Regulation of Hepatic UGT2B15 by Methylation in Adults of Asian Descent

Expression of UDP Glucuronosyltransferases 2B15 and 2B17 is associated with methylation status in prostate cancer cells

Studies have suggested that abrogated expression of detoxification enzymes, UGT2B15 and UGT2B17, are associated with prostate tumour risk and progression. We investigated the role of EGF on the expression of these enzymes since it interacts with signalling pathways to also affect prostate tumour progression and is additionally associated with decreased DNA methylation. The expression of UGT2B15, UGT2B17, de novo methyltransferases, DNMT3A and DNMT3B was assessed in prostate cancer cells (LNCaP) treated with EGF, an EGFR inhibitor PD16893, and the methyltransferase inhibitor, 5-azacytidine, respectively. The results showed that EGF treatment decreased levels of expression of all four genes and that their expression was reversed by PD16893. Treatment with 5-azacytidine, markedly decreased expression of UGT2B15 and UGT2B17 over 85% as well as significantly decreased expression of DNMT3B, but not the expression of DNMT3A. DNMT3B siRNA treated LNCaP cells had decreased expression of UGT2B15 and UGT2B17, while DNMT3A siRNA treated cells had only moderately decreased UGT2B15 expression. Treatment with DNMT methyltransferase inhibitor, RG108, significantly decreased UGT2B17 expression. Additionally, methylation differences between prostate cancer samples and benign prostate samples from an Illumina 450K Methylation Array study were assessed. The results taken together suggest that hypomethylation of the UGT2B15 and UGT2B17 genes contributes to increased risk of prostate cancer and may provide a putative biomarker or epigenetic target for chemotherapeutics. Mechanistic studies are warranted to determine the role of the methylation marks in prostate cancer.

The Ontogeny of UDP-glucuronosyltransferase Enzymes, Recommendations for Future Profiling Studies and Application Through Physiologically Based Pharmacokinetic Modelling

Limited understanding of drug pharmacokinetics in children is one of the major challenges in paediatric drug development. This is most critical in neonates and infants owing to rapid changes in physiological functions, especially in the activity of drug-metabolising enzymes. Paediatric physiologically based pharmacokinetic models that integrate ontogeny functions for cytochrome P450 enzymes have aided our understanding of drug exposure in children, including those under the age of 2 years. Paediatric physiologically based pharmacokinetic models have consequently been recognised by the European Medicines Agency and the US Food and Drug Administration as innovative tools in paediatric drug development and regulatory decision making. However, little is currently known about age-related changes in UDP-glucuronosyltransferase-mediated metabolism, which represents the most important conjugation reaction for xenobiotics. Therefore, the objective of the review was to conduct a thorough literature survey to summarise our current understanding of age-related changes in UDP-glucuronosyltransferases as well as associated clinical and experimental sources of variance. Our findings indicate that there are distinct differences in UDP-glucuronosyltransferase expression and activity between isoforms for different age groups. In addition, there is substantial variability between individuals and laboratories reported for human liver microsomes, which results in part from a lack of standardised experimental conditions. Therefore, we provide a number of best practice recommendations for experimental conditions, which ultimately may help improve the quality of data used for quantitative clinical pharmacology approaches, and thus for safe and effective pharmacotherapy in children.

DNA Methylation Profiles in a Group of Workers Occupationally Exposed to Nanoparticles

The risk of exposure to nanoparticles (NPs) has rapidly increased during the last decade due to the vast use of nanomaterials (NMs) in many areas of human life. Despite this fact, human biomonitoring studies focused on the effect of NP exposure on DNA alterations are still rare. Furthermore, there are virtually no epigenetic data available. In this study, we investigated global and gene-specific DNA methylation profiles in a group of 20 long-term (mean 14.5 years) exposed, nanocomposite, research workers and in 20 controls. Both groups were sampled twice/day (pre-shift and post-shift) in September 2018. We applied Infinium Methylation Assay, using the Infinium MethylationEPIC BeadChips with more than 850,000 CpG loci, for identification of the DNA methylation pattern in the studied groups. Aerosol exposure monitoring, including two nanosized fractions, was also performed as proof of acute NP exposure. The obtained array data showed significant differences in methylation between the exposed and control groups related to long-term exposure, specifically 341 CpG loci were hypomethylated and 364 hypermethylated. The most significant CpG differences were mainly detected in genes involved in lipid metabolism, the immune system, lung functions, signaling pathways, cancer development and xenobiotic detoxification. In contrast, short-term acute NP exposure was not accompanied by DNA methylation changes. In summary, long-term (years) exposure to NP is associated with DNA epigenetic alterations.

Prediction of irinotecan toxicity in metastatic colorectal cancer patients based on machine learning models with pharmacokinetic parameters

Irinotecan (CPT-11) is a drug used against a wide variety of tumors, which can cause severe toxicity, possibly leading to the delay or suspension of the cycle, with the consequent impact on the prognosis of survival. The main goal of this work is to predict the toxicities derived from CPT-11 using artificial intelligence methods. The data for this study is conformed of 53 cycles of FOLFIRINOX, corresponding to patients with metastatic colorectal cancer. Supported by several demographic data, blood markers and pharmacokinetic parameters resulting from a non-compartmental pharmacokinetic study of CPT-11 and its metabolites (SN-38 and SN-38-G), we use machine learning techniques to predict high degrees of different toxicities (leukopenia, neutropenia and diarrhea) in new patients. We predict high degree of leukopenia with an accuracy of 76%, neutropenia with 75% and diarrhea with 91%. Among other variables, this study shows that the areas under the curve of CPT-11, SN-38 and SN-38-G play a relevant role in the prediction of the studied toxicities. The presented models allow to predict the degree of toxicity for each cycle of treatment according to the particularities of each patient.

Pharmacokinetics and Drug Metabolism in Canada: The Current Landscape-A Summary of This Indispensable Special Issue

Canadian Pharmaceutical Scientists have a rich history of groundbreaking research in pharmacokinetics and drug metabolism undertaken primarily throughout its Pharmacy Faculties and within the Pharmaceutical and Biotechnology industry.[...].