Physicochemical, Interaction & Topological Descriptors vs. hMAO-A Inhibition of Aplysinopsin Analogs: A Boulevard to the Discovery of Semi-synthetic Anti-depression Agents

Rajeev K. Singla, Ghulam Md Ashraf, Magdah Ganash, G. Varadaraj Bhat, Bairong Shen

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Depression, a neurological disorder, is globally the 4th leading cause of chronic disabilities in human beings. Objective: This study aimed to model a 2D-QSAR equation that can facilitate the researchers to design better aplysi-nopsin analogs with potent hMAO-A inhibition. Methods: Aplysinopsin analogs dataset were subjected to ADME assessment for drug-likeness suitability using StarDrop software before modeled equation. 2D-QSAR equations were generated using VLife MDS 4.6. Dataset was segregated into training and test set using different methodologies, followed by variable selection. Model develop-ment was done using principal component regression, partial least square regression, and multiple regression. Results: The dataset has successfully qualified the drug-likeness criteria in ADME simulation, with more than 90% of molecules cleared the ideal conditions, including intrinsic solubility, hydrophobicity, CYP3A4 2C9pKi, hERG pIC50, etc. 112 models were developed using multiparametric consideration of methodologies. The best six models were discussed with their extent of significance and prediction capabilities. ALP97 was emerged out as the most significant model out of all, with ~83% of the variance in the training set, the internal predictive ability of ~74%, while having the external predictive capability of ~79%. Conclusion: ADME assessment suggested that aplysinopsin analogs are worth investigating. Interaction among the descriptors in the way of summation or multiplication products are quite influential and yield significant 2D-QSAR models with good prediction efficiency. This model can be used to design a more potent hMAO-A inhibitor with an aplysinopsin scaffold, which can then contribute to the treatment of depression and other neurological disorders.

Original languageEnglish
Pages (from-to)905-915
Number of pages11
JournalCurrent Drug Metabolism
Volume22
Issue number11
DOIs
Publication statusPublished - 2021

All Science Journal Classification (ASJC) codes

  • Pharmacology
  • Clinical Biochemistry

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