Disease Clinical Trials Research Articles

Analytic Challenges in Clinical Trials of Early Alzheimer’s Disease

Background: The heavily right-skewed data seen in recently reported Alzheimer’s disease (AD) clinical trials influenced treatment contrasts when data were analyzed via the typical mixed-effects model for repeated measures (MMRM). Methods: An MMRM analysis similar to what is commonly used in AD clinical trials was compared versus robust regression (RR) and the non-parametric Hodges–Lehman estimator (HL). Results: Results in simulated data patterned after AD trials showed that imbalance across treatment arms in the number of patients in the extreme right tail (those with rapid disease progression) frequently occurred. Each analysis method controlled Type I error at or below the nominal level. The RR analysis yielded smaller standard errors and more power than MMRM and HL. In data sets with appreciable imbalance in the number of rapid progressing patients, MMRM results favored the treatment arm with fewer rapid progressors. Results from HL showed the same trend but to a lesser degree. Robust regression yielded similar results regardless of the ratio of rapid progressors. Conclusions: Although more research is needed over a wider range of scenarios, it should not be assumed that MMRM is the optimal approach for trials in early Alzheimer’s disease.

Respiratory syncytial virus (RSV) vaccine effectiveness against RSV-associated hospitalisations and emergency department encounters among adults aged 60 years and older in the USA, October, 2023, to March, 2024: a test-negative design analysis

Respiratory syncytial virus vaccines first recommended for use during 2023 were efficacious against lower respiratory tract disease in clinical trials. Limited real-world data regarding respiratory syncytial virus vaccine effectiveness are available. To inform vaccine policy and address gaps in evidence from the clinical trials, we aimed to assess the effectiveness against respiratory syncytial virus-associated hospitalisations and emergency department encounters among adults aged at least 60 years. We conducted a test-negative design analysis in an electronic health records-based network in eight states in the USA, including hospitalisations and emergency department encounters with respiratory syncytial virus-like illness among adults aged at least 60 years who underwent respiratory syncytial virus testing from Oct 1, 2023, to March 31, 2024. Respiratory syncytial virus vaccination status at the time of the encounter was derived from electronic health record documentation, state and city immunisation registries, and, for some sites, medical claims. Vaccine effectiveness was estimated by immunocompromise status, comparing the odds of vaccination among respiratory syncytial virus-positive case patients and respiratory syncytial virus-negative control patients, and adjusting for age, race and ethnicity, sex, calendar day, social vulnerability index, number of underlying non-respiratory medical conditions, presence of respiratory underlying medical conditions, and geographical region. Among 28 271 hospitalisations for respiratory syncytial virus-like illness among adults aged at least 60 years without immunocompromising conditions, vaccine effectiveness was 80% (95% CI 71-85) against respiratory syncytial virus-associated hospitalisations, and vaccine effectiveness was 81% (52-92) against respiratory syncytial virus-associated critical illness (ICU admission or death, or both). Among 8435 hospitalisations for respiratory syncytial virus-like illness among adults with immunocompromising conditions, vaccine effectiveness was 73% (48-85) against associated hospitalisation. Among 36 521 emergency department encounters for respiratory syncytial virus-like illness among adults aged at least 60 years without an immunocompromising condition, vaccine effectiveness was 77% (70-83) against respiratory syncytial virus-associated emergency department encounters. Vaccine effectiveness estimates were similar by age group and product type. Respiratory syncytial virus vaccination was effective in preventing respiratory syncytial virus-associated hospitalisations and emergency department encounters among adults aged at least 60 years in the USA during the 2023-24 respiratory syncytial virus season, which was the first season after respiratory syncytial virus vaccine was approved. The Centers for Disease Control and Prevention.

Drug Repurposing for Effective Alzheimer's Disease Medicines: Existing Methods and Novel Pharmacoepidemiological Approaches.

Drug repurposing is a methodology used to identify new clinical indications for existing drugs developed for other indications and has been successfully applied in the treatment of numerous conditions. Alzheimer's disease (AD) may be particularly well-suited to the application of drug repurposing methods given the absence of effective therapies and abundance of multi-omic data that has been generated in AD patients recently that may facilitate discovery of candidate AD drugs. A recent focus of drug repurposing has been in the application of pharmacoepidemiologic approaches to drug evaluation. Here, real-world clinical datasets with large numbers of patients are leveraged to establish observational efficacy of candidate drugs for further evaluation in disease models and clinical trials. In this review, we provide a selected overview of methods for drug repurposing, including signature matching, network analysis, molecular docking, phenotypic screening, semantic network, and pharmacoepidemiological analyses. Numerous methods have also been applied specifically to AD with the aim of nominating novel drug candidates for evaluation. These approaches, however, are prone to numerous limitations and potential biases that we have sought to address in the Drug Repurposing for Effective Alzheimer's Medicines (DREAM) study, a multi-step framework for selection and validation of potential drug candidates that has demonstrated the promise of STAT3 inhibitors and re-evaluated evidence for other drug candidates, such as phosphodiesterase inhibitors. Taken together, drug repurposing holds significant promise for development of novel AD therapeutics, particularly as the pace of data generation and development of analytical methods continue to accelerate.

A Multidisciplinary Recruitment Approach for The ExTINGUISH Trial: Identifying Successful Strategies to Increasing Eligible Participant Enrollment in a Rare Disease Clinical Trial

A Multidisciplinary Recruitment Approach for The ExTINGUISH Trial: Identifying Successful Strategies to Increasing Eligible Participant Enrollment in a Rare Disease Clinical Trial

Predicting regional tau accumulation with machine learning‐based tau‐PET and advanced radiomics

AbstractINTRODUCTIONAlzheimer's disease is partially characterized by the progressive accumulation of aggregated tau‐containing neurofibrillary tangles. Although the association between accumulated tau, neurodegeneration, and cognitive decline is critical for disease understanding and clinical trial design, we still lack robust tools to predict individualized trajectories of tau accumulation. Our objective was to assess whether brain imaging biomarkers of flortaucipir‐positron emission tomography (PET), in combination with clinical and genomic measures, could predict future pathological tau accumulation.METHODSWe quantified the disease profile of participants (N = 276) using a comprehensive set of descriptors, including clinical/demographic (age, diagnosis, amyloid status, sex, race, ethnicity), genetic (apolipoprotein E [APOE]‐ε4), and flortaucipir‐PET imaging measures (regional flortaucipir standardized uptake value ratio [SUVr] and comprehensive radiomic texture features extracted from Automated Anatomical Labeling template regions). We trained an AdaBoost machine learning algorithm in a 2:1 split train‐test configuration to derive a prognostic index that (i) stratifies individualized brain regions including global (AD‐signature region) and lobar regions (frontal, occipital, parietal, temporal) into stable/slow‐ and fast‐progressors based on future tau accumulation, and (ii) forecasts individualized regional annualized‐rate‐of‐change in flortaucipir‐PET SUVr. Further, we developed an adaptive model incorporating flortaucipir‐PET measurements from the baseline and intermediate timepoints to predict annualized‐rate‐of‐change.RESULTSIn binary classification for predicting stable/slow‐ versus fast‐progressors, the area‐under‐the‐receiver‐operating‐characteristic curve was 0.86 in the AD‐signature region and 0.83, 0.82, 0.84, and 0.83 in frontal, occipital, parietal, and temporal regions, respectively. The trained models successfully predicted annualized‐rate‐of‐change of flortaucipir‐PET regional flortaucipir SUVr in AD‐signature and lobar regions (Pearson‐correlation [R]: AD‐signature = 0.73; frontal = 0.73; occipital = 0.71; parietal = 0.70; temporal = 0.69). The models’ performance in predicting annualized‐rate‐of‐change slightly increased when imaging features from intermediate timepoints were used in the adaptive setting (R: AD‐signature = 0.79; frontal = 0.87; occipital = 0.83; parietal = 0.74; temporal = 0.82).DISCUSSIONTaken together, our results propose a robust approach to predict future tau accumulation that may improve the ability to enroll, stratify, and gauge efficacy in clinical trial participants.Highlights Machine learning predicts the future rate of tau accumulation in Alzheimer's disease. Tau prediction in lobar/global regions benefits from diverse multimodal features. This prognostic index can serve as a sensitive tool for patient stratification.

Novel Strategies Enhancing Bioavailability and Therapeutical Potential of Silibinin for Treatment of Liver Disorders.

Silibinin, a bioactive component found in milk thistle extract (Silybum marianum), is known to have significant therapeutic potential in the treatment of various liver diseases. It is considered a key element of silymarin, which is traditionally used to support liver function. The main mechanisms of action of silibinin are attributed to its antioxidant properties protecting liver cells from damage caused by free radicals. Experimental studies conducted in vitro and in vivo have confirmed its ability to inhibit inflammatory and fibrotic processes, as well as promote the regeneration of damaged liver tissue. Therefore, silibinin represents a promising tool for the treatment of liver diseases. Since the silibinin molecule is insoluble in water and has poor bioavailability in vivo, new perspectives on solving this problem are being sought. The two most promising approaches are the water-soluble derivative silibinin-C-2',3-dihydrogen succinate, disodium salt, and the silibinin-phosphatidylcholine complex. Both drugs are currently under evaluation in liver disease clinical trials. Nevertheless, the mechanism underlying silibinin biological activity is still elusive and its more detailed understanding would undoubtedly increase its potential in the development of effective therapeutic strategies against liver diseases. This review is focused on the therapeutic potential of silibinin and its derivates, approaches to increase the bioavailability and the benefits in the treatment of liver diseases that have been achieved so far. The review discusses the relevant in vitro and in vivo studies that investigated the protective effects of silibinin in various forms of liver damage.

Modeling Diversity in Diabetic Kidney Disease Clinical Trials Using Real-World Data

Modeling Diversity in Diabetic Kidney Disease Clinical Trials Using Real-World Data

S1944 Recruitment of Minority Patients in Metabolic Dysfunction-Associated Steatotic Liver Disease Clinical Trials

S1944 Recruitment of Minority Patients in Metabolic Dysfunction-Associated Steatotic Liver Disease Clinical Trials

Clinical outcome-guided deep temporal clustering for disease progression subtyping

ObjectiveComplex diseases exhibit heterogeneous progression patterns, necessitating effective capture and clustering of longitudinal changes to identify disease subtypes for personalized treatments. However, existing studies often fail to design clustering-specific representations or neglect clinical outcomes, thereby limiting the interpretability and clinical utility. MethodWe design a unified framework for subtyping longitudinal progressive diseases. We focus on effectively integrating all data from disease progressions and improving patient representation for downstream clustering. Specifically, we propose a clinical Outcome-Guided Deep Temporal Clustering (OG-DTC) that generates representations informed by clustering and clinical outcomes. A GRU-based seq2seq architecture captures the temporal dynamics, and the model integrates k-means clustering and outcome regression to facilitate the formation of clustering structures and the integration of clinical outcomes. The learned representations are clustered using a Gaussian mixture model to identify distinct subtypes. The clustering results are extensively validated through reproducibility, stability, and significance tests. ResultsWe demonstrated the efficacy of our framework by applying it to three Alzheimer’s Disease (AD) clinical trials. Through the AD case study, we identified three distinct subtypes with unique patterns associated with differentiated clinical declines across multiple measures. The ablation study revealed the contributions of each component in the model and showed that jointly optimizing the full model improved patient representations for clustering. Extensive validations showed that the derived clustering is reproducible, stable, and significant. ConclusionOur temporal clustering framework can derive robust clustering applicable for subtyping longitudinal progressive diseases and has the potential to account for subtype variability in clinical outcomes.

A machine learning-based prediction of tau load and distribution in Alzheimer's disease using plasma, MRI and clinical variables.

Tau positron emission tomography (PET) is a reliable neuroimaging technique for assessing regional load of tau pathology in the brain, commonly used in Alzheimer's disease (AD) research and clinical trials. However, its routine clinical use is limited by cost and accessibility barriers. Here we explore using machine learning (ML) models to predict clinically useful tau-PET composites from low-cost and non-invasive features, e.g., basic clinical variables, plasma biomarkers, and structural magnetic resonance imaging (MRI). Results demonstrated that models including plasma biomarkers yielded the most accurate predictions of tau-PET burden (best model: R-squared=0.66-0.68), with especially high contribution from plasma P-tau217. In contrast, MRI variables stood out as best predictors (best model: R-squared=0.28-0.42) of asymmetric tau load between the two hemispheres (an example of clinically relevant spatial information). The models showed high generalizability to external test cohorts with data collected at multiple sites. Based on these results, we also propose a proof-of-concept two-step classification workflow, demonstrating how the ML models can be translated to a clinical setting. This study uncovers current potential in predicting tau-PET information from scalable cost-effective variables, which could improve diagnosis and prognosis of AD.

Outcomes of patients with prior biologic intolerance are better than those with biologic failure in clinical trials of inflammatory bowel disease

Abstract Background and Aims Inflammatory bowel disease (IBD) trials often stratify patients by prior biologic exposure, including prior biologic failure or intolerance. This study aimed to assess clinical outcomes in IBD patients with prior biologic failure versus intolerance treated with ustekinumab or vedolizumab. Methods A post-hoc analysis of ulcerative colitis (UC) and Crohn’s disease (CD) clinical trials for ustekinumab (UNITI, UNIFI) and vedolizumab (GEMINI-1, GEMINI-2) was performed. Clinical response, clinical remission, and endoscopic improvement (for UC) were compared among biologic naïve, biologic-failure, and biologic intolerant patients. Statistical analyses, including chi-square tests and logistic regression, were performed. Results 1178 UC and 1439 CD patients received either ustekinumab or vedolizumab. In UC, biologic intolerant patients exhibited higher clinical response (54.7% vs. 38.8%, aOR 1.87 [95% CI 0.93-3.73]), clinical remission (25.0% vs. 11.0%, aOR 2.84 [95% CI 1.47-5.49]), and endoscopic improvement (40.6% vs. 24.8%, aOR 2.76 [95% CI 1.28-5.94]) compared to biologic failure, with outcomes similar to biologic naïve patients. In biologic-intolerant CD patients, clinical response was similar between prior biologic failure and intolerance (34.2% vs 32.8%), but after adjustment for potential confounders, biologic intolerance was associated with higher odds of clinical response (aOR: 1.67, 95% CI 1.09-2.55), with no significant difference observed for clinical remission (aOR: 1.48, 95% CI 0.88-2.49). Conclusion Improved treatment outcomes were generally observed in patients with biologic intolerance compared to failure, especially in UC, where outcomes were similar to biologic naïve patients. Future clinical trials should meticulously differentiate prior biologic failure versus intolerance to mitigate potential bias.

Sensing data and methodology from the Adaptive DBS Algorithm for Personalized Therapy in Parkinson’s Disease (ADAPT-PD) clinical trial

Adaptive deep brain stimulation (aDBS) is an emerging advancement in DBS technology; however, local field potential (LFP) signal rate detection sufficient for aDBS algorithms and the methods to set-up aDBS have yet to be defined. Here we summarize sensing data and aDBS programming steps associated with the ongoing Adaptive DBS Algorithm for Personalized Therapy in Parkinson’s Disease (ADAPT-PD) pivotal trial (NCT04547712). Sixty-eight patients were enrolled with either subthalamic nucleus or globus pallidus internus DBS leads connected to a Medtronic PerceptTM PC neurostimulator. During the enrollment and screening procedures, a LFP (8–30 Hz, ≥1.2 µVp) control signal was identified by clinicians in 84.8% of patients on medication (65% bilateral signal), and in 92% of patients off medication (78% bilateral signal). The ADAPT-PD trial sensing data indicate a high LFP signal presence in both on and off medication states of these patients, with bilateral signal in the majority, regardless of PD phenotype.

Discontinuing the Term “Stakeholder” From the NIA IMPACT Collaboratory Engaging Partners Team: An Example of the Process of Language Change in an Organization

In this paper, we describe our process of changing language of the National Institute on Aging Imbedded Pragmatic Alzheimer's disease and AD-related dementias Clinical Trials Collaboratory (NIA IMPACT Collaboratory) “Stakeholder Engagement Team” to “Engaging Partners Team” in response to feedback from community partners regarding the problematic connotations of the term “stakeholder.” We present a brief history of the term “stakeholder” and its use in clinical and community-engaged research. Then, we summarize critiques of this term, including its colonial history and potential to reinforce complacency with generational traumas, particularly among Indigenous peoples and communities. We conclude with a detailed overview of our team and organization's multi-step process to discontinue use of the term “stakeholder,” in alignment with a theoretical model of organizational behavior change. This paper highlights the importance of critically evaluating language and responding to community partners. We hope our process can guide other researchers and organizations.

Sample size planning for estimating the global win probability with precision and assurance

Randomized controlled trials commonly employ multiple endpoints to collectively assess the intended effects of the new intervention on multiple aspects of the disease. Focusing on the estimation of the global win probability (WinP), defined as the (weighted) mean of the WinPs across the endpoints that a treated participant would have a better outcome than a control participant, we propose a closed-form sample size formula incorporating pre-specified precision and assurance, with precision denoted by the lower limit of confidence interval and assurance denoted by the probability of achieving that lower limit. We make use of the equivalence of the WinP and the area under the receiver operating characteristic curve (AUC) and adapt a formula originally developed for the difference between two AUCs to handle the global WinP. Unequal variances between treatment groups are allowed. Simulation results suggest that the method performs very well. We illustrate the proposed formula using a Parkinson's disease clinical trial design example.

Iron Chelators and Alzheimer's Disease Clinical Trials.

Alzheimer's disease (AD) is a major neurodegenerative disorder impacting millions of people with cognitive impairment and affecting activities of daily living. The deposition of neurofibrillary tangles of hyperphosphorylated tau proteins and accumulation of amyloid-β (Aβ) are the main pathological characteristics of AD. However, the actual causal process of AD is not yet identified. Oxidative stress occurs prior to amyloid Aβ plaque formation and tau phosphorylation in AD. The role of master antioxidant, glutathione, and metal ions (e.g., iron) in AD are the frontline area of AD research. Iron overload in specific brain regions in AD is associated with the rate of cognitive decline. We have presented the outcome from various interventional trials involving iron chelators intended to minimize the iron overload in AD. To date, however, no significant positive outcomes have been reported using iron chelators in AD and warrant further research.

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

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

Increasing Diversity, Equity, Inclusion, and Accessibility in Rare Disease Clinical Trials.

Diversity, equity, inclusion, and accessibility (DEIA) are foundational principles for clinical trials and medical research. In rare diseases clinical research, where numbers of participants are already challenged by rarity itself, maximizing inclusion is of particular importance to clinical trial success, as well as ensuring the generalizability and relevance of the trial results to the people affected by these diseases. In this article, we review the medical and gray literature and cite case examples to provide insights into how DEIA can be proactively integrated into rare diseases clinical research. Here, we particularly focus on genetic diversity. While the rare diseases DEIA literature is nascent, it is accelerating as many patient advocacy groups, professional societies, training and educational organizations, researcher groups, and funders are setting intentional strategies to attain DEIA goals moving forward, and to establish metrics to ensure continued improvement. Successful examples in underserved and underrepresented populations are available that can serve as case studies upon which rare diseases clinical research programs can be built. Rare diseases have historically been innovation drivers in basic, translational, and clinical research, and ultimately, all populations benefit from data diversity in rare diseases populations that deliver novel insights and approaches to how clinical research can be performed.

Novel measures of cognition and function for the AD spectrum in the Novel Measures for Alzheimer's Disease Prevention Trials (NoMAD) project: Psychometric properties, convergent validation, and contrasts with established measures.

This study derived composite scores for two novel cognitive measures, the No Practice Effect (NPE) battery and the Miami Computerized Functional Skills Assessment and Training system for use in early-stage Alzheimer's disease (AD) clinical trials. Their psychometric properties and associations with AD risk markers were compared to those of well-established measures. For 291 older adults with healthy cognition or early mild cognitive impairment, Exploratory factor analyses were used to identify the factor structure of the NPE. Factor and total scores were examined for their psychometric properties and associations with AD risk biomarkers. Composite scores from the novel cognitive and functional measures demonstrated better psychometric properties (distribution and test-retest reliability) and stronger associations with AD-related demographic, genetic, and brain risk markers than well-established measures, DISCUSSION: These novel measures have potential for use as primary cognitive and functional outcomes in early-stage AD clinical trials. Well-established cognitive tests may not accurately detect subtle cognitive changes. No Practice Effect (NPE) and Computerized Functional Skills Assessment and Training are novel measures designed to have improved psychometric properties. NPE had Executive Function, Cognitive Control/Speed, and Episodic Memory domains. Novel measures had better psychometric properties compared to established measures. Significant associations with Alzheimer's disease biomarkers were found with novel measures.

Pooling Alzheimer's disease clinical trial data to develop personalized medicine approaches is easier said than done: A proof-of-principle study and call to action.

Pooling data from past Alzheimer's disease clinical trials may help identify subgroups most likely to benefit from specific treatments and may help inform future trial design.Accessing past trial datasets is complicated, frustrating the field's communal effort to find the best treatments for the right individuals.We urge trial sponsors and the academic research community to remove data access barriers and improve collaboration through practicing open science and harmonizing outcome measures.

Recommendations for Broadening Eligibility Criteria in Inflammatory Bowel Disease Clinical Trials.

Clinical trial recruitment for patients with inflammatory bowel disease (IBD) has become more challenging over time. We aimed to develop recommendations for broadening IBD clinical trial eligibility to improve the inclusion of a more representative patient population in a more efficient timeline. We applied the RAND/UCLA Appropriateness Method focused on broadening IBD clinical trial eligibility. A literature review was performed for 7 domains, each representing a different area related to trial recruitment. Based on these domains, 32 statements were developed. A questionnaire was sent to IBD specialists to anonymously vote on each statement with regards to its appropriateness and feasibility. After the first round of voting, participants met for a moderated discussion to review all statements. At the end of the discussion a second round of anonymous voting led to the final recommendations. The final round of voting resulted in 26 statements. All were rated as feasible and 25 of 26 rated as appropriate. Recommendations generally are to be more inclusive of complicated disease phenotypes, more liberal around safety criteria, to recognize the importance of non-invasive imaging and biomarkers, to minimize the washout period and to not enforce a minimum or maximum number of prior medications, to allow a recently recorded colonoscopy to count as a baseline study, and to be less restrictive of age. Recommendations to broaden clinical trial eligibility were found to be both appropriate and feasible with a high degree of agreement amongst an international group of IBD specialists.