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Pharmacogenetics of Drug Metabolism: Overview01:27

Pharmacogenetics of Drug Metabolism: Overview

Genetic polymorphism in drug metabolism is crucial to the inter-individual variability observed in drug responses. Drug metabolism primarily involves the chemical modification of drugs and other xenobiotics to enhance their elimination by increasing their polarity. Two main classes of enzymes mediate this biotransformation process: Phase I enzymes, primarily cytochrome P450s, catalyze oxidation and reduction reactions, while other enzymes, such as esterases, mediate hydrolysis, and Phase II...
Pharmacogenetic Phenotypes: Alterations in Pharmacokinetics, Drug Targets and Biologic Milieu01:29

Pharmacogenetic Phenotypes: Alterations in Pharmacokinetics, Drug Targets and Biologic Milieu

Genetic variations significantly influence drug response through pharmacokinetics, receptor interactions, and biologic milieu modifications. Pharmacokinetic alterations impact drug metabolism and clearance, affecting efficacy and toxicity. Variants in drug-metabolizing enzymes, such as CYP2C9 and CYP2C19, alter drug activation and elimination. For example, CYP2C9 loss-of-function variants require lower warfarin doses to prevent excessive bleeding, while CYP2C19 variants reduce clopidogrel...
Overview of Metabolism01:40

Overview of Metabolism

Living cells constantly carry out various chemical reactions which are necessary for their proper functioning. These reactions are interlinked to one another via multiple pathways. The collection of these chemical reactions is known as metabolism.
Plant Metabolism
Sunlight, the primary source of energy in plants, is first absorbed by the chlorophyll pigments present in their leaves. Plants then use this energy to carry out photosynthesis, where water is oxidized into oxygen and carbon dioxide...
Dosage Regimen: Individualization01:24

Dosage Regimen: Individualization

Individualization in dosing regimens is the customization of medication doses for individual patients. Its necessity arises from the goal of maximizing therapeutic benefits while minimizing risks. This approach is pivotal because human responses to drugs can vary widely; what is effective for one person may be inadequate or excessive for another. Interpatient (intersubject) variability refers to differences in drug responses between individuals, while intrapatient (intrasubject) variability...
Regulation of Metabolism01:19

Regulation of Metabolism

Cellular needs and conditions vary from cell to cell and change within individual cells over time. For example, the required enzymes and energetic demands of stomach cells are different from those of fat storage cells, skin cells, blood cells, and nerve cells. Furthermore, a digestive cell works much harder to process and break down nutrients during the time that closely follows a meal compared with many hours after a meal. As these cellular demands and conditions vary, so do the amounts and...
Factors Affecting Drug Biotransformation: Biological01:19

Factors Affecting Drug Biotransformation: Biological

Biological factors significantly impact drug metabolism, influencing drug clearance, efficacy, and potential toxicity.
Species differences: Variations in enzyme systems across species can cause disparities in drug metabolism. For instance, humans may metabolize certain drugs faster than rodents, altering therapeutic effects.
Strain differences: Genetic variations within a species can result in differing enzyme activity, impacting drug response and toxicity. For example, some mouse strains may...

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Related Experiment Video

Updated: Jun 18, 2026

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
11:02

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Published on: November 29, 2024

Metabolism, variability and risk assessment.

J L C M Dorne1

  • 1University of Southampton, Clinical Pharmacology Group, Institute of Human Nutrition, School of Medicine, Southampton, UK. jean-lou.dorne@efsa.europa.eu

Toxicology
|November 26, 2009
PubMed
Summary
This summary is machine-generated.

New uncertainty factors are proposed for toxicokinetics, improving risk assessment for non-genotoxic carcinogens. These factors better account for human variability in metabolism and excretion, enhancing safety evaluations.

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Area of Science:

  • Toxicology and Risk Assessment
  • Pharmacokinetics and Metabolism
  • Environmental Health

Background:

  • Acceptable/Tolerable Daily Intakes (ADI/TDI) for non-genotoxic carcinogens rely on uncertainty factors (UF) to account for interspecies differences and human variability.
  • Current UF, including a 100-fold factor and a 3.16 factor for human variability, may not adequately cover genetic polymorphism and age-related differences in toxicokinetics.
  • Physiologically based models and chemical-specific adjustment factors offer refinements to traditional UF in risk assessment.

Purpose of the Study:

  • To derive pathway-related uncertainty factors for phase I, phase II metabolism, and renal excretion based on human toxicokinetic variability data.
  • To develop and validate Latin hypercube (Monte Carlo) models for predicting toxicokinetic variability and UF for compounds with multiple metabolic routes.
  • To explore novel methods for improving chemical mixture risk assessment within the EU NOMIRACLE project.

Main Methods:

  • Meta-analyses of toxicokinetic variability data from therapeutic drugs metabolized by single pathways in human subgroups.
  • Derivation of pathway-related lognormal variability for each metabolic route.
  • Development of Latin hypercube (Monte Carlo) models integrating quantitative metabolism data and pathway-related variability.

Main Results:

  • Pathway-related UF indicated that the current UF for toxicokinetics (3.16) is insufficient to address human variability due to genetic polymorphism and age differences.
  • Latin hypercube models provided accurate predictions of toxicokinetic variability and UF for multi-route compounds, with discrepancies attributed to data limitations.
  • Novel methods for chemical mixture risk assessment, including UF harmonization and use of toxicokinetic interaction data, were explored.

Conclusions:

  • Existing uncertainty factors for toxicokinetics require refinement to adequately protect vulnerable populations.
  • Modeling approaches using quantitative metabolism data and pathway-specific variability show promise for predicting toxicokinetic uncertainty.
  • Further research into statistical approaches and toxicokinetic assays is needed to optimize risk assessment for chemicals and mixtures.