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Population pharmacokinetic modeling of molibresib and its active metabolites in patients with solid tumors: A

Anu Shilpa Krishnatry1, Alexander Voelkner2, Arindam Dhar3

  • 1Clinical Pharmacology Modelling and Simulation, GlaxoSmithKline, Collegeville, PA, USA.

CPT: Pharmacometrics & Systems Pharmacology
|May 6, 2021
PubMed

Insights

Molibresib, a novel bromodomain and extraterminal (BET) protein inhibitor, shows autoinduction of metabolism in patients with advanced solid tumors. A population pharmacokinetic model was developed to describe its behavior and inform future dosing strategies.

Area of Science:

  • Pharmacology
  • Oncology
  • Drug Metabolism

Background:

  • Molibresib (GSK525762) is an investigational oral small-molecule inhibitor targeting bromodomain and extraterminal (BET) proteins for advanced solid tumors.
  • Initial clinical evaluation in a first-in-human study (BET115521) involved dose escalation and expansion cohorts across various solid tumor types.

Purpose of the Study:

  • To develop a semimechanistic liver-compartment population pharmacokinetic (PK) model for molibresib and its active metabolite composite (GSK3529246).
  • To characterize the PK profile, including autoinduction of metabolism, and identify influencing covariates.

Main Methods:

  • Utilized PK data from the BET115521 first-in-human study.
  • Developed a semimechanistic liver-compartment population PK model.
  • Performed covariate analysis to assess factors affecting PK parameters.

Main Results:

  • The developed PK model adequately described the autoinduction of molibresib clearance and the PK of molibresib and GSK3529246.
  • Body weight significantly impacted the volume of distribution for both molibresib and GSK3529246.
  • Elevated aspartate aminotransferase levels were associated with reduced GSK3529246 clearance.

Conclusions:

  • The PK model successfully captures molibresib autoinduction and metabolite PK.
  • The model provides a basis for simulating patient exposures and optimizing future dosing strategies.
  • Understanding PK variability is crucial for effective exposure-response analyses in clinical development.

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