Integrated Computational Analysis on Some Indolo-quinoline Derivatives for the Development of Novel Antiplasmodium Agents: CoMFA, Pharmacophore Mapping, Molecular Docking and ADMET Studies

Abstract

Background: The development of multi-resistant strains of the Plasmodium parasite has become a global problem. Therefore, designing of new antimalarial agents is an exclusive solution. Objective: To improve the activity and identify potentially efficacious new antimalarial agents, integrated computational perspectives such as pharmacophore mapping, 3D-QSAR and docking study have been applied to a series of indolo-quinoline derivatives. Methods: The pharmacophore mapping generated various hypotheses based on key functional features and the best hypothesis ADRRR_1 revealed that indolo-quinoline scaffold is essential for antimalarial activity. 3D-QSAR model was established based on CoMFA and CoMSIA models by using 30 indolo-quinoline analogues as training set and the rest of 19 as test set. Results: The molecular field analysis (MFA) with PLS (partial least-squares) method was used to develop significant CoMFA (q2=0.756, r2=0.996) and CoMSIA (q2=0.703, r2=0.812) models. The CoMFA and CoMSIA models showed good predictive ability with r2 pred values of 0.9623 and 0.9214 respectively. Docking studies were performed by using pfLDH to identify structural insight into the active site and results signify that the quinoline nitrogen acts as a hydrogen bond acceptor region to facilitate interaction with Glu122. Finally, designed molecules were screened through the ADMET tool to evaluate the pharmacokinetic and drug-likeness parameters. Conclusion: Thus, these studies suggested that established models have good predictability and would help in the optimization of newly designed molecules that may produce potent antimalarial activity.