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Tramadol (TRA) is a central-acting opioid whose biological activities are achieved by interaction with several bodily receptors such as μ-opioid receptors. Considering that central-acting drugs may promote oxidative stress, which could lead to neurodegeneration, this work reported the investigation of the redox behavior of TRA by electrochemical and semi-empirical quantum chemistry approaches (i.e., voltammetry and extended Hückel method—EHM) in order to study TRA pro-oxidant features. Electrochemical results showed that TRA exhibited two anodic peaks, namely: 1a at Ep1a ≈ +0.03 V and 2a at Ep2a ≈ +0.8 V; and a cathodic peak at Ep1c ≈ −0.01 V, whereas the quantum chemistry model suggested that the highest occupied molecular orbital n = 0 (HOMO-0) was associated with the tertiary amine in the TRA molecule, while HOMO-1 and the lowest unoccupied molecular orbital n = 0 (LUMO-0) were associated with the aromatic benzene ring. The findings were then used to propose an electrooxidation pathway according to the observations and compared to the literature, which further offered hints about TRA’s pro-oxidant nature. In conclusion, the work reported herein shows that voltammetric and semi-empirical quantum chemistry approaches can be correlated to investigate the redox behavior of CNS-acting compounds.

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Monoaromatic antioxidants are one of the major classes of small druggable molecules whose use is widespread as preservatives in pharmaceutical and foodstuff industry. The differentiation of these compounds according to their source is notably difficult due to their shared structural features. This work showcases how to promote the classification of natural and synthetic monoaromatic antioxidants using multivariate analysis, data mining and machine learning algorithms. Physicochemical and biopharmaceutical molecular descriptors were selected and calculated do render alignment and classification models using principal components analysis, data mining, support-vector machines (linear kernel) and multilayer perceptron. We showcased that physicochemical and biopharmaceutical molecular predictors may be suitable attributes for differentiating natural and synthetic monoaromatic antioxidants, since their outputs from multivariate analysis, data mining and machine learning algorithms generated a reliable and accurate model for prompt classification of natural and synthetic monoaromatic antioxidants. Moreover, all classification models yielded accuracies above 80%. This work therefore sheds light on the use of artificial intelligence in the development of classifiers for pharmaceutical and foodstuff applications.

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