AI Summary
→ WHAT IT COVERS John Burke, president and CEO of Applied Biomath, explains how mechanistic PKPD modeling advances drug development by incorporating mechanism-of-action biology into mathematical simulations. The episode covers model iteration across preclinical stages, competitive benchmarking, CAR-T cell therapy design, and FDA Project Optimus dose optimization for oncology IND submissions. → KEY INSIGHTS - **Model Complexity Staging:** Start with a deliberately simple mechanistic model to generate a fast go/no-go developability decision, then layer in downstream biology incrementally. Adding complexity too early wastes resources; the initial model should answer one question — is this drug feasible to develop — before expanding to best-in-class design parameters. - **CAR-T Parameter Sweeps:** Run tens of thousands to hundreds of thousands of simulations varying receptor density, binding affinity, CAR-T cell half-life, doubling time, cytokine release rates, and Treg-to-effector-T-cell ratios simultaneously. This identifies which one or two parameters most dramatically shift predicted efficacy and safety outcomes, directly guiding which molecules to advance to clinical candidacy. - **Model Update Cadence:** Update the mechanistic model two to three times across preclinical development — after in vitro potency assays, after mouse studies, and after xenograft studies. Each update uses parameter estimation to constrain unknowns, progressively narrowing uncertainty in human dose predictions before IND submission rather than accumulating all learning at once. - **Competitive Benchmarking in Silico:** Populate the model with publicly available PK/PD data from competitor molecules to run head-to-head simulations before entering the clinic. This allows teams to assess whether their candidate is best-in-class against current therapies and projected future therapies, informing the go/no-go investment decision with quantitative competitive evidence. - **FDA Project Optimus Alignment:** Mechanistic PKPD modeling supports the FDA's Project Optimus dose-optimization initiative by simulating patient population variability — including tenfold target overexpression scenarios and target-mediated drug disposition — to justify a higher, still-safe starting dose. This is especially critical for gene and cell therapies where traditional allometric scaling methods have no established framework. → NOTABLE MOMENT Burke argues that for gene and cell therapies, traditional allometric scaling is essentially unusable for human dose prediction, and that mechanistic modeling is the only scientifically defensible alternative — without it, he suggests, dose selection amounts to guessing rather than evidence-based prediction. 💼 SPONSORS [{"name": "Genetic Engineering and Biotechnology News (GEN)", "url": "https://www.genengnews.com"}] 🏷️ Mechanistic Modeling, PKPD, CAR-T Cell Therapy, Drug Development, FDA Project Optimus