Model-based clinical drug development in the past, present and future: a commentary

Area of Science:

• Pharmacology and Pharmaceutical Sciences
• Biostatistics and Computational Biology

Background:

• Clinical drug development is often empirical and costly, relying on qualitative risk assessment.
• Existing processes do not fully leverage the wealth of data generated during development.
• There is a need for modernized approaches to enhance efficiency and decision-making.

Purpose of the Study:

• To provide a historical overview of model-based drug development (MBDD) in clinical settings.
• To identify current challenges hindering the widespread adoption of MBDD.
• To explore future opportunities for MBDD application in the pharmaceutical industry.

Main Methods:

• Review of historical applications and evolution of MBDD in drug development.
• Analysis of current barriers and limitations to MBDD implementation.
• Discussion of potential advancements and future research directions for MBDD.

Main Results:

• MBDD offers a paradigm shift from empirical methods to data-driven, quantitative decision-making.
• Key challenges include data integration, regulatory acceptance, and interdisciplinary collaboration.
• Significant opportunities exist for optimizing trial design, dose selection, and risk assessment.

Conclusions:

• Model-based drug development (MBDD) represents a critical advancement for modernizing pharmaceutical research.
• Addressing current challenges will facilitate broader MBDD adoption and maximize its benefits.
• Continued exploration and application of MBDD are essential for efficient and effective drug development.