Title:Therapeutic, Molecular and Computational Aspects of Novel Monoamine Oxidase (MAO) Inhibitors
Volume: 20
Issue: 6
Author(s): Muthusamy Ramesh , Yussif M. Dokurugu, Michael D. Thompson and Mahmoud E. Soliman*
Affiliation:
- Discipline of Pharmaceutical Sciences, School of Health Sciences, University of KwaZulu-Natal, Westville, Durban-4001,south africa
Keywords:
MAO inhibition, Parkinson's disease, Alzheimer's disease, computer-aided drug design, QSAR, pharmacophore
modeling.
Abstract: Background Due to the limited number of MAO inhibitors in the clinics, several research
efforts are aimed at the discovery of novel MAO inhibitors. At present, a high specificity and a
reversible mode of inhibition of MAO-A/B are cited as desirable traits in drug discovery process.
This will help to reduce the probability of causing target disruption and may increase the duration of
action of drug.
Aim: 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.
Method: 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.
Conclusions: 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.
