Background Soluble amyloid β1–42 (Aβ42) signals via the α7 nicotinic acetylcholine receptor to hyperphosphorylate tau in Alzheimer's disease (AD). Simufilam disrupts this pathogenic signaling by binding filamin A and disrupts its linkages with inflammatory receptors to reduce neuroinflammation. We assessed simufilam in two Phase 3 clinical trials in mild-to-moderate AD. Methods Participants were age 50–87 with Stage 4 or 5 CE, a mini-mental state exam (MMSE) ≥16 and ≤27 and a Clinical Dementia Rating Global Score (CDR-GS) of 0.5, 1 or 2. The criterion supporting AD pathology was plasma phosphorylated (p)-tau181 or prior amyloid PET. RETHINK randomized participants to simufilam 100 mg or placebo for 52 weeks. REFOCUS evaluated simufilam 50 and 100 mg versus placebo for 76 weeks. Co-primary endpoints were change from baseline on ADAS-Cog12 and ADCS-ADL. Sub-studies assessed exploratory plasma biomarkers and, in REFOCUS only, CSF and imaging biomarkers. Results Both trials failed to meet co-primary, secondary or exploratory biomarker endpoints. REFOCUS was terminated early, with 22% of participants still active in the trial. In the predefined mild subgroup in REFOCUS, simufilam was associated with slower cognitive decline than placebo through Week 64 (p = 0.019). This finding disappeared at Week 76 with 45% missing data and did not replicate in RETHINK. Favorable nominal exploratory post-hoc findings amongst participants with the highest half of screening plasma p-tau181 levels occurred in RETHINK but not REFOCUS. The plasma p-tau181 entry criterion did not reliably exclude amyloid PET negativity in the sub-study. Conclusions Simufilam did not meet co-primary or secondary endpoints in these Phase 3 trials. Simufilam was safe and well tolerated. Trials registered at clinicaltrials.gov: NCT04994483 and NCT05026177

Mild cognitive impairment (MCI), a prodromal stage of Alzheimer's disease (AD), remains undiagnosed in > 90% of individuals, delaying access to timely evaluation and interventions. Self-administered digital cognitive assessments (SA-DCAs) offer scalable approaches for early detection, yet their real-world validation and clinical readiness remain uncertain. We developed a use-case-specific framework to evaluate SA-DCAs intended for community and primary-care MCI screening and applied it to a comprehensive scoping review of published evidence (2012-2025). Among 79 identified SA-DCAs, only four tools met predefined framework criteria across nine eligible studies. Common limitations included restricted population representativeness, inconsistent diagnostic performance reporting, limited biomarker anchoring, and reliance on prefiltered cohorts. Overall, the current evidence base is methodologically heterogeneous and incomplete for clinical deployment. The proposed framework characterizes requirements including anchoring strength, prevalence-adjusted performance reporting, and representative sampling establishing a foundation for advancing robust real-world evidence needed to translate SA-DCAs from research to clinical practice.

Participants who are randomized to treatment but have no post-baseline data pose a unique challenge. These participants need to be included to preserve randomization. Because there is no information about the outcome or the intercurrent event(s) that led to missing data, an estimand of interest, and the focus of this study, is a hypothetical strategy to estimate what would have been observed if participants had not discontinued. Various imputation-based and likelihood-based analyses were compared in simulated and real clinical trial data. Models that used baseline as a covariate or constrained baseline values to be equal yielded similar results and had greater power than an unconstrained analysis that fit baseline as a response. Assigning change to the first post-baseline visit as 0 and applying an analysis with baseline as a covariate controlled Type I error at the nominal level and had power equal to or greater than other methods. Treatment contrasts were not biased when the reason for missing all post-baseline data was random or treatment related. Within-group bias occurred with outcome-related missingness of all post-baseline data, but the bias was nearly equal in the two arms, leading to unbiased treatment contrasts. Bias occurred when missing all post-baseline data was related to treatment and outcome. Given the idiosyncratic nature of clinical trials, no universally best analytic approach exists for dealing with participants that have a baseline but no post-baseline data. Analysts can choose among the methods to tailor an approach to the situation at hand.

IntroductionThe spinocerebellar ataxia composite score (SCACOMS) comprises items from the functional Scale for the Assessment and Rating of Ataxia (f-SARA) and the Clinician Global Impression of Change (CGI-C). In the derivation of SCACOMS, weights reflecting 1-year responsiveness were assigned to each item using partial least squares (PLS) regression modeling. The current objective was to incorporate patient-feedback into the SCACOMS item weights, examine corresponding responsiveness of the composite scale, and discuss potential implications for future use.MethodsItem weights derived by PLS regression were compared to each item's relative importance as assigned by 16 patients with SCA during semi-structured interviews. SCACOMS item weights were adjusted using the following combinations: (1) 50/50 weighted combination of PLS and patient weights and (2) reducing the weight of CGI-C to 20% and averaging individual item weights obtained from each perspective. The 1-year mean to standard deviation ratios (MSDRs) for the resulting reweighted scales were compared, with larger MSDRs indicating greatest sensitivity to disease progression.ResultsThe PLS-derived SCACOMS had the highest MSDR (0.99). When item weights were averaged across the two sources, the resulting MSDR was 0.91. When the weight of CGI-C was set to 20%, reflecting patient preferences for higher weights on the discrete symptoms, the MSDR was 0.79.ConclusionsThis study took a novel approach to enhance the face validity of SCACOMS by incorporating patient feedback into the statistically optimized item weights. The result is the merging of objectively derived item weightings (reflecting optimal scale responsiveness) with patient-assigned relevance. While this update may increase the patient centricity of a composite measure, this comes at the expense of reduced sensitivity. This potential trade-off in sensitivity to detect change should be evaluated in the context of the composite measure's intended use.

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.

Despite advances in understanding the mechanisms, risk factors and treatment strategies for Alzheimer's disease (AD), no approved therapies exist to prevent or delay onset in at-risk individuals or those with elevated biomarkers who do not yet show symptoms. Multiple candidate interventions are now being evaluated in clinical trials in these settings, raising key questions around which populations are most appropriate and what criteria should guide regulatory and clinical decision-making. Data are expected within 1-2 years, underscoring the need for stakeholder alignment on clinically meaningful and acceptable characteristics of preventative therapies or other products. To address this need, the Global CEO Initiative on Alzheimer's Disease convened an international group of experts to develop target product profiles for therapies designed to delay or prevent the onset of clinical symptoms in AD. These target product profiles outline minimum and preferred characteristics, including intended use, target populations, safety expectations and efficacy benchmarks. This effort provides a foundational framework to accelerate therapeutic development and guide researchers, regulators and patients in the evaluation of emerging therapies for preventing symptomatic AD.

Alzheimer’s disease (AD) is a heterogeneous neurodegenerative disease driven by pathological depositions of proteins that accumulate over decades. Compelling genetic and neurobiological evidence suggests that amyloid accumulation in the brain initiates and drives early-stage AD. Measurement of fibrillar amyloid has been pivotal to the development and approval of disease-slowing treatments. Various biomarkers of AD pathophysiology provide evidence of target engagement and downstream effects on disease progression, and their use as surrogate endpoints may help identify and expeditiously bring new treatments to patients. In clinical trials, a surrogate endpoint serves as a substitute for a direct measurement of a patient’s clinical status, and its use can provide ethical, logistical, and economic advantages. Establishing biomarkers as surrogate endpoints involves evaluating scientific evidence through diverse statistical approaches to demonstrate their predictivity of clinical benefit. This article evaluated evidence supporting amyloid β plaque reduction as a surrogate endpoint in symptomatic AD by exploring regulatory considerations and guidelines for surrogate endpoints, examining the amyloid hypothesis and the current therapeutic landscape in AD, and presenting supporting evidence of surrogate endpoints from a recent clinical development program of AD.

Alzheimer’s disease (AD) presents unique challenges in clinical trials involving small molecules. Multifaceted issues plague such trials, emphasizing susceptibility to fraud from clinical sites and “professional patients”. The relative ease of simulating Alzheimer’s diagnosis, coupled with inadequate oversight by Contract Research Organizations (CROs), creates fertile ground for deceptive practices. Poor rater quality and the simplicity of falsifying data exacerbate concerns. A core difficulty is the diagnostic ambiguity of AD, where symptomatic overlap with other cognitive disorders often leads to misdiagnosis or feigned conditions by professional patients. This issue is intensified in small molecule studies, which are easier to replicate or substitute than biologics, increasing the temptation and feasibility of fraudulent activities. Lax monitoring and control by CROs contribute to these vulnerabilities, allowing unnoticed data manipulation and deceitful conduct. It is particularly alarming that sites most prone to deceitful practices include predominantly underrepresented populations, thwarting inclusion efforts. We critically analyze the inefficacy of regulatory audits in detecting and preventing fraud as clinical sites adeptly prepare regulatory documents to mask misconduct. We evaluate methods for detecting fraudulent practices based on the clinical data and clinical operations metrics. The absence of medical expertise among auditors limits their ability to discern data integrity issues and the nuances of AD symptomatology. Traditional data management review and statistical analysis methods fail to identify these types of issues. We call for a reevaluation of current methodologies in AD clinical trials. We advocate for enhanced CRO oversight, improved rater training, and incorporation of medical expertise in audits. Targeted, blinded statistical data review can identify problematic sites. Addressing these challenges can safeguard AD research integrity, accelerate development of effective therapeutics, and protect and benefit vulnerable populations.

Measures designed to comprehensively assess Parkinson’s disease (PD) irrespective of disease stage and treatment status may be unable to capture nuances in disease progression, particularly in early-stage PD. The objective of this paper is to develop PARkinson’s COMposite Scales (PARCOMS) with increased responsiveness to clinical decline using items of the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) for three discrete cohorts of patients. Patients with confirmed PD from the Parkinson’s Progression Markers Initiative (PPMI) data were assigned to three cohorts based on use of dopaminergic treatment, stage of disease, and presence of motor complication. For each cohort, items from MDS-UPDRS Part I (PARCOMS-Non-Motor) and Parts II and III (PARCOMS-Motor) were selected based on responsiveness using partial least squares (PLS) regression. The responsiveness of the scales was estimated using mean-to-standard deviation ratios (MSDRs) of their change values. Compared to the original MDS-UPDRS, MSDRs for PARCOMS-Motor increased 13.1

Amyloid-plaque reduction is currently the only recognized surrogate outcome for Alzheimer’s disease (AD) trials, allowing accelerated approval of plaque-clearing amyloid antibodies. However, plaque reduction does not facilitate the development of new non-plaque-clearing treatments. The hippocampus is among the first brain regions affected by AD pathology, exhibiting synaptic dysfunction and neurodegeneration that manifests as hippocampal atrophy and memory decline. We evaluated hippocampal volume (HV) as a potential surrogate outcome that can predict clinical benefit in disease-modification trials. Using published data from observational and interventional studies that examined both cognition and HV on volumetric magnetic resonance imaging (vMRI), we evaluated the cross-sectional correlations of HV to cognitive performance, the longitudinal correlations of HV atrophy to cognitive decline, HV sensitivity to drug effects, and the correlations between drug effects on HV atrophy and cognitive decline. We also examined the magnitude of HV protection that corresponds to meaningful clinical benefit. Analyses from 30 observational studies encompassing 13,187 individuals (2633 cognitively normal; 10,554 early AD) showed significant cross-sectional correlations between baseline HV and cognition, and longitudinal correlations between HV atrophy and cognitive decline over ≥ 1 year. The relationship of HV–cognitive drug effects was examined at the group level in nine placebo-controlled trials of five antiamyloid agents that evaluated HV in early AD trials of at least 18 months’ duration. These trials included four amyloid antibodies (aducanumab, lecanemab, donanemab, and gantenerumab) and one oral anti-oligomer agent (valiltramiprosate). Individual-level HV–cognition relationships were examined in two valiltramiprosate studies, one of which included diffusion tensor imaging (DTI) providing microstructural correlates of HV drug effects and helping distinguish neuroprotection from brain edema. Across these anti-amyloid drug trials (total N 10,000), there was a linear relationship between drug effects on slowing of cognitive decline and slowing of HV atrophy. Two anti-oligomer trials (valiltramiprosate) reported significant subject-level correlations between drug effects on HV and cognition over 18–24 months (r = −0.40 to −0.44, p < 0.005, N = 50/69), with significant correlations of drug effects on brain microstructure (decreased mean diffusivity) with both HV and cognitive benefits, supporting reduced neurodegeneration. The minimal HV preservation at the mild cognitive impairment (MCI) stage that is associated with clinical benefit is estimated to be ≥ 40 mm3 or ≥ 10

Alzheimer’s disease (AD), is heterogeneous across patients and also highly variable from one day to the next. Cognition, function and global performance are measured with patient performance, a study partner questionnaire and an interview with a clinician. The high heterogeneity, subjectivity and different collection methods contribute to low correlations between clinical endpoints, in contrast to cardiovascular disease, for instance, with objective and consistent endpoints, resulting in higher correlations. Accurate interpretation of the evidence for a treatment effect relies on knowledge of these correlations, but they are rarely considered. A novel statistical approach is proposed which parallels the informal process of reading through primary, secondary and exploratory results to assess the overall efficacy of a treatment. With each sequential endpoint, a reader assesses whether new information adds to or subtracts from the current evidence for a treatment effect, finally concluding whether a treatment works or not (See figure). Sequential Global Statistical Tests (sGSTs), can formally quantify the evidence for efficacy up to and including each outcome, accurately evaluating treatment efficacy. This sGST objectively accounts for redundancy and independence between endpoints using correlations, which are rarely considered in informal approaches. The utility of a cumulative sGST is demonstrated under simulated scenarios with high and low correlations. Although correlations are rarely considered in an informal evaluation, they dramatically impact the interpretation of multivariate results. Expecting significance on secondary and exploratory endpoints to support a significant primary endpoint is only appropriate when the correlation between outcomes is very high. With low correlation, a secondary or exploratory endpoint showing a trend, or directional consistency can still support a significant primary endpoint. This leads to incorrectly negative conclusions in AD. The high heterogeneity of AD results in low correlations between endpoints, making it more prone to misinterpretation of overall treatment effects than diseases with higher correlations. Expecting significance on all outcomes is likely at least partly responsible for the high failure rate in AD. Using an sGST addresses this concern, by statistically ensuring no double counting of results with highly correlated outcomes, and no underestimation of the evidence provided by nearly independent outcomes.

Mounting evidence indicates that the accumulation of amyloid in AD starts 20 to 30 years before clinically detectable cognitive impairment is observed, suggesting the presence of a long period of asymptomatic AD. Although the specific onset of this preclinical period is difficult to target, it is potentially significant to identify subjects in this asymptomatic preclinical stage of the disease. This is because newly developed disease modifying treatments may have increased effectiveness if begun during this asymptomatic period. Currently, the preclinical phase of AD is identified in asymptomatic individuals as abnormal amyloid levels detected via neuroimaging or fluid biomarkers. However, this approach is not feasible as a routine screening tool in a clinical environment, therefore there is a critical need to develop blood-based, cost-effective screening tools to detect AD in asymptomatic patients. Because of this, the search for preclinical blood-based biomarkers has become a major focus. 200 Participants were randomly selected from the Australian Imaging, Biomarkers, and Lifestyle study (AIBL). A total of 34 leukocyte antigens were examined by flow cytometry immunophenotyping. Leukocyte markers were used in addition to age, ApoE4 status, years of education, and sex to predict the PET Aβ status. Data were analyzed by logistic regression and receiver operating characteristic (ROC) analyses. We identified 12 specific leukocyte markers that were differentially expressed in cognitively normal (CN) patients designated as amyloid negative compared with CN individuals who are amyloid positive (Amyloid PET Centiloid >25). Combinations of these 12 markers produced AUC values as high as 0.94 in-sample, and 0.90 out-of-sample. Specifically, markers CD11c, CD59, CD91, and CD163 had high performance when predicting amyloid positivity. These markers also showed strong predictive performance at other levels of Centiloid, maintaining AUCs greater than 0.85. Results suggest that leukocyte surface biomarkers involved in Aβ transportation, innate phagocytosis and completement mediated clearance pathways are the first blood-based biomarkers that can accurately predict amyloid load and detect amyloid at 25 centiloids. These biomarkers could have a major impact on clinical practice by allowing primary care physicians to identify individuals at high risk of having amyloid burden in their brains with a simple blood test.