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Application of physiologically‐based pharmacokinetic modeling in hepatic impairment populations (1064.9)
Author(s) -
Sager Jennifer,
Hsu Matt,
Isoherranen Nina,
Wienkers Larry,
Wahlstrom Jan,
Foti Robert
Publication year - 2014
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.28.1_supplement.1064.9
Subject(s) - physiologically based pharmacokinetic modelling , pharmacokinetics , pharmacology , clearance , drug , cyp3a4 , drug metabolism , clinical pharmacology , computational biology , medicine , chemistry , biology , metabolism , cytochrome p450 , urology
Diagnosis of hepatic impairment (HI) is associated with a reduction in hepatocyte expression, a decrease in albumin and α‐1‐acid glycoprotein and a reduction in renal function, the combination of which can result in alteration of the pharmacokinetic (PK) properties of a drug. Therefore, regulatory agencies such as the U.S. Food and Drug Administration often require the assessment of PK parameters in HI populations. These studies can entail single or multiple‐dose approaches and must be of sufficient duration to capture the terminal half‐life of the drug, a parameter which may be extended in HI populations. The use of physiologically‐based pharmacokinetic (PBPK) modeling, a technique combining demographic and genetic factors, anatomical and physiological parameters and drug‐specific properties, can aid in the prediction of PK parameters and the design of clinical trials in HI populations. To that end, the utility of PBPK modeling in predicting unique clinical scenarios in HI populations was evaluated for a number of compounds including sensitive P450 substrates and drugs cleared by multiple P450s. Drug interactions, enantioselective PK changes and effects on hepatic versus intestinal CYP3A4 metabolism due to HI were predicted. Results suggest that PBPK modeling is a useful tool for predicting changes in PK parameters under multiple conditions and for contributing to the design of HI clinical trials.