Population Pharmacokinetics of Brigatinib in Healthy Volunteers and Patients With Cancer

Abstract

Background and objectives Brigatinib is an oral tyrosine kinase inhibitor approved in multiple countries for the treatment of patients with anaplastic lymphoma kinase-positive metastatic non-small cell lung cancer who have progressed on or are intolerant to crizotinib. We report a population pharmacokinetic model-based analysis for brigatinib. Methods Plasma concentration–time data were collected from 442 participants (105 healthy volunteers and 337 patients with cancer) who received single or multiple doses of oral brigatinib in one of five trials. Data were analyzed using non-linear mixed-effects modeling (NONMEM software version 7.3). Results Brigatinib plasma concentrations were best described by a three-compartment model with a transit compartment input (number of transit compartments = 2.35; mean transit time = 0.9 h). The final model included albumin as a covariate on apparent clearance. None of the additional covariates examined, including sex, age, race, body weight, mild or moderate renal impairment, total bilirubin, aspartate aminotransferase, and alanine aminotransferase, were found to meaningfully explain variability in apparent clearance, suggesting that no dose adjustment is required based on these covariates. Conclusions Results from these population pharmacokinetic analyses informed the prescribing guidance for patients with mild or moderate renal impairment in the US Prescribing Information and the European Summary of Product Characteristics for brigatinib. Electronic supplementary material The online version of this article (10.1007/s40262-020-00929-4) contains supplementary material, which is available to authorized users.

Brigatinib Dose Rationale in Anaplastic Lymphoma Kinase–Positive Non‐Small Cell Lung Cancer: Exposure–Response Analyses of Pivotal ALTA Study

“…The VPC showed good agreement between the predicted and observed rates of grade ≥ 2 rash ( Figure ). Model‐based simulations for a typical patient with albumin levels (the only predictor of brigatinib PK in the population PK model 9 ) set to 38 g/L estimated the predicted probability of experiencing a grade ≥ 2 rash was 5.92% (95% CI, 3.38–10.17) for a typical patient taking brigatinib 90 mg q.d. and 9.53% (95% CI, 6.72–13.35) for a patient taking brigatinib 180 mg q.d.…”

“…To better understand the relationship between brigatinib exposure and efficacy outcomes, exposure–response analyses were performed using exposure metrics derived from a published population PK model 9 and pooled efficacy data from 279 patients with ALK positive NSCLC treated in two clinical trials 3,4 . Use of a static metric (i.e., time‐averaged brigatinib exposure) in exposure–response analyses did not permit estimation of an exposure–efficacy relationship for PFS that could explain the dose–PFS relationship observed in the ALTA study.…”

“…The population PK model 9 was used to derive brigatinib exposure metrics over the treatment course. Individual daily time‐varying brigatinib exposures were derived using simulated rich PK profiles based on actual dosing history and individual post hoc (empirical Bayes) PK parameter estimates from the population PK model.…”

“…6,8 A population pharmacokinetics (PK) model for brigatinib was developed based on data from phase I and II trials. 9 The PK of brigatinib in both healthy volunteers and patients with cancer was best described by a three-compartment model with a transit compartment for absorption. Albumin was the only predictor of apparent oral clearance.…”

A Novel Integrated Pharmacokinetic-Pharmacodynamic Model to Evaluate Combination Therapy and Determine In Vivo Synergism

“…Rather, sorafenib systemic exposure was revealed to decrease over time in patients with hepatocellular carcinoma ( Arrondeau et al, 2012 ). Actually, differences in PK properties between distinct populations, if any, are generally associated with the variations in albumin levels, liver, or kidney functions ( Cheeti et al, 2013 ; Lacy et al, 2018 ; Gupta et al, 2020 ), which may be taken into consideration when developing PK models for particular populations. Ultimately, physiologically based PK-modeling approaches may be used toward translating the PK-PD relationship across species or populations, including the prediction of combined drug effects in patients with cancer ( Cheeti et al, 2013 ; Ande et al, 2018 ; Garcia-Cremades et al, 2019 ).…”