Clinical trial diversity: An opportunity for improved insight into the determinants of variability in drug response

Abstract

Although the number of countries participating in pivotal trials submitted to enable drug registration has nearly doubled over the past 25 years, there has not been a substantial increase in the diversity of clinical trial populations. In parallel, our understanding of factors that influence medicine response and variability has continued to evolve. The notion of intrinsic and extrinsic sources of variability has been embedded into different regulatory guidelines, including the recent guideline on the importance of enhancing the diversity of clinical trial populations. In addition to presenting the clinical and scientific reasons for ensuring that clinical trial populations represent the demographics of patient populations, this overview outlines the efforts of regulatory agencies, patient advocacy groups and clinical researchers to attain this goal through strategies to meet representation in recruitment targets and broaden eligibility criteria. Despite these efforts, challenges to participation in clinical trials remain, and certain groups continue to be underrepresented in development programmes. These challenges are amplified when the representativeness of specific groups may vary across countries and regions in a global clinical programme. Whilst enhanced trial diversity is a critical step towards ensuring that results will be representative of patient populations, a concerted effort is required to characterise further the factors influencing interindividual and regional differences in response for global populations. Quantitative clinical pharmacology principles should be applied to allow extrapolation of data across groups or regions as well as provide insight into the effect of patient-specific characteristics on a medicine's dose rationale and efficacy and safety profiles.

Keywords: age; diversity; ethnicity; recruitment; sex.

FIGURE 1

The smaller cube represents a fraction of the general patient population enrolled in clinical trials. Despite strict inclusion and exclusion criteria, this sample is assumed to be representative of the wider patient population, for whom the medicine may be indicated after marketing authorisation. Due to the limited sample size of the clinical trial population, safety data are collected during the postmarketing phase to ensure that the benefit–risk profile of the medicine remains accurate

FIGURE 2

Intrinsic and extrinsic factors contributing to interindividual variability in pharmacokinetics and pharmacodynamics, which may lead to clinically relevant differences in treatment response (adapted from Liu et al., 2021)

FIGURE 3

Age distribution of 3 populations providing information on the safety of simvastatin: the participants in clinical trials for simvastatin approval in France (n = 2221), a random sample of reimbursed simvastatin prescriptions in France (n = 500) and spontaneous report cases for 20 mg simvastatin in the French Pharmacovigilance database (n = 112; reproduced with permission from Martin et al., 2004)

FIGURE 4

Adjusted mean weekly warfarin dose (95% confidence interval) in 345 patients in the USA of different ethnic groups resulting in a goal international normalised ratio of 3 to 4 (reproduced with permission from Dang et al., 2005)

FIGURE 5

Gefitinib response in Asian and non‐Asian patients with non‐small cell lung cancer and the prevalence of somatic EGFR mutations associated with tyrosine kinase responses. (Figure reproduced with permission from Chang et al., 2006)

FIGURE 6

Regional participation in a hypothetical multiregional clinical trial enrolling 1000 participants allocated by geographic region (USA n = 200, Europe n = 400, China n = 200, Japan n = 200). The aim is to ensure 20% of participants are of African American ancestry

FIGURE 7

Illustration of an integrated drug development framework based on modelling, simulation and extrapolation concepts. In the upper section, each black circle within the nonclinical and clinical matrix represents a study conducted in a given phase of development. The solid black lines connecting the circles highlight that the data generated from each preceding study may be used to support and plan later studies, allowing for the creation of a comprehensive and contemporary body of evidence as the cumulative number of studies providing data grows. The dotted lines connecting the upper nonclinical/clinical matrix to the modelling and simulation tools described in the lower section indicate how such a framework can be used at each phase to analyse the emerging data and support decision making in subsequent stages of development (Adapted from Saeed et al. ²⁷ ). DDIs, drug–drug interactions; DDSIs, drug–disease interactions; MABEL, minimal anticipated biological effect level; NOAEL, no observed adverse effect level; PBPK, physiologically based pharmacokinetics; PopPK, population pharmacokinetics; PBTK, physiology‐based toxicology; PKPD, pharmacokinetic–pharmacodynamic