A Multi-target Approach for the Discovery of Anti Breast Cancer Agents from Plants Secondary Metabolites - Olawale, Femi; Iwaloye, Opeyemi; Elekofehinti, Olusola O.; Kikiowo, Babatomiwa; Oluwarotimi, Emmanuel Ayo; Ilesanmi, Kayode Michael; Akinropo, Isaac Damilola; Akinlosotu, Oluwaseun Benedicta; Adegboyega, Abayomi Emmanuel; Ologuntere, Taiwo Emmanuel (Letters in Drug Design & Discovery, 2021)
Background: Cancer is a multifactorial disease with multiple complications involving multiple proteins. Breast cancer is the most prevalent form of cancer among women. The pathophysiology of this cancer form has implicated several genetic alterations in its hallmark. Two of the most studied breast cancer molecular pathways are the cell cycle protein kinases and P13/AKT signaling pathway. Objective: Thus, this study identified novel inhibitors through computational screening of a library of medicinal plant compounds against cyclin-dependent kinase 2 (CDK2), phosphoinositide-3-kinase A (PI3Ka) and protein kinase B (AKT1). Methods: Rigid protein docking via Glide algorithm was applied to identify the hits from 3000 plant compounds screened against three drug targets involved in breast cancer pathogenesis. A more accurate and reliable ligand-protein docking called induced fit docking was adopted to extensively improve the scoring function by ranking favourable binding as top-scoring one. Results: Nine hit compounds were identified and found to interact with essential residues at the proteins’ binding sites. Subsequently, the hits pharmacokinetic parameters and toxicity were predicted to determine their potential as drug candidates and minimise toxic effects. The hit compounds were found to be non-carcinogenic, and five of them showed a desirable drug-like property. The built predictive QSAR models with an R2 value of 0.7684, 0.7973 and 0.5649 for CDK2, AKT1 and PI3Ka, respectively, were adopted to determine the hits inhibitory activity (pIC50) against the screened proteins; and the predictions revealed compounds with significant activity. Prediction of the hit compounds druglikeness, pharmacokinetic and toxicity properties by online web servers showed that the compounds are non-carcinogenic and showed moderate indices for ADMET parameters. The constructed QSAR models with reliable R2 coefficient value were used to predict the pIC50 of the selected compounds. The results revealed potent compounds with significant activity. Conclusion: This study thus provides insight into multi-target protein compounds which could be explored as chemotherapeutic alternatives in breast cancer treatment.
Keywords: QSAR