Computer-Aided Design for Identifying Anticancer Targets in Genome-Scale Metabolic Models of Colon Cancer

Abstract

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.

Human/SARS-CoV-2 genome-scale metabolic modeling to discover potential antiviral targets for COVID-19

“…The NHDE algorithm [ 38 , 39 ] was applied to solve the maximizing decision problem in Eq. (17) by using the gene-centric approach defined in Eq.…”

“…The antiviral target discovery (AVTD) framework described in Fig. 1 was formulated as a hierarchical optimization problem based on a modified form of IACT framework [ 38 , 39 ] to mimic a wet-lab experiment. The hierarchical screening procedure was formulated as a trilevel optimization problem consisting of an outer optimization problem with multiple objectives and subject to two loop inner optimization problems describing the characteristics of treated and perturbed cells.…”

“…The NHDE algorithm is a parallel direct search procedure and extended from the hybrid differential algorithm [47] . It has succeeded to solve the NP-hard problems such as strain design problems [29] , oncogene inference problems [18] , [19] , [20] , [21] , [22] and anticancer target design problems [ 38 , 39 ]. The performance and solution quality of the NHDE algorithm depended on three key setting factors: tolerance ratio used in migration, population size, and maximum number of iterations.…”

“…The present study integrated the SARS-CoV-2 Delta variant into the genome-scale human metabolic model Recon 3D [37] to present the viral infection. We developed a fuzzy multiobjective hieratical optimization framework based on a modification of Identifying Anticancer Target (IACT) framework [ 38 , 39 ] to mimic general wet-lab experiments and discover potential targets for treating COVID-19. The objectives were to limit viral biomass growth, maximize cell viability, and minimize metabolic deviation of the cells perturbed by potential treatments.…”

“…Conventional genetic algorithms can be extended to solve the problem to obtain global solutions [40] . This study applied the nested hybrid differential evolution (NHDE) to solve the decision-making problem [ 38 , 39 ] to obtain optimal antiviral targets.…”