Abstract 5699: Development and validation of a quantitative systems pharmacology model for prediction of preclinical efficacy of PARP inhibitors rucaparib and talazoparib combined with the ATR inhibitor gartisertib (M4344)

Introduction: Poly (ADP-ribose) polymerase (PARP) and ataxia telangiectasia and Rad3-related (ATR) inhibitors target key DNA damage response (DDR) kinases. PARP inhibitors (PARPi) suppress the catalytic activity of PARP and trap PARP in a complex with damaged DNA, resulting in the accumulation of unrepaired single-strand breaks (SSBs) and stalled replication forks. Loss of ATR activity blocks cell cycle arrest induced by single-stranded DNA and sensitizes cancer cells to agents that induce DNA replication stress. Thus, PARP inhibition synergizes (through synthetic lethality) with concurrent ATR inhibition by inducing replication fork collapse, double-strand breaks (DSBs), and PARP-DNA complex formation, with simultaneous loss of intra-S and G2/M checkpoints and suppression of DNA-damage repair, leading to mitotic catastrophe. Four PARPi are currently approved for the treatment of various cancers and several ATR inhibitors (ATRi) are in clinical trials either as monotherapies or in combination with other chemotherapeutic agents. We developed and validated a semi-mechanistic quantitative systems pharmacology (QSP) model that represents the mechanisms of action of PARPi and ATRi with minimal parameters, which could be used to inform the optimization of combination regimens. Methods: A QSP model of a growing cancer cell population was developed by considering SSBs and DSBs, and parallel DNA repair pathways relying on PARP and ATR. PARPi and ATRi mediated saturable inhibitory effects on their respective DDR pathways, while PARP-DNA trapping was represented as an increased conversion rate from SSBs to DSBs. Phenotypic impairments of the DDR such as BRCA mutations were embedded as DDR pathway deficiencies. The model was calibrated using experimental data derived from rucaparib and talazoparib combination studies with gartisertib. Results: The calibrated model captured well the tumor-growth inhibition observed in the HBCx9 PDX model for rucaparib and gartisertib, either alone or in combination, over average daily doses ranging from 50 mg/kg to 200 mg/kg (QD/BID) of rucaparib and 1-3 mg/kg (QD/BIW/QD alternate weeks) of gartisertib. The model was also able to predict the wide range of responses (from shrinkage to progressive disease) observed in a panel of triple-negative breast cancer PDX models (BRCA-mutant and wild type) treated with talazoparib and gartisertib in combination. The complete DDR model utilized 9 variable parameters, and the mechanisms of action of PARP and ATR inhibition were described by 4 parameters each. Conclusion: This newly developed QSP model provides a framework that can be applied to optimize the dosing regimens of PARP and ATR inhibitor combinations and help with clinical dosing strategy. Citation Format: Claire C. Villette, Frances Brightman, Nathalie Dupuy, Astrid Zimmermann, Florianne Lignet, Frank T. Zenke, Nadia Terranova, Jayaprakasam Bolleddula, Samer El Bawab, Christophe Chassagnole. Development and validation of a quantitative systems pharmacology model for prediction of preclinical efficacy of PARP inhibitors rucaparib and talazoparib combined with the ATR inhibitor gartisertib (M4344). [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5699.