Multiple Sclerosis (MS) is a chronic, neurodegenerative disease marked by demyelination of neurons, the formation of plaques in the central nervous system, and progressive neurological disability. Although treatments exist for relapsing forms of MS, there remains a significant unmet need for therapies that can promote myelin regeneration, improve patient quality of life, and slow disease progression. This thesis investigates in silico drug repurposing as a strategy for identifying novel therapeutic candidates for MS, leveraging hypotheses generated by the DRIVE platform, a network-based medicine tool that integrates phenotypic, biological, and pharmacological data to suggest potential drug-disease relationships.
Two distinct pathways from DRIVE were examined: the gene-disease-drug pathway and the gene-protein-drug pathway, from which paclitaxel and isotretinoin were selected as candidate compounds. A multi-step filtering process, including Gene Ontology-based filtering, proteinprotein interaction considerations, literature support, and structural availability, was used to prioritize the potential targets. Molecular docking was then conducted to evaluate the bindingaffinity of drug-target pairs.
Among the final candidates, docking analysis suggested that NLRP3 may serve as an alternative and therapeutically relevant target of paclitaxel in the context of MS. While these findings are preliminary and require further validation through molecular dynamics simulations and experimental studies, this work demonstrates the potential of combining network medicine approaches with structure-based drug repurposing tools to discover new therapies for diseases such as MS.
Multiple Sclerosis (MS) is a chronic, neurodegenerative disease marked by demyelination of neurons, the formation of plaques in the central nervous system, and progressive neurological disability. Although treatments exist for relapsing forms of MS, there remains a significant unmet need for therapies that can promote myelin regeneration, improve patient quality of life, and slow disease progression. This thesis investigates in silico drug repurposing as a strategy for identifying novel therapeutic candidates for MS, leveraging hypotheses generated by the DRIVE platform, a network-based medicine tool that integrates phenotypic, biological, and pharmacological data to suggest potential drug-disease relationships.
Two distinct pathways from DRIVE were examined: the gene-disease-drug pathway and the gene-protein-drug pathway, from which paclitaxel and isotretinoin were selected as candidate compounds. A multi-step filtering process, including Gene Ontology-based filtering, proteinprotein interaction considerations, literature support, and structural availability, was used to prioritize the potential targets. Molecular docking was then conducted to evaluate the bindingaffinity of drug-target pairs.
Among the final candidates, docking analysis suggested that NLRP3 may serve as an alternative and therapeutically relevant target of paclitaxel in the context of MS. While these findings are preliminary and require further validation through molecular dynamics simulations and experimental studies, this work demonstrates the potential of combining network medicine approaches with structure-based drug repurposing tools to discover new therapies for diseases such as MS. Read More
Related posts:
Knowledge graphs and explainable AI for drug repurposing on rare diseases
Knowledge graph-driven drug repurposing with hypothesis-generation
Discovery of Taxus chinensis fruit wine as potentially functional food against Alzheimer’s disease by UHPLC‐QE‐MS/MS, network pharmacology and molecular docking
Laminarin ameliorates alcohol‐induced liver damage and its molecular mechanism in mice
Analysis and Validation of the Network Pharmacology of the Mechanism of Glycyrrhetinic Acid for the Treatment of High‐Altitude Pulmonary Hypertension
Potential Targets and Signaling Mechanisms of Cinnamaldehyde Enhancing Intestinal Function and Nutritional Regulation in Fat Greenling (Hexagrammos otakii)