The Digital Platform and Its Emerging Role in Decentralized Clinical Trials.

Decentralized clinical trials (DCTs) are becoming increasingly popular. Digital clinical trial platforms are software environments where users complete designated clinical trial tasks, providing investigators and trial participants with efficient tools to support trial activities and streamline trial processes. In particular, digital platforms with a modular architecture lend themselves to DCTs, where individual trial activities can correspond to specific platform modules. While design features can allow users to customize their platform experience, the real strengths of digital platforms for DCTs are enabling centralized data capture and remote monitoring of trial participants and in using digital technologies to streamline workflows and improve trial management. When selecting a platform for use in a DCT, sponsors and investigators must consider the specific trial requirements. All digital platforms are limited in their functionality and technical capabilities. Integrating additional functional modules into a central platform may solve these challenges, but few commercial platforms are open to integrating third-party components. The lack of common data standardization protocols for clinical trials will likely limit the development of one-size-fits-all digital platforms for DCTs. This viewpoint summarizes the current role of digital platforms in supporting decentralized trial activities, including a discussion of the potential benefits and challenges of digital platforms for investigators and participants. We will highlight the role of the digital platform in the development of DCTs and emphasize where existing technology is functionally limiting. Finally, we will discuss the concept of the ideal fully integrated and unified DCT and the obstacles developers must address before it can be realized.

Evaluating guideline and registration policies among neurology journals: a cross-sectional analysis.

BACKGROUND: Neurological disorders have had a substantial rise the last three decades, imposing substantial burdens on both patients and healthcare costs. Consequently, the demand for high-quality research has become crucial for exploring effective treatment options. However, current neurology research has some limitations in terms of transparency, reproducibility, and reporting bias. The adoption of reporting guidelines (RGs) and trial registration policies has been proven to address these issues and improve research quality in other medical disciplines. It is unclear the extent to which these policies are being endorsed by neurology journals. Therefore, our study aims to evaluate the publishing policies of top neurology journals regarding RGs and trial registration. METHODS: For this cross-sectional study, neurology journals were identified using the 2021 Scopus CiteScore Tool. The top 100 journals were listed and screened for eligibility for our study. In a masked, duplicate fashion, investigators extracted data on journal characteristics, policies on RGs, and policies on trial registration using information from each journal's Instruction for Authors webpage. Additionally, investigators contacted journal editors to ensure information was current and accurate. No human participants were involved in this study. Our data collection and analyses were performed from December 14, 2022, to January 9, 2023. RESULTS: Of the 356 neurology journals identified, the top 100 were included into our sample. The five-year impact of these journals ranged from 50.844 to 2.226 (mean [SD], 7.82 [7.01]). Twenty-five (25.0%) journals did not require or recommend a single RG within their Instructions for Authors webpage, and a third (33.0%) did not require or recommend clinical trial registration. The most frequently mentioned RGs were CONSORT (64.6%), PRISMA (52.5%), and ARRIVE (53.1%). The least mentioned RG was QUOROM (1.0%), followed by MOOSE (9.0%), and SQUIRE (17.9%). CONCLUSIONS: While many top neurology journals endorse the use of RGs and trial registries, there are still areas where their adoption can be improved. Addressing these shortcomings leads to further advancements in the field of neurology, resulting in higher-quality research and better outcomes for patients.

Using online methods to recruit participants into mental health clinical trials: considerations and recommendations from the RE-MIND study.

BACKGROUND: Ensuring diversity in clinical trials can be a challenge, which may be exacerbated when recruiting vulnerable populations, such as participants with mental health illness. As recruitment continues to be the major cause of trial delays, researchers are turning to online recruitment strategies, e.g. social media, to reach a wider population and reduce recruitment time and costs. There is mixed evidence for the use of online recruitment strategies; therefore, the REcruitment in Mental health trials: broadening the 'net', opportunities for INclusivity through online methoDs (RE-MIND) study aimed to identify evidence and provide guidance for use of online strategies in recruitment to mental health trials, with a focus on whether online strategies can enhance inclusivity. This commentary, as part of the RE-MIND study, focusses on providing recommendations for recruitment strategy selection in future research with the aim to improve trial efficiency. A mixed-methods approach was employed involving three work packages: (I) an evidence review of a cohort of 97 recently published randomised controlled trials/feasibility or pilot studies in mental health to assess the impact of online versus offline recruitment; (II) a qualitative study investigating the experiences of n = 23 key stakeholders on use of an online recruitment approach in mental health clinical trials; (III) combining the results of WP1 and WP2 to produce recommendations on the use of an online recruitment strategy in mental health clinical trials. The findings from WP1 and 2 have been published elsewhere; this commentary represents the results of the third work package. CONCLUSION: For external validity, clinical trial participants should reflect the populations that will ultimately receive the interventions being tested, if proven effective. To guide researchers on their options for inclusive recruitment strategies, we have developed a list of considerations and practical recommendations on how to maximise the use of online recruitment methods.

Enhancing Patient Selection in Sepsis Clinical Trials Design Through an AI Enrichment Strategy: Algorithm Development and Validation.

BACKGROUND: Sepsis is a heterogeneous syndrome, and enrollment of more homogeneous patients is essential to improve the efficiency of clinical trials. Artificial intelligence (AI) has facilitated the identification of homogeneous subgroups, but how to estimate the uncertainty of the model outputs when applying AI to clinical decision-making remains unknown. OBJECTIVE: We aimed to design an AI-based model for purposeful patient enrollment, ensuring that a patient with sepsis recruited into a trial would still be persistently ill by the time the proposed therapy could impact patient outcome. We also expected that the model could provide interpretable factors and estimate the uncertainty of the model outputs at a customized confidence level. METHODS: In this retrospective study, 9135 patients with sepsis requiring vasopressor treatment within 24 hours after sepsis onset were enrolled from Beth Israel Deaconess Medical Center. This cohort was used for model development, and 10-fold cross-validation with 50 repeats was used for internal validation. In total, 3743 patients with sepsis from the eICU Collaborative Research Database were used as the external validation cohort. All included patients with sepsis were stratified based on disease progression trajectories: rapid death, recovery, and persistent ill. A total of 148 variables were selected for predicting the 3 trajectories. Four machine learning algorithms with 3 different setups were used. We estimated the uncertainty of the model outputs using conformal prediction (CP). The Shapley Additive Explanations method was used to explain the model. RESULTS: The multiclass gradient boosting machine was identified as the best-performing model with good discrimination and calibration performance in both validation cohorts. The mean area under the receiver operating characteristic curve with SD was 0.906 (0.018) for rapid death, 0.843 (0.008) for recovery, and 0.807 (0.010) for persistent ill in the internal validation cohort. In the external validation cohort, the mean area under the receiver operating characteristic curve (SD) was 0.878 (0.003) for rapid death, 0.764 (0.008) for recovery, and 0.696 (0.007) for persistent ill. The maximum norepinephrine equivalence, total urine output, Acute Physiology Score III, mean systolic blood pressure, and the coefficient of variation of oxygen saturation contributed the most. Compared to the model without CP, using the model with CP at a mixed confidence approach reduced overall prediction errors by 27.6% (n=62) and 30.7% (n=412) in the internal and external validation cohorts, respectively, as well as enabled the identification of more potentially persistent ill patients. CONCLUSIONS: The implementation of our model has the potential to reduce heterogeneity and enroll more homogeneous patients in sepsis clinical trials. The use of CP for estimating the uncertainty of the model outputs allows for a more comprehensive understanding of the model's reliability and assists in making informed decisions based on the predicted outcomes.

[Evaluation of the Functional Reserve and Exercise Tolerance in Patients with CHF in Clinical Trials (Consent Document of the Editorial board of the Journal of Cardiology, the Board of the Society of Specialists in Heart Failure (SSHF) and Working Group "Non-drug treatment methods" of SSHF)].

Assessing the functional capacity and exercise tolerance is an important and widely used research tool in patients with heart failure. It is used not only in cardiac rehabilitation and physical therapy, but also for inclusion criteria and outcome measures in studies of drug interventions. This document outlines the scope, guidelines for the implementation and interpretation, and limitations of the methods for assessing the functional capacity and exercise tolerance in clinical trials in patients with heart failure.

Cancer research provides a model for advancing clinical trials in dementia in the era of disease-modifying Alzheimer's-type dementia therapies.

Dementia and cancer are multifactorial, widely-feared, age-associated clinical syndromes that are increasing in prevalence. There have been major breakthroughs in clinical cancer research leading to some effective treatments, whereas the field of dementia has achieved comparatively limited success in clinical research. The lessons of cancer research may help those in the dementia research field in confronting some of the dilemmas faced when the clinical care regimen is not entirely safe or efficacious. Cancer clinical trials have assumed that untreated individuals with cancer are at high risk for morbidity and mortality after primary diagnoses. Thus, patients deserve a choice of clinical interventions, either standard of care or experimental, even if the benefits are not certain and the therapy's side effects are potentially severe. The prognosis for many individuals at risk for dementia carries a correspondingly high level of risk for both mortality and severe morbidity, particularly if one focuses on "health-span" rather than lifespan. Caregivers and patients can be strongly impacted by dementia and the many troubling associated symptoms that often go well beyond amnesia. Polls, surveys, and a literature on "dementia worry" strongly underscore that the public fears dementia. While there are institutional and industry hurdles that complicate enrollment in randomized trials, the gravity of the future morbidity and mortality inherent in a dementia diagnosis may require reconsideration of the current protective stance that limits the freedom of at-risk individuals (either symptomatic or asymptomatic) to participate and potentially benefit from ongoing clinical research. There is also evidence from both cancer and dementia research that individuals enrolled in the placebo arms of clinical trials have unexpectedly good outcomes, indicating that participation in clinical trial can have medical benefits to enrollees. To highlight aspects of cancer clinical research that may inform present and future dementia clinical research, this review highlights three main themes: the risk of side effects should be weighed against the often dire consequences of non-treatment; the desirability of long-term incremental (rather than "magic bullet") clinical advances; and, the eventual importance of combination therapies, reflecting that the dementia clinical syndrome has many underlying biological pathways.

Clinical Trial Data-Driven Risk Assessment of Drug-Drug Interactions: A Rapid and Accurate Decision-Making Tool.

BACKGROUND: In clinical practice, the vast array of potential drug combinations necessitates swift and accurate assessments of pharmacokinetic drug-drug interactions (DDIs), along with recommendations for adjustments. Current methodologies for clinical DDI evaluations primarily rely on basic extrapolations from clinical trial data. However, these methods are limited in accuracy owing to their lack of a comprehensive consideration of various critical factors, including the inhibitory potency, dosage, and type of the inhibitor, as well as the metabolic fraction and intestinal availability of the substrate. OBJECTIVE: This study aims to propose an efficient and accurate clinical pharmacokinetic-mediated DDI assessment tool, which comprehensively considers the effects of inhibitory potency and dosage of inhibitors, intestinal availability and fraction metabolized of substrates on DDI outcomes. METHODS: This study focuses on DDIs caused by cytochrome P450 3A4 enzyme inhibition, utilizing extensive clinical trial data to establish a methodology to calculate the metabolic fraction and intestinal availability for substrates, as well as the concentration and inhibitory potency for inhibitors ( K i or k inact / K I ). These parameters were then used to predict the outcomes of DDIs involving 33 substrates and 20 inhibitors. We also defined the risk index for substrates and the potency index for inhibitors to establish a clinical DDI risk scale. The training set for parameter calculation consisted of 73 clinical trials. The validation set comprised 89 clinical DDI trials involving 53 drugs. which was used to evaluate the reliability of in vivo values of K i and k inact / K I , the accuracy of DDI predictions, and the false-negative rate of risk scale. RESULTS: First, the reliability of the in vivo K i and k inact / K I values calculated in this study was assessed using a basic static model. Compared with values obtained from other methods, this study values showed a lower geometric mean fold error and root mean square error. Additionally, incorporating these values into the physiologically based pharmacokinetic-DDI model facilitated a good fitting of the C-t curves when the substrate's metabolic enzymes are inhibited. Second, area under the curve ratio predictions of studied drugs were within a 1.5 × margin of error in 81% of cases compared with clinical observations in the validation set. Last, the clinical DDI risk scale developed in this study predicted the actual risks in the validation set with only a 5.6% incidence of serious false negatives. CONCLUSIONS: This study offers a rapid and accurate approach for assessing the risk of pharmacokinetic-mediated DDIs in clinical practice, providing a foundation for rational combination drug use and dosage adjustments.

Feasibility of using real-world data to emulate substance use disorder clinical trials: a cross-sectional study.

INTRODUCTION: Real-world evidence is receiving considerable attention as a way to evaluate the efficacy and safety of medical products for substance use disorders (SUDs). However, the feasibility of using real-world data (RWD) to emulate clinical trials evaluating treatments for SUDs is uncertain. The aim of this study is to identify the number of clinical trials evaluating treatments for SUDs with reported results that could be feasibly emulated using observational data from contemporary insurance claims and/or electronic health record (EHR) data. METHODS: In this cross-sectional study, all phase 2-4 trials evaluating treatments for SUDs registered on ClinicalTrials.gov with reported results were identified. Each trial was evaluated to determine if the indications, interventions, at least 80% of eligibility criteria, comparators, and primary end points could be ascertained using contemporarily available administrative claims and/or structured EHR data. RESULTS: There were 272 SUD trials on ClinicalTrials.gov with reported results. Of these, when examining feasibility using contemporarily available administrative claims and/or structured EHR data, 262 (96.3%) had indications that were ascertainable; 194 (71.3%) had interventions that were ascertainable; 21 (7.7%) had at least 80% of eligibility criteria that were ascertainable; 17 (6.3%) had active comparators that were ascertainable; and 61 (22.4%) had primary end points that were ascertainable. In total, there were no trials for which all 5 characteristics were ascertainable using contemporarily available administrative claims and/or structured EHR data. When considering placebo comparators as ascertainable, there were 6 (2.2%) trials that had all 5 key characteristics classified as ascertainable from contemporarily available administrative claims and/or structured EHR data. CONCLUSIONS: No trials evaluating treatments for SUDs could be feasibly emulated using contemporarily available RWD, demonstrating a need for an increase in the resolution of data capture within a public health system to facilitate trial emulation.

Multidisciplinary considerations for implementing Bayesian borrowing in basket trials.

Drug development has historically relied on phase I-III clinical trials including participants sharing the same disease. However, drug development has evolved as the discovery of mechanistic drivers of disease demonstrated that the same therapeutic target may provide benefits across different diseases. A basket trial condenses evaluation of one therapy among multiple related diseases into a single trial and presents an opportunity to borrow information across them rather than viewing each in isolation. Borrowing is a statistical tool but requires a foundation of clinical and therapeutic mechanistic justification. We review the Bayesian borrowing approach, including its assumptions, and provide a framework for how this approach can be evaluated for successful use in a basket trial for drug development.

Recommendations for clinical trial design in acute kidney injury from the 31st acute disease quality initiative consensus conference. A consensus statement.

PURPOSE: Novel interventions for the prevention or treatment of acute kidney injury (AKI) are currently lacking. To facilitate the evaluation and adoption of new treatments, the use of the most appropriate design and endpoints for clinical trials in AKI is critical and yet there is little consensus regarding these issues. We aimed to develop recommendations on endpoints and trial design for studies of AKI prevention and treatment interventions based on existing data and expert consensus. METHODS: At the 31st Acute Disease Quality Initiative (ADQI) meeting, international experts in critical care, nephrology, involving adults and pediatrics, biostatistics and people with lived experience (PWLE) were assembled. We focused on four main areas: (1) patient enrichment strategies, (2) prevention and attenuation studies, (3) treatment studies, and (4) innovative trial designs of studies other than traditional (parallel arm or cluster) randomized controlled trials. Using a modified Delphi process, recommendations and consensus statements were developed based on existing data, with > 90% agreement among panel members required for final adoption. RESULTS: The panel developed 12 consensus statements for clinical trial endpoints, application of enrichment strategies where appropriate, and inclusion of PWLE to inform trial designs. Innovative trial designs were also considered. CONCLUSION: The current lack of specific therapy for prevention or treatment of AKI demands refinement of future clinical trial design. Here we report the consensus findings of the 31st ADQI group meeting which has attempted to address these issues including the use of predictive and prognostic enrichment strategies to enable appropriate patient selection.

Digital endpoints in clinical trials: emerging themes from a multi-stakeholder Knowledge Exchange event.

BACKGROUND: Digital technologies, such as wearable devices and smartphone applications (apps), can enable the decentralisation of clinical trials by measuring endpoints in people's chosen locations rather than in traditional clinical settings. Digital endpoints can allow high-frequency and sensitive measurements of health outcomes compared to visit-based endpoints which provide an episodic snapshot of a person's health. However, there are underexplored challenges in this emerging space that require interdisciplinary and cross-sector collaboration. A multi-stakeholder Knowledge Exchange event was organised to facilitate conversations across silos within this research ecosystem. METHODS: A survey was sent to an initial list of stakeholders to identify potential discussion topics. Additional stakeholders were identified through iterative discussions on perspectives that needed representation. Co-design meetings with attendees were held to discuss the scope, format and ethos of the event. The event itself featured a cross-disciplinary selection of talks, a panel discussion, small-group discussions facilitated via a rolling seating plan and audience participation via Slido. A transcript was generated from the day, which, together with the output from Slido, provided a record of the day's discussions. Finally, meetings were held following the event to identify the key challenges for digital endpoints which emerged and reflections and recommendations for dissemination. RESULTS: Several challenges for digital endpoints were identified in the following areas: patient adherence and acceptability; algorithms and software for devices; design, analysis and conduct of clinical trials with digital endpoints; the environmental impact of digital endpoints; and the need for ongoing ethical support. Learnings taken for next generation events include the need to include additional stakeholder perspectives, such as those of funders and regulators, and the need for additional resources and facilitation to allow patient and public contributors to engage meaningfully during the event. CONCLUSIONS: The event emphasised the importance of consortium building and highlighted the critical role that collaborative, multi-disciplinary, and cross-sector efforts play in driving innovation in research design and strategic partnership building moving forward. This necessitates enhanced recognition by funders to support multi-stakeholder projects with patient involvement, standardised terminology, and the utilisation of open-source software.

Assessing the Role of Patient Generation Techniques in Virtual Clinical Trial Outcomes.

Virtual clinical trials (VCTs) are growing in popularity as a tool for quantitatively predicting heterogeneous treatment responses across a population. In the context of a VCT, a plausible patient is an instance of a mathematical model with parameter (or attribute) values chosen to reflect features of the disease and response to treatment for that particular patient. A number of techniques have been introduced to determine the set of model parametrizations to include in a virtual patient cohort. These methodologies generally start with a prior distribution for each model parameter and utilize some criteria to determine whether a parameter set sampled from the priors should be included or excluded from the plausible population. No standard technique exists, however, for generating these prior distributions and choosing the inclusion/exclusion criteria. In this work, we rigorously quantify the impact that VCT design choices have on VCT predictions. Rather than use real data and a complex mathematical model, a spatial model of radiotherapy is used to generate simulated patient data and the mathematical model used to describe the patient data is a two-parameter ordinary differential equations model. This controlled setup allows us to isolate the impact of both the prior distribution and the inclusion/exclusion criteria on both the heterogeneity of plausible populations and on predicted treatment response. We find that the prior distribution, rather than the inclusion/exclusion criteria, has a larger impact on the heterogeneity of the plausible population. Yet, the percent of treatment responders in the plausible population was more sensitive to the inclusion/exclusion criteria utilized. This foundational understanding of the role of virtual clinical trial design should help inform the development of future VCTs that use more complex models and real data.

The development of a set of key points to aid clinicians and researchers in designing and conducting n-of-1 trials.

INTRODUCTION: n-of-1 trials are undertaken to optimise the evaluation of health technologies in individual patients. They involve a single patient receiving treatments, both interventional and control, consecutively over set periods of time, the order of which is decided at random. Although n-of-1 trials are undertaken in medical research it could be argued they have the utility to be undertaken more frequently. We undertook the National Institute for Health Research (NIHR) commissioned DIAMOND (Development of generalisable methodology for n-of-1 trials delivery for very low volume treatments) project to develop key points to assist clinicians and researchers in designing and conducting n-of-1 trials. METHODS: The key points were developed by undertaking a stakeholder workshop, followed by a discussion within the study team and then a stakeholder dissemination and feedback event. The stakeholder workshop sought to gain the perspectives of a variety of stakeholders (including clinicians, researchers and patient representatives) on the design and use of n-of-1 trials. A discussion between the study team was held to reflect on the workshop and draft the key points. Lastly, the stakeholders from the workshop were invited to a dissemination and feedback session where the proposed key points were presented and their feedback gained. RESULTS: A set of 22 key points were developed based on the insights from the workshop and subsequent discussions. They provide guidance on when an n-of-1 trial might be a viable or appropriate study design and discuss key decisions involved in the design of n-of-1 trials, including determining an appropriate number of treatment periods and cycles, the choice of comparator, recommended approaches to randomisation and blinding, the use of washout periods and approaches to analysis. CONCLUSIONS: The key points developed in the project will support clinical researchers to understand key considerations when designing n-of-1 trials. It is hoped they will support the wider implementation of the study design.

[The possibilities and advantages of conducting multicenter clinical trials on the basis of outpatient clinics in Moscow].

The article presents the special role of the outpatient unit (urban polyclinics) in the system of urban medical organizations, which has significant development potential in the field of clinical research. This activity became possible due to the systematic work on equipping outpatient clinics with the most modern diagnostic equipment, the availability of specialists trained in the organization and conduct of clinical trials according to the international rules of good clinical practice. A special value lies in the fact that the polyclinic network has an extensive database that includes millions of patients and provides the opportunity to perform the highest level of medical expertise and research.

The performance of a Bayesian value-based sequential clinical trial design in the presence of an equivocal cost-effectiveness signal: evidence from the HERO trial.

BACKGROUND: There is increasing interest in the capacity of adaptive designs to improve the efficiency of clinical trials. However, relatively little work has investigated how economic considerations - including the costs of the trial - might inform the design and conduct of adaptive clinical trials. METHODS: We apply a recently published Bayesian model of a value-based sequential clinical trial to data from the 'Hydroxychloroquine Effectiveness in Reducing symptoms of hand Osteoarthritis' (HERO) trial. Using parameters estimated from the trial data, including the cost of running the trial, and using multiple imputation to estimate the accumulating cost-effectiveness signal in the presence of missing data, we assess when the trial would have stopped had the value-based model been used. We used re-sampling methods to compare the design's operating characteristics with those of a conventional fixed length design. RESULTS: In contrast to the findings of the only other published retrospective application of this model, the equivocal nature of the cost-effectiveness signal from the HERO trial means that the design would have stopped the trial close to, or at, its maximum planned sample size, with limited additional value delivered via savings in research expenditure. CONCLUSION: Evidence from the two retrospective applications of this design suggests that, when the cost-effectiveness signal in a clinical trial is unambiguous, the Bayesian value-adaptive design can stop the trial before it reaches its maximum sample size, potentially saving research costs when compared with the alternative fixed sample size design. However, when the cost-effectiveness signal is equivocal, the design is expected to run to, or close to, the maximum sample size and deliver limited savings in research costs.

Streamlining electronic reporting of serious adverse events (SAEs) using the REDCap data collection system: the eSAE Project.

BACKGROUND: It is essential that electronic data collection (EDC) systems are both compliant with regulations and the principles of Good Clinical Practice (GCP) to allow for the timely and accurate reporting of data including safety data. For clinical trials of investigational medicinal products (CTIMPs), investigators must immediately report to the sponsor any serious adverse event (SAE) that occurs in a site for which they are responsible. It is therefore expected that sponsors provide systems for timely review and reporting should a SAE be classified as a suspected unexpected serious adverse reaction (SUSAR). Challenges arise when data related to adverse events (AEs) needs to be re-entered for SAEs; this can be prone to error and may delay reporting. Additionally, recognising what has changed from an initial SAE report when an investigator responds to queries raised can cause errors. METHOD: A multi-disciplinary working group came together from a UK academic clinical trials unit (CTU) to establish if an electronic system could be created in the unit's open-source EDC system-REDCap, to manage SAEs in an efficient way. RESULTS: A module has been created in REDCap to facilitate electronic SAE reporting: enabling an AE form to automatically trigger an SAE form for any AE which is also a SAE, prepopulating relevant fields of the SAE form, reducing the risk of delay and error when entering data into the SAE form. The system has also been developed with an embedded code to allow for instant visual recognition of any data updated following reporting to allow the sponsor to immediately review and resolve SAEs in a timely manner, complying with UK regulatory reporting. This functionality 'The eSAE Project' is now an active project for all of our new trials where data collection is undertaken using the REDCap system. CONCLUSION: The eSAE Project coded into REDCap offers a unique way of populating SAE forms with information already entered in the initial AE forms as applicable, coupled with highlighting any updates during the lifetime of the SAE for sponsors to identify any new information that needs to be reassessed to process and report the SAE.

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-ß (Aß) are the main pathological characteristics of AD. However, the actual causal process of AD is not yet identified. Oxidative stress occurs prior to amyloid Aß 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.

Key insights and challeneges in noninferiority trials.

Noninferiority clinical trials are crucial for evaluating the effectiveness of new interventions compared to standard interventions. By establishing statistical and clinical comparability, these trials can be conducted to demonstrate that a new intervention is not significantly inferior to the standard intervention. However, selecting appropriate noninferiority margins and study designs are essential to ensuring valid and reliable results. Moreover, employing the Consolidated Standards of Reporting Trials (CONSORT) statement for reporting noninferiority clinical trials enhances the quality and transparency of research findings. This article addresses key considerations and challenges faced by investigators in planning, conducting, and interpreting the results of noninferiority clinical trials.