Collaborative Platform Trials to Fight COVID-19: Methodological and Regulatory Considerations for a Better Societal Outcome

How Much More Efficient Are Adaptive Platform Trials Than Multiple Stand-Alone Trials? A Comprehensive Simulation Study for Streamlining Drug Development During a Pandemic

With the coronavirus disease 2019 (COVID-19) pandemic, there is growing interest in utilizing adaptive platform clinical trials (APTs), in which multiple drugs are compared with a single common control group, such as a placebo or standard-of-care group. APTs evaluate several drugs for one disease and accept additions or exclusions of drugs as the trials progress; however, little is known about the efficiency of APTs over multiple stand-alone trials. In this study, we simulated the total development period, total sample size, and statistical operating characteristics of APTs and multiple stand-alone trials in drug development settings for hospitalized patients with COVID-19. Simulation studies using selected scenarios reconfirmed several findings regarding the efficiency of APTs. The APTs without staggered addition of drugs showed a shorter total development period than stand-alone trials, but the difference rapidly diminished if patient's enrollment was accelerated during the trials owing to the spread of infection. APTs with staggered addition of drugs still have the possibility of reducing the total development period compared with multiple stand-alone trials in some cases. Our study demonstrated that APTs could improve efficiency relative to multiple stand-alone trials regarding the total development period and total sample size without undermining statistical validity; however, this improvement varies depending on the speed of patient enrollment, sample size, presence/absence of family-wise error rate adjustment, allocation ratio between drug and placebo groups, and interval of staggered addition of drugs. Given the complexity of planning and implementing APT, the decision to implement APT during a pandemic must be made carefully.

Online error rate control for platform trials

Platform trials evaluate multiple experimental treatments under a single master protocol, where new treatment arms are added to the trial over time. Given the multiple treatment comparisons, there is the potential for inflation of the overall type I error rate, which is complicated by the fact that the hypotheses are tested at different times and are not necessarily pre-specified. Online error rate control methodology provides a possible solution to the problem of multiplicity for platform trials where a relatively large number of hypotheses are expected to be tested over time. In the online multiple hypothesis testing framework, hypotheses are tested one-by-one over time, where at each time-step an analyst decides whether to reject the current null hypothesis without knowledge of future tests but based solely on past decisions. Methodology has recently been developed for online control of the false discovery rate as well as the familywise error rate (FWER). In this article, we describe how to apply online error rate control to the platform trial setting, present extensive simulation results, and give some recommendations for the use of this new methodology in practice. We show that the algorithms for online error rate control can have a substantially lower FWER than uncorrected testing, while still achieving noticeable gains in power when compared with the use of a Bonferroni correction. We also illustrate how online error rate control would have impacted a currently ongoing platform trial.

Governance of adaptive platform trials

Adaptive Clinical Trials (ACT) differ from conventional clinical trials because they permit continual modifications to key components of trial design during the trial. ACTs have grown in prevalence in recent years, with Adaptive Platform Trials (APTs), in particular, having demonstrated their significant scientific, clinical, and public health utility in relation to the COVID-19 pandemic. There has been a steady increase in the number of regulations and guidelines aimed at guiding the conduct of clinical trials. However, despite the potential of APTs to expedite the testing of new interventions in emergency situations, there is a relative dearth of published literature on why and how such trials should be governed. This work attempts to address this knowledge gap.

Montelukast as a potential treatment for COVID-19

INTRODUCTION

Montelukast is a leukotriene inhibitor that is widely used to treat chronic asthma and allergic rhinitis. The drug interferes with molecular signaling pathways produced by leukotrienes in a variety of cells and tissues throughout the human body that lead to tightening of airway muscles, production of aberrant pulmonary fluid (airway edema), and in some cases, pulmonary inflammation.

AREAS COVERED

Montelukast has also been noted to have anti-inflammatory properties, suggesting it may have a role in the treatment of coronavirus disease 2019 (COVID-19), the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has been noted to induce misfiring of the immune system in some patients. A literature search of PubMed was performed to identify all relevant studies of montelukast and SARS-CoV-2 through 27 January 2023.

EXPERT OPINION

Montelukast has been the subject of small studies of SARS-CoV-2 and will be included in a large, randomized, double-blind, placebo-controlled study of outpatients with COVID-19 sponsored by the United States National Institutes of Health known as Accelerating COVID-19 Therapeutic Interventions and Vaccines-6. This paper reviews what is known about montelukast, an inexpensive, well-tolerated, and widely available medication, and examines the rationale for using this drug to potentially treat patients with COVID-19.

Estimands and Complex Innovative Designs

Since the release of the ICH E9(R1) (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use Addendum on Estimands and Sensitivity Analysis in Clinical Trials to the Guideline on Statistical Principles for Clinical Trials) document in 2019, the estimand framework has become a fundamental part of clinical trial protocols. In parallel, complex innovative designs have gained increased popularity in drug development, in particular in early development phases or in difficult experimental situations. While the estimand framework is relevant to any study in which a treatment effect is estimated, experience is lacking as regards its application to these designs. In a basket trial for example, should a different estimand be specified for each subpopulation of interest, defined, for example, by cancer site? Or can a single estimand focusing on the general population (defined, for example, by the positivity to a certain biomarker) be used? In the case of platform trials, should a different estimand be proposed for each drug investigated? In this work we discuss possible ways of implementing the estimand framework for different types of complex innovative designs. We consider trials that allow adding or selecting experimental treatment arms, modifying the control arm or the standard of care, and selecting or pooling populations. We also address the potentially data-driven, adaptive selection of estimands in an ongoing trial and disentangle certain statistical issues that pertain to estimation rather than to estimands, such as the borrowing of nonconcurrent information. We hope this discussion will facilitate the implementation of the estimand framework and its description in the study protocol when the objectives of the trial require complex innovative designs.

Novel Strategies for the Treatment of COVID-19

On 4 September, 2020, the US National Institutes of Health launched a new clinical trial, “A Multicenter, Adaptive, Randomized Controlled Platform Trial of the Safety and Efficacy of Antithrombotic and Additional Strategies in Hospitalized Adults with COVID-19.” This open-label, placebo-controlled, multicenter, adaptive platform study was designed to evaluate therapeutic options for patients hospitalized with mild, moderate, or severe COVID-19. A variety of drugs and drug classes were selected, including heparin, the monoclonal antibody crizanlizumab, sodium-glucose cotransporter-2 inhibitors, and purinergic signaling receptor Y₁₂ inhibitors. These medications have been widely used in the treatment of other conditions, from sick cell disease to type 2 diabetes mellitus and some forms of cardiovascular disease, but their inclusion in a study of COVID-19 was somewhat unexpected. This article examines the rationale behind the use of these disparate agents in the treatment and prevention of adverse outcomes in patients with COVID-19 and explores how these strategies may be utilized in the future to address the severe acute respiratory syndrome coronavirus 2 pandemic.

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.

Therapeutic strategies to address monkeypox

Introduction

Monkeypox is a viral zoonosis, with symptoms similar to those seen in smallpox patients, although the clinical presentation may be less severe. Until recently, human monkeypox infection was rare, and primarily occurred in Central and West Africa.

Areas covered

An international outbreak began in May 2022, and monkeypox has now been detected on every continent except Antarctica. The first recognized case from the current outbreak was confirmed in the United Kingdom on 6 May 2022, in an adult with travel links to Nigeria, but it has been suggested that cases had been spreading in Europe for months. On 23 July 2022 the Director-General of the World Health Organization declared the monkeypox outbreak a public health emergency of international concern.

Expert opinion

There are no treatments specifically for monkeypox virus infections. However, monkeypox and smallpox viruses are genetically similar, and therapeutics developed to combat smallpox may be used to treat monkeypox. This manuscripts reviews what is known about these potential treatments, including tecovirimat and brincidofovir, based on a literature search of PubMed through 9 August 2022, and explores how these therapeutics may be used in the future to address the expanding monkeypox pandemic.

Uptake of the multi-arm multi-stage (MAMS) adaptive platform approach: a trial-registry review of late-phase randomised clinical trials

BACKGROUND

For medical conditions with numerous interventions worthy of investigation, there are many advantages of a multi-arm multi-stage (MAMS) platform trial approach. However, there is currently limited knowledge on uptake of the MAMS design, especially in the late-phase setting. We sought to examine uptake and characteristics of late-phase MAMS platform trials, to enable better planning for teams considering future use of this approach.

DESIGN

We examined uptake of registered, late-phase MAMS platforms in the EU clinical trials register, Australian New Zealand Clinical Trials Registry, International Standard Randomised Controlled Trial Number registry, Pan African Clinical Trials Registry, WHO International Clinical Trial Registry Platform and databases: PubMed, Medline, Cochrane Library, Global Health Library and EMBASE. Searching was performed and review data frozen on 1 April 2021. MAMS platforms were defined as requiring two or more comparison arms, with two or more trial stages, with an interim analysis allowing for stopping of recruitment to arms and typically the ability to add new intervention arms.

RESULTS

62 late-phase clinical trials using an MAMS approach were included. Overall, the number of late-phase trials using the MAMS design has been increasing since 2001 and been accelerated by COVID-19. The majority of current MAMS platforms were either targeting infectious diseases (52%) or cancers (29%) and all identified trials were for treatment interventions. 89% (55/62) of MAMS platforms were evaluating medications, with 45% (28/62) of the MAMS platforms having at least one or more repurposed medication as a comparison arm.

CONCLUSIONS

Historically, late-phase trials have adhered to long-established standard (two-arm) designs. However, the number of late-phase MAMS platform trials is increasing, across a range of different disease areas. This study highlights the potential scope of MAMS platform trials and may assist research teams considering use of this approach in the late-phase randomised clinical trial setting.

PROSPERO REGISTRATION NUMBER

CRD42019153910.

Clinical Evidence Informing Treatment Guidelines on Repurposed Drugs for Hospitalized Patients During the Early COVID-19 Pandemic: Corticosteroids, Anticoagulants, (Hydroxy)chloroquine

INTRODUCTION

In early 2020, the coronavirus disease 2019 (COVID-19) pandemic spread worldwide, overwhelming hospitals with severely ill patients and posing the urgent need for clinical evidence to guide patient care. First treatment options available were repurposed drugs to fight inflammation, coagulopathy, and viral replication. A vast number of clinical studies were launched globally to test their efficacy and safety. Our analysis describes the development of global evidence on repurposed drugs, in particular corticosteroids, anticoagulants, and (hydroxy)chloroquine in hospitalized COVID-19 patients based on different study types. We track the incorporation of clinical data in international and national treatment guidelines and identify factors that characterize studies and analyses with the greatest impact on treatment recommendations.

METHODS

A literature search in MEDLINE was conducted to assess the clinical evidence on treatment with corticosteroids, anticoagulants, and (hydroxy)chloroquine in hospitalized COVID-19 patients during the first year of the pandemic. Adoption of the evidence from this clinical data in treatment guidelines of the World Health Organization (WHO), Germany, and United States (US) was evaluated over time.

RESULTS

We identified 106 studies on corticosteroids, 141 studies on anticoagulants, and 115 studies on (hydroxy)chloroquine. Most studies were retrospective cohort studies; some were randomized clinical trials (RCTs), and a few were platform trials. These studies were compared to studies directly and indirectly referred to in WHO (7 versions), German (5 versions), and US (21 versions) guidelines. We found that initially large, well-adjusted, mainly retrospective cohort studies and ultimately large platform trials or coordinated meta-analyses of RCTs provided best available clinical evidence supporting treatment recommendations.

DISCUSSION

Particularly early in the pandemic, evidence for the efficacy and safety of repurposed drugs was of low quality, since time and scientific rigor seemed to be competing factors. Pandemic preparedness, coordinated efforts, and combined analyses were crucial to generating timely and robust clinical evidence that informed national and international treatment guidelines on corticosteroids, anticoagulants, and (hydroxy)chloroquine. Multi-arm platform trials with master protocols and coordinated meta-analyses proved particularly successful, with researchers joining forces to answer the most pressing questions as quickly as possible.

Health crisis: What opportunities for clinical drug research?

The COVID-19 pandemic led to the deployment of an unprecedented academic and industrial research effort, the sometimes redundant nature of which is regrettable, as is the lack of both national and international management. However, it must be noted that during this crisis, regulatory procedures were adapted and certain obstacles in the organisation of clinical research were partly removed to contribute to the deployment of trials as close as possible to patients and to facilitate monitoring and control procedures. The digitisation of certain processes and the decentralisation of certain activities were implemented under the cover of a mobilisation of the authorities and all institutional, academic and industrial players. While in the UK, the optimisation of resources through a single platform trial has made it possible to demonstrate or invalidate the efficacy of many treatments, in France the health crisis has highlighted the fragility of the organisation of clinical research, in particular a lack of coordination and funding, difficulties in implementing studies and a certain reluctance to share data. However, the crisis has also revealed the adaptability of the various stakeholders and has led to the improvement of several processes useful for the deployment of therapeutic innovation. Let us hope that the lessons learned during this crisis will allow for greater efficiency in the event of a new pandemic and, above all, that the progress made will continue to apply to all future clinical research activities.

Multiplicity adjustments in parallel-group multi-arm trials sharing a control group: Clear guidance is needed

Multi-arm, parallel-group clinical trials are an efficient way of testing several new treatments, treatment regimens or doses. However, guidance on the requirement for statistical adjustment to control for multiple comparisons (type I error) using a shared control group is unclear. We argue, based on current evidence, that adjustment is not always necessary in such situations. We propose that adjustment should not be a requirement in multi-arm, parallel-group trials testing distinct treatments and sharing a control group, and we call for clearer guidance from stakeholders, such as regulators and scientific journals, on the appropriate settings for adjustment of multiplicity.

Crise sanitaire : quelles opportunités pour la recherche clinique sur le médicament ?

La pandémie de coronavirus disease-19 (COVID-19) a conduit au déploiement d’un effort de recherche académique et industriel sans précédent dont on peut regretter le caractère parfois redondant ainsi que le manque de pilotage tant national qu’international. Pourtant, force est de constater qu’à l’occasion de cette crise, les procédures réglementaires ont été adaptées de même que certains freins dans l’organisation de la recherche clinique ont pu être en partie levés pour contribuer au déploiement d’essais au plus près des patients et faciliter les modalités de suivi et de contrôle. La digitalisation de certains processus et la décentralisation de certaines activités ont pu être mises en œuvre sous couvert d’une mobilisation des autorités et de l’ensemble des acteurs institutionnels, académiques ou industriels. Si outre-manche, l’optimisation des ressources, au travers d’un essai de plateforme unique, a permis de montrer ou d’infirmer l’efficacité de nombreux traitements, en France la crise sanitaire a mis en lumière la fragilité de l’organisation de la recherche clinique, notamment un déficit de coordination et de financement, des difficultés dans la mise en œuvre des études ou encore une certaine frilosité concernant le partage des données. Cependant, la crise a aussi révélé les capacités d’adaptation des différents acteurs et permis l’amélioration de plusieurs processus utiles au déploiement de l’innovation thérapeutique. Gageons que les leçons tirées à l’occasion de cette crise permettront une meilleure efficacité en cas de nouvelle pandémie et surtout que les progrès obtenus continueront de s’appliquer à l’ensemble des activités de recherche clinique futures.

Effect of anti-interleukin drugs in patients with COVID-19 and signs of cytokine release syndrome (COV-AID): a factorial, randomised, controlled trial

Background

Infections with SARS-CoV-2 continue to cause significant morbidity and mortality. Interleukin (IL)-1 and IL-6 blockade have been proposed as therapeutic strategies in COVID-19, but study outcomes have been conflicting. We sought to study whether blockade of the IL-6 or IL-1 pathway shortened the time to clinical improvement in patients with COVID-19, hypoxic respiratory failure, and signs of systemic cytokine release syndrome.

Methods

We did a prospective, multicentre, open-label, randomised, controlled trial, in hospitalised patients with COVID-19, hypoxia, and signs of a cytokine release syndrome across 16 hospitals in Belgium. Eligible patients had a proven diagnosis of COVID-19 with symptoms between 6 and 16 days, a ratio of the partial pressure of oxygen to the fraction of inspired oxygen (PaO₂:FiO₂) of less than 350 mm Hg on room air or less than 280 mm Hg on supplemental oxygen, and signs of a cytokine release syndrome in their serum (either a single ferritin measurement of more than 2000 μg/L and immediately requiring high flow oxygen or mechanical ventilation, or a ferritin concentration of more than 1000 μg/L, which had been increasing over the previous 24 h, or lymphopenia below 800/mL with two of the following criteria: an increasing ferritin concentration of more than 700 μg/L, an increasing lactate dehydrogenase concentration of more than 300 international units per L, an increasing C-reactive protein concentration of more than 70 mg/L, or an increasing D-dimers concentration of more than 1000 ng/mL). The COV-AID trial has a 2 × 2 factorial design to evaluate IL-1 blockade versus no IL-1 blockade and IL-6 blockade versus no IL-6 blockade. Patients were randomly assigned by means of permuted block randomisation with varying block size and stratification by centre. In a first randomisation, patients were assigned to receive subcutaneous anakinra once daily (100 mg) for 28 days or until discharge, or to receive no IL-1 blockade (1:2). In a second randomisation step, patients were allocated to receive a single dose of siltuximab (11 mg/kg) intravenously, or a single dose of tocilizumab (8 mg/kg) intravenously, or to receive no IL-6 blockade (1:1:1). The primary outcome was the time to clinical improvement, defined as time from randomisation to an increase of at least two points on a 6-category ordinal scale or to discharge from hospital alive. The primary and supportive efficacy endpoints were assessed in the intention-to-treat population. Safety was assessed in the safety population. This study is registered online with ClinicalTrials.gov (NCT04330638) and EudraCT (2020-001500-41) and is complete.

Findings

Between April 4, and Dec 6, 2020, 342 patients were randomly assigned to IL-1 blockade (n=112) or no IL-1 blockade (n=230) and simultaneously randomly assigned to IL-6 blockade (n=227; 114 for tocilizumab and 113 for siltuximab) or no IL-6 blockade (n=115). Most patients were male (265 [77%] of 342), median age was 65 years (IQR 54–73), and median Systematic Organ Failure Assessment (SOFA) score at randomisation was 3 (2–4). All 342 patients were included in the primary intention-to-treat analysis. The estimated median time to clinical improvement was 12 days (95% CI 10–16) in the IL-1 blockade group versus 12 days (10–15) in the no IL-1 blockade group (hazard ratio [HR] 0·94 [95% CI 0·73–1·21]). For the IL-6 blockade group, the estimated median time to clinical improvement was 11 days (95% CI 10–16) versus 12 days (11–16) in the no IL-6 blockade group (HR 1·00 [0·78–1·29]). 55 patients died during the study, but no evidence for differences in mortality between treatment groups was found. The incidence of serious adverse events and serious infections was similar across study groups.

Interpretation

Drugs targeting IL-1 or IL-6 did not shorten the time to clinical improvement in this sample of patients with COVID-19, hypoxic respiratory failure, low SOFA score, and low baseline mortality risk.

Funding

Belgian Health Care Knowledge Center and VIB Grand Challenges program.

Selection bias, investment decisions and treatment effect distributions

When making decisions regarding the investment and design for a Phase 3 programme in the development of a new drug, the results from preceding Phase 2 trials are an important source of information. However, only projects in which the Phase 2 results show promising treatment effects will typically be considered for a Phase 3 investment decision. This implies that, for those projects where Phase 3 is pursued, the underlying Phase 2 estimates are subject to selection bias. We will in this article investigate the nature of this selection bias based on a selection of distributions for the treatment effect. We illustrate some properties of Bayesian estimates, providing shrinkage of the Phase 2 estimate to counteract the selection bias. We further give some empirical guidance regarding the choice of prior distribution and comment on the consequences for decision-making in investment and planning for Phase 3 programmes.