PK/PD Models for Continuous Endpoints
Learn how PK/PD models describe continuous clinical and biomarker endpoints, including linear, Emax, sigmoid Emax, baseline, indirect-response, and exposure-response models.
PK/PD Models for Binary Response
Learn how PK/PD models describe binary clinical or pharmacodynamic responses, including logistic and probit models, exposure-response relationships, probability of response, covariates, variability, and model interpretation.
PK/PD Modeling of Adverse Events
Learn how PK/PD models can characterize relationships between drug exposure and adverse events, including binary, count, continuous, time-to-event, and longitudinal safety outcomes.
PK/PD Modeling for Clinical Trial Design
Learn how PK/PD models support clinical trial design by connecting dose, concentration, exposure, and pharmacologic response to dose selection, sampling, endpoint definition, simulation, and trial decision making.
Natural History Models for Drug Development
Learn how natural history models describe disease progression over time and how they support drug development, trial design, endpoint interpretation, placebo modeling, exposure-response analysis, and quantitative decision-making.
Model-Based Dose Selection for Phase 3
Learn how model-based dose selection uses PK, PD, exposure-response, population variability, safety, efficacy, and simulation to support dose selection for Phase 3 clinical development.
Model-Based Dose Selection for Phase 2
Learn how model-based dose selection supports Phase 2 development by integrating pharmacokinetics, exposure-response relationships, pharmacodynamics, variability, uncertainty, and clinical trial simulation to select doses for confirmatory development.
Model-Based Dose Selection Using PK/PD
Learn how pharmacokinetic and pharmacodynamic models can be used together to select doses by connecting dose, exposure, biomarker response, efficacy, and safety.
Metabolite PK/PD Modeling
Learn how metabolite PK/PD models connect parent drug concentrations, metabolite formation and elimination, active metabolites, exposure, and pharmacodynamic effects.
Mechanistic vs. Empirical PK/PD Models
Understand the differences between mechanistic and empirical PK/PD models, including their assumptions, parameters, data requirements, interpretation, applications, strengths, limitations, and practical model-selection considerations.