Screening for Combination Cancer Therapies With Dynamic Fuzzy Modeling and Multi-Objective Optimization.

ABSTRACT: Combination therapies proved to be a valuable strategy in the fight against cancer, thanks to their increased efficacy in inducing tumor cell death and in reducing tumor growth, metastatic potential, and the risk of developing drug resistance. The identification of effective combinations of drug targets generally relies on costly and time consuming processes based on in vitro experiments. Here, we present a novel computational approach that, by integrating dynamic fuzzy modeling with multi-objective optimization, allows to efficiently identify novel combination cancer therapies, with a relevant saving in working time and costs. We tested this approach on a model of oncogenic K-ras cancer cells characterized by a marked Warburg effect. The computational approach was validated by its

SUBMITTER: Spolaor S 

PROVIDER: S-EPMC8044361 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature