BESTDR Enables Bayesian Quantification of Mechanism-Specific Drug Responses.

UNLABELLED: Understanding drug responses at the cellular level is essential for elucidating mechanisms of action and advancing preclinical drug development. Traditional dose-response models rely on simplified metrics, limiting their ability to quantify parameters like cell division, death, and transition rates between cell states. To address these limitations, we developed Bayesian Estimation of STochastic processes for Dose-Response (BESTDR), a framework modeling cell growth and treatment response dynamics to estimate concentration-response relationships using longitudinal cell count data. BESTDR enables quantification of rates in multistate systems across multiple cell lines using hierarchical modeling to support high-throughput screening. Validation of BESTDR with synthetic and experimental datasets demonstrates its accuracy and robustness in estimating drug response. By integrating mechanistic modeling of cytotoxic, cytostatic, and other drug effects, BESTDR enhances dose-response studies, facilitating robust drug comparisons and mechanism-specific analyses. BESTDR offers a versatile tool for early-stage preclinical research, paving the way for drug discovery and informed experimental design.

SIGNIFICANCE: BESTDR leverages time-course cell count data to provide mechanistic insights into drug actions, distinguishing cytostatic, cytotoxic, and state transitions, thus advancing dose-response modeling crucial for preclinical research and development of targeted therapies. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .