In Silico Re-Optimization of Atezolizumab Dosing Using Population Pharmacokinetic Simulation and Exposure–Response Simulation

Cody J. Peer*, Keith T. Schmidt, Oluwatobi Arisa, William J. Richardson, Koosha Paydary, Daniel A. Goldstein, James L. Gulley, William D. Figg, Mark J. Ratain

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Atezolizumab, a humanized monoclonal antibody against programmed cell death ligand 1 (PD-L1), was initially approved in 2016, around the same time that the sponsor published the minimum serum concentration to maintain the saturation of receptor occupancy (6 μg/mL). The initially approved dose regimen of 1200 mg every 3 weeks (q3w) was subsequently modified to 840 mg q2w or 1680 mg q4w through pharmacokinetic simulations. Yet, each standard regimen yields steady-state trough concentrations (CMIN,SS) far exceeding (≈ 40-fold) the stated target concentration. Additionally, the steady-state area under the plasma drug concentration–time curve (AUCSS) at 1200 mg q3w was significantly (P =.027) correlated with the probability of adverse events of special interest (AESIs) in patients with non-small cell lung cancer (NSCLC) and, coupled with excess exposure, this provides incentive to explore alternative dose regimens to lower the exposure burden while maintaining an effective CMIN,SS. In this study, we first identified 840 mg q6w as an extended-interval regimen that could robustly maintain a serum concentration of 6 μg/mL (≥99% of virtual patients simulated, n = 1000), then applied this regimen to an approach that administers 2 “loading doses” of standard-interval regimens for a future clinical trial aiming to personalize dose regimens. Each standard dose was simulated for 2 loading doses, then 840 mg q6w thereafter; all yielded cycle-7 CMIN,SS values of >6 μg/mL in >99% of virtual patients. Further, the AUCSS from 840 mg q6w resulted in a flattening (P =.63) of the exposure–response relationship with adverse events of special interest (AESIs). We next aim to verify this in a clinical trial seeking to validate extended-interval dosing in a personalized approach using therapeutic drug monitoring.

Original languageEnglish
Pages (from-to)672-680
Number of pages9
JournalJournal of Clinical Pharmacology
Volume63
Issue number6
DOIs
StatePublished - Jun 2023
Externally publishedYes

Keywords

  • clinical trials
  • immunopharmacology
  • modeling & simulation
  • oncology
  • population pharmacokinetics

Fingerprint

Dive into the research topics of 'In Silico Re-Optimization of Atezolizumab Dosing Using Population Pharmacokinetic Simulation and Exposure–Response Simulation'. Together they form a unique fingerprint.

Cite this