A Bayesian approach for the study of synergistic interaction effects in in-vitro drug combination experiments
share
Increasing effort is devoted to the study of the combined effect of two drugs when they are administered simultaneously to the same cell culture. In this paper, we introduce a new approach to estimate which part of the effect of the two drugs is due to the interaction of the compounds, i.e. which is due to synergistic or antagonistic effects of the two drugs, compared to a reference value representing the condition when the combined compounds do not interact, called zero-interaction. We interpret an in-vitro cell viability experiment as a random experiment, by interpreting cell viability as the probability of a cell in the experiment to be viable after treatment, and including information relative to different exposure conditions. We propose a flexible Bayesian spline regression framework for modelling the response surface of two drugs combined. Since the proposed approach is based on a statistical model, it allows to naturally include replicates of the experiments, to evaluate the uncertainty around the estimates, and to perform prediction. We test the model fit and prediction performance on a simulation study, and on a diffuse large B-cell lymphoma (DLBCL) high-throughput screening dataset, comprising more than 400 drug combinations. Posterior estimates of the zero-interaction level and of the interaction term, obtained via adaptive MCMC algorithms, are used to compute novel measures of efficacy of the combined experiment.
READ FULL TEXT