Design and optimisation of novel benzimidazole hybrid structures capable of simultaneous peroxisome proliferator activated receptor γ and angiotensin receptor modulation

Abstract

Metabolic syndrome, associated with an increased risk of diabetes mellitus and cardiovascular disease, is poorly controlled with polypharmacy, prevalent in current management. Dual PPARγ/ATR modulators have potential in metabolic syndrome management. The aims of this study were to use the binding interactions of the benzimidazole scaffold with PPARγ and ATR to model structures with simultaneous high affinity for both loci using virtual screening and de novo fragment-based drug design. A consensus pharmacophore was generated in LigandScout® v4.4 by superimposing telmisartan and olmesartan from PDB crystallographic depositions 3VN2 and 4ZUD respectively, together with HTR-04. This pharmacophore was read into ZINCPharmer® with Rule of 3 filters applied. Hits were docked into protomols, describing the energetically unsatisfied space, within the PPARγ and ATR to measure affinity of hits to protomols. In de novo fragment-based drug design, two-dimensional topology maps of HTR-04 within the PPARγ and ATR ligand binding pockets guided the generation of seeds derived from HTR-04. Seeds were used for de novo growth at predesignated loci within the PPARγ and ATR ligand binding pockets. The study was conducted at the Department of Pharmacy, University of Malta. 519 Lipinski-Rule compliant hits were identified during the virtual screening phase. After docking into protomols, affinity of the 10 highest-ranking hits, expressed as a total score, ranged from 4.28 to 5.08. Using the de novo design approach, 4132 molecules were generated from 1 successful seed, of which the top 200 structures were further subjected to filtration for Lipinski Rule compliance. The benzimidazole scaffold shows potential in the development of dual PPARγ/ATR modulators for MetS management. The virtual screening and de novo design approaches both identified high-affinity drug candidate molecules suitable for further optimisation.