Finding key characteristics of promising compounds for anticancer drug discovery

Abstract

Multidrug resistance is the simultaneous resistance to two or more chemically unrelated therapeutics, including some therapeutics the cell has never been exposed to. It is one of the biggest obstacles to effective cancer chemotherapy treatments. Multidrug resistance can be caused by drug efflux, an otherwise useful body mechanism that prevents a too-high drug concentration in cells, by using proteins called transporters. Some chemical compounds have the ability to sensitize the cells to the drugs by disabling these transporters. The focus of this work is to find key characteristics of compounds that may disable a specific transporter, the P-glycoprotein. Three datasets listing compounds, their values for different features, and their ability to disable the transporters are provided by experts. Using the programming language R, various data analytics methods are applied to these datasets with the objective of predicting whether compounds are P-glycoprotein inhibitors or not. The main issue encountered is the fact that the most important dataset did not contain enough samples for the number of predictor variables. Ultimately, the decision tree and random forest models prove to be the most effective in predicting the compounds' ability to disable the transporter.