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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

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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...
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Effect of Hepatic Disease on Pharmacokinetics: Drug Dosing and Hepatic Blood Flow01:26

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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...
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Related Experiment Video

Updated: Jan 9, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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High-Throughput Screening Using the Self-Controlled Tree-Based Scan Statistic to Identify Medications Associated With

Vincent Lo Re1,2, Craig W Newcomb2, Dean M Carbonari2

  • 1Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Pharmacoepidemiology and Drug Safety
|December 5, 2025
PubMed
Summary
This summary is machine-generated.

This study used tree-based scan statistics on real-world data to identify drugs linked to severe acute liver injury (ALI). Findings highlight potential hepatotoxic medications for further investigation in pharmacoepidemiology research.

Keywords:
acute liver injurydrug‐induced liver injuryhepatotoxicityhigh‐throughput screeningtree‐based scan statistics

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Area of Science:

  • Pharmacovigilance
  • Real-world data analysis
  • Hepatotoxicity detection

Background:

  • Traditional identification of drug-induced liver injury relies on case reports.
  • High-throughput screening of real-world data offers a scalable approach to detect hepatotoxicity signals.

Purpose of the Study:

  • To apply tree-based scan statistics to real-world data for identifying drugs associated with hospitalization for severe acute liver injury (ALI).
  • To conduct separate analyses for patients without liver/biliary disease and those with chronic liver disease (CLD).

Main Methods:

  • A self-controlled case-crossover design was implemented using Veterans Health Administration data (2000-2023).
  • Conditional Bernoulli tree-based scan statistics were used to identify potential drug-ALI associations (p < 0.3).
  • Analyses were performed on patients hospitalized for laboratory-confirmed severe ALI.

Main Results:

  • Among patients without liver/biliary disease, associated drugs included acid-suppressives (ranitidine, omeprazole) and antiemetics (ondansetron).
  • In patients with chronic liver disease (CLD), associated drugs included diuretics (spironolactone, furosemide), antiemetics, and analgesics (morphine, oxycodone, fentanyl).
  • Specific antibiotics and chemotherapy agents were also identified in both patient groups.

Conclusions:

  • High-throughput screening with tree-based scan statistics effectively detected potentially hepatotoxic drugs.
  • These findings warrant further investigation in pharmacoepidemiology studies to confirm drug safety signals.