Combined network pharmacology and virtual reverse pharmacology approaches for identification of potential targets to treat vascular dementia

• Published in: Scientific reports Vol. 10; no. 1; p. 257
• Main Authors: Lagunin, Alexey A, Ivanov, Sergey M, Gloriozova, Tatyana A, Pogodin, Pavel V, Filimonov, Dmitry A, Kumar, Sandeep, Goel, Rajesh K
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 14.01.2020; Nature Publishing Group UK
• Subjects: Computer applications; Databases, Factual; Dementia; Dementia disorders; Dementia, Vascular - drug therapy; Drug Evaluation, Preclinical - methods; Genes; Medicinal plants; Molecular Targeted Therapy; Pharmacology; Proteomes; Therapeutic targets; User-Computer Interface; Vascular dementia; Vascular diseases
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-019-57199-9

Dementia is a major cause of disability and dependency among older people. If the lives of people with dementia are to be improved, research and its translation into druggable target are crucial. Ancient systems of healthcare (Ayurveda, Siddha, Unani and Sowa-Rigpa) have been used from centuries for the treatment vascular diseases and dementia. This traditional knowledge can be transformed into novel targets through robust interplay of network pharmacology (NetP) with reverse pharmacology (RevP), without ignoring cutting edge biomedical data. This work demonstrates interaction between recent and traditional data, and aimed at selection of most promising targets for guiding wet lab validations. PROTEOME, DisGeNE, DISEASES and DrugBank databases were used for selection of genes associated with pathogenesis and treatment of vascular dementia (VaD). The selection of new potential drug targets was made by methods of NetP (DIAMOnD algorithm, enrichment analysis of KEGG pathways and biological processes of Gene Ontology) and manual expert analysis. The structures of 1976 phytomolecules from the 573 Indian medicinal plants traditionally used for the treatment of dementia and vascular diseases were used for computational estimation of their interactions with new predicted VaD-related drug targets by RevP approach based on PASS (Prediction of Activity Spectra for Substances) software. We found 147 known genes associated with vascular dementia based on the analysis of the databases with gene-disease associations. Six hundred novel targets were selected by NetP methods based on 147 gene associations. The analysis of the predicted interactions between 1976 phytomolecules and 600 NetP predicted targets leaded to the selection of 10 potential drug targets for the treatment of VaD. The translational value of these targets is discussed herewith. Twenty four drugs interacting with 10 selected targets were identified from DrugBank. These drugs have not been yet studied for the treatment of VaD and may be investigated in this field for their repositioning. The relation between inhibition of two selected targets (GSK-3, PTP1B) and the treatment of VaD was confirmed by the experimental studies on animals and reported separately in our recent publications.