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. 2024 Sep;20(9):6374-6383.
doi: 10.1002/alz.14134. Epub 2024 Jul 29.

Scenarios for the long-term efficacy of amyloid-targeting therapies in the context of the natural history of Alzheimer's disease

Lars Lau Raket  1   2 , Jeffrey Cummings  3 , Alexis Moscoso  4 , Nicolas Villain  5   6 , Michael Schöll  4   7   8
Affiliations

Scenarios for the long-term efficacy of amyloid-targeting therapies in the context of the natural history of Alzheimer's disease

Lars Lau Raket et al. Alzheimers Dement. 2024 Sep.

Abstract

Introduction: Recent clinical trials of amyloid beta (Aβ)-targeting therapies in Alzheimer's disease (AD) have demonstrated a clinical benefit over 18 months, but their long-term impact on disease trajectory is not yet understood. We propose a framework for evaluating realistic long-term scenarios.

Methods: Results from recent phase 3 trials of Aβ-targeting antibodies were integrated with an estimate of the long-term patient-level natural history trajectory of the Clinical Dementia Rating-Sum of Boxes (CDR-SB) score to explore realistic long-term efficacy scenarios.

Results: Three distinct long-term efficacy scenarios were examined, ranging from conservative to optimistic. These extrapolations of positive phase 3 trials suggested treatments delayed onset of severe dementia by 0.3 to 0.6 years (conservative), 1.1 to 1.9 years (intermediate), and 2.0 to 4.2 years (optimistic).

Discussion: Our study provides a common language for long-term impact of disease-modifying treatments. Our work calls for studies with longer follow-up and results from early intervention trials to provide a comprehensive assessment of these therapies' true long-term impact.

Highlights: We present long-term scenarios of the efficacy of AD therapies. In this framework, scenarios are defined relative to the natural history of AD. Long-term projections with different levels of optimism can be compared. It provides a common language for expressing beliefs about long-term efficacy.

Keywords: amyloid‐targeting therapies; clinical trials; disease modeling; disease modification; long‐term efficacy; time saving.

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Conflict of interest statement

LLR is an employee and shareholder of Eli Lilly and Company. JC has provided consultation to Acadia, Actinogen, Acumen, AlphaCognition, Aprinoia, AriBio, Artery, Biogen, BioVie, Cassava, Cerecin, Diadem, EIP Pharma, Eisai, GemVax, Genentech, GAP Innovations, Janssen, Jocasta, Karuna, Lilly, Lundbeck, LSP, Merck, NervGen, Novo Nordisk, Oligomerix, Optoceutics, Ono, Otsuka, PRODEO, Prothena, ReMYND, Roche, Sage Therapeutics, Signant Health, Simcere, Suven, SynapseBio, TrueBinding, Vaxxinity, and Wren pharmaceutical, assessment, and investment companies. AM reports no conflicts of interest. NV has received research support from Fondation Bettencourt‐Schueller, Fondation Servier, Union Nationale pour les Intárêts de la Mádecine (UNIM), Fondation Claude Pompidou, Fondation Alzheimer and Fondation pour la Recherche sur l'Alzheimer; travel grants from the Movement Disorders Society, Merz‐Pharma, UCB Pharma, and GE Healthcare SAS; is an unpaid local principal investigator or subinvestigator in NCT04241068 and NCT05310071 (aducanumab, Biogen), NCT05399888 (BIIB080, Biogen), NCT03352557 (gosuranemab, Biogen), NCT05463731 (remternetug, Eli Lilly), NCT04592341 (gantenerumab, Roche), NCT03887455 (lecanemab, Eisai), NCT03828747 and NCT03289143 (semorinemab, Roche), NCT04619420 (JNJ‐63733657, Janssen—Johnson & Johnson), NCT04374136 (AL001, Alector), NCT04592874 (AL002, Alector), NCT04867616 (bepranemab, UCB Pharma), NCT04777396 and NCT04777409 (semaglutide, Novo Nordisk), NCT05469360 (NIO752, Novartis), is an unpaid national coordinator for NCT05564169 (masitinib, ABScience), NCT (AD04, ADvantage Therapeutics GmbH); has given unpaid lectures at symposia organized by Eisai and the Servier Foundation; and has been an unpaid expert for Janssen and Johnson & Johnson, all outside of this work. MS has served on advisory boards for and received funding from Roche Diagnostics and Novo Nordisk (outside scope of submitted work). Author disclosures are available in the supporting information.

Figures

FIGURE 1
FIGURE 1
Clinical Dementia Rating‐Sum of Boxes (CDR‐SB) results of EMERGE, CLARITY AD, and TRAILBLAZER‐ALZ 2. Results are based on publicly reported results of change from baseline in CDR‐SB analyzed using the mixed model for repeated measures with the reported average baseline score added to the results to bring them to the CDR‐SB scale.
FIGURE 2
FIGURE 2
Estimated natural history trajectory of Clinical Dementia Rating‐Sum of Boxes (thick line) from Raket et al. Four examples (thin lines) illustrate how hypothetical interventions that produce (A) 30% stage‐independent continued slowing of disease progression and (B) 30% stage‐dependent fading slowing would change the trajectory based on when the intervention was started (0, 5, 10, or 15 years after Aβ PET positivity).
FIGURE 3
FIGURE 3
Trial results mapped to long‐term Clinical Dementia Rating‐Sum of Boxes (CDR‐SB) trajectory with extrapolations (dotted lines). The gradient trajectory represents the estimated natural history trajectory, and the shaded bands represent the 90% prediction intervals for baseline CDR‐SB scores in the different studies.
See this image and copyright information in PMC

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