Towards the routine use of in silico screenings for drug discovery using metabolic modelling
| Autor: | Maria Irene Pires Pacheco, Tamara Jean Rita Bintener, Thomas Sauter |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Drug
media_common.quotation_subject In silico Systems biology Drug resistance Computational biology Biochemistry 03 medical and health sciences 0302 clinical medicine Drug Delivery Systems Neoplasms Databases Genetic Drug Discovery Medicine Animals Humans Computer Simulation metabolic modelling Review Articles Repurposing drug target discovery 030304 developmental biology media_common Cancer Oligonucleotide Array Sequence Analysis 0303 health sciences drug repurposing Drug discovery business.industry Genome Human Drug Repositioning Computational Biology systems biology personalized medicine Drug repositioning Metabolism Models Chemical Personalized medicine business 030217 neurology & neurosurgery Algorithms Gene Deletion Metabolic Networks and Pathways |
| Zdroj: | Biochemical Society Transactions |
| ISSN: | 1470-8752 0300-5127 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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