Chemometric Methods—A Valuable Tool for Investigating the Interactions Between Antifungal Drugs (Including Antifungal Antibiotics) and Food
Background/Objectives: Developing antifungal drugs with lower potential for interactions with food may help to optimize treatment and reduce the risk of antimicrobial resistance. Chemometrics uses statistical and mathematical methods to analyze multivariate chemical data, enabling the identification...
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MDPI AG
2025-01-01
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| Series: | Antibiotics |
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| author | Agnieszka Wiesner-Kiełczewska Paweł Zagrodzki Alicja Gawalska Paweł Paśko |
| author_facet | Agnieszka Wiesner-Kiełczewska Paweł Zagrodzki Alicja Gawalska Paweł Paśko |
| author_sort | Agnieszka Wiesner-Kiełczewska |
| collection | DOAJ |
| description | Background/Objectives: Developing antifungal drugs with lower potential for interactions with food may help to optimize treatment and reduce the risk of antimicrobial resistance. Chemometrics uses statistical and mathematical methods to analyze multivariate chemical data, enabling the identification of key correlations and simplifying data interpretation. We used the partial least squares (PLS) approach to explore the correlations between various characteristics of oral antifungal drugs (including antifungal antibiotics) and dietary interventions, aiming to identify patterns that could inform the optimization of antifungal therapy. Methods: We analyzed 15 oral antifungal drugs, including azoles (8), antifungal antibiotics (4), antifungal antimetabolites (1), squalene epoxidase inhibitors (1), and glucan synthase inhibitors (1). The input dataset comprised information from published clinical trials, chemical records, and calculations. We constructed PLS models with changes in the pharmacokinetic parameters (∆AUC, area under the curve; ∆C<sub>max</sub>, maximum drug concentration; and ∆T<sub>max</sub>, time to reach maximum drug concentration) after dietary intervention as the response parameters and eight groups of molecular descriptors (M1–M8) as the predictor parameters. We performed separate analyses for the different nutritional interventions. Results: In the final PLS model with food as an intervention, we effectively reduced the dimensionality of the dataset while retaining a substantial percentage of the original information (variance), as significant components explained 69.8% and 17.5% of the predictor and response parameter variances, respectively. The PLS model was significant because its components met the cross-validation criteria. We obtained six significant positive and negative correlations between the descriptors related to atoms and the postprandial ∆T<sub>max</sub>. Conclusions: The PLS method is valuable for investigating interactions between antifungal drugs (including antifungal antibiotics) and food. The correlations obtained can be used in drug modeling to predict interactions with dietary interventions based on the antifungal drug’s chemical structure. Incorporating chemometric techniques into the early drug development stages could facilitate the design of antifungal antibiotics and other antifungal agents with optimized absorption in the presence of dietary components. |
| format | Article |
| id | doaj-art-37269a03d747471d887c4d4ba9aa1773 |
| institution | Kabale University |
| issn | 2079-6382 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Antibiotics |
| spelling | doaj-art-37269a03d747471d887c4d4ba9aa17732025-01-24T13:18:50ZengMDPI AGAntibiotics2079-63822025-01-011417010.3390/antibiotics14010070Chemometric Methods—A Valuable Tool for Investigating the Interactions Between Antifungal Drugs (Including Antifungal Antibiotics) and FoodAgnieszka Wiesner-Kiełczewska0Paweł Zagrodzki1Alicja Gawalska2Paweł Paśko3Doctoral School of Medical and Health Sciences, Jagiellonian University Medical College, 31-008 Cracow, PolandDepartment of Food Chemistry and Nutrition, Faculty of Pharmacy, Jagiellonian University Medical College, 31-008 Cracow, PolandDepartment of Medicinal Chemistry, Faculty of Pharmacy, Jagiellonian University Medical College, 31-008 Cracow, PolandDepartment of Food Chemistry and Nutrition, Faculty of Pharmacy, Jagiellonian University Medical College, 31-008 Cracow, PolandBackground/Objectives: Developing antifungal drugs with lower potential for interactions with food may help to optimize treatment and reduce the risk of antimicrobial resistance. Chemometrics uses statistical and mathematical methods to analyze multivariate chemical data, enabling the identification of key correlations and simplifying data interpretation. We used the partial least squares (PLS) approach to explore the correlations between various characteristics of oral antifungal drugs (including antifungal antibiotics) and dietary interventions, aiming to identify patterns that could inform the optimization of antifungal therapy. Methods: We analyzed 15 oral antifungal drugs, including azoles (8), antifungal antibiotics (4), antifungal antimetabolites (1), squalene epoxidase inhibitors (1), and glucan synthase inhibitors (1). The input dataset comprised information from published clinical trials, chemical records, and calculations. We constructed PLS models with changes in the pharmacokinetic parameters (∆AUC, area under the curve; ∆C<sub>max</sub>, maximum drug concentration; and ∆T<sub>max</sub>, time to reach maximum drug concentration) after dietary intervention as the response parameters and eight groups of molecular descriptors (M1–M8) as the predictor parameters. We performed separate analyses for the different nutritional interventions. Results: In the final PLS model with food as an intervention, we effectively reduced the dimensionality of the dataset while retaining a substantial percentage of the original information (variance), as significant components explained 69.8% and 17.5% of the predictor and response parameter variances, respectively. The PLS model was significant because its components met the cross-validation criteria. We obtained six significant positive and negative correlations between the descriptors related to atoms and the postprandial ∆T<sub>max</sub>. Conclusions: The PLS method is valuable for investigating interactions between antifungal drugs (including antifungal antibiotics) and food. The correlations obtained can be used in drug modeling to predict interactions with dietary interventions based on the antifungal drug’s chemical structure. Incorporating chemometric techniques into the early drug development stages could facilitate the design of antifungal antibiotics and other antifungal agents with optimized absorption in the presence of dietary components.https://www.mdpi.com/2079-6382/14/1/70antifungal antibioticsantifungalschemometricspartial least squarefoodinteraction |
| spellingShingle | Agnieszka Wiesner-Kiełczewska Paweł Zagrodzki Alicja Gawalska Paweł Paśko Chemometric Methods—A Valuable Tool for Investigating the Interactions Between Antifungal Drugs (Including Antifungal Antibiotics) and Food Antibiotics antifungal antibiotics antifungals chemometrics partial least square food interaction |
| title | Chemometric Methods—A Valuable Tool for Investigating the Interactions Between Antifungal Drugs (Including Antifungal Antibiotics) and Food |
| title_full | Chemometric Methods—A Valuable Tool for Investigating the Interactions Between Antifungal Drugs (Including Antifungal Antibiotics) and Food |
| title_fullStr | Chemometric Methods—A Valuable Tool for Investigating the Interactions Between Antifungal Drugs (Including Antifungal Antibiotics) and Food |
| title_full_unstemmed | Chemometric Methods—A Valuable Tool for Investigating the Interactions Between Antifungal Drugs (Including Antifungal Antibiotics) and Food |
| title_short | Chemometric Methods—A Valuable Tool for Investigating the Interactions Between Antifungal Drugs (Including Antifungal Antibiotics) and Food |
| title_sort | chemometric methods a valuable tool for investigating the interactions between antifungal drugs including antifungal antibiotics and food |
| topic | antifungal antibiotics antifungals chemometrics partial least square food interaction |
| url | https://www.mdpi.com/2079-6382/14/1/70 |
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