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THE INSTITUTE ON METHODS AND PROTOCOLS FOR ADVANCEMENT OF CLINICAL TRIALS IN ADRD (IMPACT-AD): A NOVEL CLINICAL TRIALS TRAINING PROGRAM

 

T. Berkness1, M.C. Carrillo2, R. Sperling3,4, R. Petersen5, P. Aisen1, C. Flournoy1, H. Snyder2, R. Raman1,*,
J.D. Grill6,7,8,*

1. Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, USA; 2. Alzheimer’s Association, Division of Medical and Scientific Relations, Chicago, IL, USA; 3. Department of Neurology, Brigham and Women’s Hospital, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; 4. Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; 5. Department of Neurology, Mayo Clinic College of Medicine, Rochester, MN, USA; 6. Institute of Memory Impairment and Neurological Disorders, University of California at Irvine, Irvine, CA, USA; 7. Department of Psychiatry & Human Behavior, University of California at Irvine, Irvine, CA, USA; 8 Department of Neurobiology & Behavior, University of California at Irvine, Irvine, CA, USA; * Joint senior authors

Corresponding Author: Tyler Berkness, Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, USA, berkness@usc.edu

J Prev Alz Dis 2021;3(8):286-291
Published online April 2, 2021, http://dx.doi.org/10.14283/jpad.2021.12

 


Abstract

Background: Alzheimer’s Disease and Related Dementias (ADRD) clinical trials require multidisciplinary expertise in medicine, biostatistics, trial design, biomarkers, ethics, and informatics.
Objectives: To provide focused interactive training in ADRD clinical trials to a diverse cadre of investigators.
Design: The Institute on Methods and Protocols for Advancement of Clinical Trials in ADRD (IMPACT-AD) is a novel multidisciplinary clinical trial training program funded by the National Institute on Aging and the Alzheimer’s Association with two educational tracks. The Professionals track includes individuals who fill a broad variety of roles including clinicians, study coordinators, psychometricians, and other study professionals who wish to further their knowledge and advance their careers in ADRD trials. The Fellowship track includes current and future principal investigators and focuses on the design, conduct and analysis of ADRD clinical trials.
Setting: The 2020 inaugural iteration of IMPACT-AD was held via Zoom.
Participants: Thirty-five trainees (15 Fellowship track; 20 Professionals track) were selected from 104 applications (34% acceptance rate). Most (n=25, 71%) identified as female. Fifteen (43%) were of a non-white race; six (18%) were of Hispanic ethnicity; eight (23%) indicated they were the first person in their family to attend college.
Measurements: Participants completed daily evaluations as well as pre- and post-course assessments of learning.
Results: Across topic areas, >90% of trainees evaluated their change in knowledge based on the lectures as “very much” or “somewhat increased.” The mean proportion correct responses in pre- and post-course assessments increased from 55% to 75% for the Professionals track and from 54% to 78% for the Fellowship track.
Conclusions: IMPACT-AD successfully launched a new training opportunity amid a global pandemic that preliminarily achieved the goals of attracting a diverse cohort and providing meaningful training. The course is funded through 2025.

Key words: IMPACT-AD, training, Alzheimer’s disease, ADRD, clinical Trials, diversity.


 

Introduction

Key to the US National Plan to Address Alzheimer’s Disease and Related Dementias (ADRD) will be clinical trials of therapies that are capable of slowing or preventing the onset of symptoms (1). In addition to individuals living with dementia, ADRD trials enroll participants with mild cognitive impairment and preclinical Alzheimer’s disease stages, each requiring novel designs and methods (2, 3). There remains no FDA approved therapy for neuropsychiatric symptoms of ADRD (4) and these trials face unique challenges (5). ADRD trials incorporate a variety of clinical outcome measures, including cognitive, functional, and biomarker assessments (6-8). ADRD biomarkers can also be used as inclusion criteria and to support claims of disease modification (9). Across ADRD trial types, novel aspects of recruitment and retention (10), informed consent (11), and other ethical issues (12) such as the role of study partners, require sensitive attention. In short, ADRD trials are complex, multifaceted, and require unique training.
There is a dearth of qualified investigators with adequate training and expertise to conduct these complex studies (13). Such training is rarely provided through the traditional course of medical or biostatistical education. The complexity of ADRD trials requires a team science approach, often inclusive of medical doctors, neuropsychologists, biostatisticians, neuroimagers, and biomarker scientists, to name a few. The low availability of ADRD trialists, including clinical investigators, statisticians, and other experts represents a threat to the national ADRD research agenda. Not only must the pipeline of qualified trialists be increased, the makeup of this pool of investigators and research teams must be diversified (14).
A diverse team of investigators brings a multitude of ideas and perspectives to trial design and is essential to facilitate inclusive enrollment in ADRD trials (15-19). Diversifying study teams is a core component of the mission of the Alzheimer’s Clinical Trials Consortium (ACTC). The ACTC’s Inclusion, Diversity, and Education in Alzheimer’s disease Clinical Trials (IDEA-CT) Committee is charged with developing goals, formulating a strategic plan, and serving as a source of oversight to support the ACTC’s core values of inclusion, diversity and training in ADRD clinical trials.
To address these needs and goals, members of the ACTC IDEA-CT committee developed the Institute on Methods and Protocols for Advancement of Clinical Trials in ADRD (IMPACT-AD). IMPACT-AD is a novel multi-disciplinary clinical trial training program funded by and developed in partnership with the National Institute on Aging (NIA) and the Alzheimer’s Association. IMPACT-AD is funded through 2025 with the goal of developing a network of well-trained and diverse investigators that will shape the future of the field.
In this manuscript, we describe the development of the IMPACT-AD course and the results of the inaugural iteration, which was forced to move to a virtual format due to the COVID-19 pandemic.

 

Methods

Program Structure

We designed IMPACT-AD to include two tracks of training. A “Professionals Track” focused on training ADRD clinical trials team members who sought to further their knowledge and advance their careers in ADRD trials including clinicians, study coordinators, psychometricians, and other study professionals. A “Fellowship Track” focused on training current and future principal investigators and emphasized the design, conduct, management and analysis of ADRD clinical trials.
Four committees supported the planning and conduct of IMPACT-AD. A Curriculum Committee ensured fulfillment of learning objectives. Two application review committees evaluated applicants on merit while promoting diversity in IMPACT-AD. A Program Evaluation Committee assisted in determining the short and long-term effectiveness of the course. Thirty-seven experienced clinical trial investigators, primarily composed of ACTC site PIs and unit leaders, served as course faculty (Table 1). Sixteen “core faculty” provided mentorship in protocol development to the Fellowship track trainees.

Table 1. Course Faculty (*Core Faculty)

 

Outreach and Application Process

We employed a breadth of strategies to ensure our goal of a robust and diverse course applicant pool. A Request for Applications (RFA) announced the course and outlined the application requirements, including: 1) personal statement; 2) letter of support from a mentor or supervisor; and 3) NIH biosketch. For the Fellowship track, a draft protocol using the ACTC Protocol Synopsis template was also required. The RFA was disseminated widely. The Alzheimer’s Association’s International Society to Advance Alzheimer’s Research and Treatment (ISTAART) shared the RFA with their mailing list (n=2100) and active research awardees (n=540), including their diversity fellowship recipients. The NIA distributed the RFA to 2019 grantees (n=2300) and to alumni of the Butler-Williams Scholars Program. We sent the RFA to the ACTC steering committee members and investigative teams for numerous studies coordinated by the USC Alzheimer’s Therapeutic Research Institute (n=530) and to the National Alzheimer’s Coordinating Center’s mailing list (n=780). Applications were submitted through the Alzheimer’s Association’s centralized ProposalCentral web-based grant management service.

Selection Criteria

Each application was reviewed and scored by no fewer than five reviewers including the course co-directors. Selection criteria included: 1) demonstration of passion and commitment for ADRD clinical trials and likelihood of future involvement in ADRD research; 2) level of support from a supervising faculty member; 3) publication record; and for the Fellowship track 4) the quality of the draft protocol. Two remote study sections were convened to discuss applications and select the class of 2020.

Course Curriculum

The course curriculum included didactic lectures and active learning workshops over four days. Professionals track trainees participated for two days; Fellowship track trainees participated for the duration of the course. Didactic lectures addressed fundamental concepts in clinical trials as well as unique aspects within ADRD (Table 2). Three active learning workshops addressed scientific communication, trial publications, and securing funding. For the Fellowship track, additional protocol workgroups focused on trial design and protocol development skills. Workgroups were comprised of three Fellowship track trainees and at least three course core faculty members, including two clinical and one biostatistical faculty. Protocol workgroups focused on five specific topics: 1) trial designs; 2) selecting a sample and developing inclusion criteria; 3) selecting a primary (and other) outcome measures; 4) statistical analysis plans; 5) safety monitoring and other conduct considerations.

Table 2. Didactic Lectures and Workshop Content

* Workshops

Course Evaluations

We collected evaluations on all sessions and lectures within each session. Trainees assessed several aspects of the course including the value of each covered topic, prior knowledge of the topic and the effect on the participant’s knowledge of the lecture. Trainees scored sessions using Likert response scales tailored to each question (e.g., “Very strong”, “Strong”, “Moderate” and “Weak” as options for “What was your prior knowledge of this topic?”).
We used pre- and post-course evaluations of knowledge to determine the overall educational value of the course. Separate post-test evaluations were performed at the conclusion of Days 2 (end of the Professionals track) and 4 (end of the Fellowship track). We compared the group scores pre- and post-course completion.

 

Results

Characteristics of Applicants and Selected Trainees

We received 104 eligible applications including 48 for the Fellowship track and 56 for the Professionals track. Sixteen individuals applied to both tracks. Most applicants were female and nearly half identified as being from a non-white race and/or Hispanic/Latino ethnicity (Table 3). Twenty-three applicants (22%) indicated that they were the first in their family to attend college. Forty-six (44%) were from Institutions outside of the ACTC network.

Table 3. IMPACT-AD Applicant and Trainee Demographics

 

Thirty-five trainees (15 in the Fellowship track and 20 in the Professionals track) were selected to participate in the course, resulting in a 34% acceptance rate. Among selected trainees, the majority were female. Seven (20%) identified as African American or Black, four (11%) as Asian, twenty (57%) as White/Caucasian, three (8.5%) as multi-racial, one (3%) as Other race and six (18%) identified as being of Hispanic ethnicity. Eight trainees (23%) identified as being the first person in their family to attend college. Eleven (31%) held Professional degrees (e.g. MD, DDS, MBBS), fifteen (43%) held Doctorate degrees (e.g. PhD, PsyD), six (17%) held Master’s degrees, and three (9%) had a Bachelor’s degree. Thirteen (37%) were from institutions outside of the ACTC network. For the Fellowship track, eleven (73%) trainees proposed trials of nonpharmacological interventions, and four (27%) proposed drug trials.

Course Evaluations and Assessment of Learning

Each day of the course achieved at least an 80% response rate for program evaluations. Table 4 overviews the course evaluations for each of the sessions. On average, lecture topics were rated as “essential” by 76% and “valuable” by 22% of trainees. None of the topics received any assessment of “not necessary.”
Across lecture topics, 22%, 26%, 42%, and 10% of trainees rated their prior knowledge of topics as “very strong,” “strong,” “moderate,” and “weak”, respectively. The areas deemed as the greatest need by trainees (most responses of weak prior knowledge) included those in statistical design and analysis, with 22% of trainees identifying their prior knowledge as weak.
Across topic areas, 52%, 39%, 7%, and 3% of trainees self-reported their change in knowledge based on the lectures as “very much increased,” “somewhat increased,” “slightly increased,” and “no change”, respectively. Based on pre- and post-course assessments, each track demonstrated a positive effect of the course on trial knowledge (Figure 1). The mean proportion correct responses for the Professionals track increased from 55% to 75%. The Fellowship track improved from 54% to 78% correct responses.

Table 4. Evaluation Summaries

Mean scores are presented for each session, which included 2-6 lectures of varying lengths.

Figure 1. Pre- and Post-Course Quizzes of Course Learning

Mean performance on pre- and post-course assessments of knowledge are presented for days 1 vs. 2 (panel A), which included both the Professionals and Fellowship tracks (n=33 pre and n=35 post), and for days 1 vs. 4 (panel B), which included only the Fellowship track (n=14 pre and n=15 post).

 

Discussion

IMPACT-AD was envisioned as an annual in-person course held at the ACTC Coordinating Center/University of Southern California’s Alzheimer’s Therapeutic Research Institute in San Diego, CA. The COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2 forced implementation of a virtual format for the inaugural iteration of IMPACT-AD. As a result, we significantly adjusted the course’s structure and format in an effort to accommodate trainees’ time zones and ensure achievement of the course objectives. The course days had to be shortened and morning and evening activities were cancelled. The planned educational content remained largely intact. The results presented here indicate that these efforts were successful.
The course achieved its primary educational goals. Trainees received instruction in key topics related to ADRD interventional research and showed increased knowledge as a result of their training. Long-term evaluations will assess whether trainees continue their roles in ADRD trials, whether they achieve career advances supported by their participation in the course, and whether Fellowship track trainees successfully conduct their proposed trials.
Increasing investigator diversity is an important goal for the ACTC and specifically the IDEA-CT committee and more broadly for the field of ADRD research (20, 21). The inaugural IMPACT-AD course achieved the goal of including a diverse cohort of trainees. Trainees were diverse in sex, race and ethnicity, as well as professional backgrounds and current positions. Notably, eight trainees were the first in their families to attend college. While these diverse trainees were generally already working in ADRD trials, the course aims to give them added tools to be successful, advance in their careers, and inspire them to continue their work in the field.
A main goal of the IMPACT-AD course is to establish a network of peers that can remain connected, learn from each other, and support each other’s careers. Establishing this sense of camaraderie was made more challenging by the necessitated virtual conduct of the course. In partnership with the trainees, however, we created an IMPACT-AD Alumni Platform through the professional networking site LinkedIn. Thirty-one of 35 trainees (89%) have enlisted in this group. The Alumni Platform plans to interact virtually to discuss recent publications, plan and hold seminars, and discuss available funding and collaboration opportunities. The group is led by an IMPACT-AD Alumni Committee, composed of four trainees (two from each track). We also plan to hold an in-person event with the Class of 2020 at the earliest safe opportunity and will pursue other opportunities to connect alumni from subsequent iterations of the course.
IMPACT-AD has received funding to hold an annual course for the next four years. Based on the first year’s conduct, several changes are planned. Applicants will be required to select only one track. We anticipate holding informational webinars to answer potential applicant questions and offer guidance on the qualities that distinguished successful applications. Course content will be reorganized, emphasizing fundamental information on trial design (randomization, blinding, etc.) earlier in the agenda. We also anticipate developing some recorded lectures or webinars that will be offered to participants prior to the course to address the areas acknowledged by trainees as greatest needs (i.e., basic design and statistical analysis). We aim to improve evaluation completion rates.

 

Conclusions

The first year of the IMPACT-AD course was successful, despite unforeseen challenges resulting from the COVID-19 global pandemic. A diverse cohort of trainees was recruited and trained, and available data suggest that the training was effective. With strong partnerships with the NIA, the Alzheimer’s Association, and ACTC, the IMPACT-AD course is poised to continue its mission to train and diversify the next generation of ADRD trial investigators.

 

Funding: This work was supported by NIA U13AG067696, NIA U24AG057437, and Alzheimer’s Association SG-20-693744. JDG is supported by NIA AG066519 and NCATS UL1 TR001414.

Acknowledgments: We thank Dr. Laurie Ryan and Dr. Kristina McLinden from the National Institute on Aging for their key contributions in leadership and scientific guidance on developing and holding the inaugural iteration of IMPACT-AD. We thank the USC ATRI Events and IT teams and Ms. Chelsea Cox and Ms. Kirsten Klein from UCI MIND for their invaluable support.

Ethical standards: This paper does not describe human subjects research and therefore there was no IRB approval needed.

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

 

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A NOVEL COMPUTERIZED COGNITIVE STRESS TEST TO DETECT MILD COGNITIVE IMPAIRMENT

 
R.E. Curiel Cid1, E.A. Crocco1, M. Kitaigorodsky1, L. Beaufils2, P.A. Peña2, G. Grau1, U. Visser2, D.A. Loewenstein1
 

1. Center for Cognitive Neuroscience and Aging, Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, 1695 NW 9th Avenue, Miami, Florida, 33136. U.S.A; 2. Department of Computer Science, University of Miami, 1365 Memorial Drive, Coral Gables, Florida 33146, U.S.A.

Corresponding Author: Rosie E. Curiel, Psy.D., Associate Professor of Psychiatry & Behavioral Sciences, University of Miami Miller School of Medicine, 1695 NW 9th Avenue, Suite 3202, Miami, FL 33136. RCuriel2@miami.edu

J Prev Alz Dis 2021;
Published online January 19, 2021, http://dx.doi.org/10.14283/jpad.2021.1

 


Abstract

BACKGROUND: The Loewenstein Acevedo Scales of Semantic Interference and Learning (LASSI-L) is a novel and increasingly employed instrument that has outperformed widely used cognitive measures as an early correlate of elevated brain amyloid and neurodegeneration in prodromal Alzheimer’s Disease (AD). The LASSI-L has distinguished those with amnestic mild cognitive impairment (aMCI) and high amyloid load from aMCI attributable to other non-AD conditions. The authors designed and implemented a web-based brief computerized version of the instrument, the LASSI-BC, to improve standardized administration, facilitate scoring accuracy, real-time data entry, and increase the accessibility of the measure.
Objective: The psychometric properties and clinical utility of the brief computerized version of the LASSI-L was evaluated, together with its ability to differentiate older adults who are cognitively normal (CN) from those with amnestic Mild Cognitive Impairment (aMCI).
Methods: After undergoing a comprehensive uniform clinical and neuropsychological evaluation using traditional measures, older adults were classified as cognitively normal or diagnosed with aMCI. All participants were administered the LASSI-BC, a computerized version of the LASSI-L. Test-retest and discriminant validity was assessed for each LASSI-BC subscale.
Results: LASSI-BC subscales demonstrated high test-retest reliability, and discriminant validity was attained.
Conclusions: The LASSI-BC, a brief computerized version of the LASSI-L is a valid and useful cognitive tool for the detection of aMCI among older adults.

Key words: Computerized test, mild cognitive impairment, Alzheimer’s disease, semantic intrusion errors, semantic interference, clinical trials.


 

Introduction

Alzheimer’s disease (AD) is a devastating condition that is expected to significantly impact the rapidly aging population. Important advancements have been made to identify novel candidate biomarkers of AD, and a research framework to stage the disease from its preclinical stage onward has been proposed, with the aim of establishing a biological definition of the disease (1). Despite these formidable advances, neuropsychological assessment remains an essential component of the evaluative process because cognitive impairment is a fundamental defining symptom of AD that emerges early, at a certain point in the transition from the preclinical to clinically symptomatic stages of the disease. Further, cognitive changes are used to detect and track disease progression over time and a measurable change in cognitive ability represents a potentially meaningful clinical outcome (2). Thus, the identification of cognitive markers that are sensitive to detecting early disease states and converge with biological markers of AD pathology, have become increasingly necessary in terms of identifying individuals at risk, monitoring disease progression, and ascertaining treatment efficacy (3).
Traditional paper-and-pencil cognitive measures employed for the detection of AD-related Mild Cognitive Impairment (MCI) are often insensitive to detecting subtle cognitive changes that occur during preclinical or prodromal disease states (5, 6). There is a developing body of literature, however, that cognitive stress paradigms can measure subtle deficiencies that are highly implicated in early AD disease states among older adults. One such paradigm that measures semantic interference in memory, the Loewenstein-Acevedo Scales for Semantic Interference and Learning (LASSI-L), was sensitive enough to differentiate older adults who are cognitively unimpaired from those with subjective memory complaints, and early amnestic MCI (7, 8). On this memory measure, proactive semantic interference (PSI) deficits and particularly, the inability to recover from PSI (frPSI) was also highly associated with brain amyloid load in older adults with otherwise normal performance on a traditional battery of cognitive tests (9). The LASSI-L has outperformed other widely used memory measures in detecting prodromal AD in both English and Spanish (10, 11), and has been found to be useful in different cultural/language groups (7, 11, 12). In addition to measuring the total number of correct targets recalled on individual LASSI-L subscales, there is evidence that semantic intrusion errors may have specific utility in the assessment of prodromal AD. Loewenstein and colleagues (4) found that semantic intrusion errors sensitive to PSI and frPSI on the LASSI-L could differentiate amyloid positive aMCI groups from amyloid negative aMCI groups with non-AD diagnoses.
While it is recognized that intrusion errors represent early manifestations of neurodegenerative brain disease, a potential limitation of previous approaches is that the number of intrusion errors are often highly dependent on an individual’s total responses on a particular trial. Thus, even a seemingly modest number of intrusion errors may actually represent an at-risk cognitive profile, depending on the total number of responses that are correct. For example, an individual may make a minimal number of intrusion errors on a given trial, which may appear to be clinically insignificant. However, if the number of total responses is low, even a modest number of intrusion errors may indicate impaired inhibitory processes and underlying brain pathology. As a result, we recently developed a novel method to evaluate semantic intrusion errors utilizing the percentage of intrusion errors (PIE) in relation to total correct responses (13). This method takes into account the observation that the number of intrusion errors a person makes is often highly dependent on their total responses on a particular trial. Thus, even a seemingly modest number of intrusion errors may represent an at-risk cognitive profile. PIE demonstrated high levels of sensitivity and specificity in differentiating CN from amyloid positive persons with preclinical AD and preliminary work suggests that it is a novel and sensitive index of early memory dysfunction (11, 13).
Traditional paper and pencil neuropsychological assessments are lengthy, require a skilled examiner, are vulnerable to human error in administration and scoring, and associated with practice effects. Moreover, some of these measures have been found to be biased among diverse ethnic/cultural and language groups. To address some of these concerns, computerized testing batteries have been developed to explore a more suitable option to mitigate some of the abovementioned limitations (14-17). However, these too have limitations in early detection of AD-associated cognitive impairment. For example, many of these computerized batteries are relatively successful at distinguishing between older adults with normal cognition and those with dementia or late stage MCI, but lack the predictive power needed to move the field forward, which is to correctly classify individuals with MCI and/or earlier on the disease continuum, and do so in a manner that is validated for use among different ethnic/cultural and language groups. This highlights a major problem with many traditional computerized batteries; they are automated versions of traditional paper-and-pencil cognitive assessment paradigms that lack sensitivity to detect AD-associated cognitive decline, and employ the same paradigms originally developed for the assessment of dementia or traumatic brain injury (17).
Recent work by Curiel and associates (5-12) led to the development of a brief computerized version of the LASSI-L, the LASSI-BC, which incorporates all the elements of this well-established cognitive stress test. The LASSI-BC is currently being studied extensively in a longitudinal study of at-risk aging adults. This novel computerized version of the instrument does not require a skilled examiner, is web-based and can remotely run on most browser-capable devices. Moreover, it is intuitive and appropriate for use among older adults that are either predominantly English or Spanish-speaking and who have varying ethnic/cultural backgrounds including Hispanics and African Americans.
In this first validation study, we examine the psychometric properties of the LASSI-BC. We also evaluate the clinical utility several LASSI-BC subscales as it relates to their ability to differentiate older adults with normal cognition from those with aMCI on measures of: i) proactive semantic interference, ii) the failure to recover from proactive semantic interference, iii) retroactive semantic interference and iv) the percentage of intrusion errors in relation to total cued recall responses by the participant. Performance on these specific subscales were selected a priori because, as noted above, our previous work using the paper-and-pencil LASSI-L has robustly demonstrated that these particular subscales are the most sensitive to cognitive breakdowns associated with MCI due to preclinical and prodromal AD.

 

Methods

This study included 64 older adults that were evaluated as part of an IRB-approved longitudinal investigation funded by the National Institute on Aging. An experienced clinician administered a standard clinical assessment protocol, which included the Clinical Dementia Rating Scale (CDR) (18), and the Mini-Mental State Examination (MMSE) (19). Subsequently, a neuropsychological battery was independently administered in either Spanish or English dependent on the participant’s dominant and preferred language. Spanish language evaluations were completed with equivalent standardized neuropsychological tests and appropriate age, education, and cultural/language normative data (20-23). Proficient bilingual (Spanish/English) psychometricians performed all the testing.
Diagnostic groups were classified using the following criteria:

Amnestic MCI group (aMCI) (n=25)

Participants met Petersen’s criteria (24)) for MCI and evidenced all of the following: a) subjective cognitive complaints by the participant and/or collateral informant; b) evidence by clinical evaluation or history of memory or other cognitive decline; c) Global Clinical Dementia Rating scale of 0.5 (18); d) below expected performance on delayed recall of the HVLT-R (23) or delayed paragraph recall from the National Alzheimer’s Coordinating Center -Unified Data Set (NACC-UDS) (25) as measured by a score that is 1.5 SD or more below the mean using age, education, and language-related norms.

Cognitively Normal Group (n=39)

Participants were classified as cognitively normal if all of the following criteria were met: a) no subjective cognitive complaints made by the participant and a collateral informant; b) no evidence by clinical evaluation or history of memory or other cognitive decline after an extensive interview with the participant and an informant; c) Global CDR score of 0; d) performance on all traditional neuropsychological tests (e.g.: Category Fluency (26), Trails A and B (27), WAIS-IV Block Design subtest (28) was not more than 1.0 SD below normal limits for age, education, and language group.

Loewenstein-Acevedo Scales for Semantic Interference and Learning, Brief Computerized Version (LASSI-BC)

The LASSI-BC was not used for diagnostic determination in this study. This computerized cognitive stress test is a novel computerized measure that is briefer than the paper-and-pencil LASSI-L, taking approximately 10 to 12 minutes to complete. The LASSI-BC contains the elements of the original LASSI-L which demonstrated the greatest differentiation between aMCI, PreMCI and CN older adults in previous studies. For example, free recall preceding the cued recall trials of the LASSI-L added time to the administration but was never as effective as cued recall in distinguishing among diagnostic groups. Developed in collaboration with the University of Miami Department of Computer Science, the LASSI-BC is a remotely accessible test available in both English and Spanish. As a web application, it can be run on devices that can run Google Chrome, including desktop computers, laptops, tablets, or even smartphones. While the LASSI-BC is a fully self-administered test with all verbal responses recorded and scored by the computer, for the purposes of this validation study, a trained study team member was present for each administration to systematically record responses, which provided a double check on the accuracy of data. The LASSI-BC utilizes Google Cloud Speech API , which has been successfully implemented for use with older adults. The test leverages Google Cloud’s Speech to Text software in conjunction with a backup lexicon for understanding the participants’ spoken words. The lexicon is designed to account for variations in participant’s pronunciation by allowing for words that the computer “mishears” to serve as alternatives to the actual word being spoken. Lexicons were chosen based on observations from participants during the test.
Upon initiating the examination, the participant is instructed in both audio and visual formats. They will see 15 words belonging to one of three semantic categories: fruits, musical instruments, or articles of clothing (five words per category). The words are then individually presented on the screen and audio for a 6-second interval. This presentation facilitates optimal encoding and storage of the to-be-remembered information. Further, this instruction style has been easily understood and accepted by older adults during pilot studies in the course of developing the LASSI-BC. After the computer presents all 15 words, participants are presented with each category cue (e.g., fruits) and asked to recall the words that belonged to that category. Participants are then presented with the same target stimuli for a second learning trial with subsequent cued recall to strengthen the acquisition and recall of the List A targets. The exposure to the semantically related list (i.e., List B) is then conducted in the same manner as exposure to List A. List B consists of 15 words different from List A, all of which belong to each of the three categories used in List A (i.e., fruits, musical instruments, and articles of clothing). Following the presentation of the List B words, the person is asked to recall each of the List B words that belonged to each of the categories. List B words are presented again, followed by a second category-cued recall trial. Finally, to assess retroactive semantic interference, participants are asked to free recall the original List A words. Primary measures used in this study are the second cued recall score for List A (maximum learning), first cued recall score for List B (susceptibility to proactive semantic interference), second cued recall of List B (failure to recover from proactive semantic interference), and the third cued recall of List A (retroactive semantic interference). In addition, we evaluated the novel ratio used with the LASSI-L, that takes into account the percentage of intrusion errors (PIE) as a function of total responses on subscales that measure proactive semantic interference and the failure to recover from proactive semantic interference. Specifically, the ratio is denoted as follows: Total Intrusion Errors/ (Total Intrusion Errors + Total Correct Responses) for LASSI-BC Cued B1 (a measure of susceptibility to proactive semantic interference) and LASSI-BC Cued B2 recall (a measure of recovery from proactive semantic interference).

 

Results

The computerized version of the LASSI-BC had psychometric properties that compared favorably to the test-retest reliabilities obtained on the original paper-and-pencil LASSI-L (7). As depicted in Table 1, CN (n=39) and aMCI (n=25) groups did not differ in terms of age, sex, or language of evaluation. Individuals diagnosed as aMCI, although well educated (Mean =14.26; SD=3.5), had less educational attainment relative to their cognitively normal counterparts (Mean =16.32; SD=2.3). As expected, aMCI participants also had lower mean MMSE scores (Mean =26.04; SD=2.3).

Table 1. Demographic Characteristics and Computerized LASSI-BC Scores among Participants who are Cognitively Normal and with Amnestic Mild Cognitive Impairment

 

Test-retest reliability

Test-retest reliability data was obtained on a subset of 15 older adults diagnosed with aMCI using Petersen’s criteria (24) for each of the LASSI-BC subscales. The mean age was 73.4 (SD=6.3); education 15.4 (SD=3.6); and the mean MMSE score for this group was 26.6 (SD=2.2). These individuals (60% primary English-speakers and 60% female) were administered the LASSI-BC on two occasions, within a 4 to 39-week interval (Mean =13.9.; SD=10.6 weeks). In our pilot work, we found robust test-retest correlations ranging from 0.55 to 0.721 on the subscales that have shown to be the most sensitive measures of cognitive decline in the original paper-and-pencil version. In this study, test-retest comparisons were conducted for Cued Recall A2 (measures maximum learning), Cued Recall B1 (measures proactive semantic interference), and Cued Recall B2 (measures the failure to recover from proactive semantic interference). One-tailed Pearson Product Moment Correlation Coefficients were obtained given the directional hypotheses concerning test-retest relationships. High, statistically significant test-retest reliabilities were obtained for Cued A2 Recall (r=.726; p<.001); Cued Recall B1 (r=.529; p=0.021); Cued Recall B2 (r=.555; p=0.016).

Discriminant validity

As depicted in Table 1, LASSI-BC scales sensitive to maximum learning (Cued A2), vulnerability to proactive semantic interference (Cued B1) and the failure to recover from proactive semantic interference (Cued B2) were statistically significant in discriminating between older adults with amnestic MCI and cognitively normal counterparts. These results were identical when demographic variables such as education were entered in the model as covariates
We then calculated areas under the Receiver Operating Characteristic (ROC) curve for LASSI-BC correct responses as well as the PIE indices for Cued B1 and Cued B2 subscales. We selected these measures a priori given that performance on these specific subscales have traditionally been the most discriminant measures on the paper-and-pencil form of the LASSI-L.
As shown in Table 2, an optimal cut-point of 5 by Youden’s criteria on correct responses for Cued Recall B1, yielded a sensitivity of 84.6% and a specificity of 86.8%. An optimal cut-point of 9 by Youden’s criteria on correct responses provided on Cued Recall B2, yielded statistically significant areas under the ROC curve of .868 (SE=0.88) and .824 (SE=.051), respectively.

Table 2. Classification of aMCI versus Cognitively Normal Participants on the LASSI-BC

 

We subsequently examined an optimal cut-point for PIE on the Cued Recall B1 and the Cued Recall B2 subscales. For PIE on Cued Recall B1, the area under the ROC was .879 (SE=.06) with a sensitivity of 92.9% and specificity of 80%, respectively using an optimal cut-point of .2540. For PIE on Cued Recall B2, the area under the ROC was .801 (SE=.07), using an optimal cut-point of .2159, which yielded a sensitivity of 78.6% and specificity of 68.0%. We selected these specific subscales because they have shown to be the strongest predictors of aMCI in the paper-and-pencil form of the LASSI-L.
We subsequently entered the statistically significant LASSI-BC subscales (Cued Recall B1 and Cued Recall B2) into a stepwise logistic regression. As seen in Table 3, the first variable to enter the logistic regression model was PIE on Cued B1 [B=6.86 (SE=1.67) Wald=17.07, p<0.001)]. On the second step of the logistic regression model, correct responses on Cued Recall B2 entered the model [B=-.34 (SE=.128), Wald= 17.1 (p=.008)]. Combining PIE Cued Recall B1 and correct responses on Cued Recall B2, yielded an overall sensitivity of 80% and specificity of 89.7%. It should be noted logistic regression weighs overall classification in a manner that favors the largest diagnostic group (in this case CN participants). Nonetheless, ROC and stepwise regression models yielded similar results indicating excellent discriminative ability.
In sum, our findings support that the LASSI-BC has equal or better psychometric properties than the original paper-and-pencil LASSI-L and demonstrates that computerized administration is both feasible, well accepted, and has excellent discriminant properties.

Table 3. Step-wise Logistic Regression Using Proactive Semantic Interference Measures on the Computerized LASSI-BC

 

Discussion

The present study was designed to examine the psychometric properties of the LASSI-BC, the brief computerized version of the LASSI-L, a cognitive stress test that utilizes a novel cognitive assessment paradigm based on semantic interference in memory. In studies conducted in the United States and abroad, the LASSI-L has shown great utility in detecting cognitive changes among individuals during the preclinical and prodromal stages of AD (4, 29) and has been found to be appropriate for use among diverse ethnic/cultural and language groups (11, 30, 12). The paradigm that this measure employs is unique in that it explicitly and from the outset organizes the examinee’s learning around specific semantic categories, which promotes active encoding, reduces the use of individualized learning strategies that can help or hinder performance, increases depth of initial learning, and is designed to tap an individual’s vulnerability to semantic interference.
The current investigation examined all salient subscales of the LASSI-BC, which were selected based on previous work with the paper-and-pencil versions. The computerized version evidenced good test-retest reliability for participants diagnosed with aMCI. Scores on all LASSI-BC subscales were higher for cognitively normal older adults, relative to aMCI participants. In addition, high levels of discriminant validity were obtained in differentiating aMCI from cognitively normal groups based on ROC analyses and logistic regression.
A potential limitation of this first validation study is that we employed modest numbers of participants who were tested in either English or Spanish on the LASSI-BC. Although, our overall findings were highly significant and the paper-and-pencil LASSI-L has been validated in different languages (i.e.- Spanish speakers in Argentina, Spanish speakers in Spain, Spanish speakers from Mexico, etc.) and with different ethnic/cultural groups (European Americans, Hispanics and African Americans), such future comparisons should be made with the LASSI-BC. Further, additional studies with the LASSI-BC will include evaluating the diagnostic utility of this computerized cognitive stress test to differentiate older adults earlier on the preclinical continuum of AD, and relate performance to biomarkers of AD pathology, as well as compare it to other traditional and widely used cognitive measures in the field.
There has been an increase in the number of computerized tests developed including the CogState (31) and the Cognition Battery from the NIH Toolbox (16), but limitations exist. For example, one of the most widely-used computerized cognitive batteries for the assessment of MCI is the CogState. As part of the Mayo Clinic Study on Aging, Mielke and associates (32) administered the CogState to eighty-six participants diagnosed with MCI who were found to have worse performance than cognitively healthy individuals; however, it is likely that individuals classified as MCI ranged from early states of MCI to late MCI, the latter of which is more cognitively similar to early dementia in terms of neuropsychological test performance, limiting evidence that this measure in sensitive to preclinical cognitive change. Further, the authors noted that their results are not generalizable to other ethnicities due to the demographic makeup of the region (Minnesota, USA). Another study conducted by Mielke and colleagues (33) aimed to examine performance on the CogState with neuroimaging biomarkers (MRI, FDG PET, and amyloid PET) among cognitively normal participants aged 51-71; however, only weak associations were found between CogState subtests and biomarkers of neurodegeneration.
With the rapidly aging population, early detection of cognitive decline in individuals at risk for AD and related disorders has become a global priority. Accurately identifying at risk individuals through the detection and monitoring of subtle, albeit sensitive cognitive changes that transpire early in the disease course is an important initiative and computerized cognitive outcome measures have the potential to greatly reduce burden for participants, clinical researchers and clinicians.
The development of computerized cognitive tests for older adults has significantly increased during the past decade. In fact, available systematic reviews have identified more than a dozen computerized measures designed to detect dementia or MCI (34, 35, 36). Moreover, the use of computerized assessments with older adults has been found to be feasible and reliable (37, 38). A recent meta-analysis has shown relatively good diagnostic accuracy, and authors further concluded that their performance distinguishing individuals with MCI and dementia is comparable with traditional paper-pencil neuropsychological measures (35). It is anticipated that as technology advances, clinical trials will include validated computerized testing to sensitively capture cognitive performance, particularly in large-scale secondary prevention efforts (39). The impact of this technological advancement in computerized, web-based cognitive testing has the potential to facilitate remote deliverability, allow for real-time data entry, improves standardization, and reduces administration and scoring errors. Moreover, computerized assessment can more readily monitor longitudinal cognitive changes for each individual, facilitating a precision-based approach. It is critical; however, that emerging cognitive tests move beyond simply computerizing outdated, insensitive cognitive paradigms and instead invest in the development and validation of cognitive paradigms that are sensitive and specific to early cognitive breakdowns that occur during the preclinical stages of AD. These too should exhibit sensitivity to biomarkers of AD (e.g., amyloid load, tau deposition, and neurodegeneration in AD-prone regions). Doing so may address some of the most critical challenges facing clinical trials including proper selection of at-risk participants, and monitoring meaningful cognitive change over time.

Funding: This research was funded by the National Institute of Aging Grant 1 R01 AG047649-01A1 (David Loewenstein, PI), 1 R01 AG047649-01A1 (Rosie Curiel Cid, PI) 5 P50 AG047726602 1Florida Alzheimer’s Disease Research Center (Todd Golde, PI), 8AZ. The sponsors had no role in the design and conduct of the study; in the collection analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript.

Ethical standard: This research study was conducted in alignment with the Declaration of Helsinki and through the approval of the University of Miami Institutional Review Board.
Conflict of interest: Drs. Curiel and Loewenstein have intellectual property used in this study.

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TRC-PAD: ACCELERATING RECRUITMENT OF AD CLINICAL TRIALS THROUGH INNOVATIVE INFORMATION TECHNOLOGY

G.A. Jimenez-Maggiora1, S. Bruschi1, R. Raman1, O. Langford1, M. Donohue1, M.S. Rafii1, R.A. Sperling2, J.L. Cummings3, P.S. Aisen1 and the TRC-PAD Investigators*

1. Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, USA; 2. Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; 3. Department of Brain Health, School of Integrated Health Sciences, University of Las Vegas, Nevada; Cleveland Clinic Lou Ruvo Center for Brain Health, USA; * TRC-PAD investigators are listed at www.trcpad.org

Corresponding Author: GA Jimenez-Maggiora, Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, USA, gustavoj@usc.edu

J Prev Alz Dis 2020;4(7):226-233
Published online August 16, 2020, http://dx.doi.org/10.14283/jpad.2020.48

 


Abstract

BACKGROUND: The Trial-Ready Cohort for Preclinical/Prodromal Alzheimer’s Disease (TRC-PAD) Informatics Platform (TRC-PAD IP) was developed to facilitate the efficient selection, recruitment, and assessment of study participants in support of the TRC-PAD program.
Objectives: Describe the innovative architecture, workflows, and components of the TRC-PAD IP.
Design: The TRC-PAD IP was conceived as a secure, scalable, multi-tiered information management platform designed to facilitate high-throughput, cost-effective selection, recruitment, and assessment of TRC-PAD study participants and to develop a learning algorithm to select amyloid-bearing participants to participate in trials of early-stage Alzheimer’s disease.
Setting: TRC-PAD participants were evaluated using both web-based and in-person assessments to predict their risk of amyloid biomarker abnormalities and eligibility for preclinical and prodromal clinical trials. Participant data were integrated across multiple stages to inform the prediction of amyloid biomarker elevation.
Participants: TRC-PAD participants were age 50 and above, with an interest in participating in Alzheimer’s research.
Measurements: TRC-PAD participants’ cognitive performance and subjective memory concerns were remotely assessed on a longitudinal basis to predict participant risk of biomarker abnormalities. Those participants determined to be at the highest risk were invited to an in-clinic screening visit for a full battery of clinical and cognitive assessments and amyloid biomarker confirmation using positron emission tomography (PET) or lumbar puncture (LP).
Results: The TRC-PAD IP supported growth in recruitment, screening, and enrollment of TRC-PAD participants by leveraging a secure, scalable, cost-effective cloud-based information technology architecture.
Conclusions: The TRC-PAD program and its underlying information management infrastructure, TRC-PAD IP, have demonstrated feasibility concerning the program aims. The flexible and modular design of the TRC-PAD IP will accommodate the introduction of emerging diagnostic technologies.

Key words: Alzheimer’s, clinical trials, informatics, recruitment.


 

Background

Alzheimer’s Disease (AD) has emerged as one of the most significant public health issues of the 21st century. In 2020, it was estimated that 5.8 million people in the United States (U.S.) were living with Alzheimer’s Disease; this number was expected to rise to 13.8 million by 2050 (1). The development of effective disease-modifying interventions for Alzheimer’s disease (AD) remains an enormously important world health need. As therapeutic research has expanded to early-stage interventions, the recruitment of minimally affected optimally selected study participants has been challenging (2). The challenges include: identifying potential asymptomatic participants with elevated amyloid levels who meet study criteria, expanding recruitment and participation of groups that have been traditionally underrepresented in AD clinical trials, and reducing the time and cost of screening failures (3). Novel approaches to improve participant selection and recruitment using sophisticated informatics platforms have been developed (4, 5). The Trial-Ready Cohort for Preclinical/Prodromal Alzheimer’s Disease (TRC-PAD) program was initiated with the overarching goal of accelerating therapeutic development for AD through the establishment of an infrastructure to ensure timely recruitment of targeted individuals into optimally designed trials (6). More specifically, TRC-PAD aimed to establish a biomarker-confirmed trial-ready cohort to facilitate recruitment into preclinical and prodromal AD trials by using an efficient, low-cost multi-stage selection process driven by an adaptive risk algorithm which combined web-based and in-person longitudinal assessment of participants. The TRC-PAD Informatics Platform (TRC-PAD IP) was developed to facilitate the selection, recruitment, and assessment of study participants in support of the TRC-PAD program. The purpose of this article is to describe the innovative architecture, workflows, and components of the TRC-PAD IP, demonstrate its utility, and discuss broader implications for the field of AD clinical trials.

 

Methods

TRC-PAD Informatics Platform

The TRC-PAD Informatics Platform (TRC-PAD IP) (Figure 1) was conceived as a secure, scalable, multi-tiered information management platform designed to facilitate high-throughput, cost-effective selection, recruitment, and assessment of TRC-PAD study participants. The program aims called for the construction of a multi-stage process that encompassed 1) a public-facing web-based registry, the Alzheimer Prevention Trials (APT) Webstudy registry, 2) an analytics platform capable of supporting the development and implementation of risk-based referral and screening algorithms, 3) a web-based referral management system, the Site Referral System (SRS), and 4) a regulatory-compliant clinical data management system to collect data for the standing Trial Ready Cohort (TRC). Given the complex structure and pathways of this process, tracking participants as they entered the program and transitioned from one stage to the next was also a critical requirement. The component systems underpinning this process are described in the following sections.

Figure 1. TRC-PAD Informatics Platform

 

APT Webstudy

The APT Webstudy (www.aptwebstudy.org) is a public-facing online longitudinal study that invited members of the general population who were over 50 years of age and had sufficient interest in participating in AD research to register for a user account. Once registered, users were presented with a guided walk-through during which they provided their contact information and preferences, current AD-related diagnosis (if known), and interest in participating in AD-related research. Subsequently, users were offered additional study information, asked to consider the benefits and risks of study participation, and allowed to consent and enroll in the APT Webstudy using an online consent process. Participants were subsequently asked to provide additional information regarding their demographics, family and medical history, lifestyle, current AD biomarker status (if know), and complete assessments of their subjective memory perceptions using the Cognitive Function Instrument (CFI), and cognitive performance using the Cogstate Brief Battery (7-9). To minimize participant burden, the registration, consent, and assessment process was designed to take approximately 15 to 20 minutes to complete. Follow-up assessments, scheduled every 3 months, were designed to be completed in 10 to 15 minutes to promote participant retention.

Website Design

The APT Webstudy was designed with several features to support usability and engagement. Users had the option to create their accounts using a «1-click» social login instead of a traditional approach which requires a username, password, and email address. Participants were provided with a dashboard to view their previous assessment scores. They could also download a report with all their information and assessment scores. Participants received a quarterly newsletter via electronic mail developed by leading AD researchers which highlighted new developments in the field as well as news related to the TRC-PAD program. The website was designed to support multiple screen sizes (phone, tablet, desktop) using responsive web design principles (10).

Longitudinal Assessments

To improve the prediction of participant risk, the APT Webstudy was designed to incorporate unsupervised cognitive assessments as part of a longitudinal observational study. Thus, participants are invited to return to the website every 3 months to complete a new round of assessments and update their information as needed. These follow-up assessments are designed to take 10 to 15 minutes to complete. To encourage compliance, participants received quarterly electronic mail reminders to log-in to the APT Webstudy website to complete their web-based assessments.

Website Architecture

An important consideration in a public-facing website is the variability of website traffic patterns. Websites with highly volatile traffic patterns can exhibit occasional periods of poor performance or unavailability. This is especially true when an unexpected media event, such as a viral social media mention or a celebrity endorsement, draws a burst of traffic toward the website. To mitigate the effects of these unexpected traffic spikes, the APT Webstudy has an architecture that dynamically adjusts its capacity to respond to incoming requests as changes in traffic patterns emerge. This architecture combines a cloud-based fleet of webservers, a multi-region database cluster, and a dynamic networking configuration to allow the website to automatically activate and deactivate computational resources on-demand to maintain performance during high-traffic periods in a cost-effective manner.

Recruitment and Retention

Recruitment into the APT Webstudy relied on a combination of traditional and digital media strategies. These efforts included community-based events, local and national paid media campaigns, social media campaigns, and earned media. Additionally, a group of preexisting registries which included the Brain Health Registry (brainhealthregistry.org), the Alzheimer’s Prevention Registry (endalznow.org), Trial Match (trialmatch.alz.org), and Healthy Brains (healthybrains.org), partnered with the TRC-PAD program to refer participants from their cohorts. These registries, collectively known as the “feeder” registries, provided the APT Webstudy with access to a large, engaged group of potential participants. The performance of these recruitment efforts was tracked by using Urchin Tracking Module (UTM) codes which associated participants with the campaign that referred them to the APT Webstudy (11).

Participant Technical Support

APT Webstudy participants were provided with both telephone- and email-based support channels. Telephone-based support requests were automatically transcribed and forwarded to the email-based support management system for follow-up. This architecture allowed the APT Webstudy support team to manage all incoming support requests via a single interface. Support cycle times and participant satisfaction were among the Key Performance Indicators (KPIs) used to assess and manage the performance of the support team.

Site Referral System

APT Webstudy participant data were regularly evaluated using an adaptive algorithm that assessed each participant’s risk of AD biomarker positivity and ranked participants based on predicted risk (12). Participants determined to have the highest risk were referred to the nearest TRC-PAD performance site based on their self-reported 5-digit Zip Code. Participant referrals were provided to performance sites via the Site Referral System (SRS), a secure website that allowed authorized site personnel to manage the site’s referral queue and ensure the timely disposition of each referral. Using the SRS, site personnel contacted participants and invited them to schedule an initial in-person screening visit to determine their eligibility for enrollment into the TRC-PAD standing cohort.

Website Design

A key concept guiding the design of the SRS was the idea that site referral queues would be managed collaboratively among multiple site personnel. To support collaboration, the SRS website incorporated several features that optimized team-based management and disposition of participant referrals. For example, as new referrals became available in the referral queue, site personnel received notifications via electronic mail. The status of every referral in the queue was summarized in a dashboard view facilitating management and reporting. Each referral provided site personnel with access to a summary of participant-reported demographic, medical history, lifestyle, and contact information, as well as status changes. As a final step, referrals were assigned an outcome code and marked complete.

Trial Ready Cohort

The TRC was conceived as the final stage in the TRC-PAD process. The initial target for the TRC was to enroll a standing longitudinal cohort of 2,000 biomarker-confirmed participants (50% preclinical and 50% prodromal) (6). An Electronic Data Capture (TRC EDC) system was developed to manage TRC participant data, based on the Alzheimer’s Therapeutic Research Institute (ATRI) EDC system. The TRC EDC was used to operationalize a multi-stage screening process and collect a rich set of clinical, neuropsychological, neuroimaging, and biospecimen data collected in a multi-site setting. The TRC EDC was validated to comply with CRF Title 21 Part 11 (13). Doing so allowed the TRC data to be eligible for use as run-in data in downstream clinical trials.

TRC EDC Design

The TRC EDC was built to provide centralized management of all critical cohort study dataflows and workflows. This approach provided study teams with broad transparency and management capabilities over the study’s complex dataflows and processes. Implementing this approach, however, has proven challenging for traditional systems, which struggle to scale up as larger and more complex data types are introduced. Historically, a solution to this problem has been the implementation of interconnected purpose-specific systems. While feasible, this solution suffers from several shortcomings: 1) supporting evolving study requirements requires complex multi-system impact analysis, 2) training on multiple systems increases the burden on study teams, and 3) data integrations require ongoing maintenance as systems are updated. The TRC EDC was designed to avoid these issues by implementing a cloud-native architecture, which allowed it to harness the full range of capabilities of the underlying cloud platform on which it was hosted. In this architecture, the TRC EDC served as an orchestration engine that coordinated and delegated computational workloads, such as uploading and processing large binary objects (e.g. medical images, sensor data, multimedia data, genetic data, graph data), to the underlying cloud-based service best-suited for the required function, without impacting the study team’s interaction with the system.

Site Network

An initial step toward the establishment of the TRC was the creation of the TRC-PAD site network, a set of 35 academic clinical sites distributed across the large population centers in the contiguous United States. Sites were selected based on multiple criteria including study team research experience and expertise, amyloid positron emission tomography (PET) imaging and radiotracer availability, and track record in AD/AD and related disorders (ADRD) clinical trials. Site selection and activation were conducted in two stages, the “vanguard” or initial phase, which included 8 pilot sites, and the broad activation phase. The vanguard phase was used as a learning exercise to test and refine TRC-PAD data management tools and processes. These learnings were applied during the broad activation phase to ensure the full site network was optimized.

Multi-stage Selection and Screening Process

The TRC-PAD selection and recruitment processes were managed by a series of learning algorithms that assessed participant data at multiple points. Newly acquired data were used to update participant risk predictions and rankings (12). These results informed the decision-making process used to graduate participants from one stage of TRC-PAD to the next, culminating with the final determination on enrollment eligibility into the standing TRC.

Referral to Downstream Clinical Trials

TRC participants who meet eligibility criteria will be referred for screening and potential enrollment to downstream clinical trials, temporarily suspending additional longitudinal assessments and data collection activities in the TRC EDC. In these cases, a link between a participant’s TRC data record and their downstream clinical trial data record will be established via the use of the National Institute on Aging’s (NIA) Global Unique Identifier (GUID) (14, 15). This link will allow for participant data to be integrated across cohorts and used to further inform the TRC-PAD learning algorithms.

Analytical Platform

TRC-PAD IP supported a single Analytical Platform (AP) that aggregated data from multiple sources in a single semi-structured repository, also known as a «data lake» (16). This approach combined the use of serverless computing methods with fast, low-cost object storage to facilitate the development and operationalization of multiple analysis workloads such as machine learning algorithms, statistical analyses, and reports.

Information Architecture

The TRC-PAD IP was constructed using open source web development and scientific computing tools hosted on Amazon Web Services (AWS), a public cloud computing platform (Table 1). The TRC-PAD program partnered with AWS’s consulting group, AWS Professional Services (AWS ProServ), to construct a secure, scalable, cost-effective computational infrastructure. The component systems of the TRC-PAD IP were built using a phased approach. Unique system-generated identification numbers (IDs) were assigned to each participant as they moved from one stage of TRC-PAD to the next. These IDs were linked across component systems to maintain the integrity of each participant’s data record while protecting confidentiality.

Table 1. Technology Components of the TRC-PAD Informatics Platform

 

Scalability and Cost

The TRC-PAD IP was built on cloud-based computational infrastructure that was optimized to support each component system. The infrastructure supporting the APT Webstudy, which was subject to temporary bursts of traffic due to recruitment campaigns or unexpected media events, was designed to dynamically adapt to changing web traffic patterns by increasing or decreasing its fleet of webservers, within set cost parameters. Likewise, the TRC EDC, which managed a large, regulatory-compliant cohort database, used a cost-effective, highly durable storage strategy. In this architecture, computational resources were used on-demand, reducing idle capacity and providing flexibility as the TRC-PAD program’s requirements evolved.

Security and Compliance

Ensuring participant confidentiality and data security were the primary requirements for the TRC-PAD IP. Thus, the TRC-PAD Informatics team worked with AWS ProServ to build a Health Insurance Portability and Accountability Act (HIPAA)-eligible architecture and implement AWS’s best practices (17). This architecture was designed to take advantage of multiple strategies to ensure data security and durability such as a multi-account structure, multi-region, encrypted data storage, and automated policy management. To further ensure data security, the TRC-PAD IP underwent annual security audits by an independent security firm.

Regulatory Oversight

Regulatory oversight of the APT Webstudy was provided by the University of Southern California Institutional Review Board (USC IRB). Regulatory oversight for the TRC was provided by Advarra, Inc., under a single IRB (sIRB) model. The sIRB model was established as a National Institute of Health (NIH) requirement for multi-site studies starting in 2018 to streamline the review of research that involves human subjects (18).

 

Results

The APT Webstudy was launched on December 22, 2017, after a 6-month development period. The SRS and TRC EDC launched in May 2019. The TRC-PAD development team worked closely with the study team employing agile software development methods to design, build, test, and deploy these systems (19, 20).
As of July 6, 2020, 36,955 users had registered for an APT account. Of these registered users, 33,259 (90.0%) enrolled in the study via online consent and had completed more than 280,000 remote assessments (Figure 2). Recruitment into the APT Webstudy was driven by earned, owned, and shared media as well as feeder-based referrals. The APT cohort was geographically distributed across all 50 U.S. states, with participants concentrated in the coastal, midwestern, and southwestern states. Participant mobile device usage (43.0%) on the APT Webstudy was higher than initially expected. The demographic characteristics of the cohort are female (73.0%), non-Hispanic White (92.4%), with a mean age of 64.6 years (SD = 8.3). 87.7% agreed to have their contact information shared with the TRC sites. After one year of quarterly follow-up, 44.7% of participants were retained. The retention rate after two years of quarterly follow-up was 29.7%.

Figure 2. APT Webstudy Enrollment (December 22, 2017 to July 6, 2020)

 

TRC sites began in-person screening of participant referrals in August 2019. As of July 6, 2020, 27 of 35 TRC sites were activated and had received 1,675 risk-ranked participant referrals via the SRS. Of these, 246 (14.7%) participants were referred to the TRC for initial screening, 123 (50.0%) participants completed the initial screening visit, 99 (80.5%) participants were authorized for amyloid testing, 55 (55.6%) participants were biomarker-confirmed using amyloid PET or CSF assessment, 26 (47.3%) participants were found to be amyloid elevated, and 23 (88.5%) participants were enrolled into the TRC (Figure 3). The demographic characteristics of the standing TRC are female (51.2%), non-Hispanic White (94.2%), with a mean age of 72 years (SD = 7.8), and a mean SUVr of 1.14 (SD = 0.22). The median (interquartile range) cycle time from initial site referral via SRS to enrollment decision in the TRC was 28 (15 to 84) days. During this period, the TRC-PAD IP proved to be a scalable, cost-effective solution to support all stages of the TRC-PAD selection and recruitment.

Figure 3. TRC-PAD Participant Flows by Stage (as of July 6, 2020)

 

Discussion

Our early experiences with the TRC-PAD program and its underlying information management infrastructure, TRC-PAD IP, have demonstrated feasibility concerning the program aims. The TRC-PAD IP supported recruitment, screening, and enrollment of TRC-PAD participants by leveraging a secure, scalable, cost-effective cloud-based information technology (IT) architecture. The APT Webstudy has demonstrated effectiveness in terms of selecting and recruiting individuals from the general population who have an elevated risk of amyloid positivity. The APT Webstudy has proven effective in remotely assessing participants on a longitudinal basis. The SRS has demonstrated effectiveness in terms of allowing TRC sites to manage site referrals promptly. The TRC EDC has supported the coordination of the multi-stage risk-based screening and enrollment process into the standing TRC. Much work remains to demonstrate the effectiveness of the TRC-PAD program in accelerating recruitment into downstream AD clinical trials.
Several limitations should be considered. First, the TRC-PAD program has struggled to select and recruit a representative sample of participants across multiple sociodemographic dimensions (21). Efforts to address this challenge have included updating the APT Webstudy to support Spanish-speaking participants, however, more work is needed. Second, the architecture has proven to be resilient during a few web traffic spikes but has yet to be subjected to the type of surge (>10-100x daily web traffic) associated with an unexpected mention in a national media platform. Third, the integrations with third-party platforms, such as the Cogstate Brief Battery, have proven to be fragile, requiring frequent maintenance and technical support for participants. Addressing these technical issues may serve to improve both participant retention and risk algorithm predictive performance.
In May 2019, the TRC-PAD principal investigators selected the first set of downstream clinical trials slated to utilize the standing TRC to recruit participants. These clinical trials are scheduled to begin recruiting participants in North America (U.S. and Canada) in May 2020. The efficiencies that will accrue to these clinical trials are two-fold: 1) by drawing participants from the TRC, the expectation is that recruitment into these clinical trials will be accelerated by reducing the number of screen failures; and 2) the TRC clinical, neuropsychological, biofluid, and imaging data will be available to use as high-quality run-in data for downstream clinical trials (22). These combined efficiencies should yield significant savings both in terms of resources and time.
As the TRC-PAD program has been established over the past few years, promising new AD biomarkers have been developed. Plasma-based AD biomarkers, for example, have been shown to effectively predict amyloid positivity (23). When fully validated, the introduction of these diagnostic tools into the TRC screening process may be used to further increase effectiveness. The flexible and modular design of the TRC-PAD IP will accommodate the introduction of these new technologies.
Finally, as news of the TRC-PAD program’s progress has spread, a global network of programs modeled on its approach has begun to take shape. Investigators in several countries have been collaborating with the TRC-PAD program leadership to establish similar programs in their home regions. By using global cloud computing infrastructure and the TRC-PAD IP as a model architecture, these programs have been able to rapidly establish similar platforms in their local jurisdictions. Once fully operational, the global TRC-PAD network aims to provide AD clinical trials with a steady stream of well-characterized, biomarker-confirmed participants yielding savings in time, effort, and expense.

 

Acknowledgments: The authors would like to thank the TRC-PAD participants and their families, sponsors and partners, investigators, site and coordinating center personnel for their contributions in support of the program. Dr. Cummings is supported by Keep Memory Alive (KMA); NIGMS grant P20GM109025; NINDS grant U01NS093334; and NIA grant R01AG053798.

Funding: This work was funded by the U.S. National Institute on Aging (NIA) (grant number 1R010AG053798).

Ethical standard: Institutional Review Boards (IRBs) approved these studies, and all participants gave informed consent before participating.

Conflict of interest: The authors report grants from National Institute on Aging, during the conduct of the study. None of the authors have additional financial interests, relationships or affiliations relevant to the subject of this manuscript.

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

 

References

1. 2020 Alzheimer’s disease facts and figures. Alzheimers Dement. 2020.
2. Watson JL, Ryan L, Silverberg N, Cahan V, Bernard MA. Obstacles And Opportunities In Alzheimer’s Clinical Trial Recruitment. Health Affairs. 2014;33(4):574-579.
3. Grill JD, Galvin JE. Facilitating Alzheimer Disease Research Recruitment. Alzheimer Disease & Associated Disorders. 2014;28(1):1-8.
4. Ritchie CW, Molinuevo JL, Truyen L, et al. Development of interventions for the secondary prevention of Alzheimer’s dementia: the European Prevention of Alzheimer’s Dementia (EPAD) project. Lancet Psychiatry. 2016;3(2):179-186.
5. Lovestone S, Consortium E. The European medical information framework: A novel ecosystem for sharing healthcare data across Europe. Learn Health Syst. 2020;4(2):e10214.
6. Aisen PS, Sperling RA, Cummings J, et al. The Trial-Ready Cohort for Preclinical/prodromal Alzheimer’s Disease (TRC-PAD) Project: An Overview. J Prev Alz Dis 2020; DOI: 10.14283/jpad.2020.45
7. Walsh SP, Raman R, Jones KB, Aisen PS, Group AsDCS. ADCS Prevention Instrument Project: the Mail-In Cognitive Function Screening Instrument (MCFSI). Alzheimer Dis Assoc Disord. 2006;20(4 Suppl 3):S170-178.
8. Li C, Neugroschl J, Luo X, et al. The Utility of the Cognitive Function Instrument (CFI) to Detect Cognitive Decline in Non-Demented Older Adults. J Alzheimers Dis. 2017;60(2):427-437.
9. Lim YY, Ellis KA, Harrington K, et al. Use of the CogState Brief Battery in the assessment of Alzheimer’s disease related cognitive impairment in the Australian Imaging, Biomarkers and Lifestyle (AIBL) study. J Clin Exp Neuropsychol. 2012;34(4):345-358.
10. Lane J, Barker T, Lewis J, Moscovitz M. Responsive Design. In: Foundation Website Creation with HTML5, CSS3, and JavaScript. Berkeley, CA: Apress; 2012.
11. Walter S, Clanton T, Langford O, et al. Recruitment into the Alzheimer Prevention Trials (APT) Webstudy for a Trial-Ready Cohort for Preclinical and Prodromal Alzheimer’s Disease (TRC-PAD). J Prev Alz Dis 2020; DOI: 10.14283/jpad.2020.46
12. Langford O, Raman R, Sperling RA, et al. Predicting amyloid burden to accelerate recruitment of secondary prevention clinical trials. J Prev Alz Dis 2020; DOI: 10.14283/jpad.2020.44
13. Title 21 Part 11 — Electronic Records; Electronic Signatures. In: Electronic Code of Federal Regulations; 1997.
14. Babcock D. The Role of GUIDs in Data Sharing. The NIA Alzheimer’s Disease Centers Program – National Alzheimer’s Coordinating Center. https://www.alz.washington.edu/NONMEMBER/FALL19/Dir/Babcock.pdf. Published 2019. Accessed May 5, 2020.
15. Johnson SB, Whitney G, McAuliffe M, et al. Using global unique identifiers to link autism collections. J Am Med Inform Assoc. 2010;17(6):689-695.
16. O’Leary D. Embedding AI and Crowdsourcing in the Big Data Lake. IEEE Intelligent Systems. 2014;29(5):70-73.
17. Amazon Web Services: AWS Well-Architected Framework. https://d1.awsstatic.com/whitepapers/architecture/AWS_Well-Architected_Framework.pdf. Published 2019. Accessed April 27, 2020.
18. National Institutes of Health: NIH Policy on the Use of a Single Institutional Review Board for Multi-Site Research. https://osp.od.nih.gov/wp-content/uploads/2016/06/NIH_sIRB_Policy_Multi_site_Research_UPDATED2016.pdf. Accessed April 28, 2020.
19. Beck K, Beedle M, van Bennekum A, et al. Manifesto for Agile Software Development. http://agilemanifesto.org/. Accessed May 13, 2020.
20. Hohl P, Klünder J, van Bennekum A, et al. Back to the future: origins and directions of the “Agile Manifesto” – views of the originators. Journal of Software Engineering Research and Development. 2018;6(1):1-27.
21. Walter S, Langford O, Clanton T, et al. The Trial-Ready Cohort for Preclinical/Prodromal Alzheimer’s disease (TRC-PAD): Experience from the first 3 years. J Prev Alz Dis 2020; DOI: 10.14283/jpad.2020.47
22. Laursen DRT, Paludan-Müller AS, Hróbjartsson A. Randomized clinical trials with run-in periods: frequency, characteristics and reporting. Clin Epidemiol. 2019;11:169-184.
23. Ovod V, Ramsey KN, Mawuenyega KG, et al. Amyloid β concentrations and stable isotope labeling kinetics of human plasma specific to central nervous system amyloidosis. Alzheimers Dement. 2017;13(8):841-849.

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INTEGRATING BIOMARKER OUTCOMES INTO CLINICAL TRIALS FOR ALZHEIMER’S DISEASE IN DOWN SYNDROME

 

M.S. Rafii1, S. Zaman2, B.L. Handen3

 

1. Alzheimer’s Therapeutic Research Institute (ATRI), Keck School of Medicine, University of Southern California, USA; 2. Cambridge Intellectual and Developmental Disabilities Research Group, Department of Psychiatry, University of Cambridge, Cambridge, UK & Cambridgeshire & Peterborough NHS Foundation Trust, Fulbourn Hospital, Cambridge, UK; 3. Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.

Corresponding Author: Michael S. Rafii, 1Alzheimer’s Therapeutic Research Institute (ATRI), Keck School of Medicine, University of Southern California, USA, mrafii@usc.edu

J Prev Alz Dis 2021;1(8):48-51
Published online June 19, 2020, http://dx.doi.org/10.14283/jpad.2020.35

 


Abstract

The NIH-funded Alzheimer’s Biomarker Consortium Down Syndrome (ABC-DS) and the European Horizon 21 Consortium are collecting critical new information on the natural history of Alzheimer’s Disease (AD) biomarkers in adults with Down syndrome (DS), a population genetically predisposed to developing AD. These studies are also providing key insights into which biomarkers best represent clinically meaningful outcomes that are most feasible in clinical trials. This paper considers how these data can be integrated in clinical trials for individuals with DS. The Alzheimer’s Clinical Trial Consortium – Down syndrome (ACTC-DS) is a platform that brings expert researchers from both networks together to conduct clinical trials for AD in DS across international sites while building on their expertise and experience.

Key words: Alzheimer’s disease, down syndrome, clinical trials.


 

Introduction

In Down syndrome (DS), which in over 95% cases is caused by trisomy of chromosome 21, the triplication of the APP gene results in increased APP protein expression along with increased Aβ production (1). This leads to the almost universal presence of Alzheimer’s disease (AD) neuropathology by the age of 40 years in people with DS and a very high prevalence rate of dementia over the ensuing 20-30 years (2). AD in DS (DSAD) is remarkably similar to AD in the non-DS population, such as autosomal dominant AD (ADAD) and sporadic, late-onset AD (LOAD) in the general population but moderated by the impact of DS neurodevelopmental factors. Although the extra copy of APP, due to its gene dose effect is the key upstream etiology of neurodegeneration that places a necessary major focus on the amyloid cascade, there are multiple downstream manifestations and biomarkers that need to be characterized. Nonetheless, it is believed that the continuum of AD, that is, the asymptomatic or ‘preclinical’, mildly symptomatic or ‘prodromal’ and fully symptomatic or ‘dementia’ stages progress sequentially in DSAD, just as they do in the other forms of AD. Understanding the predictive relationship between longitudinal changes in standard AD biomarkers and clinical outcomes is therefore critical to their successful implementation in clinical trials for DSAD (Figure 1).

 

Figure 1. Anchoring Clinical Symptoms with Robust Biomarkers Understanding longitudinal change in biomarkers will be critical for staging individuals with DS along the AD continuum and necessary for clinical trials

A-Amyloid, T-Tau; N-Neurodegeneration

 

Biomarkers play a critical role in drug development including characterization of the disease state and its progression as well as a proxy for potential efficacy of interventions. Following drug discovery, pharmacokinetic and pharmacodynamic studies, and lead optimization, the candidate compound then enters the clinical trial stages of drug development comprised of phase I, II and III. Phase I studies are first-in-human exposures beginning with single ascending doses and progressing to multiple ascending doses in small cohorts of healthy human volunteers. Phase II studies focus on collecting additional safety and tolerability data as well as biomarker data such as target engagement supporting modification of disease progression. Phase III studies are large, multicenter studies that collect data on clinical efficacy.
In considering the design of clinical trials for DSAD, whether they be for prevention or modification of the disease process, it will be essential to identify the appropriate sample population (e.g. age range, baseline intellectual functioning), the safety profile of the intervention (e.g. pharmacokinetics, common adverse events, especially those that may be unique to people with DS), as well as the proper outcome measures that will reflect impact on objective measures of disease progression and presumably clinically meaningful outcomes.
To support a claim that biomarker data and clinical results are mediated by the same underlying mechanism, the two must be strongly correlated. Preliminary correlations have been established for certain cognitive outcomes and AD biomarkers, which illustrate remarkable similarities in the behavior of biomarkers in DSAD, ADAD and LOAD, as will be discussed in the next few paragraphs.
In DS, where intellectual disability (ID) is neurodevelopmental and relatively stable in early adulthood, preceding cognitive decline associated with AD, the question arises as to what degree cognitive changes are related to the emergence of the clinical features of AD. Given the immense variability in the pre-morbid or baseline cognitive functioning in DS, it can be extremely challenging to assess cognitive change, especially among those with limited premorbid expressive language skills and/or severe ID (3). This fact further highlights the need for validated AD biomarkers in this population. Results from recent biomarker studies suggest that we may be able to accurately discriminate the progressive brain changes due to AD pathology from the baseline ID in DS and perhaps identify cognitive measures sensitive to early decline related to AD pathology in the context of DS-related ID (4).

 

Key AD Biomarkers in DS

Given the pivotal role of Aβ and tau in AD pathogenesis, it is not surprising that the main biomarkers that have been developed measure amyloid, tau and markers of neurodegeneration. Amyloid PET imaging has been performed using various tracers in adults with DS over the past decade (5-8). Levels of brain amyloid on PET are observed to increase dramatically in people with DS over age 40, mirroring the pathological finding of the universal presence of plaques seen at autopsy (9). Recent imaging studies have demonstrated that amyloid brain accumulation in DS begins in the striatum (10) which is similar to individuals with ADAD (8). Rates of accumulation of amyloid appear to be similar to those observed in the sporadic population with a substantial interval (15-20 years) between elevated brain amyloid and onset of cognitive symptoms (11). This decades-long period defines the ‘preclinical stage’ of AD and is a key target for intervention and allows for secondary prevention, before significant pathology has developed or clinical symptoms have become evident (12, 13).
In DS, the relationship between regional neurofibrillary tangles (NFTs) and atrophy has been well-established with post-mortem studies, as has the correlation of NFTs and cognitive decline (14). By using the PET tracer 18F-AV-1451, regional accumulation of NFTs in DS has been studied, and as seen in other neurodegenerative diseases, the distribution of tau pathology in DS is greatest in areas with atrophy on MRI, involving the medial temporal lobes and spreading posteriorly into the parietal lobes (15). Moreover, it appears that lower cognitive scores correlate with increased tau pathology just as has been reported in LOAD (15).
Changes in regional glucose metabolism, a measure of neuronal activity have been shown to be associated with cognitive decline in people with DS (16). Interestingly, individuals with DS who are not demented show a pattern of hypometabolism within the posterior cingulate-precuneus, a region which is typically observed as hypometabolic in LOAD (4). There also seems to be an inverse relationship between amyloid accumulation and regional glucose metabolism (6) as well as an inverse relationship between tau pathology and regional glucose metabolism (15). Both of these observations support the notion that biomarkers of DSAD are indeed behaving similarly to the other forms of AD: ADAD and LOAD.
Postmortem studies indicate that, although the brains of individuals with DS are typically smaller than age-matched individuals in the general population, a pattern of atrophy involving the medial temporal lobes is observed in the early stages of DSAD that also seems to correlate with declines in specific memory measures (17). Brain atrophy is considered a late manifestation of AD and reflects extensive neurodegeneration but remains an important AD biomarker and again illustrates similarities between DSAD, LOAD and ADAD.
Perhaps one of the most exciting developments in the field of AD biomarkers has been advances in blood-based biomarkers. Individuals with DS have higher plasma Aβ1–42 and Aβ1–40 concentrations compared with individuals without DS (18). And in CSF, elevated levels of Aβ42 are seen early in life, but with age Aβ42 levels decline (owing to their deposition into plaques) while tau levels progressively increase (18). Recent work on CSF and plasma levels of neurofilament light (NfL), a component of the axonal cytoskeleton and marker of neuronal damage and degeneration, has shown strong correlation with cognitive status in adults with DS (19). Specifically, plasma NfL levels appear to increase with age and can distinguish between normal aging in DS and AD (19, 20). Plasma NfL levels have also been shown to correlate with standard biomarkers of AD pathology and markers of neurodegeneration as well as cognitive and functional decline (21).
Despite recent advances in our understanding of AD biomarkers in DS, many questions remain. For example, which cognitive outcome measures will reflect clinical meaningfulness? Will the A/T(N) classification framework apply to DSAD and provide any diagnostic or prognostic value? How early in the course AD should we intervene and for how long should we treat? Should we consider early prevention trials prior to the appearance of AD-related neuropathology (e.g. anti-amyloid therapy for individuals with DS younger than 35 years of age)? How well will anti-amyloid therapies, currently under evaluation for ADAD and LOAD, be tolerated in persons with DS? At what stage of DSAD should anti-tau therapeutics be evaluated? How will regulatory agencies view AD in DS, in terms of an indication?

 

Ongoing Efforts

The landmark ABC-DS is setting the stage for powering clinical trials for DSAD. This observational study launched in 2015, is examining the progression of AD-related biomarkers (Aβ-, tau- and FDG-PET, MRI, cerebrospinal fluid plasma biomarkers and neuropathology) as well as cognitive and functional measures in over 400 adults with DS (22). The data from ABC-DS will enable secondary prevention trials for DSAD by anchoring standard AD biomarkers with cognitive and clinical outcomes in people with DS.
In addition, the Horizon 21 Down Syndrome Consortium in Europe is ongoing, and comprised of various existing DS cohorts from the UK (the London Down Syndrome Consortium [LonDownS] and the Cambridge Dementia in Down’s Syndrome [DiDS] cohort), Netherlands (the Rotterdam Down syndrome study), Germany (AD21 study group, Munich), France (TriAL21 for Lejeune Institute, Paris), and Spain (the Down Alzheimer Barcelona Neuroimaging Initiative (DABNI). This large consortium is collecting longitudinal data on AD-related cognitive and clinical changes along with standard AD biomarkers in over 1,000 participants with DS (23).
The NIH-funded Alzheimer’s Clinical Trial Consortium – Down Syndrome (ACTC-DS) was launched in 2018 to serve as a platform for conducting clinical trials to treat and prevent AD dementia in people with DS. ACTC-DS leverages the infrastructure of the NIA’s Alzheimer Clinical Trial Consortium (ACTC) in order to conduct trials across an international network of sites with expertise in DSAD, including many ABC-DS and Horizon 21performance sites. The first project to be conducted by ACTC-DS is the Trial Ready-Cohort – Down syndrome (TRC-DS), which will enroll 120 non-demented participants with DS into a longitudinal safety-run in study using MRI, amyloid PET, cognitive testing, and fluid biomarkers in preparation for upcoming randomized placebo-controlled clinical trials for DSAD. TRC-DS will allow participants who are medically stable, well-characterized and interested in participating in clinical trials to enroll into a phase II clinical trial of an anti-amyloid therapeutic. Additional therapeutic strategies under consideration for future trials include anti-tau immunotherapy and down-regulation of APP using anti-sense oligonucleotides.

 

Conclusions

Major advances have been made over the past decade in understanding DSAD by utilizing the latest AD biomarkers such as brain imaging and biofluid assays. Indeed, several research groups from around the world have shown that there exist remarkable similarities between DSAD, ADAD and LOAD. The ABC-DS and Horizon 21 projects are setting the stage for conducting secondary prevention trials for DSAD, while ACTC-DS will employ this knowledge and expertise to test the latest and most promising therapeutics for AD in people with DS.

 

Competing Interests: MSR is a consultant to AC Immune.

Funding: MSR was funded by NIH R61AG066543-01.

 

References

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2. Lott IT, Head E. Dementia in Down syndrome: unique insights for Alzheimer disease research. Nat Rev Neurol. 2019;15(3):135–147. doi:10.1038/s41582-018-0132-6
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6. Rafii MS, Wishnek H, Brewer JB, Donohue MC, Ness S, Mobley WC, Aisen PS, Rissman RA. The down syndrome biomarker initiative (DSBI) pilot: proof of concept for deep phenotyping of Alzheimer’s disease biomarkers in down syndrome. Front Behav Neurosci. 2015 Sep 14;9:239.
7. Jennings D, Seibyl J, Sabbagh M, Lai F, Hopkins W, Bullich S, Gimenez M, Reininger C, Putz B, Stephens A, Catafau AM, Marek K. Age dependence of brain β-amyloid deposition in Down syndrome: An [18F]florbetaben PET study. Neurology. 2015 Feb 3;84(5):500-7.
8. Annus T, Wilson LR, Hong YT, Acosta-Cabronero J, Fryer TD, Cardenas-Blanco A, Smith R, Boros I, Coles JP, Aigbirhio FI, Menon DK, Zaman SH, Nestor PJ, Holland AJ. The pattern of amyloid accumulation in the brains of adults with Down syndrome. Alzheimers Dement. 2016 May;12(5):538-45.
9. Lao PJ, Betthauser TJ, Hillmer AT, Price JC, Klunk WE, Mihaila I, Higgins AT, Bulova PD, Hartley SL, Hardison R, Tumuluru RV, Murali D, Mathis CA, Cohen AD, Barnhart TE, Devenny DA, Mailick MR, Johnson SC, Handen BL, Christian BT. The effects of normal aging on amyloid-β deposition in nondemented adults with Down syndrome as imaged by carbon 11-labeled Pittsburgh compound B. Alzheimers Dement. 2016 Apr;12(4):380-90.
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11. Lao PJ, Handen BL, Betthauser TJ, Mihaila I, Hartley SL, Cohen AD, Tudorascu DL, Bulova PD, Lopresti BJ, Tumuluru RV, Murali D, Mathis CA, Barnhart TE, Stone CK, Price JC, Devenny DA, Mailick MR, Klunk WE, Johnson SC, Christian BT. Longitudinal changes in amyloid positron emission tomography and volumetric magnetic resonance imaging in the nondemented Down syndrome population. Alzheimers Dement (Amst). 2017 May 23;9:1-9.
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13. Sperling RA, Jack CR, Jr., Aisen PS. Testing the right target and right drug at the right stage. Sci. Transl. Med. 2011:3:111-133.
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15. Rafii MS, Lukic AS, Andrews RD, Brewer J, Rissman RA, Strother SC, Wernick MN, Pennington C, Mobley WC, Ness S, Matthews DC; Down Syndrome Biomarker Initiative and the Alzheimer’s Disease Neuroimaging Initiative. PET Imaging of Tau Pathology and Relationship to Amyloid, Longitudinal MRI, and Cognitive Change in Down Syndrome: Results from the Down Syndrome Biomarker Initiative (DSBI). J Alzheimers Dis. 2017;60(2):439-450.
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18. Fortea J, Carmona-Iragui M, Benejam B, Fernandez S, Videla L, Barroeta I, et al. Plasma and CSF biomarkers for the diagnosis of Alzheimer’s disease in adults with Down syndrome: a cross-sectional study. Lancet Neurol. 2018;17(10):860-9.
19. Strydom A, Heslegrave A, Startin CM, et al. Neurofilament light as a blood biomarker for neurodegeneration in Down syndrome. Alzheimers Res Ther. 2018;10(1):39.
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DATA-DRIVEN PARTICIPANT RECRUITMENT: FINDINGS FROM THE ALZHEIMER’S DISEASE NEUROIMAGING INITIATIVE 3

 

C. Barger3, J. Fockler1,2, W. Kwang1,2, S. Moore3, D. Flenniken1,2, A. Ulbricht1,2, P. Aisen3, M.W. Weiner1,2 on behalf of the Alzheimer’s Disease Neuroimaging Initiative

 

1. Center for Imaging of Neurodegenerative Diseases, San Francisco Veteran’s Administration Medical Center, San Francisco, CA, USA; 2. University of California San Francisco (UCSF) Department of Radiology and Biomedical Imaging, San Francisco, CA, USA; 3. Alzheimer’s Therapeutic Research Institute, University of Southern California (USC) San Diego, CA, USA

Corresponding Author: Charissa Barger, Alzheimer’s Therapeutic Research Institute, University of Southern California (USC) San Diego, CA, USA, charissa.barger@syneoshealth.com, 213-253-8387

J Prev Alz Dis 2020;
Published online February 7, 2020, http://dx.doi.org/10.14283/jpad.2020.5

 


Abstract

Background: Effective and measurable participant recruitment methods are urgently needed for clinical studies in Alzheimer’s disease.
Objectives: To develop methods for measuring recruitment tactics and evaluating effectiveness.
Methods: Recruitment tactics for the Alzheimer’s Disease Neuroimaging Initiative (ADNI3) were measured using web and phone analytics, campaign metrics and survey responses.
Results: A total of 462 new participants were enrolled into ADNI3 through recruitment efforts. We collected metrics on recruitment activities including 82,003 unique visitors to the recruitment website and 3,335 calls to study phone numbers. The recruitment sources that produced the most screening and enrollment included online advertisements, local radio and newspaper coverage and emails and referrals from registries.
Conclusions: Analysis of recruitment activity obtained through tracking methods provided some insight for effective recruitment. ADNI3 can serve as an example of how a data-driven approach to centralized participant recruitment can be utilized to facilitate clinical research.

Key words: Clinical trials, recruitment, Alzheimer’s disease.


 

Introduction

In the United States, an estimated 5.8 million people are currently living with Alzheimer’s disease (AD) (1) and this is projected to increase to 13.8 million by 2050 (2). AD and other dementias are estimated to cost the United States $290 billion in healthcare, long-term care and hospice and that could reach as much as $1.1 trillion by 2050 (1). There is an increasing need of effective treatments and preventative measures for AD as the prevalence and overall costs of AD care will continue to dramatically rise within the next 30 years. However, a major obstacle in clinical research progress and developing new AD treatments is the identification and enrollment of participants in clinical research [3]. Because clinical trials commonly experience high screen failure rates, it is crucial to reach as many potential participants as possible and then implement more innovative ways to pre-screen a large volume of interested individuals. Difficulty in participant recruitment commonly results in early termination of clinical trials (4). In contrast, this issue can also lead to extending the duration of clinical trials, which in turn increases costs and delays study progress (3). Although participant recruitment has been primarily conducted at the research site-level, as studies become more competitive for participants, there is a greater need to expand efforts and provide referrals to research sites through more centralized activities, including utilizing online advertising, media coverage and web-based registries (4-8), that can reach a larger number of potential participants across multiple research sites.
Launched in 2004, the Alzheimer’s Disease Neuroimaging Initiative (ADNI), funded by the National Institutes of Health, is a longitudinal observational study aimed at discovering, optimizing, standardizing and validating clinical trial measures and biomarkers used in ongoing AD research. Participation in ADNI includes clinical/cognitive, imaging, biomarker and genetic assessments. It is now in its third phase of trials termed ADNI3, which is a continuation of ADNI, ADNI-GO and ADNI2. ADNI3’s cohort includes both new participants and participants retained from previous iterations of ADNI. Recruitment began September 2016 and is currently ongoing with aims to enroll up to 1,200 new participants across 59 research sites in the United States and Canada. Participants are ages 55 to 90 and enrolled across three cohorts: cognitively normal, mild cognitive impairment and mild AD dementia.
To address the need to expand recruitment efforts to targeted populations, the ADNI3‘s Coordinating Center utilized a range of recruitment activities that can be linked to measurable outcomes. The Coordinating Center collected recruitment metrics, which provided some preliminary insights on which methods were more effective at reaching a greater number of potential participants. By using a multi-faceted recruitment plan, ADNI3 can serve as an example of how a data-driven approach to centralized participant recruitment can be utilized to facilitate clinical research.

 

Methods

Recruitment Activities

On behalf of ADNI3, the Coordinating Center managed recruitment activities to connect potential participants directly with local research sites. These activities included: national newspaper and radio coverage; local TV and newspaper coverage; and search engine, website, social media and newspaper advertisements.
Additional recruitment activities were provided by internet-based registries including: the National Institute on Aging’s Alzheimer’s and related Dementias Education and Referral Center (ADEAR); Banner Alzheimer’s Institute’s Alzheimer’s Prevention Registry (APR); and University of California, San Francisco’s (UCSF) Brain Health Registry (BHR). These registries aim to provide a pool of screened potential participants who have provided information including contact information and demographics and have already expressed a willingness to participate in research (6, 9). The recruitment activities included: online study listings (provided by APR, ADEAR, ClinicalTrials.gov), emails to their existing list of registrants (provided by APR and BHR) and direct referrals to research sites (provided by BHR). Direct BHR referrals applied exclusion/inclusion criteria to their pool of over 65,000 participants nationally to pre-screen and refer them to local research sites (9). The selected BHR participants were provided research site contact information and a referral code to be brought with them to their screening visit.

Website and Data Collection

Most digital activities funneled visitors to the ADNI3 recruitment website, ADNI3.org, which was developed as a collaboration between the Alzheimer’s Therapeutic Research Institute (ATRI) at the University of Southern California and the Brain Health Registry (BHR) at UCSF.
The website, powered by BHR, provided education about ADNI3 with multiple links to a pre-screener questionnaire to guide visitors to complete it as the preferred call to action. The questionnaire asked study-specific questions to determine eligibility. Those who were potentially eligible were prompted to enter their contact information in a sign up form, which connected referrals to a local research site to enroll. The website also included a “research site finder” map with the ability to cater to the needs of individual research sites by pausing or stopping referrals from the website.
The website captured data using the methods listed below to track visitors during the recruitment process:
1. Trackable Links: Custom URLs were used to identify how web visitors arrived at the recruitment website. These were placed in all digital communication, advertisements, emails, articles and social media posts.
2. Sign Up Form: This web-based form collected contact information, birth year and zip codes from visitors to determine if there was a research site enrolling in their area. They were then provided with a referral code and the research sites’ contact information immediately or once their local site began enrolling.
3. Phone Numbers: Unique phone numbers were used to track phone calls to research sites from recruitment campaigns and the recruitment website. Masked local phone numbers, that rang through to the research site’s phone number, enabled tracking of how many calls were placed from the recruitment website. Also, an interactive voice response (IVR) system automatically forwarded calls for interested participants to their local site after providing their zip code. The system then logged any activity and provided data on what calls were placed and where they were forwarded. Additionally, the phone number a participant called was used as a referral code for measuring conversion into the study.
4. Contact Us Form: This web-based form allowed visitors to provide their contact information for a specific research site. This was provided to the site along with a referral code to track them into enrollment.
Additional information was collected and recorded in an electronic case report form at the in-person screening visit to determine where the participant heard about the study. Once the participant was in clinic, they were asked questions about how they heard about the study and whether they had a referral code in the screening questionnaire. Questions include: “How did you hear about the study?” and “From whom did you hear about the study?”. If the participant had a referral code, the site staff was responsible for entering the specific referral code into the online form. Any additional comments or specifics were collected in qualitative responses.

Data Analysis

All data was reported visually through an active dashboard, allowing all recruitment metrics described above to be seen in real-time, in one place. The dashboard, managed by the BHR, included, but was not limited to: the number of sign up forms completed and what site the interested participant was referred to, how many calls were placed from the recruitment website and how many consented study participants provided referral codes. Additionally, recruitment campaigns that could not have specific data attached, like radio interviews, could be filtered by date to show website activity within a specified timeframe. Google Analytics Dashboards were also used to view website traffic data and duration of web sessions. Figure 1 shows the flow of recruitment activities through the website to study enrollment with data displayed by the dashboard.
The dashboards were reviewed regularly to assess current recruitment activities and make informed decisions on recruitment tactics. We assessed increases in website traffic, use of trackable links, website activities completed and phone call volume to gauge effectiveness of recruitment activities compared to daily averages.

Figure 1. ADNI3 referral flow by recruitment Activities

Figure 1. ADNI3 referral flow by recruitment Activities

 

Results

Enrollment Summary

As of August 2019, 892 individuals enrolled into ADNI3, including 430 participants that continued from ADNI2 and 462 newly enrolled participants. The average age of new participants was 71; 13% were minority participants, defined as either Latino/Hispanic or non-White; 54% were female; and the average education was 16.6 years.
Data collected at the screening visits showed that approximately 52% of new participants were enrolled through site-driven activities, such as physician referrals, community outreach events and patient database outreach. The remaining 48% of new participants were enrolled through recruitment activities managed by the Coordinating Center, which will be focused on for this paper.

Recruitment Efforts

Figure 2 summarizes the recruitment activities utilized by the Coordinating Center and subsequent data collected. As a result of central recruitment efforts, there have been 82,003 unique visitors to ADNI3.org, 2,189 phone calls to the IVR phone number and 1,146 phone calls through local masked phone numbers. Of the visitors to the website, 15.5% engaged in some form of website activity by completing the pre-screener, contact us form, sign up form, or calling a site directly using the masked phone number. In total 12,728 referrals were provided to research sites through the recruitment website.

Figure 2. Recruitment activities conducted by the coordinating center

Figure 2. Recruitment activities conducted by the coordinating center

NOTE: Figure 2 includes 35% of screening and enrollment data that can accurately be linked to recruitment activities conducted by the Coordinating Center. Numbers included in Website Visitors and Contacted Research Site are 100 times the actual number. Condensed numbers were provided for visibility.

 

Of the 82,003 website visitors, 56,170 (68.5%) can be linked directly to the source of recruitment through trackable links. The majority of the website visitors tracked by links came from Facebook advertisements with 40,833 tracked users (72.7%). The trackable links identified only two individuals who had referral codes which were reported to site staff at the screening visit. In contrast, APR had only 2,319 tracked users (4.13%) but ultimately led to 13 referral codes reported at the screening visit.
764 in-person questionnaires were completed at the screening visit to collect information on where participants heard about the study. These included 132 referral codes but no local phone number referral codes. Of the questionnaires completed, several included conflicting information about the source of recruitment. For example, of the 206 individuals that selected a registry as the source from which they heard about the study, 13% indicated a conflicting source in how they heard about the study (5 from advertising, 2 from media coverage and 20 from site outreach).

Most Effective Recruitment Sources

The recruitment sources that produced the most referrals through ADNI3.org include: Facebook and Google advertisements targeted by age and location; radio interviews in local markets with the Principal Investigator; articles and press releases in local newspapers; and emails from Caring.com and APR. These activities resulted in 65% of the total website traffic, 46% of the total completed website pre-screeners and 26% of the total calls. 27% of new participant enrollment can be linked to a registry using the self-reported data collected at the screening visit. The most effective registry activity for recruitment was direct referrals provided by BHR resulting in at least 73 individuals screened and 48 enrolled, as shown in the referral codes collected.

Using Data to Improve Recruitment Activities

Analyzing data throughout the study provided insight on how recruitment activities were performing and helped to determine recruitment plans. Following the April 2018 email from APR, a total of 1,159 trackable links were collected on the website and 496 of these visitors (42.7%) were referred to a research site. As a result of this information, additional emails were conducted with APR to provide referrals to sites, which resulted in 1,265 more trackable links collected and 448 of these visitors (35.4%) were referred to a research site.

Use of Website Pre-screener

The most popular action on ADNI3.org was to complete the pre-screener form (12%). Of those that completed the pre-screener, 56% were deemed eligible and referred to a site. This minimized site burden by eliminating approximately 4,294 participants deemed ineligible, or 72 individuals per site.

 

Discussion

The major conclusion from this study was that quantitative data concerning all steps of the recruitment process provided useful information concerning the efficiency of each step. The data provided some insight into which recruitment activities were producing the most referrals, which was helpful for recruitment planning. We were able to connect a large number of recruitment activities to website traffic but data on the source of enrollment was only available for a small percentage due to several limitations.
Use of trackable links provided some insight on which recruitment activities led a potential participant to the website and subsequent enrollment. However, some trackable link data is not captured due to private browsing, firewalls and website blocking cookies. Furthermore, some responses to the screening questionnaire collected at the clinic visit conflicted with the referral code provided. These discrepancies might be due to human error from participants and study staff or might suggest that multiple touchpoints of recruitment are needed for successful enrollment. Our efforts to track campaigns by using local phone numbers that could serve as referral codes during the screening visit was ineffective, and no phone numbers were provided as referral codes. In addition, the more manual methods of tracking recruitment activities, such as the metrics that rely on site data entry, were completed partially or inconsistently.
We would recommend collecting self-report screening questionnaires and referral codes at the initial contact with the participant (i.e., during the pre-screening telephone call) to improve accuracy. Questions asked should be simplified for clarity with limited choices to avoid confusion and provide cleaner data. More robust methods for collecting referral codes should also be employed, such as limiting data entry fields to ensure consistency in referral codes or automating referral codes through the use of a QR codes.
Analyzing recruitment metrics, we were able to obtain a preliminary assessment of central recruitment efforts in real-time to inform recruitment planning. We increased the most successful recruitment activities based on these assessments. Additionally, we realized that the quality of the source was sometimes more important than the quantity of users reached. For instance, Facebook reached a far larger number of users than APR yet APR had a larger number of users who were screened in clinic. We believe this was because APR’s registry members already had a greater interest in participating in research studies whereas Facebook reached a broader audience. With more accurate and complete data, we would be able to more effectively measure tactics to gauge the most successful recruitment activities and connect these to actual enrollment.
Although the use of an online pre-screener is not a recruitment tactic, we found that it was an effective entry method for referrals to reduce site burden by eliminating referrals that do not meet the basic study criteria. This conclusion was possible due to tracking website activity including the number of online pre-screeners completed and the user’s subsequent eligibility status.
We found that central recruitment efforts using a multi-faceted approach of media coverage, online advertising and registry outreach can be effective at providing sites with eligible referrals, and subsequently leading to increased screening and enrollment. Driving recruitment activities to a central website with tracking capabilities allows efforts to be measured and evaluated for efficacy. This data can then be used to inform recruitment planning. Using this data-driven approach to centralized recruitment, investigators can effectively and efficiently pursue enrollment goals.

 

Acknowledgements: Michael Weiner, MD (UC San Francisco, Principal Investigator, Executive Committee); Paul Aisen, MD (UC San Diego, ADCS PI and Director of the Coordinating Center Clinical Core, Executive Committee, Clinical Core Leaders); Ronald Petersen, MD, PhD (Mayo Clinic, Rochester, Executive Committee, Clinical Core Leader); Clifford R. Jack, Jr., MD (Mayo Clinic, Rochester, Executive Committee, MRI Core Leader); William Jagust, MD (UC Berkeley, Executive Committee; PET Core Leader); John Q. Trojanowki, MD, PhD (U Pennsylvania, Executive Committee, Biomarkers Core Leader); Arthur W. Toga, PhD (USC, Executive Committee, Informatics Core Leader); Laurel Beckett, PhD (UC Davis, Executive Committee, Biostatistics Core Leader); Robert C. Green, MD, MPH (Brigham and Women’s Hospital, Harvard Medical School, Executive Committee and Chair of Data and Publication Committee); Andrew J. Saykin, PsyD (Indiana University, Executive Committee, Genetics Core Leader); John Morris, MD (Washington University St. Louis, Executive Committee, Neuropathology Core Leader); Leslie M. Shaw (University of Pennsylvania, Executive Committee, Biomarkers Core Leader); Enchi Liu, PhD (Janssen Alzheimer Immunotherapy, ADNI 2 Private Partner Scientific Board Chair); Tom Montine, MD, PhD (University of Washington) ; Ronald G. Thomas, PhD (UC San Diego); Michael Donohue, PhD (UC San Diego); Sarah Walter, MSc (UC San Diego); Devon Gessert (UC San Diego); Tamie Sather, MS (UC San Diego,); Gus Jiminez, MBS (UC San Diego); Danielle Harvey, PhD (UC Davis;); Michael Donohue, PhD (UC San Diego); Matthew Bernstein, PhD (Mayo Clinic, Rochester); Nick Fox, MD (University of London); Paul Thompson, PhD (USC School of Medicine); Norbert Schuff, PhD (UCSF MRI); Charles DeCArli, MD (UC Davis); Bret Borowski, RT (Mayo Clinic); Jeff Gunter, PhD (Mayo Clinic); Matt Senjem, MS (Mayo Clinic); Prashanthi Vemuri, PhD (Mayo Clinic); David Jones, MD (Mayo Clinic); Kejal Kantarci (Mayo Clinic); Chad Ward (Mayo Clinic); Robert A. Koeppe, PhD (University of Michigan, PET Core Leader); Norm Foster, MD (University of Utah); Eric M. Reiman, MD (Banner Alzheimer’s Institute); Kewei Chen, PhD (Banner Alzheimer’s Institute); Chet Mathis, MD (University of Pittsburgh); Susan Landau, PhD (UC Berkeley); Nigel J. Cairns, PhD, MRCPath (Washington University St. Louis); Erin Householder (Washington University St. Louis); Lisa Taylor Reinwald, BA, HTL (Washington University St. Louis); Virginia Lee, PhD, MBA (UPenn School of Medicine); Magdalena Korecka, PhD (UPenn School of Medicine); Michal Figurski, PhD (UPenn School of Medicine); Karen Crawford (USC); Scott Neu, PhD (USC); Tatiana M. Foroud, PhD (Indiana University); Steven Potkin, MD UC (UC Irvine); Li Shen, PhD (Indiana University); Faber Kelley, MS, CCRC (Indiana University); Sungeun Kim, PhD (Indiana University); Kwangsik Nho, PhD (Indiana University); Zaven Kachaturian, PhD (Khachaturian, Radebaugh & Associates, Inc and Alzheimer’s Association’s Ronald and Nancy Reagan’s Research Institute); Richard Frank, MD, PhD (General Electric); Peter J. Snyder, PhD (Brown University); Susan Molchan, PhD (National Institute on Aging/ National Institutes of Health); Jeffrey Kaye, MD (Oregon Health and Science University); Joseph Quinn, MD (Oregon Health and Science University); Betty Lind, BS (Oregon Health and Science University); Raina Carter, BA (Oregon Health and Science University); Sara Dolen, BS (Oregon Health and Science University); Lon S. Schneider, MD (University of Southern CaliforGroups Acknowledgements Journal Format nia); Sonia Pawluczyk, MD (University of Southern California); Mauricio Beccera, BS (University of Southern California); Liberty Teodoro, RN (University of Southern California); Bryan M. Spann, DO, PhD (University of Southern California); James Brewer, MD, PhD (University of California San Diego); Helen Vanderswag, RN (University of California San Diego); Adam Fleisher, MD (University of California San Diego); Judith L. Heidebrink, MD, MS (University of Michigan); Joanne L. Lord, LPN, BA, CCRC (University of Michigan); Ronald Petersen, MD, PhD (Mayo Clinic, Rochester); Sara S. Mason, RN (Mayo Clinic, Rochester); Colleen S. Albers, RN (Mayo Clinic, Rochester); David Knopman, MD (Mayo Clinic, Rochester); Kris Johnson, RN (Mayo Clinic, Rochester); Rachelle S. Doody, MD, PhD (Baylor College of Medicine); Javier Villanueva Meyer, MD (Baylor College of Medicine); Munir Chowdhury, MBBS, MS (Baylor College of Medicine); Susan Rountree, MD (Baylor College of Medicine); Mimi Dang, MD (Baylor College of Medicine); Yaakov Stern, PhD (Columbia University Medical Center); Lawrence S. Honig, MD, PhD (Columbia University Medical Center); Karen L. Bell, MD (Columbia University Medical Center); Beau Ances, MD (Washington University, St. Louis); John C. Morris, MD (Washington University, St. Louis); Maria Carroll, RN, MSN (Washington University, St. Louis); Sue Leon, RN, MSN (Washington University, St. Louis); Erin Householder, MS, CCRP (Washington University, St. Louis); Mark A. Mintun, MD (Washington University, St. Louis); Stacy Schneider, APRN, BC, GNP (Washington University, St. Louis); Angela Oliver, RN, BSN, MSG ; Daniel Marson, JD, PhD (University of Alabama Birmingham); Randall Griffith, PhD, ABPP (University of Alabama Birmingham); David Clark, MD (University of Alabama Birmingham); David Geldmacher, MD (University of Alabama Birmingham); John Brockington, MD (University of Alabama Birmingham); Erik Roberson, MD (University of Alabama Birmingham); Hillel Grossman, MD (Mount Sinai School of Medicine); Effie Mitsis, PhD (Mount Sinai School of Medicine); Leyla deToledo-Morrell, PhD (Rush University Medical Center); Raj C. Shah, MD (Rush University Medical Center); Ranjan Duara, MD (Wien Center); Daniel Varon, MD (Wien Center); Maria T. Greig, HP (Wien Center); Peggy Roberts, CNA (Wien Center); Marilyn Albert, PhD (Johns Hopkins University); Chiadi Onyike, MD (Johns Hopkins University); Daniel D’Agostino II, BS (Johns Hopkins University); Stephanie Kielb, BS (Johns Hopkins University); James E. Galvin, MD, MPH (New York University); Dana M. Pogorelec (New York University); Brittany Cerbone (New York University); Christina A. Michel (New York University); Henry Rusinek, PhD (New York University); Mony J de Leon, EdD (New York University); Lidia Glodzik, MD, PhD (New York University); Susan De Santi, PhD (New York University); P. Murali Doraiswamy, MD (Duke University Medical Center); Jeffrey R. Petrella, MD (Duke University Medical Center); Terence Z. Wong, MD (Duke University Medical Center); Steven E. Arnold, MD (University of Pennsylvania); Jason H. Karlawish, MD (University of Pennsylvania); David Wolk, MD (University of Pennsylvania); Charles D. Smith, MD (University of Kentucky); Greg Jicha, MD (University of Kentucky); Peter Hardy, PhD (University of Kentucky); Partha Sinha, PhD (University of Kentucky); Elizabeth Oates, MD (University of Kentucky); Gary Conrad, MD (University of Kentucky); Oscar L. Lopez, MD (University of Pittsburgh); MaryAnn Oakley, MA (University of Pittsburgh); Donna M. Simpson, CRNP, MPH (University of Pittsburgh); Anton P. Porsteinsson, MD (University of Rochester Medical Center); Bonnie S. Goldstein, MS, NP (University of Rochester Medical Center); Kim Martin, RN (University of Rochester Medical Center); Kelly M. Makino, BS (University of Rochester Medical Center); M. Saleem Ismail, MD (University of Rochester Medical Center); Connie Brand, RN (University of Rochester Medical Center); Ruth A. Mulnard, DNSc, RN, FAAN (University of California, Irvine); Gaby Thai, MD (University of California, Irvine); Catherine Mc Adams Ortiz, MSN, RN, A/GNP (University of California, Irvine); Kyle Womack, MD (University of Texas Southwestern Medical School); Dana Mathews, MD, PhD (University of Texas Southwestern Medical School); Mary Quiceno, MD (University of Texas Southwestern Medical School); Ramon Diaz Arrastia, MD, PhD (University of Texas Southwestern Medical School); Richard King, MD (University of Texas Southwestern Medical School); Myron Weiner, MD (University of Texas Southwestern Medical School); Kristen Martin Cook, MA (University of Texas Southwestern Medical School); Michael DeVous, PhD (University of Texas Southwestern Medical School); Allan I. Levey, MD, PhD (Emory University); James J. Lah, MD, PhD (Emory University); Janet S. Cellar, DNP, PMHCNS BC (Emory University); Jeffrey M. Burns, MD (University of Kansas, Medical Center); Heather S. Anderson, MD (University of Kansas, Medical Center); Russell H. Swerdlow, MD (University of Kansas, Medical Center); Liana Apostolova, MD (University of California, Los Angeles); Kathleen Tingus, PhD (University of California, Los Angeles); Ellen Woo, PhD (University of California, Los Angeles); Daniel H.S. Silverman, MD, PhD (University of California, Los Angeles); Po H. Lu, PsyD (University of California, Los Angeles); George Bartzokis, MD (University of California, Los Angeles); Neill R Graff Radford, MBBCH, FRCP (London) (Mayo Clinic, JacksonGroups Acknowledgements Journal Format 2 ville); Francine Parfitt, MSH, CCRC (Mayo Clinic, Jacksonville); Tracy Kendall, BA, CCRP (Mayo Clinic, Jacksonville); Heather Johnson, MLS, CCRP (Mayo Clinic, Jacksonville); Martin R. Farlow, MD (Indiana University); Ann Marie Hake, MD (Indiana University); Brandy R. Matthews, MD (Indiana University); Scott Herring, RN, CCRC (Indiana University); Cynthia Hunt, BS, CCRP (Indiana University); Christopher H. van Dyck, MD (Yale University School of Medicine); Richard E. Carson, PhD (Yale University School of Medicine); Martha G. MacAvoy, PhD (Yale University School of Medicine); Howard Chertkow, MD (McGill Univ., Montreal Jewish General Hospital); Howard Bergman, MD (McGill Univ., Montreal Jewish General Hospital); Chris Hosein, Med (McGill Univ., Montreal Jewish General Hospital); Sandra Black, MD, FRCPC (Sunnybrook Health Sciences, Ontario); Dr Bojana Stefanovic (Sunnybrook Health Sciences, Ontario); Curtis Caldwell, PhD (Sunnybrook Health Sciences, Ontario); Ging Yuek Robin Hsiung, MD, MHSc, FRCPC (U.B.C. Clinic for AD & Related Disorders); Howard Feldman, MD, FRCPC (U.B.C. Clinic for AD & Related Disorders); Benita Mudge, BS (U.B.C. Clinic for AD & Related Disorders); Michele Assaly, MA Past (U.B.C. Clinic for AD & Related Disorders); Andrew Kertesz, MD (Cognitive Neurology St. Joseph’s, Ontario); John Rogers, MD (Cognitive Neurology St. Joseph’s, Ontario); Dick Trost, PhD (Cognitive Neurology St. Joseph’s, Ontario); Charles Bernick, MD (Cleveland Clinic Lou Ruvo Center for Brain Health); Donna Munic, PhD (Cleveland Clinic Lou Ruvo Center for Brain Health); Diana Kerwin, MD (Northwestern University); Marek Marsel Mesulam, MD (Northwestern University); Kristine Lipowski, BA (Northwestern University); Chuang Kuo Wu, MD, PhD (Northwestern University); Nancy Johnson, PhD (Northwestern University); Carl Sadowsky, MD (Premiere Research Inst (Palm Beach Neurology)); Walter Martinez, MD (Premiere Research Inst (Palm Beach Neurology)); Teresa Villena, MD (Premiere Research Inst (Palm Beach Neurology)); Raymond Scott Turner, MD, PhD (Georgetown University Medical Center); Kathleen Johnson, NP (Georgetown University Medical Center); Brigid Reynolds, NP (Georgetown University Medical Center); Reisa A. Sperling, MD (Brigham and Women’s Hospital); Keith A. Johnson, MD (Brigham and Women’s Hospital); Gad Marshall, MD (Brigham and Women’s Hospital); Meghan Frey (Brigham and Women’s Hospital); Jerome Yesavage, MD (Stanford University); Joy L. Taylor, PhD (Stanford University); Barton Lane, MD (Stanford University); Allyson Rosen, PhD (Stanford University); Jared Tinklenberg, MD (Stanford University); Marwan N. Sabbagh, MD (Banner Sun Health Research Institute); Christine M. Belden, PsyD (Banner Sun Health Research Institute); Sandra A. Jacobson, MD (Banner Sun Health Research Institute); Sherye A. Sirrel, MS (Banner Sun Health Research Institute); Neil Kowall, MD (Boston University); Ronald Killiany, PhD (Boston University); Andrew E. Budson, MD (Boston University); Alexander Norbash, MD (Boston University); Patricia Lynn Johnson, BA (Boston University); Thomas O. Obisesan, MD, MPH (Howard University); Saba Wolday, MSc (Howard University); Joanne Allard, PhD (Howard University); Alan Lerner, MD (Case Western Reserve University); Paula Ogrocki, PhD (Case Western Reserve University); Leon Hudson, MPH (Case Western Reserve University); Evan Fletcher, PhD (University of California, Davis Sacramento); Owen Carmichael, PhD (University of California, Davis Sacramento); John Olichney, MD (University of California, Davis Sacramento); Charles DeCarli, MD (University of California, Davis Sacramento); Smita Kittur, MD (Neurological Care of CNY); Michael Borrie, MB ChB (Parkwood Hospital); T Y Lee, PhD (Parkwood Hospital); Dr Rob Bartha, PhD (Parkwood Hospital); Sterling Johnson, PhD (University of Wisconsin); Sanjay Asthana, MD (University of Wisconsin); Cynthia M. Carlsson, MD (University of Wisconsin); Steven G. Potkin, MD (University of California, Irvine BIC); Adrian Preda, MD (University of California, Irvine BIC); Dana Nguyen, PhD (University of California, Irvine BIC); Pierre Tariot, MD (Banner Alzheimer’s Institute); Adam Fleisher, MD (Banner Alzheimer’s Institute); Stephanie Reeder, BA (Banner Alzheimer’s Institute); Vernice Bates, MD (Dent Neurologic Institute); Horacio Capote, MD (Dent Neurologic Institute); Michelle Rainka, PharmD, CCRP (Dent Neurologic Institute); Douglas W. Scharre, MD (Ohio State University); Maria Kataki, MD, PhD (Ohio State University); Anahita Adeli, MD (Ohio State University); Earl A. Zimmerman, MD (Albany Medical College); Dzintra Celmins, MD (Albany Medical College); Alice D. Brown, FNP (Albany Medical College); Godfrey D. Pearlson, MD (Hartford Hosp, Olin Neuropsychiatry Research Center); Karen Blank, MD (Hartford Hosp, Olin Neuropsychiatry Research Center); Karen Anderson, RN (Hartford Hosp, Olin Neuropsychiatry Research Center); Robert B. Santulli, MD (Dartmouth Hitchcock Medical Center); Tamar J. Kitzmiller (Dartmouth Hitchcock Medical Center); Eben S. Schwartz, PhD (Dartmouth Hitchcock Medical Center); Kaycee M. Sink, MD, MAS (Wake Forest University Health Sciences); Jeff D. Williamson, MD, MHS (Wake Forest University Health Sciences); Pradeep Garg, PhD (Wake Forest University Health Sciences); Franklin Watkins, MD (Wake Forest University Health Sciences); Brian R. Ott, MD (Rhode Island Hospital); Henry Querfurth, MD (Rhode Island Hospital); Geoffrey Tremont, PhD (Rhode Island Groups Acknowledgements Journal Format 3 Hospital); Stephen Salloway, MD, MS (Butler Hospital); Paul Malloy, PhD (Butler Hospital); Stephen Correia, PhD (Butler Hospital); Howard J. Rosen, MD (UC San Francisco); Bruce L. Miller, MD (UC San Francisco); Jacobo Mintzer, MD, MBA (Medical University of South Carolina); Kenneth Spicer, MD, PhD (Medical University of South Carolina); David Bachman, MD (Medical University of South Carolina); Elizabeth Finger, MD (St. Joseph’s Health Care); Stephen Pasternak, MD (St. Joseph’s Health Care); Irina Rachinsky, MD (St. Joseph’s Health Care); John Rogers, MD (St. Joseph’s Health Care); Andrew Kertesz, MD (St. Joseph’s Health Care); Dick Drost, MD (St. Joseph’s Health Care); Nunzio Pomara, MD (Nathan Kline Institute); Raymundo Hernando, MD (Nathan Kline Institute); Antero Sarrael, MD (Nathan Kline Institute); Susan K. Schultz, MD (University of Iowa College of Medicine, Iowa City); Laura L. Boles Ponto, PhD (University of Iowa College of Medicine, Iowa City); Hyungsub Shim, MD (University of Iowa College of Medicine, Iowa City); Karen Elizabeth Smith, RN (University of Iowa College of Medicine, Iowa City); Norman Relkin, MD, PhD (Cornell University); Gloria Chaing, MD (Cornell University); Lisa Raudin, PhD (Cornell University); Amanda Smith, MD (University of South Florida: USF Health Byrd Alzheimer’s Institute); Kristin Fargher, MD (University of South Florida: USF Health Byrd Alzheimer’s Institute); Balebail Ashok Raj, MD (University of South Florida: USF Health Byrd Alzheimer’s Institute). Additionally, the authors would like to thank the Brain Health Registry team at UCSF, notably Rachel Nosheny, PhD, Scott Mackin, PhD, Diana Truran-Sacrey, Monica Camacho, Taylor Howell, Josh Hwang, Alexander Happ, Joseph Eichenbaum and Tirzah Williams.

Funding: The Alzheimer’s Disease Neuroimaging Initiative (ADNI) is funded by the National Institutes of Health (NIH) (Grant U19 AG024904). ADNI is made possible with funding from the NIH and private sector support raised by the Foundation for the NIH from the following organizations: AbbVie, Accelerate Cure/Treatment for Alzheimer’s Disease, Alector, Alzheimer’s Association, Araclon Biotech, BioClinica Inc, Biogen, Cogstate, Denali Therapeutics, Diamir, Eisai Inc, Eli Lilly and Company, EuroImmun, Genentech Inc, GE Healthcare, Janssen Alzheimer Immunotherapy Research & Development LLC, Lundbeck, Magou, Merck & Co Inc, PeopleBio, Pfizer Inc, Piramal, Roche, Saladax Biomedical, Servier, and Takeda.

Conflict of interest disclosure: Ms. Barger reports grants from the National Institutes of Health during the conduct of the study.

Ethical standards: Participants in ADNI3 provided written consent to participate with IRB-approved informed consent forms (ICF).

 

References

1.     Alzheimer’s Association. 2019 Alzheimer’s Disease Facts and Figures. Alzheimers Dement. 2019;15(3):321-87.
2.     Hebert LE, Weuve J, Scherr PA, Evans DA. Alzheimer disease in the United States (2010-2050) estimated using the 2010 Census. Neurology. 2013;80(19):1778-83.
3.    Fargo KN, Carrillo MC, Weiner MW, Potter WZ, Khachaturian Z. The crisis in recruitment for clinical trials in Alzheimer’s and dementia: An action plan for solutions. Alzheimers Dement. 2016;12(11):1113-5.
4.    Gauthier S, Albert M, Fox N, et al. Why has therapy development for dementia failed in the last two decades? Alzheimers Dement. 2016;12(1):60–4.
5.    Grill JD, Karlawish J. Addressing the challenges to successful recruitment and retention in Alzheimer’s disease clinical trials. Alzheimers Res Ther. 2010;2(6):34.
6.    Grill JD, Galvin JE. Facilitating Alzheimer disease research recruitment. Alzheimer Dis Assoc Disord. 2014;28(1):1–8.
7.    Watson JL, Ryan L, Silverberg N, Cahan V, Bernard MA. Obstacles and opportunities in Alzheimer’s clinical trial recruitment. Health Aff (Millwood). 2014;33(4):574–579.
8.    Knebl JA, Patki D. Recruitment of subjects into clinical trials for Alzheimer disease. J Am Osteopath Assoc. 2010;110:S43–S49.
9.    Weiner MW, Nosheny R, Camacho M, et al. The Brain Health Registry: An internet-based platform for recruitment, assessment, and longitudinal monitoring of participants for neuroscience studies. Alzheimers Dement. 2018;14(8):1063–1076.

CAN DIGITAL TECHNOLOGY ADVANCE THE DEVELOPMENT OF TREATMENTS FOR ALZHEIMER’S DISEASE?

 

M. Mc Carthy, P. Schueler

 

ICON plc, Dublin, Ireland

Corresponding Author: Marie Mc Carthy, ICON plc  Dublin, Ireland, marie.mccarthy@iconplc.com

J Prev Alz Dis

Published online September 9, 2019, http://dx.doi.org/10.14283/jpad.2019.32

 

 


Abstract

The report explores the potential digital technology has to generate novel endpoints and digital biomarkers for Alzheimer’s disease drug development studies.  Drawing from literature and novel pilots, we explore the value of innovative digital technology to digitize physiological behaviours such as sleep disturbance and gait changes. Technology now exists to monitor and quantify our use and interaction with electronics in the home, the use of social platforms and smart-phones, geolocation, sleep and activity patterns. These multimodal digital data are a feasible alternative to capturing the more complex activities of daily living that require higher cognitive processes and are a sensitive predictor of disease.  The combination of biosensors and the internet of things (IoT), offers the potential to collect highly relevant, objective data in a continuous, passive and low burden manner.  Digital endpoints and biomarkers could have value in the diagnosis, monitoring and development of therapies for patients living with Alzheimer’s disease.

Key words: Digital Biomarkers, Clinical trials, Ecological Momentary Assessment, Gait, ADL, Alzheimer’s Disease, Smartphone.


 

What is the Problem?

There have been no new Alzheimer’s disease drug therapies on the market in over a decade.  Alzheimer’s disease is a complex multifactorial disease and there are many reasons proposed for this stasis, including limited validated drug targets, lack of reliable surrogate biomarkers, slow and variable disease progression, and the dependency on soft endpoints (1). The limited sensitivity of existing tools to detect and monitor Alzheimer’s disease is compounded by the narrow set of outcomes (2). These limitations become even more impactful in trials of disease-modifying therapies;  current measures of cognition are not sensitive in individuals with very early stages of the disease (3) and profoundly affect the value of these assessments in studies where subjects with no or minimal symptomatology are followed for several years before they may reach a pre-defined outcomes.

 

The Solution?

In its most recent, draft Guidance the Food and Drug Administration (FDA) (4) accepts that cognition, in its entirety, encompassing all its constituent processes and domains, is meaningful in terms of daily function. However, it caveats this with the following statement that reinforces the dilemma facing the industry: “when measured using conventional approaches with sensitive tools directed at particular cognitive domains, the meaningfulness of measured changes may not be apparent.” This still leads to a certain need for co-primary endpoints where cognitive change needs to be accompanied by a benefit reflected by an  independent endpoint assessing daily function, operationalized as “Activities of Daily Living” (ADL).  The more complex activities linked to independent living are assessed by Instrumental Activities of Daily Living (IADLs) and include items such as housework, communication using computer and telephone, food preparation etc.   There is a growing body of evidence that subtle deficits in IADL, particularly those that are performance based, are more sensitive to early cognitive decline and may be present in mild cognitive impairment (MCI) (5, 6).

 

Smart Home Technology

Smart home technology is readily available, combining sensors and connected devices that monitor and control the use of appliances in the home (7).  These systems are a network of connected technologies that can monitor a number of activities in the home including; the opening and closing of doors, movement in specific locations, heat and light and the presence or absence of an individual.  In a short pilot, we combined smart home data (Table 1) with actigraphy data to explore the potential to generate insights more usually collected by questionnaires (8).    Connected home systems are already utilized by health agencies to support older adults living in the community (9) as part of healthy aging programs for safety and health monitoring (10).  There are significant possibilities for their use in drug development studies by providing continuous data to generate novel endpoints that have the potential to be more sensitive to change than existing methodologies.   Digital technologies could benefit Alzheimer’s disease research by generating a more patient centric assessment by removing domains from questionnaires better captured by passive digital technologies (Table 1).  These new multi-modal assessments could facilitate the capture of complex digital IADL’s, such as the ability to use a smartphone, conduct online banking, social media interaction etc.    New composite digital endpoints could emerge by mapping the discordance between subjective data of the individual’s perceived behaviour and their objective data as gathered by sensors.  Finally the high number of data points reduces the bias from rare samples during the pre-scheduled on site study visits, what should increase the robustness and reliability of the data, ultimately leading to less data variability (11).

Table 1. Objective Sensor data and related ADL question (7)

Table 1. Objective Sensor data and related ADL question (7)

 

Gait

There is growing interest in gait change as a marker for cognitive decline.  Reports of gait disturbances have been found to precede dementia by more than 5 years (12, 13). While the use of wrist or ankle worn physical activity monitors (PAM)  to collect steps and gait cadence is well established, assessing spatiotemporal gait is not a simple process and is limited to specialist clinics equipped with electronic walkways.  This significantly affects the utility of this approach in clinical trials due to the limited number of sites available for gait assessments.
New technology such as smart-insoles is emerging.  In a recent pilot, we used smart insoles to quantify gait speed and stride variability (14) in a non-clinical setting.  The potential value of smart-insoles is in the portability of the technology, enabling their use outside of specialist gait clinics and thereby monitoring gait change in the individual’s home or residential care setting. This has the potential to capture more nuanced assessment of gait change, including balance, inter-gait variability and even stance. These devices generate vast quantities of data leading to the possibility of using machine learning to identify new clinical sensitive signals within the data set.  However it was outside the scope of our pilot study to determine the minimal clinically important differences (MCID) for cognitive decline.  In addition, it should be noted that factors such as footwear and data transfer could impact the operationalization of these devices in a clinical trial.

Figure 1. The smart insoles generate data from 13 embedded tri-axial accelerometers each capable of generating 100hertz data

Figure 1. The smart insoles generate data from 13 embedded tri-axial accelerometers each capable of generating 100hertz data

The static report shows pressure distribution, single pressure values and total forces for the single sensors embedded in the insoles when a healthy volunteer engaged in different patterns of walking.

 

Smart Phones and Smart watch

The smartphone and smartwatch are emerging as significant digital tool for the collection of disease specific biomarkers and endpoints for Alzheimer’s disease.   Smartphone are widely available and have an array of inbuilt technology including accelerometers, gyroscopes, magnetometers, global positioning system (GPS), proximity sensors, ambient light sensors, microphones, cameras, touch-screen sensors.  These sensors facilitate the capture a multitude of data including; activity, cadence, speech, tremor and location.   Smartphones can facilitate the ready deployment of a growing array of applications (apps) and can be to deploy cognitive assessments and gamification. Used outside of the clinic as screening tools, frequent burst cognitive assessments have the potential to make results more reliable and can potentially offer a means of continuous longitudinal monitoring. Changes in language and voice are being evaluated as predicators of disease progression (15, 16) with the goal to develop smartphone apps that could be used to for this purpose.
Smartwatches are evolving from simple actigraphy devices that measure sleep and activity to biosensors that that contain an array of sensors including photoplethysmography (PPG) and electrodermal activity (EDA) sensors.   These biosensors can generate a myriad of endpoints including sleep disturbance, activity, heart rate, respiration rate, oxygen saturation and galvanic skin response.  These biosensors could have particular utility in the ongoing research into the influence of cardiovascular factors on the development and progression of AD (17). It is entirely feasible that biosensors capable of continuously monitoring cardiovascular and respiratory signals could play an increasingly significant role as low burden tools to generate new targets for treatment and prevention of AD.

 

What does the future look like

Clinical development programs for Alzheimer’s disease are becoming larger and longer; the sustainability of existing methodologies and study designs is questionable.  There is growing interest in the use of digital technology and exploring the transformative potential of these technologies as a means of providing additional insights (18).
The value of actigraphy devices in the study of this population has been previously discussed (19) and this report focuses on primarily on two areas gait assessment and IADL where digital technology could have a significant impact on patient centric trial design and the generation of new digital endpoints.    There is significant potential for the use of connected devices and IoT, particularly for disease-outcome driven prevention studies.  These sensors are gaining acceptance by health care systems as a means of keeping older adults in the community.  They are relatively easy to install and can passively capture individuals’ behaviour as they go about their normal activities of daily living.  These use cases are ensuring that the systems are becoming more robust in terms of connectivity and compliant in terms of data privacy and security and more robust for the generation of data for use in clinical trials.
There is potential value in combining data from multimodal digital devices and developing composite end-points that are more responsive to change then viewing each dataset as a singularity.   Advanced analytic platforms that use artificial intelligence and machine learning are available that can ingest data from multiple sources including; sensors, smartphone, smart-home, environmental, geolocation, voice and questionnaires, generating insights into behavioural changes and cognitive decline. The correlation of these data with clinical observations and laboratory biomarkers could help characterise the populations, monitor progression over time and assess the efficacy of interventions.

 

What more needs to be done

As with any new outcome assessment, digital endpoints need to be clinically validated. The technology is required to measure endpoints that are both meaningful to the patient and clinically relevant.  The data generated by the digital assessment needs to capture the concept of interest that reflects the meaningful health aspect for individuals living with Alzheimer’s disease. In addition, the data generated needs to measure meaningful change that is consistent across specific populations.  The same scientific rigor and data quality criteria are required whether considering digital or traditional methodologies. The digital technology need verification and validation to ensure there is sufficient scientific evidence to support its use in a specific study (20). Data privacy, security and storage need to align with local regulations.   The potential of Digital Biomarkers and endpoints is immense, however, if a digital strategy is to be successful, the inclusion of a conscious patient centric approach including strategies to quantify and reduce patient burden and ensure patient engagement is essential.

 

Conflict of Interest: There are no conflicts of interest.

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

 

References

1.    Schueler P, Siegfried K and Hüll M.  Lessons Learned from Major Clinical Trials Conducted Over the Past Decades; in M Bairu; Alzheimer’s Disease Trials, Elsevier, 2013.
2.    Schneider LS1, Mangialasche F, Andreasen N, et al.  Clinical trials and late-stage drug development for Alzheimer’s disease: an appraisal from 1984 to 2014. J Intern Med 2014 March; 2014;275(3): 251–283. doi:10.1111/joim.12191, 251-283.
3.    Vellas R, Bateman  K. Blennow, G et al.  Endpoints for Pre-Dementia AD Trials: A Report from the EU/US/CTAD Task Force.  J Prev Alzheimers Dis. 2015;Jun; 2(2): 128–135.
4.    U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER), 2018. Center for Biologics Evaluation and Research (CBER) Early Alzheimer’s Disease: Developing Drugs for Treatment Guidance for Industry 01/29/18.  Revision 1. https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM596728.pdf.  Accessed 11/02/2019
5.    Mlinac ME, F. M. Assessment of Activities of Daily Living, Self-Care, and Independence. , Volume 31, Issue 6, 1 September 2016, Pages 506–516,. Archives of Clinical Neuropsychology, 2016;506-516.
6.    Jekel K., Damian M, Wattmo C et al. Mild cognitive impairment and deficits in instrumental activities of daily living: a systematic review 2015;7:17. . Alzheimer’s Disease Research and therapy .
7.    Wang Ju, Bauer J,  Becker M et al, A novel approach for discovering human behavior patterns using unsupervised methods. Z Gerontol Geriat 2014;47:648–660
8.    Mc Carthy M, Walsh D, Tallon J, et al . Can wearables and sensor data be used to add context to activities of daily living questionnaires? Qual Life Res 2018;27(Suppl 1): 1 S159.
9.    Bipartisan Policy Centre. Healthy Aging Begins at Home. https://bipartisanpolicy.org/wp-content/uploads/2016/05/BPC-Healthy-Aging.pdf.accessed 2016;11/02/2019
10.    Azimi I,   Rahmani A M,  Liljeberg P et al. Internet of things for remote elderly monitoring: a study. J Ambient Intell Human Comput, 2016.
11.    Dodge HH, Zhu J, Mattek NC, et al, Use of high-frequency in-home monitoring data may reduce sample sizes needed in clinical trials. PLoS One 2015;10:e0138095.
12.    Deschamps T, Beauchet O, Annweiler C, et al. Postural control and cognitive decline in older adults: position versus velocity implicit motor strategy. Gait Posture. 2014;39(1):628-30
13.    Beauchet O, Allali G, Launay C, et al. Gait variability at fast-pace walking speed: a biomarker of mild cognitive impairment? Neurol Sci. 2013; Aug;34(8):1275-82.
14.    Mc Carthy M, Gon C. Can digital footprints capture clinically relevant gait endpoints in non clinical setting: A proof of concept. The Journal of Prevention of Alzheimer’s Disease – JPAD Volume 5, Supplement 1, 2018 S186.
15.    Au R, Piers RJ, Devine S. How Technology is Reshaping Cognitive Assessment: Lessons from the Framingham Heart Study. Neuropsychology. 2017 ;31(8): 846–861.
16.     Martínez-Sánchez F, Meilán JJG, Carro J,  et  al, A Prototype for the Voice Analysis Diagnosis of Alzheimer’s Disease. J Alzheimers Dis. 2018;64(2):473-481
17.    Santos CY,  Snyder PJ,  Wu W et al.   Pathophysiologic relationship between Alzheimer’s disease, cerebrovascular disease, and cardiovascular risk: A review and synthesis. Alzheimers Dement (Amst). 2017; 7: 69–87.
18.    Gold M, Amatniek J, Carrillo MC, et al. Digital technologies as biomarkers, clinical outcomes assessment, and recruitment tools in Alzheimer’s disease clinical trials. Alzheimers Dement 2018; 4: 234–242.
19.    M. Mc Carthy, W. Muehlhausen, P. Schüler. The case for using actigraphy generated sleep and activity endpoints in Alzheimer’s Disease clinical trials. J Prev Alz Dis 2016;3(3):173-176
20.    https://www.ctti-clinicaltrials.org/sites/www.ctti-clinicaltrials.org/files/mobile-devices-recommendations.pdf#Pg18Ln30 accessed 11/02/2019

Published online September 9, 2019, http://dx.doi.org/10.14283/jpad.2019.32

ADVANCING ALZHEIMER’S DISEASE TREATMENT: LESSONS FROM CTAD 2018

 

B. Vellas1, L.J. Bain2, J. Touchon3, P.S. Aisen4

 

1. Gerontopole, INSERM U1027, Alzheimer’s Disease Research and Clinical Center, Toulouse University Hospital, Toulouse, France; 2. Independent Science Writer, Elverson, PA, USA; 3.Montpellier University, and INSERM U1061, Montpellier, France; 4. Alzheimer’s Therapeutic Research Institute (ATRI), Keck School of Medicine, University of Southern California, San Diego, CA, USA

Corresponding Author: B. Vellas, INSERM U1027, Alzheimer’s Disease Research and Clinical Center, Toulouse University Hospital, Toulouse, France.

J Prev Alz Dis 2019;
Published online April 4, 2019, http://dx.doi.org/10.14283/jpad.2019.11

 


Abstract

The 2018 Clinical Trials on Alzheimer’s Disease (CTAD) conference showcased recent successes and failures in trials of Alzheimer’s disease treatments. More importantly, the conference provided opportunities for investigators to share what they have learned from those studies with the goal of designing future trials with a greater likelihood of success. Data from studies of novel and non-amyloid treatment approaches were also shared, including neuroprotective and regenerative strategies and those that target neuroinflammation and synaptic function. New tools to improve the efficiency and productivity of clinical trials were described, including biomarkers and machine learning algorithms for predictive modeling.

Key words: Alzheimer’s disease, clinical trials, therapeutics, recruitment, predictive modeling


 

Introduction

The Clinical Trials on Alzheimer’s Disease (CTAD) conference, held each year since 2008, has become a major venue for clinical trialists and other researchers from academia and industry to share learnings from recent clinical trials and learn about new therapeutics and diagnostics in development. Convened in Barcelona, Spain in October, 2018, CTAD welcomed approximately 1200 delegates from around the world to share data from studies conducted across the spectrum of development, ranging from early stage studies of novel therapeutics targeting a range of mechanisms, to results from later stage trials including those that failed to show efficacy. Investigators shared information on the pathophysiological underpinnings of these studies, clinical and biomarker results, as well as experiences with participant recruitment, outcome measures, trial design, and data management and analysis.

 

Gaining insight from negative trials

A central goal of the conference is to advance the development of effective treatments for Alzheimer’s disease (AD) by applying lessons learned in ongoing clinical trials as well as completed trials, including those that failed to demonstrate efficacy or that were terminated for other reasons.  For example, beta-secretase inhibitors (BACEi) emerged in recent years as one of the most promising classes of drugs for treatment of AD. However, disappointing results from multiple Phase 3 clinical trials have tempered some of the optimism surrounding BACE inhibition, prompting investigators from multiple companies developing drugs in this class to come together and jointly examine the potential benefits and risks of BACE inhibition.
BACE1 is an enzyme that cleaves the amyloid precursor protein as the first step in the production of Aβ peptide, the primary constituent of amyloid plaques in the AD brain. In animal models of AD, BACE1 deletion reduced production of Aβ and  plaque load, and improved cognition (1). Last year, Merck terminated a study of the BACE1 inhibitor verubecestat in mild to moderate AD when an interim analysis showed that the drug was ineffective at slowing the rate of cognitive decline (2). Reasoning that the drug might be more effective if given earlier in the disease process, before substantial buildup of plaque, the company continued their trial in prodromal AD. But in February 2018, the company stopped the trial after an independent data monitoring committee determined that there was no evidence of efficacy. Even more troubling, data presented at CTAD2018 showed that verubecestat treatment in prodromal AD was associated with worsening on all clinical measures and an increase in adverse events (3).
These results raise many unanswered questions. For example, the rapid onset of cognitive worsening might be related to a dramatic lowering of Aβ or to an off-target activity of the drug. Notable, the rapid loss of Aβ may have deleterious effects on neurotransmission or synaptic function (4). Key questions include whether the negative effect was related to dose, duration of treatment, or the specific population enrolled in the trial.
To promote open sharing of data from the many trials conducted by academic and industry scientists, the Alzheimer’s Association sponsored an emergency session at CTAD to explore emerging results from other BACE inhibitor trials, including Janssen’s EARLY trial of atabecestat in preclinical AD, which was terminated in May 2018 because of elevations in a liver enzyme seen in about a third of patients. Although the levels returned to baseline, Janssen determined that the benefit risk profile of the drug was not appropriate for a preventive treatment. A preliminary analysis of data from the study suggested that there was a rapid-onset, dose-related worsening on cognitive measures.  Data were also presented from a Phase 2 study of Lilly’s BACEi LY3202626, another study that was terminated early due to a low probability of success; these data also suggested adverse effects on cognition. Importantly, many of these trials have collected rich biomarker and imaging data, which will be analyzed in the coming months along with cognitive and clinical data to see what else can be learned. The newly formed Alzheimer’s Clinical Trials Consortium (ACTC) could serve as a neutral party to analyze data across multiple studies.
In a separate symposium, a panel explored whether BACE1 is a suitable drug target for the prevention and treatment of AD. Possible reasons for the disappointing results seen in many of the BACEi trials could be related to treatment starting too late or using doses that are too high. Indeed, it was suggested that BACE1 inhibition may be ideal for primary prevention and that dominantly inherited AD is a predictable model in which to study such interventions. The Dominantly Inherited Alzheimer’s Network (DIAN) and the Alzheimer’s Prevention Initiative (API) have shown that amyloid accumulation becomes significant as much as 15 years before symptoms appear in people with autosomal dominant forms of AD (5, 6), which gives investigators the opportunity to decide when and how to intervene using adaptive designs.

 

New data from ongoing trials

CTAD also provided sponsors the opportunity to share updated information on ongoing trials, especially of Phase 3 trials of amyloid targeting monoclonal antibodies. Highly anticipated data were presented to support claims presented earlier in 2018 suggesting disease-modifying effects for the anti-amyloid monoclonal antibody BAN2401, currently being developed jointly by Eisai and Biogen. The BAN2401 antibody is specifically aimed at species of the Aβ protein called protofibrils that are thought to be most toxic to brain cells. Top line results from the phase 2 study 201 indicated that the drug may slow cognitive decline and reduce plaque buildup in the brains of people with early AD. However, the data initially presented left unanswered questions about the degree to which participants’ APOE4 status may have skewed results of the study.
The reason for concern was that in the middle of study 201, health authorities decided that APOE4 carriers should no longer be randomized to the highest dose of BAN2401 because of the risk of developing amyloid-related imaging abnormalities (ARIA). This change led to an imbalance in the number of APOE4 carriers in the high-dose group, which was also the group showing the greatest treatment effect. At CTAD, investigators presented data from additional subgroup analyses as well as cerebrospinal fluid (CSF) biomarker results that were not previously available, which lent some support to Eisai’s theory that apparent treatment effects are driven by the drug itself and not to an imbalance of APOE4 carriers. Moreover, positron emission tomography (PET) imaging studies indicate that treatment with BAN2401 results in significant clearance of brain amyloid accompanied by favorable clinical effects. Nonetheless, some critics of the research remained uneasy about the complex trial design and unconvinced that concerns about the imbalance of APOE4 carriers had been laid to rest by the subgroup analyses. There was general enthusiasm for launching Phase 3 studies to provide definitive answers.

 

Revisiting older treatment strategies

At CTAD2018, vaccination re-emerged as a promising treatment strategy with data from a Phase 2a study of UB-311, an active anti-amyloid immunotherapy vaccine with the potential to prevent AD (7). Vaccination against amyloid is not a new idea. In 1999, Schenk and colleagues demonstrated in mice that active vaccination against neurotoxic forms of Aβ reduced levels of Aβ in the brain and improved performance on memory tasks (8). But a subsequent clinical trial of AN-1792, a vaccine against Aβ42, was terminated in 2002 because of an increased incidence of encephalitis, elicited by the production of cytotoxic T cells (9). The sponsor of the UB-311 study, United Neuroscience, Inc, has developed a platform that uses synthetic peptides to generate endogenous target-specific antibodies against a variety of antigens. UB-311 is a fully synthetic peptide vaccine that comprises two Aβ1-14 peptides fused to T-helper cell peptides in order to elicit a strong antibody response against Aβ while avoiding a cytotoxic T cell response (10). Data presented at CTAD showed no serious adverse events among 42 participants randomized to receive 7 doses of drug or placebo over 60 weeks, followed by 18 weeks of observations. Additional data from extensive biomarker, safety, and immunogenicity studies are expected by the end of 2019.
Another treatment approach, plasma exchange, has a long history in the treatment of other conditions and has recently emerged as a promising strategy against AD. Barcelona-based Grifols, S.A. reported encouraging results from a phase 2b/3 study of plasma exchange with albumin replacement in patients with moderate AD (AMBAR study) (11).
Grifols has been investigating the clinical use of plasma exchange with albumin replacement for the past 15 years. In an earlier pilot study, they showed that the technique resulted in a decrease in plasma Aβ, an increase in cerebrospinal fluid levels of Aβ, and improvements on clinical and functional neuroimaging measures. For the AMBAR study, they randomized nearly 350 participants with mild to moderate dementia to either placebo or one of three treatment groups. A sham procedure delivered to the placebo group simulated plasmapheresis so that patients, caregivers, and raters were all blind to whether the participant was receiving active treatment or placebo. The study was conducted at 41 sites in Spain and the United States.
All three treatment groups started the trial with conventional total plasma exchange with albumin replacement (TPE) once per week for two months followed by low-volume plasma exchange (LVPE) once per month for 12 months, with either a low (20 mg) or high (40 mg) dose of albumin. Two of the treatment groups also received an intravenous infusion of immunoglobulin (IVIG) to replace endogenous immunoglobulins. One group did not receive IVIG as an extra check on the safety of this product. Cognition, function and AD biomarkers were assessed at baseline, after the 2 months of TPE, and at 6, 9, 12, and 14 months. CSF biomarkers, magnetic resonance imaging (MRI), and fluorodeoxyglucose PET (FDG-PET) studies were conducted less frequently.
Participants in all three treatment arms showed a slowing of decline compared to placebo on both cognitive and functional measures, although the results were not statistically significant. However, when the results were reanalyzed with participants divided according to their baseline mini-mental status exam (MMSE) scores, patients with mild dementia (MMSE 22-26) showed no benefits from the treatment, while those with moderate dementia (MMSE 18-21) showed 61% slowing of decline on both the Alzheimer’s Disease Assessment Scale-cognitive subscale (ADAS-cog) and the Alzheimer’s Disease Cooperative Study-Activities of Daily Living (ADCS-ADL) scale. Safety was also assessed, and while the treated groups had a higher incidence of adverse events compared to placebo, the treatment was judged to be safe and well-tolerated. Nearly three quarters of participants (72%) completed the study despite the substantial burden of the trial.
The apparent strength of the effect in moderate but not mild AD patients was surprising and will be under further investigation. Future analyses of biomarkers and imaging studies and at least one additional clinical trial are planned to follow-up on this observation.

 

Continued interest in non-amyloid treatment strategies

The observed association of type 2 diabetes mellitus (T2D) with increased AD risk (12), along with preclinical studies that demonstrated improvement of brain function and synaptic health in mice treated with insulin has sparked interest in insulin as a treatment (13). Intranasal delivery of insulin results in rapid uptake in the central nervous system (14) and has been shown to improve memory and other cognitive measures in individuals with amnestic mild cognitive impairment (aMCI) or mild-to-moderate AD (15-17). Craft and colleagues at the Wake Forest School of Medicine in Winston-Salem, North Carolina, presented data from a multi-site Phase 2/3 study of intranasal insulin in patients with mild cognitive impairment (MCI) or mild AD (18).
For this trial, 289 participants at 26 US sites were randomized to receive either insulin or placebo daily for 12 months. The first 49 participants used Kurve Technology’s ViaNase ™ device to deliver the drug; however frequent malfunctions led the investigators to discontinue use of that device in favor of the newly-developed Precision Olfactory Delivery™ device (POD) from Impel NeuroPharma. Assessments were made at baseline and at three-month intervals until the end of the study, when participants were offered open-label insulin treatment for another 6 months. A change in cognitive function from baseline to 12 months served as the primary endpoint; secondary endpoints included assessments of daily function, imaging, and biomarker studies.
Although the study failed to show effectiveness of intranasal insulin delivered with the POD on any of the outcome measures, a pattern suggestive of benefit in the participants who used the ViaNase device was noted. Further analyses of the data, including analyses of the open-label extension and other pre-specified responder analyses are forthcoming, said Craft.
Interesting results were presented from a Phase 3 trial of another novel treatment strategy, this one using GV-971, a marine-derived oligosaccharide that has been shown to inhibit the formation of beta amyloid fibrils, reduce neuroinflammation, and affect the gut microbiota (19). The 36-week randomized controlled multicenter trial conducted by Shanghai’s Green Valley Pharmaceutical Company in 788 people diagnosed with mild-to-moderate AD demonstrated statistically significant improvements in cognition (assessed with the ADAS-cog) as early as week four, and continued improvement for the duration of the study. A non-significant trend towards improvement on the Clinicians Interview-Based Impression of Change (CIBIC+) was also observed. No improvements were seen on other secondary outcomes including measures of activities of daily living and neuropsychiatric symptoms. FDG-PET, an imaging study of brain metabolism, also showed no significant effects. The treatment was safe and well-tolerated.
A third innovative treatment strategy was presented by investigators from the Rocky Mountain Alzheimer’s Disease Center in Colorado (20). Based on the observation that people with rheumatoid arthritis have a reduced risk of AD, they hypothesized that cytokines produced by the innate immune system may be neuroprotective. To test this hypothesis, they injected transgenic AD mice with granulocyte-macrophage colony-stimulating factor (GM-CSF) and showed that it reduced brain amyloid by 40% after only seven days and reversed cognitive impairment in 14 days. They also reviewed data from bone-marrow transplant patients who had been given Leukine (also known as sargramostim), a recombinant form of human GM-CSF, and found that these patients showed significantly improved cognitive function. These data provided the rationale to test sargramostim in a placebo-controlled pilot study in patients with mild-to-moderate AD. Study participants who received a subcutaneous injection 5 days a week for three weeks showed a reduction in amyloid that correlated with an improvement in the mini-mental state examination (MMSE) score and no sign of ARIA. They are now planning a six-month trial of GM-CSF/sargramostim in mild-to-moderate AD.
A novel regenerative treatment that promotes neurogenesis in the brain is also in development as a first-in-class therapeutic for MCI and mild AD. Allopregnanolone (allo), a naturally occurring substance and metabolite of progesterone, has both regenerative and neuroprotective properties (21). Moreover, investigators at the Center for Innovation in Brain Science at the University of Arizona have demonstrated that allo induced neurogenesis and a change in synaptic connectivity and restored cognitive function in mice (22). At CTAD, they reported results from a multiple-dose Phase 1b/2a study in participants with MCI or mild AD (23).  While there was no statistically significant improvement in cognition, there was a trend in the right direction for improvements in executive function. No sex differences were seen and the treatment was well tolerated. They are now planning a Phase 2 randomized controlled trial that will enroll 200 mild AD patients to receive 4 mg of allo for 72 weeks.

 

Developing new tools to accelerate AD drug development

Developing new AD therapeutics requires not only a full pipeline of diverse agents, but novel and efficient analytic tools and trial designs as well. At CTAD, investigators described tools ranging from low to high tech that are being applied to studies testing a diverse set of therapeutic agents. At the low-tech end, for example, investigators at the Wake Forest School of Medicine are using mass mailings and the internet to identify potential participants for the COSMOS-Mind study, which is examining the potential of cocoa-flavanol extract to improve cognition in older adults (24). Interested participants were sent recruitment materials, contacted to confirm interest, screened via telephone, and if eligible, were then enrolled and randomized. Participants are assessed annually using a telephone-based cognitive composite outcome measure. Although results of the study will not be available for several years, the study has already demonstrated the feasibility of conducting a large, simple, cost-effective, and geographically diverse trial over the telephone, and has shown that older adults are willing to volunteer for telephone-based assessments.
At the other end of the technology spectrum, a machine learning algorithm has been developed by IQVIA to help identify prodromal AD patients in the general population (25). In the United States alone, there are currently 150 clinical trials seeking about 70,000 participants, making recruitment an enormous challenge. Moreover, the landscape has become more complicated as the number of procedures involved in trials has increased by about 70% and the number of countries conducting trials has more than doubled. IQVIA’s approach is to leverage huge healthcare datasets to build a predictive algorithm. To build the model, they used data from an initial cohort of more than 405 million subjects divided into a positive cohort (those with AD or who had been prescribed AD symptomatic drugs) and a control cohort (a matched sample of patients without an AD diagnosis or AD treatment). Using 24 months of medical history data from no more than 3 years prior to the AD diagnosis, they identified features such as diagnostic procedures, medical interventions, concomitant pathologies, and other characteristics that differentiated those with AD from those without AD across. They then compared the performance of different algorithms across different age groups and ranked risk factors for each age group based on the predictive value of that risk factor in that age group.
Predictive modeling can also enable precision medicine, according to data presented by Ariana Pharma in Paris, France. As part of the development of ANAVEX®2-73, an orally available selective sigma-1 receptor agonist, Ariana used an unbiased, data-driven machine learning platform that integrated clinical and genomic data to identify four key drivers of the response to the drug, including polymorphisms of two genes. This allowed them to extend their small open label study in a selected population, strengthening their hypothesis for this particular drug and supporting the notion that such precision pharmacology approaches can be used to identify patients who will benefit from particular drugs across a wide range of neurodegenerative diseases (26).
New biomarkers are also needed to increase the efficiency and productivity of trials. Speakers at CTAD described recent advances in the development of plasma biomarkers, which offer advantages for screening and diagnosis in terms of reduced cost and much greater availability to large populations. The Trial-Ready Cohort for Preclinical/Prodromal AD (TRC-PAD) project, funded by the National Institute on Aging will incorporate three different plasma biomarker assay platforms, enabling multi-center comparative field testing of the assessment tools (27). Ultrasensitive immunoassays for amyloid beta oligomers in CSF were also described, which offer the ability to demonstrate target engagement in clinical trials with a small number of participants. The Yale PET center has also recently developed a ligand for synaptic PET imaging, which could offer the first in vivo measure of synaptic density as an outcome measure in clinical trials of disease-modifying therapies, particularly those targeting synapses.

 

Take home lessons

CTAD 2018 offered a vision of new and better drugs, improved methods for identifying individuals in the earliest stages of disease, and improved diagnostic and staging tools, all aimed at providing better and more personalized treatment approaches for the millions of people living with or at risk of developing AD. The conference also highlighted lessons learned from completed and ongoing clinical trials that can be applied to future trials to improve the efficiency of those trials and accelerate the discovery and development of effective treatments for AD. While there is continued optimism regarding the potential efficacy of amyloid-targeting agents, disappointing results from many trials as well as increased understanding of the pathogenetic mechanisms that contribute to AD strongly supports pursuit of treatments targeting alternative mechanisms. Combination therapies that target multiple mechanisms may be needed, as well as treatments that address comorbid conditions that contribute to dementia. Other lessons learned at CTAD 2018 include:
•    Blood-based methods of detecting early markers of AD pathology have matured to the point of being ready for use in clinical trials but may still need to be refined for use in clinical care.
•    Incorporating a wide range of biomarkers into all clinical trials is essential to ensure that the maximum amount of information is provided by those trials.
•    Repurposing treatments that have been used for other diseases offers advantages in drug development since human safety data may be available.
•    To reduce the cost and expand access to clinical trials, remote assessments via telephone provide a feasible approach for testing cognition and detecting cognitive decline over time.
•    Extensive data in electronic medical records and genetics studies can be leveraged to predict risk of developing dementia, enable recruitment of presymptomatic individuals into clinical trials, and guide personalized approaches to treatment.

The next CTAD meeting, which will be held in San Diego December 4th through 7th, 2019 will build on each of these ideas. In addition to early reports on novel therapeutic strategies, we anticipate updates on the major industry and academic trials, validation data on leading biomarker candidates (including plasma markers of brain amyloid and neurodegeneration), and reports on new approaches to recruitment, retention, and improving diversity in AD clinical trials.  We also expect to see Phase 2 results from highly anticipated tau-targeting therapeutics.

 

Conflict of Interest: Dr. Vellas reports grants from Lilly, Merck, Roche, Lundbeck, Biogen, grants from Alzheimer’s Association, European Commission, personal fees from Lilly, Merck, Roche, Biogen, outside the submitted work. Ms. Bain reports compensation from CTAD CONGRESS for preparation of the manuscript. Dr. Touchon has nothing to disclose. Dr. Aisen reports grants from Lilly, personal fees from Proclara, other from Lilly, other from Janssen, other from Eisai, grants from Janssen, grants from NIA, grants from FNIH, grants from Alzheimer’s Association, personal fees from Merck, personal fees from Roche, personal fees from Lundbeck, personal fees from Biogen, personal fees from ImmunoBrain Checkpoint,  outside the submitted work.

 

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IMPLEMENTING A MEMORY CLINIC MODEL TO FACILITATE RECRUITMENT INTO EARLY PHASE CLINICAL TRIALS FOR MILD COGNITIVE IMPAIRMENT AND ALZHEIMER’S DISEASE

 

L. Park1, C. Kouhanim1, S. Lee1, Z. Mendoza2, K. Patrick2, L. Gertsik2, C. Aguilar1, D. Gullaba1, S. Semenova1, S. Jhee1

 

1. PAREXEL International, Glendale, CA, USA; 2. California Clinical Trials Medical Group, Glendale, CA, USA

Corresponding Author: Dr. Lovingly Park, PAREXEL International, 1560 E. Chevy Chase Dr, Suite 140, Glendale, CA 91206, USA, Phone: +1 818 254 4389, Fax: +1 818 936 6844, Email: Lovingly.Park2@Parexel.com

J Prev Alz Dis 2019;2(6):135-138
Published online February 11, 2019, http://dx.doi.org/10.14283/jpad.2019.8

 


Abstract

Background: The recruitment challenges for MCI and AD subjects into clinical trials are well known, and this is particularly true for early phase studies.  Currently, only 10-20% of all patients who are referred for research from the community are trial eligible (Grill and Karlawish, 2011).  Due to the limited and specific study objectives in early phase study designs, these rates drop to approximately one patient every two months.  Barriers to research recruitment are multi-factorial, involving patient centered factors, issues related to caregiver/study partner participation, and aspects related to the involvement of their treating physicians.  To address this challenge, we implemented a Memory Clinic within PAREXEL’s Early Phase Clinical Pharmacology Unit.  Our objective was to significantly facilitate recruitment into AD clinical trials by providing resources and education to patients, their treating physicians, and caregivers in the community.
Method:  The Clinic’s primary goals were to increase research visibility and partnerships with local organizations and referring physicians.  Members of the research team co-sponsored community outreach events with local organizations, thereby increasing awareness about the services of this memory clinic.  Secondly, physician outreach was expanded to include those who were not previously amenable to clinical trial referrals.  Finally, Memory Clinic patients were given clinical evaluations, free of charge and the results were discussed with the patients and their caregivers.  If the patients were interested in hearing more about possible research opportunities, they were referred to the early phase unit for a screening visit.
Results: We found that new referrals for research participation significantly increased as a result of this new paradigm. In 2016, 12 patients diagnosed with MCI or AD per protocol, were referred to a research study and 3 were randomized.  In 2017, 98 patients were referred and 16 were enrolled   In addition, our referral network increased with 30 physicians over a 20 mile radius.  Collaborations with national non-profit organizations also increased, thereby increasing public awareness about the importance of research participation in the development of new treatments for Alzheimer’s Disease.
Conclusions:  In summary, community engagement and providing referring physicians with a clinical service improved recruitment significantly for our phase 1 unit.  Resource education, staff training, and dedicated medical professionals can significantly improve awareness about clinical research participation and provide additional participants over and above traditional recruitment methods and trial registry enrollment in a large urban area.

Key words: Enrollment, recruitment, early phase, clinical trials, Alzheimer’s disease


 

Introduction

The prevalence of Alzheimer’s Disease (AD) is expected to reach 1.1 trillion by 2050, in the absence of effective pharmacological interventions (1). The last novel treatment approved for AD was in 2003, despite the fact that there are currently 105 candidates in development across all phases of clinical trials (2).  To address the challenge, a call to action by politicians, industry and academic leaders is ongoing, but even with the recent scientific advances in biomarker development, translational research, and clinical trial methodology, issues with AD recruitment hinder drug development (3, 4).  Only 10-20% meet criteria for participation when they are referred for research, resulting in a national enrollment rate of one patient per research site every two months (4).  Issues associated with eligibility are multi-factorial and involve patient-centered factors (e.g., comorbidities, concomitant medications, randomization to placebo), caregiver/study partner participation (e.g., availability), and limited involvement of their treating physicians (e.g., awareness about available trials).
A number of recruitment approaches exist to attract participants (e.g. appealing to previous research participants) (5).  There are also significant initiatives to create patient database registries to facilitate recruitment among community-dwelling elders (6).  However, there is limited evidence to suggest that these efforts successfully translate to enrollment into clinical trials. In response to the ongoing crisis facing patient recruitment, the National Institute of Aging and the Alzheimer’s Association funded programs to address this unmet need.  One such example is the Outreach, Recruitment, and Education Core (ORE) through the Alzheimer’s Disease Research Center (ADRC) across the United States.  Each ORE has a specific goal to educate the public about aging, Alzheimer’s Disease, cognitive health, and research participation through different outreach initiatives, expert lectures, and avenues for expert patient care and evaluation.  This resulted in the successful enrollment in some areas, proving that such models are effective and efficient ways of increasing patients’ access to research and clinical care in their communities (7).
Although ORE’s success is proven in academic settings and large teaching hospitals, the model had not been tested in commercial settings to evaluate its success in early phase (Phase I and IIa) clinical trials, where there is hesitation about research participation.  Some reasons relate to the focus on safety, pharmacokinetic and pharmacodynamics, with minimal therapeutic benefits to the patient.  Early phase studies also tend to be more burdensome due to more frequent visits (or longer inpatient stays) and complex procedures (e.g. lumbar puncture, neuroimaging, cognitive testing) (8).  These studies are also associated with higher risks, thereby excluding many older adults with common co-morbid medical conditions from participating.  As such, traditional recruitment methods of advertising and database mining, fail to make these studies attractive to patients and their families.  A more targeted and personalized approach to recruitment is required to overcome these hurdles and a model such as ORE can bridge the gap between recruitment targets and actual enrollment rates in early phase studies.
To test the model set forth by ORE, the PAREXEL Los Angeles Early Phase unit established a Memory Clinic, the goals of which were to facilitate recruitment by providing comprehensive neuropsychological evaluations to interested patients, perform consultation services to physicians, pre-identify appropriate patients for studies, and conducting educational activities about research participation. The Memory Clinic also provides information about resources available to the patient and their caregivers as it relates to healthy aging, dementia, and Alzheimer’s disease.

 

Methods

The Memory Clinic is a pilot program modeled after ORE which focuses on community events, self-referrals and increased physician outreach.  Patients are eligible for an evaluation free of charge, in exchange for hearing about clinical research and the overall goals of the clinic.  There is no obligation to participate in research studies.  Community outreach is done in a two-stage approach. First, a clinician or other member of the research team provides a lecture or a seminar on topics of interest such as healthy aging and dementia. At the completion of each lecture, psychometrists are on-site to administer questions regarding cognition and everyday life. This information is then reviewed by a clinician and an invitation for a comprehensive Memory Clinic evaluation is extended, if the complaints represented a marked decline from their previous level of functioning. Patients are also seen at the clinic through self, caregiver, or treating physician referral.  An important aspect of the clinic is to establish relationships with physicians in the community, educate them on the Memory Clinic goals and provide information on the utility of neuropsychological testing for clinical care.  Once they became familiar with the concept, they refer patients with the hope of obtaining cognitive test results to assist with  differential diagnosis and treatment planning.  Physicians are also provided with information about potential research opportunities for their patients.
During the evaluation the patient, or their surrogate, is provided with an informed consent and educated about the overall goals of the memory clinic. The patient is given a comprehensive clinical evaluation including a review of their medical history, current medications, and a semi-structured interview to obtain collateral information.  Patients are also administered tailored neuropsychological batteries, depending on the nature of the evaluation or referral question by the physician or family member.  The neuropsychological battery included tests assessing attention/processing speed, working memory, learning and memory, visuospatial ability, language, executive functioning and mood, using the Wechsler Test of Adult Reading (WTAR), Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), Trail Making Test (TMT), Controlled Oral Word Association Test (COWAT), and Geriatric Depression Scale (GDS). The WTAR is a norm-referenced standardized measure of premorbid intelligence in English-speaking individuals 16-89 years of age (9, 10).  The RBANS is a brief neuropsychological battery that consists of ten subtests which yield five index scores related to different cognitive domains (10).  The TMT is a neuropsychological test of visual attention and task switching which provides information about speed of processing, cognitive flexibility, and executive functioning and found to be significantly related to functional decline in dementia (11). The COWAT is a verbal fluency test found to have significant age related effects (12). The GDS is a 30-item self-report assessment administered to screen for depressive symptoms in the elderly (13). Functional impairment is established using semi-structured clinical interviews.  If additional tests are needed to further elucidate cognitive impairments, they are incorporated into the battery as appropriate. The tests are scored, interpreted, and written into a report that is shared with the patient and his or her medical provider upon request.  Patient performances were discussed during interdisciplinary team conferences where they were diagnosed by consensus.  If the patient meets criteria for Mild Cognitive Impairment or Alzheimer’s disease, they were subsequently invited for a feedback session to review their results with a clinician and discuss possible recommendations (14, 15). At the conclusion of the visit, participants were offered opportunities to screen for clinical research studies and if they are interested, a member of the project specific research team would meet with them individually.  If patients did not meet criteria, they were still presented with opportunities for research participation as a member of another population (i.e. healthy older adults, healthy normal volunteers, Parkinson’s disease, etc.)

 

Results

Approximately 169 individuals were referred through community outreach services targeting those with memory loss or their treating physicians. Of all those who were evaluated through the Memory Clinic, 52 were found to have MCI, 40 had AD, and 30 were determined to be cognitively normal.  47 patients were found to have cognitive impairments as a result of other etiologies (e.g. non-AD dementia, psychiatric impairments, traumatic brain injury, epilepsy, etc.).  110 patients were referred for research and the overall pool was ethnically and linguistically diverse.  Approximately 60 patients spoke English and the neuropsychological characteristics of those who completed the neuropsychological battery in English (n=40) are provided in Table 1.  Those who were non-English speaking, (e.g. Korean, Spanish, and Arabic) were administered a flexible neuropsychological assessment and a clinical interview in their native language.  A total of 19 patients were eventually enrolled in a clinical trial.  Other goals of the clinic were achieved by increasing the referral network to over 30 physicians within a 20 mile radius from the Memory Clinic location.  Collaborations with national non-profit organizations also increased, thereby increasing public awareness about the importance of research participation in the development of new treatments for Alzheimer’s disease and other related disorders.

Table 1. Patient Characteristics for Age, Education, and RBANS Standard Scores

Table 1. Patient Characteristics for Age, Education, and RBANS Standard Scores

AD=Alzheimer’s Disease; MCI= Mild Cognitive Impairment, HNE=Healthy normal elderly or cognitively normal.

 

Discussion

The findings from the Memory Clinic suggest that a model adopted in academia can be translated into a commercial clinical research setting with the proper clinicians and resources, resulting in increased awareness about clinical research and enrollment.  Comprehensive neuropsychological assessments are costly, have limited insurance coverage, and the scarcity of clinical resources can lead to waiting lists delaying a diagnosis and access to care.  Physician engagement in the research process increased as they became more aware of the importance of participation in clinical trials and the availability of research opportunities for their patients.  Connecting caregivers and their families with resources in the community was also an added incentive and the cross-referrals between professional organizations and the Memory Clinic proved to be a mutually rewarding collaboration.  As such, the Memory Clinic addressed common methodological challenges needed to improve recruitment and retention in AD clinical trials (3).
Our community outreach activities increased exponentially through the Memory Clinic.  Perceptions about clinical trials shifted as organizations were more inviting, as a result of the service to the community, as opposed to using their event as a platform solely for recruitment.  Educational seminars included information about the importance of research to the development of new treatments, as well as the medical and regulatory protections that are made on the patient’s behalf.  When patients and their families observed the relationship between members of the Memory Clinic and trusted organizations, they were more likely to request an appointment for an evaluation.
Caregiver resources were also addressed during Memory Clinic visits.  Families were taught about lifestyle changes and modifiable risk factors to improve cognition and prevent the progression of dementia.  Education related to the influences of caregiver distress on health and psychological functioning was discussed and caregivers were given information about how to seek respite to reduce their burden.  Recommendations to help improve daily function and communication with the patient were also provided.  Consistent with reported findings in the literature, caregiver access to this information through the Memory Clinic addressed an unmet need in the AD community and also increased overall interest in research participation (16).
An unintended benefit of the Memory Clinic, was the opportunity to evaluate patients from other neurologic populations and add them to the research database for future studies.  Due to the fact that the Memory Clinic was available as a community resource, patients with a wide range of cognitive impairments across different etiologies were invited for an assessment.  This was done primarily to increase our contacts with other patient populations for other future research opportunities.
While the Memory Clinic was useful in increasing our patient interactions and identifying people for research participation, many were still reluctant to participate in early phase clinical trials.  Of those who were, the selection criteria within the individual research protocols were prohibitive (e.g. many did not qualify based on the fact that they were recently diagnosed and subsequently not stabilized on their medications, or they had several comorbid conditions or medications that were prohibitive, or they were simply above the age limit).  Future research of our Memory Clinic data will include evaluating how the clinical diagnosis given as part of the clinical neuropsychological assessment differs from the criteria set forth by common AD protocols.  Moreover, strategies for recruitment will center on the importance of evolving patient engagement as outlined by other initiatives in the AD community (17).
Another common barrier to research participation was the requirement of invasive procedures as part of the early phase research studies.  Even if the patient was willing to engage in these procedures, their caregivers were reluctant to let them continue.  While innovative biomarkers serve as important pharmacokinetic and pharmacodynamic measures of signal detection in early phase trials, they also pose a challenge to recruitment. As the knowledge within the scientific community evolves, consideration may be given towards creating adaptive protocol designs to accommodate a patient’s level of comfort with the procedures and facilitate recruitment.
Existing literature shows that attitudes towards research are the strongest predictors of willingness to participate (18). A significant number of non-English speaking patients, were referred for research studies.  Utilizing multi-lingual staff with training in culturally competent neuropsychological testing increased the patient’s and their caregiver’s willingness to engage in the evaluation and hear about opportunities for research (19, 20). This has previously been proposed as a necessary component of improving recruitment rates in AD and future endeavors for the Memory Clinic will include expanding the ethnic subgroups to increase the heterogeneity of our overall research samples.
In summary, the Memory Clinic proved to be a useful model of recruitment within the setting of a commercial early phase research unit.  Addressing significant barriers to patient enrollment such as individual patient factors, caregiver influences, and involvement of their treating physicians was instrumental in the assessment of individuals interested in participation and subsequent research participation.  The Memory Clinic is a relatively recent concept in the unit and some of the original patients are returning for their annual follow-up visit.  Further work will be done to assess the patients’ longitudinal cognitive status and determine if attitudes towards research participation changes as a function of dementia severity.

 

Acknoledgement: The authors would like to thank Dr. Anita Mardian for her advocacy on behalf of the Memory Clinic, her efforts provided the PAREXEL Early Phase unit with the resources to launch this initiative.  The authors thank all patients and their caregivers for their participation in the Memory Clinic.  Their altruistic contribution to the field will hopefully lead to the development of effective treatments for AD.  Finally, we would also like to express our appreciation for our collaborators in the community.

Funding: Funding for the Memory Clinic initiative was provided by PAREXEL International.  These resources were used towards the assessment procedures, community outreach activities, testing materials, and promotional advertising.

Conflict of interest: Authors are employed by California Clinical Trials Medical Group or PAREXEL International at the time of this publication.

Ethical standards: Patients and/or legal proxies signed informed consent for the evaluation.

 

References

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3.    Vellas B, Hampel H, Rouge-Bugat ME, et al. Alzheimer’s disease therapeutic trials: EU/US Task Force report on recruitment, retention, and methodology. J Nutr Health Aging. 2012;16(4):339-345.
4.    Grill JD, Galvin JE. Facilitating Alzheimer disease research recruitment. Alzheimer Dis Assoc Disord. 2014;28(1):1-8.
5.    Grill JD, Karlawish J. Addressing the challenges to successful recruitment and retention in Alzheimer’s disease clinical trials. Alzheimers Res Ther. 2010;2(6):34.
6.    Williams DE, Vitiello MV, Ries RK, Bokan J, Prinz PN. Successful recruitment of elderly community-dwelling subjects for Alzheimer’s disease research. J Gerontol. 1988;43(3):M69-74.
7.    Bachman DL, Stuckey M, Ebeling M, et al. Establishment of a predominantly African-American cohort for the study of Alzheimer’s disease: the South Carolina Alzheimer’s disease clinical core. Dement Geriatr Cogn Disord. 2009;27(4):329-336.
8.    Watson JL, Ryan L, Silverberg N, Cahan V, Bernard MA. Obstacles and opportunities in Alzheimer’s clinical trial recruitment. Health Aff (Millwood). 2014;33(4):574-579.
9.    Duff K, Patton D, Schoenberg MR, Mold J, Scott JG, Adams RL. Age- and education-corrected independent normative data for the RBANS in a community dwelling elderly sample. Clin Neuropsychol. 2003;17(3):351-366.
10.    Randolph C, Tierney MC, Mohr E, Chase TN. The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS): preliminary clinical validity. J Clin Exp Neuropsychol. 1998;20(3):310-319.
11.    Marshall GA, Rentz DM, Frey MT, et al. Executive function and instrumental activities of daily living in mild cognitive impairment and Alzheimer’s disease. Alzheimers Dement. 2011;7(3):300-308.
12.    Rodriguez-Aranda C, Martinussen M. Age-related differences in performance of phonemic verbal fluency measured by Controlled Oral Word Association Task (COWAT): a meta-analytic study. Dev Neuropsychol. 2006;30(2):697-717.
13.    Yesavage JA. Geriatric Depression Scale. Psychopharmacol Bull. 1988;24(4):709-711.
14.    McKhann GM, Knopman DS, Chertkow H, et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7(3):263-269.
15.    Albert MS, DeKosky ST, Dickson D, et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7(3):270-279.
16.    Jennings LA, Reuben DB, Evertson LC, et al. Unmet needs of caregivers of individuals referred to a dementia care program. J Am Geriatr Soc. 2015;63(2):282-289.
17.    Vellas B. Recruitment, retention and other methodological issues related to clinical trials for Alzheimer’s disease. J Nutr Health Aging. 2012;16(4):330.
18.    Grill JD, Raman R, Ernstrom K, Aisen P, Karlawish J. Effect of study partner on the conduct of Alzheimer disease clinical trials. Neurology. 2013;80(3):282-288.
19.    Chow TW, Ross L, Fox P, Cummings JL, Lin KM. Utilization of Alzheimer’s disease community resources by Asian-Americans in California. Int J Geriatr Psychiatry. 2000;15(9):838-847.
20.    Perales J, Moore WT, Fernandez C, et al. Feasibility of an Alzheimer’s disease knowledge intervention in the Latino community. Ethn Health. 2018:1-12.

REPORT FROM THE FIRST CLINICAL TRIALS ON ALZHEIMER’S DISEASE (CTAD) ASIA-CHINA 2018 : BRINGING TOGETHER GLOBAL LEADERS

 

J.K. Chhetri1,2,3, P. Chan1,2, B. Vellas3, J. Touchon4, S. Gauthier5

 

1. Department of Neurobiology, Geriatrics and Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China; 2. China National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital of Capital Medical University, Beijing, China; 3. Department of Geriatrics, Gerontopole, World Health Organization (WHO) Collaborating Center for Frailty, Clinical Research and Geriatric training, University of Toulouse, France; 4. INSERM U 1061, University Hospital of Montpellier Montepellier, France; 5. McGill Center for Studies in Aging, Montreal, Canada

Corresponding Author: J.K Chhetri, Department of Neurobiology, Geriatrics and Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China, chhetri_jk@hotmail.com

J Prev Alz Dis
Published online January 25, 2019, http://dx.doi.org/10.14283/jpad.2019.2

 


Abstract

Population of older adults in Asia, and particularly in China is increasing rapidly. Older population are at increased risk of Alzheimer’s disease (AD) and other dementias. Soon, the Chinese population with AD will represent almost half of the world’s AD population. There is a desperate need of  disease modifying therapies to delay or slow the progression of AD, to tackle this emerging healthcare emergency. In this context, the first CTAD Asia-China conference was held in China to bring together Western and Asian leaders in AD. This meeting focused largely on how to develop successful trials in China, utilizing past experiences from the West.

Key words: Dementia, Alzheimer’s disease, China, chinese, clinical trials, CTAD.


 

 

The first Clinical Trials on Alzheimer’s Disease (CTAD) Asia-China conference was held in Shanghai, China, 1-2 September, 2018. The goal of the meeting was to bring together Asian and Western leaders on the treatment and management of Alzheimer’s disease (AD), and discuss the possibilities of next generation of AD treatment. This conference was aimed on how to develop successful AD clinical trials in Asia and particularly in China. Topics such as methodology, database management of clinical trials, biomarkers, patient recruitment, and other current challenges in AD drug trials were discussed (1).
It is projected that in every three seconds someone in the world develops dementia. Currently, over 50 million people are known to be living with AD and other dementias worldwide (2). This number is projected to double every 20 years, expecting to reach over 75 million by 2030 and 131.5 million by 2050, if we cannot find possible treatments to delay the onset or ameliorate the progression of the symptoms. In this scenario, there is a greatest need of development for disease-modifying therapies (DMTs) that will delay the clinical course of AD (3).  Pr. Jeff Cummings (Las Vegas) pointed out that clinical trials investigating DMTs are being conducted more and more outside of the United States of America (USA). Moreover, it is of greatest need to develop such clinical trials in the Asian region (in particular China) since China has the fastest growth of older population in the world and is expected to represent about half of the global AD population by the mid-century (4). However, AD is still not a top priority either for public-health policies or for healthcare investment companies in China (while it is a multi-billion dollar industry in the USA). Lessons should be learnt from the past mistakes in the USA, European union (EU) or other countries and the best ideas to suit the Chinese setting should be adopted. China has to act now if it was to reduce the upcoming burden of AD.

Major Initiatives in China

Several significant initiatives in China were presented by Pr. Zhen-Xin Zhang (Beijing), a leading researcher in dementia. Pr. Zhang also presented the clinical environment and research resources in China for preclinical and prodromal AD trials and highlighted the importance of biomarkers in such trials. Interestingly, one of these initiative- “The Chinese Biomarker Study” is being conducted in collaboration with the US Framingham study. She reported that a network of more than 40 centers (trained in good clinical practice) led by Peking Union Medical College Hospital has been already approved to conduct AD trials in China.
One of the most interesting and important discussion in the conference was the Consortium for Dementia Prevention in Chinese Populations (CDPC), was presented by Dr. Feng Lei (Singapore). According to him, the purpose of the consortium will be sharing of protocols, data and other related information. He has invited colleagues focusing on large prospective cohort studies (involving Chinese population), interventional trials in AD and other age associated cognitive decline to join the consortium. Similarly, Pr. Takeshi Iwatsubo (Tokyo) has shown successful examples of their work from the Japanese-ADNI study. He discussed for opportunities for future collaborations between Japan and China, in particular concerning the A4 (Anti-Amyloid treatment for asymptomatic AD) study which is currently recruiting (multi-country) and expected to be completed by 2022. A highly ambitious initiative of the Chinese Ministry of Science and Technology’s, CHINA (China Initiative on Neurodegeneration and Aging) project was presented by Pr. Piu Chan (Beijing). This initiative involves a network of centers/cohorts throughout China, and will implement advanced tools such as artificial intelligence (AI),  bio-banks, and big-data to support genomics, neuroimaging and clinical diagnostics (for AD and Parkinson’s disease). Another similar initiative known as the CAADI (China Aging and Alzheimer’s Disease Initiative) was described by Dr. Yong Shen (Beijing). Indeed, such initiatives greatly contribute in the development of population specific outcome measures and instruments. An example of such instrument -The Chinese version of the Baylor Profound Mental Status Examination was described by Pr. Xue Fu (Chongqing).

Epidemiological data concerning China, clinical trials and evidence based initiatives

According to Pr. Jianping Jia (Beijing) the prevalence of dementia in Chinese population is approximately 5%-6% , of which over 3% are AD and about 1.5% with Vascular dementia (VaD) (prevalence higher in rural areas) and this rate is expected to increase (5). He pointed that there is a need for qualified clinicians and adequate memory clinics to correctly diagnose AD or other forms of dementia in China. The total annual cost of AD in China was  estimated to be $167.74 billion in 2015, predicted to reach $507.49 billion in 2030 and $1.89 trillion in 2050 (6). Pr. Jia highlighted the urgency for effective anti-AD drugs, in order to reduce the social and healthcare burden in China. He pointed out some of the clinical trials with Chinese drugs that are moving to Phase III that may have the potential to fight dementia (in particular with vascular pathology), such as compound Chinese medicine Sai Luo Tong for Vascular dementia (VD) (3) and DL-3-n-butylphthalide (NBP) to treat Vascular Cognitive Impairment, no dementia (VCI-ND).  Similarly, the importance of vascular pathology in AD was further highlighted by Pr. Jiong Shi (Phoenix) in an initiative known as ICONS (The Impairment of Cognition Study) from China National Stroke Registry III. The primary objective of this initiative is to identify the potential risk factors and facilitate the prognosis of ischemic cerebrovascular disease. This database includes imaging, genomics, proteomics, metabolomics and lipidomics. This study is expected to provide numerous answers regarding post-stroke cognitive impairment (PSCI). The imaging and biomarker information could further elucidate the underlying mechanism of PSCI that would enable us to design effective DMTs.  Furthermore, Dr. Jin-Tai Yu (Qingdao) presented twenty evidence-based clinical recommendations (mainly life style and behavioral factors) for the prevention of AD based on their meta-analysis (covering 104 risk factors ) of current available prospective cohort studies.

Sharing experiences and collaboration

Experts shared their views on various operational aspects of CTAD in China. Pr. Cummings stressed the need to tackle AD from all angles globally (more importantly now in China) to overcome this healthcare emergency. Dr. Amir Kalali (San Diego) pointed out that previous methods implemented in clinical trials may not be good enough for future trials. He stressed upon the need for more information and communicative technology (ICT) involvement in the future clinical trials, as more and more population have access to technology than ever before.  Pr. Paul Aisen (San Diego) explained that it would be easier to implement ICT in the Chinese population studies, as large proportion of Chinese have already begun to use ICTs in their daily life. However, he warned about using ICT for recruitment purpose (such as in a web-based study) as there is always the risk of enrollment of population who are already amyloid beta (Aβ) positive. One of the key issues for AD clinical trials in China is the lack of experienced principle investigators (PIs) as Pr. Zheng-Xin Zhang pointed out. In addition, there was very limited time available for a PI to perform clinical trials in China, as they had to carry on with their routine hospital activities in parallel. Besides, very little funding is available in China for conducting trials and collaboration with the international community. Pr. Andreas U. Monsch (Basel) discussed about the stigma associated with diagnosis of AD, even for physicians concerned about time and cost required for testing and counseling (which might be even a bigger problem in China, given the large population and limited reimbursement by social security). He stressed that there is a need for models to predict cognitive decline or Aβ-positivity, so that patients can plan their future, including treatment options. On the contrary, individuals with AD or other forms of dementia are likely to be diagnosed later than when they can most benefit from DMTs, according to Phyllis Barkman Ferrell (Indianapolis). This will create a major healthcare crisis for patients, families and nations with no national dementia plans yet, like China in the near future. Pr. Zhang highlighted the great desire of Chinese patients to be involved in clinical trials (without actual knowledge of trials). Dr. Rachelle Doody (Basel) expressed the commitment from pharmaceutical sector to develop novel therapies for AD. The experts agreed that there is a need for more collaborative work (so as to exchange resources and expertise) for successfully tackling AD.  All leaders were hopeful that the Chinese government will immediately take initiation to overcome the challenges of CTAD in China and contribute in reducing the global burden of AD.

Neuroimaging initiatives

Biomarkers could play an extremely important role to identify the high risk population and provide opportunities to implement DMTs to target a specific mechanism of action (8). Pr. Eric Reiman (Phoenix) discussed the emerging role of biomarkers such as brain imaging, cerebrospinal fluid (CSF) biomarkers, new emerging blood tests, and genetic risk factors. Biomarkers could be used in AD observational studies (detection and tracking), in therapeutic trials (participants selection, dose selection, treatment monitoring/prediction/evaluation, accelerated evaluation of preventive therapies), in the clinical settings (diagnosis, prognosis, management , screening). Gilles Tamagnan (Connecticut) who is closely working with Pr. Piu Chan, discussed on the importance of amyloid and tau-imaging ligands in AD DMTs. He explained, although these imaging ligands are still largely unavailable or affordable globally, several Chinese hospitals have approved the production and testing of  such radiopharmaceuticals strategies.

Trials results

Recently human anti-Aβ monoclonal antibodies are being investigated as potential DMT candidates[9]. Some of these trials have been discussed during the CTAD conference.  Baseline data from the ongoing CREAD study- a Phase III trial of Crenezumab (human anti- Aβ monoclonal IgG4 antibody) in early prodromal to mild AD were presented. Exploratory results from the Phase II trial of Crenezumab suggested that improved outcomes may be achieved with earlier treatment and higher dose. Hence, the objective of the Phase III trial was to evaluate the efficacy and safety of a much higher dose of Crenezumab than in the Phase II (60 mg/kg IV every four weeks) vs placebo in patients with early AD for 105 weeks. The primary end point is a change in Clinical dementia rating -sum of boxes (CDR-SB) from baseline to 105 weeks.  A total of 813 patients were randomized between 2016-2017. Another ongoing trial of Gantenarumab (humanized anti-Aβ monoclonal IgG1 antibody), GRADUATE new Phase III was presented. Following a futility assessment of the prior Gantenerumab Phase III-SCarlet RoAD study, a new potentially efficacious and safe dose regimen for  Gantenerumab was developed. The GRADUATE new Phase III intends to use the calculated dose (up-titration to a target dose) for 24 months (anticipated enrollment of 750 subjects, CDR-SB primary endpoint). Another potential candidate, Aducanumab is currently in its Phase 1b study (PRIME) evaluating the safety, tolerability, pharmacokinetics and pharmacodynamics in patients with prodromal or mild AD. Results showed beneficial effect in cognition and reduction of amyloid plaques over 24 months. Further investigation of the clinical efficacy of Aducanumab in the ENGAGE and EMERGE Phase III trial is expected.
Results from a Phase II study of Elenbecestat (E2609), a BACE 1 (a β -site amyloid precursor protein cleaving enzyme 1) inhibitor was shown. The primary objective of the trial was to investigate the safety of Elenbecestat in subjects with mild cognitive impairment (MCI) to moderate AD (placebo vs Elenbecestat for 18 months). Elenbecestat was reported to be well tolerated, no adverse events were observed, and the group with the higher dose of Elenbecestat showed significantly lower amyloid load than the placebo. Clinical assessments showed higher dose of Elenbecestat to attenuate cognitive decline in subjects with MCI and moderate AD.  Interestingly, in another Phase II trial of Abapetalone, a BET (bromodomain and extraterminal) protein inhibitor treatment showed a significant decrease in major adverse cardiovascular events (MACE), which was most distinct in patients with diabetes and elevated inflammation. Thus, a Phase III trial BETonMACE is currently underway to evaluate the effect of Abapetalone on population who are diabetic and with a recent acute coronary syndrome and with low levels of high density lipoprotein (HDL-C). Changes in the Montreal Cognition Assessment (MoCA) is expected to provide insights on the potential of  BET inhibition to modulate cognitive function in older adults with cardiovascular risk factors. Similarly, the potential of Abapetalone treat VCI and neurodegenerative disease is also being investigated.

Promising Chinese compounds

The efficacy of a Traditional Chinese Medicine(TCM) YangXue QingNao (YXQN)  (a well-known TCM to improve cerebral blood flow) to improve short-term memory, spatial learning and memory in AD mice was demonstrated. Interestingly, YXQN was found to reduce AB plaque deposition (47%-72% reduction) in hippocampus/cortex of AD mice. Another compound GV-971 (marine derived oligosaccharide) was shown to be effective in reducing AB deposition and tau tangles in AD mice. Compound GV-971 was able to reduce the AB induced neuroinflammatory cascade and ameliorate cognitive impairment in the AD mice.

Drug development Challenges

Treating AD early in the course of the disease may be critical in improving patient outcomes. In order develop promising new DMTs there is a need for a deeper understanding of the disease biology, establish strong linkage of treatment targets, improve translational measures, select promising investigational compounds, improve clinical study design and find patients that are most likely to respond (10). Samantha Budd (Boston) explained all of these tasks are a major challenge for developing AD drugs. Moreover, conducting bigger clinical trials, involving multi-country, larger cities and multi-ethnicity could potentially lead to screening failure. Similar challenges existed even in the context of China with large population and cultural/ethnicity diversity said Pr. Zheng-Xin Zhang. More importantly such big trials require  huge financial input , are time consuming (10-15 years from drug discovery to approval) and with a very high failure rate Pr. Cummings added. He pointed out that the various levels of complexity (biological, genetics, brain circuit, population, clinical, treatment) of the disease itself are a major challenge, which require a complex solution. We are still learning from our past experiences and failures, we have learnt about novel biomarkers, intervention timing, dosing and titration effect described Dr. Rachelle Doody. She stressed on the continuous approach to target amyloid and tau pathways and attempt to modulate the associated neuroinflammation..

The key takeaway message from CTAD Asia-China

Sharing of ideas, information and knowledge on the latest achievements and progress in AD treatment across the Western world and Asia was a major achievement for participants. Possibilities of future collaboration and sharing of resources and expertise in the field of AD could facilitate the implementation of major clinical trials in the Asian region. Despite our failures in the past, progress is being made and hopefully we will have effective treatments very soon. We look forward to the next CTAD-ASIA meeting, 13-14 September in Beijing, China.

Conflict of interest: None.

Ethical standards: None.

References

1.    CTAD Asia-China and the future of Alzheimer’s disease research in Asia. https://www.linkedin.com/pulse/ctad-asia-china-future-alzheimers-disease-research-asia-judith-walker. Accessed 1 Oct 2018
2.     Dementia statistics | Alzheimer’s Disease International. https://www.alz.co.uk/research/statistics. Accessed 1 Oct 2018
3.     Cummings J, Fox N. Defining Disease Modifying Therapy for Alzheimer’s Disease. J Prev Alzheimers Dis 2017;4:109–115
4.     Feng L, Li J, Yu J-T, et al. Editorial: Prevention of Alzheimer’s Disease in Chinese Populations: Status, Challenges and Directions. J Prev Alzheimers Dis 2018;5:90–94
5.     Jia J, Wang F, Wei C, et al. The prevalence of dementia in urban and rural areas of China. Alzheimers Dement 2014;10:1–9
6.     Jia J, Wei C, Chen S, et al. The cost of Alzheimer’s disease in China and re-estimation of costs worldwide. Alzheimers Dement 2018;14:483–491
7.     Jia J, Wei C, Chen S, et al. Efficacy and safety of the compound Chinese medicine SaiLuoTong in vascular dementia: A randomized clinical trial. Alzheimers Dement (N Y) 2018;4:108–117
8.     Cummings J, Fox N, Vellas B, Aisen P, Shan G. Biomarker and Clinical Trial Design Support for Disease-Modifying Therapies: Report of a Survey of the EU/US: Alzheimer’s Disease Task Force. J Prev Alzheimers Dis 2018;5:103–109
9.     Budd Haeberlein S, O’Gorman J, Chiao P, et al. Clinical Development of Aducanumab, an Anti-Aβ Human Monoclonal Antibody Being Investigated for the Treatment of Early Alzheimer’s Disease. J Prev Alzheimers Dis 2017;4:255–263
10.     Vellas B, Aisen P, Weiner M, Touchon J. What We Learn from the CTAD (Clinical Trials Alzheimer’s Disease) 2018. J Prev Alzheimers Dis 2018;5:214–215

IDENTIFYING BETTER OUTCOME MEASURES TO IMPROVE TREATMENT OF AGITATION IN DEMENTIA: A REPORT FROM THE EU/US/CTAD TASK FORCE

 

M. Sano1, M. Soto2, M. Carrillo3, J. Cummings4, S. Hendrix5, J. Mintzer6, A. Porsteinsson7, P. Rosenberg8, L. Schneider9, J. Touchon10, P. Aisen11, B. Vellas2, C. Lyketsos8, and the EU/US/CTAD Task Force members*

 

*E.U./U.S. CTAD TASK FORCE: Susan Abushakra (Framingham); Joan Amatniek (Princeton); Sandrine Andrieu (Toulouse); Randall Bateman (Saint Louis); Joanne Bell (Wilmington); Gene Bowman (Lausanne); Sasha Bozeat (Utrecht); Samantha Budd Haeberlein (Cambridge); Marc Cantillon (Livingston); Marither Chuidian (Aliso Viejo); Doina Cosma-Roman (Aliso Viejo); Anne De Jong-Laird (Wexham); Rachelle Doody (Basel); Sanjay Dubé (Aliso Viejo); Michael Egan (North Wales); Laura Eggermont (Utrecht); Phyllis Ferrell (Indianapolis); Erin Foff (Princeton);  Terence Fullerton (New York); Sylvie Gouttefangeas (Suresnes);  Michael Grundman (San Diego); David Hewitt (Wilmington); Carole Ho (South San Francisco); Patrick Kesslak (Princeton); Valérie Legrand (Nanterre), Stefan Lind (Valby); Richard Margolin (New York); Thomas Megerian (Aliso Viejo); Annette Merdes (Munich); David Michelson (North Wales); Mark Mintun (Philadelphia); Tina Olsson (Cambridge); Ronald Petersen (Rochester); Jana Podhorna (Ingelheim am Rhein); Stephane Pollentier (Ingelheim am Rhein); Rema Raman (San Diego); Murray Raskind (Seattle); Gary Romano (Beerse); Juha Rouru (Turku); Ivana Rubino (Cambridge); Ricardo Sainz-Fuertes (Wexham); Stephen Salloway (Providence); Cristina Sampaio (Princeton); Philip Scheltens (Amsterdam); Rachel Schindler (New York); Mark Schmidt (Beerse); Jeroen Schmitt (Lausanne); Peter Schüler (Langen); Märta Segerdahl Storck (Valby); Eric Siemers (Indianapolis); John Sims (Indianapolis); LeAnne Skordos (Cambridge); Bjorn Sperling (Cambridge); Reisa Sperling (Boston); Joyce Suhy (Newark);  Serge Van der Geyten (Beerse); Philipp Von Rosenstiel (Cambridge); Michael Weiner (San Francisco); Glen Wunderlich (Ridgefield); Haichen Yang (North Wales); Jerry Yang (New York)

1. Mount Sinai School of Medicine, Bronx, NY, USA; 2. Gerontopole, INSERM U1027, Alzheimer’s Disease Research and Clinical Center, Toulouse University Hospital, Toulouse, France; 3. Alzheimer’s Association, Chicago, IL USA; 4. Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, Nevada, USA; 5. Pentara Corporation, Salt Lake City, UT, USA; 6. Roper St. Francis CBRT, Charleston, SC, USA; 7. University of Rochester School of Medicine and Dentistry, Rochester, NY, USA; 8. Johns Hopkins University School of Medicine, Baltimore, MD, USA; 9. University of Southern California, Los Angeles, CA, USA; 10. University Hospital of Montpellier, 34025 Montepellier Cedex 5, and INSERM 1061, France; 11. Alzheimer’s Therapeutic Research Institute (ATRI), Keck School of Medicine, University of Southern California, San Diego, CA, USA

Corresponding Author: Mary Sano, Mount Sinai School of Medicine, Bronx, NY, USA, mary.sano@mssm.edu

J Prev Alz Dis 2018;5(2):98-102
Published online March 21, 2018, http://dx.doi.org/10.14283/jpad.2018.15

 

 


Abstract

For the second time in the past 3 years, the EU-US CTAD Task Force addressed challenges related to designing clinical trials for agitation in dementia, which is one of the most disruptive aspects of the condition for both patients and caregivers. Six recommendations emerged from the Task Force meeting: 1 – Operationalizing agitation criteria established by the IPA; 2 – Combining clinician- and caregiver-derived outcomes as primary outcome measures; 3 – Using global ratings to define clinically  meaningful effects and power studies; 4 – Improving the accuracy of caregiver reports by better training and education of caregivers; 5 – Employing emerging technologies to collect near real-time behavioral data; and 6 – Utilizing innovative trial designs and increasing the use of biomarkers to maximize the productivity of clinical trials for neuropsychiatric symptoms.

Key words: Neuropsychiatric symptoms, agitation, dementia, Alzheimer’s disease, clinical trials, NPS outcome measures.


 

 

Introduction

Agitation and other neuropsychiatric symptoms (NPS) are the most disruptive aspects of dementia for both patients and caregivers. They are associated with worse quality of life (1), greater dementia severity, earlier institutionalization, and accelerated mortality (2, 3). Despite being a major driver of high cost care (4, 5), agitation and other NPS are poorly understood and inadequately studied. In a population-based study, agitation in dementia occurred in up to 40% of community-dwelling dementia patients and 80% of patients living in nursing homes (6). Cross-sectional studies show somewhat lower prevalence estimates (7). For persons with mild cognitive impairment (MCI) the prevalence of agitation is nearly as high, according to some studies (8). There are no approved pharmacological treatments for agitation in dementia in the USA and in Europe and Canada only short-term use of risperidone is approved for severe persistent physical aggression. Several medications with conventional and novel mechanisms of action are in development.
In 2014, the International Psychogeriatric Association (IPA) published provisional consensus definition for agitation in cognitive disorders for clinical and research use. According to this definition, agitation in dementia is characterized by emotional distress associated with the presence of at least one of the following: excessive motor activity, verbal aggression, or physical aggression. These symptoms must be severe enough to cause significant impairment in interpersonal relationships, social functioning, and/or the ability to perform or participate in activities of daily living. These symptoms must not be attributable to another psychiatric disorder, environmental or medical conditions, or the physiological effects of substance use (9).
Reaching consensus on a definition of agitation in dementia represents an important step towards identifying new treatments since improved nosology can reduce heterogeneity in defining target conditions.  These criteria need to be operationalized, so that the clinical characteristics can be aligned with what is understood about the biology and phenomenology of agitation in dementia. This will allow for the selection and assessment of measures that best capture clinically important outcomes. With this in mind, the European Union-North American Clinical Trials in Alzheimer’s Disease Task Force (EU-US CTAD Task Force) focused its 2017 meeting in Boston, Massachusetts on finding the best outcome measure for agitation in dementia trials. This Task Force comprises an international collaboration of Alzheimer’s disease (AD) investigators from industry and academia who meet yearly to review recent progress in developing effective treatments for AD, and reach agreement on common clinical trial approaches, while promoting collaboration and data sharing. The fact that the Task Force previously addressed some of the challenges in designing clinical trials for agitation and aggression only three years ago (10) attests to the importance of this issue.

 

Overview of Agitation in Dementia

Clinical presentation

In addition to the core symptoms, irritability, disinhibition, and aberrant motor activity are common. Moreover, agitation and other NPS fluctuate and overlap. For example, agitation overlaps with many other NPS, particularly depression, irritability, and anxiety, but also apathy (11). These fluctuating symptoms cluster into predictable groups with complex etiologies (12, 13). Caregivers often notice increased agitation in the late afternoon and evening, a phenomenon referred to as “sundowning” (even though it has more to do with fatigue and sensory experiences than with the sunset).

Biological mechanisms

Agitation may be due to underlying biological mechanisms or may be a consequence of delirium, environmental factors, medication, or caregiver and environmental interactions (14). Distinguishing the underlying factors that result in agitation – both biological and environmental — is important for treatment decisions. For example, sundowning may be associated with sleep disturbances, circadian rhythm disruption, or disorientation. If associated with circadian rhythm dysfunction, this symptom may be a target for chronobiologic treatments. Moreover, the underlying biology that disrupts behavior in persons with AD interacts with short-term and long-term environmental factors, medical comorbidities, etc, due to patient vulnerability.
The neurobiological mechanisms underlying agitation in dementia may differ from those that underlie agitation in other psychiatric diseases such as schizophrenia or major depressive disorder (15). These differences likely explain the fact that drugs used to treat NPS in depression and schizophrenia seem to be less effective in dementia leading to the need for novel approaches to treatment mechanism. Notably, the US Food and Drug Administration (FDA) recently approved pimavanserin for dementia-related psychosis in Parkinson’s disease, and this drug is currently in clinical trials to treat both psychosis and agitation in patients with AD and in a trial for dementia-related psychosis in multiple types of neurodegenerative disorders. Pimavanserin is a selective 5-HT2A receptor inverse agonist. If treatment responsiveness emerges across dementia causes, this could open a new window into understanding the biology and treatment of some NPS.
Neurodegeneration disrupts brain circuitry, resulting in NPS. In agitation, at least two different circuits are disrupted (15). These may be the same circuits that are disrupted in dementia-related apathy, which could explain how apathy and agitation are closely linked (16). Further understanding of how these circuits are disrupted in different patients may provide clues about patients’ differential response to treatment.  Assessment of circuit function might also play a role as a biomarker to assess or predict treatment response.
Agitation in dementia is also associated with alterations in the function of serotonergic, noradrenergic, cholinergic, and dopaminergic neurotransmitters, related to neurodegeneration of associated brain nuclei (17). Neurodegeneration also contributes indirectly to the emergence of NPS by making patients very vulnerable to short-term and long-term environmental factors, or medical comorbidities, and other influences, such that patients express the impact of these in their behavior.  Understanding the neurophysiological factors that underlie agitation in dementia should lead to more effective treatments.
With several medications with novel mechanisms in development, a question considered by the Task Force is whether treatment development should continue to focus on phenomenology or move towards targeting neurobiologic mechanisms. A focus on phenomenology may identify those more likely to respond to a specific treatment; however, the lack of attention to the complex mechanisms and genetic and environmental factors that contribute directly and indirectly to symptoms may ultimately lead to failure to identify underlying processes that need to be targeted in order to prevent these disabling NPS. For example, both affective and executive functions impaired in agitation, suggest that multiple neural circuits are disrupted. Citalopram primarily targets the affective phenotype (18).

Available treatments

Treatment options for NPS in AD have been disappointing. Antipsychotics, anticonvulsants, benzodiazepines, and antidepressants are frequently prescribed despite little evidence of efficacy and an increased risk of adverse side effects. Citalopram, a selective serotonin reuptake inhibitor (SSRI) is one apparent success story, since clinical trials showed that this drug reduces agitation without the negative side effects associated with other SSRIs (17). Worsening cognition and cardiac side effects were observed in some patients on citalopram, potentially limiting usefulness, although these side effects might be mitigated by using the S-enantiomer of racemic citalopram (19) A subgroup analysis investigating the heterogeneity of the treatment response concluded that patients with moderate agitation and lower levels of cognitive impairment were more likely to benefit from citalopram, while those with more severe agitation and greater cognitive impairment were at higher risk of adverse responses (20).

 

Assessing agitation in clinical trials

Choosing the best outcome measure for clinical trials is key to treatment development for NPS. Over time, different trials have used different outcome measures, as no gold standard has previously emerged. Instruments used to assess agitation in clinical trials include broad-spectrum scales such as the Neuropsychiatric Inventory (NPI) (21) and the clinician-rated NPI-C (22), the Neurobehavioral Rating Scale (NBRS), and the Behavioral Pathology in Alzheimer’s Disease Rating Scale (BEHAVE-AD) (23); as well as agitation subscales of these instruments (NBRS, NPI, and NPI-C) or agitation-focused scales such as the Cohen-Mansfield Agitation Inventory (CMAI) (24).
The Neuropsychiatric Inventory (NPI) and its variants the NPI-Q and NPI-NH (for nursing homes), which is based on informant report, are widely used but may miss granularity as they are not designed for use as free-standing agitation instruments. The NPI-C, the clinician-rated form of the NPI (22) has a broader range that includes NPS characteristics of MCI and severe dementia, high inter-rater reliability, strong convergent validity for depression (assessed with the Cornell Scale for Depression in Dementia [CSDD]), psychosis (assessed with the Brief Psychiatric Rating Scale [BPRS]), apathy (vs. Apathy Evaluation Scale [AES]), and agitation/aggression (vs. the CMAI). The main strength of the NPI-C is that final scoring is based on experienced clinician ratings and not on subjective caregiver’s input that include the so called “filter” whereby NPS reported to affect the patient reflect the caregiver’s mental state instead (25). NPI-C has been translated into several languages offering advantages for international multi-site trials. Limitations of the NPI-C include its length as it has twice as many items as the NPI, and takes longer to complete, and it may be more costly as it must be administered by a skilled clinician.  To date there is a lack of data concerning the agitation and aggression components of the NPI-C since this new measure has been rarely used in cohorts or trials.  (Note: NPI-C was found to be feasible to use in a multi-center trial of scyllo-inositol for agitation but results have not been published).
Global scales have been widely used in clinical trials of treatments for NPS including agitation. The Alzheimer’s Disease Cooperative Study-Clinical Global Impression of Change (ADCS-CGIC), published in 1997 following a consensus process involving AD clinicians [26], has been modified for NPS trials (27, 28). The mADCS-CGIC uses an interview structure with worksheets to remind raters to evaluate specific NPS (e.g., depression, apathy, agitation) as well as the broader dementia. While its administration is less structured than a rating scale, this is intentional and allows the focus to be on “gestalt” of the NPS syndrome being rated. As such, it is less likely pick up trivial effects that are irrelevant to the clinical setting. In the Citalopram for Agitation in Alzheimer’s Disease Study (CitAD), significant improvement compared to placebo was seen using two instruments as primary outcome measures — the mADCS-CGIC and the NBRS-A (17).
The CMAI is agitation specific and very detailed although it historically was developed for use in nursing homes. It covers a range of clinically-relevant agitation behaviors (not symptoms) and can usually be administered in 15-20 minutes. Drawbacks include its subjective nature, since it is administered to caregivers with no clinician input, and the observation that it focuses on the assessment of many behaviors seen in advanced dementia, typically not relevant to outpatients. In addition, questions remain about what is a clinically meaningful effect size on the CMAI. In CitAD, CMAI and the NPI were used as secondary outcome measures, enabling investigators to compare these measures in terms of sensitivity to change.

 

Evidence for a single construct vs. symptom clusters

CMAI calculates agitation scores using a diverse group of behaviors, which are grouped into symptom clusters. Although the manual  states that it is not useful to add all categories to calculate a total score since different agitated behaviors occur under different circumstances and in different people, it also indicates that behaviors may be weighted according to disruptive impact and then combined (29, 30). The long version’s 29-items tend to fit models involving 3-4 factors in principal component analyses: aggressive behavior, physically non-aggressive behavior, verbally agitated behavior, and/or hiding and hoarding. The question remains of whether symptom clusters provide more clinically relevant information for clinical trials than a total score calculated by summing all categories. Further, if a total score is used, should the factors be weighted differently? Weighting would have to be clinically determined and take into account the shape of behavioral trajectories and the clinical importance of behavioral changes.
Factor analyses from studies in different populations suggest that CMAI item scores do not cluster in a way that supports the use of factor (31). In different studies, different items “load” onto different factors and in some cases do not load at all or load on more than one factor (31, 32). If factor scores were to be used as separate endpoints, claims could be based on any one of those factors; however, this only makes sense if the different factors are independent, which is not the case. Since CMAI items as a whole represent a single construct (e.g., agitation, aggression), reflect aspects of the IPA provisional criteria, and have change scores consistent across factors (31, 32), it is most appropriate to combine them into a single total CMAI score. Separating factors may also reduce the power from the convergence of evidence across multiple domains. Change scores cluster to a greater extent than endpoint scores, and within each factor there are items that show significance while others do not, depending on the study (i.e., there is litle consistency across studies). For example, using data from a risperidone study, only change in “hitting behaviors” was significant in the physical aggression factor (33). Moreover, this study indicated that the biggest predictor of which items will show significance in treatment effects is how big the placebo effect is; thus signal-to-noise in the placebo group negatively predicts treatment effect (32).

 

Conclusions

To accelerate development of improved treatments for agitation, novel measures are needed to better capture behaviors that are of most concern to patients and caregivers. Operationalizing the IPA criteria is a needed first step, for diagnosis and to establish entry criteria for clinical studies. Some Task Force members advocated developing a single measure that reflects agitation as a unitary phenomenon. Since the development of new scales from whole cloth will result in additional regulatory challenges the consensus was that the best approach moving forward is to use existing datasets to construct an evidence-based single novel measure of agitation by selecting item subsets of existing scales (e.g., NPI-C or CMAI) that best reflect the IPA criteria and the situations in which agitation occurs. These data sets may include critical descriptors of setting, demographic or clinical characteristics, disease severity which could be used to improve sensitivity of outcomes for specific trials.  [Recommendation #1].
The Task Force agreed that since clinician-derived and caregiver-reported assessments have overlapping strengths primary outcome measures in agitation trials should combine the two as is the case with mADCS or NPI-C, with secondary outcomes focusing on caregiver report alone, as with CMAI or NPI [Recommendation #2].
Further, global ratings should be used to define clinically meaningful effect sizes and to power studies [Recommendation #3]. This will mitigate concerns about defining meaningful benefit based on individual symptoms that are not relevant to the specific care setting. It will also streamline trials by supporting the use of smaller samples sizes and making “no-go” decisions easier.
Better engagement of caregiver-informants is critical to future treatment development [Recommendation #4]. Caregivers play an important role in the management of NPS. They not only feel the consequences of disruptive behaviors but may also be the cause of those behaviors. Giving caregivers a greater voice in management and treatment development should be coupled with efforts to improve data quality. To improve accuracy of caregiver reports it is essential to reduce the effects of inexperience and subjectivity (e.g., the caregiver “filter”). To this end, caregivers should be trained to understand what is meant by the term “agitation” and about the phenotype of individual symptoms which may provide data that is more valid and has less inherent variance.
Collection of near real-time data on agitation symptoms through briefer more frequent (e.g., daily) data collection contacts or caregiver diaries should be pursued. Technology may offer novel solutions for this objective, for example, by using digital assistants to remind people to submit assessments [Recommendation #5].
Critical improvements in clinical trial design should be implemented to ensure the provision of high quality care and to minimize placebo responses [Recommendation #6]. Trials should use systematic approaches to ensure that non-pharmacologic therapies have been considered prior to enrolling patients in medication trials (e.g, the DICE (Describe, Investigate, Create, Evaluate) approach (14)). Further, to exclude participants who do not need to be on medication, a placebo lead-in or withdrawal design should be considered. In clinical trials for severe agitation, there has been a strong bias for not selecting people who are easier to manage, although these patients may be less responsive to medication.
In the future, assessing levels of agitation or response to treatment could be improved through the use of biomarkers. The fact that agitation reflects multiple biological pathways suggests that multiple types of biomarkers – genetic, pharmacogenetic, proteomic, and performance – will be needed. In clinical and observational studies, these biomarkers will need to be validated in populations with a range of behaviors and in different settings.
Since agitation has multiple causes and mechanisms, there is no simple, single, or unique treatment. This observation demands that treatment development move towards better understanding the causes of agitation, including neurobiological factors, and the interaction with patient factors (e.g, dementia severity, co-morbidities), and environmental factors. Since different phenotypes may reflect different types of agitation addressing causes should precede treating the symptoms. Ultimately, the field should coalesce around the development of sequential algorithms that combine “eco-psycho-social” with pharmacologic treatments for agitation, and by extension for all NPS.

 

Acknowledgements: The authors thank Lisa J. Bain for assistance in the preparation of this manuscript. JC is supported in part by Keep Memory Alive (KMA) and NIGMS grant P20GM109025.

Conflict of interest: JC owns the copyright of the NPI. Other authors have no conflicts of interest with this paper. The Task Force was partially funded by registration fees from industrial participants. These corporations placed no restrictions on this work.

 

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