<![CDATA[Current Drug Metabolism (Volume 24 - Issue 12)]]> https://www.benthamscience.com/journal/15 RSS Feed for Journals | BenthamScience EurekaSelect (+https://www.benthamscience.com) 2024-01-12 <![CDATA[Current Drug Metabolism (Volume 24 - Issue 12)]]> https://www.benthamscience.com/journal/15 <![CDATA[The Impacts and Changes Related to the Cancer Drug Resistance Mechanism]]>https://www.benthamscience.com/article/1367662024-01-12Background: Cancer drug resistance remains a difficult barrier to effective treatment, necessitating a thorough understanding of its multi-layered mechanism.

Objective: This study aims to comprehensively explore the diverse mechanisms of cancer drug resistance, assess the evolution of resistance detection methods, and identify strategies for overcoming this challenge. The evolution of resistance detection methods and identification strategies for overcoming the challenge.

Methods: A comprehensive literature review was conducted to analyze intrinsic and acquired drug resistance mechanisms, including altered drug efflux, reduced uptake, inactivation, target mutations, signaling pathway changes, apoptotic defects, and cellular plasticity. The evolution of mutation detection techniques, encompassing clinical predictions, experimental approaches, and computational methods, was investigated. Strategies to enhance drug efficacy, modify pharmacokinetics, optimizoptimizee binding modes, and explore alternate protein folding states were examined.

Results: The study comprehensively overviews the intricate mechanisms contributing to cancer drug resistance. It outlines the progression of mutation detection methods and underscores the importance of interdisciplinary approaches. Strategies to overcome drug resistance challenges, such as modulating ATP-binding cassette transporters and developing multidrug resistance inhibitors, are discussed. The study underscores the critical need for continued research to enhance cancer treatment efficacy.

Conclusion: This study provides valuable insights into the complexity of cancer drug resistance mechanisms, highlights evolving detection methods, and offers potential strategies to enhance treatment outcomes.

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<![CDATA[Nutritional Therapy Strategies Targeting Tumor Energy Metabolism]]>https://www.benthamscience.com/article/1368372024-01-12 <![CDATA[Drug-Protein Interactions Prediction Models Using Feature Selection and Classification Techniques]]>https://www.benthamscience.com/article/1368572024-01-12Background: Drug-Protein Interaction (DPI) identification is crucial in drug discovery. The high dimensionality of drug and protein features poses challenges for accurate interaction prediction, necessitating the use of computational techniques. Docking-based methods rely on 3D structures, while ligand-based methods have limitations such as reliance on known ligands and neglecting protein structure. Therefore, the preferred approach is the chemogenomics-based approach using machine learning, which considers both drug and protein characteristics for DPI prediction.

Methods: In machine learning, feature selection plays a vital role in improving model performance, reducing overfitting, enhancing interpretability, and making the learning process more efficient. It helps extract meaningful patterns from drug and protein data while eliminating irrelevant or redundant information, resulting in more effective machine-learning models. On the other hand, classification is of great importance as it enables pattern recognition, decision-making, predictive modeling, anomaly detection, data exploration, and automation. It empowers machines to make accurate predictions and facilitates efficient decision-making in DPI prediction. For this research work, protein data was sourced from the KEGG database, while drug data was obtained from the DrugBank data machine-learning base.

Results: To address the issue of imbalanced Drug Protein Pairs (DPP), different balancing techniques like Random Over Sampling (ROS), Synthetic Minority Over-sampling Technique (SMOTE), and Adaptive SMOTE were employed. Given the large number of features associated with drugs and proteins, feature selection becomes necessary. Various feature selection methods were evaluated: Correlation, Information Gain (IG), Chi-Square (CS), and Relief. Multiple classification methods, including Support Vector Machines (SVM), Random Forest (RF), Adaboost, and Logistic Regression (LR), were used to predict DPI. Finally, this research identifies the best balancing, feature selection, and classification methods for accurate DPI prediction.

Conclusion: This comprehensive approach aims to overcome the limitations of existing methods and provide more reliable and efficient predictions in drug-protein interaction studies.

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<![CDATA[Inhibitory Effects of Tricyclic Antidepressants on Human Liver Microsomal Morphine Glucuronidation: Application of IVIVE to Predict Potential Drug-Drug Interactions in Humans]]>https://www.benthamscience.com/article/1368382024-01-12 Background: Tricyclic antidepressants (TCAs) are commonly co-administered with morphine as an adjuvant analgesic. Nevertheless, there remains a lack of information concerning metabolic drug-drug interactions (DDIs) resulting from TCA inhibition on morphine glucuronidation.

Objective: This study aimed to (i) examine the inhibitory effects of TCAs (viz., amitriptyline, clomipramine, imipramine, and nortriptyline) on human liver microsomal morphine 3- and 6-glucuronidation and (ii) evaluate the potential of DDI in humans by employing in vitro-in vivo extrapolation (IVIVE) approaches.

Method: The inhibition parameters for TCA inhibition on morphine glucuronidation were derived from the in vitro system containing 2% BSA. The Ki values were employed to predict the DDI magnitude in vivo by using static and dynamic mechanistic PBPK approaches

Results: TCAs moderately inhibited human liver microsomal morphine glucuronidation, with clomipramine exhibiting the most potent inhibition potency. Amitriptyline, clomipramine, imipramine, and nortriptyline competitively inhibited morphine 3- and 6-glucuronide formation with the respective Ki values of 91 ± 7.5 and 82 ± 11 μM, 23 ± 1.3 and 14 ± 0.7 μM, 103 ± 5 and 90 ± 7 μM, and 115 ± 5 and 110 ± 3 μM. Employing the static mechanistic IVIVE, a prediction showed an estimated 20% elevation in the morphine AUC when co-administered with either clomipramine or imipramine, whereas the predicted increase was <5% for amitriptyline or nortriptyline. PBPK modelling predicted an increase of less than 10% in the morphine AUC due to the inhibition of clomipramine and imipramine in both virtual healthy and cirrhotic populations.

Conclusion: The results suggest that the likelihood of potential clinical DDIs arising from tricyclic antidepressant inhibition on morphine glucuronidation is low.

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<![CDATA[Acknowledgements To Reviewers]]>https://www.benthamscience.com/article/1370482024-01-12