Jeličić Mario-Livio, Kovačić Jelena, Cvetnić Matija, Mornar Ana, Amidžić Klarić Daniela
Department of Pharmaceutical Analysis, Faculty of Pharmacy and Biochemistry, University of Zagreb, Ante Kovačića 1, 10000 Zagreb, Croatia.
Department of Analytical Chemistry, Faculty of Chemical Engineering and Technology, University of Zagreb, Marulićev trg 19, 10000 Zagreb, Croatia.
Pharmaceuticals (Basel). 2022 Jun 24;15(7):791. doi: 10.3390/ph15070791.
Since oxidative stress has been linked to several pathological conditions and diseases, drugs with additional antioxidant activity can be beneficial in the treatment of these diseases. Therefore, this study takes a new look at the antioxidant activity of frequently prescribed drugs using the HPLC-DPPH method. The antioxidative activity expressed as the TEAC value of 82 drugs was successfully determined and is discussed in this work. Using the obtained values, the QSAR model was developed to predict the TEAC based on the selected molecular descriptors. The results of QSAR modeling showed that four- and seven-variable models had the best potential for TEAC prediction. Looking at the statistical parameters of each model, the four-variable model was superior to seven-variable. The final model showed good predicting power ( = 0.927) considering the selected descriptors, implying that it can be used as a fast and economically acceptable evaluation of antioxidative activity. The advantage of such model is its ability to predict the antioxidative activity of a drug regardless of its structural diversity or therapeutic classification.
由于氧化应激与多种病理状况和疾病相关，具有额外抗氧化活性的药物可能有益于这些疾病的治疗。因此，本研究采用高效液相色谱-二苯基苦味酰基自由基（HPLC-DPPH）法重新审视常用药物的抗氧化活性。成功测定了82种药物以TEAC值表示的抗氧化活性，并在本研究中进行了讨论。利用所得数据，基于选定的分子描述符建立了定量构效关系（QSAR）模型来预测TEAC。QSAR建模结果表明，四变量模型和七变量模型在预测TEAC方面具有最佳潜力。从每个模型的统计参数来看，四变量模型优于七变量模型。考虑到选定的描述符，最终模型显示出良好的预测能力（ = 0.927），这意味着它可用于对抗氧化活性进行快速且经济上可接受的评估。这种模型的优点在于其能够预测药物的抗氧化活性，而无需考虑其结构多样性或治疗分类。
