Population Pharmacokinetics, Pharmacogenomics, and Adverse Events of Osimertinib and its Two Active Metabolites, AZ5104 and AZ7550, in Japanese Patients with Advanced Non-small Cell Lung Cancer: a Prospective Observational Study

Abstract

Background: Potential novel strategies for adverse event (AE) management of osimertinib therapy, including therapeutic drug monitoring and the use of biomarkers, have not yet been fully investigated. This study aimed to evaluate (1) the relationship between exposure to osimertinib, especially its active metabolites (AZ5104 and AZ7550), and AEs, and (2) the relationship between germline polymorphisms and AEs. Methods: We conducted a prospective, longitudinal observational study of 53 patients with advanced non-small cell lung cancer receiving osimertinib therapy from February 2019 to April 2022. A population pharmacokinetic model was developed to estimate the area under the serum concentration–time curve from 0 to 24 h (AUC0–24) of osimertinib and its metabolites. Germline polymorphisms were analyzed using TaqMan® SNP genotyping and CycleavePCR® assays. Results: There was a significant association between the AUC0–24 of AZ7550 and grade ≥ 2 paronychia (p = 0.043) or anorexia (p = 0.011) and between that of osimertinib or AZ5104 and grade ≥ 2 diarrhea (p = 0.026 and p = 0.049, respectively). Furthermore, the AUC0–24 of AZ5104 was significantly associated with any grade ≥ 2 AEs (p = 0.046). EGFR rs2293348 and rs4947492 were associated with severe AEs (p = 0.019 and p = 0.050, respectively), and ABCG2 rs2231137 and ABCB1 rs1128503 were associated with grade ≥ 2 AEs (p = 0.008 and p = 0.038, respectively). Conclusion: Higher exposures to osimertinib, AZ5104, and AZ7550 and polymorphisms in EGFR, ABCG2, and ABCB1 were related to higher severity of AEs; therefore, monitoring these may be beneficial for osimertinib AE management.

Exploring inter-ethnic and inter-patient variability and optimal dosing of osimertinib: a physiologically based pharmacokinetic modeling approach

“…The simulations conducted in this study strongly demonstrated that albumin level has a substantial impact on the PK variables of OSI as well as EGFRm + inhibition. These findings align well with multiple clinical observations where albumin levels in patients have shown a strong correlation with clinical efficacy ( Brown et al, 2017 ; Yokota et al, 2022 ; Ishikawa et al, 2023 ).…”

“…Variations in f up can result in greater PK variability. Moreover, several clinical studies have demonstrated that the level of plasma albumin, which predominantly binds to OSI ( Food and Drug Administration FDA, 2015 ), can influence the distribution and clearance of OSI in NSCLC patients, and have a statistically significant correlation with the efficacy of OSI in NSCLC patients ( Yokota et al, 2022 ; Ishikawa et al, 2023 ).…”

Comparing the Efficacy of Two Generations of EGFR-TKIs: An Integrated Drug–Disease Mechanistic Model Approach in EGFR-Mutated Lung Adenocarcinoma

“…For this reason, the PBPK models of gefitinib and osimertinib were built using the same equations. Nonetheless, a particularity of osimertinib is that one of its metabolites, AZ5104, also acts as an EGFR-TKI [ 11 ]. The metabolization of osimertinib to AZ5104 in the liver has therefore been modeled, as well as the equations of distribution of AZ5104 from the liver to different tissues and tumors.…”

Integrated PBPK-EO Modeling of Osimertinib: Predicting Pharmacokinetics, Intracranial EGFR Engagement, and Optimal Dosing Strategies in Clinical Settings

“…While ABCB1 CL int,u and EGFR T 0 did not exhibit a signi cant impact EO trough in the sensitivity analysis, further research was still made to evaluate the effect of these modeling parameters on EO trough in BRT. This decision was in uenced by the signi cant impact of ABCB1 activity on OSI exposure observed in clinical studies [14,15] , as well as the association EGFR expression with worse progression in patients [48] .…”