Abstract 6507: Simulation driven identification of combinations for the WEE1 inhibitor Debio 0123 results in synergistic effect with cabozantinib validated in vivo
Authors: Elek Boldizsar , Chessex Anne V. , Piggott Luke , Papp Orsolya , Fekete Ivan , Veres Daniel 
Published: March 2024

Introduction: Debio 0123, a potential best-in-class, brain-penetrant, WEE1 inhibitor is being studied as monotherapy and in combination with standard of care therapies in patients with solid tumors in Phase I trials. The compound's mechanism of action enables a wide range of combination treatment options with the aim to increase therapeutic window, and improve treatment outcomes in cancer. WEE1 is a tyrosine kinase that is activated in response to DNA-damage acting at the G2/M and S-phase checkpoints, allowing DNA damage repair before entering mitosis. Inhibition of WEE1 increases cell sensitivity to DNA damaging agents leading to genomic instability and apoptosis. Debiopharm, in collaboration with Turbine set out to identify novel combination opportunities for Debio 0123.

Methods: Two approaches were used to identify synergistic combinations of Debio 0123. (1) an in vitro screening panel of 35 cancer cell lines and 70 compounds and (2) using Turbine’s proprietary Simulated CellTM solution. The in silico approach combines simulation with machine learning, modelling how thousands of signalling proteins interact thereby characterizing cellular level cancer behaviour and response or resistance to treatment. Millions of simulations can be carried out, identifying responder/resistant cell lines to single-or combined drug treatments, thus informing wet lab screening. Turbine built a comprehensive in silico model of WEE1 and associated mechanistic pathways based on scientific literature. Network-based in silico avatars of 116 cancer cell lines were established using public/proprietary genomic and transcriptomic data, and trained on in vitro dose-response to a representative set of reference compounds. High-resolution combination matrices of 59 compounds and Debio-0123 were tested in silico. Synergistic combinations were identified and taken forward to in vitro assays and in vivo studies.

Results: Sensitizing combinations, including DNA damaging agents as well as targeted therapies were identified using both the in silico and in vitro screens. Turbine predicted synergistic anti-tumour activity in silico between Debio 0123 and cabozantinib. Although previously tested using traditional screening methods, this combination had not been identified as a synergistic combination for Debio 0123. Further in vitro testing in multiple cell lines demonstrated synergy between these agents at all doses tested. In vivo testing in a CAKI-1 (renal cell carcinoma) model showed moderate monotherapy effects but significant synergy when Debio 0123 and cabozantinib were dosed in combination.

Conclusion: We demonstrated that an in silico computational approach to model cancer signalling pathways can complement conventional screening methodologies to identify novel, clinically actionable combination opportunities for Debio 0123.

Citation Format: Boldizsar Elek, Anne V. Chessex, Luke Piggott, Orsolya Papp, Ivan Fekete, Daniel Veres. Simulation driven identification of combinations for the WEE1 inhibitor Debio 0123 results in synergistic effect with cabozantinib validated in vivo [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6507.