Deep dive into the origins, rationale, and practical implementation of quantitative systems pharmacology (QSP), structured around the six-stage workflow first articulated by Gadkar et al. (2016). Key highlights include:

• Introduction to QSP & MotivationA concise overview of QSP’s role at the interface of pharmacology, systems biology, and engineering, emphasizing the need for standardized workflows to improve reproducibility and stakeholder communication.

• Stage 1: Project Needs & GoalsDiscussion of how to engage collaborators, define decision-making timelines, and scope project questions so that modeling efforts align with real drug-development milestones.

• Stage 2: Reviewing the BiologyGuidance on literature mining, expert interviews, data aggregation, and visual diagramming to delineate the biological scope and identify knowledge gaps before building any equations.

• Stage 3: Model Structure DevelopmentExamination of approaches—supervised vs. unsupervised, logic-based vs. differential equations—to translate biological diagrams into mathematical topologies, with examples of pathway and multiscale models.

• Stage 4: Calibration of Reference SubjectsInsights on sensitivity and dynamical analyses, parameter estimation strategies, and the use of a small set of “reference virtual subjects” to ensure the model can recapitulate core behaviors.

• Stage 5: Exploring Variability & UncertaintyDescription of generating alternate parameter sets (virtual subjects), assembling virtual cohorts, and weighting them into virtual populations to capture heterogeneity and test predictive robustness.

• Stage 6: Experimental & Clinical Design SupportHow model outputs inform optimal experiment design, biomarker selection, and clinical trial simulations, and how new data feed back into iterative refinement.

• Concluding ThoughtsEmphasis on the cyclical, collaborative nature of the workflow and the value of “wrong” predictions in generating new biological hypotheses.