Empowering Clinical Development With Disease Progression Modeling: Recommendations From the Clinical Trials Transformation Initiative
Year: 2026
Type: article
Abstract: Evaluating the benefit-risk profile of a medical product requires comprehensive evidence that integrates information across development stages. Technological advances and broader use of real-world data are enabling innovative quantitative paradigms characterized by greater efficiency, patient centricity, and sustainability. Despite regulatory recognition of model-informed drug development, disease progression modeling (DPM) remains underutilized compared with more established pharmacokinetic/pharmacodynamic modeling and simulation approaches. To inform adoption decisions by clinical, regulatory, and portfolio leaders, the Clinical Trials Transformation Initiative (CTTI) developed evidence-based, cross-sector recommendations that describe when and how to use DPM in medical product development and how to effectively communicate its impactful implementation across functions. In this article, we (i) define DPM and summarize its unique value in clinical development, (ii) describe CTTI's collaborative process to generate a set of nine DPM recommendations and a practical considerations framework, and (iii) present examples of strategic development questions showing how DPM can answer high-value questions about indication, population, endpoints, and dose selection. By providing a shared vocabulary and structured questions for decision makers and modelers, these recommendations may lower barriers to DPM implementation, support fit-for-purpose use of existing and new models, and enable more efficient, patient-focused, and sustainable clinical development.
DOI: 10.1111/cts.70686
Institutions: Duke University, Duke Clinical Research Institute, Critical Path Institute, United States Food and Drug Administration, Center for Drug Evaluation and Research, Genentech, National Institutes of Health, National Institute of Allergy and Infectious Diseases, EMD Serono, Inc. (United States), Merck Healthcare KGaA, Darmstadt (Germany)
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,630
OpenAlex ID: W7172521308
