Accelerating the Drug Development Pipeline with Genome‐Scale Metabolic Network Reconstructions

Dougherty, Bonnie V.; Moutinho, Thomas J.; Papin, Jason A.

doi:10.1002/9783527696130.ch5

Cited by 7 publications

(8 citation statements)

References 89 publications

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“…Using the model building algorithm (MBA) (Jerby et al, 2010), which generates tissue-specific models, a focused model for cancer metabolism has been created containing a core set of reactions known to be common for 60 variant cancer cell lines. Using this model and the knowledge that uncontrolled cell growth and altered metabolism are characterizing hallmarks for cancer cells, it was possible to identify two different types of drug targets (Hanahan and Weinberg, 2011;Dougherty et al, 2017). The first target type was growth-supporting genes (found via in silico gene deletion screens) that resulted in identification of 52 metabolic drug targets; 8 of these currently correspond to cancer therapeutics.…”

Section: Reconstructions Can Be Made Specific By Integrating -Omics Datamentioning

confidence: 99%

“…In addition, a set of genes were identified in the healthy cell model network that were downregulated in the cancer model. By inhibiting the genes more highly expressed in cancer cells, targeting could be achieved (Dougherty et al, 2017).…”

Section: Reconstructions Can Be Made Specific By Integrating -Omics Datamentioning

confidence: 99%

“…Using the HMR and transcriptomic data from both healthy and obese individuals, a GEM with the objective function defined as acetyl-CoA production and formation of lipid droplets was produced. Through this analysis, two possible drug targets were identified by considering reactions with significantly changed flux values, and a potential biomarker for obesity was identified through reporter metabolites, which is an algorithm allowing the analysis of transcriptomic data in the light of the metabolic network structure to predict highly affected metabolites (Bordbar et al, 2011;Väremo et al, 2013;Levian et al, 2014;Dougherty et al, 2017).…”

Section: Comparing Models Of Cells In Different Circumstances Can Promentioning

confidence: 99%

“…Community models or multi-cell models are another avenue to be explored as a potential use for GEMs to enhance the use of hMSCs in regenerative medicine. These models can provide insight into the metabolic functions of possible interacting organisms or the various cell types residing within the same organism (Levy and Borenstein, 2013;Dougherty et al, 2017). Given that hMSCs are intended to be integrated into a host system for clinical application (Han et al, 2012;Levy and Borenstein, 2013;Burke et al, 2016;Archambault et al, 2017;Goldberg et al, 2017;Moreira et al, 2017;Cunningham et al, 2018;Hu et al, 2018;Ibraheim et al, 2018;Solarte et al, 2018), this could provide useful insights.…”

Section: Multi-cell Modelsmentioning

confidence: 99%

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Current Status and Future Prospects of Genome-Scale Metabolic Modeling to Optimize the Use of Mesenchymal Stem Cells in Regenerative Medicine

Sigmarsdóttir

McGarrity

Rolfsson

et al. 2020

Front. Bioeng. Biotechnol.

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Mesenchymal stem cells are a promising source for externally grown tissue replacements and patient-specific immunomodulatory treatments. This promise has not yet been fulfilled in part due to production scaling issues and the need to maintain the correct phenotype after re-implantation. One aspect of extracorporeal growth that may be manipulated to optimize cell growth and differentiation is metabolism. The metabolism of MSCs changes during and in response to differentiation and immunomodulatory changes. MSC metabolism may be linked to functional differences but how this occurs and influences MSC function remains unclear. Understanding how MSC metabolism relates to cell function is however important as metabolite availability and environmental circumstances in the body may affect the success of implantation. Genome-scale constraint based metabolic modeling can be used as a tool to fill gaps in knowledge of MSC metabolism, acting as a framework to integrate and understand various data types (e.g., genomic, transcriptomic and metabolomic). These approaches have long been used to optimize the growth and productivity of bacterial production systems and are being increasingly used to provide insights into human health research. Production of tissue for implantation using MSCs requires both optimized production of cell mass and the understanding of the patient and phenotype specific metabolic situation. This review considers the current knowledge of MSC metabolism and how it may be optimized along with the current and future uses of genome scale constraint based metabolic modeling to further this aim.

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Section: Reconstructions Can Be Made Specific By Integrating -Omics Datamentioning

confidence: 99%

Section: Reconstructions Can Be Made Specific By Integrating -Omics Datamentioning

confidence: 99%

Section: Comparing Models Of Cells In Different Circumstances Can Promentioning

confidence: 99%

Section: Multi-cell Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Current Status and Future Prospects of Genome-Scale Metabolic Modeling to Optimize the Use of Mesenchymal Stem Cells in Regenerative Medicine

Sigmarsdóttir

McGarrity

Rolfsson

et al. 2020

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

show abstract

“…This could be a result of ligands with the activity having affinity for several targets and a possible interplay of cellular pathways affected by these interactions. Therefore, the target selection has to be informed by knowledge-based approaches and using a target pool relevant to the disease of interest [51,53], and further complemented by systems biology computational analysis [54] and network biology [55].…”

Section: Body Of the Textmentioning

confidence: 99%

The Benefits of In Silico Modeling to Identify Possible Small-Molecule Drugs and Their Off-Target Interactions

Zloh

Kirton

2018

Future Med. Chem.

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The research into the use of small molecules as drugs continues to be a key driver in the development of molecular databases, computer-aided drug design software and collaborative platforms. The evolution of computational approaches is driven by the essential criteria that a drug molecule has to fulfill, from the affinity to targets to minimal side effects while having adequate absorption, distribution, metabolism, and excretion (ADME) properties. A combination of ligand- and structure-based drug development approaches is already used to obtain consensus predictions of small molecule activities and their off-target interactions. Further integration of these methods into easy-to-use workflows informed by systems biology could realize the full potential of available data in the drug discovery and reduce the attrition of drug candidates.

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Genome-scale integration of transcriptome and metabolome unveils squalene synthase and dihydrofolate reductase as targets against AML cells resistant to chemotherapy

Karakitsou

Foguet

Mostazo

et al. 2021

Computational and Structural Biotechnology Journal

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Accelerating the Drug Development Pipeline with Genome‐Scale Metabolic Network Reconstructions

Cited by 7 publications

References 89 publications

Current Status and Future Prospects of Genome-Scale Metabolic Modeling to Optimize the Use of Mesenchymal Stem Cells in Regenerative Medicine

Current Status and Future Prospects of Genome-Scale Metabolic Modeling to Optimize the Use of Mesenchymal Stem Cells in Regenerative Medicine

The Benefits of In Silico Modeling to Identify Possible Small-Molecule Drugs and Their Off-Target Interactions

Genome-scale integration of transcriptome and metabolome unveils squalene synthase and dihydrofolate reductase as targets against AML cells resistant to chemotherapy

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