Abstract
Currently, the development of new effective drugs for cancer therapy is not only hindered by development costs, drug efficacy, and drug safety but also by the rapid occurrence of drug resistance in cancer. Hence, new tools are needed to study the underlying mechanisms in cancer. Here, we discuss the current use of metabolic modelling approaches to identify cancer-specific metabolism and find possible new drug targets and drugs for repurposing. Furthermore, we list valuable resources that are needed for the reconstruction of cancer-specific models by integrating various available datasets with genome-scale metabolic reconstructions using model-building algorithms. We also discuss how new drug targets can be determined by using gene essentiality analysis, an in silico method to predict essential genes in a given condition such as cancer and how synthetic lethality studies could greatly benefit cancer patients by suggesting drug combinations with reduced side effects.
Highlights
Since Otto Warburg, it is known that some cancer cells have an altered metabolism such as preferring the production of ATP from aerobic glycolysis over oxidative phosphorylation [1]
Genome-scale and context-specific models [10], that have been successfully used for the integration of -omics data, are very promising approaches that allow, among others, to understand how mutations affect cancer metabolism by mapping them onto context-specific models to study their metabolism [11] and to determine if the phenotype can be rescued by alternative pathways
More cancer-specific models shortly followed by integrating cancer data with different genome-scale reconstructions and model-building algorithms for data integration such as models for each of the cell lines in the NCI-60 to identify metabolic sub-pathways that provide energy and lipids for cancer growth [62] or HCC models that allow stratifying patients according to acetate utilization
Summary
Towards the routine use of in silico screenings for drug discovery using metabolic modelling. The development of new effective drugs for cancer therapy is hindered by development costs, drug efficacy, and drug safety and by the rapid occurrence of drug resistance in cancer. New tools are needed to study the underlying mechanisms in cancer. We discuss the current use of metabolic modelling approaches to identify cancer-specific metabolism and find possible new drug targets and drugs for repurposing. We discuss how new drug targets can be determined by using gene essentiality analysis, an in silico method to predict essential genes in a given condition such as cancer and how synthetic lethality studies could greatly benefit cancer patients by suggesting drug combinations with reduced side effects
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