The ENDS of assumptions; an online tool for the Epistemic Nonparametric Drug-response Scoring

Abstract

MotivationThe drugs 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. ResultsWe present a class of nonparametric 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 IC50 and AUC. Specifically, three nonparametric models including Spline, Monotonic, and Bayesian (npS, npM, npB) and the parametric Logistic (pL) are implemented. Other indices including Maximum Effective Dose (MED) and Drug-response Span Gradient (DSG) pertinent to the npS are also provided to facilitate the interpretation of the fit. The collection of these models are implemented in an online app, standing as useful resource for drug dose-response curve fitting and analysis. AvailabilityThe ENDS is freely available online at https://irscope.shinyapps.io/ENDS/ and source codes can be obtained from https://github.com/AmiryousefiLab/ENDS. Supplementary informationSupplementary data are available at Bioinformatics and https://irscope.shinyapps.io/ENDS/ Contactali.amiryousefi@helisnki.fi; jing.tang@helisnki.fi. ContributionsAA conceived the study and developed the models, AA and BW adopted and implemented the methods, JT provided the funding, AA, BW, MJ, and JT wrote the paper.