Investigating the inflammatory signaling characterizing lysosomal storage disorders (LSDs)
A new computational method for drug repurposing
- Area
- Systems Pharmacology
- Expertise
- Data integration
- Knowledge extraction
Challenge
Develop a systems pharmacology workflow that integrates different data types such as genomics, transcriptomics, and literature to identify in silico drug repurposing candidates
What we did
The analytical workflow that we devised consists of three interconnected parts. First, we establish a list of disease genes from the results of genome-wide association studies and literature mining results. By mapping the identified genes on to pre-established tissue-specific biological networks, we identify disease modules. Then, drug targets and drug perturbed genes are mapped on the networks to build drug modules. Finally, the impact of existing drugs on the disease is assessed by a proximity score, which combines network-based distance and functional similarity. The resulting drug candidates are prioritized by annotating them with additional information gathered from biological databases and from the literature.
Outcome
By applying this computational framework to metabolic syndrome, we confirmed the important contribution of immune dysregulation in metabolic syndrome and we identified the BTK inhibitor ibrutinib as a candidate drug for lowering the chronic inflammation associated with obesity. The experimental validation using a high-fat diet-induced obesity model in zebrafish larvae confirmed the effectiveness of ibrutinib in lowering the inflammatory load due to macrophage accumulation.
Partner
The Department CIBIO – at the University of Trento pursues the task of creating a suitable environment for merging classical cellular and molecular biology approaches with the new powerful tools of systems and synthetic biology.
https://www.cibio.unitn.itPublications
Misselbeck K, Parolo S, Lorenzini F, Savoca V, Leonardelli L, Bora P, Morine MJ, Mione MC, Domenici E, Priami C. A network-based approach to identify deregulated pathways and drug effects in metabolic syndrome. Nat Commun. 2019 Nov 18;10(1):5215. doi: 10.1038/s41467-019-13208-z.
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