PI-096 - DEVELOPMENT OF IMMUNO-ONCOLOGY (IO) QSP PLATFORM MODEL FOR EFFICACY PREDICTION OF CHECKPOINT INHIBITORS (CPI) AND ADC COMBINATION THERAPY IN PHASE II/III CLINICAL TRIALS.

S. Pallikonda Chakravarthy¹, B. Paleja¹, R. Sing¹, K. Chandrasekaran¹, H. Sandhu¹, T. Ray¹, M. Channavazzala¹, R. Kumar²; ¹Vantage Research, Chennai, Tamilnadu, India, ²Vantage Research, Wilmington, DE, USA.

Background: We have developed an IO QSP platform model calibrated to clinical readouts of CPI and ADC monotherapies to predict the phase II/III responses for the CPI + ADC combination therapy . The model can be used to test new combination trial designs to support drug development, utility of combinations in new indications and comparison of combo against the standard of care.

Methods: Modular design approaches are used to capture systemic pharmacokinetics using minimal PBPK framework, drug disposition at cellular level, accounting for antigen expression, drug-antigen interaction, internalization, intracellular payload disposition, tumor growth inhibition (TGI) module and bystander killing by the drug [1], as a function of payload property.

Anti-PD1 therapy Pembrolizumab + Enfortumab vedotin (EV) combination prediction is analyzed with monotherapy information taken from Keynote 12 and NCT032885452 [2]. The model parameters are identified through physiological and clinical constraints. Virtual population (Vpop) is generated to capture the clinical response for monotherapies which is then used to predict the clinical response for the combination.

Results: The model predicted a range of Objective Response Rate (ORR) for the combination based on different levels of synergy between the two therapies and alternate clinical dosing strategies. Population subclass analysis based on PDL1 expression and NECTIN-4 is run to help in the phase II patient enrichment.

Conclusion: The developed IO QSP model can be used to simulate virtual clinical trials and can generate Waterfall and RECIST for a novel ADC + CPI combination. Modular design of the model will allow for repurposing to test alternative dosing strategies, patient enrichment and combination prioritizations.

[1] Singh et al., Evolution of the Systems Pharmacokinetics-Pharmacodynamics Model for Antibody-Drug Conjugates to Characterize Tumor Heterogeneity and In Vivo Bystander Effect. J. Pharmacol. Exp. Ther. 2020 Jul;374(1):184-199.
[2] Plimack et al., Safety and activity of pembrolizumab in patients with locally advanced or metastatic urothelial cancer (KEYNOTE-012): a non-randomised, open-label, phase 1b study. Lancet Oncol. 2017 Feb;18(2):212-220.