A novel cognitive disease progression model for clinical trials in autosomal-dominant Alzheimer's disease

Disease progression trajectory curves to estimate saved time in Alzheimer's disease trials

With the recent successful disease-modifying therapies against Alzheimer's disease (AD), there have been discussions on easily interpretable measures for treatment effects. Among them, saved time for patients treated with a new drug as compared to patients randomized to the placebo group offers easier interpretation than the reduced percentage in outcome decline at last visit which were commonly used in AD trials. The existing method to calculate saved time utilized the disease progression trajectory of the placebo group and the treatment effect at the last visit. We propose to develop two new methods that use disease progression trajectories of both groups: (1) slope adjusted method; and (2) area under the curve method. We used data from the two donanemab trials and the donepezil trial to illustrate the application of the proposed methods and conducted simulation studies to compare these methods. When a drug has a constant treatment effect over time or early and middle difference in the disease progression, the area under the curve method often has the saved time being longer than the existing method. When the treatment effect is an increasing function of time before the last visit as observed in disease-modifying therapy trials, the slope adjusted method could have a larger saved time as compared to the existing method. In many cases, the area under the curve method often has the smallest standard deviation of saved time.

Sample size estimates for biomarker-based outcome measures in clinical trials in autosomal dominant Alzheimer's disease

INTRODUCTION

Alzheimer disease (AD)-modifying therapies are approved for treatment of early-symptomatic AD. Autosomal dominant AD (ADAD) provides a unique opportunity to test therapies in presymptomatic individuals.

METHODS

Using data from the Dominantly Inherited Alzheimer Network (DIAN), sample sizes for clinical trials were estimated for various cognitive, imaging, and CSF outcomes. Sample sizes were computed for detecting a reduction of either absolute levels of AD-related pathology (amyloid, tau) or change over time in neurodegeneration (atrophy, hypometabolism, cognitive change).

RESULTS

Biomarkers measuring amyloid and tau pathology had required sample sizes below 200 participants per arm (examples CSF Aβ42/40: 47[95 %CI 25,104], cortical PIB 49[28,99], CSF p-tau181 74[48,125]) for a four-year trial in presymptomatic individuals (CDR=0) to have 80 % power (5 % statistical significance) to detect a 25 % reduction in absolute levels of pathology, allowing 40 % dropout. For cognitive, MRI, and FDG, it was more appropriate to detect a 50 % reduction in rate of change. Sample sizes ranged from 250 to 900 (examples hippocampal volume: 338[131,2096], cognitive composite: 326[157,1074]). MRI, FDG and cognitive outcomes had lower sample sizes when including indivduals with mild impairment (CDR=0.5 and 1) as well as presymptomatic individuals (CDR=0).

DISCUSSION

Despite the rarity of ADAD, presymptomatic clinical trials with feasible sample sizes given the number of cases appear possible.

Sample size estimates for biomarker-based outcome measures in clinical trials in autosomal dominant Alzheimer's disease

INTRODUCTION

Alzheimer disease (AD)-modifying therapies are approved for treatment of early-symptomatic AD. Autosomal dominant AD (ADAD) provides a unique opportunity to test therapies in presymptomatic individuals.

METHODS

Using data from the Dominantly Inherited Alzheimer Network (DIAN), sample sizes for clinical trials were estimated for various cognitive, imaging, and CSF outcomes. Sample sizes were computed for detecting a reduction of either absolute levels of AD-related pathology (amyloid, tau) or change over time in neurodegeneration (atrophy, hypometabolism, cognitive change).

RESULTS

Biomarkers measuring amyloid and tau pathology had required sample sizes below 200 participants per arm (examples CSF Aβ42/40: 47[95%CI 25,104], cortical PIB 49[28,99], CSF p-tau181 74[48,125]) for a four-year trial in presymptomatic individuals (CDR=0) to have 80% power (5% statistical significance) to detect a 25% reduction in absolute levels of pathology, allowing 40% dropout. For cognitive, MRI, and FDG, it was more appropriate to detect a 50% reduction in rate of change. Sample sizes ranged from 250-900 (examples hippocampal volume: 338[131,2096], cognitive composite: 326[157,1074]). MRI, FDG and cognitive outcomes had lower sample sizes when including indivduals with mild impairment (CDR=0.5 and 1) as well as presymptomatic individuals (CDR=0).

DISCUSSION

Despite the rarity of ADAD, presymptomatic clinical trials with feasible sample sizes given the number of cases appear possible.

Harnessing Greater Statistical Power: Comprehensive Evaluation of Disease Modifying Treatment Effects Across All or Multiple Post-Baseline Visits Compared to the Last Visit for Alzheimer's Disease Clinical Trials

Background

In Alzheimer's disease (AD) clinical trials, efficacy inference is traditionally based on the last visit (e.g., 18 months). However, recent studies suggest that disease-modifying treatment effects may emerge as early as 3 months post-baseline.

Objective

To explore this further, our study aimed to assess the increased statistical power achieved by incorporating all or multiple post-baseline visits to estimate treatment effect, compared to relying solely on the last visit.

Methods

We developed explicit formulas for the base functions of the natural cubic spline model, ensuring compatibility with standard SAS procedures. Through simulations using disease progression trajectories from ClarityAD and TRAILBLAZER-ALZ2 trials, we comprehensively evaluated various models in terms of power and type I error. Additionally, we offer SAS codes that to facilitate seamless implementation of different modeling approaches.

Results

Simulations based on ClarityAD and TRAILBLAZER-ALZ2 disease trajectories demonstrated that models incorporating multiple or all post-baseline visits yield greater power than those using only the last visit, while maintaining Type I error control. Furthermore, when three post-baseline visits were included, adding more visits resulted in minimal power gains.

Conclusions

Our findings support prioritizing statistical models that incorporate multiple or all post-baseline visits for treatment efficacy inference, as they offer greater efficiency than models relying solely on the last visit.

Clinical Progression on CDR-SB©: Progression Free Time at Each 0.5-unit Level in Dominantly Inherited and Sporadic Alzheimer's Disease Populations

INTRODUCTION

CDR-SB is a reliable and clinically meaningful composite for assessing treatment effects in Alzheimer's disease (AD) clinical trials. Small CDR-SB differences at the end of a trial often lead to controversy in deriving clinically meaningful interpretations.

METHODS

We estimated progression-free time participants remained at each 0.5-unit CDR-SB increment in dominantly inherited AD (DIAD) and sporadic AD populations, evaluating its potential as an alternative measure of treatment effects.

RESULTS

Progression-free time is longer at CDR-SB ≤ 2.0 (1-2 years) and shorter at CDR-SB ≥ 5 (0.33 or less) in the ADNI cohort. The DIAD cohort showed similar but shorter times. Using progression-free time, continuous lecanemab treatment for three years is estimated to delay disease progression by 0.7 years in the sporadic population.

DISCUSSION

Progression-free time provides a benchmark for expressing clinical progression and treatment effects and can be applied particularly during open-label extensions and single-arm trials without placebo comparisons.

The Potential of Disease Progression Modeling to Advance Clinical Development and Decision Making

While some model-informed drug development frameworks are well recognized as enabling clinical trials, the value of disease progression modeling (DPM) in impacting medical product development has yet to be fully realized. The Clinical Trials Transformation Initiative assembled a diverse project team from across the patient, academic, regulatory, and industry sectors of practice to advance the use of DPM for decision making in clinical trials and medical product development. This team conducted a scoping review to explore current applications of DPM and convened a multi-stakeholder expert meeting to discuss its value in medical product development. In this article, we present the scoping review and expert meeting output and propose key questions that medical product developers and regulators may use to inform clinical development strategy, appreciate the therapeutic context and endpoint selection, and optimize trial design with disease progression models. By expanding awareness of the unique value of DPM, this article does not aim to be technical in nature but rather aims to highlight the potential of DPM to improve the quality and efficiency of medical product development.

Unlocking Cognitive Analysis Potential in Alzheimer's Disease Clinical Trials: Investigating Hierarchical Linear Models for Analyzing Novel Measurement Burst Design Data

Measurement burst designs typically administer brief cognitive tests four times per day for 1 week, resulting in a maximum of 28 data points per week per test for every 6 months. In Alzheimer's disease clinical trials, utilizing measurement burst designs holds great promise for boosting statistical power by collecting huge amount of data. However, appropriate methods for analyzing these complex datasets are not well investigated. Furthermore, the large amount of burst design data also poses tremendous challenges for traditional computational procedures such as SAS mixed or Nlmixed. We propose to analyze burst design data using novel hierarchical linear mixed effects models or hierarchical mixed models for repeated measures. Through simulations and real-world data applications using the novel SAS procedure Hpmixed, we demonstrate these hierarchical models' efficiency over traditional models. Our sample simulation and analysis code can serve as a catalyst to facilitate the methodology development for burst design data.

Increasing hub disruption parallels dementia severity in autosomal dominant Alzheimer's disease

Hub regions in the brain, recognized for their roles in ensuring efficient information transfer, are vulnerable to pathological alterations in neurodegenerative conditions, including Alzheimer's disease (AD). Computational simulations and animal experiments have hinted at the theory of activity-dependent degeneration as the cause of this hub vulnerability. However, two critical issues remain unresolved. First, past research has not clearly distinguished between two scenarios: hub regions facing a higher risk of connectivity disruption (targeted attack) and all regions having an equal risk (random attack). Second, human studies offering support for activity-dependent explanations remain scarce. We refined the hub disruption index to demonstrate a hub disruption pattern in functional connectivity in autosomal dominant AD that aligned with targeted attacks. This hub disruption is detectable even in preclinical stages, 12 years before the expected symptom onset and is amplified alongside symptomatic progression. Moreover, hub disruption was primarily tied to regional differences in global connectivity and sequentially followed changes observed in amyloid-beta positron emission tomography cortical markers, consistent with the activity-dependent degeneration explanation. Taken together, our findings deepen the understanding of brain network organization in neurodegenerative diseases and could be instrumental in refining diagnostic and targeted therapeutic strategies for AD in the future.

New Horizons of Model Informed Drug Development in Rare Diseases Drug Development

Model-informed approaches provide a quantitative framework to integrate all available nonclinical and clinical data, thus furnishing a totality of evidence approach to drug development and regulatory evaluation. Maximizing the use of all available data and information about the drug enables a more robust characterization of the risk-benefit profile and reduces uncertainty in both technical and regulatory success. This offers the potential to transform rare diseases drug development, where conducting large well-controlled clinical trials is impractical and/or unethical due to a small patient population, a significant portion of which could be children. Additionally, the totality of evidence generated by model-informed approaches can provide confirmatory evidence for regulatory approval without the need for additional clinical data. In the article, applications of novel quantitative approaches such as quantitative systems pharmacology, disease progression modeling, artificial intelligence, machine learning, modeling of real-world data using model-based meta-analysis and strategies such as external control and patient-reported outcomes as well as clinical trial simulations to optimize trials and sample collection are discussed. Specific case studies of these modeling approaches in rare diseases are provided to showcase applications in drug development and regulatory review. Finally, perspectives are shared on the future state of these modeling approaches in rare diseases drug development along with challenges and opportunities for incorporating such tools in the rational development of drug products.

A framework for translating tauopathy therapeutics: Drug discovery to clinical trials

The tauopathies are defined by pathological tau protein aggregates within a spectrum of clinically heterogeneous neurodegenerative diseases. The primary tauopathies meet the definition of rare diseases in the United States. There is no approved treatment for primary tauopathies. In this context, designing the most efficient development programs to translate promising targets and treatments from preclinical studies to early-phase clinical trials is vital. In September 2022, the Rainwater Charitable Foundation convened an international expert workshop focused on the translation of tauopathy therapeutics through early-phase trials. Our report on the workshop recommends a framework for principled drug development and a companion lexicon to facilitate communication focusing on reproducibility and achieving common elements. Topics include the selection of targets, drugs, biomarkers, participants, and study designs. The maturation of pharmacodynamic biomarkers to demonstrate target engagement and surrogate disease biomarkers is a crucial unmet need. HIGHLIGHTS: Experts provided a framework to translate therapeutics (discovery to clinical trials). Experts focused on the "5 Rights" (target, drug, biomarker, participants, trial). Current research on frontotemporal degeneration, progressive supranuclear palsy, and corticobasal syndrome therapeutics includes 32 trials (37% on biologics) Tau therapeutics are being tested in Alzheimer's disease; primary tauopathies have a large unmet need.

Clinical Trials for Wolfram Syndrome Neurodegeneration: Novel Design, Endpoints, and Analysis Models

Objective

Wolfram syndrome, an ultra-rare condition, currently lacks effective treatment options. The rarity of this disease presents significant challenges in conducting clinical trials, particularly in achieving sufficient statistical power (e.g., 80%). The objective of this study is to propose a novel clinical trial design based on real-world data to reduce the sample size required for conducting clinical trials for Wolfram syndrome.

Methods

We propose a novel clinical trial design with three key features aimed at reducing sample size and improve efficiency: (i) Pooling historical/external controls from a longitudinal observational study conducted by the Washington University Wolfram Research Clinic. (ii) Utilizing run-in data to estimate model parameters. (iii) Simultaneously tracking treatment effects in two endpoints using a multivariate proportional linear mixed effects model.

Results

Comprehensive simulations were conducted based on real-world data obtained through the Wolfram syndrome longitudinal observational study. Our simulations demonstrate that this proposed design can substantially reduce sample size requirements. Specifically, with a bivariate endpoint and the inclusion of run-in data, a sample size of approximately 30 per group can achieve over 80% power, assuming the placebo progression rate remains consistent during both the run-in and randomized periods. In cases where the placebo progression rate varies, the sample size increases to approximately 50 per group.

Conclusions

For rare diseases like Wolfram syndrome, leveraging existing resources such as historical/external controls and run-in data, along with evaluating comprehensive treatment effects using bivariate/multivariate endpoints, can significantly expedite the development of new drugs.

15 Years of Longitudinal Genetic, Clinical, Cognitive, Imaging, and Biochemical Measures in DIAN

This manuscript describes and summarizes the Dominantly Inherited Alzheimer Network Observational Study (DIAN Obs), highlighting the wealth of longitudinal data, samples, and results from this human cohort study of brain aging and a rare monogenic form of Alzheimer's disease (AD). DIAN Obs is an international collaborative longitudinal study initiated in 2008 with support from the National Institute on Aging (NIA), designed to obtain comprehensive and uniform data on brain biology and function in individuals at risk for autosomal dominant AD (ADAD). ADAD gene mutations in the amyloid protein precursor (APP), presenilin 1 (PSEN1), or presenilin 2 (PSEN2) genes are deterministic causes of ADAD, with virtually full penetrance, and a predictable age at symptomatic onset. Data and specimens collected are derived from full clinical assessments, including neurologic and physical examinations, extensive cognitive batteries, structural and functional neuro-imaging, amyloid and tau pathological measures using positron emission tomography (PET), flurordeoxyglucose (FDG) PET, cerebrospinal fluid and blood collection (plasma, serum, and whole blood), extensive genetic and multi-omic analyses, and brain donation upon death. This comprehensive evaluation of the human nervous system is performed longitudinally in both mutation carriers and family non-carriers, providing one of the deepest and broadest evaluations of the human brain across decades and through AD progression. These extensive data sets and samples are available for researchers to address scientific questions on the human brain, aging, and AD.

Statistical considerations when estimating time-saving treatment effects in Alzheimer's disease clinical trials

INTRODUCTION

Estimating treatment effects as time savings in disease progression may be more easily interpretable than assessing the absolute difference or a percentage reduction. In this study, we investigate the statistical considerations of the existing method for estimating time savings and propose alternative complementary methods.

METHODS

We propose five alternative methods to estimate the time savings from different perspectives. These methods are applied to simulated clinical trial data that mimic or modify the Clinical Dementia Rating Sum of Boxes progression trajectories observed in the Clarity AD lecanemab trial.

RESULTS

Our study demonstrates that the proposed methods can generate more precise estimates by considering two crucial factors: (1) the absolute difference between treatment arms, and (2) the observed progression rate in the treatment arm.

DISCUSSION

Quantifying treatment effects as time savings in disease progression offers distinct advantages. To provide comprehensive estimations, it is important to use various methods.

HIGHLIGHTS

We explore the statistical considerations of the current method for estimating time savings. We proposed alternative methods that provide time savings estimations based on the observed absolute differences. By using various methods, a more comprehensive estimation of time savings can be achieved.

Novel non-linear models for clinical trial analysis with longitudinal data: A tutorial using SAS for both frequentist and Bayesian methods

Longitudinal data from clinical trials are commonly analyzed using mixed models for repeated measures (MMRM) when the time variable is categorical or linear mixed-effects models (ie, random effects model) when the time variable is continuous. In these models, statistical inference is typically based on the absolute difference in the adjusted mean change (for categorical time) or the rate of change (for continuous time). Previously, we proposed a novel approach: modeling the percentage reduction in disease progression associated with the treatment relative to the placebo decline using proportional models. This concept of proportionality provides an innovative and flexible method for simultaneously modeling different cohorts, multivariate endpoints, and jointly modeling continuous and survival endpoints. Through simulated data, we demonstrate the implementation of these models using SAS procedures in both frequentist and Bayesian approaches. Additionally, we introduce a novel method for implementing MMRM models (ie, analysis of response profile) using the nlmixed procedure.

Presenilin-1 mutation position influences amyloidosis, small vessel disease, and dementia with disease stage

INTRODUCTION

Amyloidosis, including cerebral amyloid angiopathy, and markers of small vessel disease (SVD) vary across dominantly inherited Alzheimer's disease (DIAD) presenilin-1 (PSEN1) mutation carriers. We investigated how mutation position relative to codon 200 (pre-/postcodon 200) influences these pathologic features and dementia at different stages.

METHODS

Individuals from families with known PSEN1 mutations (n = 393) underwent neuroimaging and clinical assessments. We cross-sectionally evaluated regional Pittsburgh compound B-positron emission tomography uptake, magnetic resonance imaging markers of SVD (diffusion tensor imaging-based white matter injury, white matter hyperintensity volumes, and microhemorrhages), and cognition.

RESULTS

Postcodon 200 carriers had lower amyloid burden in all regions but worse markers of SVD and worse Clinical Dementia Rating® scores compared to precodon 200 carriers as a function of estimated years to symptom onset. Markers of SVD partially mediated the mutation position effects on clinical measures.

DISCUSSION

We demonstrated the genotypic variability behind spatiotemporal amyloidosis, SVD, and clinical presentation in DIAD, which may inform patient prognosis and clinical trials.

HIGHLIGHTS

Mutation position influences Aβ burden, SVD, and dementia. PSEN1 pre-200 group had stronger associations between Aβ burden and disease stage. PSEN1 post-200 group had stronger associations between SVD markers and disease stage. PSEN1 post-200 group had worse dementia score than pre-200 in late disease stage. Diffusion tensor imaging-based SVD markers mediated mutation position effects on dementia in the late stage.

Assessment of Efficacy and Safety of Zagotenemab: Results From PERISCOPE-ALZ, a Phase 2 Study in Early Symptomatic Alzheimer Disease

BACKGROUND AND OBJECTIVES

Zagotenemab (LY3303560), a monoclonal antibody that preferentially targets misfolded, extracellular, aggregated tau, was assessed in the PERISCOPE-ALZ phase 2 study to determine its ability to slow cognitive and functional decline relative to placebo in early symptomatic Alzheimer disease (AD).

METHODS

Participants were enrolled across 56 sites in North America and Japan. Key eligibility criteria included age of 60-85 years, Mini-Mental State Examination score of 20-28, and intermediate levels of brain tau on PET imaging. In this double-blind study, participants were equally randomized to 1,400 mg or 5,600 mg of zagotenemab, or placebo (IV infusion every 4 weeks for 100 weeks). The primary outcome was change on the Integrated AD Rating Scale (iADRS) assessed by a Bayesian Disease Progression model. Secondary measures include mixed model repeated measures analysis of additional cognitive and functional endpoints as well as biomarkers of AD pathology.

RESULTS

A total of 360 participants (mean age = 75.4 years; female = 52.8%) were randomized, and 218 completed the treatment period. Demographics and baseline characteristics were reasonably balanced among arms. The mean disease progression ratio (proportional decline in the treated vs placebo group) with 95% credible intervals for the iADRS was 1.10 (0.959-1.265) for the zagotenemab low-dose group and 1.05 (0.907-1.209) for the high-dose, where a ratio less than 1 favors the treatment group. Secondary clinical endpoint measures failed to show a drug-placebo difference in favor of zagotenemab. No treatment effect was demonstrated by flortaucipir PET, volumetric MRI, or neurofilament light chain (NfL) analyses. A dose-related increase in plasma phosphorylated tau181 and total tau was demonstrated. Zagotenemab treatment groups reported a higher incidence of adverse events (AEs) (85.1%) compared with the placebo group (74.6%). This difference was not attributable to any specific AE or category of AEs.

DISCUSSION

In participants with early symptomatic AD, zagotenemab failed to achieve significant slowing of clinical disease progression compared with placebo. Imaging biomarker and plasma NfL findings did not show evidence of pharmacodynamic activity or disease modification.

TRIAL REGISTRATION INFORMATION

ClinicalTrials.gov: NCT03518073.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that for patients with early symptomatic AD, zagotenemab does not slow clinical disease progression.

Increasing hub disruption parallels dementia severity in autosomal dominant Alzheimer disease

Hub regions in the brain, recognized for their roles in ensuring efficient information transfer, are vulnerable to pathological alterations in neurodegenerative conditions, including Alzheimer Disease (AD). Given their essential role in neural communication, disruptions to these hubs have profound implications for overall brain network integrity and functionality. Hub disruption, or targeted impairment of functional connectivity at the hubs, is recognized in AD patients. Computational models paired with evidence from animal experiments hint at a mechanistic explanation, suggesting that these hubs may be preferentially targeted in neurodegeneration, due to their high neuronal activity levels-a phenomenon termed "activity-dependent degeneration". Yet, two critical issues were unresolved. First, past research hasn't definitively shown whether hub regions face a higher likelihood of impairment (targeted attack) compared to other regions or if impairment likelihood is uniformly distributed (random attack). Second, human studies offering support for activity-dependent explanations remain scarce. We applied a refined hub disruption index to determine the presence of targeted attacks in AD. Furthermore, we explored potential evidence for activity-dependent degeneration by evaluating if hub vulnerability is better explained by global connectivity or connectivity variations across functional systems, as well as comparing its timing relative to amyloid beta deposition in the brain. Our unique cohort of participants with autosomal dominant Alzheimer Disease (ADAD) allowed us to probe into the preclinical stages of AD to determine the hub disruption timeline in relation to expected symptom emergence. Our findings reveal a hub disruption pattern in ADAD aligned with targeted attacks, detectable even in pre-clinical stages. Notably, the disruption's severity amplified alongside symptomatic progression. Moreover, since excessive local neuronal activity has been shown to increase amyloid deposition and high connectivity regions show high level of neuronal activity, our observation that hub disruption was primarily tied to regional differences in global connectivity and sequentially followed changes observed in Aβ PET cortical markers is consistent with the activity-dependent degeneration model. Intriguingly, these disruptions were discernible 8 years before the expected age of symptom onset. Taken together, our findings not only align with the targeted attack on hubs model but also suggest that activity-dependent degeneration might be the cause of hub vulnerability. This deepened understanding could be instrumental in refining diagnostic techniques and developing targeted therapeutic strategies for AD in the future.

Targeting amyloid β in Alzheimer's disease: Meta-analysis of low-dose solanezumab in Alzheimer's disease with mild dementia studies

INTRODUCTION

Solanezumab is a monoclonal antibody that binds to the mid-domain of soluble amyloid β peptide. This meta-analysis evaluated the effect of low-dose solanezumab on clinical progression in three phase 3 studies.

METHODS

The population comprised patients aged ≥55 years with Alzheimer's disease (AD) with mild dementia, randomized to 400 mg solanezumab or placebo every 4 weeks for 80 weeks. Frequentist mixed-model repeated-measures (MMRM) and Bayesian disease progression model (DPM) longitudinal analyses were conducted.

RESULTS

Pooled MMRM analyses showed a statistically significant effect of solanezumab across cognitive and functional outcome measures. DPM results were generally consistent with MMRM results, ranging from 15% to 30% slowing of clinical progression.

DISCUSSION

These analyses suggest low-dose solanezumab slows clinical progression of AD with mild dementia. The ongoing A4 solanezumab study in participants with preclinical AD will ascertain the effect of a higher dose of solanezumab in an earlier disease stage.

HIGHLIGHTS

Individual EXPEDITION studies were negative but suggest low-dose solanezumab had an effect in slowing the clinical progression of Alzheimer's disease (AD) with mild dementia. At 80 weeks, mixed-model repeated-measures analyses showed numeric reductions in measures of clinical decline in solanezumab-treated arms compared with placebo across almost every outcome measure, and statistical significance in multiple outcome measures in each study. Pooled analyses suggest a high probability that low-dose solanezumab has at least some effect on slowing the clinical progression of AD with mild dementia. Across cognitive and functional outcome measures, estimates from disease progression model analyses range from 15% to 30% slowing of decline with low-dose solanezumab in AD with mild dementia.

Disease progression model anchored around clinical diagnosis in longitudinal cohorts: example of Alzheimer's disease and related dementia

BACKGROUND

Alzheimer's disease and related dementia (ADRD) are characterized by multiple and progressive anatomo-clinical changes including accumulation of abnormal proteins in the brain, brain atrophy and severe cognitive impairment. Understanding the sequence and timing of these changes is of primary importance to gain insight into the disease natural history and ultimately allow earlier diagnosis. Yet, modeling changes over disease course from cohort data is challenging as the usual timescales (time since inclusion, chronological age) are inappropriate and time-to-clinical diagnosis is available on small subsamples of participants with short follow-up durations prior to diagnosis. One solution to circumvent this challenge is to define the disease time as a latent variable.

METHODS

We developed a multivariate mixed model approach that realigns individual trajectories into the latent disease time to describe disease progression. In contrast with the existing literature, our methodology exploits the clinical diagnosis information as a partially observed and approximate reference to guide the estimation of the latent disease time. The model estimation was carried out in the Bayesian Framework using Stan. We applied the methodology to the MEMENTO study, a French multicentric clinic-based cohort of 2186 participants with 5-year intensive follow-up. Repeated measures of 12 ADRD markers stemmed from cerebrospinal fluid (CSF), brain imaging and cognitive tests were analyzed.

RESULTS

The estimated latent disease time spanned over twenty years before the clinical diagnosis. Considering the profile of a woman aged 70 with a high level of education and APOE4 carrier (the main genetic risk factor for ADRD), CSF markers of tau proteins accumulation preceded markers of brain atrophy by 5 years and cognitive decline by 10 years. However we observed that individual characteristics could substantially modify the sequence and timing of these changes, in particular for CSF level of A[Formula: see text].

CONCLUSION

By leveraging the available clinical diagnosis timing information, our disease progression model does not only realign trajectories into the most homogeneous way. It accounts for the inherent residual inter-individual variability in dementia progression to describe the long-term anatomo-clinical degradations according to the years preceding clinical diagnosis, and to provide clinically meaningful information on the sequence of events.

TRIAL REGISTRATION

clinicaltrials.gov, NCT01926249. Registered on 16 August 2013.

The longitudinal progression of MRI changes in pre-ataxic carriers of SCA3/MJD

BACKGROUND

The natural history of magnetic resonance imaging (MRI) in pre-ataxic stages of spinocerebellar ataxia type 3/Machado-Joseph disease (SCA3/MJD) is not well known. We report cross-sectional and longitudinal data obtained at this stage.

METHODS

Baseline (follow-up) observations included 32 (17) pre-ataxic carriers (SARA < 3) and 20 (12) related controls. The mutation length was used to estimate the time to onset (TimeTo) of gait ataxia. Clinical scales and MRIs were performed at baseline and after a median (IQR) of 30 (7) months. Cerebellar volumetries (ACAPULCO), deep gray-matter (T1-Multiatlas), cortical thickness (FreeSurfer), cervical spinal cord area (SCT) and white matter (DTI-Multiatlas) were assessed. Baseline differences between groups were described; variables that presented a p < 0.1 after Bonferroni correction were assessed longitudinally, using TimeTo and study time. For TimeTo strategy, corrections for age, sex and intracranial volume were done with Z-score progression. A significance level of 5% was adopted.

RESULTS

SCT at C1 level distinguished pre-ataxic carriers from controls. DTI measures of the right inferior cerebellar peduncle (ICP), bilateral middle cerebellar peduncles (MCP) and bilateral medial lemniscus (ML), also distinguished pre-ataxic carriers from controls, and progressed over TimeTo, with effect sizes varying from 0.11 to 0.20, larger than those of the clinical scales. No MRI variable showed progression over study time.

DISCUSSION

DTI parameters of the right ICP, left MCP and right ML were the best biomarkers for the pre-ataxic stage of SCA3/MJD. TimeTo is an interesting timescale, since it captured the longitudinal worsening of these structures.

Design and Statistical Innovations in a Platform Trial for Amyotrophic Lateral Sclerosis

Platform trials allow efficient evaluation of multiple interventions for a specific disease. The HEALEY ALS Platform Trial is testing multiple investigational products in parallel and sequentially in persons with amyotrophic lateral sclerosis (ALS) with the goal of rapidly identifying novel treatments to slow disease progression. Platform trials have considerable operational and statistical efficiencies compared with typical randomized controlled trials due to their use of shared infrastructure and shared control data. We describe the statistical approaches required to achieve the objectives of a platform trial in the context of ALS. This includes following regulatory guidance for the disease area of interest and accounting for potential differences in outcomes of participants within the shared control (potentially due to differences in time of randomization, mode of administration, and eligibility criteria). Within the HEALEY ALS Platform Trial, the complex statistical objectives are met using a Bayesian shared parameter analysis of function and survival. This analysis serves to provide a common integrated estimate of treatment benefit, overall slowing in disease progression, as measured by function and survival while accounting for potential differences in the shared control group using Bayesian hierarchical modeling. Clinical trial simulation is used to provide a better understanding of this novel analysis method and complex design. ANN NEUROL 2023;94:547-560.

Brain network decoupling with increased serum neurofilament and reduced cognitive function in Alzheimer's disease

Neurofilament light chain, a putative measure of neuronal damage, is measurable in blood and CSF and is predictive of cognitive function in individuals with Alzheimer's disease. There has been limited prior work linking neurofilament light and functional connectivity, and no prior work has investigated neurofilament light associations with functional connectivity in autosomal dominant Alzheimer's disease. Here, we assessed relationships between blood neurofilament light, cognition, and functional connectivity in a cross-sectional sample of 106 autosomal dominant Alzheimer's disease mutation carriers and 76 non-carriers. We employed an innovative network-level enrichment analysis approach to assess connectome-wide associations with neurofilament light. Neurofilament light was positively correlated with deterioration of functional connectivity within the default mode network and negatively correlated with connectivity between default mode network and executive control networks, including the cingulo-opercular, salience, and dorsal attention networks. Further, reduced connectivity within the default mode network and between the default mode network and executive control networks was associated with reduced cognitive function. Hierarchical regression analysis revealed that neurofilament levels and functional connectivity within the default mode network and between the default mode network and the dorsal attention network explained significant variance in cognitive composite scores when controlling for age, sex, and education. A mediation analysis demonstrated that functional connectivity within the default mode network and between the default mode network and dorsal attention network partially mediated the relationship between blood neurofilament light levels and cognitive function. Our novel results indicate that blood estimates of neurofilament levels correspond to direct measurements of brain dysfunction, shedding new light on the underlying biological processes of Alzheimer's disease. Further, we demonstrate how variation within key brain systems can partially mediate the negative effects of heightened total serum neurofilament levels, suggesting potential regions for targeted interventions. Finally, our results lend further evidence that low-cost and minimally invasive blood measurements of neurofilament may be a useful marker of brain functional connectivity and cognitive decline in Alzheimer's disease.

Natural cubic splines for the analysis of Alzheimer's clinical trials

Mixed model repeated measures (MMRM) is the most common analysis approach used in clinical trials for Alzheimer's disease and other progressive diseases measured with continuous outcomes over time. The model treats time as a categorical variable, which allows an unconstrained estimate of the mean for each study visit in each randomized group. Categorizing time in this way can be problematic when assessments occur off-schedule, as including off-schedule visits can induce bias, and excluding them ignores valuable information and violates the intention to treat principle. This problem has been exacerbated by clinical trial visits which have been delayed due to the COVID19 pandemic. As an alternative to MMRM, we propose a constrained longitudinal data analysis with natural cubic splines that treats time as continuous and uses test version effects to model the mean over time. Compared to categorical-time models like MMRM and models that assume a proportional treatment effect, the spline model is shown to be more parsimonious and precise in real clinical trial datasets, and has better power and Type I error in a variety of simulation scenarios.

Neuroinflammation and amyloid deposition in the progression of mixed Alzheimer and vascular dementia

BACKGROUND

Alzheimer's disease (AD) and vascular contributions to cognitive impairment and dementia (VCID) pathologies coexist in patients with cognitive impairment. Abnormal amyloid beta (Aβ) deposition is the hallmark pathologic biomarker for AD. Neuroinflammation may be a pathophysiological mechanism in both AD and VCID. In this study, we aimed to understand the role of neuroinflammation and Aβ deposition in white matter hyperintensities (WMH) progression and cognitive decline over a decade in patients with mixed AD and VCID pathologies.

METHODS

Twenty-four elderly participants (median [interquartile range] age 78 [64.8, 83] years old, 14 female) were recruited from the Knight Alzheimer Disease Research Center. 11C-PK11195 standard uptake value ratio (SUVR) and 11C-PiB mean cortical binding potential (MCBP) were used to evaluate neuroinflammation and Aβ deposition in-vivo, respectively. Fluid-attenuated inversion recovery MR images were acquired to obtain baseline WMH volume and its progression over 11.5 years. Composite cognitive scores (global, processing speed and memory) were computed at baseline and follow-up over 7.5 years. Multiple linear regression models evaluated the association between PET biomarkers (11C-PK11195 SUVR and 11C-PiB MCBP) and baseline WMH volume and cognitive function. Moreover, linear mixed-effects models evaluated whether PET biomarkers predicted greater WMH progression or cognitive decline over a decade.

RESULTS

Fifteen participants (62.5%) had mixed AD (positive PiB) and VCID (at least one vascular risk factor) pathologies. Elevated 11C-PK11195 SUVR, but not 11C-PiB MCBP, was associated with greater baseline WMH volume and predicted greater WMH progression. Elevated 11C-PiB MCBP was associated with baseline memory and global cognition. Elevated 11C-PK11195 SUVR and elevated 11C-PiB MCBP independently predicted greater global cognition and processing speed declines. No association was found between 11C-PK11195 SUVR and 11C-PiB MCBP.

CONCLUSIONS

Neuroinflammation and Aβ deposition may represent two distinct pathophysiological pathways, and both independently contributed to the progression of cognitive impairment in mixed AD and VCID pathologies. Neuroinflammation, but not Aβ deposition, contributed to WMH volume and progression.

A multidimensional ODE-based model of Alzheimer's disease progression

Data-driven Alzheimer's disease (AD) progression models are useful for clinical prediction, disease mechanism understanding, and clinical trial design. Most dynamic models were inspired by the amyloid cascade hypothesis and described AD progression as a linear chain of pathological events. However, the heterogeneity observed in healthy and sporadic AD populations challenged the amyloid hypothesis, and there is a need for more flexible dynamical models that accompany this conceptual shift. We present a statistical model of the temporal evolution of biomarkers and cognitive tests that allows diverse biomarker paths throughout the disease. The model consists of two elements: a multivariate dynamic model of the joint evolution of biomarkers and cognitive tests; and a clinical prediction model. The dynamic model uses a system of ordinary differential equations to jointly model the rate of change of an individual's biomarkers and cognitive tests. The clinical prediction model is an ordinal logistic model of the diagnostic label. Prognosis and time-to-onset predictions are obtained by computing the clinical label probabilities throughout the forecasted biomarker trajectories. The proposed dynamical model is interpretable, free of one-dimensional progression hypotheses or disease staging paradigms, and can account for the heterogeneous dynamics observed in sporadic AD. We developed the model using longitudinal data from the Alzheimer's Disease Neuroimaging Initiative. We illustrate the patterns of biomarker rates of change and the model performance to predict the time to conversion from MCI to dementia.

Progression of Clinical and Eye Movement Markers in Preataxic Carriers of Machado-Joseph Disease

BACKGROUND

Little is known about preclinical stages of Machado-Joseph disease, a polyglutamine disorder characterized by progressive adult-onset ataxia.

OBJECTIVE

We aimed to describe the longitudinal progression of clinical and oculomotor variables in the preataxic phase of disease.

METHODS

Carriers and noncarriers were assessed at three visits. Preataxic carriers (Scale for Assessment and Rating of Ataxia score < 3) expected to start ataxia in ≤4 years were considered near onset (PAN). Progressions of ataxic and preataxic carriers, considering status at the end of the study, were described according to the start (or its prediction) of gait ataxia (TimeToAfterOnset) and according to the study time.

RESULTS

A total of 35 ataxics, 38 preataxics, and 22 noncarriers were included. The "TimeToAfterOnset" timeline showed that Neurological Examination Scale for Spinocerebellar Ataxias (NESSCA; effect size, 0.09), Inventory of Non-Ataxia Symptoms (INAS0.07), and the vestibulo-ocular reflex gain (0.12) progressed in preataxic carriers, and that most slopes accelerate in PAN, turning similar to those of ataxics. In the study time, NESSCA (1.36) and vertical pursuit gain (1.17) significantly worsened in PAN, and 6 of 11 PANs converted to ataxia. For a clinical trial with 80% power and 2-year duration, 57 PANs are needed in each study arm to detect a 50% reduction in the conversion rate.

CONCLUSIONS

NESSCA, INAS, vestibulo-ocular reflex, and vertical pursuit gains significantly worsened in the preataxic phase. The "TimeToAfterOnset" timeline unveiled that slopes of most variables are small in preataxics but increase and reach the ataxic slopes from 4 years before the onset of ataxia. For future trials in preataxic carriers, we recommend recruiting PANs and using the conversion rate as the primary outcome. © 2022 International Parkinson and Movement Disorder Society.

Consistency of efficacy results across various clinical measures and statistical methods in the lecanemab phase 2 trial of early Alzheimer's disease

BACKGROUND

Lecanemab (BAN2401) is a humanized IgG1 monoclonal antibody that preferentially targets soluble aggregated Aβ species (protofibrils) with activity at insoluble fibrils and slowed clinical decline in an 18-month phase 2 proof-of-concept study (Study 201; ClinicalTrials.gov NCT01767311) in 856 subjects with early Alzheimer's disease (AD). In this trial, subjects were randomized to five lecanemab dose regimens or placebo. The primary efficacy endpoint was change from baseline in the Alzheimer's Disease Composite Score (ADCOMS) at 12 months with Bayesian analyses. The key secondary endpoints were ADCOMS at 18 months and Clinical Dementia Rating-Sum-of-Boxes (CDR-SB) and Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog14) at 18 months. The results have been published previously. Herein, we describe the results of sensitivity analyses evaluating the consistency of the lecanemab efficacy results in Study 201 at the identified dose, the ED90, across multiple statistical methods and multiple endpoints over the duration of the study.

METHODS

The protocol-specified analysis model was a mixed model for repeated measures (MMRM). Sensitivity analyses address the consistency of the conclusions using multiple statistical methods. These include a disease progression model (DPM), a natural cubic spline (NCS) model, a quadratic mixed model (QMM), and 2 MMRMs with additional covariates.

RESULTS

The sensitivity analyses showed positive lecanemab treatment effects for all endpoints and all statistical models considered. The protocol-specified ADCOMS analysis showed a 29.7% slower decline than placebo for ADCOMS at 18 months. The various other analyses of 3 key endpoints showed declines ranging from 26.5 to 55.9%. The results at 12 months are also consistent with those at 18 months.

CONCLUSIONS

The conclusion of the primary analysis of the lecanemab Study 201 is strengthened by the consistently positive conclusions across multiple statistical models, across efficacy endpoints, and over time, despite missing data. The 18-month data from this trial was utilized in the design of the confirmatory phase 3 trial (Clarity AD) and allowed for proper powering for multiple, robust outcomes.

Amyloid-Related Imaging Abnormalities in the DIAN-TU-001 Trial of Gantenerumab and Solanezumab: Lessons from a Trial in Dominantly Inherited Alzheimer Disease

OBJECTIVE

To determine the characteristics of participants with amyloid-related imaging abnormalities (ARIA) in a trial of gantenerumab or solanezumab in dominantly inherited Alzheimer disease (DIAD).

METHODS

142 DIAD mutation carriers received either gantenerumab SC (n = 52), solanezumab IV (n = 50), or placebo (n = 40). Participants underwent assessments with the Clinical Dementia Rating® (CDR®), neuropsychological testing, CSF biomarkers, β-amyloid positron emission tomography (PET), and magnetic resonance imaging (MRI) to monitor ARIA. Cross-sectional and longitudinal analyses evaluated potential ARIA-related risk factors.

RESULTS

Eleven participants developed ARIA-E, including 3 with mild symptoms. No ARIA-E was reported under solanezumab while gantenerumab was associated with ARIA-E compared to placebo (odds ratio [OR] = 9.1, confidence interval [CI][1.2, 412.3]; p = 0.021). Under gantenerumab, APOE-ɛ4 carriers were more likely to develop ARIA-E (OR = 5.0, CI[1.0, 30.4]; p = 0.055), as were individuals with microhemorrhage at baseline (OR = 13.7, CI[1.2, 163.2]; p = 0.039). No ARIA-E was observed at the initial 225 mg/month gantenerumab dose, and most cases were observed at doses >675 mg. At first ARIA-E occurrence, all ARIA-E participants were amyloid-PET+, 60% were CDR >0, 60% were past their estimated year to symptom onset, and 60% had also incident ARIA-H. Most ARIA-E radiologically resolved after dose adjustment and developing ARIA-E did not significantly increase odds of trial discontinuation. ARIA-E was more frequently observed in the occipital lobe (90%). ARIA-E severity was associated with age at time of ARIA-E.

INTERPRETATION

In DIAD, solanezumab was not associated with ARIA. Gantenerumab dose over 225 mg increased ARIA-E risk, with additional risk for individuals APOE-ɛ4(+) or with microhemorrhage. ARIA-E was reversible on MRI in most cases, generally asymptomatic, without additional risk for trial discontinuation. ANN NEUROL 2022;92:729-744.

Application of Bayesian methods to accelerate rare disease drug development: scopes and hurdles

Background

Design and analysis of clinical trials for rare and ultra-rare disease pose unique challenges to the practitioners. Meeting conventional power requirements is infeasible for diseases where sample sizes are inherently very small. Moreover, rare disease populations are generally heterogeneous and widely dispersed, which complicates study enrollment and design. Leveraging all available information in rare and ultra-rare disease trials can improve both drug development and informed decision-making processes.

Main text

Bayesian statistics provides a formal framework for combining all relevant information at all stages of the clinical trial, including trial design, execution, and analysis. This manuscript provides an overview of different Bayesian methods applicable to clinical trials in rare disease. We present real or hypothetical case studies that address the key needs of rare disease drug development highlighting several specific Bayesian examples of clinical trials. Advantages and hurdles of these approaches are discussed in detail. In addition, we emphasize the practical and regulatory aspects in the context of real-life applications.

Conclusion

The use of innovative trial designs such as master protocols and complex adaptive designs in conjunction with a Bayesian approach may help to reduce sample size, select the correct treatment and population, and accurately and reliably assess the treatment effect in the rare disease setting.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13023-022-02342-5.

The amyloid hypothesis in Alzheimer disease: new insights from new therapeutics

Many drugs that target amyloid-β (Aβ) in Alzheimer disease (AD) have failed to demonstrate clinical efficacy. However, four anti-Aβ antibodies have been shown to mediate the removal of amyloid plaque from brains of patients with AD, and the FDA has recently granted accelerated approval to one of these, aducanumab, using reduction of amyloid plaque as a surrogate end point. The rationale for approval and the extent of the clinical benefit from these antibodies are under intense debate. With the aim of informing this debate, we review clinical trial data for drugs that target Aβ from the perspective of the temporal interplay between the two pathognomonic protein aggregates in AD - Aβ plaques and tau neurofibrillary tangles - and their relationship to cognitive impairment, highlighting differences in drug properties that could affect their clinical performance. On this basis, we propose that Aβ pathology drives tau pathology, that amyloid plaque would need to be reduced to a low level (~20 centiloids) to reveal significant clinical benefit and that there will be a lag between the removal of amyloid and the potential to observe a clinical benefit. We conclude that the speed of amyloid removal from the brain by a potential therapy will be important in demonstrating clinical benefit in the context of a clinical trial.

Adaptive Platform Trials to Transform Amyotrophic Lateral Sclerosis Therapy Development

Current therapeutic development in amyotrophic lateral sclerosis (ALS) relies on individual randomized clinical trials to test a specific investigational product in a single patient population. This approach has intrinsic limitations, including cost, time, and lack of flexibility. Adaptive platform trials represent a novel approach to investigate several interventions for a single disease in a continuous manner. Already in use in oncology, this approach is now being employed more often in neurology. Here, we describe a newly launched platform trial for ALS. The Healey ALS Platform Trial is testing multiple investigational products concurrently in people with ALS, with the goal of rapidly identifying novel treatments, biomarkers, and trial endpoints. ANN NEUROL 2022;91:165-175.

Influence of Subject-Specific Effects in Longitudinal Modelling of Cognitive Decline in Alzheimer's Disease

BACKGROUND

Accurate longitudinal modelling of cognitive decline is a major goal of Alzheimer's disease and related dementia (ADRD) research. However, the impact of subject-specific effects is not well characterized and may have implications for data generation and prediction.

OBJECTIVE

This study seeks to address the impact of subject-specific effects, which are a less well-characterized aspect of ADRD cognitive decline, as measured by the Alzheimer's Disease Assessment Scale's Cognitive Subscale (ADAS-Cog).

METHODS

Prediction errors and biases for the ADAS-Cog subscale were evaluated when using only population-level effects, robust imputation of subject-specific effects using model covariances, and directly known individual-level effects fit during modelling as a natural control. Evaluated models included pre-specified parameterizations for clinical trial simulation, analogous mixed-effects regression models parameterized directly, and random forest ensemble models. Assessment used a meta-database of Alzheimer's disease studies with validation in simulated synthetic cohorts.

RESULTS

All models observed increases in variance under imputation leading to increased prediction error. Bias decreased with imputation except under the pre-specified parameterization, which increased in the meta-database, but was attenuated under simulation. Known fitted subject effects gave the best prediction results.

CONCLUSION

Subject-specific effects were found to have a profound impact on predicting ADAS-Cog. Reductions in bias suggest imputing random effects assists in calculating results on average, as when simulating clinical trials. However, reduction in error emphasizes population-level effects when attempting to predict outcomes for individuals. Forecasting future observations greatly benefits from using known subject-specific effects.

The effects of green tea on cognitive impairments in the rat model of Alzheimer's disease: protection against inflammatory and oxidative damage

INTRODUCTION

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by a decrement in the number of synapses, an increment in the production of oxygen free radicals and inflammatory cytokines. Green tea (GT) plays a defensive performance in different neurodegenerative conditions, such as cognition deficit. This study investigated the neuroprotective effect of green tea (GT) on cognitive disorder, inflammation, and oxidative stress in the streptozotocin (STZ)- induced AD model.

MATERIALS AND METHODS

The rats were divided into four groups: (1) Control, (2) GT, (3) Alz, and (4) GT + Alz. AD was induced by the injection of STZ (3 mg/kg, bilaterally, ICV). Morris water maze and passive avoidance tests were done to evaluate the memory and learning of rats. Biochemical parameters were measured with specialized ELISA kits.

RESULTS

Briefly, data analysis revealed that GT administration for 21 days improved memory impairment induced by the injection of STZ. Pretreatment with GT enhanced time spent in the goal quarter and reduced latency time and path length. Furthermore, pretreatment with GT prevented the increment of malondialdehyde (MDA) concentration in STZ-treated rats. As a pro-inflammatory cytokine, tumor necrosis factor- α (TNF-α) concentration was suppressed with the GT pretreatment. Total antioxidant capacity was increased after GT administration in rats treated compared with AD rats.

CONCLUSIONS

GT pretreatment attenuated STZ-induced learning and memory impairment through the suppression of TNF-α and MDA concentrations. The beneficial effects of GT on memory could be attributed to its protective effects on oxidative defenses.

A trial of gantenerumab or solanezumab in dominantly inherited Alzheimer's disease

Dominantly inherited Alzheimer's disease (DIAD) causes predictable biological changes decades before the onset of clinical symptoms, enabling testing of interventions in the asymptomatic and symptomatic stages to delay or slow disease progression. We conducted a randomized, placebo-controlled, multi-arm trial of gantenerumab or solanezumab in participants with DIAD across asymptomatic and symptomatic disease stages. Mutation carriers were assigned 3:1 to either drug or placebo and received treatment for 4-7 years. The primary outcome was a cognitive end point; secondary outcomes included clinical, cognitive, imaging and fluid biomarker measures. Fifty-two participants carrying a mutation were assigned to receive gantenerumab, 52 solanezumab and 40 placebo. Both drugs engaged their Aβ targets but neither demonstrated a beneficial effect on cognitive measures compared to controls. The solanezumab-treated group showed a greater cognitive decline on some measures and did not show benefits on downstream biomarkers. Gantenerumab significantly reduced amyloid plaques, cerebrospinal fluid total tau, and phospho-tau181 and attenuated increases of neurofilament light chain. Amyloid-related imaging abnormalities edema was observed in 19.2% (3 out of 11 were mildly symptomatic) of the gantenerumab group, 2.5% of the placebo group and 0% of the solanezumab group. Gantenerumab and solanezumab did not slow cognitive decline in symptomatic DIAD. The asymptomatic groups showed no cognitive decline; symptomatic participants had declined before reaching the target doses.

Bayesian adaptive design for clinical trials in Duchenne muscular dystrophy

A Bayesian adaptive design is proposed for a clinical trial in Duchenne muscular dystrophy. The trial was designed to demonstrate treatment efficacy on an ambulatory-based clinical endpoint and to identify early success on a biomarker (dystrophin protein levels) that can serve as a basis for accelerated approval in the United States. The trial incorporates placebo augmentation using placebo data from past clinical trials. A thorough simulation study was conducted to understand the operating characteristics of the trial. This trial design was selected for the US FDA Complex Innovative Trial Design Pilot Meeting Program and the experience in that program is summarized.

Donanemab in Early Alzheimer's Disease

BACKGROUND

A hallmark of Alzheimer's disease is the accumulation of amyloid-β (Aβ) peptide. Donanemab, an antibody that targets a modified form of deposited Aβ, is being investigated for the treatment of early Alzheimer's disease.

METHODS

We conducted a phase 2 trial of donanemab in patients with early symptomatic Alzheimer's disease who had tau and amyloid deposition on positron-emission tomography (PET). Patients were randomly assigned in a 1:1 ratio to receive donanemab (700 mg for the first three doses and 1400 mg thereafter) or placebo intravenously every 4 weeks for up to 72 weeks. The primary outcome was the change from baseline in the score on the Integrated Alzheimer's Disease Rating Scale (iADRS; range, 0 to 144, with lower scores indicating greater cognitive and functional impairment) at 76 weeks. Secondary outcomes included the change in scores on the Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB), the 13-item cognitive subscale of the Alzheimer's Disease Assessment Scale (ADAS-Cog₁₃), the Alzheimer's Disease Cooperative Study-Instrumental Activities of Daily Living Inventory (ADCS-iADL), and the Mini-Mental State Examination (MMSE), as well as the change in the amyloid and tau burden on PET.

RESULTS

A total of 257 patients were enrolled; 131 were assigned to receive donanemab and 126 to receive placebo. The baseline iADRS score was 106 in both groups. The change from baseline in the iADRS score at 76 weeks was -6.86 with donanemab and -10.06 with placebo (difference, 3.20; 95% confidence interval, 0.12 to 6.27; P = 0.04). The results for most secondary outcomes showed no substantial difference. At 76 weeks, the reductions in the amyloid plaque level and the global tau load were 85.06 centiloids and 0.01 greater, respectively, with donanemab than with placebo. Amyloid-related cerebral edema or effusions (mostly asymptomatic) occurred with donanemab.

CONCLUSIONS

In patients with early Alzheimer's disease, donanemab resulted in a better composite score for cognition and for the ability to perform activities of daily living than placebo at 76 weeks, although results for secondary outcomes were mixed. Longer and larger trials are necessary to study the efficacy and safety of donanemab in Alzheimer's disease. (Funded by Eli Lilly; TRAILBLAZER-ALZ ClinicalTrials.gov number, NCT03367403.).

Longitudinal Accumulation of Cerebral Microhemorrhages in Dominantly Inherited Alzheimer Disease

OBJECTIVE

To investigate the inherent clinical risks associated with the presence of cerebral microhemorrhages (CMHs) or cerebral microbleeds and characterize individuals at high risk for developing hemorrhagic amyloid-related imaging abnormality (ARIA-H), we longitudinally evaluated families with dominantly inherited Alzheimer disease (DIAD).

METHODS

Mutation carriers (n = 310) and noncarriers (n = 201) underwent neuroimaging, including gradient echo MRI sequences to detect CMHs, and neuropsychological and clinical assessments. Cross-sectional and longitudinal analyses evaluated relationships between CMHs and neuroimaging and clinical markers of disease.

RESULTS

Three percent of noncarriers and 8% of carriers developed CMHs primarily located in lobar areas. Carriers with CMHs were older, had higher diastolic blood pressure and Hachinski ischemic scores, and more clinical, cognitive, and motor impairments than those without CMHs. APOE ε4 status was not associated with the prevalence or incidence of CMHs. Prevalent or incident CMHs predicted faster change in Clinical Dementia Rating although not composite cognitive measure, cortical thickness, hippocampal volume, or white matter lesions. Critically, the presence of 2 or more CMHs was associated with a significant risk for development of additional CMHs over time (8.95 ± 10.04 per year).

CONCLUSION

Our study highlights factors associated with the development of CMHs in individuals with DIAD. CMHs are a part of the underlying disease process in DIAD and are significantly associated with dementia. This highlights that in participants in treatment trials exposed to drugs, which carry the risk of ARIA-H as a complication, it may be challenging to separate natural incidence of CMHs from drug-related CMHs.

Relative efficiency of equal versus unequal cluster sizes in cluster randomized trials with a small number of clusters

To calculate sample sizes in cluster randomized trials (CRTs), the cluster sizes are usually assumed to be identical across all clusters for simplicity. However, equal cluster sizes are not guaranteed in practice, especially when the number of clusters is limited. Therefore, it is important to understand the relative efficiency (RE) of equal versus unequal cluster sizes when designing CRTs with a limited number of clusters. In this paper, we are interested in the RE of two bias-corrected sandwich estimators of the treatment effect in the Generalized Estimating Equation (GEE) models for CRTs with a small number of clusters. Specifically, we derive the RE of two bias-corrected sandwich estimators for binary, continuous, or count data in CRTs under the assumption of an exchangeable working correlation structure. We consider different scenarios of cluster size distributions and investigate RE performance through simulation studies. We conclude that the number of clusters could be increased by as much as 42% to compensate for efficiency loss due to unequal cluster sizes. Finally, we propose an algorithm of increasing the number of clusters when the coefficient of variation of cluster sizes is known and unknown.

Relationships between big-five personality factors and Alzheimer's disease pathology in autosomal dominant Alzheimer's disease

INTRODUCTION

Changes in personality characteristics are associated with the onset of symptoms in Alzheimer's disease (AD) and may even precede clinical diagnosis. However, personality changes caused by disease progression can be difficult to separate from changes that occur with normal aging. The Dominantly Inherited Alzheimer Network (DIAN) provides a unique cohort in which to relate measures of personality traits to in vivo markers of disease in a much younger sample than in typical late onset AD.

METHODS

Personality traits measured with the International Personality Item Pool at baseline from DIAN participants were analyzed as a function of estimated years to onset of clinical symptoms and well-established AD biomarkers.

RESULTS

Both neuroticism and conscientiousness were correlated with years to symptom onset and markers of tau pathology in the cerebrospinal fluid. Self-reported conscientiousness and both neuroticism and conscientiousness ratings from a collateral source were correlated with longitudinal rates of cognitive decline such that participants who were rated as higher on neuroticism and lower on conscientiousness exhibited accelerated rates of cognitive decline.

DISCUSSION

Personality traits are correlated with the accumulation of AD pathology and time to symptom onset, suggesting that AD progression can influence an individual's personality characteristics. Together these findings suggest that measuring neuroticism and conscientiousness may hold utility in tracking disease progression in AD.

Awareness of genetic risk in the Dominantly Inherited Alzheimer Network (DIAN)

INTRODUCTION

Although some members of families with autosomal dominant Alzheimer's disease mutations learn their mutation status, most do not. How knowledge of mutation status affects clinical disease progression is unknown. This study quantifies the influence of mutation awareness on clinical symptoms, cognition, and biomarkers.

METHODS

Mutation carriers and non-carriers from the Dominantly Inherited Alzheimer Network (DIAN) were stratified based on knowledge of mutation status. Rates of change on standard clinical, cognitive, and neuroimaging outcomes were examined.

RESULTS

Mutation knowledge had no associations with cognitive decline, clinical progression, amyloid deposition, hippocampal volume, or depression in either carriers or non-carriers. Carriers who learned their status mid-study had slightly higher levels of depression and lower cognitive scores.

DISCUSSION

Knowledge of mutation status does not affect rates of change on any measured outcome. Learning of status mid-study may confer short-term changes in cognitive functioning, or changes in cognition may influence the determination of mutation status.

Two-period linear mixed effects models to analyze clinical trials with run-in data when the primary outcome is continuous: Applications to Alzheimer's disease

Introduction

Study outcomes can be measured repeatedly based on the clinical trial protocol before randomization during what is known as the “run-in” period. However, it has not been established how best to incorporate run-in data into the primary analysis of the trial.

Methods

We proposed two-period (run-in period and randomization period) linear mixed effects models to simultaneously model the run-in data and the postrandomization data.

Results

Compared with the traditional models, the two-period linear mixed effects models can increase the power up to 15% and yield similar power for both unequal randomization and equal randomization.

Discussion

Given that analysis of run-in data using the two-period linear mixed effects models allows more participants (unequal randomization) to be on the active treatment with similar power to that of the equal-randomization trials, it may reduce the dropout by assigning more participants to the active treatment and thus improve the efficiency of AD clinical trials.

Staging biomarkers in preclinical autosomal dominant Alzheimer's disease by estimated years to symptom onset

INTRODUCTION

Staging preclinical Alzheimer disease (AD) by the expected years to symptom onset (EYO) in autosomal dominant AD (ADAD) through biomarker correlations is important.

METHODS

We estimated the correlation matrix between EYO/cognition and imaging/CSF biomarkers, and searched for the EYO cutoff where a change in the correlations occurred before and after the cutoff among the asymptomatic mutation carriers of ADAD. We then estimated the longitudinal rate of change for biomarkers/cognition within each preclinical stage defined by the EYO.

RESULTS

Based on the change in the correlations, the preclinical ADAD was divided by EYOs -7 and -13 years. Mutation carriers demonstrated a temporal ordering of biomarker/cognition changes across the three preclinical stages.

DISCUSSION

Duration of each preclinical stage can be estimated in ADAD, facilitating better planning of prevention trials with the EYO cutoffs under the recently released FDA guidance. The generalization of these results to sporadic AD warrants further investigation.

Simultaneously evaluating the effect of baseline levels and longitudinal changes in disease biomarkers on cognition in dominantly inherited Alzheimer's disease

INTRODUCTION

As the role of biomarkers is increasing in Alzheimer's disease (AD) clinical trials, it is critical to use a comprehensive temporal biomarker profile that reflects both baseline and longitudinal assessments to establish a more precise association between the change in biomarkers and change in cognition. Because age of onset of dementia symptoms is highly predictable, and there are relatively few age-related comorbidities, the Dominantly Inherited Alzheimer Network autosomal dominant AD population affords a unique opportunity to investigate these relationships in a well-characterized population.

METHODS

A novel joint statistical model was used to simultaneously evaluate how a comprehensive AD biomarker profile predicts change in cognition using amyloid positron emission tomography (PET), CSF Aβ42, CSF total tau and Ptau181, cortical metabolism using [F-18] fluorodeoxyglucose-PET, and hippocampal volume from participants enrolled in the Dominantly Inherited Alzheimer Network (n = 262) with mean (SD) duration of follow-up of 2.7 (1.2) years.

RESULTS

Baseline amyloid PET levels and CSF biomarkers were associated with change in cognition in contrast to the rate of change of brain metabolism and hippocampal volume, which predicted change in cognition.

CONCLUSIONS

This study suggests that the baseline value of amyloid PET and CSF Aβ42 measures may be useful for screening participants for AD trials; however, brain hippocampus atrophy and hypometabolism are only useful as repeated longitudinal assessments for tracking cognition and disease progression. This suggests that measures of amyloid plaques predict future cognitive decline, but only longitudinal measures of neurodegeneration correlate with cognitive decline. The novel statistical model used in this study can be easily applied to any pair of outcomes and has potential to be widely used by the AD research community.

Bayesian model of disease progression in GNE myopathy

One Sentence Summary: A Bayesian repeated measures model based on quantitative muscle strength data from a prospective Natural History Study was developed to determine disease progression and design clinical trials for GNE myopathy, a rare and slowly progressive muscle disease. GNE myopathy is a rare muscle disease characterized by slowly progressive weakness and atrophy of skeletal muscles. To address the significant challenges of defining the natural history and designing clinical trials for GNE myopathy, we developed a Bayesian latent variable repeated measures model to determine disease progression. The model is based on longitudinal quantitative muscle strength data collected as part of a prospective Natural History Study. The GNE Myopathy Progression Model provides an understanding of disease progression that would have otherwise required a natural history of unfeasible duration. "Disease age," the model-generated measure of disease progression, highly correlates with a variety of clinical, functional and patient-reported outcomes. With the incorporation of a treatment effect parameter to the GNE Disease Progression Model, we describe a novel GNE Myopathy Disease Modification Analysis that significantly increases power and reduces the number of subjects required to test the effectiveness of novel therapies when compared to more traditional analysis methods. The GNE Myopathy Disease Progression Model and Disease Modification Analysis can be applied to muscle diseases with prospectively collected muscle strength data, and a variety of rare and slowly progressive diseases.