Network-Based Target Prioritization and Drug Candidate Identification for Multiple Sclerosis: From Analyzing "Omics Data" to Druggability Simulations.

Multiple sclerosis (MS) is the most common chronic inflammatory demyelinating disease of the central nervous system. While the drugs currently available for MS provide symptomatic benefit, there is no curative treatment. The emergence of large-scale multiomics data and network theory provide new opportunities for drug discovery in MS, as these are promising strategies for developing novel drugs. In this study, we proposed a computational framework that combined biomolecular network modeling and structural dynamics analysis to facilitate the discovery of new drugs with potential activity in MS. First, we developed a new shortest path-based algorithm that prioritized differentially expressed genes using a newly topological and functional exploration of protein-protein interaction network. Then, pathway enrichment analysis and an assessment of target druggability suggested that TNF-α-induced protein 3 (TNFAIP3), which is involved in NF-κ B signaling, could be a potential therapeutic target for MS. Finally, druggability simulations and mutation enrichment analysis of the TNFAIP3 dimer presented two druggable sites. Follow-up pharmacophore model-based virtual screening of the two sites yielded 30 hit compounds with low energy scores. In summary, this novel method based on analyzing "omics data" and performing druggability simulations, is a systematic approach that unravels disease mechanisms and links them to the chemical space to develop treatments and can be applied to other complex diseases.