Phytochemicals in Pancreatic Cancer Treatment: A Machine Learning Study.

Abstract

The discovery of new strategies and novel therapeutic agents is crucial to improving the current treatment methods and increasing the efficacy of cancer therapy. Phytochemicals, naturally occurring bioactive constituents derived from plants, have great potential in preventing and treating various diseases, including cancer. This study reviewed 74 literature studies published between 2006 and 2022 that conducted in vitro cytotoxicity and cell apoptosis analyses of the different concentrations of phytochemicals and their combinations with conventional drugs or supplementary phytochemicals on human pancreatic cell lines. From 34 plant-derived phytochemicals on 20 human pancreatic cancer cell lines, a total of 11 input and 2 output variables have been used to construct the data set that contained 2161 different instances. The machine learning approach has been implemented using random forest for regression, whereas association rule mining has been used to determine the effects of individual phytochemicals. The random forest models developed are generally good, indicating that the phytochemical type, its concentration, and the type of cell line are the most important descriptors for predicting the cell viability. However, for predicting cell apoptosis the primary phytochemical type is the most significant descriptor . Among the studied phytochemicals, catechin and indole-3-carbinol were found to be non-cytotoxic at all concentrations irrespective of the treatment time. On the other hand, berbamine and resveratrol were strongly cytotoxic with cell viabilities of less than 40% at a concentration range between 10 and 100 μM and above 100 μM, respectively, which brings them forward as potential therapeutic agents in the treatment of pancreatic cancer.