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P.S. Aisen1, J. Cummings2, R. Doody3, L. Kramer4, S. Salloway5, D.J. Selkoe6, J. Sims7, R.A. Sperling6, B. Vellas8 and the EU/US CTAD 2019 Task Force*


* EU/US/CTAD TASK FORCE: Susan Abushakra (Framingham) ; Paul Aisen (San Diego) ; John Alam (Boston) ; Sandrine Andrieu (Toulouse) ; Anu Bansal (Simsbury) ; Monika Baudler (Basel) ; Joanne Bell (Wilmington) ; Mickaël Beraud (Zaventem); Tobias Bittner (Basel); Samantha Budd Haeberlein (Cambridge) ; Szofia Bullain (Basel) ; Marc Cantillon (Gilbert) ; Maria Carrillo (Chicago) ; Carmen Castrillo-Viguera (Cambridge) ; Ivan Cheung (Woodcliff Lake) ; Julia Coelho (San Francisco) ; Jeffrey Cummings (Las Vegas) ; Michael Detke (San Francisco) ; Daniel Di Giusto (Basel) ; Rachelle Doody (South San Francisco) ; John Dwyer (Washington) ; Michael Egan (North Wales) ; Colin Ewen (Slough) ; Charles Fisher (San Francisco) ; Serge Gauthier (Montreal) ; Michael Gold (North Chicago) ; Harald Hampel (Woodcliff Lake) ; Ping He (Cambridge) ; Suzanne Hendrix (Salt Lake City) ; David Henley (Titusville) ; Michael Irizarry (Woodcliff Lake) ; Atsushi Iwata (Tokyo) ; Takeshi Iwatsubo (Tokyo) ; Michael Keeley (South San Francisco) ; Geoffrey Kerchner (South San Francisco) ; Gene Kinney (San Francisco) ; Hartmuth Kolb (Titusville) ; Marie Kosco-Vilbois (Lausanne) ; Lynn Kramer (Westport) ; Ricky Kurzman (Woodcliff Lake) ; Lars Lannfelt (Uppsala) ; John Lawson (Malvern) ; Jinhe Li (Gilbert) ; Frank Longo (Stanford) ; Mark Mintun (Philadelphia) ; Vaidrius Navikas (Valby) ; Gerald Novak (Titusville) ; Gunilla Osswald (Stockholm) ; Susanne Ostrowitzki (South San Francisco) ; Anton Porsteinsson (Rochester) ; Rema Raman (San Diego) ; Ivana Rubino (Cambridge) ; Marwan Sabbagh (Las Vegas) ; Stephen Salloway (Providence) ; Rachel Schindler (New York) ; Lon Schneider (Los Angeles) ; Hiroshi Sekiya (Malvern) ; Dennis Selkoe (Boston) ; Eric Siemers (Zionsville) ; John Sims (Indianapolis) ; Lisa Sipe (San Marcos) ; Olivier Sol (Lausanne) ; Reisa Sperling (Boston) ; Andrew Stephens (Berlin) ; Johannes Streffer (Braine-l’Alleud) ; Joyce Suhy (Newark) ; Chad Swanson (Woodcliff Lake) ; Gilles Tamagnan (New Haven) ; Rudolph Tanzi (Boston) ; Pierre Tariot (Phoenix); Edmond Teng (South San Francisco) ; Martin Tolar (Framingham) ; Jacques Touchon (Montpellier) ; Martin Traber (Basel) ; Bruno Vellas (Toulouse) ; Andrea Vergallo (Woodcliff Lake) ; Christian Von Hehn (Cambridge) ; George Vradenburg (Washington) ; Judy Walker (Singapore) ; Michael Weiner (San Francisco) ; Glen Wunderlich (Ridgefield) ; Jennifer Ann Zimmeri (Indianapolis) ; Haichen Yang (North Wales) ; Wagner Zago (San Francisco) ; Thomas Zoda (Austin)

1. Alzheimer’s Therapeutic Research Institute (ATRI), Keck School of Medicine, University of Southern California, San Diego, CA, USA; 2. Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas, and Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, Nevada, USA; 3. Genentech/Roche, Basel, Switzerland; 4. Eisai Co., Ltd., Eisai, Inc., Woodcliff Lake, NJ, USA; 5. The Warren Alpert Medical School of Brown University, Providence RI, USA; 6. Brigham and Women’s Hospital, Boston MA, USA; 7. Eli Lilly and Company, Indianapolis, IN, 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,

J Prev Alz Dis 2020;3(7):146-151
Published online April 17, 2020,



The termination of many clinical trials of amyloid-targeting therapies for the treatment of Alzheimer’s disease (AD) has had a major impact on the AD clinical research enterprise. However, positive signals in recent studies have reinvigorated support for the amyloid hypothesis and amyloid-targeting strategies. In December 2019, the EU-US Clinical Trials on Alzheimer’s Disease (CTAD) Task Force met to share learnings from these studies in order to inform future trials and promote the development of effective AD treatments. Critical factors that have emerged in studies of anti-amyloid monoclonal antibody therapies include developing a better understanding of the specific amyloid species targeted by different antibodies, advancing our insight into the mechanism by which those antibodies may reduce pathology, implementing more comprehensive repertoires of biomarkers into trials, and identifying appropriate doses. Studies suggest that Amyloid-Related Imaging Abnormalities – effusion type (ARIA-E) are a manageable safety concern and that caution should be exercised before terminating studies based on interim analyses. The Task Force concluded that opportunities for developing effective treatments include developing new biomarkers, intervening in early stages of disease, and use of combination therapies.

Key words: Alzheimer’s disease, dementia, amyloid hypothesis, monoclonal antibody treatment, BACE inhibitors, combination therapy.



Despite encouraging results from the aducanumab Phase 1 and BAN2401 Phase 2 anti-amyloid antibody clinical trials, amyloid-beta protein (Aß)-based strategies for the treatment of Alzheimer’s disease (AD) appeared to take a crippling blow in March 2019 when Biogen announced it was terminating two clinical trials (EMERGE and ENGAGE) of the anti-Aß monoclonal antibody aducanumab based on the results of an interim analysis demonstrating a lack of benefit or ‘futility.’ The field had another major challenge in July when Novartis, Amgen, and the Banner Alzheimer’s Institute announced termination of pivotal trials of the beta-site amyloid precursor protein cleaving enzyme (BACE) inhibitor umibecestat after an interim analysis identified cognitive worsening in trial participants. This marked the fifth failed BACE inhibitor in less than two years, two with trials stopped because of adverse events (Merck’s verubecestat and Janssen’s atabecestat) and two trials stopped for lack of efficacy (Astra Zeneca and Eli Lilly’s lanabecestat and Eli Lilly’s LY3202626) (1–3). A fifth BACE inhibitor trial of Eisai and Biogen’s elenbecestat was halted in September 2019 due to an unfavorable risk/benefit profile (4). Trials for another anti- Aß monoclonal antibody, Genentech and Roche’s crenezumab, were terminated in 2019 for futility (5).
Then, in October, a stunning reversal: Biogen announced that the futility analysis in the aducanumab trial was misleading. Analysis of a larger data set indicated that aducanumab did indeed slow cognitive decline in trial participants who received a higher dose of the drug for longer periods of time in one of the two studies. Following this announcement, Biogen indicated they planned to submit aducanumab to the U.S. Food and Drug Administration (FDA) for regulatory approval. Any form of approval for aducanumab has the potential to transform the AD field, providing hope for patients and researchers alike. Regulatory success could also reinvigorate support for the amyloid cascade hypothesis, which posits that deposition of Aβ in the brain leads to the neurodegeneration and dementia that characterize AD. This hypothesis has driven the development of AD therapeutics for decades. Secretase inhibitors block production of Aβ, while anti-Aβ antibodies are designed to clear Aβ and prevent the formation of amyloid plaques as well as neutralize soluble Aß oligomers. Prior to the announcement of aducanumab’s potential beneficial effects, no secretase inhibitor and only two monoclonal antibodies — BAN2401 and gantenerumab — had preliminary evidence of possible efficacy against Aß, and there was much speculation in the field that the amyloid hypothesis was dead or at least unhelpful in guiding development of AD therapeutics. However substantial emerging evidence supports the amyloid cascade hypothesis (6).
To better understand the implications of these clinical trial results and the future of amyloid-based therapies, the European Union and United States Clinical Trials on Alzheimer’s Disease Task Force (EU/US CTAD-TF) convened a meeting in San Diego on December 4, 2019, bringing together industry scientists involved in clinical trials of anti- Aß and other AD therapies along with representatives from pharmaceutical, biotechnology, diagnostics, and medical device companies, academic researchers, clinicians, and non-profit organizations. Their goal was to articulate lessons learned from these trials with the hope of enabling future successful trials that will lead to the approval of effective treatments for AD.


Learnings from trials of anti-amyloid monoclonal antibody trials

The Task Force discussed five anti-amyloid monoclonal antibody therapies currently in clinical development: aducanumab (7), BAN2401 (6), gantenerumab (8), solanezumab (9–13) , and donanemab. Other anti-amyloid monoclonal antibodies (e.g., crenezumab) are also in development (5, 14). As summarized in Table 1, these antibodies target different forms of amyloid, may have different mechanisms of action, and are being tested for efficacy at different stages of disease.


Table 1. Anti-amyloid monoclonal antibody therapies


The importance of dose

The futility analysis in the ENGAGE and EMERGE aducanumab trials – two identically designed Phase 3 studies — was based on a pooled interim dataset of approximately 50% of enrolled participants using a probability calculation that assumed non-heterogeneity between the two studies. A subsequent analysis of a larger dataset, however, revealed that protocol amendments allowing increased dosing in apolipoprotein E epsilon 4 (APOE4) carriers had differential effects on the two studies due to the relative timing of enrollment. This analysis demonstrated a statistically significant reduction in clinical decline across multiple clinical endpoints among early AD patients in EMERGE, likely due to high dose exposure to the drug. Participants in the ENGAGE trial who had received higher doses (10 mg/kg) for at least 10 doses had clinical effects similar to those of the EMERGE participants. Amyloid positron emission tomography (PET) studies demonstrated dose-dependent reduction of brain amyloid deposition across both trials.
Other trials have also demonstrated substantial dose-related amyloid lowering. Study 201 of BAN2401 used an adaptive randomization design with six arms to understand the impact of dose and minimize the number of participants treated with ineffective doses. The highest dose (10 mg/kg biweekly) produced the greatest slowing of disease progression and most robust reduction in brain amyloid levels compared to placebo and is used in the recently-launched Phase 3 Clarity AD study.
Open-label extensions of two early Phase 3 gantenerumab trials, in which study participants were assigned one of five titration schemes, also showed that five times higher dose of ganternerumab than was used in the earlier phase 3 studies drove increased amyloid reduction assessed with amyloid PET imaging (15). These findings prompted the initiation of a new Phase 3 program using this five-fold higher doses.

Mechanism matters

Amyloid is not a monolithic target but a family of monomers, oligomers, protofibrils, and fibrils; and different anti-Aβ antibodies target partially different species. The molecular dynamics by which targeting different species results in variable effects on plaque burden and brain volume loss are not well understood; however, these differential mechanisms may help explain the different trial effects observed.
Solanezumab was hypothesized to remove brain amyloid through what is called the “peripheral sink hypothesis,” i.e., by increasing the clearance of soluble Aβ via the formation of antibody-Aβ complexes in the plasma. However, pharmacodynamic studies showed that a reduction of Aβ in the peripheral compartment failed to shift the equilibrium between Aβ species enough to cause a substantial reduction of fibrillary Aβ in the brain (16); the possible beneficial effect of solanezumab on cognitive decline may nonetheless be mediated by its binding to smaller, diffusible forms. Other possible mechanisms of anti-Aβ antibodies include direct targeting of Aβ plaques or other toxic species of Aβ for removal or activating phagocytosis of Aβ by microglia (17). Clinical trials of solanezumab in mild-moderate AD and in prodromal/mild AD failed to show a drug-placebo difference and no effects on biomarkers were observed. Solanezumab continues in the Anti-Amyloid treatment of Asymptomatic Alzheimer’s disease (A4) study of cognitively asymptomatic participants with positive amyloid imaging.
The effects of anti-Aβ antibodies on brain volume loss is poorly understood. In the EXPEDITION trials, treatment with solanezumab showed a modest but statistically insignificant slowing of brain atrophy (13). Gantenerumab produced no such effects on the measures collected (8). One theory suggests that driving down amyloid may itself be reflected as a reduction in brain volume. The effects on brain volume, however, could differ depending on which form of amyloid the antibody targets (e.g. plaques versus oligomeric forms). Further analysis of data from anti-Aβ antibody trials may help clarify this issue. The correlation of treatment-related brain volume loss and disease progression is also unclear.

ARIA appears to be a manageable safety concern

The incidence of amyloid-related imaging abnormalities – effusion type (ARIA-E) associated with anti-Aβ antibody treatment has been a substantial concern in the development of these therapies (18). For example, in the aducanumab trials, ARIA-E was seen in more than one-third of participants, although these episodes were typically asymptomatic and resolved within 4-16 weeks without long-term sequelae. ARIA-E was also observed in about 10% of participants in the BAN2401 Phase 2 study, occurring primarily in the first three months of treatment.
Recent studies suggest that ARIA-E can be safely managed by titrating drug to the target dose. For example, in the gantenerumab studies, titrating to the target dose reduced ARIA-E incidence in both APOE4 carriers and non-carriers and the majority of episodes were asymptomatic. Other studies have suggested that APOE4 carriers are at higher risk for ARIA-E. While ARIA-E appears to be manageable, uncertainty remains about whether even a minimal risk could be problematic for preclinical AD patients, or whether ARIA-E occurs less frequently in earlier stages of disease or in individuals with lower levels of vascular amyloid.
Although it may be challenging, it will be necessary to develop criteria that could be used in primary care settings for safely beginning treatment and monitoring for ARIA-E should an anti-Aβ monoclonal antibody treatment be approved for AD,. A better understanding of the mechanisms involved could relieve concerns among primary care physicians once these therapies become available.

Interim and futility analyses are useful only if appropriately designed

Futility analyses are designed to protect participants from unnecessary exposure to drugs that have little chance of providing benefits, but if they result in premature termination of a trial, participants and sponsors alike – indeed, the entire field – may suffer adverse consequences from a failure to identify efficacious treatments and the failure to collect a complete dataset from the trial (19). The aducanumab Phase 3 program is not the only example in the field in which interim analysis wrongly predicted futility, raising questions about the design and appropriateness of futility analyses.
Among the fundamental tenets of futility analyses is that participants included in the analysis are representative of those in the full dataset and that drop-outs are equally distributed across all treatment groups. Protocol amendments made in the course of the aducanumab study, however, resulted in non-identical interim and final populations and in cohorts that had received different doses for different periods of time
All futility analyses come with a price: loss of statistical power to demonstrate efficacy. This cost must be carefully weighed against any benefits from early termination. While there are clear advantages to stopping early when failure is inevitable, the possibility of misleading futility analyses suggests that criteria for defining failure versus success need to be very carefully specified. To implement criteria for interim analyses requires a better understanding of the clinical-biological trajectories of disease progression in stratified patient populations (19,20). Interim analyses could also benefit from looking at the totality of evidence and by aggregating signals to reduce noise.

Responder analyses could help identify subgroup differences

To determine the disease stage at which a treatment may be efficacious, the optimal duration of treatment, and other patient characteristics that may affect efficacy, responder analyses of trial data and data from open-label extension studies can be valuable. Post-hoc exploratory data analyses may yield improved understanding of study results and inform the design of future studies. For example, in the SCarlet RoAD study of gantenerumab, an exploratory analysis that classified participants according to whether they were slow or fast progressors suggested that fast progressors showed a greater exposure-dependent slowing of clinical and cognitive decline with treatment (8). While not a classic responder analysis, the exploration of the faster progressing subset allowed modeling related to a drug-placebo difference and helped to define inclusion criteria for the ongoing Phase 3 GRADUATE program with higher dose of gantenerumab.


Moving forward with amyloid-based therapies

Genetic, neuropathologic, biochemical, and now clinical trials support the amyloid hypothesis of AD while recognizing that downstream pathological processes contribute importantly to the development of the disease (6). Many questions remain to be answered in order to translate the amyloid hypothesis into efficacious therapies. For example, further research is needed to determine which Aβ species are most important to target, whether relevant Aβ species change over the course of disease, if there is an optimal time for targeting a particular Aβ species, and whether at some point amyloid becomes less relevant or irrelevant. Developing a larger repertoire of biomarkers to predict disease onset and progression, e.g. microglial activation biomarkers, may help clarify the role of amyloid-related mechanisms as well as other mechanisms in disease progression (21). Preliminary data from the monoclonal antibody trials suggest there are “downstream” effects on cerebrospinal fluid levels of neurofilament light, neurogranin, and tau. These may be crucial measures of the biological effects of interventions and that can eventually be compared across trials.
An effective treatment may also require an Aß-targeting drug in combination with a drug targeting another mechanism (e.g. neuroinflammation) or two drugs that target different amyloid mechanisms (e.g. production and clearance of Aβ) (22). Investigators have explored targeting Aß in combination with tau, the protein found in the neurofibrillary tangles that along with amyloid plaques represent the major pathological hallmarks of AD. Moving this approach forward, however, will require a better understanding of the value of various tau-related targets, the relationship of amyloid to the level of tau burden as well as the time lag between amyloid deposition, tau deposition, and cognitive impairment (23). Employing tau PET studies in clinical trials may help define these aspects of the role of tau in AD (24,25). Other tau biomarkers are in development. For example, Walsh and colleagues have shown that an N-terminal fragment of tau (NT1) and p-tau in plasma are significantly increased in AD and mild cognitive impairment (MCI) (26).
Analysis of data from several failed clinical trials of amyloid-targeting drugs suggest that to slow or prevent disease progression, it may be necessary to intervene at very early, pre-symptomatic stages of the disease (27,28). Studies currently underway to test this include the A4 study in clinically normal older individuals with elevated amyloid levels on screening PET; and the AHEAD 3-45 study in clinically normal individuals with elevated or intermediate amyloid. Other prevention trials are underway in clinically normal participants at increased genetic risk of developing AD, including the Alzheimer’s Prevention Initiative (API) Colombia Trial (20). [The DIAN-TU studies involving both clinically normal and symptomatic autosomal dominant mutation carriers recently reported negative topline results.] The challenges inherent in these prevention trials include the difficulty of detecting a slowing of progression in cognitively normal individuals and the resulting large sample size and long trial durations required; the hope of preventing AD has motivated many individuals around the world to volunteer for these studies.
Very early intervention, including primary prevention, may be more feasible with active vaccination or oral therapy rather than passive immunotherapy requiring repeated intravenous or subcutaneous administration. Active vaccination against Aß remains a plausible strategy (e.g. CAD-106; UB-311). Orally bioavailable BACE inhibitor programs have been halted with concern about observations of cognitive worsening in trials; however, evidence that this cognitive toxicity is dose-related and reversible raises hope that viable regimens may eventually move forward.



The termination of multiple clinical trials for futility or adverse events has had a major impact on the AD clinical research enterprise. However, evidence strongly supports amyloid as a viable target although not the only important target. Given the complexity of AD pathology, combination treatment will likely be needed. If antibody trials are sufficiently positive, they could represent a good first step towards combination treatment and lead to financial coverage and use of amyloid PET, which would be a major advance for the clinical care of AD.
To optimize the potential benefits and reduce the potential risks to participants as much as possible, methodological improvements in the design and conduct of clinical trials are needed. For example, adaptive dose finding studies may result in more patients assigned to an effective dose and avoid exposure of patients to ineffective doses. In addition, since disease modification depends on protecting neurons from the pathology, a better understanding of neuroprotection, the relationship of the biological underpinnings of the aging process (30), and the development of intermediate biomarkers of neuroprotection are needed. Advancing understanding of the complexity underlying the development of AD and potential interventions that could slow or halt the disease pathophysiological progression will require more discovery science as well as increased use of platform trials. Public-private partnerships with strong collaborations and data sharing will be necessary to accelerate these efforts, along with broad public engagement.


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

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, grants from NIA, grants from FNIH, grants from Alzheimer’s Association, grants from Eisai, personal fees from Merck, personal fees from Biogen, personal fees from Roche, personal fees from Lundbeck, personal fees from Proclara, personal fees from Immunobrain Checkpoint, outside the submitted work; Dr Cummings is a consultant for Acadia, Actinogen, AgeneBio, Alkahest, Alzheon, Annovis, Avanir, Axsome, Biogen, Cassava, Cerecin, Cerevel, Cognoptix, Cortexyme, EIP Pharma, Eisai, Foresight, Gemvax, Green Valley, Grifols, Karuna, Nutricia, Orion, Otsuka, Probiodrug, ReMYND, Resverlogix, Roche, Samumed, Samus Therapeutics, Third Rock, Signant Health, Sunovion, Suven, United Neuroscience pharmaceutical and assessment companies, and the Alzheimer Drug Discovery Foundation; and owns stock in ADAMAS, BioAsis, MedAvante, QR Pharma, and United Neuroscience. Dr. Doody is an employee of Genentech/F Hoffman-LaRoche and holds stock in the company; Dr. Kramer is an employee of Eisai Company, Ltd. Dr. Kramer is an employee of Eisai Company Ltd; Dr. S. Salloway: NC; Dr. Selkoe: NC; Dr. Sims reports other from Employee of Eli Lilly and Company, outside the submitted work; Dr. Sperling reports personal fees from AC Immune, personal fees from Biogen, personal fees from Janssen, personal fees from Neurocentria , personal fees from Eisai, personal fees from GE Healthcare, personal fees from Roche, personal fees from InSightec, personal fees from Takeda Pharmaceuticals, grants from Eli Lilly , grants from Janssen, grants from Digital Cognition Technologies, grants from Eisai, grants from NIA , grants from Alzheimer’s Association, personal fees and other from Novartis, personal fees and other from AC Immune, personal fees and other from Janssen, 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.

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1. Egan MF, Kost J, Voss T, Mukai Y, Aisen PS, Cummings JL, et al. Randomized Trial of Verubecestat for Prodromal Alzheimer’s Disease. N Engl J Med. 2019 Apr 11;380(15):1408–20.
2. Henley D, Raghavan N, Sperling R, Aisen P, Raman R, Romano G. Preliminary Results of a Trial of Atabecestat in Preclinical Alzheimer’s Disease. N Engl J Med. 2019 11;380(15):1483–5.
3. Wessels AM, Tariot PN, Zimmer JA, Selzler KJ, Bragg SM, Andersen SW, et al. Efficacy and Safety of Lanabecestat for Treatment of Early and Mild Alzheimer Disease: The AMARANTH and DAYBREAK-ALZ Randomized Clinical Trials. JAMA Neurol. 2019 Nov 25;
4. Panza F, Lozupone M, Solfrizzi V, Sardone R, Piccininni C, Dibello V, et al. BACE inhibitors in clinical development for the treatment of Alzheimer’s disease. Expert Rev Neurother. 2018 Nov;18(11):847–57.
5. Cummings JL, Cohen S, van Dyck CH, Brody M, Curtis C, Cho W, et al. ABBY: A phase 2 randomized trial of crenezumab in mild to moderate Alzheimer disease. Neurology. 2018 22;90(21):e1889–97.
6. Selkoe DJ, Hardy J. The amyloid hypothesis of Alzheimer’s disease at 25 years. EMBO Mol Med. 2016 Jun;8(6):595–608.
7. Sevigny J, Chiao P, Bussière T, Weinreb PH, Williams L, Maier M, et al. The antibody aducanumab reduces Aβ plaques in Alzheimer’s disease. Nature. 2016 01;537(7618):50–6.
8. Ostrowitzki S, Lasser RA, Dorflinger E, Scheltens P, Barkhof F, Nikolcheva T, et al. A phase III randomized trial of gantenerumab in prodromal Alzheimer’s disease. Alzheimers Res Ther. 2017 Dec 8;9(1):95.
9. Farlow M, Arnold SE, van Dyck CH, Aisen PS, Snider BJ, Porsteinsson AP, et al. Safety and biomarker effects of solanezumab in patients with Alzheimer’s disease. Alzheimers Dement. 2012 Jul;8(4):261–71.
10. Doody RS, Thomas RG, Farlow M, Iwatsubo T, Vellas B, Joffe S, et al. Phase 3 trials of solanezumab for mild-to-moderate Alzheimer’s disease. N Engl J Med. 2014 Jan 23;370(4):311–21.
11. Siemers ER, Sundell KL, Carlson C, Case M, Sethuraman G, Liu-Seifert H, et al. Phase 3 solanezumab trials: Secondary outcomes in mild Alzheimer’s disease patients. Alzheimers Dement. 2016 Feb;12(2):110–20.
12. Honig LS, Vellas B, Woodward M, Boada M, Bullock R, Borrie M, et al. Trial of Solanezumab for Mild Dementia Due to Alzheimer’s Disease. N Engl J Med. 2018 25;378(4):321–30.
13. Schwarz AJ, Sundell KL, Charil A, Case MG, Jaeger RK, Scott D, et al. Magnetic resonance imaging measures of brain atrophy from the EXPEDITION3 trial in mild Alzheimer’s disease. Alzheimers Dement (N Y). 2019;5:328–37.
14. van Dyck CH. Anti-Amyloid-β Monoclonal Antibodies for Alzheimer’s Disease: Pitfalls and Promise. Biol Psychiatry. 2018 Feb 15;83(4):311–9.
15. Klein G, Delmar P, Voyle N, Rehal S, Hofmann C, Abi-Saab D, et al. Gantenerumab reduces amyloid-β plaques in patients with prodromal to moderate Alzheimer’s disease: a PET substudy interim analysis. Alzheimers Res Ther. 2019 Dec 12;11(1):101.
16. Willis BA, Sundell K, Lachno DR, Ferguson-Sells LR, Case MG, Holdridge K, et al. Central pharmacodynamic activity of solanezumab in mild Alzheimer’s disease dementia. Alzheimers Dement (N Y). 2018;4:652–60.
17. Panza F, Lozupone M, Seripa D, Imbimbo BP. Amyloid-β immunotherapy for alzheimer disease: Is it now a long shot? Annals of Neurology. 2019;85(3):303–15.
18. Sperling RA, Jack CR, Black SE, Frosch MP, Greenberg SM, Hyman BT, et al. Amyloid-related imaging abnormalities in amyloid-modifying therapeutic trials: recommendations from the Alzheimer’s Association Research Roundtable Workgroup. Alzheimers Dement. 2011 Jul;7(4):367–85.
19. Doody, R. The role of futility analyses in AD clinical trials. J Prev Alz Dis. 2020;7(in press).
20. Aisen PS. Editorial: Failure After Failure. What Next in AD Drug Development? J Prev Alzheimers Dis. 2019;6(3):150.
21. Zetterberg H, Burnham SC. Blood-based molecular biomarkers for Alzheimer’s disease. Molecular Brain. 2019 Mar 28;12(1):26.
22. Gauthier S, Alam J, Fillit H, Iwatsubo T, Liu-Seifert H, Sabbagh M, et al. Combination Therapy for Alzheimer’s Disease: Perspectives of the EU/US CTAD Task Force. J Prev Alzheimers Dis. 2019;6(3):164–8.
23. Cummings J, Blennow K, Johnson K, Keeley M, Bateman RJ, Molinuevo JL, et al. Anti-Tau Trials for Alzheimer’s Disease: A Report from the EU/US/CTAD Task Force. J Prev Alzheimers Dis. 2019;6(3):157–63.
24. Harrison TM, Maass A, Adams JN, Du R, Baker SL, Jagust WJ. Tau deposition is associated with functional isolation of the hippocampus in aging. Nat Commun. 2019 Oct 25;10(1):4900.
25. Pontecorvo MJ, Devous MD, Kennedy I, Navitsky M, Lu M, Galante N, et al. A multicentre longitudinal study of flortaucipir (18F) in normal ageing, mild cognitive impairment and Alzheimer’s disease dementia. Brain. 2019 Jun 1;142(6):1723–35.
26. Chen Z, Mengel D, Keshavan A, Rissman RA, Billinton A, Perkinton M, et al. Learnings about the complexity of extracellular tau aid development of a blood-based screen for Alzheimer’s disease. Alzheimers Dement. 2019;15(3):487–96.
27. Aisen PS, Siemers E, Michelson D, Salloway S, Sampaio C, Carrillo MC, et al. What Have We Learned from Expedition III and EPOCH Trials? Perspective of the CTAD Task Force. J Prev Alzheimers Dis. 2018;5(3):171–4.
28. Sperling RA, Jack CR, Aisen PS. Testing the right target and right drug at the right stage. Sci Transl Med. 2011 Nov 30;3(111):111cm33.
29. Bateman RJ, Benzinger TL, Berry S, Clifford DB, Duggan C, Fagan AM, et al. The DIAN-TU Next Generation Alzheimer’s prevention trial: adaptive design and disease progression model. Alzheimers Dement. 2017 Jan;13(1):8–19.
30. Sierra F. Geroscience and the role of aging in the etiology and management of alzheimer’s disease. Journal of Prevention of Alzheimer’s Disease [Internet]. 2019 Mar 1 [cited 2020 Jan 30]; Available from:

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S. Tomaszewski1, S. Gauthier2, A. Wimo3, P. Rosa-Neto2


1. Faculty of Medicine, McGill University, Montreal, QC, Canada; 2. Alzheimer Disease Research Unit, McGill Centre for Studies in Aging, Douglas Mental Health Research Institute, Montreal, QC, Canada; 3. Karolinska Institute, Stockholm, Sweden

Corresponding Author: Serge Gauthier, McGill Center for Studies in Aging, Douglas Mental Health University Institute, Douglas Hospital, Verdun, QC, Canada. Phone: +1 514-766-2010; Fax: +1 514-888-4050. Email:

J Prev Alz Dis 2016;3(3):164-172
Published online November 27, 2015,


Current drugs for treatment of mild to severe dementia of the Alzheimer’s type include cholinesterase inhibitors and the NMDA non-competitive receptor antagonist memantine. There is controversy as to the additive benefit of these symptomatic drugs, and their effects are clinically modest. Patients with Alzheimer’s disease (AD) are known to have characteristic pathology, including senile plaques with amyloid beta-protein aggregates and neurofibrillary tangles with assembled tau proteins, which start in the hippocampus and spread to neighboring areas. Amyloid and tau modifying drugs are under clinical testing. Based on this pathophysiology, it is crucial to investigate whether anti-amyloid and anti-tau combined therapy would show efficacy in early stage of AD, beyond what could be achieved with anti-amyloid or anti-tau monotherapy.  It is equally important to consider the socio-economic implications of such a combination therapy, if effective. We hypothesize that the high costs of combination therapy for early-stage AD patients will require societal and public health initiatives to ensure universal access to AD treatment. In order to better predict these socio-economic implications, we summarize the management of other combination therapies used for tuberculosis, HIV/AIDS, and breast cancer, based on a database search of PubMed and other relevant sources. We put forward a framework for testing a potential anti-amyloid and anti-tau disease modifying combination therapy for early-stage AD patients and present an analysis of the socio-economic implications of such a combination therapy. 


Key words: Alzheimer’s disease, dementia, combination therapy, anti-amyloid, anti-tau, socio-economic considerations, tuberculosis, HIV/AIDS, breast cancer.  



Alzheimer’s disease (AD) comes third on the scale of most expensive disorders in the United States, outranked only by cancer and coronary heart disease. The care given to AD patients can use up to 75% of household income (1). In the United Kingdom, dementia is found to be the number one brain-related disorder in terms of costliness, followed by mood and psychotic disorders (2). Worldwide, dementia affects as many as 46.8 million patients in 2015 and remains on the rise; the prevalence is expected to double every two decades, in parallel with population aging – the strongest risk factor contributing to the dementia epidemic (3). This prediction underpins the growing socio-economic impact of AD on patients, society, and healthcare systems in both the developed and developing world.       

To address the Alzheimer’s epidemic with its growing human burden and associated economic tribulations, health ministers from eight top industrialized nations, have committed at the G8 Dementia Summit in UK (2013) to increasing research efforts with the goal to develop a cure or a disease modifying treatment for dementia within the next decade, by 2025 (4). In parallel, the socio-economic challenges with current symptomatic AD therapy and potential future AD drug combinations should guide the elaboration of public health policies, advocating for the quality of life of AD patients and working towards lowering the heavy burden of AD on patients, families, and society as a whole. Similar challenges have been encountered when dealing with tuberculosis (TB), HIV/AIDS, and breast cancer, due to the high cost of combination therapies implicated in the management of those diseases. 

This position paper outlines potential solutions to a hypothetical AD combination treatment inspired by the management of other diseases that rely on multiple-drug therapies.


Rationale for combination therapy in AD

Currently, the recommended drug therapy for AD patients includes a cholinesterase inhibitor, such as donepezil, rivastigmine, or galantamine. There is ongoing controversy about the additive benefit of NMDA receptor antagonist memantine in the moderate to severe stages of AD. To date, research in drug development has been centered on monotherapies but there is emerging interest in testing drug combinations in AD (4) and this has been encouraged by the regulators (5).

In a multifactorial disease like Alzheimer’s involving various genetic and environmental risk factors, with resulting amyloid and tau abnormalities, it seems valid to target more than one element in the disease pathology to potentially better the therapeutic outcome. An analogous logic is applied in cancer care, where combination therapies act on multiple pathways to target cancer cells for apoptosis as efficiently as possible, while decreasing, as a side-benefit, the likelihood of drug resistance (6). The potential disease modifying combination therapy for AD, similarly to cancer combination regimens (6), will have to be closely tested for efficacy and safety, considering the potential risks of overlapping toxicity between agents and/or antagonistic neutralizing effects. Recent epidemiological results support the multi-factorial shape of AD and indicate that cardiovascular risk factors also may be involved in the pathogenesis of AD (and other dementias) (7); this may be one explanation to the possible, but not yet surely confirmed decline in prevalence of dementia (8, 9).


Anti-tau and anti-amyloid combination

Alzheimer’s patients present with two hallmark neuropathological findings: senile plaques and neurofibrillary tangles (NFTs). Senile plaques are aggregates of toxic fragments of the amyloid precursor protein, which collect progressively in the brain, often years before the symptomatic phase of AD. On the other hand, NFTs with intracellular hyperphosphorylated tau aggregates are found throughout the brain of AD patients and account for the disruption of neuronal function (10). Based on this pathological process (also shown in figure 1), it becomes intuitive to predict that disease modifying effects might be obtained by halting the progression of brain amyloid and tau pathology. Thus, the following protocol will outline the step-by-step approach to a future trial on the therapeutic effectiveness of anti-amyloid and anti-tau combination therapy, as compared to an anti-amyloid and anti-tau monotherapy.


Figure 1. Different biomarker levels throughout the phases of AD, following normal clinical progression (Source:


Protocol to demonstrate benefit of combination therapy in AD 

Anti-amyloid and anti-tau combination therapy could be tested clinically following a specific framework summarized in table 1. 


Table 1. 2 x 2 factorial design testing the effectiveness of anti-amyloid (A) and anti-tau (T) combination therapy versus A or T monotherapy

(Adapted from


The target population could be selected from older patients with amnestic mild cognitive impairment (MCI) with positive biomarker suggestive of AD pathology, such as low CSF Aβ42 and high phospho-tau levels (11). 

Potential alternative trial populations could be the subjects living in Antioquia, Colombia, who carry  the presenilin-1 E280A mutation (PS1)—a genetic change that translates into early-onset familial AD (EOFAD) with severe cognitive decline early in life (often starting in the third decade) and severe amyloid-β and tau pathology (12)—or the more heterogeneous population of mutation carriers in the Dominantly Inherited Alzheimer Network (DIAN; (13)).

This potential drug combination could be tested according to a 2 x 2 factorial design (shown in table 2), the ideal framework for comparing the effectiveness of a drug combination versus monotherapy. The idea would be to separate the sample population into four subgroups receiving the following regimens: (1) anti-amyloid (A) and anti-tau (T) drug combination therapy, (2) anti-amyloid (A) drug combined with placebo, (3) anti-tau (T) drug combined with placebo, and (4) two placebos. 

Clinical outcomes could be assessed using three distinct tools comparing differences between baseline and 12 months of treatment. The Clinical Dementia Rating – Sum of Boxes (CDR-SB) score, as a composite scale for the cognitive and functional state of AD patients, could serve as a primary outcome measure. Supportive secondary outcome measures could include biomarkers in the CSF, namely Aβ1-42 and phospho-tau-181, as well as the score on the Alzheimer’s Disease Assessment Scale-cognitive subscale (ADAS-cog), a tool for evaluating cognitive impairment. 


Table 2. Summary of Drug Protocol for AD disease modifying anti-amyloid and anti-tau combination therapy


Lessons from combination therapy in tuberculosis, HIV/AIDS and breast cancer

To maximize the quality of life of AD patients, it is crucial to anticipate the potential financial burden of an anti-amyloid and anti-tau combination drug therapy as well as be ready to propose effective solutions to mitigate costs. Thus, we will look at socio-economic success stories in TB, HIV/AIDS and breast cancer care in order to guide the elaboration of effective cost-lowering strategies for AD patients.


Tuberculosis (TB) is a valid public health concern considering its infectious nature and its large patient population. In 2004, 8.9 million new cases were estimated worldwide. Despite the existence of standard TB regimens, tuberculosis remains the treatable infectious disease with the highest mortality (14). To fight this epidemic, WHO set up the Stop TB partnership (2001) which proposed a Global Plan to Stop TB, 2001-2005 (with further developments for 2006-2015), advocating six key WHO-recommended elements, for instance improving disease detection and cure, involving all care providers, and investing in research (15). These global disease-specific targets, guiding public health initiatives, complement the United Nations’ Millennium Development Goal (MDG) 6, whose objective is to stop and progressively reverse the incidence of major infectious diseases, such as malaria, HIV/AIDS, and TB, by 2015 (16). 

Out of the 10 anti-TB drugs approved by the U.S. Food and Drug Administration (FDA), the current first-line treatment for tuberculosis consists of a two-month combination therapy of isoniazid (INH), rifampin (RIF), ethambutol (EMB), and pyrazinamide (PZA), followed by a four-month INH and RIF regimen (17). In the 1950s, a variety of novel TB drugs with distinct mechanisms of action were developed, paving the path for a new combination therapy (18). Already in 1966, a pivotal paper was published regarding the combination of isoniazid with para-amino salicylic acid (PAS) for patients with pulmonary TB. This combination turned out to be equally safe for administration at home as in a sanatorium, under supervision of the health care team, without real risk of onward transmission to close family contacts. Thus, this pivotal study served as the ground base for the introduction of ambulatory care in TB (19). One year later, rifampicin, a new bactericidal antituberculous drug, was synthesized, marking a major breakthrough in TB drug development. When RIF was included in the TB cocktail-drug treatment, along with another potent agent, PZA, the new resulting regimen was a revelation: it increased the long-term cure rates to 95% or more, while shortening the duration of treatment by over a half (20). 

Initially, those advances, despite their therapeutic benefits, imposed a large financial burden on TB patients, namely due to the high cost of the first-line combination therapy. Other direct expenditures involve the costs for: diagnostics and follow-up tests, inpatient care, diet supplements, transportation to doctor visits, to name a few. Indirect expenditures, such as lost productivity, are also a huge concern in TB given that the patient population is quite young and a large portion is on sickness leave from work until recovery (21).

To alleviate part of the financial struggle experienced by many TB victims, partnerships on a local, national and international level have mobilized to ensure free or at least more affordable antituberculous drug combinations, especially in high TB-burden countries. The Global Drug Facility (GDF), established by the Stop TB partnership in 2001, is one such initiative that offers antituberculous drugs for free or at little cost to countries in need (22, 23). GDF also strengthens national TB programmes by helping them with proper drug distribution and administration (22). GDF, in line with the mission of the Stop TB partnership, managed to grant drugs at lower prices thanks to the expansion of the drug supplier base, bulk purchasing, and competitive bidding at an international level (23).

In the early 1990s, China implemented the WHO-recommended TB control strategy, DOTS (Directly Observed Treatment – Shortcourse), thus becoming a pioneer country in offering free TB services. The DOTS approach is an example of public health intervention aimed at better global disease control. It comprises five main elements centered on sustained political mobilization with strategic partnerships and proper funding, improved diagnostics, standard supervised therapy with adequate patient support, continued drug supply, and monitoring systems with data reporting for outcome measurement. An expansion to this strategy, DOTS-plus (1999), addresses the high cost of second-line TB drugs for multidrug-resistant TB (MDR-TB) patients. Many countries have also appealed to external bodies for financial support to allow proper TB management. In Swaziland, a South-African sovereign state, the government has entered into partnership with international nongovernmental organizations in order to maximize access to medicines and TB care services throughout the country (24).

Another cost-lowering strategy involves non-profit generic companies and philanthropic donors. For instance, the Global Drug Facility (GDF) provides drugs at low prices thanks to the generosity of donors who help fund medicine stockpiles for the treatment of many illnesses, TB included (23). The Global Fund to fight AIDS, Tuberculosis and Malaria (2002)  as well as other international sources of financial aid have a variety of grants available to support TB care in candidate countries (24). 

In the United States, two fixed-dose combinations have been approved for use in TB management by the Food and Drug Administration (FDA): Rifamate© (isoniazid and rifampin) and Rifater© (isoniazid, rifampin, and pyrazinamide). According to expert opinion, while no evidence would suggest a superior pharmacologic activity and therapeutic effect of fixed-dose combinations compared to individual medicines, such drug formulations are recommended in cases where DOT is administered daily or, on the contrary, when DOT is not given at all. Using fixed-dose combinations reduces the amount of capsules or tablets that a patient has to take, thus offering an advantage in terms of easier drug administration and improved adherence, as well as potentially decreasing the likelihood of drug resistance since patients cannot mistakenly take one type of pill selectively out of the other drugs prescribed in their combination therapy (17, 25).

It can be argued that TB drug combinations should be provided for free to all affected patients, since many of them would not be able to afford the treatment otherwise. Another argument is of a social construct; the logic is that “treatment has benefits that extend to society as a whole (cure prevents transmission to others)” given the infectious nature of TB (26). Strategies to mitigate the costs of drug combinations also indirectly promote an increased access to medicines by the very principle that less expensive drugs can be more easily afforded by a larger patient population. Another method used in TB care to optimize treatment accessibility is the inclusion of anti-TB drugs into the WHO Model List of Essential Medicines (27). WHO formulates an “Access Framework” (2004) around that model list to promote universal access of these essential medicines by advocating strategic selection and use of these drugs, along with reasonable prices, continued funding, and efficient management and delivery of the drug supply (28). A wide variety of organizations (international, nongovernmental, non-profit, etc.), such as UNICEF, UNHCR and UNFPA, use the Model List as a basis for their drug supply system (29).


Figure 2. Four key elements of WHO’s “Access Framework” meant to maximize access to essential medicines – thus reflecting Millennium Development Goals, Target 17 (Source:



The worldwide prevalence of HIV was estimated to be 35.3 million in 2012, with 70.8% living in Sub-Saharan Africa, compared to 31.0 million in 2002. In parallel, new cases of infection decreased from 3.3 million to 2.3 million within the same time period. Clearly, progress has been made thanks to the introduction of combination antiretroviral (ARV) therapies in the late 1990s, which transformed HIV into a chronic yet controllable condition. The increased longevity thus achieved explains the growing prevalence of HIV/AIDS (30).

The standard ARV regimen prescribed nowadays is a triple therapy; it involves one non-nucleoside reverse transcriptase inhibitor (NNRTI), integrase inhibitor or protease inhibitor (PI), combined with two nucleoside reverse transcriptase inhibitors (NRTIs) (30). As shown in the Merck protocol 035, a pivotal clinical trial, despite the pill burden and the associated treatment cost, the benefit of delayed disease progression with a triple therapy of two NRTIs with a PI (versus two NRTIs or a PI alone) can partially dwarf the costs of treatment. Indeed, according to an economic model developed by Cook et al (31) which was applied to patients from the Merck Protocol 035, the costs of triple therapy, given that suppression lasts up to five years, would exceed the costs of double therapy, yet 81% could be discounted based on the fact that fewer cases progressed to AIDS.

To further fight the spread of HIV/AIDS and promote universal access to treatment, similarly to the AIDS targets of the Millennium Development Goal 6 (16), the Joint United Nations Programme on HIV/AIDS (UNAIDS) launched in 2012 a global action plan called Treatment 2015. This initiative included a specific target known as “15 by 15” meaning that 15 million patients will have access to HIV antiretroviral drug regimens by 2015. Despite many challenges, this “15 by 15” goal turned into a success story, and even better, it made history, because it was the first time that a quantifiable treatment objective in global health had been met before its deadline (32).

To make ARV drugs more affordable, especially for low-income nations, in 2001, former United Nations Secretary-General Kofi Annan brought forward the idea of a global fund for HIV treatment that would offer free drugs after having bought them at little cost. He argued that ARV drugs remained expensive for disadvantaged patient populations, even after price reductions by pharmaceutical companies. This valid argument underpinned the need for a drastic change in the health economic management of ARV drug therapies; one year later, in 2002, the Global Fund to Fight AIDS, Tuberculosis and Malaria was established. In the next decade, US$ 20 billion have been pledged, the majority of funds going into HIV/AIDs treatment and care services. This amount is the largest pledge that has ever been made for a single health condition in such a short time period (24).

Additional funding for ARV drugs, which are the basis of HIV/AIDS treatment, has been made possible by the United States President’s Emergency Plan for AIDS Relief (PEPFAR), which works in close collaboration with UNAIDS, UNICEF, and more broadly with WHO. From 2003 to 2011, PEPFAR dedicated sustained efforts into procuring the funds necessary for the purchase of ARV drugs to prevent mother-child transmission, thus successfully protecting 340,000 babies from HIV infection. Thanks to PEPFAR, during 2004-2011, the US government has collected and disbursed the unforeseen value of US$ 32 billion for HIV (24). As part of PEPFAR, the Supply Chain Management System (SCMS) was launched in 2005. This initiative is focused on strengthening and creating new drug supply chains for HIV to ensure accessibility and best-value of ARV drugs, HIV test kits and other products (33).

Analyzing the situation of AIDS, Berwick (58) postulates that price reductions should go beyond the public sector; they should be applied across the board. In a more radical tone, the author claims that AIDS—–the most deadly epidemic in all history—–could be quickly resolved if pharmaceutical companies decided to improve the fate of the world and provide HIV drugs for free to disadvantaged populations in lower-income countries (58). Actually, five world-leading pharmaceutical companies agreed to decrease prices of HIV drugs in sub-Saharan Africa, some even offering 90% price reductions (34). 

There seems to be a need for putting more pressure on pharmaceutical companies to sell quality drugs for less. In 1986, Burroughs Wellcome & Co. synthesized a new very expensive drug, Zidovudine, initially intended for cancer care, and accidentally observed to delay progression to AIDS via its effect on maintaining a low viral load. The high annual cost of US$10,000 per patient linked with a Zidovudine regimen would have prevented many HIV victims from using such drugs, had it not been for social pressure from activists that forced the pharmaceutical company to lower the price by 20%. Despite alleviating part of the financial burden, this drug remains highly unaffordable in resource-poor countries (24). 

To alleviate the financial burden of HIV combination therapy and other associated medical or nonmedical costs, the HIV/AIDS Policy, Coordination and Programs Division, under the Public Health Agency of Canada, offers five funds to help fight this chronic illness, as part of a federal mission plan, the “Federal Initiative to Address HIV/AIDS in Canada” (35). Nonprofit generic companies, subsidized by philanthropic foundations, could also help ensure lower drug prices and greater accessibility to treatment. This has been already achieved with considerable success in Africa, when dealing with the AIDS epidemic (36). 

To attain the most affordable prices for essential medicines such as the ARV drugs, it is crucial to have generic competition for bio-equivalent drugs (58). Equally, comparative drug price information needs to be made publicly available to inform the patient population on their options, thus helping them make cost-effective decisions. The International Drug Price Indicator Guide (updated annually after its first edition in 1986) is one such tool developed by the Management Sciences for Health (MSH), a non-profit international health organization, working with WHO since 2000. This guide comprises a price comparison of different pharmaceutical products, diagnostic tests, and other medical tools, from many suppliers (both commercial and non-profit), for the prevention and treatment of various prevalent illnesses like HIV/AIDS. This price list is used as a basis for the publication on “Sources and Prices of Selected Drugs and Diagnostics for People Living with HIV/AIDS”. Both these documents help patients select the least expensive option for their prescribed regimen, while also improving the drug procurement system on a societal dimension, by encouraging competition and negotiations for the best price (28, 33).

Turning to patent rules, Brazil can serve as a case study for the use of compulsory licensing as a way of exerting positive pressure on pharmaceutical companies; the Brazilian government managed on many occasions to lower prices of ARV drugs under the threat of breaking the companies’ drug patents and allowing domestic production of their products. Thanks to this negotiation tactic, Brazil was able to force lower drug prices for nelfinavir and efavirenz on Roche and Merck, the respective drug manufacturers. Compulsory licensing, although acceptable under Brazil’s patent law, became a reason of concern for pharmaceutical companies and other stakeholders. This led the World Trade Organization (WTO) to open a discussion panel on compulsory licencing, following a request from the United States on behalf of its pharmaceutical companies. This panel was dedicated to assessing the validity of Brazil’s appeal to compulsory licensing in light of the Trade-Related Aspects of Intellectual Property Rights (TRIPS) agreement—–a binding law ensuring that copyright, patent and other property rights are protected. Despite the clear political and financial dilemma, soon after, WTO dropped its dispute panel regarding Brazil’s clashing interpretation of the TRIPS agreement. In November 2001, the Doha Declaration was released to clarify when a state is justified in resorting to compulsory licencing: the conclusion is that this “negotiating tool” can be used to address public health emergencies. More commonly, Brazil brings drug prices down by encouraging domestic production of medicines at a lower cost, thus largely reducing the need for international import. In 1999 for instance, nearly half of national ARV drugs were produced in state or privately-run firms (37). 

There are currently over 20 distinct ARV drugs, out of which a few have been joined together into fixed-dose combination (FDC) products. FDCs have the advantage of facilitating distribution and administration due to a lower pill burden, and potentially decreasing treatment costs. However, only selected ARV drugs can be combined given the possibility of antagonistic reactions and overlapping toxicity. Despite the potential risks of FDCs, if not properly tested for efficacy and safety, some newer FDCs (still waiting for FDA approval) have already been marketed in resource-poor countries where HIV reached crisis levels in order to benefit from the indispensable advantages of FDCs (38).

Breast Cancer

Cancer care is a very costly endeavor, especially now that patients tend to survive longer, thus prolonging the treatment period (39). Breast cancer, specifically, is the main cause of cancer-related mortality among women throughout the world. Incidence rates are highest in high-income countries and although low at baseline in more resource-limited settings they are on the rise (40). This is why a new approach in health-care economics needs to be considered. Many governments have already taken some steps in this direction in their respective National Cancer Plans. Also known as “national cancer control programmes” by the World Health Organisation, they lay out strategies on how to best deal with new cancer cases. Although they differ slightly across the different jurisdictions, they typically emphasize prevention as well as early detection to increase likelihood of a successful treatment and thus lower costs (41). 

Pharmacologic treatment of breast cancer includes a large number of anticancer agents with distinct mechanisms of action and varying effects on tumor cells. Common examples are: “methotrexate, 5-fluorouracil (5-FU), cyclophosphamide, anthracyclines, taxanes, trastuzumab, tamoxifen, and aromatase inhibitors” (42).  These and other medications are often combined to maximize treatment outcomes by targeting different cell pathways and receptors. The majority of drug combinations are not approved by the FDA, unlike the individual drugs that they consist of, yet they are still commonly used. Standard chemotherapy regimens can include any of the following combinations: AC (Adriamycin (A) + Cyclophosphamide (C)), AC-T (A + C + Paclitaxel (Taxol)), CAF (C + A + Fluorouracil (F)), CMF (C + Methotrexate + F), FEC (F + Epirubicin Hydrochloride + C), and TAC (Docetaxel (Taxotere) + A + C) (43).

Addressing cancer therapies, an American study mentions the need for better evidence-based national guidelines; one suggestion is to replace the current available list of treatment options for each type of cancer with a comparative cost-effective analysis of different generic drugs, including risks and benefits for patients, which would facilitate treatment choices (36). The Breast Health Global Initiative (BHGI) works on a specific set of guidelines, culturally adapted to poorer nations that lack sustainable healthcare systems, to improve health outcomes for patients with breast cancer (44). 

An interesting field in development is the study of pharmacogenomics that looks at predictive factors to maximize treatment responsiveness, thus potentially increasing the cost-effectiveness of otherwise costly combination therapies. Before opting for genetic screening, an economic analysis is needed to evaluate the quantity of savings gained from a more targeted treatment, i.e. administered to patients on the basis of whether they express or do not express a specific protein predictive of therapeutic success, with respect to the additional costs of testing and decreased revenue for drug producers linked with a more restricted patient selection. The resulting disadvantage of genetic testing for pharmaceutical companies may force them to raise the prices of the drug combinations in question. This likely outcome needs to be weighed against the societal and health benefits. The concept of pharmacogenetics is growing in importance in oncological conditions (and HIV/AIDS, to a slightly lesser extent) (45).

When conducting a socio-economic analysis of combination therapies, it is crucial to adopt a far-reaching view on their financial impact. For example, although offering an adjuvant therapy implies additional immediate costs, on the long term such a regimen (if optimal) can be cost-saving, because it has the potential to inhibit metastases and limit recurrences of tumor (39).


Combination therapy for AD  

A large number of patients, reaching epidemic proportions, are suffering from dementia; as of 2015, there were 46.8 million cases of dementia worldwide (3), exceeding highly prevalent diseases like tuberculosis and HIV/AIDS.». This number is expected to reach 74.7 million by 2030, almost doubling in the space of 20 years (3). To counter the high associated worldwide cost of dementia, of the amount of US$ 818 billion in 2015 (3), different cost-lowering strategies must be applied, based on success stories from other prevalent conditions, such as TB, HIV/AIDS, and breast cancer. There is also no state of opposition with the potentially positive effects of prevention activities in AD and other dementias as indicated by the FINGER study (46) and a pharmaceutical combined disease modifying treatment. 

As in other illnesses, effective management of AD costs requires putting pressure on pharmaceutical companies by encouraging competition for bio-equivalent drugs. Also, another measure is to stimulate competition at the level of drug regulators, which can be achieved by privatizing existing certification boards. Privatised regulators would come up with specific standards of regulation based on the choice requirements of those who select drug regimens, thus making the approval process both smoother and faster. In the same vein, development costs will be potentially reduced, thus allowing more drugs to be produced for a given monetary value – this means the drug supply will become less expensive and come closer to matching the demand, thus increasing access to medicines (47).

While a potential anti-amyloid and anti-tau combination therapy for early-stage AD patients would imply a higher cost of prescription medications, if indeed found to have a disease modifying effect, we would expect the resulting caregiver time needed to be reduced, thus balancing out this increase in drug cost, or at least relieving some of the financial burden on society. 

Delaying the onset and evolution of AD by 1 year via preventive interventions and early therapeutics can save around 9 million people worldwide from having an Alzheimer’s diagnosis in 2050. This may serve as a cost-benefit argument for encouraging preventive measures in dementia care (48). Similarly, delaying institutionalization at the moderate to severe AD phases, even only by one month, was shown to generate cost savings of US$1863 monthly. From a socio-economic perspective, it appears advantageous to prolong home care as much as possible (given that the condition of the patient permits it); it can help reduce direct healthcare costs as well as decrease the global burden of disease by enhancing the quality of life of AD patients, for whom home care is often the number one preference (49). However, cost-effectiveness per se does not necessarily imply that cost savings must be achieved. There is a societal willingness to pay (WTP) for improvement in care. An effective disease modifying treatment will probably prolong survival and since treatment will start in predementia states (where costs of care also without treatment are rather low), the aggregated treatment cost will be significant (50). Even if the total aggregated costs during the whole period, from early symptoms to death, may be higher with than without disease modifying treatment, it may be regarded as cost-effective due to significant effects in the outcomes, supporting good value for money. 

In the event that the anti-amyloid and anti-tau drug combination, proposed in this paper, is found be an effective disease modifying therapy, the use of pharmacogenomics would become more ethically acceptable, since a positive genetic test would no longer “[condemn] an innocent person to death without his being able to escape his fate” as would be the case in the absence of preventive or curative treatment (10).

However, to study the long-term cost-effectiveness of a combination therapy is not easy. Several designs need to be used (51). While efficacy is analysed in phase 3 trials, cost-effectiveness analysis based on empirical within trial data is often taking place in phase 4 trials.  Since resource use and cost data are frequently skewed, the power analysis will show that the needed sample sizes are much larger than for clinical efficacy measures (52). Furthermore, to cover the whole survival in controlled trials is not possible and thus health economic modelling is needed (53). 

New genetic tests exist to check the status of the apolipoprotein E type 4 gene, the main genetic indicator of risk for AD (10). The ApoE gene can also serve as a predictive factor to select patients who are more likely to respond to a symptomatic and/or disease modifying drug therapy (54). Furthermore, developing biomarkers for diagnostic and prognostic purposes could help improve detection of disease (55) while decreasing costs, by being used as a replacement for the more expensive PET scans. The use of effective biomarkers is also essential to avoid cases meeting clinical criteria for dementia without Alzheimer’s disease pathophysiology in the early diagnostics of AD (56). However, it remains elusive which biomarkers would be sufficient to identify carriers of AD pathophysiology. 

There is also a clear need for improving the drug development process to maximize efficiency and decrease costs. A potential solution to this challenge is brought forward by Accelerating Medicines Partnership (AMP), which involves the collaboration between four types of actors: US National Institutes of Health (NIH), US Food and Drug Administration (FDA), 10 pharmaceutical industries, and a group of non-profit organizations, working in three disease areas: AD, type 2 diabetes, and autoimmune disorders of rheumatoid arthritis and systemic lupus erythematosus. All partners accept to pool their data on biomarkers and potential drug targets thus increasing the likelihood of developing targeted therapies without the typical failure rate. Clearly, it is important to ensure an optimal choice of drug target and design before conducting the actual trial, and AMP facilitates this task. Normally, over 95% of candidate drugs do not pass the extensive testing which spans over many years; those that fail in the late phase clinical trials are responsible for the greatest waste of money and time. Thus, improving the drug development process is an essential cost-lowering strategy needed to revolutionize the health economics of AD (57). This is of great importance since 58% of people with AD and other dementias worldwide presently live in low and middle income countries and this proportion is estimated to increase to 63% in 2030 and 68% in 2050 (3).   

Access to treatment with the current drugs is today limited in many countries. If a combination disease modifying treatment will enter the market, its price will be significant for most people with dementia worldwide and the whole issue of pricing and reimbursement will be crucial. Cost-lowering strategies used or predicted to be of use in TB, HIV/AIDS and breast cancer are equally applicable to Alzheimer’s disease. These include: local, national or international partnerships aiming to provide more affordable drugs, drug price indicators with cost-effective data, inclusion of anti-tau and anti-amyloid drug combination in WHO’s list of essential medicines, appeal to philanthropic donors, public health policies, compulsory licensing, fixed-dose combinations, maximization of domestic production, improved drug distribution to remote areas or underprivileged populations, and raised awareness of the disease and its management among patients and caregivers. 



When designing a protocol for a potential disease modifying therapy, the socio-economic impact of such a pharmacological construct also has to be speculated and evaluated prior to its chemical development and introduction into the market, in order to reduce its future monetary burden and increase access to such therapy. This research achieves exactly that. It goes beyond the current symptomatic treatment alternatives for AD; it introduces a framework for testing a potential anti-tau and anti-amyloid disease modifying combination therapy for early-stage AD patients and includes a socio-economic analysis of such a combination therapy, based on the cost-lowering strategies used in other prevalent diseases, such as TB, HIV/AIDS, and breast cancer. 

While a cost-effectiveness analysis of the potential anti-amyloid and anti-tau combination therapy is important for treatment decisions, it cannot be the only determining factor that will guide physicians to prescribe the given regimen or not. Optimizing the quality of AD care does not mean blindly applying cost-effectiveness study results to clinical practice. Patient and family goals and expectations need to be considered case by case in order to provide the most human and compassionate care in Alzheimer’s disease. 


Disclosures: The authors declare no conflict of interests.

Acknowledgements: SG and PRN receive peer-reviewed funding from the CIHR, and the ELSI program of the Canadian Consortium for Neurodegeneration in Aging (CCNA) funded the summer studentship of ST.



1. Castro DM, Dillon C, Machnicki G, Allegri RF. The economic cost of Alzheimer’s disease: family or public health burden?. Dement. Neuropsychol. 2010;4(4):262-267.

2. Fineberg NA, Haddad PM, Carpenter L, Gannon B, Sharpe R, Young AH, et al. The size, burden and cost of disorders of the brain in the UK. J Psychopharmacology. 2013;27(9):761-70.

3. Alzheimer’s Disease International. World Alzheimer report 2015 the global impact of dementia: an analysis of prevalence, incidence, cost and trends. 2015;1-88.

4. Long R. Finding a path for the cure for dementia: an independent report into an integrated approach to dementia research [Internet]. 2015;1-28.

5. U.S. Food and Drug Administration. Guidance for industry Alzheimer’s disease: developing drugs for the treatment of early stage disease (Draft Guidance). New Hampshire: U.S. Department of Health and Human Services FDA/Center for Drug Evaluation and Research. 2013.

6. LoRusso PM, Canetta R, Wagner JA, Balogh EP, Nass SJ, Boerner SA, et al. Accelerating cancer therapy development: the importance of combination strategies and collaboration. Summary of an Institute of Medicine workshop. Clin Cancer Res. 2012;18(22):6101-9.

7. Kivipelto M, Mangialasche F, Solomon A, Johansson G, Lannfelt L, Fratiglioni L, et al. 9th Key symposium introduction: updating Alzheimer’s disease diagnosis implications for prevention and treatment. J Intern Med. 2014;275(3):202-3.

8. Matthews FE, Arthur A, Barnes LE, Bond J, Jagger C, Robinson L, et al. A two-decade comparison of prevalence of dementia in individuals aged 65 years and older from three geographical areas of England: results of the Cognitive Function and Ageing Study I and II. Lancet. 2013;382(9902):1405-12.

9. Rocca WA, Petersen RC, Knopman DS, Hebert LE, Evans DA, Hall KS, et al. Trends in the incidence and prevalence of Alzheimer’s disease, dementia, and cognitive impairment in the United States. Alzheimers Dement. 2011;7(1):80-93.

10. Poirier J, Gauthier S. Alzheimer’s disease : the complete introduction. Toronto: Dundurn; 2014.

11. Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, 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. Alzheimer’s & Dementia. 2011;7(3):270-9.

12. Sepulveda-Falla D, Glatzel M, Lopera F. Phenotypic profile of early-onset familial Alzheimer’s disease caused by presenilin-1 E280A mutation. J Alzheimers Dis. 2012;32(1):1-12.

13. Bateman RJ, Xiong C, Benzinger TLS, Fagan AM, Goate A, Fox NC, et al. Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N Eng J Med. 2012;367(9):795-804.

14. Dye C. Global epidemiology of tuberculosis. The Lancet.367(9514):938-40.

15. Stop TB partnership. The global plan to stop TB, 2006-2015 [Internet]. World Health Organization; 2015:1-172. Available from: 

16. World Health Organization. MDG 6: combat HIV/AIDS, malaria and other diseases [Internet]. World Health Organization; 2014. Available from:

17. Centers for Disease Control and Prevention. Core curriculum on Tuberculosis: what the clinician should know [Internet]. 2013;139-187. Available from:

18. Zumla A, Nahid P, Cole ST. Advances in the development of new tuberculosis drugs and treatment regimens. Nat Rev Drug Discov. 2013;12(5):388-404.

19. Kamat SR, Dawson JJ, Devadatta S, Fox W, Janardhanam B, Radhakrishna S, et al. A controlled study of the influence of segregation of tuberculous patients for one year on the attack rate of tuberculosis in a 5-year period in close family contacts in South India. Bull World Health Organ. 1966;34(4):517-32.

20. Geraint D. Chemotherapy including drug-resistant therapy.  Clinical Tuberculosis, 5th ed: CRC Press; 2014. p. 229-40.

21. Tiemersma EW, Collins D, van den Hof S. Costs faced by (multidrug resistant) tuberculosis patients during diagnosis and treatment: report from a pilot study in Ethiopia, Indonesia and Kazakhstan. [place unknown]:KNCV Tuberculosis Foundation/Management Sciences for Health; 2014.

22. Matiru R, Ryan T. The Global Drug Facility: a unique, holistic and pioneering approach to drug procurement and management. Bull World Health Org. 2007;85(5):348-53.

23. Lunte K, Cordier-Lassalle T, Keravec J. Reducing the price of treatment for multidrug-resistant tuberculosis through the Global Drug Facility. Bull World Health Org. 2015;93(4):279-82.

24. World Health Organization. Bugs, drugs, & smoke: stories from public health [Internet]. World Health Organization; 2011. Available from:

25. Mukund U, Diana W. WHO’s Stop TB Strategy.  Tuberculosis. Lung Biology in Health and Disease: CRC Press; 2009. p. 300-24.

26. Singh JA, Bhan A, Upshur R. Diagnosis of drug-resistant TB and provision of second-line TB treatment in India: some ethical considerations. Indian J Med Ethics. 2013;10(2):110-4.

27. World Health Organization, Stop TB Initiative. Treatment of tuberculosis:  guidelines. 4th ed. Geneva: World Health Organization; 2010.

28. van Boxtel CJ, Santoso B, Edwards IR. Drug benefits and risks: international textbook of clinical pharmacology. 2nd ed. Amsterdam; Uppsala, Sweden: IOS Press; Uppsala Monitoring Centre; 2008.

29. World Health Organization. Essential medicines [Internet]. World Health Organization; 2015. Available from:

30. Maartens G, Celum C, Lewin SR. HIV infection: epidemiology, pathogenesis, treatment, and prevention. Lancet. 2014;384(9939):258-71.

31. Cook J, Dasbach E, Coplan P, Markson L, Yin D, Meibohm A, et al. Modeling the long-term outcomes and costs of HIV antiretroviral therapy using HIV RNA levels: application to a clinical trial. AIDS Res Hum Retroviruses. 1999;15(6):499-508.

32. UNAIDS. “15 by 15”: a global target achieved [Internet]. Geneva: Joint United Nations Programme on HIV/AIDS; 2015. Available from:

33. Frye JE, editor. International drug price indicator guide [Internet]. Cambridge, MA: Management for Services for Health/WHO; 2010. Available from:

34. Gottlieb S. Companies reduce prices for HIV drugs in developing countries. Bull World Health Org. 2000;78(6):862.

35. Public Health Agency of Canada. National HIV/AIDS grants and contributions funds [Internet]. Public Health Agency of Canada; 2006. Available from:

36. Siddiqui M, Rajkumar SV. The high cost of cancer drugs and what we can do about it. Mayo Clin Proc. 2012;87(10):935-43.

37. Perlman D, Roy A. The practice of international health : a case-based orientation. Oxford; New York: Oxford University Press; 2009.

38. U.S Food and Drug Administration. Guidance for industry – fixed dose combination and co-packaged drug products for treatment of HIV [Internet]. Silver Spring: U.S. Food and Drug Administration: 2004.

39. Beckmann MW, Lux MP. Health economics in breast cancer. Breast Care (Basel). 2013;8(1):5-6.

40. Key TJ, Verkasalo PK, Banks E. Epidemiology of breast cancer. Lancet Oncol. 2001;2(3):133-40.

41. World Health Organization. National cancer control programmes [Internet]. Geneva: World Health Organization; 2015. Available from:

42. Navolanic PM, McCubrey JA. Pharmacological breast cancer therapy (review). Int J Oncol. 2005;27(5):1341-4.

43. National Cancer Institute. Drugs approved for breast cancer [Internet]. National Cancer Institute. 2014. Available from:

44. Galukande M, Kiguli-Malwadde E. Rethinking breast cancer screening strategies in resource-limited settings. Afr Health Sci. 2010;10(1):89-92.

45. Danzon P, Towse A. The economics of gene therapy and of pharmacogenetics. Value Health. 2002;5(1):5-13.

46. Ngandu T, Lehtisalo J, Solomon A, Levalahti E, Ahtiluoto S, Antikainen R, et al. A 2 year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): a randomised controlled trial. Lancet. 2015;385(9984):2255-63.

47. Sauer C, Sauer RM. Reducing barriers to the development of high quality, low cost medicines: a proposal for reforming the drug approval process [Internet]. London: Hanway Print Center; 2005.

48. Andrieu S, Coley N, Lovestone S, Aisen PS, Vellas B. Prevention of sporadic Alzheimer’s disease: lessons learned from clinical trials and future directions. Lancet Neurol. 2015;14(9):926-44.

49. Zhu CW, Sano M. Economic considerations in the management of Alzheimer’s disease. Clin Interv Aging. 2006;1(2):143-54.

50. Skoldunger A, Johnell K, Winblad B, Wimo A. Mortality and treatment costs have a great impact on the cost-effectiveness of disease modifying treatment in Alzheimer’s disease – a simulation study. Curr Alzheimer Res. 2013;10(2):207-16.

51. Wimo A. Long-term effects of Alzheimer’s disease treatment. Lancet Neurol. 2015:1-2.

52. Briggs A. Economic evaluation and clinical trials: size matters (editorial). BMJ. 2000;321(7273):1362-3.

53. Wimo A, Ballard C, Brayne C, Gauthier S, Handels R, Jones RW, et al. Health economic evaluation of treatments for Alzheimer’s disease: impact of new diagnostic criteria. J Intern Med. 2014;275(3):304-16.

54. Liu C-C, Kanekiyo T, Xu H, Bu G. Apolipoprotein E and Alzheimer disease: risk, mechanisms, and therapy. Nat Rev Neurol. 2013;9(2):106-18.

55. Fougère B VJ, Delrieu AJ, Sinclair A, Wimo CJ, Herman H, et al. The road ahead to cure and prevent Alzheimer’s disease: implementing prevention into primary care. Prev Alz Dis. 2015; 2(3):199-211.

56. Salloway S, Sperling R, Brashear HR. Phase 3 trials of solanezumab and bapineuzumab for Alzheimer’s disease. N Engl J Med. 2014;370(15):1460.

57. National Institutes of Health. Accelerating medicines partnership [Internet]. Bethesda: U.S. Department of Health and Human Services; 2015. Available from:

58. Berwick D. «We all have AIDS»: case for reducing the cost of HIV drugs to zero. BMJ. 2002;324(7331):214-6.


S. Hendrix1, N. Ellison1, S. Stanworth1, V. Otcheretko2, P.N. Tariot3

1. Pentara Corporation, Salt Lake City, UT, USA; 2. Forest Research Institute, Jersey City, NJ, USA, an affiliate of Actavis, Inc.; 3. Banner Alzheimer’s Institute, Phoenix, AZ, USA

Corresponding Author: Suzanne Hendrix, Pentara Corporation, Salt Lake City, UT, USA, Telephone: ++1-801-898-724, Fax: ++1-801-486-7467,

J Prev Alz Dis 2015;2(3):165-171
Published online May 12, 2015,


Background: Several randomized trials have demonstrated superiority of memantine-cholinesterase inhibitor combination therapy in patients with moderate to severe Alzheimer’s disease, yet a recent publication reported no additional benefit of add-on memantine therapy compared to donepezil alone.

Objectives: In this post hoc analysis, we sought to re-evaluate the results from the DOMINO study using common statistical tools and to apply the statistical models used in the DOMINO study to a pooled data set of 24- to 28-week randomized trials of memantine in patients with moderate to severe AD in order to explore the robustness of the primary findings from the DOMINO study.

Design: DOMINO study: Randomized, double-blind, placebo-controlled trial (Current Controlled Trial number, ISRCTN49545035); Memantine Clinical Trial Program: Pooled analysis from four randomized, double-blind, placebo-controlled trials.

Setting: DOMINO study: United Kingdom; Memantine Clinical Trial Program: Multinational.

Participants: DOMINO study: 295 participants enrolled during the period of February 2008 to March 2010; Memantine Clinical Trial Program: 1417 participants enrolled between August 1998 and January 2008.

Measurements: In the DOMINO study, the co-primary outcome measures were scores on the Standardized Mini-Mental State Examination and the Bristol Activities of Daily Living Scale; Neuropsychiatric Inventory was a secondary measure. In the Memantine Clinical Trial Program, outcome measures included the Severe Impairment Battery, the 19-item Alzheimer’s Disease Cooperative Study – Activities of Daily Living scale, Neuropsychiatric Inventory, and a 4-Domain Composite Index (Z-score; a post hoc assessment).

Results: Both the pooled analysis of the Memantine Clinical Trial Program and the re-assessment of the DOMINO study with common statistical tools showed that adding memantine to donepezil therapy is associated with benefits across multiple clinical domains.

Conclusions: The current analyses suggest that the results of the DOMINO study do not contradict previous studies which investigated the combined effects of memantine-cholinesterase inhibitor treatment.

Key words: Memantine, donepezil, combination therapy, treatment strategy.  


Treatment guidelines and health policy decisions critically depend on the outcomes of clinical trials and the conclusions reported by the investigators. However, sources of uncertainty and bias — eg, chance, sample size, enrollment criteria, study design, data analysis plan, choice of statistical tools (1)— remain inherent to any clinical study.

Therefore, it is common for seemingly similar studies, or a formal reanalysis of trial outcomes, to result in different conclusions or conclusions that are contrary to previous results (2). For example, several lines of evidence, including 6-month randomized clinical trials (3-6), post hoc analyses (7, 8), and long-term (1-14 year) observational cohort effectiveness studies (9-11), suggest that memantine-cholinesterase inhibitor (ChEI) combination therapy in patients with moderate to severe AD is superior to monotherapy with either drug or drug class. In contrast, the authors of a randomized, 52-week Donepezil and Memantine in Moderate to Severe Alzheimer’s Disease (DOMINO) study (12) concluded, based on a subgroup analysis, that the addition of memantine to ongoing donepezil treatment was not superior to donepezil or memantine monotherapy. However, due to a variety of methodological limitations, including small sample size, the failure to demonstrate a «statistically significant» effect of addition of memantine does not mean or even imply that there is no effect (see Discussion). The current treatment paradigm for AD consists of monotherapy with a ChEI (eg, donepezil) in the earlier stages (13) and the addition of memantine in the moderate or severe stages (14-16). Reconciling the apparently dissimilar conclusions of the DOMINO publication and the previous clinical trial results is crucial for determining the best standard of care for patients with moderate to severe AD, and is the goal of this paper.

The purpose of the current analysis was twofold: (1) Reassess the published DOMINO results using common statistical tools, and (2) apply the statistical models from the DOMINO study to a pooled data set of 24- to 28-week randomized trials of memantine in moderate to severe AD (3-6) in order to examine the statistical support for the DOMINO conclusions.


Trial Characteristics and Treatment Groups

DOMINO Study (12)

The DOMINO study was a one-year, multicenter, double-blind, placebo-controlled, clinical trial with a two-by-two factorial design. The participants were community-dwelling individuals with moderate to severe probable AD (N=295) (Standardized Mini-Mental State Examination [SMMSE] score range: 5-13) who had been receiving donepezil continuously for ≥3 months with a daily dose of 10 mg for ≥6 weeks and whose prescribing physicians were considering change of treatment (stopping donepezil or introducing memantine). A randomized minimization procedure and stratification by donepezil treatment duration (3-6 months or >6 months), age (<60 years, 60-74 years, or >74 years), and SMMSE score (5-9 or 10-13) were used to assign participants to one of the following treatments: discontinue donepezil (n=73), discontinue donepezil and start 20 mg memantine (n=76), continue 10 mg donepezil (n=73), or continue donepezil 10 mg and start 20 mg memantine (n=73). 

Memantine Clinical Trial Program

The four pooled trials of the Memantine Clinical Trial Program were 6-month, randomized (1:1), multicenter, parallel-group, double-blind, and placebo-controlled. Outpatients (N=1,957) met standardized clinical criteria for moderate to severe probable AD (combined MMSE range: 3-16) and were either not receiving background antidementia therapy (4, 5) (memantine monotherapy trials) or receiving ChEI therapy continuously for ≥6 months, of which a stable dose for ≥3 months (3, 6)(add-on/combination trials). The active treatment was memantine (10 mg bid) (3-5) or memantine extended-release (28 mg qd) (6). The pooled populations (n=1,417, excluding non-donepezil ChEIs) were divided into four treatment categories created to mimic the DOMINO groups: placebo-treated participants from the two memantine monotherapy trials (4, 5) (PBO; n=281), memantine-treated participants from the two monotherapy trials (4, 5) (MEM; n=289), placebo-treated participants who were concurrently taking donepezil in the two add-on trials (3, 6) (DON/PBO; n=418), or memantine-treated participants who were concurrently taking donepezil in the two add-on trials (3, 6) (DON/MEM; n=429). Data from participants who were on a ChEI other than donepezil at Baseline were not included.

The treatment groups in the DOMINO study and the pooled dataset of the Memantine Clinical Trial Program are shown in Table 1. It is important to note that all treatment assignments in DOMINO were randomized, whereas in the pooled studies memantine and placebo treatments were randomly assigned but donepezil pretreatment was not, meaning that observed memantine effects are due to group assignment, but observed donepezil effects could be due to group assignment, selection issues or population differences.   

Table 1. DOMINO Study and Pooled Sample of the Memantine Clinical Trial Program: Treatment Groups, Assessments, and Statistical Analyses

*Total population, not population with all visit data available; 4D indicates four-dimensional; ADCS-ADL19, the 19-item Alzheimer’s Disease Cooperative Study – Activities of Daily Living scale (score range: 0-54); BADLS, Bristol Activities of Daily Living Scale (score range: 0-60); CI, indicates confidence interval; DON, donepezil; MEM, memantine; MMRM, multilevel modeling with repeated measures; NPI, Neuropsychiatric Inventory (score range: 0-144); PBO, placebo; SMMSE, Standardized Mini-Mental State Examination (score range: 0-30); SIB, Severe Impairment Battery (score range: 0-100).

Efficacy Assessments and Outcomes

In the DOMINO study, the co-primary outcome measures were the Standardized Mini-Mental State Examination (SMMSE) (17) and the Bristol Activities of Daily Living Scale (BADLS) (18). The SMMSE is a 12-item, 30-point scale designed to assess cognitive performance, in which higher scores indicate better cognitive abilities. The BADLS is a 20-item, 60-point scale designed to assess the ability of someone with dementia to carry out daily activities, in which higher scores indicate greater impairment. A secondary outcome measure was the Neuropsychiatric Inventory (NPI) (19), which is a 12-item, 144-point scale designed to assess behavior, with higher scores indicating greater behavioral problems.

In the Memantine Clinical Trial Program, the cognitive outcome measure was the Severe Impairment Battery (SIB) (20), a 40-item, 100-point scale designed to assess cognitive performance in patients with moderate to severe AD, in which lower scores indicate greater impairment. Daily function was assessed using the 19-item Alzheimer’s Disease Cooperative Study – Activities of Daily Living scale (ADCS-ADL19) (21), a 54-point instrument designed to assess functional abilities in patients with moderate to severe AD, in which lower scores indicate greater impairment. Behavioral symptoms were assessed with the NPI. Lastly, a post hoc 4-Domain (4D) Composite Index (Z-score) was an equally weighted composite measure consisting of the four outcome measures — SIB, ADCS-ADL19, NPI, and the Clinician’s Interview-Based Impression of Change Plus Caregiver Input (CIBIC-Plus), a measure of patient’s global clinical status (22) — based on the distribution of the baseline scores. In order to make the contributing scales uniform in their direction of improvement, the direction of improvement for the NPI and CIBIC-Plus was reversed.  

Statistical Analysis 

Results of the DOMINO study were re-assessed using a multilevel modeling regression with repeated measures (MMRM) with effects for memantine, donepezil, and memantine by donepezil interaction, adjusted for baseline scores and for the four stratification factors (center, duration of donepezil treatment before study entry, baseline SMMSE score, and age), with random effects for each visit and a variance component structure that was chosen because an unstructured covariance matrix in the DOMINO study did not converge (12). In addition, confidence intervals (CIs) were converted into p-values (two-sided). Data from the Memantine Clinical Trial Program were analyzed using a similar regression model to that employed in the DOMINO trial by applying the same two covariance structures: an unstructured covariance structure, which did converge for the pooled memantine data, and a variance components structure, which was used for the DOMINO results.

The three objectives stated in the DOMINO publication were 1) to assess the main effect of donepezil continuation vs discontinuation, 2) to assess the main effect of memantine addition vs matched placebo, and 3) to test whether the addition of memantine to ongoing donepezil treatment would provide additive or synergistic benefits in patients with moderate to severe AD. The distinction between additivity and synergy, according to the publication, was based on homogeneous versus heterogeneous effects for each treatment alone (i.e., monotherapy) versus those of the combined treatments (12). Although the specific statistical tests were not explicitly stated, additivity would be demonstrated if donepezil and memantine treatment effects were homogeneous in the presence and absence of the other treatment, while synergy (either positive or negative) would be consistent with heterogeneous outcomes. Our analysis used the traditional definitions of synergy and additivity, with a significant positive or negative interaction between the two treatments being the criterion for synergy (i.e., heterogeneity), and the absence of a significant interaction in the presence of significant main effects being the criterion for additivity (i.e., homogeneity) (23). In other words, positive or negative synergy would be determined by a combined effect that is larger or smaller, respectively, than the sum of the two individual effects, and that is associated with a significant treatment interaction; a combined effect that is larger or smaller than the sum of the individual effects, but without significant interaction, would indicate additivity.

A comparison of the statistical analyses used for the DOMINO trial and Memantine Clinical Trial Program is presented in Table 1. Baseline demographics and characteristics of the pooled data set were assessed using summary statistics and compared by means of ANOVA (continuous variables) or a chi-squared test (dichotomous variables). No adjustments for multiple comparisons were made (i.e., each variable was considered independently) (Table 2). Center was included as a random effect in the model for the Memantine Clinical Trial Program. Previous time on donepezil was not available for the monotherapy studies included in the Memantine Clinical Trial Program and was therefore not included in the model.

Table 2. DOMINO and Memantine Clinical Trial Program: Baseline Characteristics

*Mean ± standard deviation; †Analysis of variance (ANOVA); ‡Chi-squared test;  BADLS indicates Bristol Activities of Daily Living Scale (score range: 0-60); DON, donepezil; MEM, memantine; MMSE, Mini Mental State Examination (score range: 0-30); NPI, Neuropsychiatric Inventory (score range: 0-144); PBO, placebo; SMMSE, Standardized Mini-Mental State Examination.



Patient Demographics and Baseline Characteristics

In the DOMINO study, a total of 295 participants were enrolled during the period of February 2008 to March 2010. Recruitment was slower than anticipated and the public funder of the study did not allow extending the recruitment period, which resulted in a sample size notably smaller than the one originally planned (800) or the one of 430 that, according to an interim analysis, would have yielded power of 80% to 96%. The baseline characteristics of the DOMINO study participants and the four study populations from the Memantine Clinical Trial Program are shown in Table 2. In the DOMINO study, the baseline characteristics were similar among treatment groups. In the Memantine Clinical Trial Program, statistically significant differences across groups were primarily due to differences in baseline characteristics between the trials with background donepezil and those without (however, note that each pooled trial was randomized, so the memantine and placebo groups within each donepezil group were comparable).

Efficacy Assessments


Based on the confidence intervals presented in the primary paper (12), the DOMINO study found a significant overall advantage of continued donepezil versus donepezil discontinuation (with or without memantine addition) across all visits for SMMSE and BADLS, but not for NPI, with p-values based on our reassessment of <0.001 (SMMSE, BADLS) and 0.081 (NPI) (Figure 1). A significant overall advantage was also seen for memantine addition versus placebo addition (with or without discontinuation of donepezil) for SMMSE (p<0.001), BADLS (p=0.015), and NPI (p=0.002) (Figure 1). There was no evidence of negative or positive synergistic interaction between donepezil and memantine on the SMMSE (p=0.14), NPI (p=0.42) and BADLS score (p=0.09) across the entire trial period (12).

Figure 1: DOMINO Data, Reassessed

Continued DON vs Discontinued DON: *p<0.05; **p<0.01; ***p<0.001; Active MEM vs PBO MEM: †p<0.05; ††p<0.01; †††p<0.001; All differences are presented with 95% CIs. Data for Active MEM vs PBO MEM are average values across participants who continued or discontinued donepezil. Data for Continued DON and Discontinued DON are average values across participants who received active or placebo memantine; BADLS indicates Bristol Activities of Daily Living Scale (score range: 0-60); CI, confidence interval; DON, donepezil; MEM, memantine; NPI, Neuropsychiatric Inventory (score range: 0-144); PBO, placebo; SMMSE, Standardized Mini-Mental State Examination (score range: 0-30).

Visit by visit, there was a significant advantage of continued donepezil versus donepezil discontinuation on the SMMSE (Weeks 6, 18, 30, and 52; Figure 1A), the BADLS (Weeks 6, 18, 30, and 52; Figure 1B), and on the NPI (Week 18; Figure 1C) at a 2-sided α=0.05. For memantine addition, there were visit-by-visit significant effects of adding memantine versus adding placebo on the SMMSE (Weeks 6, 18, and 30; Figure 1A), the NPI (Weeks 6, 30, and 52; Figure 1C), and on the BADLS (Weeks 18 and 30; Figure 1B), also at 2-sided α=0.05.

Memantine Clinical Trial Program

Applying the statistical approach used in the DOMINO study to data from the Memantine Clinical Trial Program largely corroborated the DOMINO findings. Overall, there were statistically significant main effects for background donepezil versus placebo on the SIB (p<0.001), ADCS-ADL19 (p=0.020), NPI (p<0.001), and the Composite Index (p=0.001) (Figure 2A, C-D), and a statistically significant advantage of memantine versus placebo in all four assessments (SIB, p<0.001; ADCS-ADL19, p=0.017; NPI, p<0.001; Composite Index, p<0.001) (Figure 2A-D). No significant positive or negative interactions were seen between background donepezil and memantine for any of the outcomes (all p-values ≥0.170).

Visit-by-visit data indicated significant effects of donepezil versus placebo at Weeks 4, 12, and 24 on the SIB (Figure 2A), Week 24 on the ADCS-ADL19 (Figure 2B), and Weeks 12 and 24 on the NPI and the Composite Index (Figure 2C & 2D). There were significant effects of adding memantine versus adding placebo at Weeks 4, 12, and 24 on the SIB and the Composite Index (Figure 2A & 2D), Week 24 on the ADCS-ADL19 (Figure 2B), and Weeks 12 and 24 on the NPI (Figure 2C).

The covariance structure had minimal influence on the results. The outcomes of the unstructured covariance model are shown for all outcomes.

Figure 2: Pooled Memantine Clinical Trial Program Data

Background DON vs No Background DON: *p<0.05; **p<0.01; ***p<0.001; Add-on MEM vs Add-on PBO: †p<0.05; ††p<0.01; †††p<0.001; All differences are presented with 95% confidence intervals. Add-on MEM vs Add-on PBO values represent average values across participants with or without background DON treatment. Data for Background DON vs No Background DON represent average values across participants who received Add-on MEM or Add-on PBO; 4D, indicates four-dimensional; ADCS-ADL19, 19-item Alzheimer’s Disease Cooperative Study – Activities of Daily Living scale (score range: 0-54); DON indicates donepezil; MEM, memantine; NPI, Neuropsychiatric Inventory (score range: 0-144); PBO, placebo; SIB, Severe Impairment Battery (score range: 0-100).


 The reassessment of the DOMINO results with common statistical tools and the meta analysis of the Memantine Clinical Trial Program data, in the context of standard statistical definitions of additivity and synergy, provide converging evidence that the addition of memantine to existing donepezil therapy is associated with benefits across multiple clinical domains. There was statistical evidence of an additive effect for the memantine and donepezil treatments in the DOMINO study and in the pooled population of the Memantine Clinical Trial Program, but no statistically significant evidence of a negative or positive synergistic interaction (i.e., heterogeneity). The current analyses suggest that the results of the DOMINO study are consistent with the previous, larger studies that investigated the effects of memantine-ChEI combination therapy in patients with moderate to severe AD (3-6, 8-11). The precise mechanisms by which two drugs as different as memantine and donepezil may interact in the human brain are not entirely understood (24). Cholinergic neurons receive glutamatergic inputs (24) and vice versa (25, 26). As acetylcholine and choline acetyltransferase levels do not begin to fall significantly until dementia is advanced, there would be a good theoretical basis to assume benefit from simultaneous administration of memantine and donepezil into the moderate and severe stages of the disease (27).

A primary aim of the DOMINO study (12) was to determine whether initiating memantine treatment at the moderate and severe stages of AD is beneficial. The conclusions of the original publication were that initiation of memantine provided significant benefit for both primary outcomes and for the NPI. In addition, statistically significant benefits were seen for the continuation of donepezil for both primary outcomes, but not for the NPI. The DOMINO authors also reported that they “did not find significant heterogeneity in the efficacy of donepezil or memantine in the presence or absence of the other drug,” based on non-significant donepezil by memantine interaction effects. However, the conclusion that there were no significant benefits of the combination of donepezil and memantine treatment over donepezil treatment alone (12) was based on separating a homogeneous population into small subgroups for analysis rather than using the primary ITT population to obtain estimates from the mixed model. This conclusion elicited criticism, in part because of the issues noted above as well as the small subgroup end-of-study sample sizes (20-38/group) and critical differences in dropout rates among the treatment groups (28). The current reassessment of the DOMINO study data suggested significant advantages of memantine compared to placebo in the presence or absence of donepezil for overall cognition (SMMSE), function (BADLS), and behavior (NPI) measures, thereby corroborating several lines of clinical evidence supporting the superiority of memantine-ChEI combination therapy over the component monotherapies (3, 6-11). Another aim of Howard et al (2012) was to address whether patients with moderate to severe AD already receiving donepezil benefit from continuing donepezil treatment (12). The superiority of continued donepezil treatment over discontinuation/placebo donepezil was demonstrated in the original publication (12), the current reassessment of the DOMINO study data, and in a soon to be published area-under-the-curve (AUC) analysis of the pooled data from the Memantine Clinical Trial Program, in which the 4D Composite Index for the 24/28 week treatment period showed additive and cumulative clinical advantages of memantine-donepezil combination over monotherapies with donepezil or memantine (7).

A key limitation of the current analysis is that the conclusions are based on post hoc assessments with non-randomized groups for the donepezil comparisons, and baseline differences that may influence the donepezil comparisons. In addition, the statistical model used in DOMINO could not be used in its entirety for the pooled data since the non-donepezil patients did not have previous time on donepezil (12), which eliminated this covariate from the model.

In conclusion, this re-analysis suggests that the results of the DOMINO trial do not contradict previous studies in moderate to severe AD that found benefits of the combined memantine-donepezil treatment that exceed those of the monotherapies.

Conflict of interests: V. Otcheretko is an employee of the study sponsor. S. Hendrix, N. Ellison, and S. Stanworth are employees of Pentara Corporation, a contractor of the study’s sponsor. P. N. Tariot received grants from the study sponsor. 

Ethical standards: All participants, or their caregivers or legally authorized representatives, provided written informed consent before any study  procedures were conducted, as noted in the original study publications (3-6, 12).

Funding: This analysis was sponsored by Forest Research Institute, LLC, an affiliate of Actavis, Inc. The sponsor was involved in the collection of a subset of data included in this analysis, in interpretation of results, preparation of the manuscript, and its review and approval.

Acknoledgments: Editorial support (development of tables and graphs, editorial suggestions, formatting for submission) was provided by Prescott Medical Communications Group (Chicago, IL, USA), a contractor of the study sponsor.


1. Gluud LL. Bias in clinical intervention research. American journal of epidemiology 2006;163, 493-501.

2. Ebrahim S, Sohani ZN, Montoya L, Agarwal A, Thorlund K, Mills EJ, Ioannidis JP. Reanalyses of randomized clinical trial data. JAMA 2014;312, 1024-1032.

3. Tariot PN, Farlow MR, Grossberg GT, Graham SM, McDonald S, Gergel I. Memantine treatment in patients with moderate to severe Alzheimer disease already receiving donepezil: a randomized controlled trial. JAMA 2004;291, 317-324.

4. Reisberg B, Doody R, Stoffler A, Schmitt F, Ferris S, Mobius HJ. Memantine in moderate-to-severe Alzheimer’s disease. N Engl J Med 2003;348, 1333-1341.

5. van Dyck CH, Tariot PN, Meyers B, Malca Resnick E. A 24-week randomized, controlled trial of memantine in patients with moderate-to-severe Alzheimer disease. Alzheimer Dis Assoc Disord 2007;21, 136-143.

6. Grossberg GT, Manes F, Allegri RF, Gutierrez-Robledo LM, Gloger S, Xie L, Jia XD, Pejovic V, Miller ML, Perhach JL, Graham SM. The safety, tolerability, and efficacy of once-daily memantine (28 mg): a multinational, randomized, double-blind, placebo-controlled trial in patients with moderate-to-severe Alzheimer’s disease taking cholinesterase inhibitors. CNS Drugs 2013;27, 469-478.

7. Atri A, Hendrix S, Pejović V, Hofbauer RK, Edwards J, Molinuevo JL, Graham SM. Cumulative, additive benefits of memantine-donepezil combination over component monotherapies in moderate to severe Alzheimer’s dementia: a pooled area under the curve analysis. Alzheimer Research & Therapy Accepted, 2015.

8. Atri A, Molinuevo JL, Lemming O, Wirth Y, Pulte I, Wilkinson D. Memantine in patients with Alzheimer’s disease receiving donepezil: new analyses of efficacy and safety for combination therapy. Alzheimers Res Ther 2013;5.

9. Atri A, Shaughnessy LW, Locascio JJ, Growdon JH. Long-term course and effectiveness of combination therapy in Alzheimer disease. Alzheimer Dis Assoc Disord 2008;22, 209-221.

10. Lopez OL, Becker J, Wahed A, Saxton J, Sweet R, Wolk D, Klunk W, Dekosky S. Long-term effects of the concomitant use of memantine with cholinesterase inhibition in Alzheimer disease. Journal of Neurology, Neurosurgery & Psychiatry 2009;80, 600-607.

11. Rountree SD, Chan W, Pavlik VN, Darby EJ, Siddiqui S, Doody RS. Persistent treatment with cholinesterase inhibitors and/or memantine slows clinical progression of Alzheimer disease. Alzheimers Res Ther 2009;1, 7.

12. Howard R, McShane R, Lindesay J, Ritchie C, Baldwin A, Barber R, Burns A, Dening T, Findlay D, Holmes C, Hughes A, Jacoby R, Jones R, Jones R, McKeith I, Macharouthu A, O’Brien J, Passmore P, Sheehan B, Juszczak E, Katona C, Hills R, Knapp M, Ballard C, Brown R, Banerjee S, Onions C, Griffin M, Adams J, Gray R, Johnson T, Bentham P, Phillips P. Donepezil and memantine for moderate-to-severe Alzheimer’s disease. N Engl J Med 2012;366, 893-903.

13. Birks J. Cholinesterase inhibitors for Alzheimer’s disease Cochrane Database Syst Rev. 2006  25;(1):CD005593..

14. McShane R, Areosa Sastre A, Minakaran N. Memantine for dementia. Cochrane Database Syst Rev. 2006 2:CD003154.

15. Rabins P, Blacker D, Rovner B, Rummans T, Schneider L, Tariot P, Blass D, McIntyre J, Charles S, Anzia D. APA Work Group on Alzheimer’s Disease and other Dementias. American Psychiatric Association practice guideline for the treatment of patients with Alzheimer’s disease and other dementias. Am J Psychiatry 2007;164, 5-56.

16. Geldmacher DS, Kerwin DR. Practical diagnosis and management of dementia due to Alzheimer’s disease in the primary care setting: an evidence-based approach. The primary care companion for CNS disorders 2013;15:(4). pii: PCC.12r01474.

17. Molloy DW, Standish TI. A guide to the standardized Mini-Mental State Examination. Int Psychogeriatr 1997; 9 Suppl 1, 87-94; discussion 1997;143-150.

18. Bucks RS, Ashworth DL, Wilcock GK, Siegfried K. Assessment of activities of daily living in dementia: development of the Bristol Activities of Daily Living Scale. Age Ageing 1996;25, 113-120.

19. Cummings JL, Mega M, Gray K, Rosenberg-Thompson S, Carusi DA, Gornbein J. The Neuropsychiatric Inventory: comprehensive assessment of psychopathology in dementia. Neurology 1994;44, 2308-2314.

20. Saxton J, Swihart AA. Neuropsychological assessment of the severely impaired elderly patient. Clin Geriatr Med 1989;5, 531-543.

21. Galasko D, Bennett D, Sano M, Ernesto C, Thomas R, Grundman M, Ferris S. An inventory to assess activities of daily living for clinical trials in Alzheimer’s disease. The Alzheimer’s Disease Cooperative Study. Alzheimer Dis Assoc Disord 1997;11 Suppl 2, S33-39.

22. Schneider LS, Olin JT, Doody RS, Clark CM, Morris JC, Reisberg B, Schmitt FA, Grundman M, Thomas RG, Ferris SH. Validity and reliability of the Alzheimer’s Disease Cooperative Study-Clinical Global Impression of Change. The Alzheimer’s Disease Cooperative Study. Alzheimer Dis Assoc Disord 1997;11 Suppl 2, S22-32.

23. Slinker BK. The statistics of synergism. Journal of molecular and cellular cardiology 1998;30, 723-731.

24. Parsons CG, Danysz W, Dekundy A, Pulte I. Memantine and cholinesterase inhibitors: complementary mechanisms in the treatment of Alzheimer’s Disease. Neurotoxicity research 2013;24, 358-369.

25. Henny P, Jones BE. Projections from basal forebrain to prefrontal cortex comprise cholinergic, GABAergic and glutamatergic inputs to pyramidal cells or interneurons. European Journal of Neuroscience 2008;27, 654-670.

26. Arroyo S, Bennett C, Hestrin S. Nicotinic modulation of cortical circuits. Frontiers in neural circuits 2014;8:30. doi: 10.3389/fncir.2014.00030.

27. Jones R, Sheehan B, Phillips P, Juszczak E, Adams J, Baldwin A, Ballard C, Banerjee S, Barber B, Bentham P. DOMINO-AD protocol: donepezil and memantine in moderate to severe Alzheimer’s disease–a multicentre RCT. Trials 2009;10, 57.

28. Tariot PN. Cessation of donepezil is associated with clinical decline in patients with moderate-to-severe Alzheimer’s disease compared to continuation of donepezil or addition or substitution of memantine. Evidence Based Medicine 2013;18, 62-63.