Innovative approaches for vaccine trials as a key component of pandemic preparedness - a white paper.

BACKGROUND: WHO postulates the application of adaptive design features in the global clinical trial ecosystem. However, the adaptive platform trial (APT) methodology has not been widely adopted in clinical research on vaccines. METHODS: The VACCELERATE Consortium organized a two-day workshop to discuss the applicability of APT methodology in vaccine trials under non-pandemic as well as pandemic conditions. Core aspects of the discussions are summarized in this article. RESULTS: An "ever-warm" APT appears ideally suited to improve efficiency and speed of vaccine research. Continuous learning based on accumulating APT trial data allows for pre-planned adaptations during its course. Given the relative design complexity, alignment of all stakeholders at all stages of an APT is central. Vaccine trial modelling is crucial, both before and in a pandemic emergency. Various inferential paradigms are possible (frequentist, likelihood, or Bayesian). The focus in the interpandemic interval may be on research gaps left by industry trials. For activation in emergency, template Disease X protocols of syndromal design for pathogens yet unknown need to be stockpiled and updated regularly. Governance of a vaccine APT should be fully integrated into supranational pandemic response mechanisms. DISCUSSION: A broad range of adaptive features can be applied in platform trials on vaccines. Faster knowledge generation comes with increased complexity of trial design. Design complexity should not preclude simple execution at trial sites. Continuously generated evidence represents a return on investment that will garner societal support for sustainable funding. Adaptive design features will naturally find their way into platform trials on vaccines.

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.

A Bayesian model with application for adaptive platform trials having temporal changes.

Temporal changes exist in clinical trials. Over time, shifts in patients' characteristics, trial conduct, and other features of a clinical trial may occur. In typical randomized clinical trials, temporal effects, that is, the impact of temporal changes on clinical outcomes and study analysis, are largely mitigated by randomization and usually need not be explicitly addressed. However, temporal effects can be a serious obstacle for conducting clinical trials with complex designs, including the adaptive platform trials that are gaining popularity in recent medical product development. In this paper, we introduce a Bayesian robust prior for mitigating temporal effects based on a hidden Markov model, and propose a particle filtering algorithm for computation. We conduct simulation studies to evaluate the performance of the proposed method and provide illustration examples based on trials of Ebola virus disease therapeutics and hemostat in vascular surgery.

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.

Coordination of COVID-19 platform trials in Europe.

To ensure optimal coordination of the EU-funded COVID-19 platform trials, a double coordination mechanism was established. It included the Trial Coordination Board (TCB) to promote the dialogue between investigators and relevant public health stakeholders and the Joint Access Advisory Mechanism (JAAM) to streamline access of new intervention arms to the platform trials. Both the TCB and the JAAM emerged as efficient instruments to promote cooperation and optimise the use of resources within EU-funded adaptive platform trials. In addition, an adaptive platform trial toolbox was developed to collect information and literature on challenges and solutions identified to date. The recently funded 'Coordination MEchanism for Cohorts and Trials' (CoMeCT) project will endeavour to make this model sustainable, with a further expansion to other emerging infectious diseases, as part of the governance of the current and future platform trials for pandemic preparedness. This example could serve as a model for platform trial coordination in other disease areas.

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 non-concurrent 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.

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.

Adapting and Evaluating an AI-Based Chatbot Through Patient and Stakeholder Engagement to Provide Information for Different Health Conditions: Master Protocol for an Adaptive Platform Trial (the MARVIN Chatbots Study).

BACKGROUND: Artificial intelligence (AI)-based chatbots could help address some of the challenges patients face in acquiring information essential to their self-health management, including unreliable sources and overburdened health care professionals. Research to ensure the proper design, implementation, and uptake of chatbots is imperative. Inclusive digital health research and responsible AI integration into health care require active and sustained patient and stakeholder engagement, yet corresponding activities and guidance are limited for this purpose. OBJECTIVE: In response, this manuscript presents a master protocol for the development, testing, and implementation of a chatbot family in partnership with stakeholders. This protocol aims to help efficiently translate an initial chatbot intervention (MARVIN) to multiple health domains and populations. METHODS: The MARVIN chatbots study has an adaptive platform trial design consisting of multiple parallel individual chatbot substudies with four common objectives: (1) co-construct a tailored AI chatbot for a specific health care setting, (2) assess its usability with a small sample of participants, (3) measure implementation outcomes (usability, acceptability, appropriateness, adoption, and fidelity) within a large sample, and (4) evaluate the impact of patient and stakeholder partnerships on chatbot development. For objective 1, a needs assessment will be conducted within the setting, involving four 2-hour focus groups with 5 participants each. Then, a co-construction design committee will be formed with patient partners, health care professionals, and researchers who will participate in 6 workshops for chatbot development, testing, and improvement. For objective 2, a total of 30 participants will interact with the prototype for 3 weeks and assess its usability through a survey and 3 focus groups. Positive usability outcomes will lead to the initiation of objective 3, whereby the public will be able to access the chatbot for a 12-month real-world implementation study using web-based questionnaires to measure usability, acceptability, and appropriateness for 150 participants and meta-use data to inform adoption and fidelity. After each objective, for objective 4, focus groups will be conducted with the design committee to better understand their perspectives on the engagement process. RESULTS: From July 2022 to October 2023, this master protocol led to four substudies conducted at the McGill University Health Centre or the Centre hospitalier de l'Université de Montréal (both in Montreal, Quebec, Canada): (1) MARVIN for HIV (large-scale implementation expected in mid-2024), (2) MARVIN-Pharma for community pharmacists providing HIV care (usability study planned for mid-2024), (3) MARVINA for breast cancer, and (4) MARVIN-CHAMP for pediatric infectious conditions (both in preparation, with development to begin in early 2024). CONCLUSIONS: This master protocol offers an approach to chatbot development in partnership with patients and health care professionals that includes a comprehensive assessment of implementation outcomes. It also contributes to best practice recommendations for patient and stakeholder engagement in digital health research. TRIAL REGISTRATION: ClinicalTrials.gov NCT05789901; https://classic.clinicaltrials.gov/ct2/show/NCT05789901. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/54668.

Core protocol for the adaptive Platform Trial In COVID-19 Vaccine priming and BOOsting (PICOBOO).

BACKGROUND: The need for coronavirus 2019 (COVID-19) vaccination in different age groups and populations is a subject of great uncertainty and an ongoing global debate. Critical knowledge gaps regarding COVID-19 vaccination include the duration of protection offered by different priming and booster vaccination regimens in different populations, including homologous or heterologous schedules; how vaccination impacts key elements of the immune system; how this is modified by prior or subsequent exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and future variants; and how immune responses correlate with protection against infection and disease, including antibodies and effector and T cell central memory. METHODS: The Platform Trial In COVID-19 priming and BOOsting (PICOBOO) is a multi-site, multi-arm, Bayesian, adaptive, randomised controlled platform trial. PICOBOO will expeditiously generate and translate high-quality evidence of the immunogenicity, reactogenicity and cross-protection of different COVID-19 priming and booster vaccination strategies against SARS-CoV-2 and its variants/subvariants, specific to the Australian context. While the platform is designed to be vaccine agnostic, participants will be randomised to one of three vaccines at trial commencement, including Pfizer's Comirnaty, Moderna's Spikevax or Novavax's Nuvaxovid COVID-19 vaccine. The protocol structure specifying PICOBOO is modular and hierarchical. Here, we describe the Core Protocol, which outlines the trial processes applicable to all study participants included in the platform trial. DISCUSSION: PICOBOO is the first adaptive platform trial evaluating different COVID-19 priming and booster vaccination strategies in Australia, and one of the few established internationally, that is designed to generate high-quality evidence to inform immunisation practice and policy. The modular, hierarchical protocol structure is intended to standardise outcomes, endpoints, data collection and other study processes for nested substudies included in the trial platform and to minimise duplication. It is anticipated that this flexible trial structure will enable investigators to respond with agility to new research questions as they arise, such as the utility of new vaccines (such as bivalent, or SARS-CoV-2 variant-specific vaccines) as they become available for use. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry ACTRN12622000238774. Registered on 10 February 2022.

Lopinavir-ritonavir and hydroxychloroquine for critically ill patients with COVID-19: REMAP-CAP randomized controlled trial.

PURPOSE: To study the efficacy of lopinavir-ritonavir and hydroxychloroquine in critically ill patients with coronavirus disease 2019 (COVID-19). METHODS: Critically ill adults with COVID-19 were randomized to receive lopinavir-ritonavir, hydroxychloroquine, combination therapy of lopinavir-ritonavir and hydroxychloroquine or no antiviral therapy (control). The primary endpoint was an ordinal scale of organ support-free days. Analyses used a Bayesian cumulative logistic model and expressed treatment effects as an adjusted odds ratio (OR) where an OR > 1 is favorable. RESULTS: We randomized 694 patients to receive lopinavir-ritonavir (n = 255), hydroxychloroquine (n = 50), combination therapy (n = 27) or control (n = 362). The median organ support-free days among patients in lopinavir-ritonavir, hydroxychloroquine, and combination therapy groups was 4 (- 1 to 15), 0 (- 1 to 9) and-1 (- 1 to 7), respectively, compared to 6 (- 1 to 16) in the control group with in-hospital mortality of 88/249 (35%), 17/49 (35%), 13/26 (50%), respectively, compared to 106/353 (30%) in the control group. The three interventions decreased organ support-free days compared to control (OR [95% credible interval]: 0.73 [0.55, 0.99], 0.57 [0.35, 0.83] 0.41 [0.24, 0.72]), yielding posterior probabilities that reached the threshold futility (≥ 99.0%), and high probabilities of harm (98.0%, 99.9% and > 99.9%, respectively). The three interventions reduced hospital survival compared with control (OR [95% CrI]: 0.65 [0.45, 0.95], 0.56 [0.30, 0.89], and 0.36 [0.17, 0.73]), yielding high probabilities of harm (98.5% and 99.4% and 99.8%, respectively). CONCLUSION: Among critically ill patients with COVID-19, lopinavir-ritonavir, hydroxychloroquine, or combination therapy worsened outcomes compared to no antiviral therapy.

Why participation in an international clinical trial platform matters during a pandemic? Launching REMAP-CAP in Japan.

REMAP-CAP, a randomized, embedded, multifactorial adaptive platform trial for community-acquired pneumonia, is an international clinical trial that is rapidly expanding its scope and scale in response to the COVID-19 pandemic. Japan is now joining REMAP-CAP with endorsement from Japanese academic societies. Commitment to REMAP-CAP can significantly contribute to population health through timely identification of optimal COVID-19 therapeutics. Additionally, it will promote the establishment of a national and global network of clinical trials to tackle future pandemics of emerging and re-emerging infectious diseases, in collaboration with multiple stakeholders, including front-line healthcare workers, governmental agencies, regulatory authorities, and academic societies.