Adaptive Platform Trial Research Articles

Using the ‘Leapfrog’ Design as a Simple Form of Adaptive Platform Trial to Develop, Test, and Implement Treatment Personalization Methods in Routine Practice

The route for the development, evaluation and dissemination of personalized psychological therapies is complex and challenging. In particular, the large sample sizes needed to provide adequately powered trials of newly-developed personalization approaches means that the traditional treatment development route is extremely inefficient. This paper outlines the promise of adaptive platform trials (APT) embedded within routine practice as a method to streamline development and testing of personalized psychological therapies, and close the gap to implementation in real-world settings. It focuses in particular on a recently-developed simplified APT design, the ‘leapfrog’ trial, illustrating via simulation how such a trial may proceed and the advantages it can bring, for example in terms of reduced sample sizes. Finally it discusses models of how such trials could be implemented in routine practice, including potential challenges and caveats, alongside a longer-term perspective on the development of personalized psychological treatments.

Associate Principal Investigators and the HEAL-COVID trial: good for trainees, good for trials

BackgroundThe NIHR’s Associate Principal Investigator (API) Scheme in the United Kingdom was expanded nationally in 2020 with the aim of training clinicians to become Principal Investigators for clinical research in the future. The HEAL-COVID adaptive platform trial is an urgent public health study registered with the API Scheme. Within eighteen months of opening, the trial had recruited almost 1200 patients with over 100 active sites. Here we describe our experiences of APIs working on the trial with two broad objectives. Firstly, we aim to explore through qualitative methods the impact that the scheme has had on the APIs’ professional development. Secondly, we aim to quantify the impact that the APIs have had on the recruitment of patients into the trial.MethodsThe professional backgrounds of the APIs are described from data from their application forms to the scheme. The HEAL-COVID API Network is described from records of the monthly meetings. The APIs’ experiences are reviewed from data from the NIHR exit surveys at 6 months and from a reflective practice exercise at the final network meeting. Data of patient recruitment to HEAL-COVID was analysed for centres with and without APIs via a multivariate analysis.ResultsForty-two APIs were registered with the HEAL-COVID trial with a diversity of backgrounds in terms of gender, country, profession, grade and specialty. Eleven monthly network meetings took place with the dual objectives of facilitating trial activity and providing educational content. Fourteen APIs completed the NIHR survey with all reporting Good Clinical Practice completion, local promotional activity of the trial, patient recruitment and support from their respective PI. Sites with at least one API recruited over 3.5 times more patients than sites without an API (medians 4 vs 14.5, p < 0.05), independent of factors including type of hospital or number of inpatient beds.DiscussionThis study adds to the growing literature that the NIHR’s API Scheme is effective in meeting its objectives in providing research training to clinicians, thus building a workforce of future clinical researchers. Moreover, data from the HEAL-COVID trial shows that sites with an API are associated with higher recruitment. Overall, registering a trial with the API Scheme not only trains future clinical researchers, but it is also likely to increase the number of patients recruited (amongst other benefits), increasing the efficiency of trials and improving access for patients.

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.

Effects of Allocation Method and Time Trends on Identification of the Best Arm in Multi-Arm Trials

Many trial designs, such as dose-finding trials, shared-control designs, or adaptive platform trials, investigate multiple therapies simultaneously. Often these trials seek to identify the best arm and compare it to a control. Adaptive trials are commonly considered in this space, focusing on methods that drop arms or adjust allocation in response to accumulating information. These methods continue to be compared in the literature, most recently with an emphasis on the effect of time trends during the experiment. Here we compare several methods, considering their performance with and without time trends present. The four procedures are: (a) fixed allocation, (b) arm dropping based on p-values (two variants), (c) arm dropping based on the posterior probability each arm is best (two variants), and (d) response-adaptive randomization. These procedures are compared in terms of their ability to identify the best arm, statistical power, accuracy of estimation, and potential benefit to participants inside the trial. We find arm dropping based on the probability each arm is best and RAR among the best options from the methods considered. Arm dropping based on p-values performs moderately worse, and fixed allocation is much worse on all metrics within this context.

Precision Promise (PrP) Bayesian platform trial for metastatic pancreatic cancer (mPDAC): Results of the first experimental arm, SM-88 as second line therapy.

675 Background: PrP is a phase 3 Bayesian adaptive platform trial for efficiently testing multiple arms against a common control. Its design is what FDA calls a Complex Innovative Design (www.fda.gov/media/130897/download). Experimental arms in stage 1 are adaptively randomized against other arms. The maximum size of an arm’s stage 1 is 100 subjects. Strong efficacy in stage 1 leads to a fixed randomization stage 2. Stages 1 and 2 are combined for the arm’s final analysis and, if positive, would be submitted for drug registration. SM-88 (D,L-alpha-metyrosine) is a dysfunctional, racemic tyrosine mixture designed to interrupt PDAC cellular metabolism and induce oxidative stress. Methoxsalen, phenytoin and sirolimus are added to SM-88 to potentially enhance oxidative stress, and form a 4-drug oral regimen. SM-88 was selected for testing in 2nd line mPDAC using the PrP platform. Methods: Primary endpoint overall survival (OS) is analyzed based on modified intention to treat (mITT). Randomization is 7:3 to experimental and control arms. Efficacy is defined by OS hazard ratio (HR, experimental vs control). All statistical measures in PrP are Bayesian. Superiority (HR &lt; 1) is assessed monthly and is claimed should the Bayesian probability of benefit be ≥ 98%. Futility analyses occur monthly once 50 subjects have accrued to the arm. Accrual ends for futility if Bayesian predictive power (PP) of eventual success in PrP is &lt; 20% for all the arm’s signatures. Follow-up continues for 12 months after arm accrual stops. PrP experimental arm designs are flexible: an arm’s protocol appendix can supersede the master protocol. However, type I error is controlled at &lt; 0.025, as shown by simulation. Results: SM-88 entered PrP in Apr 2020. As the first experimental arm in PrP, it was randomized 7:3 against control arms. Pooled controls for SM-88 were gemcitabine/nab-paclitaxel and mFOLFIRINOX at standard doses, since neither therapy was a backbone for SM-88. Between 4/2020 - 10/2021, 142 subjects were screened, 73 subjects started therapy and are included in the mITT cohort. 55 subjects were enrolled in the SM-88 arm and 18 in control arms. Median age was 65, 41% were female and 67% had ECOG-1. As per protocol, accrual stopped at its first futility analysis in Nov 2021 based on Predictive power (PP) 0.0001 (see table), which is less than the protocol bound 0.20. SM-88 toxicity was mild. Interim results at accrual stop and final results (12 month follow up) shown below. mOS was 4.1m for SM-88 vs 8.1m for control. Conclusions: Leveraging small sample sizes of SM-88 and controls, PrP efficiently and compellingly concluded SM-88 futility in 2nd line mPDAC. PrP continues to assess other therapies, utilizing time-adjusted, as well as concurrently randomized, controls. Clinical trial information: NCT04229004 . [Table: see text]

Clinical antiviral efficacy of favipiravir in early COVID-19 (PLATCOV): an open-label, randomised, controlled, adaptive platform trial

Brief summaryIn early symptomatic COVID-19 treatment, high dose oral favipiravir did not accelerate viral clearance.BackgroundFavipiravir, an anti-influenza drug, has in vitro antiviral activity against SARS-CoV-2. Clinical trial evidence to date is inconclusive. Favipiravir has been recommended for the treatment of COVID-19 in some countries.MethodsIn a multicentre open-label, randomised, controlled, adaptive platform trial, low-risk adult patients with early symptomatic COVID-19 were randomised to one of ten treatment arms including high dose oral favipiravir (3.6g on day 0 followed by 1.6g daily to complete 7 days treatment) or no study drug. The primary outcome was the rate of viral clearance (derived under a linear mixed-effects model from the daily log10 viral densities in standardised duplicate oropharyngeal swab eluates taken daily over 8 days [18 swabs per patient]), assessed in a modified intention-to-treat population (mITT). The safety population included all patients who received at least one dose of the allocated intervention. This ongoing adaptive platform trial was registered at ClinicalTrials.gov (NCT05041907) on 13/09/2021.ResultsIn the final analysis, the mITT population contained data from 114 patients randomised to favipiravir and 126 patients randomised concurrently to no study drug. Under the linear mixed-effects model fitted to all oropharyngeal viral density estimates in the first 8 days from randomisation (4,318 swabs), there was no difference in the rate of viral clearance between patients given favipiravir and patients receiving no study drug; a -1% (95% credible interval: -14 to 14%) difference. High dose favipiravir was well-tolerated.InterpretationFavipiravir does not accelerate viral clearance in early symptomatic COVID-19. The viral clearance rate estimated from quantitative measurements of oropharyngeal eluate viral densities assesses the antiviral efficacy of drugs in vivo with comparatively few studied patients.

Flex-IT! Applying “Platform Trials” Methodology to Immunotherapy for Food Allergy in Research and Clinical Practice

There is an increasing trend in the management of food allergy toward active treatment using allergen immunotherapy (AIT). Although AIT is efficacious, treatment-related adverse events are common, particularly with oral immunotherapy in those with high levels of allergen-specific IgE sensitization. In clinical practice, these adverse events inevitably create challenges: clinicians and patients routinely face decisions whether to alter the dose itself, the frequency of dosing, and the pace of escalation, or indeed discontinue AIT altogether. Flexibility is therefore needed to adapt treatment, particularly in clinical practice, so that participants are "treated-to-target." For example, this may entail a significant change in the dosing protocol or even switching from one route of administration to another in response to frequent adverse events. We refer to this approach as flexible immunotherapy. However, there is little evidence to inform clinicians as to what changes to treatment are most likely to result in treatment success. Classical clinical trials rely, by necessity, on relatively rigid updosing protocols. To provide an evidence base to optimize AIT, the food allergy community should adopt adaptive platform trials, where a "master protocol" facilitates more efficient evaluation, including longer-term outcomes of multiple interventions. Within a single clinical trial, participants are able to switch between different treatment arms; interventions can be added or dropped without compromising the integrity of the trial. Developing platform trials for food AIT may initially be costly, but they represent a significant opportunity to grow the evidence base (with respect to both treatment outcomes and biomarker discovery) at scale. In addition, they could help understand longitudinal disease trajectories that are difficult to study in clinical trials for food allergy due to the time needed to demonstrate changes in efficacy. Finally, their adoption would achieve greater collaboration and consistency in approaches to proactive management of food allergy in routine clinical practice. As a community, we need to actively pursue this with funders and established research collaborations to deliver the very best outcomes for our patients and their families.

Treatment Comparisons in Adaptive Platform Trials Adjusting for Temporal Drift

An adaptive platform trial (APT) is a multi-arm trial in the context of a single disease where treatment arms are allowed to enter or leave the trial based on some decision rule. If a treatment enters the trial later than the control arm, there exist nonconcurrent controls who were not randomized between the two arms under comparison. As APTs typically take long periods of time to conduct, temporal drift may occur, which requires the treatment comparisons to be adjusted for this temporal change. Under the causal inference framework, we propose two approaches for treatment comparisons in APTs that account for temporal drift, both based on propensity score weighting. In particular, to address unmeasured confounders, one approach is doubly robust in the sense that it remains valid so long as either the propensity score model is correctly specified or the time effect model is correctly specified. Simulation study shows that our proposed approaches have desirable operating characteristics with well controlled Type I error rates and high power with or without unmeasured confounders.

Evaluating the impact of a SIMPlified LaYered consent process on recruitment of potential participants to the Staphylococcus aureus Network Adaptive Platform trial: study protocol for a multicentre pragmatic nested randomised clinical trial (SIMPLY-SNAP trial)

IntroductionInformed consent forms (ICFs) for randomised clinical trials (RCTs) can be onerous and lengthy. The process has the potential to overwhelm patients with information, leading them to miss elements of...

A blueprint for a multi-disease, multi-domain Bayesian adaptive platform trial incorporating adult and paediatric subgroups: the Staphylococcus aureus Network Adaptive Platform trial

The Staphylococcus aureus Network Adaptive Platform (SNAP) trial is a multifactorial Bayesian adaptive platform trial that aims to improve the way that S. aureus bloodstream infection, a globally common and severe infectious disease, is treated. In a world first, the SNAP trial will simultaneously investigate the effects of multiple intervention modalities within multiple groups of participants with different forms of S. aureus bloodstream infection. Here, we formalise the trial structure, modelling approach, and decision rules that will be used for the SNAP trial. By summarising the statistical principles governing the design, our hope is that the SNAP trial will serve as an adaptable template that can be used to improve comparative effectiveness research efficiency in other disease areas.Trial registrationNCT05137119. Registered on 30 November 2021.

Towards achieving transnational research partnership equity: lessons from implementing adaptive platform trials in low- and middle-income countries.

Use of adaptive clinical trials, particularly adaptive platform trials, has grown exponentially in response to the coronavirus disease (COVID-19) pandemic. Implementation of these trials in low- and middle-income countries (LMICs) has been fostered through the formation or modification of transnational research partnerships, typically between research groups from LMICs and high-income countries (HICs). While these partnerships are important to promote collaboration and overcome the structural and economic disadvantages faced by LMIC health researchers, it is critical to focus attention on the multiple dimensions of partnership equity. Based on informal literature reviews and a meeting with leaders of one of the multinational COVID-19 adaptive platform trials, we describe some important considerations about research partnership equity in this context. We organize these considerations into eight thematic categories: 1) epistemic structures, 2) funding, 3) ethics oversight, 4) regulatory oversight, 5) leadership, 6) post-trial access to interventions, data, and specimens, 7) knowledge translation and dissemination, and 8) research capacity strengthening and maintenance. Within each category we review normative claims that support its relevance to research partnership equity followed by discussion of how adaptive platform trials highlight new dimensions, considerations, or challenges. In aggregate, these observations provide insight into procedural and substantive equity-building measures within transnational global health research partnerships more broadly.

Adaptive platform trials: the impact of common controls on type one error and power

ABSTRACT Platform trials offer a framework to study multiple interventions in one trial with the opportunity of opening and closing arms. The use of common controls can increase efficiency as compared to individual controls. The need for multiplicity adjustment because of common controls is currently a debate among researchers, pharmaceutical companies, and regulators. The impact of common controls on the type one error in a fixed platform trial, i.e. when all treatments start and end recruitment at the same time, has been discussed in the literature before. We complement these findings by investigating the impact of a common control on the type one error and power in a flexible platform trial, i.e. when one arm joins the platform later. We derived the correlation of test statistics to assess the impact of the overlap and compared the results to a trial with individual controls. Furthermore, we evaluate the power, and the impact of multiplicity adjustment on the power in fixed and flexible platform trials. These methodological considerations are complemented by a regulatory guideline review. With multiple arms, the FWER is inflated when no multiplicity adjustment is applied. However, the FWER inflation is smaller with common controls than with individual controls. Even after multiplicity adjustment, a trial with common controls is often beneficial in terms of sample size and power. However, in some cases, the trial with common controls loses the efficiency gain and it might be advisable to run a separate trial rather than joining a platform trial.

REMAP Periop: a randomised, embedded, multifactorial adaptive platform trial protocol for perioperative medicine to determine the optimal enhanced recovery pathway components in complex abdominal surgery patients within a US healthcare system

IntroductionImplementation of enhanced recovery pathways (ERPs) has resulted in improved patient-centred outcomes and decreased costs. However, there is a lack of high-level evidence for many ERP elements. We have designed...

378. Cost-effectiveness of an adaptive platform trial design compared to sequential conventional clinical trials for comparative drug evaluations in bloodstream infections: a simulation study

Abstract Background Adaptive platform trials (APTs) have become increasingly popular in recent years in infectious diseases research. However, few studies have compared APT design against conventional randomized clinical trial (RCT) design from a cost-effectiveness standpoint. We aimed to evaluate the cost-effectiveness of APT versus conventional RCTs and quantify the trade-offs involved in choosing between these designs. Methods We conducted a model-based economic evaluation using a two-level, hierarchical simulation model comparing two strategies: (1) APT comparing three drugs simultaneously against a single control group, and (2) three sequential 2-arm parallel group conventional RCTs (Fig 1). Cost inputs were obtained from a recently completed conventional RCT studying bloodstream infections (BSI) and a recently launched APT for Gram-negative BSI (Table 1). 1000 Monte Carlo 2nd order iterations were performed to simulate 1000 RCTs to determine empirical Type I and II error rates across several scenario analyses.Figure 1:Schematic illustrating adaptive platform trial and conventional clinical trial design used in model.Table 1:Cost inputs for adaptive platform trial and conventional clinical trial design.SD = standard deviation. All costs are stated in Canadian dollars. * Input standard deviations stated if costs input as gamma distributions. Results In the base case analysis where a less stringent interim analysis stopping rule was used, and the drugs being tested were effective, APT design was associated with lower cost ($5,368,000 vs $8,655,000), shorter duration (135 vs 242 weeks), and lower mean type II error (0.086 vs 0.213) (Table 2). However, results were highly sensitive to different scenario analyses where more stringent stopping rules were applied or if the tested drugs had no true effect. Effect sizes were less precise and on average were over-estimated with APT design (Fig 2). Type I error rates were also consistently higher with the APT strategy (mean error rates of 0.20 and 0.077 using liberal and strict χ2crit values of 3.841 and 6.635 respectively) compared to conventional design (fixed at 0.05 by design) (Fig 3).Table 2:Results of base case and scenario analyses.All cost stated are in Canadian dollars.Figure 2:Distribution of relative risk over 1000 RCTs associated with APT and conventional RCT strategies for base case and scenario analyses.(a) Distribution of RR when drugs have true effect (RR of 0.7, 0.75, and 0.8 respectively). Adaptive platform trial design was associated with less precise estimates (wider ranges) and on average over-estimated the effect size. (b) Distribution of RR when drugs have no true effect (RR of 1.0 for all three drugs). (c) and (d) represent the same scenarios as (a) and (b) but with stricter interim analysis cut-offs (χ2 critical values of 6.635 vs 3.841; corresponding to p-value of 0.01 vs 0.05 respectively.Figure 3:Empirical type I and type II error associated with APT and conventional RCT strategies for base case and scenario analyses.(a) Empirical type II error associated with APT and conventional trials, calculated by determining the proportion of 1000 RCTs where no significant difference was concluded when drugs were simulated to have a true effect. Conventional trial design had type II error rates of about 0.20 by design (in sample size calculation). APT design was associated with lower type II error rates. The same effect was seen in (c) where stricter cut-off values for interim analysis was used. (b) Empirical type I error associated with APT and conventional trials, calculated by determining the proportion of 1000 RCTs where a significant difference was concluded when drugs were simulated to have no true effect (RR of 1.0). APT was associated with a consistently higher type I error rate, even when a stricter interim analysis cut-off was used (d). Conclusion We show a proof-of-concept that simulation methods can be used to compare APT and conventional RCT designs for trial planning purposes. Neither strategy was consistently superior in terms of cost-effectiveness. Trade-offs in cost, sample size, and error rates are highly scenario dependent. Choice of trial design should depend on multiple variables, including the study question, probability of efficacy of the drug, and priorities of the investigator (Table 3).Table 3:Factors affecting choice of RCT design. Disclosures All Authors: No reported disclosures

Nonconcurrent Controls in Platform Trials: Can We Borrow Their Concurrent Observation Data?

Adaptive platform trials (APTs) offer an innovative approach to studying multiple therapeutic interventions more efficiently through flexible features such as adding and dropping interventions as evidence emerges, creating a seamless process that avoids enrollment disruption. The benefits and practical challenges of implementing APTs have been widely discussed in the literature; however, less consideration has been given to how to use the nonconcurrent control (NCC) data (i.e., the data generated by patients recruited in the control arm before a new treatment is added) when the outcome of interest is a time to event endpoint. Including the NCC can increase the power of the trial. However, due to the omnipresent change of standard care over time, complete borrowing of the NCC survival data may lead to some bias in the estimation. In this article, we propose an alternative approach to borrow the concurrent observation part of the NCC data by left truncation using a simple decision-making flowchart, which can reduce the bias due to the change of standard care under certain assumptions. Then, the restricted mean survival time (RMST), estimated by the Kaplan-Meier method, is used to compare the treatment versus the pooled control group. We present two simulation studies to illustrate the performance of the decision-making flowchart method under different scenarios. We advocate researchers and drug developers to apply and validate this simple approach in practice.

A Systematic Review of Potential Biomarkers for Bacterial Burden and Treatment Efficacy Assessment in Tuberculosis 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; β, early and end-of treatment end points, phase 2b-c trials; γ, posttreatment 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 22 864 participants. From 1356 biomarker entries, 318 were reported with the performance metrics of interest, with 103 promising and 41 good predictors. Group results were: α, mycobacterial RNA and lipoarabinomannan (LAM) in sputum, and host metabolites in urine; β, mycobacterial RNA and host transcriptomic or cytokine signatures for early treatment response; and γ, host transcriptomics for recurrence. A combination of biomarkers from different categories could help in designing more efficient platform trials. Efforts to develop efficacy surrogates should be better coordinated.

CTNI-17. APPLICATION OF THE GBM-X DATA PLATFORM TO ESTIMATE TREATMENT EFFECTS OF THE EXPERIMENTAL THERAPIES OF THE RANDOMIZED PHASE 2 INDIVIDUALIZED SCREENING TRIAL OF INNOVATIVE GLIOBLASTOMA THERAPIES

Abstract BACKGROUND Leveraging external data has the potential to increase efficiency and precision of drug development in glioblastoma by complementing or replacing data from in-trial controls. We used an external control arm (ECA) to re-analyze data of the abemaciclib, neratinib and CC-115 arms of the Individualized Screening Trial of Innovative Glioblastoma Therapy (INSIGhT) trial. METHODS INSIGhT (NCT02977780) is an ongoing phase 2 adaptive platform trial in newly diagnosed MGMT unmethylated glioblastoma with a shared control arm of patients receiving radiation therapy with concurrent and maintenance temozolomide. The initial experimental arms (abemaciclib, neratinib, CC-115) have not shown a survival benefit relative to the shared control arm. We re-analyzed the experimental arm data and replaced the shared control arm with an ECA. The ECA was developed from a collection of trial datasets and real-world data available through the Glioblastoma External (GBM-X) Data Platform (). Propensity score matching was used to adjust for possible differences between pretreatment covariates (age, sex, resection, KPS) in the ECA and shared control arm. Cox proportional hazards models were used to estimate treatment effects. RESULTS On INSIGhT, patients were randomized to abemaciclib (n = 74), neratinib (n = 80), and CC-115 (n = 12). There were 687 unmethylated MGMT patients in the GBM-X ECA. After propensity score matching, there was no significant treatment effect associated with abemaciclib (HR 1.091, 95%CI:0.834-1.433, p = 0.536), neratinib (HR = 1.043, 95%CI:0.787-1.383, p = 0.770), or CC-115 (HR 1.088, 95%CI:0.834-1.433, p = 0.794) compared to the ECA. CONCLUSION In comparison to an ECA, there was no significant survival benefit with abemaciclib, neratinib and CC-115. Treatment effects estimates were similar to the primary analyses based on the original shared control arm of INSIGhT. The use of external control arms in early phase testing of new therapies is supported by our findings and could significantly reduce costs and time to conduct trials in newly diagnosed glioblastoma.

CTNI-85. GBM AGILE PLATFORM TRIAL FOR NEWLY DIAGNOSED AND RECURRENT GBM: RESULTS OF FIRST EXPERIMENTAL ARM, REGORAFENIB

Abstract GBM AGILE (NCT03970447;https://www.gcaresearch.org/research/gbm-agile) is a phase 3 Bayesian adaptive platform trial that efficiently tests multiple arms against common control, with 6 arms included to date. Primary endpoint is overall survival (OS). Stage 1 experimental arms are adaptively randomized against other arms. Demonstrated efficacy in stage 1 leads to fixed randomization stage 2. Stages 1 and 2 are combined for registration. Control randomization is fixed. Regorafenib, a multikinase-inhibitor, entered into GBM AGILE as the first arm and therefore was equally randomized against control. Regorafenib showed OS benefit in recurrent disease (RD) in randomized phase 2 REGOMA trial. METHODS: Patient subtypes in GBM AGILE are newly diagnosed unmethylated (NDU), RD, and—not considered for regorafenib—ND methylated (NDM). Arm indications (signatures) are combinations of subtypes. Control is temozolomide (ND) and lomustine (RD). Efficacy is assessed by OS hazard ratio(HR), arm/control. Efficacy is demonstrated when Bayesian probability of benefit (HR&amp;lt; 1.00) ≥ 98% (roughly analogous P-value: 0.02). Futility occurs at any monthly analysis when Bayesian predictive power (PP) is &amp;lt; 25% for all signatures. Follow-up continues for 12 months after arm’s accrual stops. RESULTS: for regorafenib: When PP for all 3 pre-defined signatures was &amp;lt; 25%, regorafenib’s accrual was stopped for futility. Regorafenib/control sample sizes were 49/51, 126/128, 175/179 for signatures NDU, RD, and both. Respective PPs: 0.138, 0.030, 0.025—none close to 0.25. Respective mean HRs: 1.26, 1.25, 1.23. Probabilities of benefit (HR&amp;lt; 1.00): 0.35, 0.18, 0.17. At final analysis, mean HRs were 1.07, 1.12, 1.10 with final probabilities of benefit (HR&amp;lt; 1.00) equal to 0.43, 0.24, 0.24—none close to 0.98. CONCLUSION: GBM AGILE efficiently and compellingly addressed regorafenib’s role in GBM, in RD and NDU. These findings are germane as they fail to confirm the REGOMA results in RD. GBM AGILE continues to efficiently assess other therapies, including utilizing concurrent and previously accrued controls.

Early Oral Antibiotic Switch in Staphylococcus aureus Bacteraemia: The Staphylococcus aureus Network Adaptive Platform (SNAP) Trial Early Oral Switch Protocol.

Staphylococcus aureus bloodstream infection (bacteremia) is traditionally treated with at least 2 weeks of intravenous (IV) antibiotics in adults, 3-7 days in children, and often longer for those with complicated disease. The current practice of treating S. aureus bacteremia (SAB) with prolonged IV antibiotics (rather than oral antibiotics) is based on historical observational research and expert opinion. Prolonged IV antibiotic therapy has significant disadvantages for patients and healthcare systems, and there is growing interest in whether a switch to oral antibiotics following an initial period of IV therapy is a safe alternative for clinically stable patients. The early oral switch (EOS) domain of the S. aureus Network Adaptive Platform (SNAP) trial will assess early switch to oral antibiotics compared with continued IV treatment in clinically stable patients with SAB. The primary endpoint is 90-day all-cause mortality. Hospitalised SAB patients are assessed at platform day 7 ±2 (uncomplicated SAB) and day 14 ±2 (complicated SAB) to determine their eligibility for randomization to EOS (intervention) or continued IV treatment (current standard of care). Recruitment is occurring in the EOS domain of the SNAP trial. As of August 2023, 21% of all SNAP participants had been randomized to the EOS domain, a total of 264 participants across 77 centers, with an aim to recruit at least 1000 participants. We describe challenges and facilitators to enrolment in this domain to aid those planning similar trials.

Antiviral efficacy of molnupiravir versus ritonavir-boosted nirmatrelvir in patients with early symptomatic COVID-19 (PLATCOV): an open-label, phase 2, randomised, controlled, adaptive trial

Molnupiravir and ritonavir-boosted nirmatrelvir are the two leading oral COVID-19 antiviral treatments, but their antiviral activities in patients have not been compared directly. The aim of this ongoing platform trial is to compare different antiviral treatments using the rate of viral clearance as the measure of antiviral effect. PLATCOV is an open-label, multicentre, phase 2, randomised, controlled, adaptive pharmacometric platform trial running in Thailand, Brazil, Pakistan, and Laos. The component of the trial reported here was conducted in the Hospital for Tropical Diseases, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand. We recruited low-risk adult patients aged 18-50 years with early symptomatic COVID-19 (<4 days of symptoms). Eligible patients were randomly assigned using block randomisation via a centralised web app to one of seven treatment groups: molnupiravir, ritonavir-boosted nirmatrelvir, casirivimab-imdevimab, tixagevimab-cilgavimab, favipiravir, fluoxetine, or no study drug. The no study drug group comprised a minimum proportion of 20% of patients at all times, with uniform randomisation ratios applied across the active treatment groups. Results for the concurrently randomised molnupiravir, ritonavir-boosted nirmatrelvir, and no study drug groups are reported here. The primary endpoint was the rate of oropharyngeal viral clearance assessed in a modified intention-to-treat population, defined as patients with more than 2 days of follow-up. Safety was assessed in all participants who took at least one dose of the medication. The viral clearance rate was derived under a Bayesian hierarchical linear model fitted to the log10 viral densities in standardised duplicate oropharyngeal swab eluates taken daily over 1 week (18 measurements). Treatment groups with a probability of more than 0·9 that viral clearance was accelerated by more than 20% compared with no drug entered a non-inferiority comparison (with a 10% non-inferiority margin) compared with the platform's current most effective drug. This ongoing trial is registered at ClinicalTrials.gov, NCT05041907. Between June 6, 2022, and Feb 23, 2023, 209 patients in Thailand were enrolled and concurrently randomly assigned to molnupiravir (n=65), ritonavir-boosted nirmatrelvir (n=59), or no study drug (n=85). 129 (62%) of the patients were female and 80 (38%) were male. Relative to the no study drug group, the rates of viral clearance were 37% (95% credible interval 16-65) faster with molnupiravir and 84% (54-119) faster with ritonavir-boosted nirmatrelvir. In the non-inferiority comparison, viral clearance was 25% (10-38) slower with molnupiravir than ritonavir-boosted nirmatrelvir. Molnupiravir was removed from the study platform when it reached the prespecified inferiority margin of 10% compared with ritonavir-boosted nirmatrelvir. Median estimated viral clearance half-lives were 8·5 h (IQR 6·7-10·1) with ritonavir-boosted nirmatrelvir, 11·6 h (8·6-15·4) with molnupiravir, and 15·5 h (11·9-21·2) with no study drug. Viral rebound occurred more frequently following nirmatrelvir (six [10%] of 58) compared with the no study drug (one [1%] of 84; p=0·018) or the molnupiravir (one [2%] of 65; p=0·051) groups. Persistent infections following molnupiravir had more viral mutations (three of nine patients had an increased number of single nucleotide polymorphisms in samples collected at 7 or more days compared with those at baseline) than after nirmatrelvir (zero of three) or no study drug (zero of 19). There were no adverse events of grade 3 or worse, or serious adverse events in any of the reported treatment groups. Both molnupiravir and ritonavir-boosted nirmatrelvir accelerate oropharyngeal SARS-CoV-2 viral clearance in patients with COVID-19, but the antiviral effect of ritonavir-boosted nirmatrelvir was substantially greater. Measurement of oropharyngeal viral clearance rates provides a rapid and well tolerated approach to the assessment and comparison of antiviral drugs in patients with COVID-19. It should be evaluated in other acute viral respiratory infections. Wellcome Trust through the COVID-19 Therapeutics Accelerator.