Therapeutic, Molecular and Computational Aspects of Novel Monoamine Oxidase (MAO) Inhibitors

Abstract

Most of the existing MAO inhibitors lead to side effects due to the lack of affinity and selectivity. Therefore, there is an urgent need to design novel, potent, reversible and selective inhibitors for MAO-A/B. Selective inhibition of MAO-A results in the elevated level of serotonin and noradrenaline. Hence, MAO-A inhibitors can be used for improving the symptoms of depression. The selective MAO-B inhibitors are used with L-DOPA and/or dopamine agonists in the symptomatic treatment of Parkinson's disease. The present study was aimed to describe the recently developed hits of MAO inhibitors. At present, CADD techniques are gaining an attention in rationale drug discovery of MAO inhibitors, and several research groups employed CADD approaches on various chemical scaffolds to identify novel MAO inhibitors. These computational techniques assisted in the development of lead molecules with improved pharmacodynamics / pharmacokinetic properties toward MAOs. Further, CADD techniques provided a better understanding of structural aspects of molecular targets and lead molecules. The present review describes the importance of structural features of potential chemical scaffolds as well as the role of computational approaches like ligand docking, molecular dynamics, QSAR and pharmacophore modeling in the development of novel MAO inhibitors.