Enhancing the drug discovery process: Bayesian inference for the analysis and comparison of dose–response experiments

摘要

MotivationThe efficacy of a chemical compound is often tested through dose–response experiments from which efficacy metrics, such as the IC50, can be derived. The Marquardt–Levenberg algorithm (non-linear regression) is commonly used to compute estimations for these metrics. The analysis are however limited and can lead to biased conclusions. The approach does not evaluate the certainty (or uncertainty) of the estimates nor does it allow for the statistical comparison of two datasets. To compensate for these shortcomings, intuition plays an important role in the interpretation of results and the formulations of conclusions. We here propose a Bayesian inference methodology for the analysis and comparison of dose–response experiments.