Population pharmacokinetic analysis of sorafenib in patients with solid tumours

Lokesh Jain1, Sukyung Woo, Erin R Gardner

  • 1Clinical Pharmacology Program Medical Oncology Branch, National Cancer Institute, NIH, Bethesda, MD 20892, USA.

Abstract

Insights

This study characterized sorafenib pharmacokinetics (PK) in cancer patients, revealing no significant impact from demographic or genetic factors on drug disposition. The developed model aids in optimizing sorafenib dosing and understanding exposure-response relationships.

Area of Science:

  • Pharmacology
  • Oncology
  • Clinical Pharmacy

Background:

  • Sorafenib is a multikinase inhibitor approved for renal cell carcinoma (RCC) and hepatocellular carcinoma (HCC).
  • Sorafenib exhibits high inter-individual pharmacokinetic variability, likely due to limited solubility and enterohepatic recycling (EHC).

Purpose of the Study:

  • To characterize sorafenib pharmacokinetics (PK) in solid tumor patients.
  • To assess the influence of demographic, clinical, and pharmacogenetic factors on sorafenib disposition.

Main Methods:

  • Population PK analysis using NONMEM in 111 patients receiving sorafenib (200 or 400 mg BID).
  • Assessment of covariates including bodyweight, BSA, age, gender, liver/kidney function, and CYP3A4/CYP3A5/UGT1A9 genotypes.
  • Model validation through visual predictive checks and nonparametric bootstrap analysis.

Main Results:

  • A one-compartment model with four transit absorption compartments and EHC accurately described sorafenib PK.
  • Baseline bodyweight was a significant covariate for volume of distribution, explaining 4% of inter-individual variability.
  • Key PK parameters were estimated, including clearance, volume, absorption time, and fraction undergoing EHC.

Conclusions:

  • The population PK model aligns with known characteristics of oral sorafenib, including delayed absorption and EHC.
  • No clinically significant covariates were identified, suggesting limited impact of tested factors on sorafenib PK.
  • The model can support simulations for alternative dosing strategies and exposure-response analyses.

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