Abstract 1222: Integrating i<i>n vitro</i> and <i>in vivo</i> data with algorithm-driven PK/PD modeling: a fast and efficient tool for evaluating cancer therapies in preclinical models

Preclinical in vivo PK/PD studies of cancer therapies are invaluable for cancer drug discovery and the translation of therapies from bench to bedside. However, conventional in vivo experimentation are often time-intensive, resource-demanding, and may require redesign due to inefficient and/or toxic range of concentrations, or suboptimal study parameters. To address these challenges, we developed an integrated approach combining in vitro and in vivo data to generate in silico PK/PD models of anti-cancer compounds. Our algorithm was verified using standard chemotherapies, including Cisplatin and Dacarbazine, against tumoral cell lines for e.g. B16F10 and LLC. The algorithm was trained with the cells doubling time in vitro and with the tumor growth rate in mice. We then conducted an in vitro time-course experiment using a range of concentrations to assess cellular growth inhibition. This data was integrated into the algorithm thus it can calculate the IC50, the drug plasma concentration, and the efficacy over-time of each drug against the engrafted cell lines in mice. Finally, we validated our model in vivo using selected doses from the calculated efficacy ranges, achieving tumor volume reductions comparable to the model’s projections for the tested drugs. In conclusion, we generated a fast and resource-efficient method for PK/PD studies, broadly applicable to cancer drug candidates with minimal prior in vivo efficacy data. Citation Format: Karina Adamowicz, David Cebrian, Fabio dell' Aquila, Leanne Doyle, Agnieszka Drzał, Simone Esposito, Bartosz Płóciennik, Katarzyna Urbańska, Martyna Sniegocka. Integrating in vitro and in vivo data with algorithm-driven PK/PD modeling: a fast and efficient tool for evaluating cancer therapies in preclinical models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1222.