Platform Randomised trial of INterventions against COVID-19 In older peoPLE (PRINCIPLE): protocol for a randomised, controlled, open-label, adaptive platform, trial of community treatment of COVID-19 syndromic illness in people at higher risk

Democratising clinical trials research to strengthen primary health care

The World Health Assembly has called for clinical trials to be strengthened, with broader demographic and geographical inclusion of populations. The objective of this paper is to highlight the importance of rigorous evidence to maximise the health gains of primary health care, and to identify strategies for strengthening clinical trials in primary care. Clinical trials should evaluate interventions of all kinds, including preventive manoeuvres, diagnostics, health service research questions, behavioural and educational interventions, vaccines, therapeutics, and policies. Single question trials can be inefficient and seldom strengthen health systems. New approaches that develop or strengthen health research infrastructure and embed research in primary care will identify effective interventions faster, how to deliver them better, and more accurately determine to whom they should be applied. When patients and community members, together with researchers, contribute to conception, design, and delivery, research will result in more useful, relevant evidence. Traditional site-based recruitment (where the participant comes to the trial) can be complemented by approaches that give people the opportunity to contribute regardless of where they live and receive their health care (taking the trials to the people). However, this cannot be done until regulation is modernised to make it easier for health-care professionals, researchers, and research participants to co-design, deliver, and implement such trials, and to develop processes to coordinate and monitor progress against goals for budget shifts, delivery, engagement, trials activity, and impact. Strengthening primary care trials is especially important in those regions where primary care is most under-resourced and is key to pandemic preparedness. Not doing so risks widening inequities further.

Identification and Estimation of Causal Effects Using Non-Concurrent Controls in Platform Trials

Platform trials are multi-arm designs that simultaneously evaluate multiple treatments for a single disease within the same overall trial structure. Unlike traditional randomized controlled trials, they allow treatment arms to enter and exit the trial at distinct times while maintaining a control arm throughout. This control arm comprises both concurrent controls, where participants are randomized concurrently to either the treatment or control arm, and non-concurrent controls, who enter the trial when the treatment arm under study is unavailable. While flexible, platform trials introduce the challenge of using non-concurrent controls, raising questions about estimating treatment effects. Specifically, which estimands should be targeted? Under what assumptions can these estimands be identified and estimated? Are there any efficiency gains? In this article, we discuss issues related to the identification and estimation assumptions of common choices of estimand. We conclude that the most robust strategy to increase efficiency without imposing unwarranted assumptions is to target the concurrent average treatment effect (cATE), the ATE among only concurrent units, using a covariate-adjusted doubly robust estimator. Our studies suggest that, for the purpose of obtaining efficiency gains, collecting important prognostic variables is more important than relying on non-concurrent controls. We also discuss the perils of targeting ATE due to an untestable extrapolation assumption that will often be invalid. We provide simulations illustrating our points and an application to the ACTT platform trial, resulting in a 20% improvement in precision compared to the naive estimator that ignores non-concurrent controls and prognostic variables.

Democratising the design and delivery of large-scale randomised, controlled clinical trials in primary care: A personal view

BACKGROUND

Rapid identification of effective treatments for use in the community during a pandemic is vital for the well-being of individuals and the sustainability of healthcare systems and society. Furthermore, identifying treatments that do not work reduces research wastage, spares people unnecessary side effects, rationalises the cost of purchasing and stockpiling medication, and reduces inappropriate medication use. Nevertheless, only a small minority of therapeutic trials for SARS-CoV-2 infections have been in primary care: most opened too late, struggled to recruit, and few produced actionable results. Participation in research is often limited by where one lives or receives health care, and trial participants may not represent those for whom the treatments are intended.

INNOVATIVE TRIALS

The ALIC4E, PRINCIPLE and the ongoing PANORAMIC trial have randomised over 40,500 people with COVID-19. This personal view describes how these trials have innovated in: trial design (by using novel adaptive platform designs); trial delivery (by complementing traditional site-based recruitment ('the patient comes to the research') with mechanisms to enable sick, infectious people to participate without having to leave home ('taking research to the people'), and by addressing the 'inverse research participation law,' which highlights disproportionate barriers faced by those who have the most to contribute, and benefit from, research, and; in transforming the evidence base by evaluating nine medicines to support guidelines and care decisions world-wide for COVID-19 and contribute to antimicrobial stewardship.

CONCLUSION

The PRINCIPLE and PANORAMIC trials represent models of innovation and inclusivity, and exemplify the potential of primary care to lead the way in addressing pressing global health challenges.

Towards an understanding of the ethics of electronic consent in clinical trials

There are good practical reasons to use electronic consent (e-consent) in randomised trials, especially when conducting large-scale clinical trials to answer population-level health research questions. However, determining ethical reasons for e-consent is not so clear and depends on a proper understanding of what e-consent means when used in clinical trials and its ethical significance. Here we focus on four features of ethical significance which give rise to a range of ethical considerations relating to e-consent and merit further focused ethics research.

Bringing platform trials closer to reality by enabling with digital research environment (DRE) connectivity

Platform trials are generally regarded as an innovative approach to address clinical valuation of early stage candidates, regardless of modality as the evidence evolves. As a type of randomized clinical trial (RCT) design construct in which multiple interventions are evaluated concurrently against a common control group allowing new interventions to be added and the control group to be updated throughout the trial, they provide a dynamic and efficient mechanism to compare and potentially discriminate new treatment candidates. Their recent use in the evaluation of new therapies for COVID-19 has spurred new interest in the approach. The paucity of platform trials is less influenced by the novelty and operational requirements as opposed to concerns regarding the sharing of intellectual property (IP) and the lack of infrastructure to operationalize the conduct in the context of IP and data sharing. We provide a mechanism how this can be accomplished through the use of a digital research environment (DRE) providing a safe and secure platform for clinical researchers, quantitative and physician scientists to analyze and develop tools (e.g., models) on sensitive data with the confidence that the data and models developed are protected. A DRE, in this context, expands on the concept of a trusted research environment (TRE) by providing remote access to data alongside tools for analysis in a securely controlled workspace, while allowing data and tools to be findable, accessible, interoperable, and reusable (FAIR), version-controlled, and dynamically grow in size or quality as a result of each treatment evaluated in the trial.

Primary and secondary care collaboration in clinical research

More patients are seen in primary care than in any other part of the health system in the UK. Our NHS datasets are the envy of the world and provide us with huge opportunities to support our patients and populations. In this paper, we demonstrate the breadth of primary care research, recruitment and delivery options. We show how research can affect many different aspects of patient care and demonstrate, through the delivery and publication of game-changing research, the ability of recruitment in primary care to answer questions that are relevant to secondary care activity. Indeed, these complex and innovative study designs and their collaborative delivery across the multitude of diseases (acute and chronic) show the strength of primary care. Collaboration across boundaries, specialties and healthcare settings will provide increased opportunities for clinical research development and, most importantly, deliver the highest quality research to support our patients.

Design and Statistical Innovations in a Platform Trial for Amyotrophic Lateral Sclerosis

Platform trials allow efficient evaluation of multiple interventions for a specific disease. The HEALEY ALS Platform Trial is testing multiple investigational products in parallel and sequentially in persons with amyotrophic lateral sclerosis (ALS) with the goal of rapidly identifying novel treatments to slow disease progression. Platform trials have considerable operational and statistical efficiencies compared with typical randomized controlled trials due to their use of shared infrastructure and shared control data. We describe the statistical approaches required to achieve the objectives of a platform trial in the context of ALS. This includes following regulatory guidance for the disease area of interest and accounting for potential differences in outcomes of participants within the shared control (potentially due to differences in time of randomization, mode of administration, and eligibility criteria). Within the HEALEY ALS Platform Trial, the complex statistical objectives are met using a Bayesian shared parameter analysis of function and survival. This analysis serves to provide a common integrated estimate of treatment benefit, overall slowing in disease progression, as measured by function and survival while accounting for potential differences in the shared control group using Bayesian hierarchical modeling. Clinical trial simulation is used to provide a better understanding of this novel analysis method and complex design. ANN NEUROL 2023;94:547-560.

Platform trials to evaluate the benefit-risk of COVID-19 therapeutics: Successes, learnings, and recommendations for future pandemics

BACKGROUND

In response to the COVID-19 global pandemic, multiple platform trials were initiated to accelerate evidence generation of potential therapeutic interventions. Given a rapidly evolving and dynamic pandemic, platform trials have a key advantage over traditional randomized trials: multiple interventions can be investigated under a master protocol sharing a common infrastructure.

METHODS

This paper focuses on nine platform trials that were instrumental in advancing care in COVID-19 in the hospital and community setting. A semi-structured qualitative interview was conducted with the principal investigators and lead statisticians of these trials. Information from the interviews and public sources were tabulated and summarized across trials, and recommendations for best practice for the next health crisis are provided.

RESULTS

Based on the information gathered takeaways were identified as 1) the existence of some aspect of trial design or conduct (e.g., existing network of investigators or colleagues, infrastructure for data capture and relevant statistical expertise) was a key success factor; 2) the choice of treatments (e.g., repurposed drugs) had major impact on the trials as did the choice of primary endpoint; and 3) the lack of coordination across trials was flagged as an area for improvement.

CONCLUSION

These trials deployed during the COVID-19 pandemic demonstrate how to achieve both speed and quality of evidence generation regarding clinical benefit (or not) of existing therapies to treat new pathogens in a pandemic setting. As a group, these trials identified treatments that worked, and many that did not, in a matter of months.

Core protocol development for phase 2/3 clinical trials in the leukodystrophy vanishing white matter: a consensus statement by the VWM consortium and patient advocates

BACKGROUND

The leukodystrophy "Vanishing White Matter" (VWM) is an orphan disease with neurological decline and high mortality. Currently, VWM has no approved treatments, but advances in understanding pathophysiology have led to identification of promising therapies. Several investigational medicinal products are either in or about to enter clinical trial phase. Clinical trials in VWM pose serious challenges, as VWM has an episodic disease course; disease phenotype is highly heterogeneous and predictable only for early onset; and study power is limited by the small patient numbers. To address these challenges and accelerate therapy delivery, the VWM Consortium, a group of academic clinicians with expertise in VWM, decided to develop a core protocol to function as a template for trials, to improve trial design and facilitate sharing of control data, while permitting flexibility regarding other trial details. Overall aims of the core protocol are to collect safety, tolerability, and efficacy data for treatment assessment and marketing authorization.

METHODS

To develop the core protocol, the VWM Consortium designated a committee, including clinician members of the VWM Consortium, family and patient group advocates, and experts in statistics, clinical trial design and alliancing with industries. We drafted three age-specific protocols, to stratify into more homogeneous patient groups, of ages ≥ 18 years, ≥ 6 to < 18 years and < 6 years. We chose double-blind, randomized, placebo-controlled design for patients aged ≥ 6 years; and open-label non-randomized natural-history-controlled design for patients < 6 years. The protocol describes study populations, age-specific endpoints, inclusion and exclusion criteria, study schedules, sample size determinations, and statistical considerations.

DISCUSSION

The core protocol provides a shared uniformity across trials, enables a pool of shared controls, and reduces the total number of patients necessary per trial, limiting the number of patients on placebo. All VWM clinical trials are suggested to adhere to the core protocol. Other trial components such as choice of primary outcome, pharmacokinetics, pharmacodynamics, and biomarkers are flexible and unconstrained by the core protocol. Each sponsor is responsible for their trial execution, while the control data are handled by a shared research organization. This core protocol benefits the efficiency of parallel and consecutive trials in VWM, and we hope accelerates time to availability of treatments for VWM.

TRIAL REGISTRATION

NA. From a scientific and ethical perspective, it is strongly recommended that all interventional trials using this core protocol are registered in a clinical trial register.

Platform adaptive trial of novel antivirals for early treatment of COVID-19 In the community (PANORAMIC): protocol for a randomised, controlled, open-label, adaptive platform trial of community novel antiviral treatment of COVID-19 in people at increased risk of more severe disease

INTRODUCTION

There is an urgent need to determine the safety, effectiveness and cost-effectiveness of novel antiviral treatments for COVID-19 in vaccinated patients in the community at increased risk of morbidity and mortality from COVID-19.

METHODS AND ANALYSIS

PANORAMIC is a UK-wide, open-label, prospective, adaptive, multiarm platform, randomised clinical trial that evaluates antiviral treatments for COVID-19 in the community. A master protocol governs the addition of new antiviral treatments as they become available, and the introduction and cessation of existing interventions via interim analyses. The first two interventions to be evaluated are molnupiravir (Lagevrio) and nirmatrelvir/ritonavir (Paxlovid).

ELIGIBILITY CRITERIA

community-dwelling within 5 days of onset of symptomatic COVID-19 (confirmed by PCR or lateral flow test), and either (1) aged 50 years and over, or (2) aged 18-49 years with qualifying comorbidities. Registration occurs via the trial website and by telephone. Recruitment occurs remotely through the central trial team, or in person through clinical sites. Participants are randomised to receive either usual care or a trial drug plus usual care. Outcomes are collected via a participant-completed daily electronic symptom diary for 28 days post randomisation. Participants and/or their Trial Partner are contacted by the research team after days 7, 14 and 28 if the diary is not completed, or if the participant is unable to access the diary. The primary efficacy endpoint is all-cause, non-elective hospitalisation and/or death within 28 days of randomisation. Multiple prespecified interim analyses allow interventions to be stopped for futility or superiority based on prespecified decision criteria. A prospective economic evaluation is embedded within the trial.

ETHICS AND DISSEMINATION

Ethical approval granted by South Central-Berkshire REC number: 21/SC/0393; IRAS project ID: 1004274. Results will be presented to policymakers and at conferences, and published in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

ISRCTN30448031; EudraCT number: 2021-005748-31.

A rapid review of community engagement and informed consent processes for adaptive platform trials and alternative design trials for public health emergencies

Background : Public Health Emergencies (PHE) demand expeditious research responses to evaluate new or repurposed therapies and prevention strategies. Alternative Design Trials (ADTs) and Adaptive Platform Trials (APTs) have enabled efficient large-scale testing of biomedical interventions during recent PHEs. Design features of these trials may have implications for engagement and/or informed consent processes. We aimed to rapidly review evidence on engagement and informed consent for ADTs and APTs during PHE to consider what (if any) recommendations can inform practice. Method : In 2022, we searched 8 prominent databases for relevant peer reviewed publications and guidelines for ADTs/APTs in PHE contexts. Articles were selected based on pre-identified inclusion and exclusion criteria. We reviewed protocols and informed consent documents for a sample of large platform trials and consulted with key informants from ADTs/APT trial teams. Data were extracted and summarised using narrative synthesis. Results : Of the 49 articles included, 10 were guidance documents, 14 discussed engagement, 10 discussed informed consent, and 15 discussed both. Included articles addressed ADTs delivered during the West African Ebola epidemic and APTs delivered during COVID-19. PHE clinical research guidance documents highlight the value of ADTs/APTs and the importance of community engagement, but do not provide practice-specific guidance for engagement or informed consent. Engagement and consent practice for ADTs conducted during the West African Ebola epidemic have been well-documented. For COVID-19, engagement and consent practice was described for APTs primarily delivered in high income countries with well-developed health service structures. A key consideration is strong communication of the complexity of trial design in clear, accessible ways. Conclusion: We highlight key considerations for best practice in community engagement and informed consent relevant to ADTs and APTs for PHEs which may helpfully be included in future guidance. Protocol: The review protocol is published online at Prospero on 15/06/2022: registration number CRD42022334170.

Methodology and design of platform trials: a meta-epidemiological study

OBJECTIVES

Adaptive platforms allow for the evaluation of multiple interventions at a lower cost and have been growing in popularity, especially during the COVID-19 pandemic. The objective of this review is to summarize published platform trials, examine specific methodological design features among these studies, and hopefully aid readers in the evaluation and interpretation of platform trial results.

METHODS

We performed a systematic review of EMBASE, MEDLINE, Cochrane Central Register of Controlled Trials (CENTRAL), and clinicaltrials.gov from January 2015 to January 2022 for protocols or results of platform trials. Pairs of reviewers, working independently and in duplicate, collected data on trial characteristics of trial registrations, protocols, and publications of platform trials. We reported our results using total numbers and percentages, as well as medians with interquartile range (IQR) when appropriate.

RESULTS

We identified 15,277 unique search records and screened 14,403 titles and abstracts after duplicates were removed. We identified 98 unique randomized platform trials. Sixteen platform trials were sourced from a systematic review completed in 2019, which included platform trials reported prior to 2015. Most platform trials (n = 67, 68.3%) were registered between 2020 and 2022, coinciding with the COVID-19 pandemic. The included platform trials primarily recruited or plan to recruit patients from North America or Europe, with most subjects being recruited from the United States (n = 39, 39.7%) and the United Kingdom (n = 31, 31.6%). Bayesian methods were used in 28.6% (n = 28) of platform RCTs and frequentist methods in 66.3% (n = 65) of trials, including 1 (1%) that used methods from both paradigms. Out of the twenty-five trials with peer-reviewed publication of results, seven trials used Bayesian methods (28%), and of those, two (8%) used a predefined sample size calculation while the remainder used pre-specified probabilities of futility, harm, or benefit calculated at (pre-specified) intervals to inform decisions about stopping interventions or the entire trial. Seventeen (68%) peer-reviewed publications used frequentist methods. Out of the seven published Bayesian trials, seven (100%) reported thresholds for benefit. The threshold for benefit ranged from 80% to >99%.

CONCLUSION

We identified and summarized key components of platform trials, including the basics of the methodological and statistical considerations. Ultimately, improving standardization and reporting in platform trials require an understanding of the current landscape. We provide the most updated and rigorous review of platform trials to date.

Additional consensus recommendations for conducting complex innovative trials of oncology agents: a post-pandemic perspective

In our 2020 consensus paper, we devised ten recommendations for conducting Complex Innovative Design (CID) trials to evaluate cancer drugs. Within weeks of its publication, the UK was hit by the first wave of the SARS-CoV-2 pandemic. Large CID trials were prioritised to compare the efficacy of new and repurposed COVID-19 treatments and inform regulatory decisions. The unusual circumstances of the pandemic meant studies such as RECOVERY were opened almost immediately and recruited record numbers of participants. However, trial teams were required to make concessions and adaptations to these studies to ensure recruitment was rapid and broad. As these are relevant to cancer trials that enrol patients with similar risk factors, we have added three new recommendations to our original ten: employing pragmatism such as using focused information sheets and collection of only the most relevant data; minimising negative environmental impacts with paperless systems; and using direct-to-patient communication methods to improve uptake. These recommendations can be applied to all oncology CID trials to improve their inclusivity, uptake and efficiency. Above all, the success of CID studies during the COVID-19 pandemic underscores their efficacy as tools for rapid treatment evaluation.

Molnupiravir plus usual care versus usual care alone as early treatment for adults with COVID-19 at increased risk of adverse outcomes (PANORAMIC): an open-label, platform-adaptive randomised controlled trial

BACKGROUND

The safety, effectiveness, and cost-effectiveness of molnupiravir, an oral antiviral medication for SARS-CoV-2, has not been established in vaccinated patients in the community at increased risk of morbidity and mortality from COVID-19. We aimed to establish whether the addition of molnupiravir to usual care reduced hospital admissions and deaths associated with COVID-19 in this population.

METHODS

PANORAMIC was a UK-based, national, multicentre, open-label, multigroup, prospective, platform adaptive randomised controlled trial. Eligible participants were aged 50 years or older-or aged 18 years or older with relevant comorbidities-and had been unwell with confirmed COVID-19 for 5 days or fewer in the community. Participants were randomly assigned (1:1) to receive 800 mg molnupiravir twice daily for 5 days plus usual care or usual care only. A secure, web-based system (Spinnaker) was used for randomisation, which was stratified by age (<50 years vs ≥50 years) and vaccination status (yes vs no). COVID-19 outcomes were tracked via a self-completed online daily diary for 28 days after randomisation. The primary outcome was all-cause hospitalisation or death within 28 days of randomisation, which was analysed using Bayesian models in all eligible participants who were randomly assigned. This trial is registered with ISRCTN, number 30448031.

FINDINGS

Between Dec 8, 2021, and April 27, 2022, 26 411 participants were randomly assigned, 12 821 to molnupiravir plus usual care, 12 962 to usual care alone, and 628 to other treatment groups (which will be reported separately). 12 529 participants from the molnupiravir plus usual care group, and 12 525 from the usual care group were included in the primary analysis population. The mean age of the population was 56·6 years (SD 12·6), and 24 290 (94%) of 25 708 participants had had at least three doses of a SARS-CoV-2 vaccine. Hospitalisations or deaths were recorded in 105 (1%) of 12 529 participants in the molnupiravir plus usual care group versus 98 (1%) of 12 525 in the usual care group (adjusted odds ratio 1·06 [95% Bayesian credible interval 0·81-1·41]; probability of superiority 0·33). There was no evidence of treatment interaction between subgroups. Serious adverse events were recorded for 50 (0·4%) of 12 774 participants in the molnupiravir plus usual care group and for 45 (0·3%) of 12 934 in the usual care group. None of these events were judged to be related to molnupiravir.

INTERPRETATION

Molnupiravir did not reduce the frequency of COVID-19-associated hospitalisations or death among high-risk vaccinated adults in the community.

FUNDING

UK National Institute for Health and Care Research.

The Bayesian Time Machine: Accounting for temporal drift in multi-arm platform trials

BACKGROUND

Multi-arm platform trials investigate multiple agents simultaneously, typically with staggered entry and exit of experimental treatment arms versus a shared control arm. In such settings, there is considerable debate whether to limit analyses for a treatment arm to concurrent randomized control subjects or to allow comparisons to both concurrent and non-concurrent (pooled) control subjects. The potential bias from temporal drift over time is at the core of this debate.

METHODS

We propose time-adjusted analyses, including a "Bayesian Time Machine," to model potential temporal drift in the entire study population, such that primary analyses can incorporate all randomized control subjects from the platform trial. We conduct a simulation study to assess performance relative to utilizing concurrent or pooled controls.

RESULTS

In multi-arm platform trials with staggered entry, analyses adjusting for temporal drift (either Bayesian or frequentist) have superior estimation of treatment effects and favorable testing properties compared to analyses using either concurrent or pooled controls. The Bayesian Time Machine generally provides estimates with greater precision and smaller mean square error than alternative approaches, at the risk of small bias and small Type I error inflation.

CONCLUSIONS

The Bayesian Time Machine provides a compromise between bias and precision by smoothing estimates across time and leveraging all available data for the estimation of treatment effects. Prior distributions controlling the behavior of dynamic smoothing across time must be pre-specified and carefully calibrated to the unique context of each trial, appropriately accounting for the population, disease, and endpoints.

Understanding the clinical utility of favipiravir (T-705) in coronavirus disease of 2019: a review

The coronavirus disease of 2019 (COVID-19) has caused significant morbidity and mortality among infected individuals across the world. High transmissibility rate of the causative virus - Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) - has led to immense strain and bottlenecking of the health care system. While noteworthy advances in vaccine development have been made amid the current global pandemic, most therapeutic agents are repurposed from use in other viral infections and are being evaluated for efficacy in COVID-19. Favipiravir, an orally administered drug originally developed in Japan against emerging influenza viral strains, has been shown to have widespread application and safety across multiple ribonucleic acid (RNA) viral infections. With a strong affinity toward the viral RNA-dependent RNA polymerase (RdRp), favipiravir could be a promising therapy against SARS-CoV-2, by targeting downstream viral RNA replication. Initial trials for usage in COVID-19 have suggested that favipiravir administration during initial infection stages, in individuals with mild to moderate infection, has a strong potential to improve clinical outcomes. However, additional well-designed clinical trials are required to closely examine ideal timing of drug administration, dosage, and duration, to assess the role of favipiravir in COVID-19 therapy. This review provides evidence-based insights and throws light on the current clinical trials examining the efficacy of favipiravir in tackling COVID-19, including its mechanism, pharmacodynamics, and pharmacokinetics.