Suzanne Hendrix

In clinical studies, it is scientifically important, and a regulatory expectation, that objectives be translated into key clinical questions by specifically defining treatment effects to be estimated. Estimands are part of a structured framework, as presented in International Council for Harmonisation (ICH) E9(R1), by which study objectives are linked to a suitable study design and tools for estimation. Estimands are constructed using five attributes: treatment, population, variable (or endpoint), population‐level summary for the variable, and intercurrent events (ICEs). ICEs occur after treatment initiation and can affect the existence or interpretation of the measurements. In Alzheimer's disease (AD), potential ICEs include additional AD medication use, discontinuation of treatment, and death. We describe estimands in recent clinical studies of anti‐amyloid therapies, including gantenerumab, lecanemab, and donanemab, in AD and use the evoke and evoke+ studies of semaglutide in early‐stage symptomatic AD as examples of estimand application in AD trials.

In progressive diseases such as Alzheimer's, treatments that slow progression should start early to preserve higher levels of functioning for a longer period. In corresponding clinical trials, treatment effects are usually expressed as mean differences on a clinical scale at fixed time points. Early in the disease course, however, these mean differences may appear small but may nonetheless correspond to an important slowing of disease progression. This complicates the appreciation of the relevance of observed treatment effects. We introduce a class of target parameters that quantify treatment effects on the time scale in longitudinal studies; for instance, in terms of time saved or percentage slowing of progression. We focus on data from randomized trials where the target parameters are identified under regularity assumptions. These target parameters remain well defined if treatment was not randomized, but additional untestable assumptions are required for identification. We propose general two‐step estimators. In the first step, the data can be analyzed with standard methods for longitudinal data and standard software can thus be used. In the second step, summary statistics from the first step are used for inferences about the target parameters. The second step has been implemented in the TCT R package. We study the asymptotic properties and efficiency of these two‐step estimators, and evaluate them in an extensive simulation study. These estimators are used in a phase 2/3 clinical trial for Alzheimer's disease, leading to important additional insights into the treatment effect.

INTRODUCTION: The importance of biomarkers as a primary outcome or as supportive evidence of clinical effect is rising as the field shifts toward disease‐modifying treatments and earlier intervention, because they have lower variability and can indicate disease progression earlier than clinical outcomes. This study assessed the performance of plasma pTau 181 and 217 as a predictive biomarker and potential primary endpoint in early‐phase Alzheimer's disease (AD) trials. METHODS: Summary data from recent monoclonal antibody (mAb) trials including plasma pTau 181 and 217 were analyzed to evaluate associations between plasma pTau 181/217 and clinical outcomes. The suitability of plasma pTau 181/217 as a surrogate endpoint for internal decision making was assessed using Prentice criteria. Simulations were conducted to explore the statistical power of using plasma pTau 181/217 as a primary outcome in dose‐escalation, proof‐of‐concept (POC) trial designs. Additional criteria for biomarker validation were applied to simulated data. RESULTS: A strong group‐level correlation ( r = 0.781) was observed between treatment effects on plasma pTau 181/217 and Clinical Dementia Rating scale – Sum of Boxes (CDR‐SB). Mean change in plasma pTau 181/217 significantly predicted mean change in CDR‐SB ( p = 0.013). The treatment effect on pTau 181/217 was ∼2.6 times greater than on CDR‐SB. Prentice Criteria 1, 2, and 4 were met or reasonably met; Criterion 3 is not applicable in the POC setting. CONCLUSION: Plasma pTau 181/217 at 6 months shows future promise to reasonably likely predict clinical benefit for drugs that reduce pTau 181/217 levels, supporting its use as a primary endpoint in early‐phase trials. With effect sizes similar to those seen with donanemab, adequately powered trials may require as few as 100 participants. Such trials should include prespecified analyses to evaluate individual‐level Prentice criteria, and pTau 181/217 results can be used to predict potential Phase 3 clinical outcomes. Highlights: The group‐level correlation between a biomarker treatment effect and clinical endpoint treatment effect is a measurement of the biomarker's ability to predict clinical outcome. The correlation of group level plasma pT217 or pT181 effect size at 6 months with clinical outcome Clinical Dementia Rating scale – Sum of Boxes (CDR‐SB) effect size at 12 months was approximately 0.781 with p values of 0.013. Cohen's d effect size of plasma pTau as an outcome was 2.6 times greater than the Cohen's d of CDR‐SB, leading to higher power or lower sample sizes. As a primary endpoint, plasma pTau meets or reasonably meets Prentice Criteria 1, 2, and 4, while Criterion 3 was deemed not applicable in the proof‐of‐concept study setting.

Background: Alzheimer’s disease (AD) clinical trials led to the recent successes with monoclonal antibodies targeting amyloid, which opens up many new directions for research into treatment for AD in the future. Gaining greater understanding from these successes and failures will help researchers to focus their efforts on avenues that have the highest potential benefit.Method: We performed a meta‐analysis of over a hundred studies of 70+ AD treatments. A global statistical test (GST) combining ADAS‐cog, ADCS‐ADL and CDR‐sb was used to assess the overall efficacy in these studies, in a fair way across multiple outcomes. Disease modifying treatment effects and symptomatic effects impacting all disease symptoms will perform better on this GST than treatments impacting only one symptom. In addition, a false positive effect is less likely to occur on all 3 outcomes simultaneously, making the GST a more reliable outcome for detecting true treatment effects. Composite scores, ADCOMS and iADRS, were used to target true disease progression in the lecanemab and donanemab phase 2 studies, respectively, and have similar advantages to a GST. A similar approach would have possibly averted much of the controversy surrounding the aducanumab approval.Result: Some studies that were previously determined to be failures show some indication of positive effects. And conversely, some studies previously thought to be promising are shown to be more clearly failures. Meta‐analyses combining similar treatments across programs illuminate overlooked mechanisms as well as more conclusive failures.Conclusion: The success and failure of Alzheimer’s treatments is partly hidden by including 3 different domains of clinical efficacy: cognition, function and global. True treatment efficacy and true lack of efficacy are much easier to detect with multiple, combined outcomes.