The Rise of Adaptive Platform Trials in Critical Care.

As durable learning research systems, adaptive platform trials represent a transformative new approach to accelerating clinical evaluation and discovery in critical care. This Perspective provides a brief introduction to the concept of adaptive platform trials, describes several established and emerging platforms in critical care, and surveys some opportunities and challenges for their implementation and impact.

Advantages and Challenges of Platform Trials for Disease Modifying Therapies in Parkinson's Disease.

Traditional drug development in Parkinson's disease (PD) faces significant challenges because of its protracted timeline and high costs. In response, innovative master protocols are emerging and designed to address multiple research questions within a single overarching protocol. These trials may offer advantages such as increased efficiency, agility in adding new treatment arms, and potential cost savings. However, they also present organizational, methodological, funding, regulatory, and sponsorship challenges. We review the potential of master protocols, focusing on platform trials, for disease modifying therapies in PD. These trials share a common control group and allow for the termination or addition of treatment arms during a trial with non-predetermined end. Specific issues exist for a platform trial in the PD field considering the heterogeneity of patients in terms of phenotype, genotype and staging, the confounding effects of symptomatic treatments, and the choice of outcome measures with no consensus on a non-clinical biomarker to serve as a surrogate and the slowness of PD progression. We illustrate these aspects using the examples of the main PD platform trials currently in development with each one targeting distinct goals, populations, and outcomes. Overall, platform trials hold promise in expediting the evaluation of potential therapies for PD. However, it remains to be proven whether these theoretical benefits will translate into increased production of high-quality trial data. Success also depends on the willingness of pharmaceutical companies to engage in such trials and whether this approach will ultimately hasten the identification and licensing of effective disease-modifying drugs. © 2024 International Parkinson and Movement Disorder Society.

A multi-arm multi-stage platform design that allows preplanned addition of arms while still controlling the family-wise error.

There is growing interest in platform trials that allow for adding of new treatment arms as the trial progresses as well as being able to stop treatments part way through the trial for either lack of benefit/futility or for superiority. In some situations, platform trials need to guarantee that error rates are controlled. This paper presents a multi-stage design, that allows additional arms to be added in a platform trial in a preplanned fashion, while still controlling the family-wise error rate, under the assumption of known number and timing of treatments to be added, and no time trends. A method is given to compute the sample size required to achieve a desired level of power and we show how the distribution of the sample size and the expected sample size can be found. We focus on power under the least favorable configuration which is the power of finding the treatment with a clinically relevant effect out of a set of treatments while the rest have an uninteresting treatment effect. A motivating trial is presented which focuses on two settings, with the first being a set number of stages per active treatment arm and the second being a set total number of stages, with treatments that are added later getting fewer stages. Compared to Bonferroni, the savings in the total maximum sample size are modest in a trial with three arms, <1% of the total sample size. However, the savings are more substantial in trials with more arms.

Pooling Different Placebos as a Control Group in a Randomized Platform Trial: Benefits and Challenges From Experience in the ACTIV-2 COVID-19 Trial.

Adaptive platform trials were implemented during the coronavirus disease 2019 (COVID-19) pandemic to rapidly evaluate therapeutics, including the placebo-controlled phase 2/3 ACTIV-2 trial, which studied 7 investigational agents with diverse routes of administration. For each agent, safety and efficacy outcomes were compared to a pooled placebo control group, which included participants who received a placebo for that agent or for other agents in concurrent evaluation. A 2-step randomization framework was implemented to facilitate this. Over the study duration, the pooled placebo design achieved a reduction in sample size of 6% versus a trial involving distinct placebo control groups for evaluating each agent. However, a 26% reduction was achieved during the period when multiple agents were in parallel phase 2 evaluation. We discuss some of the complexities implementing the pooled placebo design versus a design involving nonoverlapping control groups, with the aim of informing the design of future platform trials. Clinical Trials Registration. NCT04518410.

A systematic review of potential biomarkers for bacterial burden and treatment efficacy assessment in TB platform-based clinical trials.

Adaptive platform trials can be more efficient than classic trials for developing new treatments. Moving from culture-based to simpler- or faster-to-measure biomarkers as efficacy surrogates may enhance this advantage. We performed a systematic review of treatment efficacy biomarkers in adults with tuberculosis. Platform trials can span different development phases. We grouped biomarkers as: α, bacterial load estimates used in phase 2a trials; ß, early and end-of treatment endpoints, phase 2b-c trials; γ, post-treatment or trial-level estimates, phase 2c-3 trials. We considered as analysis unit (biomarker entry) each combination of biomarker, predicted outcome, and their respective measurement times or intervals. Performance metrics included: sensitivity, specificity, area under the receiver-operator curve (AUC), and correlation measures, ,and classified as poor, promising, or good. Eighty-six studies included 22864 participants. From 1356 biomarker entries, 318 were reported with the performance metrics of interest, with 103 promising and 41 good predictors. Group results: α, mycobacterial RNA and Lipoarabinomannan in sputum, and host metabolites in urine; ß, mycobacterial RNA and host transcriptomic or cytokine signatures for early treatment response; γ, host transcriptomics for recurrence. A combination of biomarkers from different categories could help designing more efficient platform trials. Efforts to develop efficacy surrogates should be better coordinated.

Platform trials to overcome major shortcomings of traditional clinical trials in non-alcoholic steatohepatitis? Pros and cons.

Non-alcoholic fatty liver disease is a condition that affects 25% of the population. Non-alcoholic steatohepatitis (NASH) is a progressive form of the disease that can lead to severe complications such as cirrhosis and hepatocellular carcinoma. Despite its high prevalence, no drugs are currently approved for the treatment of NASH. The drug development pipeline in NASH is very active, yet most assets do not progress to phase III trials and those that do reach phase III often fail to achieve the endpoints necessary for approval by regulatory agencies. Amongst other reasons, the methodological and operational features of traditional clinical trials in NASH might impede optimal drug development. In this regard, platform trials might be an attractive complement or alternative to conventional clinical trials. Platform trials use a master protocol which enables evaluation of multiple investigational medicinal products concurrently or sequentially with a single, shared control arm. Through Bayesian interim analyses, these trials allow for early exit of drugs from the trial based on success or futility, while providing participants better chances of receiving active compounds through adaptive randomisation. Overall, platform trials represent an alternative for patients, pharmaceutical companies, and clinicians in the quest to accelerate the approval of pharmacologic treatments for NASH.

Drug repurposing for COVID-19: current evidence from randomized controlled adaptive platform trials and living systematic reviews.

INTRODUCTION: The coronavirus disease 2019 (COVID-19) pandemic resulted in a race to develop effective treatments largely through drug repurposing via adaptive platform trials on a global scale. Drug repurposing trials have focused on potential antiviral therapies aimed at preventing viral replication, anti-inflammatory agents, antithrombotic agents and immune modulators through a number of adaptive platform trials. Living systematic reviews have also enabled evidence synthesis and network meta-analysis as clinical trial data emerge globally. SOURCES OF DATA: Recent published literature. AREAS OF AGREEMENT: Corticosteroids and immunomodulators that antagonize the interleukin-6 (IL-6) receptor have been shown to play a critical role in modulating inflammation and improving clinical outcomes in hospitalized patients. Inhaled budesonide reduces the time to recovery in older patients with mild-to-moderate COVID-19 managed in the community. AREAS OF CONTROVERSY: The clinical benefit of remdesivir remains controversial with conflicting evidence from different trials. Remdesivir led to a reduction in time to clinical recovery in the ACTT-1 trial. However, the World Health Organization SOLIDARITY and DISCOVERY trial did not find a significant benefit on 28-day mortality and clinical recovery. GROWING POINTS: Other treatments currently being investigated include antidiabetic drug empagliflozin, antimalarial drug artesunate, tyrosine kinase inhibitor imatinib, immunomodulatory drug infliximab, antiviral drug favipiravir, antiparasitic drug ivermectin and antidepressant drug fluvoxamine. AREAS TIMELY FOR DEVELOPING RESEARCH: The timing of therapeutic interventions based on postulated mechanisms of action and the selection of clinically meaningful primary end points remain important considerations in the design and implementation of COVID-19 therapeutic trials.

Improving the efficiency of clinical trials in multiple sclerosis.

BACKGROUND: Phase 3 clinical trials for disease-modifying therapies in relapsing-remitting multiple sclerosis (RRMS) have utilized a limited number of conventional designs with a high degree of success. However, these designs limit the types of questions that can be addressed, and the time and cost required. Moreover, trials involving people with progressive multiple sclerosis (MS) have been less successful. OBJECTIVE: The objective of this paper is to discuss complex innovative trial designs, intermediate and composite outcomes and to improve the efficiency of trial design in MS and broaden questions that can be addressed, particularly as applied to progressive MS. METHODS: We held an international workshop with experts in clinical trial design. RESULTS: Recommendations include increasing the use of complex innovative designs, developing biomarkers to enrich progressive MS trial populations, prioritize intermediate outcomes for further development that target therapeutic mechanisms of action other than peripherally mediated inflammation, investigate acceptability to people with MS of data linkage for studying long-term outcomes of clinical trials, use Bayesian designs to potentially reduce sample sizes required for pediatric trials, and provide sustained funding for platform trials and registries that can support pragmatic trials. CONCLUSION: Novel trial designs and further development of intermediate outcomes may improve clinical trial efficiency in MS and address novel therapeutic questions.

Online multiple hypothesis testing.

Modern data analysis frequently involves large-scale hypothesis testing, which naturally gives rise to the problem of maintaining control of a suitable type I error rate, such as the false discovery rate (FDR). In many biomedical and technological applications, an additional complexity is that hypotheses are tested in an online manner, one-by-one over time. However, traditional procedures that control the FDR, such as the Benjamini-Hochberg procedure, assume that all p-values are available to be tested at a single time point. To address these challenges, a new field of methodology has developed over the past 15 years showing how to control error rates for online multiple hypothesis testing. In this framework, hypotheses arrive in a stream, and at each time point the analyst decides whether to reject the current hypothesis based both on the evidence against it, and on the previous rejection decisions. In this paper, we present a comprehensive exposition of the literature on online error rate control, with a review of key theory as well as a focus on applied examples. We also provide simulation results comparing different online testing algorithms and an up-to-date overview of the many methodological extensions that have been proposed.

Discussion on "Optimal test procedures for multiple hypotheses controlling the familywise expected loss" by Willi Maurer, Frank Bretz, and Xiaolei Xun.

This comment builds on the familywise expected loss (FWEL) framework suggested by Maurer, Bretz, and Xun in 2022. By representing the populationwise error rate (PWER) as FWEL, it is illustrated how the FWEL framework can be extended to clinical trials with multiple and overlapping populations and the PWER can be generalized to more general losses. The comment also addresses the question of how to deal with midtrial changes in the posttrial risks and related losses that are caused by data-driven decisions. Focusing on multiarm trials with the possibility of dropping treatments midtrial, we suggest to switch from control of the unconditional expected loss to control of the conditional expected loss that is related to the actual risks and is conditional on the sample event that causes the change in the risks. The problem and here suggested solution is also motivated with a sequence of independent trials for a hitherto incurable disease which ends when an efficient treatment is found. No multiplicity adjustment is applied in this case and we show how this can be justified by the consideration of the changing out-trial risks and with control of conditional type I error rates and losses.

Inference in response-adaptive clinical trials when the enrolled population varies over time.

A common assumption of data analysis in clinical trials is that the patient population, as well as treatment effects, do not vary during the course of the study. However, when trials enroll patients over several years, this hypothesis may be violated. Ignoring variations of the outcome distributions over time, under the control and experimental treatments, can lead to biased treatment effect estimates and poor control of false positive results. We propose and compare two procedures that account for possible variations of the outcome distributions over time, to correct treatment effect estimates, and to control type-I error rates. The first procedure models trends of patient outcomes with splines. The second leverages conditional inference principles, which have been introduced to analyze randomized trials when patient prognostic profiles are unbalanced across arms. These two procedures are applicable in response-adaptive clinical trials. We illustrate the consequences of trends in the outcome distributions in response-adaptive designs and in platform trials, and investigate the proposed methods in the analysis of a glioblastoma study.

Allocation in platform trials to maintain comparability across time and eligibility.

Platform trials, with arms entering and leaving the trial over time, are complex. In addition to trial changes over time, certain arms in a platform may come with patient restrictions. Both of these issues (time and eligibility) can create biases in comparing active arms to control. The largest of these biases, using non-concurrent controls or including control patients that were ineligible for an active arm, have been extensively discussed in the literature. Here we show that even restricting to concurrent, eligible controls can induce biases if proper allocation ratios are not maintained throughout the platform. We also build on results in Ventz et al. Biostat., 19:199-215, 2018 to describe an algorithm that guarantees comparability between active and control groups in arm analyses in both time and eligibility, and allows for both re-randomization of patients and two-stage randomization procedures. The resulting method is both flexible and easily implemented, allowing robust comparisons when assumptions that underlie alternative randomization methods are in doubt.

Electronic health records (EHRs) in clinical research and platform trials: Application of the innovative EHR-based methods developed by EU-PEARL.

OBJECTIVE: Electronic Health Record (EHR) systems are digital platforms in clinical practice used to collect patients' clinical information related to their health status and represents a useful storage of real-world data. EHRs have a potential role in research studies, in particular, in platform trials. Platform trials are innovative trial designs including multiple trial arms (conducted simultaneously and/or sequentially) on different treatments under a single master protocol. However, the use of EHRs in research comes with important challenges such as incompleteness of records and the need to translate trial eligibility criteria into interoperable queries. In this paper, we aim to review and to describe our proposed innovative methods to tackle some of the most important challenges identified. This work is part of the Innovative Medicines Initiative (IMI) EU Patient-cEntric clinicAl tRial pLatforms (EU-PEARL) project's work package 3 (WP3), whose objective is to deliver tools and guidance for EHR-based protocol feasibility assessment, clinical site selection, and patient pre-screening in platform trials, investing in the building of a data-driven clinical network framework that can execute these complex innovative designs for which feasibility assessments are critically important. METHODS: ISO standards and relevant references informed a readiness survey, producing 354 criteria with corresponding questions selected and harmonised through a 7-round scoring process (0-1) in stakeholder meetings, with 85% of consensus being the threshold of acceptance for a criterium/question. ATLAS cohort definition and Cohort Diagnostics were mainly used to create the trial feasibility eligibility (I/E) criteria as executable interoperable queries. RESULTS: The WP3/EU-PEARL group developed a readiness survey (eSurvey) for an efficient selection of clinical sites with suitable EHRs, consisting of yes-or-no questions, and a set-up of interoperable proxy queries using physicians' defined trial criteria. Both actions facilitate recruiting trial participants and alignment between study costs/timelines and data-driven recruitment potential. CONCLUSION: The eSurvey will help create an archive of clinical sites with mature EHR systems suitable to participate in clinical trials/platform trials, and the interoperable proxy queries of trial eligibility criteria will help identify the number of potential participants. Ultimately, these tools will contribute to the production of EHR-based protocol design.

MIDAS-2: an enhanced Bayesian platform design for immunotherapy combinations with subgroup efficacy exploration.

Although immunotherapy combinations have revolutionised cancer treatment, the rapid screening of effective and optimal therapies from large numbers of candidate combinations, as well as exploring subgroup efficacy, remains challenging. This necessitates innovative, integrated, and efficient trial designs. In this study, we extend the MIDAS design to include subgroup exploration and propose an enhanced Bayesian information borrowing platform design called MIDAS-2. MIDAS-2 enables quick and continuous screening of promising combination strategies and exploration of their subgroup effects within a unified platform design framework. We use a regression model to characterize the efficacy pattern in subgroups. Information borrowing is applied through Bayesian hierarchical modelling to improve trial efficiency considering the limited sample size in subgroups. Time trend calibration is also employed to avoid potential baseline drifts. Simulation results demonstrate that MIDAS-2 yields high probabilities for identifying the effective drug combinations as well as promising subgroups, facilitating appropriate selection of the best treatments for each subgroup. The proposed design is robust against small time trend drifts, and the type I error is successfully controlled after calibration when a large drift is expected. Overall, MIDAS-2 provides an adaptive drug screening and subgroup exploring framework to accelerate immunotherapy development in an efficient, accurate, and integrated fashion.

Bayes' Theorem in Neurocritical Care: Principles and Practice.

Patients with critical neurological illness are diverse. As a result of the heterogeneity of this patient population, standardized approaches to patient management might not confer benefit. A precision medicine approach to neurocritical care is therefore urgently needed to improve our understanding of neurocritical illness and the care provided to this vulnerable cohort. Research designs and approaches based on Bayesian models have the potential to meet this need, as they are specifically designed to evolve with emerging evidence. This adaptability provides a benefit over the popular frequentist statistical approach, as it provides a way of adjusting hypotheses and trial procedures to maximize efficacy. This review summarizes the current state of knowledge on Bayes' theorem, and its potential applications to the field of neurocritical care. We review the basic principles underlying Bayes' theorem, compare the use of Bayesian versus frequentist statistics in medicine, and discuss the relevance of Bayesian statistics to the field of neuroscience and to clinical research. Finally, we explore the potential benefits of employing Bayesian methods within the field of neurocritical care as a steppingstone toward implementing precision medicine approaches to improve patient outcomes for complex, heterogeneous disorders.

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.

A state-of-the-art review of stratified medicine in cancer: towards a future precision medicine strategy in cancer.

BACKGROUND: Building on the success of targeted therapy in certain well-defined cancer genotypes, three platform studies-NCI-MATCH, LUNG-MAP and The National Lung Matrix Trial (NLMT)-have attempted to discover new genotype-matched therapies for people with cancer. PATIENTS AND METHODS: We review the outputs from these platform studies. This review led us to propose a series of recommendations and considerations that we hope will inform future precision medicine programmes in cancer. RESULTS: The three studies collectively screened over 13 000 patients. Across 37 genotype-matched cohorts, there have been 66/875 responders, with an overall response rate of 7.5%. Targeting copy number gain yielded 5/199 responses across nine biomarker-drug matched cohorts, with a response rate of 2.5%. CONCLUSIONS: The majority of these studies used single-agent targeted therapies. Whilst preclinical data can suggest rational combination treatment to reverse adaptive resistance or block parallel activated pathways, there is an essential need for accurate modelling of the toxicity-activity trade-off of combinations. Agent selection is often suboptimal; dose expansion should only be carried out with agents with clear clinical proof of mechanism and high target selectivity. Targeting copy number change has been disappointing; it is crucial to define the drivers on shared amplicons that include the targeted aberration. Maximising outcomes with currently available targeted therapies requires moving towards a more contextualised stratified medicine acknowledging the criticality of the genomic, transcriptional and immunological context on which the targeted aberration is inscribed. Genomic complexity and instability is likely to be a leading cause of targeted therapy failure in genomically complex cancers. Preclinical models must be developed that more accurately capture the genomic complexity of human disease. The degree of attrition of studies carried out after standard-of-care therapy suggests that serious efforts be made to develop a suite of precision medicine studies in the minimal residual disease setting.

On model-based time trend adjustments in platform trials with non-concurrent controls.

BACKGROUND: Platform trials can evaluate the efficacy of several experimental treatments compared to a control. The number of experimental treatments is not fixed, as arms may be added or removed as the trial progresses. Platform trials are more efficient than independent parallel group trials because of using shared control groups. However, for a treatment entering the trial at a later time point, the control group is divided into concurrent controls, consisting of patients randomised to control when that treatment arm is in the platform, and non-concurrent controls, patients randomised before. Using non-concurrent controls in addition to concurrent controls can improve the trial's efficiency by increasing power and reducing the required sample size, but can introduce bias due to time trends. METHODS: We focus on a platform trial with two treatment arms and a common control arm. Assuming that the second treatment arm is added at a later time, we assess the robustness of recently proposed model-based approaches to adjust for time trends when utilizing non-concurrent controls. In particular, we consider approaches where time trends are modeled either as linear in time or as a step function, with steps at time points where treatments enter or leave the platform trial. For trials with continuous or binary outcomes, we investigate the type 1 error rate and power of testing the efficacy of the newly added arm, as well as the bias and root mean squared error of treatment effect estimates under a range of scenarios. In addition to scenarios where time trends are equal across arms, we investigate settings with different time trends or time trends that are not additive in the scale of the model. RESULTS: A step function model, fitted on data from all treatment arms, gives increased power while controlling the type 1 error, as long as the time trends are equal for the different arms and additive on the model scale. This holds even if the shape of the time trend deviates from a step function when patients are allocated to arms by block randomisation. However, if time trends differ between arms or are not additive to treatment effects in the scale of the model, the type 1 error rate may be inflated. CONCLUSIONS: The efficiency gained by using step function models to incorporate non-concurrent controls can outweigh potential risks of biases, especially in settings with small sample sizes. Such biases may arise if the model assumptions of equality and additivity of time trends are not satisfied. However, the specifics of the trial, scientific plausibility of different time trends, and robustness of results should be carefully considered.

Combining factorial and multi-arm multi-stage platform designs to evaluate multiple interventions efficiently.

BACKGROUND: Factorial designs and multi-arm multi-stage (MAMS) platform designs have many advantages, but the practical advantages and disadvantages of combining the two designs have not been explored. METHODS: We propose practical methods for a combined design within the platform trial paradigm where some interventions are not expected to interact and could be given together. RESULTS: We describe the combined design and suggest diagrams that can be used to represent it. Many properties are common both to standard factorial designs, including the need to consider interactions between interventions and the impact of intervention efficacy on power of other comparisons, and to standard multi-arm multi-stage designs, including the need to pre-specify procedures for starting and stopping intervention comparisons. We also identify some specific features of the factorial-MAMS design: timing of interim and final analyses should be determined by calendar time or total observed events; some non-factorial modifications may be useful; eligibility criteria should be broad enough to include any patient eligible for any part of the randomisation; stratified randomisation may conveniently be performed sequentially; and analysis requires special care to use only concurrent controls. CONCLUSION: A combined factorial-MAMS design can combine the efficiencies of factorial trials and multi-arm multi-stage platform trials. It allows us to address multiple research questions under one protocol and to test multiple new treatment options, which is particularly important when facing a new emergent infection such as COVID-19.

Design and implementation of an international, multi-arm, multi-stage platform master protocol for trials of novel SARS-CoV-2 antiviral agents: Therapeutics for Inpatients with COVID-19 (TICO/ACTIV-3).

BACKGROUND/AIMS: Safe and effective therapies for COVID-19 are urgently needed. In order to meet this need, the Accelerating COVID-19 Therapeutic Interventions and Vaccines public-private partnership initiated the Therapeutics for Inpatients with COVID-19. Therapeutics for Inpatients with COVID-19 is a multi-arm, multi-stage platform master protocol, which facilitates the rapid evaluation of the safety and efficacy of novel candidate antiviral therapeutic agents for adults hospitalized with COVID-19. Five agents have so far entered the protocol, with rapid answers already provided for three of these. Other agents are expected to enter the protocol throughout 2021. This protocol contains a number of key design and implementation features that, along with challenges faced by the protocol team, are presented and discussed. METHODS: Three clinical trial networks, encompassing a global network of clinical sites, participated in the protocol development and implementation. Therapeutics for Inpatients with COVID-19 utilizes a multi-arm, multi-stage design with an agile and robust approach to futility and safety evaluation at 300 patients enrolled, with subsequent expansion to full sample size and an expanded target population if the agent shows an acceptable safety profile and evidence of efficacy. Rapid recruitment to multiple agents is enabled through the sharing of placebo, the confining of agent-specific information to protocol appendices, and modular consent forms. In collaboration with the Food and Drug Administration, a thorough safety data collection and Data and Safety Monitoring Board schedule was developed for the study of potential therapeutic agents with limited in-human data in hospitalized patients with COVID-19. RESULTS: As of 8 August 2021, five agents have entered the Therapeutics for Inpatients with COVID-19 master protocol and a total of 1909 participants have been randomized to one of these agents or matching placebo. There were a number of challenges faced by the study team that needed to be overcome in order to successfully implement Therapeutics for Inpatients with COVID-19 across a global network of sites. These included ensuring drug supply and reliable recruitment allowing for changing infection rates across the global network of sites, the need to balance the collection of data and samples without overburdening clinical staff and obtaining regulatory approvals across a global network of sites. CONCLUSION: Through a robust multi-network partnership, the Therapeutics for Inpatients with COVID-19 protocol has been successfully used across a global network of sites for rapid generation of efficacy data on multiple novel antiviral agents. The protocol design and implementation features used in this protocol, and the approaches to address challenges, will have broader applicability. Mechanisms to facilitate improved communication and harmonization among country-specific regulatory bodies are required to achieve the full potential of this approach in dealing with a global outbreak.