BESTDR: Bayesian quantification of mechanism-specific drug response in cell culture
McDonald, T. O.; Bruno, S.; Roney, J. P.; Zervantonakis, I. K.; Michor, F.
Show abstract
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 novel framework modeling cell growth and treatment response dynamics to estimate concentration-response relationships using longitudinal cell count data. BESTDR quantifies rates in multi-state systems across multiple cell lines using hierarchical modeling to support high-throughput screening. We validated BESTDR with synthetic and experimental datasets, demonstrating its robustness and accuracy in estimating drug response. By integrating mechanistic modeling of cytotoxic, cytostatic and other 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.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Multiplexed single-cell profiling of post-perturbation transcriptional responses to define cancer vulnerabilities and therapeutic mechanism of action 95%
- Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones 95%
- A versatile information retrieval framework for evaluating profile strength and similarity 95%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Defining subpopulations of differential drug response to reveal novel target populations 95%
- Single-cell characterization of step-wise acquisition of carboplatin resistance in ovarian cancer 94%
- Network-driven cancer cell avatars for combination discovery and biomarker identification for DNA Damage Response inhibitors 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.