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Related Concept Videos

Effect of Hepatic Disease on Pharmacokinetics: Pathophysiologic Assessment and Liver Function Test01:22

Effect of Hepatic Disease on Pharmacokinetics: Pathophysiologic Assessment and Liver Function Test

In clinical practice, the direct measurement of hepatic blood flow to evaluate liver function presents significant challenges due to the intricate and specialized nature of the necessary techniques. Consequently, healthcare professionals often rely on empirical estimates derived from thorough patient examinations and liver function tests to gauge liver health. Among the tools at their disposal, the Child–Pugh and MELD scoring systems stand out for their ability to categorize and assess the...
Effect of Hepatic Disease on Pharmacokinetics: Drug Dosing and Hepatic Blood Flow01:26

Effect of Hepatic Disease on Pharmacokinetics: Drug Dosing and Hepatic Blood Flow

Chronic liver disease significantly impacts drug metabolism due to alterations in hepatic blood flow and enzyme accessibility. This disruption affects the body's pharmacokinetics—the movement and processing of drugs within the system. Key enzymes crucial for metabolizing medications become less accessible, changing how drugs are processed and utilized. Furthermore, liver disease influences the synthesis of plasma proteins, such as albumin and globulins, which play critical roles in drug binding...

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Related Experiment Video

Updated: Jul 4, 2026

Human Liver Microphysiological System for Assessing Drug-Induced Liver Toxicity In Vitro
11:06

Human Liver Microphysiological System for Assessing Drug-Induced Liver Toxicity In Vitro

Published on: January 31, 2022

Assessing chronic liver toxicity based on relative gene expression data.

Kedar Kulkarni1, Peter Larsen, Andreas A Linninger

  • 1Laboratory for Product and Process Design, Department of Chemical and Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA.

Journal of Theoretical Biology
|July 1, 2008
PubMed
Summary

Quantifying drug-induced liver damage is challenging. A new Toxicologic Prediction Network (TPN) model uses gene expression data to predict chronic hepatotoxicity in rats, improving risk assessment.

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Cell Type-specific Gene Expression Profiling in the Mouse Liver
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Cell Type-specific Gene Expression Profiling in the Mouse Liver

Published on: September 17, 2019

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Last Updated: Jul 4, 2026

Human Liver Microphysiological System for Assessing Drug-Induced Liver Toxicity In Vitro
11:06

Human Liver Microphysiological System for Assessing Drug-Induced Liver Toxicity In Vitro

Published on: January 31, 2022

Cell Type-specific Gene Expression Profiling in the Mouse Liver
10:06

Cell Type-specific Gene Expression Profiling in the Mouse Liver

Published on: September 17, 2019

Area of Science:

  • Toxicology
  • Computational Biology
  • Pharmacology

Background:

  • Assessing chronic toxicity of hepatotoxic drugs is time-consuming using traditional animal studies.
  • Current methods lack quantitative approaches to link gene expression changes to long-term toxic effects.
  • There is a need for predictive models to evaluate drug safety and risk efficiently.

Purpose of the Study:

  • To introduce the Toxicologic Prediction Network (TPN), a novel mathematical model for assessing chronic hepatotoxicity.
  • To correlate subchronic hepatic gene expression data in rats with potential chronic toxicological outcomes.
  • To provide a quantitative method for estimating phenotypical exposure risks associated with drug compounds.

Main Methods:

  • Development of a directed graph model representing interactions between drugs, genes, and hepatotoxicity.
  • Utilizing a knowledge-based mathematical model to estimate specific phenotypical risks (e.g., toxic hepatopathy, fatty liver, tumors).
  • Employing an inversion problem to determine network interaction strengths by minimizing discrepancies in gene expression and toxicity data.

Main Results:

  • The TPN model successfully inferred chronic health risks of halogenated aromatic hydrocarbons from subchronic gene expression data in a case study.
  • Demonstrated the model's capability in estimating the toxicological impact of novel drugs and drug mixtures.
  • Showcased the TPN's utility in optimizing drug formulations for maximum efficacy and minimal adverse effects.

Conclusions:

  • The Toxicologic Prediction Network (TPN) offers a powerful quantitative approach to predict chronic hepatotoxicity from gene expression data.
  • This model can accelerate drug development by enabling early toxicity assessments and formulation optimization.
  • TPN predictions in animals may offer future relevance for human risk assessment, enhancing drug safety evaluations.