Computational drug discovery approaches identify mebendazole as a candidate treatment for autosomal dominant polycystic kidney disease

• Main Authors: Philip W. Brownjohn (Author), Azedine Zoufir (Author), Daniel J. O'Donovan (Author), Saatviga Sudhahar (Author), Alexander Syme (Author), Rosemary Huckvale (Author), John R. Porter (Author), Hester Bange (Author), Jane Brennan (Author), Neil T. Thompson (Author)
• Format: Book
• Published: Frontiers Media S.A., 2024-05-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 1663-9812; 10.3389/fphar.2024.1397864
• Keywords: autosomal dominant polycystic kidney disease; rare diseases; gene expression profiling; machine learning; drug discovery; drug repositioning; Therapeutics. Pharmacology; RM1-950
• Source: Frontiers in Pharmacology, Vol 15 (2024)

Autosomal dominant polycystic kidney disease (ADPKD) is a rare genetic disorder characterised by numerous renal cysts, the progressive expansion of which can impact kidney function and lead eventually to renal failure. Tolvaptan is the only disease-modifying drug approved for the treatment of ADPKD, however its poor side effect and safety profile necessitates the need for the development of new therapeutics in this area. Using a combination of transcriptomic and machine learning computational drug discovery tools, we predicted that a number of existing drugs could have utility in the treatment of ADPKD, and subsequently validated several of these drug predictions in established models of disease. We determined that the anthelmintic mebendazole was a potent anti-cystic agent in human cellular and in vivo models of ADPKD, and is likely acting through the inhibition of microtubule polymerisation and protein kinase activity. These findings demonstrate the utility of combining computational approaches to identify and understand potential new treatments for traditionally underserved rare diseases.