{'fraction_non_alphanumeric': 0.025309639203015617, 'fraction_numerical': 0.03338718362950996, 'mean_word_length': 6.2578125, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Characterization of the Ontogeny of Hepatic UDP‐Glucuronosyltransferase Enzymes Based on Glucuronidation Activity Measured in Human Liver Microsomes

An understanding of the postnatal development of hepatic UDP‐glucuronosyltransferase (UGT) enzymes is required for accurate prediction of the age‐dependent changes in pharmacokinetics of many drugs used in children. However, the maturation rate of hepatic UGT isoforms remains a major knowledge gap. This study aimed to establish the age‐associated changes in glucuronidation activity of 10 major hepatic UGT isoforms in humans, namely, UGT1A1, UGT1A3, UGT1A4, UGT1A6, UGT1A9, UGT2B4, UGT2B7, UGT2B10, UGT2B15, and UGT2B17. Human liver microsomes from pediatric and adult donors were incubated under optimized incubation conditions to assess the activity rates of hepatic UGT isoforms using a panel of 19 in vitro UGT probe substrates and clinically used drugs. Statistically strong correlations of glucuronidation activities allowed the ontogeny of UGT1A1, UGT1A4, UGT2B7, UGT2B10, and UGT2B15 to be established using multiple selective UGT substrates and matched human liver microsome samples. The postnatal development of hepatic UGTs is isoform‐dependent using either individual or cross‐correlated selective isoform substrates. Maximal adult activity was reached at different times ranging from within a month (UGT1A1, UGT2B4, UGT2B7, UGT2B10, and UGT2B15), during infancy (UGT1A3, UGT1A4, and UGT1A9), to adolescence (UGT1A6 and UGT2B17). This study provides an extensive characterization of the postnatal ontogeny profiles of hepatic UGT enzymes that are instrumental for predicting drug disposition via in vitro–in vivo extrapolation algorithms and verifying pharmacokinetic predictions against in vivo observations via pediatric physiologically based pharmacokinetic modeling in pediatric patients.