Integration of genomic variants and bioinformatic-based approach to drive drug repurposing for multiple sclerosis

Arief Rahman Afief; Lalu Muhammad Irham; Wirawan Adikusuma; Dyah Aryani Perwitasari; Ageng Brahmadhi; Rockie Chong

doi:10.1016/j.bbrep.2022.101337

Integration of genomic variants and bioinformatic-based approach to drive drug repurposing for multiple sclerosis

Biochem Biophys Rep. 2022 Sep 5:32:101337. doi: 10.1016/j.bbrep.2022.101337. eCollection 2022 Dec.

Authors

Arief Rahman Afief¹, Lalu Muhammad Irham¹, Wirawan Adikusuma², Dyah Aryani Perwitasari¹, Ageng Brahmadhi³, Rockie Chong⁴

Affiliations

¹ Faculty of Pharmacy, Universitas Ahmad Dahlan, Yogyakarta, Indonesia.
² Department of Pharmacy, University of Muhammadiyah Mataram, Mataram, Indonesia.
³ Faculty of Medicine, Universitas Muhammadiyah Purwokerto, Purwokerto, Central Java, Indonesia.
⁴ Department of Chemistry and Biochemistry, University of California, Los Angeles, USA.

Abstract

Multiple sclerosis (MS) is a chronic autoimmune disease in the central nervous system (CNS) marked by inflammation, demyelination, and axonal loss. Currently available MS medication is limited, thereby calling for a strategy to accelerate new drug discovery. One of the strategies to discover new drugs is to utilize old drugs for new indications, an approach known as drug repurposing. Herein, we first identified 421 MS-associated SNPs from the Genome-Wide Association Study (GWAS) catalog (p-value < 5 × 10^-8), and a total of 427 risk genes associated with MS using HaploReg version 4.1 under the criterion r² > 0.8. MS risk genes were then prioritized using bioinformatics analysis to identify biological MS risk genes. The prioritization was performed based on six defined categories of functional annotations, namely missense mutation, cis-expression quantitative trait locus (cis-eQTL), molecular pathway analysis, protein-protein interaction (PPI), genes overlap with knockout mouse phenotype, and primary immunodeficiency (PID). A total of 144 biological MS risk genes were found and mapped into 194 genes within an expanded PPI network. According to the DrugBank and the Therapeutic Target Database, 27 genes within the list targeted by 68 new candidate drugs were identified. Importantly, the power of our approach is confirmed with the identification of a known approved drug (dimethyl fumarate) for MS. Based on additional data from ClinicalTrials.gov, eight drugs targeting eight distinct genes are prioritized with clinical evidence for MS disease treatment. Notably, CD80 and CD86 pathways are promising targets for MS drug repurposing. Using in silico drug repurposing, we identified belatacept as a promising MS drug candidate. Overall, this study emphasized the integration of functional genomic variants and bioinformatic-based approach that reveal important biological insights for MS and drive drug repurposing efforts for the treatment of this devastating disease.

Keywords: Autoimmune disease; Bioinformatics; Drug repurposing; Genomic variants; Multiple sclerosis.