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PLATFORM TRIALS TO EXPEDITE DRUG DEVELOPMENT IN ALZHEIMER’S DISEASE: A REPORT FROM THE EU/US CTAD TASK FORCE

 

 

P.S. Aisen1, R.J. Bateman2, M. Carrillo3, R. Doody4, K. Johnson5, J.R. Sims6, R. Sperling7, B. Vellas8 and the EU/US CTAD Task Force

 

*EU/US/CTAD TASK FORCE: Rebecca Amariglio (Boston); Sandrine Andrieu (Toulouse); Rhoda Au (Boston); Clive Ballard (Exeter); Matthew Barton (Raleigh); Monika Baudler (Basel); Joanne Bell (Willmington); Kevin Biglan (Indianapolis); Adam Boxer (San Francisco); Sasha Bozeat (Basel); Claudine Brisard (Issy les Moulineaux); Miroslaw Brys (Indianapolis); Marc Cantillon (Gilbert); Bill Chan (Beijing); Ivan Cheung (Woodcliff Lake); Min Cho (Woodcliff Lake); Julia Coelho (San Francisco); Shobha Dhadda (Woodcliff Lake); Daniel Di Giusto (Basel); Michael Donohue (San Diego); John Dwyer (Washington); Michael Egan (North Wales); Rianne Esquivel (Malvern); Howard Fillit (New York); Wendy Galpern (New Jersey); Harald Hampel (Woodcliff Lake); Jason Hassenstab (St Louis); David Henley (New Jersey); Joseph Herring (North Wales); Carole Ho (South San Francisco); Michael Irizarry (Woodcliff Lake); Takeshi Iwatsubo (Tokyo); Gustavo Jimenez-Maggiora (San Diego); Jeffrey Kaye (Portland); Geoffrey Kerchner (South San Francisco); Gene Kinney (South San Francisco); Shailaja Korukonda (Woodcliff Lake); Lynn Kramer (Woodcliff Lake); Jaren Landen (Cambridge); Ishani Landri (Woodcliff Lake); Lars Lannfelt (Uppsala); Valérie Legrand (Nanterre); Simon Lovestone (Oxford); Manoj Malhotra (Woodcliff Lake); Eric McDade (St Louis); Fanni Natanegara (Indianapolis); Francisco Nogueira (South San Francisco); Gerald Novak (New Jersey); Gunilla Osswald (Stockholm); Susanne Ostrowitzki (South San Francisco); Amanda Paley (New York); Kathryn Papp (Boston); Martin Rabe (Woodcliff Lake); Rema Raman (San Diego); Elena Ratti (Cambridge); Laurie Ryan (Bethesda); Stephen Salloway (Providence); Peter Schüler (Langen); Hiroshi Sekiya (Malvern); Jiong Shi (Las Vegas); Melanie Shulman (Cambridge); Eric Siemers (Zionsville); Kaycee Sink (South San Francisco); Maria Soto-Martin (Toulouse); Joyce Suhy (Newark); Jina Swartz (London); Pierre Tariot (Phoenix); Edmond Teng (South San Francisco); Jacques Touchon (Montpellier); Martin Traber (Basel); Andrea Vergallo (Woodcliff Lake); Judy Walker (Singapore); Jon Walsh (San Francisco); Michael Weiner (San Francisco); Alette Wessels (Indianapolis); Haichen Yang (North Wales); Wagner Zago (San Francisco); Kenton Zavitz (Cambridge)

1. Alzheimer’s Therapeutic Research Institute (ATRI), Keck School of Medicine, University of Southern California, San Diego, CA, USA; 3. Alzheimer’s Association, Chicago, IL, USA; 4. Genentech/Roche, Basel, Switzerland; 5. Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; 6 – Eli Lilly and Company, Indianapolis, IN, USA; 7. Brigham and Women’s Hospital, Boston MA, USA; 8. Gerontopole, INSERM U1027, Alzheimer’s Disease Research and Clinical Center, Toulouse University Hospital, Toulouse, France

Corresponding Author: P.S. Aisen, University of Southern California Alzheimer’s Therapeutic Research Institute, San Diego, CA, USA, paisen@usc.edu

J Prev Alz Dis 2021;3(8):306-312
Published online May 24, 2021, http://dx.doi.org/10.14283/jpad.2021.21

 


Abstract

A diverse range of platforms has been established to increase the efficiency and speed of clinical trials for Alzheimer’s disease (AD). These platforms enable parallel assessment of multiple therapeutics, treatment regimens, or participant groups; use uniform protocols and outcome measures; and may allow treatment arms to be added or dropped based on interim analyses of outcomes. The EU/US CTAD Task Force discussed the lessons learned from the Dominantly Inherited Alzheimer’s Network Trials Unit (DIAN-TU) platform trial and the challenges addressed by other platform trials that have launched or are in the planning stages. The landscape of clinical trial platforms in the AD space includes those testing experimental therapies such as DIAN-TU, platforms designed to test multidomain interventions, and those designed to streamline trial recruitment by building trial-ready cohorts. The heterogeneity of the AD patient population, AD drugs, treatment regimens, and analytical methods complicates the design and execution of platform trials, yet Task Force members concluded that platform trials are essential to advance the search for effective AD treatments, including combination therapies.

Key words: Alzheimer’s disease, anti-amyloid therapies, anti-tau therapies, platform trials, adaptive trial design, shared placebos, master protocols, secondary prevention.


 

Introduction

Disappointing clinical trial results continue to mount in the search for effective treatments for Alzheimer’s disease (AD) despite increasing knowledge about underlying mechanisms and potential therapeutic targets (1,2). As trials move into earlier stages of the disease with a focus on secondary and even primary prevention, the need for innovative trial designs and improved biomarker technologies has become increasingly evident. In December 2020 as the COVID-19 pandemic caused the suspension of many non-COVID-19 clinical trials worldwide (3), the EU/US Clinical Trials in Alzheimer’s Disease Task Force met virtually to explore platform trials as a means of accelerating and improving the efficiency and success of AD drug development. The Task Force brought together leaders from existing and planned AD platform trials to discuss their experiences to date as well as plans for moving forward. Together, they examined recent experience from the Dominantly Inherited Alzheimer Network Trials Unit (DIAN-TU) platform trial and other platform trials that have launched or are in the planning stages, and that provide lessons applicable to the design of future trials.
Platform trials are those conducted within an infrastructure that enables simultaneous and perpetual assessment of multiple therapeutics, treatment regimens, and/or participant groups. They have the potential to increase efficiency by minimizing screen failures; using uniform outcome measures, protocols, and consents; and testing multiple targets and drugs in parallel rather than serially. Adaptive platform trials may allow new arms to be added or individual arms to be stopped if interim analysis indicates a failure to demonstrate efficacy.
However, platform trials can be highly complex both operationally and analytically, requiring a great deal of coordination to bring multiple stakeholders together at the same time and to produce convincing results.

 

Lessons learned from DIAN-TU

DIAN-TU and the DIAN Observational study (DIAN-Obs) are public-private partnerships created to facilitate the development of AD therapeutics and advance scientific understanding of the optimal ways to prevent and treat AD. To accomplish these objectives, DIAN enrolled participants from around the world with dominantly inherited AD (DIAD) mutations that confer nearly 100 percent certainty of developing AD. By longitudinally monitoring both symptomatic and presymptomatic participants with clinical assessments and physiologic and pathologic biomarkers, DIAN established the progression of biomarkers across the continuum of the disease and demonstrated a biomarker profile associated with presymptomatic disease (4). This provided a rationale for starting a prevention trial in the presymptomatic stage. Figure 1 illustrates the relationship of a range of biomarkers to disease stage and how that translates to the potential for primary and secondary prevention as well as symptomatic treatment.

Figure 1. Stages of Pathology and Disease in Dominantly Inherited Alzheimer’s Disease

Bateman, Randall J., Chengjie Xiong, Tammie L.S. Benzinger, Anne M. Fagan, Alison Goate, Nick C. Fox, Daniel S. Marcus, et al. “Clinical and Biomarker Changes in Dominantly Inherited Alzheimer’s Disease.” New England Journal of Medicine 367, no. 9 (August 30, 2012): 795–804. https://doi.org/10.1056/NEJMoa1202753. Reprinted with permission.

 

DIAN-TU built a flexible and robust platform based on observational data from DIAN-Obs along with disease progression models, comprehensive biomarker studies, input from participants and family members, and inclusive discussions with stakeholders. The platform enabled the design and execution of prevention studies that can accommodate a range of prevention and treatment trials. Since the rarity of these mutations limits the number of trial participants, DIAN-TU has established study sites around the globe: in North and South American, Europe, Japan, Korea, and Australia, with additional sites planned in China and other countries.
The DIAN-TU Trial Platform is designed to improve the efficiency of trials by:
• testing multiple drugs and targets in parallel
• optimizing the trial design in view of the limited number of participants
• using a pooled placebo and control group, building up data over time to accumulate power to detect a drug effect
• evaluating the magnitude of target engagement based on each drug’s mechanism of action
• adapting the trial in response to biomarker findings, magnitude of drug effects at different doses, and safety signals
• incorporating novel biomarkers
• applying learnings from each study into future studies in the platform.

The DIAN-TU platform launched in 2012 as a 4-year secondary prevention trial in participants with DIAD mutations and expected onset ranging from -15 to +10 years and Clinical Dementia Rating (CDR) score of 0 (cognitively normal), 0.5 (very mild cognitive impairment), or 1 (mild cognitive impairment consistent with early dementia) (6). Nearly 200 participants were enrolled, randomized to receive Eli Lilly’s solanezumab (a soluble anti-amyloid-beta monoclonal antibody), Roche’s gantenerumab (an aggregated anti-amyloid monoclonal antibody), or placebo. A third drug arm testing a beta-secretase inhibitor was launched but was terminated early because of safety concerns.
As the two-drug trial continued, adaptations to the platform were made. Planned as a two-year biomarker trial, the study transitioned to a four-year cognitive endpoint trial after the biomarker analysis confirmed that the drugs were engaging their targets. Part way through the trial, a decision was made to increase the dose of both drugs based on external data.
Other components were also added to the platform. These included a run-in period during which data from cognitive measures and tau PET studies were collected in preparation for launching three tau next generation (NexGen) drug arms. Meanwhile, a primary prevention trial in DIAD participants 10 years before symptom onset is in the launch preparation stage. This trial will use a beta amyloid (Aβ)-targeting drug with the aim of preventing the development of plaques before they start.
Topline results for the solanezumab/gantenerumab trial, reported in early 2020, showed that both drugs failed to meet the prespecified primary cognitive endpoint (7). Nonetheless, the platform itself succeeded in meeting many of its objectives and has provided extensive data that continues to be analyzed. While DIAN-TU has not yet confirmed the clinical or cognitive effectiveness of a therapy for DIAD, the ongoing studies have gathered important data relevant to the validity of the amyloid hypothesis, the timing of treatment, and the use of biomarkers to track therapeutic effectiveness.
These analyses suggest two possible reasons that may explain the failure to detect a significant cognitive difference between groups: 1) although the dose was increased part way through the trial, most participants had received lower doses; by the time they were switched to the high dose, many symptomatic participants had already declined substantially, thus limiting the ability to evaluate the effect of the high dose; 2) some asymptomatic trial participants failed to decline and in some cases improved; thus it was not possible to detect a drug effect in those participants.
The continuing data analysis also suggested that gantenerumab showed significant biologic effects on downstream biomarkers, suggesting that higher doses and longer treatment periods may be needed, especially for asymptomatic mutation carriers. While substantially advancing the field, these results were not able to definitively test the hypothesis. Nonetheless, gantenerumab is being studied in an ongoing exploratory open label extension (8).
Other lessons learned from the trial include the need to enrich the study for participants likely to show signs of cognitive decline during the treatment period. The optimal approach to this challenge is not clear, but further analysis of biomarker data from this trial may provide some insight. In addition, other research suggests that some novel biomarkers or combinations of markers may be predictive of incipient cognitive decline. How they would be implemented in screening for trials, however, would present additional complexity. Researchers also continue to search for more sensitive cognitive assessments and tools that minimize practice effects, which could also potentially improve the ability to detect a treatment effect. Yet even if a test is developed to detect subtle cognitive change, the ability of that test to be applied to an AD trial could be limited if the cognitive change results from non-AD aging effects.

 

AHEAD 3-45 Platform Study

The AHEAD Study is a platform-based study comprising two Phase 3 secondary prevention trials — the A45 trial and the A3 trial. Both four-year trials are testing Eisai’s anti-Aβ monoclonal antibody lecanemab (BAN 2401) but use a different dosing regimen depending on baseline amyloid burden. Together they are evaluating the efficacy of the treatment across the continuum of early-to-late preclinical AD as determined by positron emission tomography (PET) amyloid assessment. Using different radioligands, PET imaging enables the visualization of both amyloid plaques and tau neurofibrillary tangles. The AHEAD trials build on the findings of the Harvard Aging Brain Study (HABS) showing that early cortical amyloid accumulation is associated with subsequent tau accumulation in the inferior temporal neocortex, which is associated with neurodegeneration and cognitive decline (9). This study and others in separate cohorts have shown that a period of rapid acceleration in rate of Aβ accumulation immediately precedes reaching the threshold of amyloid positivity that heralds cognitive decline (10–12). These studies led to the hypothesis that decreasing amyloid accumulation at the earliest detectable stage will provide the best opportunity to slow progression and prevent cognitive decline.
The A3 trial aims to capture people with a measurable amyloid PET signal that is below a conventional threshold of amyloidosis. These participants with “intermediate” levels of amyloid (early preclinical or “pre-preclinical” AD) will be randomized to receive the drug or placebo monthly. Amyloid PET will be used as the primary outcome measure to determine whether lecanemab slows amyloid accumulation. Tau PET will be a key secondary outcome and cognitive testing will be an exploratory outcome.
In the A45 study, participants with elevated amyloid will be randomized to receive the drug or placebo biweekly for the first two years followed by monthly maintenance dosing, with cognitive testing as the primary outcome. Amyloid and tau PET will be key secondary outcomes, and biomarker studies will also be an essential component of the trial along with additional cognitive and participant-reported outcomes.
The first participants in AHEAD were screened in July and randomized in September. Worldwide, 100 sites have been identified. The AHEAD platform will use a common screening protocol for the two trials, 18F NAV4694 amyloid and 18F MK6240 tau PET at baseline, 2 and 4 years, and will enroll participants between the ages of 55 and 80. Participants under age 65 will need to have one additional risk factor (e.g., family history, APOE carriage, or known amyloid status). In response to the COVID-19 pandemic, the study team also plans to implement home infusion and potentially, supervised remote assessments.

 

Tau platforms

Other platforms are currently being planned to evaluate the efficacy of anti-tau drugs. While anti-tau therapies have gained support in the Alzheimer’s community because tau is thought responsible for neurodegeneration and cognitive impairment and because of the disappointing results of so many anti-amyloid therapies, many challenges remain due to the complexity of tau biology and the incomplete understanding of the relationship between tau and Aβ (13,14). Moreover, several recent trials of anti-tau monoclonal antibodies have failed to demonstrate efficacy, raising concerns that the disappointing search for an anti-amyloid therapy may be repeated for tau therapies (15). Nonetheless, these negative anti-tau trials may in fact highlight even more strongly the importance of a platform approach.
Eli Lilly and Company has proposed using a master protocol, which should facilitate greater efficiency and accelerated drug development through the use of a common protocol, trial design, and infrastructure. Master protocols can be used in umbrella, basket, or adaptive platform trials to evaluate multiple therapies in the context of a single disease, a single therapy in the context of multiple diseases or disease subtypes, or multiple therapies in a single disease with arms added and/or terminated over time (16). For example, Lilly developed an innovative master protocol to evaluate multiple interventions for chronic pain, in which three different therapeutics were tested across three different pain states.
Master protocols offer the opportunity for increased flexibility to explore multiple hypotheses and different drug combinations or patient populations. As with other platform trials they allow for shared placebo data, unbalanced randomization so that more participants are assigned to the active drug arm, and the potential for a lower screen failure rate. While master protocols may offer increased efficiency for testing multiple therapeutics in parallel, tradeoffs include increased complexity, reduced efficiency gains if fewer therapeutics enter as well as diminished flexibility compared to a single therapeutic trial. It is essential to consider the mechanistic differences among candidate tau therapeutics (and among immunotherapies targeting different epitopes) which may point to different selection criteria and outcome measures.
To study treatments for tauopathies, a master protocol could be used to test multiple therapeutics in different tauopathies (e.g. AD, Primary Supranuclear Palsy [PSP], and Corticobasal degeneration [CBD]); multiple therapeutics in one tauopathy at different stages of disease (late stage AD, early symptomatic AD, and preclinical AD); or multiple therapeutics in only one stage (e.g., preclinical AD). Lilly’s consideration of a master protocol approach to testing multiple anti-tau assets could use one or a combination of these structures, although all of them present substantial operational costs and risks.
A second tau platform protocol is being developed by the Alzheimer’s Clinical Trial Consortium (ACTC). Developers of the Alzheimer’s Tau Platform (ATP) envision a proof-of-concept platform to accelerate decision making in tau therapeutic development by simultaneously testing different anti-tau mechanisms in sporadic AD. They argue that a platform that enables testing multiple molecules and multiple regimens using factorial adaptive designs would improve the efficiency of recruitment, trial startup, and analyses. They also suggest that demonstrating efficacy may be possible more quickly with anti-tau versus anti-amyloid therapies since recent evidence suggest that tau PET agents can detect highly dynamic change in some individuals, e.g. those with higher levels of amyloid (17).
DIAN-TU is also planning to add three anti-tau drug arms to its study in 2021. Since platforms can be especially helpful in assessing treatment response at different stages of disease, each arm will likely enroll both presymptomatic and symptomatic participants. Moreover, by testing different mechanisms and targets in the platform and using short-term biomarker studies, the platform approach may be able to get information on which targets are druggable at which stages of disease.

 

The landscape of AD platform trials

The concept of platform trials is broad. DIAN-TU was designed to enable testing of multiple drugs using a shared placebo arm, with adaptive allocation based on randomization data. In contrast, the AHEAD 3-45 Platform provides benefits that are primarily operational rather than statistical comparisons between treatment regimens. Platform trials may also be used to explore multifaceted hypotheses, for example, to ask questions such as whether amyloid accumulation must be cleared before targeting tau. Different arms of a platform study can ask different questions simultaneously rather than sequentially to increase efficiency and accelerate drug development.
Longitudinal biomarker and clinical studies in clearly defined cohorts (e.g. APOE homozygotes and heterozygotes) are needed to inform the design of platform programs. A matrix of outcomes (including tau and amyloid biomarkers, imaging, and the neural tool kit) across the continuum of disease could eventually lead to a surrogate marker of efficacy, which could dramatically accelerate the search for effective therapies. For example, the European Prevention of Alzheimer’s Dementia (EPAD) platform began with a longitudinal cohort study to serve as a readiness cohort for proof-of-concept secondary prevention AD trials (18).
Meanwhile, an international network of multidomain intervention clinical trials has been established to replicate the groundbreaking Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER), which demonstrated that a combination of lifestyle interventions reduces the risk of cognitive decline in older adults with vascular risk factors that increase their risk of dementia. The Worldwide FINGERS initiative (WW-FINGERS) provides a platform for investigators in different countries and cultures to adapt the FINGERS protocol while using similar protocols and sharing data (19).
The Trial-Ready Cohort for Preclinical/prodromal Alzheimer’s Disease (TRC-PAD) project is a collaborative effort to establish an efficient mechanism for recruiting participants into very early stage Alzheimer’s disease trials (20, 21). Clinically normal and mildly symptomatic individuals are followed longitudinally in a web-based component called the Alzheimer’s Prevention Trial Webstudy (APT Webstudy), with quarterly assessment of cognition and subjective concerns. The Webstudy data is used to predict the likelihood of brain amyloid elevation; individuals at relatively high risk are invited for in-person assessment in the TRC screening phase, during which a cognitive battery is administered and APOE genotype is obtained followed by reassessment of risk of amyloid elevation (22–24). After an initial validation study, plasma amyloid peptide ratios will be included in this risk assessment. Based on this second risk calculation, individuals may have amyloid testing by PET scan or lumbar puncture, with those potentially eligible for trials followed in the TRC, while the rest are invited to remain in the APT Webstudy.

 

Addressing the challenges of AD platform trials

The heterogeneity of the AD patient population creates several complications in designing and executing platform trials. For example, the substantial clinical and biomarker heterogeneity in sporadic AD makes screening particularly challenging and increases the sample size needed to obtain adequate power. Regarding screening, accumulating data suggests that blood-based biomarkers have adequate sensitivity and specificity to be advantageous for selecting subjects with AD pathology although they are not yet supported for use as endpoints (25). Incorporating plasma Aβ testing into screening protocols could dramatically reduce the number of PET scans required. Many of the AD platform trials underway or in the planning stages rely on PET scans prior to randomization to determine how far the disease as progressed. For trials testing tau-based therapies, tau PET promises to be especially useful since deposition of neocortical tau is seen even in the presymptomatic stage. PET scans are also commonly used as endpoints and to assess target engagement and treatment response; however, the spectrum of baseline amyloidosis is broad. The growing utility of plasma measures indicative of tau abnormalities will extend biomarker coverage beyond the fibrillar deposits measurable by PET scans. The incorporation of brain donation protocols in DIAN-TU and other trials enables the comparison and confirmation of AD pathology to biomarkers and drug effects on AD pathology.
The heterogeneity of the drugs themselves also increase both the potential benefits and the complexity of platform trials. Multi-arm platforms enable the parallel testing of different approaches and are particularly suited to testing combination therapies. However, the complexity of combination trials is decreased if one drug in a combination approach has been approved for a certain population. While there may be theoretical reasons for pursuing a combination of drugs that target both amyloid and tau, designing a regimen that allows for maximal biological engagement remains unclear, i.e., should amyloid be cleared or neutralized prior to targeting tau, or can targeting tau even in the presence of amyloid stop tau spread and downstream tau effects? Another issue with combination therapies is that two drugs with separate development plans may not be compatible from a logistical standpoint.
Different modes of administration further complicate platform trials. Participants’ acceptance of a treatment may vary by mode of administration (e.g., oral more likely to be accepted than an intrathecal treatment). Combining placebos when there are different modes of administration may not be feasible.
Platform trials may employ different analytical methods to test hypotheses, address subgroup effects, and compare outcomes against control groups (26). Interim analysis offers both benefits and risks. While interim analysis may be informative and can protect subjects from risks of harm or better target limited resources by eliminating a drug that has little chance of success, early decisions may be inaccurate and prematurely ending an arm may also limit what can be learned from the study or reduce confidence in an approach that may ultimately work. Task Force members agreed that planning for interim analysis ahead of time is essential. This includes determining the types of data to be analyzed (e.g. biomarkers vs. cognitive outcomes) and the timepoints at which interim analyses should be conducted. For exploratory, hypothesis-generating platform trials where the goal is to elucidate pathophysiological interactions between targets and drugs, shorter studies with no interim analysis may be appropriate. However, in platform trials with registration as an ultimate goal, carefully designed interim analysis may be appropriate.
Platform studies may also be challenging from a company perspective since they are expensive, complex, and may compete for resources with individual molecule programs. Collaboration of different company sponsors has the potential to share costs and risks, but presents other challenges including protection of intellectual property and data. Industry representatives acknowledged, however, that as data accumulate in a platform and the ability to use that data grows, care must be taken to ensure the data are appropriately used. The Task Force also recognized the importance of ensuring that the community of funders and sponsors understand the benefits and challenges associated with platform trials and that both public and private support are essential for this complex endeavor.
Despite the challenges, many Task Force members believe that platform trials are essential to ensure that the field builds on and moves past the previous disappointments that have plagued amyloid trials. Moreover, they are necessary since conducting trials sequentially, drug by drug and severity stage by severity stage, takes much too long. Platform trials provide other opportunities that are essential for AD drug development by testing the predictive value of biomarker status and the surrogate value of biomarkers; evaluating individual drugs across multiple stages of disease; and accelerating the timing of testing combinations of drugs, which have historically waited until each drug in the combination has shown efficacy. Additionally, the Task Force advocated the importance of continued collaborations and partnerships to ensure that multiple investigators analyze multiple datasets, share data and knowledge, and learn from one another to gain insight into how to defeat this deadly disease.

 

Acknowledgements: The authors thank Lisa J. Bain for assistance in the preparation of this manuscript. 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 su

Conflicts of interest: The Task Force was partially funded by registration fees from industrial participants. These corporations placed no restrictions on this work. Dr. Aisen reports grants from Janssen, NIA, FNIH, Alzheimer’s Association, and Eisai, personal fees from Merk, Biogen, Roche, ImmunoBrain Checkpoint, Abbvie, Rainbow Medical, and personal fees from Shionogi, outside the submitted work. Dr. Bateman reports grants from the Alzheimers Association, NIH, FNIH, GHR Foundation, Eli Lilly and Company, Hoffman-LaRoche, Avid Radiopharmaceuticals, Janssen, Eisai, Genetech Abbvie, Biogen, Centene, United Neuroscience, and an anonymous organization. In-kind support from CogState and Signant. Personal fees from Hoffman-LaRoche, Janssen, Eisai, C2N Diagnostics, AC Immune, Amgen, and Pfizer. Dr. Carrillo does not have any COI and is a full time empolyee of the Alzheimer’s Assn. Dr. Doody is a full-time employee of F. Hoffman LaRoche/Genentech; Dr. Johnson reports personal fees from Novartis, AC Immune, Janssen and Cerveau, outside the submitted work. Dr. Sims is an empolyee of Lilly. Dr. Sperling reports grants from Eli Lilly, NIA, Alzheimer’s Association, Janssen, Eisai, personal fees from Shionogi, Genentech, Oligomerix, Inc., Cytox, Prothena, Acumen, JOMDD, Renew, Alnylam Pharmaceuticals, Neuraly, Janssen, Neurocentria, AC Immune, Biogen, Eisai, Roche and Takeda Pharmaceuticals, outside the submitted work. 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.

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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THE COMPUTERIZED COGNITIVE COMPOSITE (C3) IN A4, AN ALZHEIMER’S DISEASE SECONDARY PREVENTION TRIAL

 

K.V. Papp1,2, D.M. Rentz1,2, P. Maruff3,4, C.-K. Sun5, R. Raman5, M.C. Donohue5, A. Schembri4, C. Stark6, M.A. Yassa6, A.M. Wessels7, R. Yaari7, K.C. Holdridge7, P.S. Aisen5, R.A. Sperling1,2 on behalf of the A4 Study Team*

 

1. Department of Neurology, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA; 2. Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; 3. The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia; 4. Cogstate, Ltd, Melbourne, Victoria, Australia; 5. Alzheimer Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego, CA, USA; 6. Center for the Neurobiology of Learning and Memory and Department of Neurobiology and Behavior, University of California Irvine, Irvine, California, USA; 7. Eli Lilly and Company, Indianapolis, Indiana, USA; *Full listing of A4 Study team and site personnel available at A4STUDY.org

Corresponding Author: Kathryn V. Papp, Center for Alzheimer Research and Treatment; 60 Fenwood Road; Boston, MA 02115, Telephone: 617-643-5322; Fax: 857-5461, Email Address: kpapp@bwh.harvard.edu

J Prev Alz Dis 2021;1(8):59-67
Published online June 19, 2020, http://dx.doi.org/10.14283/jpad.2020.38

 


Abstract

Background: Computerized cognitive assessments may improve Alzheimer’s disease (AD) secondary prevention trial efficiency and accuracy. However, they require validation against standard outcomes and relevant biomarkers.
Objective: To assess the feasibility and validity of the tablet-based Computerized Cognitive Composite (C3).
Design: Cross-sectional analysis of cognitive screening data from the A4 study (Anti-Amyloid in Asymptomatic AD).
Setting: Multi-center international study.
Participants: Clinically normal (CN) older adults (65-85; n=4486)
Measurements: Participants underwent florbetapir-Positron Emission Tomography for Aβ+/- classification. They completed the C3 and standard paper and pencil measures included in the Preclinical Alzheimer’s Cognitive Composite (PACC). The C3 combines memory measures sensitive to change over time (Cogstate Brief Battery-One Card Learning) and measures shown to be declining early in AD including pattern separation (Behavioral Pattern Separation Test- Object- Lure Discrimination Index) and associative memory (Face Name Associative Memory Exam- Face-Name Matching). C3 acceptability and completion rates were assessed using qualitative and quantitative methods. C3 performance was explored in relation to Aβ+/- groups (n=1323/3163) and PACC.
Results: C3 was feasible for CN older adults to complete. Rates of incomplete or invalid administrations were extremely low, even in the bottom quartile of cognitive performers (PACC). C3 was moderately correlated with PACC (r=0.39). Aβ+ performed worse on C3 compared with Aβ- [unadjusted Cohen’s d=-0.22 (95%CI: -0.31,-0.13) p<0.001] and at a magnitude comparable to the PACC [d=-0.32 (95%CI: -0.41,-0.23) p<0.001]. Better C3 performance was observed in younger, more educated, and female participants.
Conclusions: These findings provide support for both the feasibility and validity of C3 and computerized cognitive outcomes more generally in AD secondary prevention trials.

Key words: Digital biomarkers, cognition, computerized testing, preclinical Alzheimer’s disease, secondary prevention.


 

Introduction

Computerized cognitive assessments have the potential to significantly reduce data administration and scoring errors, site burden, and cost in Alzheimer’s disease (AD) secondary prevention trials as cognitive screening tools and outcome measures. These assessments have yet to replace paper and pencil measures as primary outcomes given several remaining questions: How feasible are computerized assessments in normal older adults and older adults who progress to Mild Cognitive Impairment (MCI) over the course of a trial? How reliable is the data collected? And finally, how valid are computerized cognitive assessments, that is, are they related to gold-standard paper and pencil primary outcomes and AD pathology targeted in a given intervention?
The Anti-Amyloid in Asymptomatic Alzheimer’s (A4) study (1, 2) offers a unique opportunity to address some of these questions by assessing the feasibility and validity of the Computerized Cognitive Composite (C3) in a very large multi-site AD secondary prevention study targeting clinically normal (CN) older adults with elevated cerebral amyloid (2). The C3 is derived using two well-validated memory paradigms from the cognitive neuroscience literature: the Face Name Associative Memory Exam (FNAME) and the Behavioral Pattern Separation Task-Object (BPS-O). It also includes measures from the Cogstate Brief Battery (CBB) which uses playing cards to assess visual memory in addition to reaction time (RT) and working memory and was designed to be sensitive to change over time with randomized alternate forms. The CBB has been studied in relationship to AD neuroimaging markers in several cohort studies of normal older adults (3, 4). Behavioral versions of the FNAME (5, 6) and a modified version of the BPS-O (7) were selected for inclusion in the C3 as they have been shown to elicit aberrant activity in the medial temporal lobes during functional imaging studies in individuals at risk for AD based on biomarkers (8-10). More specifically, these individuals fail to habituate to repeated stimuli (FNAME) or during both correct rejections and false alarms (BPS-O), neural signatures consonant with successful memory formation. The C3 was identified a-priori to include one primary memory outcome from each component measure including: the BPS-O lure discrimination index, Face-Name Matching accuracy, and One-Card Learning accuracy.
The aim of this study was to assess the feasibility and validity of the C3 in CN older adults participating in a secondary prevention trial. Specific goals included determining whether reliable C3 data was consistently captured using a touchscreen tablet and whether data reliability decreased in the lowest cognitive performers. To assess the validity of the C3, we investigated 1) whether the C3 was related to the primary study outcome: performance on traditional paper and pencil measures (i.e., the Preclinical Alzheimer’s Cognitive Composite- PACC) 2) whether the C3 was related to cerebral amyloid (Aβ) and 3) whether the magnitude of this relationship was comparable to that observed between PACC and Aβ+/-. In addition to our main aims, we explored whether improved performance with C3 retesting using alternate forms differentiated between Aβ+/- individuals above and beyond cross-sectional performance. Finally, we explored performance on the constituent tests from the C3 and their relationships with Aβ status, demographic characteristics, and paper and pencil measures. The implications of these findings as they relate to the design and use of future computerized outcomes in secondary prevention trials are discussed.

 

Methods

Participants and Study Design

The A4 Study is a double-blind, placebo-controlled 240-week Phase 3 trial of an anti-Aβ monoclonal antibody in CN older adults with preclinical AD (2) occurring across 67 sites. Participants interested in enrolling in A4 were required to be aged 65 to 85 and were deemed clinically normal (CN) based on Mini Mental Status Exam (MMSE) ranging from 25-30 and Global Clinical Dementia (CDR) Rating Score of 0. During their initial screening visit, participants completed traditional and computerized cognitive testing (detailed further below). Prior to enrollment, they underwent a florbetapir Positron Emission Tomography (PET) for classification of Aβ status (Table 1) at a second visit. On their third visit, all potential participants completed computerized testing and were subsequently provided with results of their AD biomarker imaging and informed about whether they were eligible (Aβ+) or ineligible (Aβ-) to enroll in the trial. The current study includes cognitive screening data at 2 timepoints for Aβ+ and Aβ- individuals.

Table 1. Participant Characteristics by Aβ Status

NOTE. Two-sample t-test with unequal variances were used for continuous variables and Fisher’s Exact test for categorical variables. Values are Mean (Standard Deviation) unless otherwise indicated.

 

Cognitive Measures

The primary outcome for the A4 Study is performance on the PACC, a multi-domain composite of paper and pencil measures (11). Measures contributing to the C3 are administered on a touchscreen tablet using the Cogstate platform and serve as an exploratory outcome. All participants completed the PACC and C3 at the first screening visit (Visit 1) and an alternate C3 within 90 days (mean=55 days) at the study eligibility visit (Visit 3) prior to study eligibility disclosure.

Paper and Pencil Cognitive Testing: The PACC

The PACC, described in detail elsewhere (11), is calculated as the sum of mean performance across four measures normalized using a z-score including the MMSE (0–30), the WMS-R Logical Memory Delayed Recall (LMDR; 0–25), the Digit-Symbol Coding Test (DSC; 0–93), and the Free and Cued Selective Reminding Test–Free + Total Recall (FCSRT96; 0–96) (2).

Computerized Testing: The C3

Figure 1 provides a schematic of C3 Components: BPS-O, FNAME and the CBB. An examiner is present in the testing room and initially guides administration, but the battery has the potential to be completed largely independently in the context of written on-screen instructions and automatic transitions between tasks (12).

 

Figure 1. C3 Task Schematic

NOTE. All tasks are completed on a tablet using a touchscreen. Stimuli in gray are not scored.

 

Behavioral Pattern Separation- Object (BPS-O; more recently termed the Mnemonic Similarity Test)

Participants are presented with images of 40 everyday objects serially and are allotted 5 seconds to determine whether the item is for use “indoors” or “outdoors” to ensure adequate attentiveness to stimuli (7). Participants are subsequently shown 20 of the same items interspersed with both novel images and lure images. They are asked to categorize each image as: Old, Similar, or New within 5 seconds. Accuracy and RT measures are collected. Of interest is the rate at which participants can correctly identify lures as “Similar” rather than as “Old.” The lure discrimination index (LDI) is computed as the proportion of “Similar” responses given to lure items minus the ratio of “Similar” responses given to the foils (the latter is to correct for response bias). The LDI is the primary outcome from the BPS-O task. A higher LDI indicates better pattern separation performance.

Face-Name Associative Memory Exam (FNAME)

Participants are shown 12 face-name pairs presented serially. For each face-name pair, the participant is asked whether the name “fits” or “doesn’t fit” the face to ensure adequate attentiveness to the stimuli. Participants are allowed 5 seconds to respond and are asked to try to remember the face-name pair. Following the learning phase, the CBB tests serve as a 12 to 15-minute delay. Subsequently, there are three measures of memory including face recognition (FSBT), first letter name recall (FNLT) and face-name matching (FNMT). In FSBT, participants are asked to identify the previously learned faces, presented alongside two distractor faces of matching age, race, and sex. The target face is subsequently presented with a touchscreen keyboard and the participant selects the first letter of the name paired with that face (FNLT). Finally, the target face is presented with three names (target name, a re-paired same-sex name, and an age and sex-matched foil name) and the participant must select the correct name (FNMT). Accuracy for each component is scored /12 with FNMT number of correct matches serving as the primary outcome of interest.

Cogstate Brief Battery (CBB)

The CBB (13, 14) uses playing cards as stimuli and includes a measure of attention (Detection-DET),reaction time RT (Identification-IDN), working memory (One-Back Test-ONB), and visual memory (One-Card Learning-OCL). Measures of RT and accuracy are recorded. To address skewness, a log10 transformation is applied to RT measures and an arcsin sqrt transformation is applied to accuracy measures. In DET, participants are required to tap ‘Yes’ as quickly as possible in response to a stimulus card turning face-up. The task continues until 35 correct trials are recorded. The outcome is RT. In IDN, a participant must select whether the card is red or not red; thirty correct trials are required. RT is the primary outcome for IDN; IDN accuracy was also examined. In ONB, participants must indicate “yes” or “no” whether the current card is equivalent to the previously seen card. In OCL, participants must learn a series of playing cards by responding ‘yes’ or ‘no’ to whether the card has been previously seen in the task. For ONB and OCL, both RT and accuracy are computed. Here, we examined RT and Accuracy for both IDN and ONB. We examined only RT for DET and only Accuracy for OCL.

The C3

Constituents of the C3 were identified a-priori and include one primary memory outcome from each measure including the BPS-O LDI, FNMT, and OCL. The C3 is computed as the average of these z-scored outcomes derived from the study population at Visit 1.

Data Quality

Data from individual C3 measures were included in analyses if they met pre-specified task-specific completion checks (Supplementary Table 1). For example, OCL for a given participant is included in analyses if the participant responds in ≥75% of trials. Study rater comments were also reviewed to better determine C3 usability and acceptability.

Amyloid PET Imaging

Eligible participants completed a florbetapir PET scan at Visit 2. Scan acquisition occurred over 50-70 minutes following an injection of 10mCi of florbetapir-F18. Aβ binding was assessed using mean standardized uptake value ratio (SUVr) with whole cerebellar gray as a reference region. Participants were deemed eligible (Aβ+) versus not eligible (Aβ-) using an algorithm combining both quantitative SUVr (>1.15) information and a centrally-determined visual read (2).

Statistical Analyses

Primary analyses were performed on the C3 at Visit 1. To assess C3 feasibility and data validity, test completion rates and performance checks were computed (Supplementary Table 1) and rates subsequently compared between Aβ+/- groups using Chi-square tests. Rater comments were systematically reviewed and observations by raters were grouped into categories (e.g., technical issue, interruptions) and the frequency of observations made in each category were computed. To infer C3 feasibility and data validity in those who may develop impairment over the course of the A4 study, we compared test completion rates and performance checks between the lowest cognitive performers (bottom quartile on PACC) with typical cognitive performers using chi square tests.
Demographic differences between Aβ+/- groups were assessed using Welch’s two-sample t-tests for continuous variables and Fisher’s Exact test for categorical variables (e.g., age, APOE). Linear models were fit to compare cognitive performance across males and females. Linear models were fit to compare cognitive performance across Aβ+/- while adjusting for covariates: age, sex, and education. Effect size was computed as a Cohen’s d (mean difference between Aβ+ and Aβ- groups divided by the pooled standard deviation) with 0.01 representing a “very small” effect, 0.20 representing a “small” effect, and 0.5 representing a “medium” effect (15). Comparable linear models were performed and effect sizes calculated for individual C3 components to examine Aβ+/- group differences on individual C3 measures (e.g., OCL, ONB, BPS-O). No adjustments were made for multiple comparisons; however, results are reported as point estimates and 95% confidence intervals.
Differences in performance between Visit 1 and Visit 3 were examined using linear models of difference scores with Aβ status, age, sex, and education as covariates.
Pearson correlation coefficients were computed to assess the relationships between C3 and demographic characteristics as well as C3 and the PACC. Pearson correlation coefficients were similarly used to assess the relationships among C3 components and PACC components to assess the convergent and discriminant validity between memory versus non-memory tasks on C3 versus PACC.
Linear models were also fit to compare cognitive performance between ε4+/- while adjusting for covariates: age, sex, and education.
All analyses were conducted using R version 3.6.1 (R-project.org).

 

Results

Feasibility of the C3

Completion and performance checks were met in >98% of individual test administrations within the C3 (Supplementary Table 1) and equivalent by Aβ+/. Raters reported issues in approximately 4% of C3 administrations. The most commonly reported problem (reflecting 0.7% of administrations) was that the tablet was insufficiently responsive to a participant’s finger taps and/or the participant was mis-tapping by either hovering their fingers too closely to the screen or by tapping too quickly. The second most commonly reported issue (0.5% of administrations) was overly deliberative responding on BPS-O and FNAME causing items to time-out. This was followed by non-specific technical issues (e.g., frozen program, interruptions from low battery signal or software update, glitches such as stimulus not loading or items auto-proceeding). Report of confusion with task instructions was very low (reported in 0.3% of administrations). Participants most commonly had difficulty understanding instructions for ONB and OCL; additionally, some reported confusion regarding the goal of the judgment component of BPS-O and FNAME learning components (i.e., indoor vs. outdoor, fits vs. doesn’t fit). Despite this, few participants (<3%) failed to make an “indoor/outdoor” or a “fits” judgment on more than 3 items. Participants refused to continue C3 testing in <0.002% of administrations with the most common reasons including frustration and fatigue.

Predictions for the Feasibility of the C3 Longitudinally

To preliminarily estimate whether the C3 (to be completed at 6-month intervals for the A4 study duration) will remain feasible in participants experiencing cognitive decline, we examined C3 performance in the lowest cognitive performers on PACC. The magnitude of the C3 Aβ group difference increased by a factor of 5.2 when restricting the Aβ+ group to the bottom quartile of PACC [adjusted cohen’s d=-0.57 (95%CI:-0.68, -0.45) p<0.001], however, no significant changes in rates of performance completion and performance checks were observed.

Demographic and Clinical Characteristics

Aβ+ were older compared with Aβ- (Table 1). There were no group differences for sex or education level. Aβ+ exhibited a higher rate of ε4 positivity and higher proportion of Caucasians compared with Aβ-.

C3 Performance

Aβ+ performed worse on the C3 compared with Aβ- (unadjusted d=-0.22, adjusted d=-0.11), mirroring the Aβ+/- performance difference on the PACC (unadjusted d=-0.32, adjusted d=-0.18) (Figure 2; Table 2). Importantly, the majority of participants were performing in the normal range, with performance in Aβ+ on average only -0.08 standard deviations below the mean. In addition to Aβ positivity (Beta=-0.07 p=0.002), older age (Beta= -0.04 p<0.0001), less education (Beta= 0.03 p<0.0001), and male sex (Beta=-0.10 p<0.0001) contributed to overall worse C3 performance. Models adjusted for demographic features generally resulted in smaller Aβ+/- effect sizes compared with unadjusted models (Figure 2). For example, there was 66% decrease in effect size between the unadjusted (d=-0.22) and adjusted C3 (d=-0.11). C3 and PACC were moderately correlated (r=0.39, p<0.001). However, both contributed unique explanatory variance about Aβ+/- status when modeled together (Supplementary Table 2 Model A).
Improved performance at re-testing was observed for C3 with an average increase of 0.25 standard deviations between visits (Beta=0.25, p<0.0001). However, there was no relationship between Aβ status and differential improvement on C3 re-testing (Beta= 0.00, p=0.961). Importantly, Aβ+ continued to perform worse on the C3 compared with Aβ- and this group difference was at a comparable magnitude as compared with initial testing (re-testing cohen’s d=-0.21, p<0.0001).

Table 2. Group Differences Between Aβ+ versus Aβ- on C3 at Screening Visit 1

Note. M=mean, SD=standard deviation; PACC=Preclinical Alzheimer’s Cognitive Composite; C3= Computerized Cognitive Composite; BPS-O= Behavioral Pattern Separation Task-Object; LDI=Lure Discrimination Index; FNAME=Face-Name Associative Memory Exam; FNLT=1st letter Name Recall; FNMT=Face-Name Matching; FSBT=Facial Recognition; CBB=Cogstate Brief Battery; RT=reaction time; Acc=Accuracy; DET=Detection; IDN=Identification; ONB=One-Back Test; OCL=One-Card Learning.

 

 

Figure 2. Covariate-Unadjusted and Adjusted Group Differences (Effect Sizes: Cohen’s d) Between Aβ+/Aβ- Groups at Screening Visit 1

Note. Smaller effect size (Cohen’s d) is associated with worse performance in Aβ+ (n=1323) relative to Aβ- (n=3163). Top (unadjusted) and bottom (covariate-adjusted). PACC=Preclinical Alzheimer’s Cognitive Composite; C3= Computerized Cognitive Composite; FNAME=Face-Name Associative Memory Exam; CBB=Cogstate Brief Battery; RT=reaction time; Acc=Accuracy

 

Individual C3 Components

Individual C3 components which showed statistically significant differences between groups were BPS-O LDI, FNAME FNMT, CBB IDN accuracy, ONB accuracy and RT, and OCL accuracy. When adjusting for demographics, FNAME FNMT and ONB RT were no longer significant. Interestingly, for IDN RT, Aβ+ exhibited a statistical trend towards unexpectedly faster RT compared with Aβ- (adjusted d=-0.06, p=0.055). Despite a trend towards being slightly faster, Aβ+ were less accurate for IDN compared with Aβ- (unadjusted d=-0.25, adjusted d=-0.14). IDN Accuracy was correlated with IDN RT (r= -0.30, p<0.001) such that generally faster RT for correct responses was associated with reduced overall accuracy. However, when both IDN Accuracy and IDN RT were incorporated into the sample model to predict Aβ status, only reduced IDN Accuracy was a significant predictor (Supplementary Table 2 Model B).

Correlations Among C3 Components, Demographics, PACC

Age

Greater age was associated with worse performance across all C3 outcomes (Table 3). This association was strongest for the overall C3 Composite (r=-0.29, p<0.001). Age was least associated with RT tasks including DET (r=-0.13, p<0.001) and IDN (r=-0.11, p<0.001).

Table 3. Pearson correlation coefficients (r) Among C3 Components and Demographics

Note. Higher value represents better performance. PACC=Preclinical Alzheimer Cognitive Composite; C3= Computerized Cognitive Composite; BPS-O= Behavioral Pattern Separation Task-Object; LDI=Lure Discrimination Index; FNAME=Face-Name Associative Memory Exam; FNLT=1st letter Name Recall; FNMT=Face-Name Matching; FSBT=Facial Recognition; CBB=Cogstate Brief Battery; RT=reaction time; Acc=Accuracy; DET=Detection; IDN=Identification; ONB=One-Back Test; OCL=One-Card Learning; FCSRT=Free and Cued Selective Reminding Test; DSST=Digit Symbol Substitution Test

 

Education

Higher education was associated with better performance on all individual C3 outcomes, with the largest impact on OCL accuracy (r= 0.13, p<0.001) followed by the overall C3 (r=0.12, p<0.001). The only exception was ONB RT where faster performance was associated with lower education.

Sex

Women outperformed men on all components of FNAME including FNLT (d= -0.46, p<0.0001), FNMT (d= -0.36, p<0.0001), and FSBT (d= -0.39, p<0.0001). Women also outperformed men on IDN Accuracy (d= -0.16, p<0.0001) and ONB Accuracy (d=-0.08, p=0.019). Interestingly, however, men outperformed women on DET (d= -0.23, p<0.0001) and ONB RT (d= -0.12, p<0.001). Performance between the sexes was comparable for BPS-O, IDN RT, and OCL Accuracy.
On OCL, Aβ+ females did not perform differently compared with Aβ- females [Estimate=-0.00 (0.01), p=0.468]. However, Aβ- males performed worse compared with Aβ+ males [Estimate=-0.02 (0.01), p=0.0006]. This suggests that OCL captures subtle decrements in memory between Aβ+/- men but not women. A non-significant statistical trend toward the same pattern was observed in BPS-O.

PACC and C3

Correlations among components of the 2 composites tended to be more strongly-related in a domain-specific manner providing support for convergent and discriminant validity (Table 3). For example, DET and IDN were correlated with DSST at r=0.26 and 0.31, respectively while not being significantly related to memory components of the PACC (FCSRT, Story Memory) or MMSE.

The C3 and APOE Status

There was no difference in performance between APOEε4 carriers vs. non-carriers on the C3 [adjusted d= -0.03 (95% CI: -0.09, 0.03), p=0.379] or on individual C3 outcomes (not shown). The model for carrier vs. non-carrier group differences did not improve with the removal of demographic covariates in contrast with models for Aβ+/- [unadjusted d= 0.03 (95% CI: -0.05, 0.10), p=0.470]. Finally, we did not observe an interaction between E4 and Aβ status on the C3.

 

Discussion

Among a large sample of CN older adults screening for an AD secondary prevention trial, assessment of cognition using a tablet-based measure (C3) was feasible. Diminished C3 performance was associated with worse PACC performance and elevated Aβ. Although the magnitude of the Aβ+/- group difference was statistically small (d= -0.11, once adjusted for covariates) it was comparable to that observed on well-established and clinically meaningful paper and pencil measures included in the primary outcome, i.e., the PACC (d= -0.18). Performance on the C3 was also reliable, with an equal Aβ+/- group effect on the C3 at retesting within 90 days. More broadly, these findings suggest that computerized testing has the potential to replace traditional paper and pencil primary outcomes in future trials- representing a potential shift in clinical trial cognitive assessment methodology. Additionally, these results further confirm the small but consistent association between Aβ burden and cognition cross-sectionally within a CN population.

Usability/Acceptability of the C3

The very low rates of incomplete and/or invalid administrations for the C3 battery indicate that in the older adults assessed, even those with little computer literacy, the supervised tablet-based cognitive testing has high acceptability. Rates of completion and performance check failures remained low in a subset of low PACC performers, providing early evidence for C3 feasibility longitudinally as some participants show progressive cognitive decrements over the course of the study. Study procedures required a rater to supervise C3 testing, however, raters noted that many participants did not require significant assistance after completing the first few measures. This was further evidence by improved performance on re-testing as participants gained familiarity with the device and tasks. Future trials may consider further optimizing computerized tasks to be self-guided to reduce rater training and time. Potential barriers to tablet-based testing were infrequent, largely addressable, and unlikely to systematically affect performance on the C3. These included inexperience with tablets leading both to mis-tapping and difficulty registering finger taps. Many older adults emphasized accuracy over speed during learning trials, resulting in time-outs. Several of these issues can be addressed with modifications to instructions and design (e.g., including a timer indicator) while others will diminish over time with secular trends toward increased familiarity with digital technology.

The C3 Composite and Individual C3 Measures by Aβ+/-

Components of C3 tests which differed between Aβ+/- groups were primarily in memory (BPS-O; OCL) but also included working memory (ONB). The difference in pattern separation memory performance between Aβ+/- participants extends previous fMRI works showing an association between AD biomarkers (including Aβ -PET) and aberrant fMRI activity during learning on a pattern separation task in normal older adults (9) to a difference in frank performance. The BPS-O (10) was designed in part to capture a weakened “novelty signal”, that is, a reduced ability to correctly discriminate between stimuli that are similar but not identical to previously encountered targets. This tendency to misidentify similar lures as targets has been conceptualized as an error in pattern separation (16). Aβ group differences were also observed on face-name memory but this effect was significantly attenuated when controlling for demographic features. In contrast with other C3 memory measures (OCL Accuracy and BPS-O) there was a significant sex effect whereby women generally performed better on all aspects of FNAME compared with men. This may be attributable to a general female advantage in verbal memory (17), however, it may be related to the nature of the information. Previous work with FNAME indicates a diminishment of the sex effect when requiring memory for occupation-face versus name-face pairs (5, 18). Our findings from the CBB measures were consistent with previous results examining this battery in relationship to AD neuroimaging markers in normal older adults. Poorer performance on OCL has been associated with higher levels of CSF phosphorylated-tau/Abeta42 in late middle-aged participants in the Wisconsin Registry for Alzheimer’s Prevention (4). Similarly, we found that OCL was sensitive. However, we also found that working memory (ONB) was also relatively strongly associated with elevated Aβ. While C3 constituents were selected theoretically and a-priori, ONB may be considered for inclusion in future optimized and/or data-driven C3 versions. Interestingly, the Aβ+ group made more errors on a Cogstate RT task (IDN) but paradoxically also performed the task more quickly compared with the Aβ- group. These findings suggest that faster RT may, in fact, be a sign of subtle decrements. One explanation for this finding is an age-associated decrease in inhibition of pre-potent responses (19) may be more pronounced in preclinical AD. More broadly, it confirms that early cognitive changes in preclinical AD extend beyond memory (20, 21).
Part of the impetus for combining outcomes from the BPS-O, FNAME, and CBB into a C3, is aligned with the rationale for cognitive composites as primary endpoints (22) to maximize signal to noise ratio in a population expected to exhibit subtle cognitive decrements. This was confirmed in our data whereby the combination of FNMT, BPS-O, and OCL into the C3 resulted in a numerically larger effect size compared with any single one of these measures alone. However, there are multiple means of constructing composites including data-driven approaches; for example, selecting measures most associated with Aβ cross-sectionally or measures most sensitive to change. The current C3 was theoretically derived on the basis of previous literature and longitudinal data is needed to confirm its sensitivity over time. Importantly, different memory measures provided related but partially unique information about Aβ status. For example, both BPS-O and OCL were significant predictors of Aβ status when included in the same model (Supplementary Table 2 Model C). More recent work examining the heterogeneity of cognitive decline in early AD suggests that different atrophy patterns are associated with different cognitive trajectories (23). A cognitive composite would thus benefit from being sufficiently broad to avoid under/overestimating decline in a given subgroup.
Our finding that OCL differentiated Aβ+ vs Aβ- men but not women highlights the issue of heterogeneity in a different light. Males and females performed equivalently for visual memory of playing cards (OCL) but females outperformed males on face-name memory. We hypothesize that visual card-based tasks may be both more engaging and an area of relative strength for males versus females in contrast with name memory (17). Regardless, these findings highlight the rationale for composite scores and the opportunity to use C3 to better understand demographic and individual differences in performance and cognitive trajectories.

C3 Performance and ε4 Status

The lack of a group difference in C3 performance between ε4 carriers vs. non-carriers is not unexpected given the specific recruitment of CN older adults and the current cross-sectional analysis. This is evidenced by the further diminishment of group differences between e4+ vs. e4- participants when including age as a covariate. In contrast, removal of age as a covariate systematically increased the Aβ+ vs. Aβ- group differences.

C3 and Re-testing

Consistent with the literature, participants performed slightly better on re-testing which is consistent with increased familiarity with the tablet and task demands (3). Diminished practice effects have been shown to predict incident MCI and/or dementia (24, 25) and have been suggested as a screening tool (26). However, we did not observe differential improvement in performance by Aβ group status. Future adjustments to the FNAME paradigm emphasizing item versus task familiarity may increase the relevance of a diminished practice effect. More specifically, using repeated versus alternate stimuli may capture more AD-specific learning over repeated exposures to the same material (27). C3 practice effects are likely to diminish significantly after the second administration (24). Likewise, item familiarity practice effects are unlikely to contribute to C3 trajectories over time given that all remaining versions are unique.

 

Conclusions

Within the context of AD secondary prevention trials, our results indicate that computerized (tablet-based) cognitive testing is feasible in older adults in a secondary prevention trial setting and we provide support for the validity of such testing as the C3 was 1) correlated with the primary outcome of paper and pencil composite performance (PACC), 2) related to AD pathological burden (Aβ+/-) and 3) related to Aβ+/- at a similar magnitude as the PACC. Positive relationships with AD biomarkers and PACC suggest that the C3 is capturing meaningful cognitive decrements and, has the potential to serve as a proxy for paper and pencil measures in future trials. In addition to reducing staff time and allowing the possibility for remote assessment, computerized testing has the potential to capture a greater quantity and more nuanced quality of data for each measure. Future work will determine the sensitivity of the C3 to change over time in the context of an anti-amyloid treatment trial.

 

Acknowlegments and funding: The A4 Study is a secondary prevention trial in preclinical Alzheimer’s disease, aiming to slow cognitive decline associated with brain amyloid accumulation in clinically normal older individuals. The A4 Study is funded by a public-private-philanthropic partnership, including funding from the National Institutes of Health-National Institute on Aging (U19AG010483; R01AG063689), Eli Lilly and Company, Alzheimer’s Association, Accelerating Medicines Partnership, GHR Foundation, an anonymous foundation and additional private donors, with in-kind support from Avid, Cogstate, Albert Einstein College of Medicine, US Against Alzheimer’s disease, and Foundation for Neurologic Diseases. The companion observational Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) Study is funded by the Alzheimer’s Association and GHR Foundation. The A4 and LEARN Studies are led by Dr. Reisa Sperling at Brigham and Women’s Hospital, Harvard Medical School and Dr. Paul Aisen at the Alzheimer’s Therapeutic Research Institute (ATRI), University of Southern California. The A4 and LEARN Studies are coordinated by ATRI at the University of Southern California, and the data are made available through the Laboratory for Neuro Imaging at the University of Southern California. The participants screening for the A4 Study provided permission to share their de-identified data in order to advance the quest to find a successful treatment for Alzheimer’s disease. We would like to acknowledge the dedication of all the participants, the site personnel, and all of the partnership team members who continue to make the A4 and LEARN Studies possible. The complete A4 Study Team list is available on: a4study.org/a4-study-team.

Conflicts of interest: K Papp has served as a consultant for Biogen Idec and Digital Cognition Technologies. D Rentz has served as a consultant for Eli Lilly, Biogen Idec, Lundbeck Pharmaceuticals, and serves as a member of the Scientific Advisory Board for Neurotrack. P Maruff is a full-time employee of Cogstate Ltd. C-K. Sun has no disclosures to report. R. Raman has no disclosures to report. M. Donohue has served on scientific advisory boards for Biogen, Eli Lilly, and Neurotrack; and has consulted for Roche. His spouse is a full-time employee of Janssen. A. Schembri is a full-time employee of Cogstate Ltd. C. Stark has no disclosures to report. M Yassa has served as a consultant for Pfizer, Eli Lilly, Lundbeck and Dart Neuroscience and is chief scientific officer of Signa Therapeutics, LLC. A. Wessels is a full-time employee of Eli Lilly and Company. R. Yaari is a full-time employee of Eli Lilly and Company. K. Holdridge is a full-time employee of Eli Lilly and Company. P. Aisen has received research funding from NIA, FNIH, the Alzheimer’s Association, Janssen, Lilly and Eisai, and personal fees from Merck, Roche, Biogen, ImmunoBrain Checkpoint and Samus. R.A. Sperling has received research funding from NIH, Alzheimer’s Association and Eli Lilly for this research. She has served as a consultant for AC Immune, Biogen, Eisai, Janssen, Neurocentria and Roche. Her spouse has served as a consultant to Biogen, Janssen, and Novartis.

Ethical Standards: Study procedures were conducted in accordance with consensus ethics principles derived from international ethics guidelines, including the Declaration of Helsinki and Council for International Organizations of Medical Sciences (CIOMS) International Ethical Guidelines.

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.

 

SUPPLEMENTARY MATERIAL

 

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