tetano
Editor, Senior Moderator
J Taiwan Inst Chem Eng
. 2022 Feb 15;104273.
doi: 10.1016/j.jtice.2022.104273. Online ahead of print.
Human/SARS-CoV-2 Genome-Scale Metabolic Modeling to Discover Potential Antiviral Targets for COVID-19
Feng-Sheng Wang[SUP] 1 [/SUP], Ke-Lin Chen[SUP] 1 [/SUP], Sz-Wei Chu[SUP] 1 [/SUP]
Affiliations
Abstract
Background: Coronavirus disease 2019 (COVID-19) has caused a substantial increase in mortality and economic and social disruption. The absence of US Food and Drug Administration-approved drugs for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) highlights the need for new therapeutic drugs to combat COVID-19.
Methods: The present study proposed a fuzzy hierarchical optimization framework for identifying potential antiviral targets for COVID-19. The objectives in the decision-making problem were not only to evaluate the elimination of the virus growth, but also to minimize side effects causing treatment. The identified candidate targets could promote processes of drug discovery and development.
Significant findings: Our gene-centric method revealed that dihydroorotate dehydrogenase (DHODH) inhibition could reduce viral biomass growth and metabolic deviation by 99.4% and 65.6%, respectively, and increase cell viability by 70.4%. We also identified two-target combinations that could completely block viral biomass growth and more effectively prevent metabolic deviation. We also discovered that the inhibition of two antiviral metabolites, cytidine triphosphate (CTP) and uridine-5'-triphosphate (UTP), exhibits effects similar to those of molnupiravir, which is undergoing phase III clinical trials. Our predictions also indicate that CTP and UTP inhibition blocks viral RNA replication through a similar mechanism to that of molnupiravir.
Keywords: Flux balance analysis; bioprocess systems engineering; computer-aided drug discovery; constraint-based modeling; evolutionary optimization; fuzzy optimization.
. 2022 Feb 15;104273.
doi: 10.1016/j.jtice.2022.104273. Online ahead of print.
Human/SARS-CoV-2 Genome-Scale Metabolic Modeling to Discover Potential Antiviral Targets for COVID-19
Feng-Sheng Wang[SUP] 1 [/SUP], Ke-Lin Chen[SUP] 1 [/SUP], Sz-Wei Chu[SUP] 1 [/SUP]
Affiliations
- PMID: 35186172
- PMCID: PMC8843340
- DOI: 10.1016/j.jtice.2022.104273
Abstract
Background: Coronavirus disease 2019 (COVID-19) has caused a substantial increase in mortality and economic and social disruption. The absence of US Food and Drug Administration-approved drugs for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) highlights the need for new therapeutic drugs to combat COVID-19.
Methods: The present study proposed a fuzzy hierarchical optimization framework for identifying potential antiviral targets for COVID-19. The objectives in the decision-making problem were not only to evaluate the elimination of the virus growth, but also to minimize side effects causing treatment. The identified candidate targets could promote processes of drug discovery and development.
Significant findings: Our gene-centric method revealed that dihydroorotate dehydrogenase (DHODH) inhibition could reduce viral biomass growth and metabolic deviation by 99.4% and 65.6%, respectively, and increase cell viability by 70.4%. We also identified two-target combinations that could completely block viral biomass growth and more effectively prevent metabolic deviation. We also discovered that the inhibition of two antiviral metabolites, cytidine triphosphate (CTP) and uridine-5'-triphosphate (UTP), exhibits effects similar to those of molnupiravir, which is undergoing phase III clinical trials. Our predictions also indicate that CTP and UTP inhibition blocks viral RNA replication through a similar mechanism to that of molnupiravir.
Keywords: Flux balance analysis; bioprocess systems engineering; computer-aided drug discovery; constraint-based modeling; evolutionary optimization; fuzzy optimization.