Local vs. Global Sensitivity Analysis
Learn the difference between local and global sensitivity analysis in QSP and pharmacometric models, including derivatives, parameter perturbations, variance-based methods, Morris screening, Sobol indices, interpretation, and practical workflows.
Introduction to Quantitative Systems Pharmacology
An introduction to quantitative systems pharmacology (QSP), including mechanistic models, biological pathways, pharmacokinetics, pharmacodynamics, biomarkers, disease processes, virtual populations, model development, calibration, validation, and simulation.
Disease Biology Models in QSP
Learn how disease biology models in quantitative systems pharmacology represent disease mechanisms, biological pathways, disease progression, biomarkers, treatment effects, and interactions between disease processes and pharmacology.
Credibility Assessment for Mechanistic Models
Learn how to assess the credibility of mechanistic models using context of use, model risk, verification, validation, uncertainty analysis, and predictive performance for model-informed drug development.
Conservation Laws in QSP
Learn how conservation laws are used in quantitative systems pharmacology models to represent mass balance, species conservation, biological compartments, binding, turnover, and mechanistic constraints.
Communicating QSP Model Results
Learn how to communicate quantitative systems pharmacology model results clearly, including model predictions, uncertainty, sensitivity analysis, scenario comparisons, validation, limitations, and decision-relevant interpretation.
Building a QSP Model from Biological Knowledge
Learn how to build a quantitative systems pharmacology model from biological knowledge, including conceptual modeling, biological pathways, compartments, mechanisms, equations, parameters, calibration, validation, and simulation.
Biomarker Dynamics in QSP
Learn how biomarker dynamics are represented in quantitative systems pharmacology, including turnover models, production and loss, drug effects, indirect response, feedback, delays, and biomarker-driven disease progression.
Biological Network Models for QSP
Learn how biological network models represent signaling, regulation, feedback, and interactions among biological components in quantitative systems pharmacology, and how these networks are translated into mathematical QSP models.
Bayesian Calibration of QSP Models
Learn how Bayesian calibration combines mechanistic QSP models with experimental data to estimate parameter distributions, quantify uncertainty, evaluate model fit, and generate probabilistic predictions.