Chair: Jingjing Ye (BeiGene)
Co-Chair: Freda Cooner (FDA, CBER)
Abstract: The development of safe, effective, and targeted medications for pediatric populations has long been a significant challenge. Key obstacles include the small number of pediatric patients, the limited availability of detailed physiological data, and the ethical complexities of conducting research with children. These factors collectively slow the progress of pediatric drug development, often resulting in significant delays compared to the approval timelines of drugs for adults. As regulatory requirements for pediatric studies have evolved, innovative research methods, advanced technologies, and collaborative frameworks have emerged to drive progress in this critical field of medicine.
Regional guidelines discussing pediatric extrapolation have been previously issued by various regulatory agencies, including both FDA and EMA. The recently released ICH E11A guideline provides recommendations for, and promotes international harmonization of, the use of pediatric extrapolation to support the development and authorization of pediatric medicines. Recently, a review on pediatric labeling changes in US also showed that the use of extrapolation increased the approval rates of new and expanded pediatric indication (Ye et al., 2023). ICH E11A encourages use of pediatric extrapolation based on evaluation of existing evidence between adult and pediatric population: 1) similarity in disease, 2) similarity of drug pharmacology, and 3) similarity of response to treatment, to reduce the burden of conducting pediatric studies. The level of evidence would depend on the existing strength of evidence and thus the approaches for extrapolation may be different.
Explorations are typically conducted between adult and pediatric populations for the same drug. Recently, the mechanism of action (MOA) based extrapolation has been proposed. This MOA-based strategy broadens the scope of data sources beyond just the same drug to include other drugs with the same or similar MOA. As a result, it allows for the integration of diverse data types that are highly relevant to both the pediatric population and the drug under development. Within this context, Bayesian methodologies are emerging as a powerful tool, offering innovative ways to maximize the use of existing data, optimize trial designs, and ensure robust statistical analysis.
Represented by American Statistical Association (ASA) Biopharmaceutical Section Statistics in Pediatric Drug Development Scientific Workgroup (SPDRx), this session aims to explore cutting-edge approaches to pediatric drug development, focusing on how these methods can bridge the gap between adult and pediatric populations and streamline trial designs. It will feature three expert-led presentations, followed by a discussion led by the PhRMA Topic Lead for the ICH E11A pediatric extrapolation expert working group, who will provide insights into the latest developments in international harmonization efforts and the role of extrapolation in pediatric medicine.
Speaker: Zhongheng Cai, PhD (St. Jude)
Title: Bayesian Extrapolation Design: Exposure-Response Curve Comparison between Pediatric and Adult Populations
Abstract: Developing effective treatments for pediatric populations presents unique scientific and ethical challenges, particularly given the small population size. Both U.S. and EU regulations advocate for pediatric extrapolation, a strategy that leverages existing adult data to assess its relevance to children. This approach often depends on demonstrating similar disease progression, pharmacology, and clinical responses between adults and children. In pharmacology, similarity is typically evaluated through the exposure-response (E-R) relationship. However, current methods for comparing E-R curves between these groups are limited, often focusing on isolated data points rather than the entire curve (Zhang et al., 2021).
To address this gap, we propose an innovative Bayesian approach for a comprehensive comparison of E-R curves between adults and pediatric populations. This method evaluates the full spectrum of the curve using logistic regression for binary endpoints. We developed an algorithm to determine optimal sample size and key design parameters, including the Bayesian posterior probability threshold, and use the maximum curve distance as a similarity metric.
By integrating Bayesian and frequentist principles, our approach simulates datasets under both null and alternative hypotheses, ensuring type I error control while maximizing statistical power. Simulation studies demonstrate that this method offers better type I error control and greater power compared to traditional frequentist approaches (Dette et al., 2018).
Speaker: Yanyan Zhu, PhD (presenter) and Ming-Hui Chen, PhD (University of Connecticut)
Title: Bayesian Re-Design of a Single Arm Pediatric Trial via Borrowing Information from the Concurrent Adult Trials and Historical Pediatric and Adult Trials from the Same Class of Drugs
Abstract: Pediatric trials pose unique and challenging circumstances for several reasons: a small patient population, limited physiological data, and ethical complexity. Consequently, pediatric drug development often lags behind that of adults post-drug approvals. To address this issue, ICH E11A proposes a complementary strategy, known as pediatric extrapolation. The approach involves assessing the relevance of existing information from the adult/reference population to the pediatric/target population. It focuses on aspects of similarity in the disease, drug pharmacology and clinical response to treatment. The aim is to identify the gaps or level of uncertainty that must be addressed to extend conclusions regarding adequate evidence of efficacy and safety. Despite the inherent challenges, extrapolation based on mechanism-of-action (MOA) emerges as a viable option in this context, e.g., extrapolation from adult to the pediatric population using clinical trials within the same drug class can be leveraged. In this paper, we propose a new randomized Bayesian test for designing a single-arm superiority trial within a general Bayesian decision rule-based framework. The analytic form of the test statistic for binary primary outcomes is derived and a search algorithm to achieve the exact type I error is developed. The desirable theoretical properties of the proposed test are established. Several priors are investigated for leveraging multiple historical data. The Type I error and the power are computed exactly without resorting Monte Carlo sampling. Furthermore, an analytical procedure is devised to determine the amount of borrowing from the historical data in order to control a pre-specified inflation level of Type I error. The usefulness of the proposed methodology is further demonstrated via re-designing a new pediatric superiority trial borrowing information data from concurrent adult trials and historical pediatric and adult trials from the same class of drugs with the same or similar MOA.
Speaker: Huan Wang, PhD (FDA)
Title: To be announced.
Abstract: To be announced.
