Current Drug Metabolism - Volume 23, Issue 13, 2022

Metabolic reactions in the body transform the administered drug into metabolites. These metabolites exhibit diverse biological activities. Drug metabolism is the major underlying cause of drug overdose-related toxicity, adversative drug effects and the drug’s reduced efficacy. Though metabolic reactions deactivate a drug, drug metabolites are often considered pivotal agents for off-target effects or toxicity. On the other side, in combination drug therapy, one drug may influence another drug’s metabolism and clearance and is thus considered one of the primary causes of drug-drug interactions. Today with the advancement of machine learning, the metabolic fate of a drug candidate can be comprehensively studied throughout the drug development procedure. Naïve Bayes, Logistic Regression, k-Nearest Neighbours, Decision Trees, different Boosting and Ensemble methods, Support Vector Machines and Artificial Neural Network boosted Deep Learning are some machine learning algorithms which are being extensively used in such studies. Such tools are covering several attributes of drug metabolism, with an emphasis on the prediction of drug-drug interactions, drug-target-interactions, clinical drug responses, metabolite predictions, sites of metabolism, etc. These reports are crucial for evaluating metabolic stability and predicting prospective drug-drug interactions, and can help pharmaceutical companies accelerate the drug development process in a less resourcedemanding manner than what in vitro studies offer. It could also help medical practitioners to use combinatorial drug therapy in a more resourceful manner. Also, with the help of the enormous growth of deep learning, traditional fields of computational drug development like molecular interaction fields, molecular docking, quantitative structure-toactivity relationship (QSAR) studies and quantum mechanical simulations are producing results which were unimaginable couple of years back. This review provides a glimpse of a few contextually relevant machine learning algorithms and then focuses on their outcomes in different studies.

Cancer is characterized by disrupted molecular variables caused by cells that deviate from regular signal transduction. The uncontrolled segment of such cancerous cells annihilates most of the tissues that contact them. Gene therapy, immunotherapy, and nanotechnology advancements have resulted in novel strategies for anticancer drug delivery. Furthermore, diverse dispersion of nanoparticles in normal stroma cells adversely affects the healthy cells and disrupts the crosstalk of tumour stroma. It can contribute to cancer cell progression inhibition and, conversely, to acquired resistance, enabling cancer cell metastasis and proliferation. The tumour's microenvironment is critical in controlling the dispersion and physiological activities of nano-chemotherapeutics which is one of the targeted drug therapy. As it is one of the methods of treating cancer that involves the use of medications or other substances to specifically target and kill off certain subsets of malignant cells. A targeted therapy may be administered alone or in addition to more conventional methods of care like surgery, chemotherapy, or radiation treatment. The tumour microenvironment, stromatogenesis, barriers and advancement in the drug delivery system across tumour tissue are summarised in this review.

Background: The use of herbal medicines has tremendously increased over the past few decades. Case reports and controlled clinical investigations of herbal-drug interactions have been reported. Since Cytochrome P450 (CYP) enzymes play an important role in drug interactions. The evaluation of the influence of herbal medicines on the activities of CYPs is beneficial to promote scientific and rational clinical use of herbal medicines. Objective: Herein, we aimed to develop and validate a method to simultaneously quantify seven CYP cocktail probe drugs consisting of phenacetin (PNC), bupropion (BPP), losartan potassium (LK), omeprazole (OMP), dextromethorphan (DM), chlorzoxazone (CZZ) and midazolam (MDZ) and their respective metabolites in a single acquisition run and use this method to evaluate the influence of Zhuanggu Guanjie Pill (ZGGJP) on seven CYPs. Methods: A cost-effective and simple UHPLC-(±)ESI-MS/MS method for simultaneous determination of seven probe drugs and metabolites in rat plasma was developed and validated. Male and female rats were randomly divided into three groups and treated with 1.2 g/kg/d ZGGJP, 5 g/kg/d ZGGJP and 0.5% CMC-Na for 14 consecutive days. After 24 h of the last administration, all rats were administrated orally with probe drugs. The influence of ZGGJP on the CYPs was carried out by comparing the metabolic ratio (Cmax, AUC0-t) of metabolites/probe drugs in rats. Results: The calibration curves were linear, with correlation coefficient > 0.99 for seven probe drugs and their corresponding metabolites. Intra- and inter-day precisions were not greater than 15% RSD and the accuracies were within ± 15% of nominal concentrations. The ZGGJP showed significant inductive effect on CYP1A2, CYP2B6, CYP2C9 and CYP3A in male and female rats. Conclusion: ZGGJP had inductive effects on CYP1A2, CYP2B6, CYP2C9 and CYP3A in male and female rats.

Background: Previous studies have shown that haloperidol biotransformation is mainly metabolized by CYP2D6. The CYP2D6 gene is highly polymorphic, contributing to inter-individual differences in enzymatic activity, and may impact haloperidol biotransformation rates, resulting in variable drug efficacy and safety profiles. Objective: The study aimed to investigate the correlation of the CYPD6 activity with haloperidol's efficacy and safety rates in patients with alcohol-induced psychotic disorders. Methods: One hundred male patients received 5-10 mg/day haloperidol by injections for 5 days. The efficacy and safety assessments were performed using PANSS, UKU, and SAS-validated psychometric scales. Results: No relationship between haloperidol efficacy or safety and the experimental endogenous pharmacometabolomic marker for CYP2D6 activity, urinary 6-HO-THBC/pinoline ratio was identified. In contrast, we found a statistically significant association between haloperidol adverse events and the most common CYP2D6 loss-of-function allele CYP2D6*4 (p<0.001). Conclusion: Evaluation of the single polymorphism rs3892097 that defines CYP2D6*4 can predict the safety profile of haloperidol in patients with AIPD, whereas metabolic evaluation using an endogenous marker was not a suitable predictor. Furthermore, our results suggest haloperidol dose reductions could be considered in AIPD patients with at least one inactive CYP2D6 allele.

Background: Iron-chelation therapy is life-saving in patients on a chronic transfusion regimen as it reduces organ damage related to iron deposition in the tissues. Deferasirox, an iron-chelator, is characterized by pharmacokinetics variability, and some patients may discontinue the treatment due to toxicities. Objective: Understanding whether deferasirox plasma levels are related to patients' specific characteristics could help to optimize DFX dosage. Methods: We analyzed deferasirox plasma concentration in 57 transfusion-dependent anemic patients using the HPLC method in this prospective-retrospective cohort study. All outpatients (3 to 98 years) were treated with deferasirox (film-coated tablet) for at least one year (median dose, 16.5 mg/Kg once a day). Deferasirox plasma concentration was normalized for dose/Kg (C/dose) and corrected with a linear regression model that relates C/dose and the time of blood sampling (C^ref/dose). Results: No significant differences in C^ref/dose were found between males and females, either between different types of hemoglobinopathies or depending on the presence of the UGT1A1*28 polymorphism. C^ref/dose has a positive and significant correlation with age, creatinine, and direct bilirubin. C^ref/dose, instead, has a negative and significant correlation with Liver Iron Concentration (LIC), ferritin, and eGFR. C^ref/dose was significantly different between three age categories <18yrs, 18-50yrs, and >50yrs, with C^ref/dose median values of 1.0, 1.2, and 1.5, respectively. Conclusion: The study evidenced that to ensure the efficacy of deferasirox in terms of control over LIC and, at the same time, a lesser influence on renal function, the dose of the drug to be administered to an elderly patient could be reduced.

Background: 101BHG-D01 is a novel selective anti-muscarinic (M) 3 receptor-blocking drug. 101BHGD01 nasal spray is intended to be used to relieve sneezing and runny nose symptoms caused by allergic rhinitis. Methods: In this study, we examined the plasma pharmacokinetics, tissue distribution, and major excretion mode of 101BHG-D01 in Beagle dogs and rats following nasal spray and intranasal administration, respectively, using HPLCMS/ MS. Results/Discussion: We found that the pharmacokinetics of 101BHG-D01 was linear in dogs. 101BHG-D01 entered the bloodstream rapidly following nasal spray. Its plasma half-life was approximately 6 h and resided at least 24 h in the body. Moreover, 101BHG-D01 retained a significant amount in the nasal cavity. Finally, we found that 101BHGD01 was eliminated mainly in the form of stools in rats. Conclusion: In conclusion, we provided pertinent reference information regarding the design and optimization of drug delivery regimens for clinical trials.