Design and optimisation of dual PPAR ɣ/α agonists based on the novel SR10171 scaffold

Abstract

SR10171, a dual peroxisome proliferator activated receptor (PPAR) ɣ/α agonist, is capable of improving insulin sensitivity and eliciting bone formation, through inverse agonism at PPARɣ and full agonism at PPARα. It can potentially treat type 2 diabetes mellitus while protecting bone integrity and is the lead molecule in this study from which high-affinity analogous structures can be identified and optimised from an affinity and bioavailability perspective. Protein data bank (PDB) crystallographic depositions, PDB 2Q8s and PDB 3G8I, were chosen to represent PPARɣ and PPARα respectively. The original molecules in the ligand binding pocket (LBP) of each deposition were extracted, prior to docking SR10171 into each LBP. Conformational analysis was carried out to identify the optimal SR10171 conformation in each LBP, based on affinity and stability. This study consists of two approaches; a virtual screening (VS) approach and a de novo approach. Through VS, molecular structures sourced from a molecular database, will be filtered according to the degree of analogy to a consensus pharmacophore, representing an average of the bioactive coordinates of the optimal SR10171 conformation and the established ligands of each receptor subtype. These molecules will be identified as having ‘lead-like’ properties and by deduction, would be able to establish interactions within the LBP of the corresponding receptor subtype, potentially having a clinical effect. In the de novo approach, a two-dimensional map depicting the interactions between the optimal SR10171 conformer and the receptor subtype’s LBP will be generated for each subtype. Seed structures for each receptor subtype will be modelled based on the identified interactions, and planted at PPARɣ and PPARα LBP’s, to allow de novo growth within their confines. The generated molecular structures will be classified according to pharmacophore family and filtered for Lipinski compliance, and the optimal structures will be identified based on affinity. Conformational analysis will be performed on the highest scoring molecules and the best conformers of each molecule will be planted at the opposite receptor subtype’s LBP. The molecules having sufficient affinity and acceptable stability at both receptor subtypes will be proposed for evaluation of potential clinical efficacy.