The ENDS of assumptions: an online tool for the epistemic non-parametric drug–response scoring

MOTIVATION: The drug sensitivity analysis is often elucidated from drug dose–response curves. These curves capture the degree of cell viability (or inhibition) over a range of induced drugs, often with parametric assumptions that are rarely validated. RESULTS: We present a class of non-parametric models for the curve fitting and scoring of drug dose–responses. To allow a more objective representation of the drug sensitivity, these epistemic models devoid of any parametric assumptions attached to the linear fit, allow the parallel indexing such as half-maximal inhibitory concentration and area under curve. Specifically, three non-parametric models including spline (npS), monotonic and Bayesian and the parametric logistic are implemented. Other indices including maximum effective dose and drug–response span gradient pertinent to the npS are also provided to facilitate the interpretation of the fit. The collection of these models is implemented in an online app, standing as useful resource for drug dose–response curve fitting and analysis. AVAILABILITY AND IMPLEMENTATION: The ENDS is freely available online at https://irscope.shinyapps.io/ENDS/ and source codes can be obtained from https://github.com/AmiryousefiLab/ENDS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.