ArticleDrug metabolism and disposition: the biological fate of chemicals2024
Age-Dependent Changes in Cytochrome P450 Abundance and Composition in Human Liver.
Article in Drug metabolism and disposition: the biological fate of chemicals, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Safety signals for drug-related respiratory depression: a disproportionality analysis of the FDA Adverse Event Reporting System (FAERS).Journal of thoracic disease · 2026Article
- Characterization of Lysosomal Hydrolases and Transporters and Their Age-Dependent Variability: Relevance to Drug Metabolism and Transport of Small Molecule and Biologic Drugs.Clinical pharmacology and therapeutics · 2026Article
- Article
- Development of an Empirical Approach for the Prediction of Cytochrome P450-Based Drug-Drug Interactions in Pediatric Patients.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Optimizing Pediatric Drug Therapy: Pediatric Pharmacokinetics and Administration Routes.The journal of pediatric pharmacology and therapeutics : JPPT : the official journal of PPAG · 2026Article
- Hepatobiliary disorders associated with triazole antifungal agents: a disproportionality and Bayesian analysis based on the FAERS database.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Pharmacogenetics of antidepressant response: a focused review on CYP2C19, CYP2D6, SLC6A4, and HTR2A polymorphisms.Frontiers in pharmacology · 2026Review
- Developmental age and clinical illness driveFrontiers in pharmacology · 2026Article
- Severe acute respiratory syndrome coronavirus 2-mediated dysregulation of drug processing genes is dependent on pathogenic variants, target site of infection, age, and sex.Drug metabolism and disposition: the biological fate of chemicals · 2025Article
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2 authors.
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Abstract
Cytochrome P450 (CYP) superfamily represents the major drug-metabolizing enzymes responsible for metabolizing over 65% of therapeutic drugs, including those for pediatric use. CYP-ontogeny based physiologically based pharmacokinetic (PBPK) modeling has emerged as useful approach to mechanistically extrapolate adult pharmacokinetic data to children. However, these models integrate physiological differences in the pediatric population including age-dependent differences in the abundances of CYP enzymes. Conventionally, developmental changes in CYP enzymes have been reported using protein abundance and activity data from subcellular fractions such as microsomes, which are prone to high technical variability. Similarly, the available pediatric pharmacokinetic data suffer from the lack of specific CYP substrates, especially in younger children. In the present study, we used viable hepatocytes from 50 pediatric (age, 1 day-18 years) and 8 adult human donors and carried out global proteomics-based quantification of all major hepatic CYP enzymes, including orphan enzymes that have not been studied previously. While CYPs 2B6, 3A5, 4A11, 4F3, and 4V2 did not show a significant association with age, all other quantified isoforms either increased or decreased with age. CYPs 1A2, 2C8, 2C18, and 2C19 were absent or barely detected in the neonatal group, while CYP3A7 was the highest in this group. The >1 to 2 years age group showed the highest total abundance of all CYP enzymes. The age-dependent differences in CYP enzymes reported in this study can be used to develop ontogeny-based PBPK models, which in turn can help improve pediatric dose prediction based on adult dosing, leading to safer drug pharmacology in children. SIGNIFICANCE STATEMENT: We quantified the age-dependent differences in the abundances of hepatic CYP enzymes using a large set of viable pediatric and adult hepatocytes using quantitative global proteomics. We report for the first time the ontogeny in the abundance of CYP enzymes in human hepatocytes, especially, orphan CYPs 20A1, 27A1, 51A1, 7B1, and 8B1 and CYP4 subfamily of enzymes. Our study provides important data about CYP ontogeny that can be used for the better prediction of pediatric pharmacokinetics using physiologically based pharmacokinetic modeling.
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