Shotgun: A Bayesian seamless phase I-II design to accelerate the development of targeted therapies and immunotherapy

Three steps toward dose optimization for oncology dose finding

Background

Traditional dose selection for oncology registration trials typically employs a one- or two-step single maximum tolerated dose (MTD) approach. However, this approach may not be appropriate for molecularly targeted therapy, which tends to have toxicity profiles that are markedly different than cytotoxic agents. The US Food and Drug Administration launched Project Optimus to reform dose optimization in oncology drug development and has recently released a related guidance for industry.

Methods

We propose a "three steps toward dose optimization" procedure, in response to these initiatives, and discuss the details in dose-optimization designs and analyses. The first step is dose escalation to identify the MTD or maximum administered dose with an efficient hybrid design, which can offer good overdose control and increases the likelihood of the recommended MTD being close to the true MTD. The second step is the selection of appropriate recommended doses for expansion (RDEs), based on all available data, including emerging safety, pharmacokinetics, pharmacodynamics, and other biomarker information. The third step is dose optimization, which uses data from a randomized fractional factorial design with multiple RDEs explored in multiple tumor cohorts during the expansion phase to ensure a feasible dose is selected for registration trials, and that the tumor type most sensitive to the investigative treatment is identified.

Conclusion

We believe using this three-step approach can increase the likelihood of selecting an optimal dose for a registration trial that demonstrates a balanced safety profile while retaining much of the efficacy observed at the MTD.

Biomarker-driven basket trial designs: origins and new methodological developments

Due to increased use of gene sequencing techniques, understanding of cancer on a molecular level has evolved, in terms of both diagnosis and evaluation in response to initial therapies. In parallel, clinical trials meant to evaluate molecularly-driven interventions through assessment of both treatment effects and putative predictive biomarker effects are being employed to advance the goals of precision medicine. Basket trials investigate one or more biomarker-targeted therapies across multiple cancer types in a tumor location agnostic fashion. The review article offers an overview of the traditional forms of such designs, the practical challenges facing each type of design, and then review novel adaptations proposed in the last few years, categorized into Bayesian and Classical Frequentist perspectives. The review article concludes by summarizing potential advantages and limitations of the new trial design solutions.

Adaptive Bayesian information borrowing methods for finding and optimizing subgroup-specific doses

In precision oncology, integrating multiple cancer patient subgroups into a single master protocol allows for the simultaneous assessment of treatment effects in these subgroups and promotes the sharing of information between them, ultimately reducing sample sizes and costs and enhancing scientific validity. However, the safety and efficacy of these therapies may vary across different subgroups, resulting in heterogeneous outcomes. Therefore, identifying subgroup-specific optimal doses in early-phase clinical trials is crucial for the development of future trials. In this article, we review various innovative Bayesian information-borrowing strategies that aim to determine and optimize subgroup-specific doses. Specifically, we discuss Bayesian hierarchical modeling, Bayesian clustering, Bayesian model averaging or selection, pairwise borrowing, and other relevant approaches. By employing these Bayesian information-borrowing methods, investigators can gain a better understanding of the intricate relationships between dose, toxicity, and efficacy in each subgroup. This increased understanding significantly improves the chances of identifying an optimal dose tailored to each specific subgroup. Furthermore, we present several practical recommendations to guide the design of future early-phase oncology trials involving multiple subgroups when using the Bayesian information-borrowing methods.

A basket trial design based on constrained hierarchical Bayesian model for latent subgroups

It is well known a basket trial consisting of multiple cancer types has the potential of borrowing strength across the baskets defined by the cancer types, leading to an efficient design in terms of sample size and trial duration. The treatment effects in those baskets are often heterogeneous and categorized by the cancer types being sensitive or insensitive to the treatment. Hence, the assumption of exchangeability in many existing basket trials may be violated, and there is a need to design trials without this assumption. In this paper, we simplify the constrained hierarchical Bayesian model for latent subgroups (CHBM-LS) for two classifiers to deal with the potential heterogeneity of treatment effects due to the single classifier of the cancer type. Different baskets are aggregated into subgroups using a latent subgroup modeling approach. The treatment effects are similar and exchangeable to facilitate information borrowing within each latent subgroup. Applying the simplified CHBM-LS approach to the real basket trials where baskets defined by only cancer types shows better performance than other available approaches. Further simulation study also demonstrates this CHBM-LS approach outperforms other approaches with higher statistical power and better-controlled type I error rates under various scenarios.

SAM: Self-adapting mixture prior to dynamically borrow information from historical data in clinical trials

Mixture priors provide an intuitive way to incorporate historical data while accounting for potential prior-data conflict by combining an informative prior with a noninformative prior. However, prespecifying the mixing weight for each component remains a crucial challenge. Ideally, the mixing weight should reflect the degree of prior-data conflict, which is often unknown beforehand, posing a significant obstacle to the application and acceptance of mixture priors. To address this challenge, we introduce self-adapting mixture (SAM) priors that determine the mixing weight using likelihood ratio test statistics or Bayes factors. SAM priors are data-driven and self-adapting, favoring the informative (noninformative) prior component when there is little (substantial) evidence of prior-data conflict. Consequently, SAM priors achieve dynamic information borrowing. We demonstrate that SAM priors exhibit desirable properties in both finite and large samples and achieve information-borrowing consistency. Moreover, SAM priors are easy to compute, data-driven, and calibration-free, mitigating the risk of data dredging. Numerical studies show that SAM priors outperform existing methods in adopting prior-data conflicts effectively. We developed R package "SAMprior" and web application that are freely available at CRAN and www.trialdesign.org to facilitate the use of SAM priors.

Basket trials in oncology: a systematic review of practices and methods, comparative analysis of innovative methods, and an appraisal of a missed opportunity

Background

Basket trials are increasingly used in oncology drug development for early signal detection, accelerated tumor-agnostic approvals, and prioritization of promising tumor types in selected patients with the same mutation or biomarker. Participants are grouped into so-called baskets according to tumor type, allowing investigators to identify tumors with promising responses to treatment for further study. However, it remains a question as to whether and how much the adoption of basket trial designs in oncology have translated into patient benefits, increased pace and scale of clinical development, and de-risking of downstream confirmatory trials.

Methods

Innovation in basket trial design and analysis includes methods that borrow information across tumor types to increase the quality of statistical inference within each tumor type. We build on the existing systematic reviews of basket trials in oncology to discuss the current practices and landscape. We conceptually illustrate recent innovative methods for basket trials, with application to actual data from recently completed basket trials. We explore and discuss the extent to which innovative basket trials can be used to de-risk future trials through their ability to aid prioritization of promising tumor types for subsequent clinical development.

Results

We found increasing adoption of basket trial design in oncology, but largely in the design of single-arm phase II trials with a very low adoption of innovative statistical methods. Furthermore, the current practice of basket trial design, which does not consider its impact on the clinical development plan, may lead to a missed opportunity in improving the probability of success of a future trial. Gating phase II with a phase Ib basket trial reduced the size of phase II trials, and losses in the probability of success as a result of not using innovative methods may not be recoverable by running a larger phase II trial.

Conclusion

Innovative basket trial methods can reduce the size of early phase clinical trials, with sustained improvement in the probability of success of the clinical development plan. We need to do more as a community to improve the adoption of these methods.

Bayesian approach for design and analysis of medical device trials in the era of modern clinical studies

Medical device technology develops rapidly, and the life cycle of a medical device is much shorter than drugs. It is necessary to evaluate the safety and effectiveness of a medical device in a timely manner to keep up with technology flux. Bayesian methods provides an efficient approach to addressing this challenge. In this article, we review the characteristics of the Bayesian approach and some Bayesian designs that were commonly used in medical device regulatory setting, including Bayesian adaptive design, Bayesian diagnostic design, Bayesian multiregional design, and Bayesian label expansion study. We illustrate these designs with medical devices approved by the US Food and Drug Administration (FDA). We also review several innovative Bayesian information borrowing methods, and briefly discuss the challenges and future directions of the Bayesian application in medical device trials. Our objective is to promote the use of the Bayesian approach to accelerate the development of innovative medical devices and their accessibility to patients for effective disease diagnoses and treatments.

A Bayesian adaptive design for dual-agent phase I-II oncology trials integrating efficacy data across stages

Combination of several anticancer treatments has typically been presumed to have enhanced drug activity. Motivated by a real clinical trial, this paper considers phase I-II dose finding designs for dual-agent combinations, where one main objective is to characterize both the toxicity and efficacy profiles. We propose a two-stage Bayesian adaptive design that accommodates a change of patient population in-between. In stage I, we estimate a maximum tolerated dose combination using the escalation with overdose control (EWOC) principle. This is followed by a stage II, conducted in a new yet relevant patient population, to find the most efficacious dose combination. We implement a robust Bayesian hierarchical random-effects model to allow sharing of information on the efficacy across stages, assuming that the related parameters are either exchangeable or nonexchangeable. Under the assumption of exchangeability, a random-effects distribution is specified for the main effects parameters to capture uncertainty about the between-stage differences. The inclusion of nonexchangeability assumption further enables that the stage-specific efficacy parameters have their own priors. The proposed methodology is assessed with an extensive simulation study. Our results suggest a general improvement of the operating characteristics for the efficacy assessment, under a conservative assumption about the exchangeability of the parameters a priori.

Bayesian optimal phase II designs with dual-criterion decision making

The conventional phase II trial design paradigm is to make the go/no-go decision based on the hypothesis testing framework. Statistical significance itself alone, however, may not be sufficient to establish that the drug is clinically effective enough to warrant confirmatory phase III trials. We propose the Bayesian optimal phase II trial design with dual-criterion decision making (BOP2-DC), which incorporates both statistical significance and clinical relevance into decision making. Based on the posterior probability that the treatment effect reaches the lower reference value (statistical significance) and the clinically meaningful value (clinical significance), BOP2-DC allows for go/consider/no-go decisions, rather than a binary go/no-go decision. BOP2-DC is highly flexible and accommodates various types of endpoints, including binary, continuous, time-to-event, multiple, and coprimary endpoints, in single-arm and randomized trials. The decision rule of BOP2-DC is optimized to maximize the probability of a go decision when the treatment is effective or minimize the expected sample size when the treatment is futile. Simulation studies show that the BOP2-DC design yields desirable operating characteristics. The software to implement BOP2-DC is freely available at www.trialdesign.org.

Shotgun-2: A Bayesian phase I/II basket trial design to identify indication-specific optimal biological doses

For novel molecularly targeted agents and immunotherapies, the objective of dose-finding is often to identify the optimal biological dose, rather than the maximum tolerated dose. However, optimal biological doses may not be the same for different indications, challenging the traditional dose-finding framework. Therefore, we proposed a Bayesian phase I/II basket trial design, named "shotgun-2," to identify indication-specific optimal biological doses. A dose-escalation part is conducted in stage I to identify the maximum tolerated dose and admissible dose sets. In stage II, dose optimization is performed incorporating both toxicity and efficacy for each indication. Simulation studies under both fixed and random scenarios show that, compared with the traditional "phase I  +  cohort expansion" design, the shotgun-2 design is robust and can improve the probability of correctly selecting the optimal biological doses. Furthermore, this study provides a useful tool for identifying indication-specific optimal biological doses and accelerating drug development.

Basket Trials: Review of Current Practice and Innovations for Future Trials

Advances in biology and immunology have elucidated genetic and immunologic origins of cancer. Innovations in sequencing technologies revealed that distinct cancer histologies shared common genetic and immune phenotypic traits. Pharmacologic developments made it possible to target these alterations, yielding novel classes of targeted agents whose therapeutic potential span multiple tumor types. Basket trials, one type of master protocol, emerged as a tool for evaluating biomarker-targeted therapies among multiple tumor histologies. Conventionally conducted within the phase II setting and designed to estimate high and durable objective responses, basket trials pose challenges to statistical design and interpretation of results. This article reviews basket trials implemented in oncology studies and discusses issues related to their statistical design and analysis.

A bayesian platform trial design to simultaneously evaluate multiple drugs in multiple indications with mixed endpoints

In the era of targeted therapies and immunotherapies, the traditional drug development paradigm of testing one drug at a time in one indication has become increasingly inefficient. Motivated by a real-world application, we propose a master-protocol-based Bayesian platform trial design with mixed endpoints (PDME) to simultaneously evaluate multiple drugs in multiple indications, where different subsets of efficacy measures (e.g., objective response and landmark progression-free survival) may be used by different indications as single or multiple endpoints. We propose a Bayesian hierarchical model to accommodate mixed endpoints and reflect the trial structure of indications that are nested within treatments. We develop a two-stage approach that first clusters the indications into homogeneous subgroups and then applies the Bayesian hierarchical model to each subgroup to achieve precision information borrowing. Patients are enrolled in a group-sequential way and adaptively assigned to treatments according to their efficacy estimates. At each interim analysis, the posterior probabilities that the treatment effect exceeds prespecified clinically relevant thresholds are used to drop ineffective treatments and "graduate" effective treatments. Simulations show that the PDME design has desirable operating characteristics compared to existing method. This article is protected by copyright. All rights reserved.

Designing Clinical Trials for Combination Immunotherapy: A Framework for Glioblastoma

Immunotherapy has revolutionized treatment for many hard-to-treat cancers but has yet to produce significant improvement in outcomes for patients with glioblastoma. This reflects the multiple and unique mechanisms of immune evasion and escape in this highly heterogeneous tumor. Glioblastoma engenders profound local and systemic immunosuppression and is remarkably effective at inducing T-cell dysfunction, posing a challenge to any immunotherapy-based approach. To overcome these mechanisms, multiple disparate modes of immune-oriented therapy will be required. However, designing trials that can evaluate these combinatorial approaches requires careful consideration. In this review, we explore the immunotherapy resistance mechanisms that have been encountered to date and how combinatorial approaches may address these. We also describe the unique aspects of trial design in both preclinical and clinical settings and consider endpoints and markers of response best suited for an intervention involving multiple agents.

Constrained hierarchical Bayesian model for latent subgroups in basket trials with two classifiers

The basket trial in oncology is a novel clinical trial design that enables the simultaneous assessment of one treatment in multiple cancer types. In addition to the usual basket classifier of the cancer types, many recent basket trials further contain other classifiers like biomarkers that potentially affect the clinical outcomes. In other words, the treatment effects in those baskets are often categorized by not only the cancer types but also the levels of other classifiers. Therefore, the assumption of exchangeability is often violated when some baskets are more sensitive to the targeted treatment, whereas others are less. In this article, we propose a constrained hierarchical Bayesian model for latent subgroups (CHBM-LS) to deal with potential heterogeneity of treatment effects due to both the cancer type (first classifier) and another classifier (second classifier) in basket trials. Different baskets defined by multiple cancer types and multiple levels of the second classifier are aggregated into subgroups using a latent subgroup modeling approach. Within each latent subgroup, the treatment effects are similar and approximately exchangeable to borrow information. The CHBM-LS approach evaluates the treatment effect for each basket while allowing adaptive information borrowing across the baskets by identifying latent subgroups. The simulation study shows that the CHBM-LS approach outperforms other approaches with higher statistical power and better-controlled type I error rates under various scenarios with heterogeneous treatment effects across baskets.