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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...
Hepatitis01:25

Hepatitis

Hepatitis is an inflammatory condition of the liver most commonly caused by hepatotropic viruses (A–E), though non-infectious causes such as alcohol and drugs also exist.Hepatitis AHepatitis A virus (HAV) is a non-enveloped RNA virus of the Picornaviridae family. It is primarily transmitted via the fecal-oral route, typically through ingestion of contaminated food or water. After ingestion, HAV enters the bloodstream through the oropharynx or intestinal epithelium and reaches the liver. The...

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Related Experiment Video

Updated: May 12, 2026

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Hepatitis B In Silico Trials Capture Functional Cure, Indicate Mechanistic Pathways, and Suggest Prognostic Biomarker

Javiera Cortés-Ríos1, Tianjing Ren1, Nathan Hanan2

  • 1Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, State University of New York at Buffalo, USA.

Clinical Pharmacology and Therapeutics
|May 30, 2025
PubMed
Summary

In silico trials for chronic Hepatitis B accelerate drug development by simulating patient responses to therapies. This virtual approach accurately predicts functional cure and identifies key biomarkers for treatment success.

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Area of Science:

  • Computational biology
  • Immunology
  • Hepatology

Background:

  • Chronic Hepatitis B (CHB) treatment requires better predictive models for drug development.
  • Current clinical trials are lengthy and costly, necessitating innovative approaches.

Purpose of the Study:

  • To develop and validate a virtual trial framework for CHB using a mechanistic mathematical model.
  • To simulate clinical protocols and patient characteristics for predicting treatment outcomes.

Main Methods:

  • Developed a mechanistic mathematical model for CHB.
  • Simulated clinical trials with standard-of-care therapies (nucleos(t)ide analogs, pegylated interferon).
  • Utilized machine learning on synthetic virology biomarker datasets to predict functional cure.

Main Results:

  • The model accurately simulated functional cure and complex clinical observations.
  • Identified enhanced cytotoxic immunity and serum-alanine transaminase increases as potential response biomarkers.
  • A higher baseline Hepatitis B surface antigen correlated with lower treatment response due to an immune ceiling.

Conclusions:

  • In silico trials can accelerate CHB drug development and generate mechanistic hypotheses.
  • Virtual patients and machine learning can predict functional cure with high accuracy (~95%).
  • Biomarker signatures identified may aid in predicting treatment outcomes in CHB.