Chemometric Methods—A Valuable Tool for Investigating the Interactions Between Antifungal Drugs (Including Antifungal Antibiotics) and Food

• Published in: Antibiotics (Basel) Vol. 14; no. 1; p. 70
• Main Authors: Wiesner-Kiełczewska, Agnieszka, Zagrodzki, Paweł, Gawalska, Alicja, Paśko, Paweł
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2025; MDPI
• Subjects: Antacids; Antibiotics; Antifungal activity; Antifungal agents; antifungal antibiotics; antifungals; Antimetabolites; Antimicrobial resistance; Azoles; Beverages; Bioavailability; Carbohydrates; Chemometrics; Clinical trials; Correlation; Data analysis; Data interpretation; Datasets; Dietary supplements; Drug development; Drug interaction; Drug interactions; Drug resistance; Drugs; Food; Fungicides; Glucan; Heterocyclic compounds; Infections; Inhibitors; interaction; Mathematical models; Methods; Multivariate analysis; Optimization; Parameters; partial least square; Permeability; Pharmacokinetics; Posaconazole; Risk reduction; Software; Squalene epoxidase; Statistical methods; chemometrics; antimicrobial resistance; interaction; antifungals; partial least square; food–drug interaction; antifungal antibiotics; food
• ISSN: 2079-6382; 2079-6382
• DOI: 10.3390/antibiotics14010070

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; ∆Cmax, maximum drug concentration; and ∆Tmax, 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 ∆Tmax. 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.