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Semiphysiologically Based Pharmacokinetic Model of Leflunomide Disposition in Rheumatoid Arthritis Patients
A M Hopkins1, M D Wiese2, S M Proudman3
1University of South Australia, Australian Centre for Pharmacometrics, School of Pharmacy and Medical Sciences Adelaide, South Australia, Australia ; University of South Australia, Sansom Institute for Health Research, School of Pharmacy and Medical Sciences Adelaide, South Australia, Australia.
CPT: Pharmacometrics & Systems Pharmacology
|July 31, 2015
Summary
A new pharmacokinetic model explains the high variability in teriflunomide (active metabolite) levels in rheumatoid arthritis (RA) patients. Fat-free mass and liver function significantly impact these concentrations.
Area of Science:
- Pharmacokinetics and Pharmacodynamics
- Translational Pharmacology
- Rheumatology
Background:
- Leflunomide administration leads to teriflunomide, the active metabolite, with significant interindividual variability in plasma concentrations.
- Previous models exploring this variability were simplistic, often focusing only on total teriflunomide concentrations.
- Factors like enterohepatic recycling, protein binding, and genetic variations (CYP1A2, CYP2C19, ABCG2) are suspected contributors to this variability.
Purpose of the Study:
- To develop and apply a semiphysiologically based pharmacokinetic (semi-PBPK) population model to investigate teriflunomide concentration variability.
- To evaluate the influence of enterohepatic recycling, protein binding, and genetic polymorphisms on teriflunomide pharmacokinetics.
- To identify demographic and genotypic covariates affecting both total and free teriflunomide concentrations in rheumatoid arthritis patients.
Main Methods:
- Development of a 15-compartment semi-PBPK population model using total and free teriflunomide concentrations from rheumatoid arthritis patients.
- Literature data on teriflunomide concentrations after leflunomide or teriflunomide administration were incorporated.
- Screening of demographic (e.g., fat-free mass) and genotypic covariates (e.g., CYP1A2, CYP2C19, ABCG2) to assess their impact on model predictions.
Main Results:
- The developed semi-PBPK model successfully predicted both total and free teriflunomide concentrations.
- Fat-free mass and liver function (ALT levels) were identified as significant covariates improving prediction accuracy.
- Enterohepatic recycling, protein binding, and genetic variability did not significantly improve the model's predictive performance beyond demographic factors.
Conclusions:
- The semi-PBPK model provides a robust framework for evaluating multiple covariates influencing teriflunomide pharmacokinetics.
- Fat-free mass and liver function are key determinants of teriflunomide concentration variability in rheumatoid arthritis patients.
- This advanced modeling approach surpasses previous simplistic models in assessing factors affecting teriflunomide exposure.
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