Bayesynergy: flexible Bayesian modelling of synergistic interaction effects in in vitro drug combination experiments. Issue 6 (23rd July 2021)

• Record Type: Journal Article
• Authors: Rønneberg, Leiv; Cremaschi, Andrea; Hanes, Robert; Enserink, Jorrit M; Zucknick, Manuela
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 6(2021)
• Publication Date: 2021-07-23
• Subjects: dose–response -- viability assay -- drug synergy -- Bayesian -- semi-parametric -- Gaussian process; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbab251
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: The effect of cancer therapies is often tested pre-clinically via in vitro experiments, where the post-treatment viability of the cancer cell population is measured through assays estimating the number of viable cells. In this way, large libraries of compounds can be tested, comparing the efficacy of each treatment. Drug interaction studies focus on the quantification of the additional effect encountered when two drugs are combined, as opposed to using the treatments separately. In the bayesynergy R package, we implement a probabilistic approach for the description of the drug combination experiment, where the observed dose response curve is modelled as a sum of the expected response under a zero-interaction model and an additional interaction effect (synergistic or antagonistic). Although the model formulation makes use of the Bliss independence assumption, we note that the posterior estimates of the dose–response surface can also be used to extract synergy scores based on other reference models, which we illustrate for the Highest Single Agent model. The interaction is modelled in a flexible manner, using a Gaussian process formulation. Since the proposed approach is based on a statistical model, it allows the natural inclusion of replicates, handles missing data and uneven concentration grids, and provides uncertainty quantification around the results. The model is implemented in the open-source Stan programming language providing a computationally efficient sampler, a fast approximation of the posterior through variational inference, and features parallel processing for working with large drug combination screens.