Beyond Deterministic Models in Drug Discovery and Development.

Area of Science:

• Pharmacometrics
• Quantitative Systems Pharmacology
• Mathematical Biology

Background:

• Model-informed drug discovery and development is established, leveraging pharmacokinetics/pharmacodynamics (PK/PD) and systems biology.
• Current pharmacometric approaches, while successful, have limitations in specific applications.
• Quantitative methods beyond standard PK/PD are underutilized.

Purpose of the Study:

• To raise awareness of stochastic modeling approaches in pharmacometrics.
• To highlight the importance of stochastic models for small populations where random events are significant.
• To explore the integration of stochastic modeling with existing techniques for enhanced drug-disease models.

Main Methods:

• Review of existing literature on stochastic modeling in biological and pharmaceutical sciences.
• Discussion of the principles and applications of stochastic processes in pharmacometrics.
• Conceptual framework for combining stochastic models with population PK/PD and systems pharmacology.

Main Results:

• Stochastic models are crucial for accurately representing systems with inherent randomness, especially in small populations.
• Combining stochastic approaches with traditional pharmacometrics can improve model predictability.
• The integration offers a pathway to more robust and realistic drug-disease modeling.

Conclusions:

• Stochastic modeling represents a valuable, yet underused, quantitative approach for pharmacometrics.
• Integrating stochastic models can significantly enhance the versatility and realism of drug-disease models.
• Further adoption of stochastic methods is recommended for advancing drug discovery and development.