Dose-response characterization: A key to success in drug development

Abstract

Dose selection is a key component of drug development, yet inadequate dose-response characterization remains a major challenge, contributing to late-stage attrition and post-marketing regulatory commitments. Effective dose-response characterization for both efficacy and safety supports benefit-risk assessments of therapeutic interventions and relies on two main elements: Trial design and trial analysis. In trial design, selecting an appropriate dose range, determining the number of dose levels, and ensuring proper dose spacing are essential to capture both the steep and plateau regions of a dose-response curve. Adaptive trial designs provide additional flexibility to address uncertainties during trial planning and execution, increasing the chances of identifying optimal doses and improving trial efficiency. In trial analysis, modeling approaches support dose-response characterization by utilizing data across dose levels to fit a continuous curve rather than analyzing each dose level separately. Model-based methods, such as Emax modeling or MCP-Mod (which combines multiple comparison procedures and modeling), incorporate assumptions about the dose-response relationship to improve the precision of dose-response and target dose estimation. Additional precision can often be achieved by modeling dose-exposure-response relationships, recognizing that exposure (e.g. drug concentration in the plasma) often mediates the relationship between dose and clinical response. Dose-exposure response models may also enable the prediction of dose-response relationships of alternative regimens (e.g. when applying a different frequency of administration than the tested ones). This article reviews key considerations for the design and analysis of dose-response trials, focusing on strategies to improve decision-making and regulatory alignment.

Keywords: Clinical pharmacology; dose exposure response; dose finding; model uncertainty; model-informed drug development; modeling; pharmacometrics; trial analysis; trial design.