QSP Approaches Accelerate a PD-1 x TIM-3 Therapeutic in Immuno-oncology

Collaboration with Jounce Therapeutics


The goal of this collaboration was to provide early quantitative decision making guidance for the project team by developing and interrogating a quantitative systems pharmacology (QSP) model of the co-modulation inhibitory receptors PD-1 and TIM-3 in immuno-oncology. The QSP model was to: (1) provide predictions of the best-in-class profile for a PD-1 and TIM-3 dual antagonist, (2) accelerate project timelines, (3) provide biological
insights, and (4) reduce experimental costs.

The QSP model was based on first principles as a system of elementary mass-action, mechanistic PKPD, ordinary differential equations. The model parameters and reactions were based on biophysics, and are interpretable. The model reactions include protein synthesis and elimination, ligand-receptor and drug-target formation and turnover, and drug administration and first order clearance. There were four versions of the model: PD-1 monospecific, TIM-3 monospecific, PD-1 x TIM-3 bispecific and fixed dose combination (FDC) targeting PD-1 and TIM-3. The monospecific models were then benchmarked against published data such that model parameter values were set to known values and unknown parameters were estimated. Once benchmarked, the FDC and bispecific models were analyzed by systematically investigating how tuning the model parameters (e.g., affinity, avidity, dose, half-life, target expression, etc.) impacted target inhibition, and to simulate patient variability.

The model was in good agreement with published clinical data from nivolumab and pembrolizumab, and data from RMT3-23 in the TIM-3 driven mouse model. QSP model analysis predicted: (1) there would be diminishing returns on very tight binding biologics due to Target Mediated Drug Disposition (TMDD) that offsets potency, and (2) there is no advantage between FDC, 2-2 bispecific, and 2-1 bispecific formats, which are predicted to be roughly equivalent. As a result of these analyses, there was a significant reduction in the number of experiments, and acceleration of project timelines by (1) eliminating rounds of affinity maturation, as drug leads were in predicted optimal drug parameter ranges, and (2) eliminating the need to construct and evaluate bi-specific constructs and proceed with FDCs.

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