Abstract
Simple SummaryDiscovery of anticancer targets with minimal side effects is a major challenge in drug discovery and development. This study developed a fuzzy optimization framework for identifying anticancer targets. The framework was applied to identify not only gene regulator targets but also metabolite- and reaction-centric targets. The computational results show that the combination of a carbon metabolism target and any one-target gene that participates in the sphingolipid, glycerophospholipid, nucleotide, cholesterol biosynthesis, or pentose phosphate pathways is more effective for treatment than one-target inhibition is, and a two-target combination of 5-FU and folate supplement can improve cell viability, reduce metabolic deviation, and reduce side effects of normal cells.The efficient discovery of anticancer targets with minimal side effects is a major challenge in drug discovery and development. Early prediction of side effects is key for reducing development costs, increasing drug efficacy, and increasing drug safety. This study developed a fuzzy optimization framework for Identifying AntiCancer Targets (IACT) using constraint-based models. Four objectives were established to evaluate the mortality of treated cancer cells and to minimize side effects causing toxicity-induced tumorigenesis on normal cells and smaller metabolic perturbations. Fuzzy set theory was applied to evaluate potential side effects and investigate the magnitude of metabolic deviations in perturbed cells compared with their normal counterparts. The framework was applied to identify not only gene regulator targets but also metabolite- and reaction-centric targets. A nested hybrid differential evolution algorithm with a hierarchical fitness function was applied to solve multilevel IACT problems. The results show that the combination of a carbon metabolism target and any one-target gene that participates in the sphingolipid, glycerophospholipid, nucleotide, cholesterol biosynthesis, or pentose phosphate pathways is more effective for treatment than one-target inhibition is. A clinical antimetabolite drug 5-fluorouracil (5-FU) has been used to inhibit synthesis of deoxythymidine-5-triphosphate for treatment of colorectal cancer. The computational results reveal that a two-target combination of 5-FU and a folate supplement can improve cell viability, reduce metabolic deviation, and reduce side effects of normal cells.
Highlights
The process of drug discovery and development is challenging as well as cost and time intensive
The Genome-scale metabolic networks (GSMNs) of Recon 3D consisted of 5835 species, 10,600 reactions, and 2248 associated genes
We found the two-target combination of PTDSS1 and PTDSS2 increase metabolic deviation grade to 0.674, but decrease cell viability grade to 0.762
Summary
The process of drug discovery and development is challenging as well as cost and time intensive. Recent progress in omics fields (e.g., genomics [1], transcriptomics [2], proteomics [3], metabolomics [4], and fluxomics [5]) can promote the development of technology for discovering new drug targets [6]. Advancements in high-throughput data acquisition have been combined with systems biology approaches to increase the time and cost effectiveness of drug target discovery through computer-aided simulation techniques. Genome-scale metabolic networks (GSMNs) relate metabolites and reactions and represent the full set of intracellular metabolic processes curated using knowledge of cellular functions from the literature. GSMNs combined with constraint-based approaches are leading approaches for developing methods to simulate cell behavior, such as flux balance analysis (FBA) [12,13]
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