APLICAÇÃO DE TÉCNICAS COMPUTACIONAIS NA IDENTIFICAÇÃO DE POTENCIAIS INIBIDORES DA PROTEÍNA TRANSPORTADORA DE DOPAMINA

Abstract

Neuropsychiatric disorders that involve dysregulation in dopaminergic neurotransmission, attributed mainly to the dopamine transporter (DAT), are public health problems of considerable relevance, having gained prominence in recent years, due to the high rates of disabilities and numerous deaths evidenced throughout worldwide, demonstrating a significant impact on health systems. Based on these facts, we proposed to carry out an in silico study using computational techniques to identify potential dopamine reuptake inhibitors, which may contribute positively to the treatment of neuropsychiatric disorders. Thirty-six compounds derived from methylamines were used to build a mathematical model of quantitative structure activity relationship (QSAR), based on a two-dimensional structure (2D) and suitable for the validation metrics evaluated, as a tool for predicting the biological activity of new compounds that were identified through a 2D similarity-based virtual screening study. After a series of reductions of the set through the evaluation of toxicity, applicability domain, and in silico pharmacokinetic properties, a total of seven hit compounds were obtained, becoming scaffolds for the development of new DAT inhibitors. Additionally, the drug benzotropine, classified as an atypical DAT inhibitor because it does not generate drug-dependent effects such as those observed by the action of drugs of abuse, was used as a reference molecule in a virtual screening study based on pharmacophores, and after the application of toxicity filters and in silico pharmacokinetic properties, in addition to a molecular docking study to evaluate the interactions of the main ligands with the crystallized macromolecular structure of DAT, allowed the identification of five hit compounds, which proved to be the most promising to become new DAT inhibitors, based on their interaction mechanisms and biological activities predicted in a QSAR model.