A Web Application for Predicting Drug Combination Efficacy Using Monotherapy Data and IDACombo

Author(s): Yunong Xia, Alexander L. Ling, Weijie Zhang, Adam Lee, Mei-Chi Su, Robert F. Gruener, Sampreeti Jena, Yingbo Huang, Siddhika Pareek, Yuting Shan, and R. Stephanie Huang.

Summary: We recently reported a computational method (IDACombo) designed to predict the efficacy of cancer drug combinations using monotherapy response data and the assumptions of independent drug action. Given the strong agreement between IDACombo predictions and measured drug combination efficacy in vitro and in clinical trials, we believe IDACombo can be of immediate use to researchers who are working to develop novel drug combinations. While we previously released our method as an R package, we have now created an R Shiny application to allow researchers without programming experience to easily utilize this method. The app provides a graphical interface which enables users to easily generate efficacy predictions with IDACombo using provided data from several high-throughput cell line screens or using custom, user-provided data. Availability and Implementation: The R Shiny app itself can be accessed at https://oncotherapyinformatics.org/idacombo/. The source code for the R Shiny app is available on GitHub (https://github.com/yunong-xia/IDACombo-Shiny-App). The R package IDACombo upon which this app is based is also available on GitHub (https://github.com/Alexander-Ling/IDACombo/).