Exploring Ethnic Differences in Toxicity in Early-Phase Clinical Trials for Oncology Drugs

East Asian Gynecologic Oncology Trial Group (EAGOT): founding history and future perspective

Racial and regional differences exist in morbidity, histology, drug response, toxicity, and prognosis of gynecologic cancer. However, most large-scale phase III studies have been conducted in Western countries, and these data on Asians, who account for more than half of the world's population, are limited. To build a global clinical trial network in Asia, four clinical trial groups with high expertise and international competitiveness in East Asia, namely the Japanese Gynecologic Oncology Group in Japan, the Korean Gynecologic Oncology Group in Korea, the Taiwanese Gynecologic Oncology Group in Taiwan, and the Chinese Gynecologic Cancer Society in the People's Republic of China, established a new group called the East Asia Gynecologic Oncology Trial Group (EAGOT) on November 19, 2021. It includes four committees: the Cervical Cancer Committee, Uterine Corpus Cancer Committee, Ovarian Cancer Committee, and Translational Research Committee. The purpose of EAGOT is to conduct international clinical trials in an effort to provide the best treatments for Asian women affected by gynecologic cancer. Discussions on new collaborative clinical trials have already begun. The first Annual EAGOT Meeting was held on May 25-27, 2023 in Niigata, Japan. EAGOT, the largest healthcare/investigational innovation network in Asia in the area of gynecologic cancers, will become a platform for establishing standards of care and lead to guidelines for Asian women suffering from gynecologic cancer. The harmonization of regulatory/investigator-initiated clinical trials, simultaneous approval of unapproved drugs in the four countries under a common protocol, and expansion of indications will improve the prognosis of gynecologic cancers in Asia in the near future.

Estimating Similarity of Dose-Response Relationships in Phase I Clinical Trials-Case Study in Bridging Data Package

Bridging studies are designed to fill the gap between two populations in terms of clinical trial data, such as toxicity, efficacy, comorbidities and doses. According to ICH-E5 guidelines, clinical data can be extrapolated from one region to another if dose–reponse curves are similar between two populations. For instance, in Japan, Phase I clinical trials are often repeated due to this physiological/metabolic paradigm: the maximum tolerated dose (MTD) for Japanese patients is assumed to be lower than that for Caucasian patients, but not necessarily for all molecules. Therefore, proposing a statistical tool evaluating the similarity between two populations dose–response curves is of most interest. The aim of our work is to propose several indicators to evaluate the distance and the similarity of dose–toxicity curves and MTD distributions at the end of some of the Phase I trials, conducted on two populations or regions. For this purpose, we extended and adapted the commensurability criterion, initially proposed by Ollier et al. (2019), in the setting of completed phase I clinical trials. We evaluated their performance using three synthetic sets, built as examples, and six case studies found in the literature. Visualization plots and guidelines on the way to interpret the results are proposed.

Bridging across patient subgroups in phase I oncology trials that incorporate animal data

In this paper, we develop a general Bayesian hierarchical model for bridging across patient subgroups in phase I oncology trials, for which preliminary information about the dose-toxicity relationship can be drawn from animal studies. Parameters that re-scale the doses to adjust for intrinsic differences in toxicity, either between animals and humans or between human subgroups, are introduced to each dose-toxicity model. Appropriate priors are specified for these scaling parameters, which capture the magnitude of uncertainty surrounding the animal-to-human translation and bridging assumption. After mapping data onto a common, 'average' human dosing scale, human dose-toxicity parameters are assumed to be exchangeable either with the standardised, animal study-specific parameters, or between themselves across human subgroups. Random-effects distributions are distinguished by different covariance matrices that reflect the between-study heterogeneity in animals and humans. Possibility of non-exchangeability is allowed to avoid inferences for extreme subgroups being overly influenced by their complementary data. We illustrate the proposed approach with hypothetical examples, and use simulation to compare the operating characteristics of trials analysed using our Bayesian model with several alternatives. Numerical results show that the proposed approach yields robust inferences, even when data from multiple sources are inconsistent and/or the bridging assumptions are incorrect.

Bayesian dose-finding phase I trial design incorporating historical data from a preceding trial

We consider the problem of incorporating historical data from a preceding trial to design and conduct a subsequent dose-finding trial in a possibly different population of patients. In oncology, for example, after a phase I dose-finding trial is completed in Caucasian patients, investigators often conduct a further phase I trial to determine the maximum tolerated dose in Asian patients. This may be due to concerns about possible differences in treatment tolerability between populations. In this study, we propose to adaptively incorporate historical data into prior distributions assumed in a new dose-finding trial. Our proposed approach aims to appropriately borrow strength from a previous trial to improve the maximum tolerated dose determination in another patient population. We define a "historical-to-current (H-C)" parameter representing the degree of borrowing based on a retrospective analysis of previous trial data. In simulation studies, we examine the operating characteristics of the proposed method in comparison with 3 alternative approaches and assess how the H-C parameter functions across a variety of realistic settings.