Antioxidant Activity of Pharmaceuticals: Predictive QSAR Modeling for Potential Therapeutic Strategy
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-DP...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Pharmaceuticals |
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| Online Access: | https://www.mdpi.com/1424-8247/15/7/791 |
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| author | Mario-Livio Jeličić Jelena Kovačić Matija Cvetnić Ana Mornar Daniela Amidžić Klarić |
| author_facet | Mario-Livio Jeličić Jelena Kovačić Matija Cvetnić Ana Mornar Daniela Amidžić Klarić |
| author_sort | Mario-Livio Jeličić |
| collection | DOAJ |
| description | 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 (<i>r</i> = 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. |
| first_indexed | 2024-03-09T13:13:03Z |
| format | Article |
| id | doaj.art-e81516667a0748ca9e472dbc8ae6ef05 |
| institution | Directory Open Access Journal |
| issn | 1424-8247 |
| language | English |
| last_indexed | 2024-03-09T13:13:03Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Pharmaceuticals |
| spelling | doaj.art-e81516667a0748ca9e472dbc8ae6ef052023-11-30T21:40:04ZengMDPI AGPharmaceuticals1424-82472022-06-0115779110.3390/ph15070791Antioxidant Activity of Pharmaceuticals: Predictive QSAR Modeling for Potential Therapeutic StrategyMario-Livio Jeličić0Jelena Kovačić1Matija Cvetnić2Ana Mornar3Daniela Amidžić Klarić4Department of Pharmaceutical Analysis, Faculty of Pharmacy and Biochemistry, University of Zagreb, Ante Kovačića 1, 10000 Zagreb, CroatiaDepartment of Pharmaceutical Analysis, Faculty of Pharmacy and Biochemistry, University of Zagreb, Ante Kovačića 1, 10000 Zagreb, CroatiaDepartment of Analytical Chemistry, Faculty of Chemical Engineering and Technology, University of Zagreb, Marulićev trg 19, 10000 Zagreb, CroatiaDepartment of Pharmaceutical Analysis, Faculty of Pharmacy and Biochemistry, University of Zagreb, Ante Kovačića 1, 10000 Zagreb, CroatiaDepartment of Pharmaceutical Analysis, Faculty of Pharmacy and Biochemistry, University of Zagreb, Ante Kovačića 1, 10000 Zagreb, CroatiaSince 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 (<i>r</i> = 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.https://www.mdpi.com/1424-8247/15/7/791pharmaceuticalsantioxidative activityDPPHHPLCQSAR prediction |
| spellingShingle | Mario-Livio Jeličić Jelena Kovačić Matija Cvetnić Ana Mornar Daniela Amidžić Klarić Antioxidant Activity of Pharmaceuticals: Predictive QSAR Modeling for Potential Therapeutic Strategy Pharmaceuticals pharmaceuticals antioxidative activity DPPH HPLC QSAR prediction |
| title | Antioxidant Activity of Pharmaceuticals: Predictive QSAR Modeling for Potential Therapeutic Strategy |
| title_full | Antioxidant Activity of Pharmaceuticals: Predictive QSAR Modeling for Potential Therapeutic Strategy |
| title_fullStr | Antioxidant Activity of Pharmaceuticals: Predictive QSAR Modeling for Potential Therapeutic Strategy |
| title_full_unstemmed | Antioxidant Activity of Pharmaceuticals: Predictive QSAR Modeling for Potential Therapeutic Strategy |
| title_short | Antioxidant Activity of Pharmaceuticals: Predictive QSAR Modeling for Potential Therapeutic Strategy |
| title_sort | antioxidant activity of pharmaceuticals predictive qsar modeling for potential therapeutic strategy |
| topic | pharmaceuticals antioxidative activity DPPH HPLC QSAR prediction |
| url | https://www.mdpi.com/1424-8247/15/7/791 |
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