Considerations for biomarker strategies in clinical trials investigating tau-targeting therapeutics for Alzheimer's disease.

The use of biomarker-led clinical trial designs has been transformative for investigating amyloid-targeting therapies for Alzheimer's disease (AD). The designs have ensured the correct selection of patients on these trials, supported target engagement and have been used to support claims of disease modification and clinical efficacy. Ultimately, this has recently led to approval of disease-modifying, amyloid-targeting therapies for AD; something that should be noted for clinical trials investigating tau-targeting therapies for AD. There is a clear overlap of the purpose of biomarker use at each stage of clinical development between amyloid-targeting and tau-targeting clinical trials. However, there are differences within the potential context of use and interpretation for some biomarkers in particular measurements of amyloid and utility of soluble, phosphorylated tau biomarkers. Given the complexities of tau in health and disease, it is paramount that therapies target disease-relevant tau and, in parallel, appropriate assays of target engagement are developed. Tau positron emission tomography, fluid biomarkers reflecting tau pathology and downstream measures of neurodegeneration will be important both for participant recruitment and for monitoring disease-modification in tau-targeting clinical trials. Bespoke design of biomarker strategies and interpretations for different modalities and tau-based targets should also be considered.

Approaches and experiences implementing remote, electronic consent at the Leeds Clinical Trials Research Unit.

BACKGROUND: Use of electronic methods to support informed consent ('eConsent') is increasingly popular in clinical research. This commentary reports the approach taken to implement electronic consent methods and subsequent experiences from a range of studies at the Leeds Clinical Trials Research Unit (CTRU), a large clinical trials unit in the UK. MAIN TEXT: We implemented a remote eConsent process using the REDCap platform. The process can be used in trials of investigational medicinal products and other intervention types or research designs. Our standard eConsent system focuses on documenting informed consent, with other aspects of consent (e.g. providing information to potential participants and a recruiter discussing the study with each potential participant) occurring outside the system, though trial teams can use electronic methods for these activities where they have ethical approval. Our overall process includes a verbal consent step prior to confidential information being entered onto REDCap and an identity verification step in line with regulator guidance. We considered the regulatory requirements around the system's generation of source documents, how to ensure data protection standards were upheld and how to monitor informed consent within the system. We present four eConsent case studies from the CTRU: two randomised clinical trials and two other health research studies. These illustrate the ways eConsent can be implemented, and lessons learned, including about differences in uptake. CONCLUSIONS: We successfully implemented a remote eConsent process at the CTRU across multiple studies. Our case studies highlight benefits of study participants being able to give consent without having to be present at the study site. This may better align with patient preferences and trial site needs and therefore improve recruitment and resilience against external shocks (such as pandemics). Variation in uptake of eConsent may be influenced more by site-level factors than patient preferences, which may not align well with the aspiration towards patient-centred research. Our current process has some limitations, including the provision of all consent-related text in more than one language, and scalability of implementing more than one consent form version at a time. We consider how enhancements in CTRU processes, or external developments, might affect our approach.

Upstrapping to determine futility: predicting future outcomes nonparametrically from past data.

BACKGROUND: Clinical trials often involve some form of interim monitoring to determine futility before planned trial completion. While many options for interim monitoring exist (e.g., alpha-spending, conditional power), nonparametric based interim monitoring methods are also needed to account for more complex trial designs and analyses. The upstrap is one recently proposed nonparametric method that may be applied for interim monitoring. METHODS: Upstrapping is motivated by the case resampling bootstrap and involves repeatedly sampling with replacement from the interim data to simulate thousands of fully enrolled trials. The p-value is calculated for each upstrapped trial and the proportion of upstrapped trials for which the p-value criteria are met is compared with a pre-specified decision threshold. To evaluate the potential utility for upstrapping as a form of interim futility monitoring, we conducted a simulation study considering different sample sizes with several different proposed calibration strategies for the upstrap. We first compared trial rejection rates across a selection of threshold combinations to validate the upstrapping method. Then, we applied upstrapping methods to simulated clinical trial data, directly comparing their performance with more traditional alpha-spending and conditional power interim monitoring methods for futility. RESULTS: The method validation demonstrated that upstrapping is much more likely to find evidence of futility in the null scenario than the alternative across a variety of simulations settings. Our three proposed approaches for calibration of the upstrap had different strengths depending on the stopping rules used. Compared to O'Brien-Fleming group sequential methods, upstrapped approaches had type I error rates that differed by at most 1.7% and expected sample size was 2-22% lower in the null scenario, while in the alternative scenario power fluctuated between 15.7% lower and 0.2% higher and expected sample size was 0-15% lower. CONCLUSIONS: In this proof-of-concept simulation study, we evaluated the potential for upstrapping as a resampling-based method for futility monitoring in clinical trials. The trade-offs in expected sample size, power, and type I error rate control indicate that the upstrap can be calibrated to implement futility monitoring with varying degrees of aggressiveness and that performance similarities can be identified relative to considered alpha-spending and conditional power futility monitoring methods.

Integrating qualitative research within a clinical trials unit: developing strategies and understanding their implementation in contexts.

BACKGROUND/AIMS: The value of using qualitative methods within clinical trials is widely recognised. How qualitative research is integrated within trials units to achieve this is less clear. This paper describes the process through which qualitative research has been integrated within Cardiff University's Centre for Trials Research (CTR) in Wales, UK. We highlight facilitators of, and challenges to, integration. METHODS: We held group discussions on the work of the Qualitative Research Group (QRG) within CTR. The content of these discussions, materials for a presentation in CTR, and documents relating to the development of the QRG were interpreted at a workshop attended by group members. Normalisation Process Theory (NPT) was used to structure analysis. A writing group prepared a document for input from members of CTR, forming the basis of this paper. RESULTS: Actions to integrate qualitative research comprised: its inclusion in Centre strategies; formation of a QRG with dedicated funding/roles; embedding of qualitative research within operating systems; capacity building/training; monitoring opportunities to include qualitative methods in studies; maximising the quality of qualitative research and developing methodological innovation. Facilitators of these actions included: the influence of the broader methodological landscape within trial/study design and its promotion of the value of qualitative research; and close physical proximity of CTR qualitative staff/students allowing sharing of methodological approaches. Introduction of innovative qualitative methods generated interest among other staff groups. Challenges included: pressure to under-resource qualitative components of research, preference for a statistical stance historically in some research areas and funding structures, and difficulties faced by qualitative researchers carving out individual academic profiles when working across trials/studies. CONCLUSIONS: Given that CTUs are pivotal to the design and conduct of RCTs and related study types across multiple disciplines, integrating qualitative research into trials units is crucial if its contribution is to be fully realised. We have made explicit one trials unit's experience of embedding qualitative research and present this to open dialogue on ways to operationalise and optimise qualitative research in trials. NPT provides a valuable framework with which to theorise these processes, including the importance of sense-making and legitimisation when introducing new practices within organisations.

Using Bayesian statistics in confirmatory clinical trials in the regulatory setting: a tutorial review.

Bayesian statistics plays a pivotal role in advancing medical science by enabling healthcare companies, regulators, and stakeholders to assess the safety and efficacy of new treatments, interventions, and medical procedures. The Bayesian framework offers a unique advantage over the classical framework, especially when incorporating prior information into a new trial with quality external data, such as historical data or another source of co-data. In recent years, there has been a significant increase in regulatory submissions using Bayesian statistics due to its flexibility and ability to provide valuable insights for decision-making, addressing the modern complexity of clinical trials where frequentist trials are inadequate. For regulatory submissions, companies often need to consider the frequentist operating characteristics of the Bayesian analysis strategy, regardless of the design complexity. In particular, the focus is on the frequentist type I error rate and power for all realistic alternatives. This tutorial review aims to provide a comprehensive overview of the use of Bayesian statistics in sample size determination, control of type I error rate, multiplicity adjustments, external data borrowing, etc., in the regulatory environment of clinical trials. Fundamental concepts of Bayesian sample size determination and illustrative examples are provided to serve as a valuable resource for researchers, clinicians, and statisticians seeking to develop more complex and innovative designs.

Why Perfluorocarbon nanoparticles encounter bottlenecks in clinical translation despite promising oxygen carriers?

Artificial Oxygen Carriers (AOCs) have emerged as ground-breaking biomedical solutions, showcasing tremendous potential for enhancing human health and saving lives. Perfluorocarbon (PFC)-based AOCs, in particular, have garnered significant interest among researchers, leading to numerous clinical trials since the 1980 s. However, despite decades of exploration, the success rate has remained notably limited. This comprehensive review article delves into the landscape of clinical trials involving PFC compounds, shedding light on the challenges and factors contributing to the lack of clinical success with PFC nanoparticles till date. By scrutinizing the existing trials, the article aims to uncover the underlying issues like pharmacological side effects of the PFC and the nanomaterials used for the designing, complex formulation strategy and poor clinical trial designs of the formulation. More over each generation of the PFC formulation were discussed with details for their failure in the clinical trials limitations that block the path of PFC-based AOCs' full potential. Furthermore, the review emphasizes a forward-looking approach by outlining the future pathways and strategies essential for achieving success in clinical trials. AOCs require advanced yet biocompatible single-componentformulations. The new trend might be a novel drug delivery technique, like gel emulsion or reverse PFC emulsion with fluoro surfactants. Most importantly, well-planned clinical trials may end in a success story.

Coordination of COVID-19 platform trials in Europe.

To ensure optimal coordination of the EU-funded COVID-19 platform trials, a double coordination mechanism was established. It included the Trial Coordination Board (TCB) to promote the dialogue between investigators and relevant public health stakeholders and the Joint Access Advisory Mechanism (JAAM) to streamline access of new intervention arms to the platform trials. Both the TCB and the JAAM emerged as efficient instruments to promote cooperation and optimise the use of resources within EU-funded adaptive platform trials. In addition, an adaptive platform trial toolbox was developed to collect information and literature on challenges and solutions identified to date. The recently funded 'Coordination MEchanism for Cohorts and Trials' (CoMeCT) project will endeavour to make this model sustainable, with a further expansion to other emerging infectious diseases, as part of the governance of the current and future platform trials for pandemic preparedness. This example could serve as a model for platform trial coordination in other disease areas.

Value of Information for Clinical Trial Design: The Importance of Considering All Relevant Comparators.

Value of Information (VOI) analyses calculate the economic value that could be generated by obtaining further information to reduce uncertainty in a health economic decision model. VOI has been suggested as a tool for research prioritisation and trial design as it can highlight economically valuable avenues for future research. Recent methodological advances have made it increasingly feasible to use VOI in practice for research; however, there are critical differences between the VOI approach and the standard methods used to design research studies such as clinical trials. We aimed to highlight key differences between the research design approach based on VOI and standard clinical trial design methods, in particular the importance of considering the full decision context. We present two hypothetical examples to demonstrate that VOI methods are only accurate when (1) all feasible comparators are included in the decision model when designing research, and (2) all comparators are retained in the decision model once the data have been collected and a final treatment recommendation is made. Omitting comparators from either the design or analysis phase of research when using VOI methods can lead to incorrect trial designs and/or treatment recommendations. Overall, we conclude that incorrectly specifying the health economic model by ignoring potential comparators can lead to misleading VOI results and potentially waste scarce research resources.

Use of quantitative bias analysis to evaluate single-arm trials with real-world data external controls.

PURPOSE: Use of real-world data (RWD) for external controls added to single-arm trials (SAT) is increasingly prevalent in regulatory submissions. Due to inherent differences in the data-generating mechanisms, biases can arise. This paper aims to illustrate how to use quantitative bias analysis (QBA). METHODS: Advanced non-small cell lung cancer (NSCLC) serves as an example, where many small subsets of patients with molecular tumor subtypes exist. First, some sources of bias that may occur in oncology when comparing RWD to SAT are described. Second, using a hypothetical immunotherapy agent, a dataset is simulated based on expert input for survival analysis of advanced NSCLC. Finally, we illustrate the impact of three biases: missing confounder, misclassification of exposure, and outcome evaluation. RESULTS: For each simulated scenario, bias was induced by removing or adding data; hazard ratios (HRs) were estimated applying conventional analyses. Estimating the bias-adjusted treatment effect and uncertainty required carefully selecting the bias model and bias factors. Although the magnitude of each biased and bias-adjusted HR appeared moderate in all three hypothetical scenarios, the direction of bias was variable. CONCLUSION: These findings suggest that QBA can provide an intuitive framework for bias analysis, providing a key means of challenging assumptions about the evidence. However, the accuracy of bias analysis is itself dependent on correct specification of the bias model and bias factors. Ultimately, study design should reduce bias, but QBA allows us to evaluate the impact of unavoidable bias to assess the quality of the evidence.

Reporting of interventional clinical trial results in an academic center: a survey of completed studies.

BACKGROUND: The dissemination of clinical trial results is an important scientific and ethical endeavour. This survey of completed interventional studies in a French academic center describes their reporting status. METHODS: We explored all interventional studies sponsored by Rennes University Hospital identified on the French Open Science Monitor which tracks trials registered on EUCTR or clinicaltrials.gov, and provides an automatic assessment of the reporting of results. For each study, we ascertained the actual reporting of results using systematic searches on the hospital internal database, bibliographic databases (Google Scholar, PubMed), and by contacting all principal investigators (PIs). We describe several features (including total budget and numbers of trial participants) of the studies that did not report any results. RESULTS: The French Open Science Monitor identified 93 interventional studies, among which 10 (11%) reported results. In contrast, our survey identified 36 studies (39%) reporting primary analysis results and an additional 18 (19%) reporting results for secondary analyses (without results for their primary analysis). The overall budget for studies that did not report any results was estimated to be €5,051,253 for a total of 6,735 trial participants. The most frequent reasons for the absence of results reported by PIs were lack of time for 18 (42%), and logistic difficulties (e.g. delay in obtaining results or another blocking factor) for 12 (28%). An association was found between non-publication and negative results (adjusted Odds Ratio = 4.70, 95% Confidence Interval [1.67;14.11]). CONCLUSIONS: Even allowing for the fact that automatic searches underestimate the number of studies with published results, the level of reporting was disappointingly low. This amounts to a waste of trial participants' implication and money. Corrective actions are needed. TRIAL REGISTRATION: https://osf.io/q5hcs.

Utilization of fluid-based biomarkers as endpoints in disease-modifying clinical trials for Alzheimer's disease: a systematic review.

BACKGROUND: Clinical trials in Alzheimer's disease (AD) had high failure rates for several reasons, including the lack of biological endpoints. Fluid-based biomarkers may present a solution to measure biologically relevant endpoints. It is currently unclear to what extent fluid-based biomarkers are applied to support drug development. METHODS: We systematically reviewed 272 trials (clinicaltrials.gov) with disease-modifying therapies starting between 01-01-2017 and 01-01-2024 and identified which CSF and/or blood-based biomarker endpoints were used per purpose and trial type. RESULTS: We found that 44% (N = 121) of the trials employed fluid-based biomarker endpoints among which the CSF ATN biomarkers (Aß (42/40), p/tTau) were used most frequently. In blood, inflammatory cytokines, NFL, and pTau were most frequently employed. Blood- and CSF-based biomarkers were used approximately equally. Target engagement biomarkers were used in 26% (N = 72) of the trials, mainly in drugs targeting inflammation and amyloid. Lack of target engagement markers is most prominent in synaptic plasticity/neuroprotection, neurotransmitter receptor, vasculature, epigenetic regulators, proteostasis and, gut-brain axis targeting drugs. Positive biomarker results did not always translate to cognitive effects, most commonly the small significant reductions in CSF tau isoforms that were seen following anti-Tau treatments. On the other hand, the positive anti-amyloid trials results on cognitive function were supported by clear effect in most fluid markers. CONCLUSIONS: As the field moves towards primary prevention, we expect an increase in the use of fluid-based biomarkers to determine disease modification. Use of blood-based biomarkers will rapidly increase, but CSF markers remain important to determine brain-specific treatment effects. With improving techniques, new biomarkers can be found to diversify the possibilities in measuring treatment effects and target engagement. It remains important to interpret biomarker results in the context of the trial and be aware of the performance of the biomarker. Diversifying biomarkers could aid in the development of surrogacy biomarkers for different drug targets.

The role of quality of life data as an endpoint for collecting real-world evidence within geroscience clinical trials.

With geroscience research evolving at a fast pace, the need arises for human randomized controlled trials to assess the efficacy of geroprotective interventions to prevent age-related adverse outcomes, disease, and mortality in normative aging cohorts. However, to confirm efficacy requires a long-term and costly approach as time to the event of morbidity and mortality can be decades. While this could be circumvented using sensitive biomarkers of aging, current molecular, physiological, and digital endpoints require further validation. In this review, we discuss how collecting real-world evidence (RWE) by obtaining health data that is amenable for collection from large heterogeneous populations in a real-world setting can help speed up validation of geroprotective interventions. Further, we propose inclusion of quality of life (QoL) data as a biomarker of aging and candidate endpoint for geroscience clinical trials to aid in distinguishing healthy from unhealthy aging. We highlight how QoL assays can aid in accelerating data collection in studies gathering RWE on the geroprotective effects of repurposed drugs to support utilization within healthy longevity medicine. Finally, we summarize key metrics to consider when implementing QoL assays in studies, and present the short-form 36 (SF-36) as the most well-suited candidate endpoint.

Biomarkers as Endpoints in Clinical Trials for Alzheimer's Disease.

Background: Alzheimer's disease (AD) is a neurodegenerative disease that imposes economic and societal burden. Biomarkers have played a crucial role in the recent approval of aducanumab and lecanemab as disease-modifying therapies which marked a significant milestone for the treatment of AD. The inclusion of biomarkers in AD trials facilitates precise diagnosis, monitors safety, demonstrates target engagement, and supports disease modification. Objective: This study analyzed the utilization state and trends of biomarkers as endpoints in AD trials. Methods: In this retrospective study, trials were collected by searching clinicaltrials.gov using the term "Alzheimer". Primary and secondary outcomes were analyzed separately for each phase. Results: Among the 1,048 analyzed trials, 313 (29.87%) adopted biomarkers as primary endpoints and 364 (34.73%) as secondary endpoints, mainly in phases 1 and 2. The top three biomarkers adopted as primary endpoints in phases 1, 2, and 3 were amyloid-PET, tau-PET, and MRI. The top three biomarkers adopted as secondary endpoints, in phase 1, were cerebrospinal fluid (CSF) amyloid-ß (Aß), blood Aß and amyloid-PET; in phase 2, they were MRI, CSF Aß, and CSF phospho-tau; and in phase 3, they were amyloid PET, MRI, and blood Aß. There was a statistically significant increase in the adoption of biomarkers as primary endpoints in phase 2 trials (p = 0.001) and secondary endpoints in phase 3 trials (p = 0.001). Conclusions: The growing recognition of the importance of biomarkers in AD trial' design and drug development is evident by the significant steady increase in biomarkers' utilization in phases 2 and 3.

Glucagon-like peptide-1 class drugs show clear protective effects in Parkinson's and Alzheimer's disease clinical trials: A revolution in the making?

Parkinson's disease (PD) is a complex syndrome for which there is no disease-modifying treatment on the market. However, a group of drugs from the Glucagon-like peptide-1 (GLP-1) class have shown impressive improvements in clinical phase II trials. Exendin-4 (Bydureon), Liraglutide (Victoza, Saxenda) and Lixisenatide (Adlyxin), drugs that are on the market as treatments for diabetes, have shown clear effects in improving motor activity in patients with PD in phase II clinical trials. In addition, Liraglutide has shown improvement in cognition and brain shrinkage in a phase II trial in patients with Alzheimer disease (AD). Two phase III trials testing the GLP-1 drug semaglutide (Wegovy, Ozempic, Rybelsus) are ongoing. This perspective article will summarize the clinical results obtained so far in this novel research area. We are at a crossroads where GLP-1 class drugs are emerging as a new treatment strategy for PD and for AD. Newer drugs that have been designed to enter the brain easier are being developed already show improved effects in preclinical studies compared with the older GLP-1 class drugs that had been developed to treat diabetes. The future looks bright for new treatments for AD and PD.

AlpaPICO: Extraction of PICO frames from clinical trial documents using LLMs.

In recent years, there has been a surge in the publication of clinical trial reports, making it challenging to conduct systematic reviews. Automatically extracting Population, Intervention, Comparator, and Outcome (PICO) from clinical trial studies can alleviate the traditionally time-consuming process of manually scrutinizing systematic reviews. Existing approaches of PICO frame extraction involves supervised approach that relies on the existence of manually annotated data points in the form of BIO label tagging. Recent approaches, such as In-Context Learning (ICL), which has been shown to be effective for a number of downstream NLP tasks, require the use of labeled examples. In this work, we adopt ICL strategy by employing the pretrained knowledge of Large Language Models (LLMs), gathered during the pretraining phase of an LLM, to automatically extract the PICO-related terminologies from clinical trial documents in unsupervised set up to bypass the availability of large number of annotated data instances. Additionally, to showcase the highest effectiveness of LLM in oracle scenario where large number of annotated samples are available, we adopt the instruction tuning strategy by employing Low Rank Adaptation (LORA) to conduct the training of gigantic model in low resource environment for the PICO frame extraction task. More specifically, both of the proposed frameworks utilize AlpaCare as base LLM which employs both few-shot in-context learning and instruction tuning techniques to extract PICO-related terms from the clinical trial reports. We applied these approaches to the widely used coarse-grained datasets such as EBM-NLP, EBM-COMET and fine-grained datasets such as EBM-NLPrev and EBM-NLPh. Our empirical results show that our proposed ICL-based framework produces comparable results on all the version of EBM-NLP datasets and the proposed instruction tuned version of our framework produces state-of-the-art results on all the different EBM-NLP datasets. Our project is available at https://github.com/shrimonmuke0202/AlpaPICO.git.