A novel approach for estimating ingested dose associated with paracetamol overdose.

Area of Science:

• Pharmacology
• Computational Biology
• Toxicology

Background:

• Paracetamol (acetaminophen) overdose requires accurate dose estimation for effective treatment.
• Current treatment relies on generic nomograms, lacking individualization.
• Tissue-specific pharmacokinetics (PK) and ingested dose are crucial for personalized patient care.

Purpose of the Study:

• To present a novel methodology for estimating tissue-specific paracetamol PK and ingested dose.
• To utilize a computational framework with standard serum paracetamol measurements.
• To provide health care providers with data for individualized overdose management.

Main Methods:

• Development and evaluation of a physiologically-based pharmacokinetic (PBPK) model using human PK data.
• Application of Bayesian inference for parameter and dose estimation, incorporating inter-study variability.
• Calculation of uncertainty in model outputs for robust predictions.

Main Results:

• PBPK model simulations closely matched experimental blood paracetamol concentrations across various dosing conditions.
• Predicted administered doses aligned well with clinical and emergency setting PK data.
• The platform determined optimal blood sampling times and assessed the role of paracetamol conjugates in dose estimation.

Conclusions:

• The developed computational framework enables personalized serum and liver pharmacokinetic profiles and dose estimates.
• This approach moves beyond generic nomograms for paracetamol overdose management.
• Individualized treatment and follow-up plans can be informed by patient-specific data generated by the framework.