Optimal Bayesian hierarchical model to accelerate the development of tissue-agnostic drugs and basket trials

New clinical trial design borrowing information across patient subgroups based on fusion-penalized regression models

In cancer research, basket trials aim to assess the efficacy of a drug using baskets, wherein patients are organized into subgroups according to their tumor type. In this context, using information borrowing strategy may increase the probability of detecting drug efficacy in active baskets, by shrinking together the estimates of the parameters characterizing the drug efficacy in baskets with similar drug activity. Here, we propose to use fusion-penalized logistic regression models to borrow information in the setting of a phase 2 single-arm basket trial with binary outcome. We describe our proposed strategy and assess its performance via a simulation study. We assessed the impact of heterogeneity in drug efficacy, prevalence of each tumor types and implementation of interim analyses on the operating characteristics of our proposed design. We compared our approach with two existing designs, relying on the specification of prior information in a Bayesian framework to borrow information across similar baskets. Notably, our approach performed well when the effect of the drug varied greatly across the baskets. Our approach offers several advantages, including limited implementation efforts and fast computation, which is essential when planning a new trial as such planning requires intensive simulation studies.

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 generalized calibrated Bayesian hierarchical modeling approach to basket trials with multiple endpoints

A basket trial simultaneously evaluates a treatment in multiple cancer subtypes, offering an effective way to accelerate drug development in multiple indications. Many basket trials are designed and monitored based on a single efficacy endpoint, primarily the tumor response. For molecular targeted or immunotherapy agents, however, a single efficacy endpoint cannot adequately characterize the treatment effect. It is increasingly important to use more complex endpoints to comprehensively assess the risk-benefit profile of such targeted therapies. We extend the calibrated Bayesian hierarchical modeling approach to monitor phase II basket trials with multiple endpoints. We propose two generalizations, one based on the latent variable approach and the other based on the multinomial-normal hierarchical model, to accommodate different types of endpoints and dependence assumptions regarding information sharing. We introduce shrinkage parameters as functions of statistics measuring homogeneity among subgroups and propose a general calibration approach to determine the functional forms. Theoretical properties of the generalized hierarchical models are investigated. Simulation studies demonstrate that the monitoring procedure based on the generalized approach yields desirable operating characteristics.

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.

Clinical trial considerations for pediatric cancer drug development

Oncology has been one of the most active therapeutic areas in medicinal products development. Despite this fact, few drugs have been approved for use in pediatric cancer patients when compared to the number approved for adults with cancer. This disparity could be attributed to the fact that many oncology drugs have had orphan drug designation and were exempt from Pediatric Research Equity Act (PREA) requirements. On August 18, 2017, the RACE for Children Act, i.e. Research to Accelerate Cures and Equity Act, was signed into law as Title V of the 2017 FDA Reauthorization Act (FDARA) to amend the PREA. Pediatric investigation is now required if the drug or biological product is intended for the treatment of an adult cancer and directed at a molecular target that FDA determines to be "substantially relevant to the growth or progression of a pediatric cancer." This paper discusses the specific considerations in clinical trial designs and statistical methodologies to be implemented in oncology pediatric clinical programs.

Challenges and Solutions for the Benefit Assessment of Tumor-Agnostic Therapies in Germany

OBJECTIVES

Precision medicine is increasingly important in cancer treatment. Tumor-agnostic therapies are used regardless of tumor entity because they target specific biomarkers in tumors. In Germany, the benefit assessment of oncological pharmaceuticals has traditionally been entity specific. Thus, the assessment of tumor-agnostic therapies leaves stakeholders with various challenges. Our aim was to systematically identify challenges and possible solutions for the benefit assessment of therapies in tumor-agnostic indications using a 2-step sequential qualitative approach.

METHODS

To identify relevant challenges, we conducted qualitative interviews with different stakeholders who were involved in previous benefit assessments of tumor-agnostic therapies in Germany. To identify possible solutions for these challenges, we systematically searched MEDLINE, Embase, and the websites of European health technology assessment bodies for relevant literature.

RESULTS

We identified 9 categories of challenges of which the following were deemed particularly relevant: the absence of direct comparative studies, challenges regarding the use of basket studies and indirect comparisons, challenges in determining the appropriate comparative therapy in a tumor-agnostic indication, and challenges on the system side. Seven categories of solutions were identified, including an increased use of real-world evidence, making conditional decisions in the context of systematic reassessments, splitting the field of application, and finding (new) ways to design and analyze basket studies.

CONCLUSION

A range of possible solutions, which can help to meet the identified challenges in Germany, have been found. Future research should investigate the acceptance and feasibility of these solutions.

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.

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.