In this episode of The Deep Dive, we explore the groundbreaking application of quantitative systems pharmacology (QSP) to one of medicine’s most complex challenges: inflammatory bowel disease (IBD). Guided by research led by Katherine V. Rogers and colleagues, we unpack how advanced computational models are helping scientists understand the tangled web of immune pathways in Crohn’s disease and ulcerative colitis.

You’ll learn how researchers built a dynamic, mechanistic model that simulates the human immune system in the gut capturing key players like T-cells, cytokines, and neutrophils and used it to mirror real-world patient responses to treatments like infliximab and ustekinumab. We explore how this virtual patient population can help identify likely drug responders, test combinations, and refine future clinical trials; all without stepping into a lab.

This episode isn’t just about math and molecules. It’s about a new way of thinking in medicine, and how computational tools are shaping the future of drug development and precision care.

In this episode, we delve into how a multi-scale Quantitative Systems Pharmacology model is transforming our understanding of metastatic melanoma. You’ll hear a detailed discussion on integrating lesion-, patient-, and population-level dynamics to uncover hidden progression drivers, dissect mechanism-specific effects of checkpoint inhibitors, and guide the design of next-generation combination therapies. A must-listen for anyone interested in the quantitative modelling revolution in immuno-oncology.

Here is the link to the paper : https://ascpt.onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.12637

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.