The U.S. Food and Drug Administration’s (FDA’s) Center for Drug Evaluation and Research (CDER) Quantitative Medicine Center of Excellence (QM CoE), in collaboration with CDER’s Office of Translational Sciences and the Critical Path Institute (C-Path), has created a free, web-based training modules on model-informed drug development (MIDD).
One module in particular is an excellent summary of the different types of mathematical methods used in MIDD. Here is a link for that module:
Here is a summary of the transcript:
Stacey Tannenbaum of Astellas Pharma presents an in-depth overview of model types utilized in model-informed drug development (MIDD), particularly focusing beyond basic pharmacokinetics (PK) to include disease progression and the impact of drugs at macro and micro levels. The discussion covers various aspects of disease modeling, quantitative systems pharmacology (QSP), and the nuances of drug effects on disease progression.
Key points include:
- Foundational PK Principles: Emphasis on understanding drug disposition and exposure as critical to optimizing downstream pharmacokinetic/pharmacodynamic (PK/PD) interactions.
- Disease Progression Models: Exploration of disease behavior without intervention and the various external factors that can alter disease progression, such as lifestyle changes or non-pharmaceutical interventions like placebo effects.
- Quantitative Systems Pharmacology: Delving into QSP to analyze disease at a micro level, integrating drug intervention models to predict therapeutic outcomes.
- Exposure Response Relationships: Discussion on different exposure metrics like area under the curve (AUC), C-max, and the importance of understanding these relationships to optimize dosing.
- Model Types and Selection: Insights into choosing appropriate models based on disease mechanisms, including Emax models and target-mediated drug disposition (TMDD) models.
- Challenges in MIDD: Highlighting the complexity of translating pharmacometric models into clinical practice, including regulatory challenges and the necessity for model validation.
The presentation aims to provide drug development scientists with a comprehensive view of the role of advanced modeling in understanding drug effects, optimizing dosages, and predicting clinical outcomes, emphasizing the shift from empirical to more mechanistic models in drug development.