Recent CTTI Publication Reviews Current Applications and Future Opportunities for Disease Progression Modeling

Recent CTTI Publication Reviews Current Applications and Future Opportunities for Disease Progression Modeling

Recent CTTI Publication Reviews Current Applications and Future Opportunities for Disease Progression Modeling

CTTI News | October 17, 2024

Topics Included: Innovative Trials, Regulatory Submissions + Approvals

new CTTI publication, published in Clinical Pharmacology & Therapeutics, reviews current applications of disease progression modeling (DPM) and opportunities to advance the awareness and value of DPM in clinical trials. Use of modeling and simulation during drug development, otherwise known as model-informed drug development (MIDD), can help researchers make informed decisions when planning and executing clinical trials. DPM is one form of MIDD that integrates multiple types of data from various sources – including translational, clinical trial, and real-world data – to optimize trial design and execution and support regulatory decision-making. The potential of DPM has yet to be fully realized.  

To advance the use of disease progression modeling for decision making in clinical trials and medical product development, CTTI assembled a diverse project team, conducted a scoping literature review to identify current DPM applications, and shared these results at a multi-partner Expert Meeting with representatives from academia, data/tech organizations, government, the medical product industry, patients, and trade/professional organizations. They discussed the value of disease progression modeling and identified key areas that need to be addressed to incorporate DPM approaches more effectively in medical product development and regulatory decision-making. 

The paper presents the scoping review results, opportunities to increase uptake identified at the Expert Meeting, and proposes key questions that medical product developers and regulators may use to inform clinical development strategy and optimize trial design with disease progression models.