Model-Based Bridging Strategies
Learn how model-based bridging strategies use pharmacokinetic, population PK, PBPK, exposure-response, and model-informed drug development approaches to connect clinical evidence across populations, regions, formulations, and development settings.
Model Uncertainty in Pharmacometric Simulations
Learn how model uncertainty affects pharmacometric simulations, including structural model uncertainty, parameter uncertainty, covariate uncertainty, residual variability, model averaging, scenario analysis, and simulation-based decision making.
Model Averaging in Pharmacometric Modeling
Learn how model averaging can account for structural model uncertainty in pharmacometric modeling, including candidate models, model weights, parameter averaging, predictions, uncertainty, and practical implementation.
MIDD Across the Drug Development Lifecycle
Learn how Model-Informed Drug Development is applied across the drug development lifecycle, from discovery and first-in-human studies through dose selection, clinical trials, regulatory submissions, and post-approval evidence generation.
Introduction to Model-Informed Drug Development
An introduction to model-informed drug development (MIDD), including pharmacokinetic and pharmacodynamic models, population modeling, exposure-response analysis, simulation, clinical trial design, and how quantitative models support drug development decisions.
FDA and EMA Expectations for Model-Informed Drug Development
A practical guide to FDA and EMA expectations for model-informed drug development, including ICH M15, context of use, model risk, planning, evaluation, reporting, regulatory interactions, and pharmacometric submissions.
Extrapolation Across Populations Using Models
Learn how pharmacokinetic and pharmacometric models can support extrapolation across populations, including covariate effects, ethnic and geographic differences, pediatric and special populations, uncertainty, model validation, and simulation.
Best Practices for Pharmacometric Model Reporting
A practical guide to best practices for reporting pharmacometric models, including structural models, variability, covariates, estimation, diagnostics, parameter uncertainty, simulations, model evaluation, reproducibility, and regulatory communication.
Bayesian Model Averaging for PK/PD
Learn how Bayesian model averaging accounts for structural model uncertainty in PK/PD modeling by combining posterior inference and predictions across competing pharmacokinetic and pharmacodynamic models.