Rare disease clinical trials in the European Union: navigating regulatory and clinical challenges.

BACKGROUND: Clinical development for orphan drugs presents significant difficulties and challenges. There is no unique or standard design, conduct, and outcome assessment methodology and it is sometimes impractical to fit design models of rare disease trials in any practiced and well-known framework. In the European Union (EU) these challenges encompass a broad array of subjects, including trial design, study outcomes, patient recruitment, trial conduct ethics, trial cost, and chances of success. This literature-based review study aims to provide a thorough overview of the critical aspects of rare disease trials in the EU by analyzing the current landscape of rare disease trials, highlighting key challenges, delving into regulatory and research initiatives and innovation in trial designs, and proposing multi-faceted solutions to implement effective rare disease clinical trials in the region. DISCUSSION: Traditional clinical trial designs, validation, and evaluation methodologies used for nonorphan drugs often prove unsuitable for orphan drugs, given the small patient populations, sometimes fewer than 1000 cases. There is an increasing need for accessible therapies and both regulators as well as industry are trying to develop affordable and effective drugs to address this need. Despite several steps that have been taken, the timely development of drugs remains a challenge. One of the reasons behind the long development timeline is the recruitment, retention, and conduct of rare disease trials. To optimize the development timelines of orphan drugs in the EU, it is important to ensure that the safety and efficacy of the product is not compromised. Industry and regulatory agencies must implement innovative trial designs, devise flexible policies, and incorporate real-world data for assessing clinical outcomes. CONCLUSION: Collaboration among academic institutions, pharmaceutical companies (both small and major), patient groups, and health authorities is crucial in overcoming obstacles related to clinical trials and providing assistance and creative ideas. The ultimate objective of granting rare disease patients timely and affordable access to medications with a positive balance between benefits and risks is to be met.

Effects of sceptical priors on the performance of adaptive clinical trials with binary outcomes.

It is unclear how sceptical priors impact adaptive trials. We assessed the influence of priors expressing a spectrum of scepticism on the performance of several Bayesian, multi-stage, adaptive clinical trial designs using binary outcomes under different clinical scenarios. Simulations were conducted using fixed stopping rules and stopping rules calibrated to keep type 1 error rates at approximately 5%. We assessed total sample sizes, event rates, event counts, probabilities of conclusiveness and selecting the best arm, root mean squared errors (RMSEs) of the estimated treatment effect in the selected arms, and ideal design percentages (IDPs; which combines arm selection probabilities, power, and consequences of selecting inferior arms), with RMSEs and IDPs estimated in conclusive trials only and after selecting the control arm in inconclusive trials. Using fixed stopping rules, increasingly sceptical priors led to larger sample sizes, more events, higher IDPs in simulations ending in superiority, and lower RMSEs, lower probabilities of conclusiveness/selecting the best arm, and lower IDPs when selecting controls in inconclusive simulations. With calibrated stopping rules, the effects of increased scepticism on sample sizes and event counts were attenuated, and increased scepticism increased the probabilities of conclusiveness/selecting the best arm and IDPs when selecting controls in inconclusive simulations without substantially increasing sample sizes. Results from trial designs with gentle adaptation and non-informative priors resembled those from designs with more aggressive adaptation using weakly-to-moderately sceptical priors. In conclusion, the use of somewhat sceptical priors in adaptive trial designs with binary outcomes seems reasonable when considering multiple performance metrics simultaneously.

Clinical trials and their impact on policy during COVID-19: a review.

Background: Of over 8,000 recorded randomised trials addressing COVID-19, around 80% were of treatments, and 17% have reported results. Approximately 1% were adaptive or platform trials, with 25 having results available, across 29 journal articles and 10 preprint articles. Methods: We conducted an extensive literature review to address four questions about COVID-19 trials, particularly the role and impact of platform/adaptive trials and lessons learned. Results: The key findings were: Q1. Social value in conducting trials and uptake into policy? COVID-19 drug treatments varied substantially and changed considerably, with drugs found effective in definitive clinical trials replacing unproven drugs. Dexamethasone has likely saved ½-2 million lives, and was cost effective across a range of countries and populations, whereas the cost effectiveness of remdesivir is uncertain. Published economic and health system impacts of COVID-19 treatments were infrequent. Q2. Issues with adaptive trial designs. Of the 77 platform trials registered, 6 major platform trials, with approximately 50 treatment arms, recruited ~135,000 participants with funding over $100 million. Q3. Models of good practice. Streamlined set-up processes such as flexible and fast-track funding, ethics, and governance approvals are vital. To facilitate recruitment, simple and streamlined research processes, and pre-existing research networks to coordinate trial planning, design, conduct and practice change are crucial to success. Q4. Potential conflicts to avoid? When treating patients through trials, balancing individual and collective rights and allocating scarce resources between healthcare and research are challenging. Tensions occur between commercial and non-commercial sectors, and academic and public health interests, such as publication and funding driven indicators and the public good. Conclusion: There is a need to (i) reduce small, repetitive, single centre trials, (ii) increase coordination to ensure robust research conducted for treatments, and (iii) a wider adoption of adaptive/platform trial designs to respond to fast-evolving evidence landscape.

Effects of duration of follow-up and lag in data collection on the performance of adaptive clinical trials.

Different combined outcome-data lags (follow-up durations plus data-collection lags) may affect the performance of adaptive clinical trial designs. We assessed the influence of different outcome-data lags (0-105 days) on the performance of various multi-stage, adaptive trial designs (2/4 arms, with/without a common control, fixed/response-adaptive randomisation) with undesirable binary outcomes according to different inclusion rates (3.33/6.67/10 patients/day) under scenarios with no, small, and large differences. Simulations were conducted under a Bayesian framework, with constant stopping thresholds for superiority/inferiority calibrated to keep type-1 error rates at approximately 5%. We assessed multiple performance metrics, including mean sample sizes, event counts/probabilities, probabilities of conclusiveness, root mean squared errors (RMSEs) of the estimated effect in the selected arms, and RMSEs between the analyses at the time of stopping and the final analyses including data from all randomised patients. Performance metrics generally deteriorated when the proportions of randomised patients with available data were smaller due to longer outcome-data lags or faster inclusion, that is, mean sample sizes, event counts/probabilities, and RMSEs were larger, while the probabilities of conclusiveness were lower. Performance metric impairments with outcome-data lags ≤45 days were relatively smaller compared to those occurring with ≥60 days of lag. For most metrics, the effects of different outcome-data lags and lower proportions of randomised patients with available data were larger than those of different design choices, for example, the use of fixed versus response-adaptive randomisation. Increased outcome-data lag substantially affected the performance of adaptive trial designs. Trialists should consider the effects of outcome-data lags when planning adaptive trials.

A Repurposing Programme Evaluating Transdermal Oestradiol Patches for the Treatment of Prostate Cancer Within the PATCH and STAMPEDE Trials: Current Results and Adapting Trial Design.

AIMS: Androgen deprivation therapy (ADT), usually achieved with luteinising hormone releasing hormone analogues (LHRHa), is central to prostate cancer management. LHRHa reduce both testosterone and oestrogen and are associated with significant long-term toxicity. Previous use of oral oestrogens as ADT was curtailed because of cardiovascular toxicity. Transdermal oestrogen (tE2) patches are a potential alternative ADT, supressing testosterone without the associated oestrogen-depletion toxicities (osteoporosis, hot flushes, metabolic abnormalities) and avoiding cardiovascular toxicity, and we here describe their evaluation in men with prostate cancer. MATERIALS AND METHODS: The PATCH (NCT00303784) adaptive trials programme (incorporating recruitment through the STAMPEDE [NCT00268476] platform) is evaluating the safety and efficacy of tE2 patches as ADT for men with prostate cancer. An initial randomised (LHRHa versus tE2) phase II study (n = 251) with cardiovascular toxicity as the primary outcome measure has expanded into a phase III evaluation. Those with locally advanced (M0) or metastatic (M1) prostate cancer are eligible. To reflect changes in both management and prognosis, the PATCH programme is now evaluating these cohorts separately. RESULTS: Recruitment is complete, with 1362 and 1128 in the M0 and M1 cohorts, respectively. Rates of androgen suppression with tE2 were equivalent to LHRHa, with improved metabolic parameters, quality of life and bone health indices (mean absolute change in lumbar spine bone mineral density of -3.0% for LHRHa and +7.9% for tE2 with an estimated difference between arms of 9.3% (95% confidence interval 5.3-13.4). Importantly, rates of cardiovascular events were not significantly different between the two arms and the time to first cardiovascular event did not differ between treatment groups (hazard ratio 1.11, 95% confidence interval 0.80-1.53; P = 0.54). Oncological outcomes are awaited. FUTURE: Efficacy results for the M0 cohort (primary outcome measure metastases-free survival) are expected in the final quarter of 2023. For M1 patients (primary outcome measure - overall survival), analysis using restricted mean survival time is being explored. Allied translational work on longitudinal samples is underway.

An explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized controlled trials.

Randomized controlled trials (RCT) represent the cornerstone of evidence-based medicine but are resource-intensive. We propose and evaluate a machine learning (ML) strategy of adaptive predictive enrichment through computational trial phenomaps to optimize RCT enrollment. In simulated group sequential analyses of two large cardiovascular outcomes RCTs of (1) a therapeutic drug (pioglitazone versus placebo; Insulin Resistance Intervention after Stroke (IRIS) trial), and (2) a disease management strategy (intensive versus standard systolic blood pressure reduction in the Systolic Blood Pressure Intervention Trial (SPRINT)), we constructed dynamic phenotypic representations to infer response profiles during interim analyses and examined their association with study outcomes. Across three interim timepoints, our strategy learned dynamic phenotypic signatures predictive of individualized cardiovascular benefit. By conditioning a prospective candidate's probability of enrollment on their predicted benefit, we estimate that our approach would have enabled a reduction in the final trial size across ten simulations (IRIS: -14.8% ± 3.1%, pone-sample t-test=0.001; SPRINT: -17.6% ± 3.6%, pone-sample t-test<0.001), while preserving the original average treatment effect (IRIS: hazard ratio of 0.73 ± 0.01 for pioglitazone vs placebo, vs 0.76 in the original trial; SPRINT: hazard ratio of 0.72 ± 0.01 for intensive vs standard systolic blood pressure, vs 0.75 in the original trial; all with pone-sample t-test<0.01). This adaptive framework has the potential to maximize RCT enrollment efficiency.

Ethics of Adaptive Designs for Randomized Controlled Trials.

Over recent decades, adaptive trial designs have been used more and more often for clinical trials, including randomized controlled trials (RCTs). This rise in the use of adaptive RCTs has been accompanied by debates about whether such trials offer ethical and methodological advantages over traditional, fixed RCTs. This study examined how experts on clinical trial methods and ethics believe that adaptive RCTs, compared to fixed ones, affect the ethical character of clinical research. We conducted in-depth interviews with 17 researchers from bioethics, epidemiology, biostatistics, and/or medical backgrounds. While about half believed that adaptive trials are more complex and may thus threaten autonomy, these respondents also expressed that this challenge is not insurmountable. Most respondents expressed that efficiency and potential for participant benefit were the main justifications for adaptive trials. There was tension about whether adaptive randomization in response to increasing information disrupts clinical equipoise, with some respondents insisting that uncertainty still exists and therefore clinical equipoise is not disrupted. These findings suggest that further discussion is needed to increase the awareness and utility of these study designs.

How Electronic Medical Record Integration Can Support More Efficient Critical Care Clinical Trials.

Large volumes of data are collected on critically ill patients, and using data science to extract information from the electronic medical record (EMR) and to inform the design of clinical trials represents a new opportunity in critical care research. Using improved methods of phenotyping critical illnesses, subject identification and enrollment, and targeted treatment group assignment alongside newer trial designs such as adaptive platform trials can increase efficiency while lowering costs. Some tools such as the EMR to automate data collection are already in use. Refinement of data science approaches in critical illness research will allow for better clinical trials and, ultimately, improved patient outcomes.

The Future Glioblastoma Clinical Trials Landscape: Early Phase 0, Window of Opportunity, and Adaptive Phase I-III Studies.

PURPOSE OF REVIEW: Innovative clinical trial designs for glioblastoma (GBM) are needed to expedite drug discovery. Phase 0, window of opportunity, and adaptive designs have been proposed, but their advanced methodologies and underlying biostatistics are not widely known. This review summarizes phase 0, window of opportunity, and adaptive phase I-III clinical trial designs in GBM tailored to physicians. RECENT FINDINGS: Phase 0, window of opportunity, and adaptive trials are now being implemented for GBM. These trials can remove ineffective therapies earlier during drug development and improve trial efficiency. There are two ongoing adaptive platform trials: GBM Adaptive Global Innovative Learning Environment (GBM AGILE) and the INdividualized Screening trial of Innovative GBM Therapy (INSIGhT). The future clinical trials landscape in GBM will increasingly involve phase 0, window of opportunity, and adaptive phase I-III studies. Continued collaboration between physicians and biostatisticians will be critical for implementing these trial designs.

Recent innovations in adaptive trial designs: A review of design opportunities in translational research.

Clinical trials are constantly evolving in the context of increasingly complex research questions and potentially limited resources. In this review article, we discuss the emergence of "adaptive" clinical trials that allow for the preplanned modification of an ongoing clinical trial based on the accumulating evidence with application across translational research. These modifications may include terminating a trial before completion due to futility or efficacy, re-estimating the needed sample size to ensure adequate power, enriching the target population enrolled in the study, selecting across multiple treatment arms, revising allocation ratios used for randomization, or selecting the most appropriate endpoint. Emerging topics related to borrowing information from historic or supplemental data sources, sequential multiple assignment randomized trials (SMART), master protocol and seamless designs, and phase I dose-finding studies are also presented. Each design element includes a brief overview with an accompanying case study to illustrate the design method in practice. We close with brief discussions relating to the statistical considerations for these contemporary designs.

An overview of methodological considerations regarding adaptive stopping, arm dropping, and randomization in clinical trials.

BACKGROUND AND OBJECTIVES: Adaptive features may increase flexibility and efficiency of clinical trials, and improve participants' chances of being allocated to better interventions. Our objective is to provide thorough guidance on key methodological considerations for adaptive clinical trials. METHODS: We provide an overview of key methodological considerations for clinical trials employing adaptive stopping, adaptive arm dropping, and response-adaptive randomization. We cover pros and cons of different decisions and provide guidance on using simulation to compare different adaptive trial designs. We focus on Bayesian multi-arm adaptive trials, although the same general considerations apply to frequentist adaptive trials. RESULTS: We provide guidance on 1) interventions and possible common control, 2) outcome selection, follow-up duration and model choice, 3) timing of adaptive analyses, 4) decision rules for adaptive stopping and arm dropping, 5) randomization strategies, 6) performance metrics, their prioritization, and arm selection strategies, and 7) simulations, assessment of performance under different scenarios, and reporting. Finally, we provide an example using a newly developed R simulation engine that may be used to evaluate and compare different adaptive trial designs. CONCLUSION: This overview may help trialists design better and more transparent adaptive clinical trials and to adequately compare them before initiation.

Feasibility of a Platform Trial Design for the Development of Mobile Health Applications: A Review.

Background: A novel adaptive trial design called platform trials (PTs) may offer an effective, efficient, and unbiased approach to evaluate different developer versions of mobile health (m-health) apps. However, the feasibility of their use for this purpose is yet to be explored. Objective: This literature review aims to explore the reported challenges associated with the adaptive PT design to assess its feasibility for the development of m-health apps. Methods: A descriptive literature review using two databases (MEDLINE and Embase) was conducted. Documents published in English between 1947 and September 20, 2020, were eligible for inclusion. Results: The titles and abstracts of 758 records were screened after which 179 full-text articles were assessed for eligibility. A total of 41 articles were included in the synthesis, all published after the year 2000. The synthesis yielded eight distinct categories of challenging issues with PTs relevant to their application in m-health app development, along with potential solutions. These categories are ethical issues (e.g., related to informed consent, equipoise, justice) (with 19 articles contributing content), biases (7 articles), temporal drift (4 articles), miscellaneous statistical issues (3 articles), logistical issues (e.g., cost and human resources, frequent amendments; 6 articles), sample size and power conflict (2 articles), generalizability of the results (2 articles), and operational challenges (1 article). Conclusion: Although PT designs are relatively new, they have promising feasibility for the seamless evaluation of interventions that undergo continuous development, including m-health apps; however, various challenges may hinder their successful implementation.

Decision rules for identifying combination therapies in open-entry, randomized controlled platform trials.

Platform trials have become increasingly popular for drug development programs, attracting interest from statisticians, clinicians and regulatory agencies. Many statistical questions related to designing platform trials-such as the impact of decision rules, sharing of information across cohorts, and allocation ratios on operating characteristics and error rates-remain unanswered. In many platform trials, the definition of error rates is not straightforward as classical error rate concepts are not applicable. For an open-entry, exploratory platform trial design comparing combination therapies to the respective monotherapies and standard-of-care, we define a set of error rates and operating characteristics and then use these to compare a set of design parameters under a range of simulation assumptions. When setting up the simulations, we aimed for realistic trial trajectories, such that for example, a priori we do not know the exact number of treatments that will be included over time in a specific simulation run as this follows a stochastic mechanism. Our results indicate that the method of data sharing, exact specification of decision rules and a priori assumptions regarding the treatment efficacy all strongly contribute to the operating characteristics of the platform trial. Furthermore, different operating characteristics might be of importance to different stakeholders. Together with the potential flexibility and complexity of a platform trial, which also impact the achieved operating characteristics via, for example, the degree of efficiency of data sharing this implies that utmost care needs to be given to evaluation of different assumptions and design parameters at the design stage.

Measuring the effects of a personalized music intervention on agitated behaviors among nursing home residents with dementia: design features for cluster-randomized adaptive trial.

BACKGROUND: Agitated and aggressive behaviors (behaviors) are common in nursing home (NH) residents with dementia. Medications commonly used to manage behaviors have dangerous side effects. NHs are adopting non-pharmacological interventions to manage behaviors, despite a lack of effectiveness evidence and an understanding of optimal implementation strategies. We are conducting an adaptive trial to evaluate the effects of personalized music on behaviors. Adaptive trials may increase efficiency and reduce costs associated with traditional RCTs by learning and making modifications to the trial while it is ongoing. METHODS: We are conducting two consecutive parallel cluster-randomized trials with 54 NHs in each trial (27 treatment, 27 control). Participating NHs were recruited from 4 corporations which differ in size, ownership structure, geography, and residents' racial composition. After randomization, there were no significant differences between the NHs randomized to each trial with respect to baseline behaviors, number of eligible residents, degree of cognitive impairment, or antipsychotic use. Agitated behavior frequency is assessed via staff interviews (primary outcome), required nursing staff conducted resident assessments (secondary outcome), and direct observations of residents (secondary outcome). Between the two parallel trials, the adaptive design will be used to test alternative implementation strategies, increasingly enroll residents who are likely to benefit from the intervention, and seamlessly conduct a stage III/IV trial. DISCUSSION: This adaptive trial allows investigators to estimate the impact of a popular non-pharmaceutical intervention (personalized music) on residents' behaviors, under pragmatic, real-world conditions testing two implementation strategies. This design has the potential to reduce the research timeline by improving the likelihood of powered results, increasingly enrolling residents most likely to benefit from intervention, sequentially assessing the effectiveness of implementation strategies in the same trial, and creating a statistical model to reduce the future need for onsite data collection. The design may also increase research equity by enrolling and tailoring the intervention to populations otherwise excluded from research. Our design will inform pragmatic testing of other interventions with limited efficacy evidence but widespread stakeholder adoption because of the real-world need for non-pharmaceutical approaches. {2A} TRIAL REGISTRATION: ClinicalTrials.gov NCT03821844 . Registered on January 30, 2019. This trial registration meets the World Health Organization (WHO) minimum standard.

Clinical Trials for Idiopathic Pulmonary Fibrosis and the Role of Health Systems.

We are in the midst of transformative innovation in health care delivery and clinical trials in idiopathic pulmonary fibrosis (IPF). Health systems are uniquely positioned at the crossroad of these shifting paradigms, equipped with the resources to expand the research pipeline in IPF through visionary leadership and targeted investments. The authors hope that by prioritizing development of health information technology, supporting a broader range of clinical trial designs, and cultivating broad stakeholder engagement, health systems will generate data to address knowledge-evidence-practice gaps in IPF. This will continue to improve the ability to deliver high-quality, safe, and effective care.

The time is now: role of pragmatic clinical trials in guiding response to global pandemics.

Along with its heavy toll of morbidity and mortality, the coronavirus disease 2019 (COVID-19) pandemic exposed several limitations of the current global research response. The slow and inefficient process of carrying out traditional randomized clinical trials led regulatory authorities to hastily approve treatments and tests without sufficient evidence of safety and efficacy.We here outline issues with the current research platform, summarize shortcomings of traditional randomized clinical trials particularly apparent at the time of pandemics, and highlight the advantages of pragmatic clinical trials as an alternative to rapidly generate the needed clinical evidence. We further discuss barriers and challenges to pragmatic clinical trials implementation and explore opportunities for research institutions and regulatory authorities to facilitate widespread adoption of this vital research tool.As a subsequent wave of COVID-19, and/or another epidemic, are all but inevitable in our lifetime, we must ensure that our research infrastructure is conducive to carrying out pragmatic clinical trials to expeditiously generate the needed evidence and blunt the epidemic's toll on human lives and livelihoods.

Exact confidence limits after a group sequential single arm binary trial.

Group sequential single arm designs are common in phase II trials as well as attribute testing and acceptance sampling. After the trial is completed, especially if the recommendation is to proceed to further testing, there is interest in full inference on treatment efficacy. For a binary response, there is the potential to construct exact upper and lower confidence limits, the first published method for which is Jennison and Turnbull (1983). We place their method within the modern theory of exact confidence limits and provide a new general result that ensures that the exact limits are consistent with the test result, an issue that has been largely ignored in the literature. Amongst methods based on the minimal sufficient statistic, we propose two exact methods that out-perform Jennison and Turnbull's method across 10 selected designs. One of these we prefer and recommend for practical and theoretical reasons. We also investigate a method based on inverting Fisher's combination test, as well as a pure tie-breaking variant of it. For the range of designs considered, neither of these methods result in large enough improvements in efficiency to justify violation of the sufficiency principle. For any nonadaptive sequential design, an R-package is provided to select a method and compute the inference from a given realization.