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.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Introduction
Osimertinib, a third-generation epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI), is a first-line treatment for patients with advanced EGFR-positive non-small cell lung cancer (NSCLC) [1]. It has an excellent efficacy and safety profile; however, over 80% and 30% of patients administered osimertinib experience grade ≥ 2 and ≥ 3 adverse events (AEs), respectively [2]. It was initially approved as a second or later-line treatment for patients with acquired EGFR T790M mutation, but the indication was extended to include it as a first-line treatment and adjuvant therapy after tumor resection [3,4,5,6]. Therefore, long-term administration to asymptomatic early-stage patients is expected, increasing the importance of AE management for osimertinib treatment.
Therapeutic drug monitoring (TDM) and the use of biomarkers can be novel strategies for AE management. The potential benefits of TDM in TKI treatments have been suggested [7], and osimertinib treatments may also benefit from TDM, as it has characteristics suitable for TDM: long-term administration is required [8]; a validated bioanalytical method is available [9]; high interindividual variability of exposure with coefficients of variation of 27.6–37.6% is observed [10]; and a correlation between the exposure to osimertinib parent compound and the occurrence of AEs exists [11]. Osimertinib has two active metabolites, AZ5104 and AZ7550, which may be essential compounds for TDM. AZ5104 is produced by cytochrome P450 (CYP) 3A4 through demethylation of osimertinib indole N-methyl, whereas AZ7550 is produced by demethylation of the osimertinib terminal amine [12]. Both active metabolites circulate at 10% exposure of the parent compound. However, AZ5104 has a 15-fold higher potency compared with the parent compound against wild-type EGFR. In contrast, AZ7550 has a similar potency but a longer half-life; therefore, higher accumulation is expected for it than for the parent compound [8, 10]. Thus, TDM of these two active metabolites may be beneficial for AE management, but the relevance of monitoring these compounds has not yet been elucidated.
Some germline polymorphisms in the target EGFR, breast cancer resistance protein (BCRP/ABCG2), drug transporter P-glycoprotein (MDR1/ABCB1), and metabolism-related genes (cytochrome P450 oxidoreductase, POR) are associated with the occurrence of AEs or pharmacokinetics (PK) of the first- and second-generation EGFR-TKIs (gefitinib, erlotinib, and afatinib) [13,14,15,16,17,18,19,20]. These polymorphisms can act as novel biomarkers to predict osimertinib AEs, since the active metabolite of osimertinib has a high potency against wild-type EGFR, is a substrate of ABCG2 and ABCB1, and is metabolized by cytochrome P450, which requires electron transfer via POR [8].
While there have been reports on the association between exposure to osimertinib parent compound and its efficacy/safety [11, 21], no studies have explored the relevance of monitoring the two active metabolites of osimertinib for AE management. Additionally, germline polymorphisms in EGFR, ABCG2, ABCB1, or POR have a significant impact on the AEs of first- and second-generation EGFR-TKIs [13,14,15,16,17,18,19,20], but those of osimertinib have not been investigated.
This study aimed to evaluate (1) the exposure–toxicity relationship and (2) the association of germline polymorphisms with osimertinib AEs to provide evidence for safe treatment and quality-of-life improvement for patients with NSCLC treated with long-term administration of osimertinib.
Methods
Study design and patients
This prospective, longitudinal observational study was designed and conducted at the Ageo Central General Hospital from February 2019 to July 2020 and Keio University Hospital from June 2020 to April 2022. The primary endpoint was the association of exposures to osimertinib, AZ5104, AZ7550, or germline polymorphisms with AE severity. Patients with EGFR-mutation-positive NSCLC aged ≥ 20 years who were orally administered osimertinib (standard dose: 80 mg tablet/day) were included in this study. The inclusion criteria did not restrict the type of EGFR mutation, disease or treatment history, or line of treatment. Patients who were mentally or physically incapable of providing informed consent were excluded from the study. The protocol of this study was reviewed and approved by the ethics committees of Ageo Central General Hospital (Approval No. 564), Keio University School of Medicine (Approval No. 20,200,098), and Keio University Faculty of Pharmacy (Approval No. 210,118–3 and 200,710–1), and written informed consent was obtained from all participants. The study was conducted with adherence to the Declaration of Helsinki.
Data collection
AEs were assessed during hospital stays for 3 months or at three outpatient visits, when blood was collected at 2 months or later after the initial osimertinib administration (onset of most EGFR-TKI AEs are reported to be within 2–4 months after initial administration [22,23,24]). The severity of AEs (Online Resource, Table D1) was scaled according to the Common Toxicity Criteria for Adverse Effects (CTCAE) version v5.0 [25] by pharmacists and physicians.
Serum samples for PK analysis and whole peripheral blood samples for genotyping were opportunistically collected (i.e., leftovers from routine laboratory blood analysis) once every 1–2 months after commencing osimertinib therapy. Patients were asked to determine the time of drug intake. The collected serum and whole peripheral blood samples were stored at − 80 °C until analysis.
The serum concentrations of the osimertinib parent compound, AZ5104, and AZ7550 were analyzed as previously described [9]. Briefly, osimertinib, AZ5104, and AZ7550 were extracted from 100 µL serum using a protein precipitation method and analyzed simultaneously using liquid chromatography–tandem mass spectrometry.
Genotyping
Polymorphisms in EGFR, ABCG2, ABCB1, and POR (Online Resource, Appendix A) were analyzed using TaqMan® probe-based assays (Applied Biosystems, Foster City, CA, USA), whereas the ABCG2 polymorphism (rs2231137) was studied using the CycleavePCR® assay (TaKaRa Bio Inc., Kusatsu, Japan). The detailed genotyping method is provided in the Online Resource (Appendix A).
Exposure–toxicity and pharmacogenomics–toxicity relationship
A population pharmacokinetic (PopPK) model was developed using Phoenix® NLME™ 8.3 software (Certara, Princeton, NJ, USA) to estimate the area under the serum concentration–time curve from 0 to 24 h (AUC0–24) of the osimertinib parent compound, AZ5104, and AZ7550, applied as exposure measures. The detailed method for PopPK model development is provided in the Online Resource (Appendix B).
The worst grade of AE that occurred in each patient scaled using CTCAE was used for the exposure–toxicity relationship analysis. The AUC0–24 was simulated using the developed PopPK model based on individual predicted concentrations at the time closest to the occurrence of AEs. For patients who did not experience AEs, a median of three simulated AUC0–24 from three concentration data points was applied for the analysis.
Germline polymorphism and the worst grade of AE occurring in each patient were analyzed for pharmacogenomics–toxicity relationship analysis.
Statistical analysis
The cut-off date for data collection was April 4, 2022. Continuous variables such as AUC0–24 and laboratory data are presented as median (interquartile range, IQR); estimates of PopPK parameters are presented as mean (standard error, SE). Exposure–toxicity and pharmacogenomics–exposure analyses (comparison of continuous variables) were performed using Mann–Whitney U test. Receiver operating characteristic (ROC) curve analysis was used to evaluate the discrimination potential of AUC0–24 for grade ≥ 2 AEs. Significance of deviation of allele and genotype frequencies from Hardy–Weinberg equilibrium was tested, and pharmacogenomics–toxicity analysis (comparison of categorical variables) was performed using Fisher’s exact test. For the pharmacogenomics association analysis, multiple statistical analyses were performed using additive, recessive, and dominant genetic models. All p-values were two-sided, and statistical significance was set at p < 0.05. IBM® SPSS® statistics version 28.0 (SPSS, Inc., Chicago, IL, USA) was used for all the statistical analyses.
Results
Data collection
Patient characteristics are summarized in Table 1. A total of 302 serum samples for PK analysis, median (IQR) time after administration of 6.3 (2.0–23.6) h, and 53 whole peripheral blood samples for genotyping were collected from 53 patients. The median number of serum samples per patient was five, collected within a median of 5 months. Osimertinib safety was evaluated in 51 patients; we were unable to collect enough safety data from 2 out of 53 patients because of transfer to another hospital and withdrawal of consent. The lymphocytes for one of the patients and creatine phosphokinase for 20 patients were not tested in routine clinical laboratory blood analysis. The most prevalent grade ≥ 2 AE was skin disorders (63%, Online Resource, Table D1). Approximately 25% of the patients experienced severe AEs (grade 3 or leading to dose discontinuation). The patient characteristic significantly associated with severe AEs was the EGFR-TKI treatment line, with higher frequency of severe AEs in patients receiving therapy as first-line treatment (Online Resource, Table D2, p = 0.004). Other characteristics, including sex, age, ECOG PS, and somatic EGFR mutation, were not associated with any grade ≥ 2 or ≥ 3 AEs.
Development and evaluation of the PopPK model
The final PopPK parameters are listed in Table 2. Albumin was identified as a significant covariate for the clearance of the parent compound and AZ5104, whereas body weight (BW) was identified as a significant covariate for the clearance of AZ7550 (Online Resource, Table C1–C3, p < 0.05). The PopPK parameters were estimated using models (1), (2), and (3) for osimertinib, AZ5104, and AZ7550, respectively. The estimated values were close to the mean values calculated from the bootstrap sampling of n = 974 (success rate, 97.4%), n = 1000 (success rate, 100%), and n = 1000 (success rate, 100%), respectively, and all fell within the 95% percentile confidence intervals (Table 2). The detailed results for the development and evaluation of the PopPK model are provided in the Online Resource (Appendix C).
Exposure–toxicity relationship
The median (IQR) values of AUC0–24 at steady state (estimated using the final PopPK model) of the osimertinib parent compound, AZ5104, and AZ7550 were 4278 (3328–5589) ng/mL*h, 414 (309–584) ng/mL*h, and 367 (275–516) ng/mL*h, respectively, regardless of dosage.
There was a significant association between the AUC0–24 of the active metabolite AZ7550 and grade ≥ 2 paronychia or grade ≥ 2 anorexia; AUC0–24 of the osimertinib parent compound or active metabolite AZ5104 and grade ≥ 2 diarrhea; and AUC0–24 of the parent or either of the two active metabolites and grade ≥ 2 increased creatinine. Overall, the AUC0–24 of AZ5104 was significantly associated with any grade ≥ 2 AEs (Fig. 1).
The cut-off AUC0–24 of the parent compound, AZ5104, and AZ7550 to predict grade ≥ 2 AEs were 4938 ng/mL*h, 540 ng/mL*h, and 462 ng/mL*h, respectively, based on the ROC curve. The areas under the curve (sensitivity and specificity) were 0.680 (51% and 90%), 0.705 (42% and 100%), and 0.651 (49% and 90%), respectively (Online Resource, Fig. E1). The frequency of the aforementioned AEs (grade ≥ 2) was higher in patients with ≥ 4938 ng/mL*h parent compound, ≥ 540 ng/mL*h AZ5104, or ≥ 462 ng/mL*h AZ7550 AUC0–24 (Online Resource, Table E1–E3).
Pharmacogenomics-toxicity relationship
All analyzed genotypes were in Hardy–Weinberg equilibrium (p > 0.050), except for EGFR rs4947492, EGFR rs2227983, ABCG2 rs2622604, and ABCB1 rs1045642 (p = 0.047, p = 0.020, p = 0.049, and p = 0.028, respectively), but the allele frequency was similar to that reported by Togo Var [26].
There was a significant relationship between polymorphisms in germline EGFR and severe AEs or anorexia. EGFR rs2293348 C > T (C/T genotype) and EGFR rs4947492 G > A (A/A genotype) were both associated with any severe AEs (Table 3, p = 0.019 in the additive model, and p = 0.050 in the recessive model). EGFR rs2293348 C > T (C/T genotype) was also significantly associated with grade ≥ 1 or ≥ 2 anorexia (Online Resource, Table F1, p < 0.001 or p = 0.023, respectively, in an additive model).
The ABCG2 rs2231137 G > A polymorphism (G/A and A/A genotypes) was significantly associated with both AEs and PK. A higher frequency of any grade ≥ 2 AEs (Table 3, p = 0.008 in a dominant model) was observed in patients with ABCG2 rs2231137 G/A or A/A genotypes. Furthermore, patients with the G/A or A/A genotype had a significantly higher exposure (AUC0–24) to the osimertinib parent compound (Fig. 2, p = 0.018).
A significant association was observed between ABCB1 rs1128503 C > T polymorphism (C/T and T/T genotypes) and any grade ≥ 2 AEs (Table 3, p = 0.038 in a dominant model). Additionally, the ABCB1 rs2032582 G > T/A polymorphism (A/T and G/T genotypes) was associated with any grade ≥ 2 skin disorders and grade ≥ 2 lymphocyte count decrease (Online Resource, Table F2, p = 0.017; Table F3, p = 0.033; respectively, in an additive model). Polymorphisms in the POR were not significantly associated with any AEs or PK of the drug.
Discussion
To the best of our knowledge, this is the first report to evaluate the association between exposures to the two active metabolites of osimertinib (AZ5104 and AZ7550) or germline polymorphisms (EGFR, ABCG2, ABCB1, and POR) and the severity of AEs.
PopPK modeling was performed to estimate the AUC0–24, which was selected as an exposure measure given the suggested linear relationship between the AUC of osimertinib parent compound (at a steady state of any dosing interval) and AEs [11]. The median estimated AUC0–24 of osimertinib at steady state was consistent or slightly lower than that in previous reports because some patients included in our study received a reduced dose [27, 28]. Albumin level was identified as a significant covariate for the clearance of the parent compound and AZ5104 (positive correlation), consistent with a previous report [29]. C-reactive protein (CRP) is a covariate of osimertinib clearance [21]. Decrease in albumin level and an increase in CRP level occur during inflammation, which can decrease CYP3A4 activity [30,31,32]. Therefore, decreased albumin level may be an indicator of increased exposures to the osimertinib parent compound and AZ5104—a consequence of decreased CYP3A4 activity during inflammation. However, elucidating these mechanisms is outside the scope of this study.
To our knowledge, our study is the first to report a PopPK model and a significant covariate (BW) for AZ7550. The BW may have an impact on the clearance of AZ7550 because AZ7550 has a longer half-life than the parent compound and AZ5104 (72.7 h versus 59.7 and 52.6 h, respectively), and greater distribution and accumulation are expected [10].
The exposure–toxicity relationships were different among the three compounds: osimertinib parent, AZ5104, and AZ7550. Exposure to AZ7550 was associated with grade ≥ 2 paronychia and anorexia, both of which are potentially related to direct epidermal or mucosal damage. The mechanisms underlying EGFR-TKI-induced skin disorders, such as paronychia, involve decreased keratinocyte proliferation and increased keratinocyte differentiation at the epidermis, resulting in impaired skin barrier function [33]. However, anorexia can be caused by multiple factors, and assessing causal associations is difficult. Although the mechanism responsible for EGFR-TKI-induced anorexia has not been elucidated, one hypothesis for anorexia induction is gastric mucosal injury, since inhibition of EGFR in gastric parietal cells can interfere with gastric mucosal membrane protection and repair of mucosal injury [34, 35]. Moreover, cancer cachexia should be considered a confounder of the AZ7550 exposure–anorexia relationship. The main characteristics of cancer cachexia are anorexia and inflammation, and the elevated production of proinflammatory cytokines can cause a decrease in CYP3A4 activity, resulting in alterations in AZ7550 metabolism [31]. However, because our PopPK analysis indicated that the albumin level, which may be related to inflammation, was not a significant covariate for AZ7550 clearance, we considered that inflammation was unlikely to affect the PK of AZ7550. It is more likely that the increase in exposure to AZ7550 increases direct epidermal or mucosal damage, leading to a higher severity of paronychia or anorexia.
Our results also indicated that exposures to the osimertinib parent compound and AZ5104 were associated with grade ≥ 2 diarrhea and increase in creatinine level. Similarly, a previous report suggested a linear relationship between the exposure to the parent compound and the occurrence of diarrhea [11], whereas another report suggested that exposure to the parent compound and grade ≥ 1 diarrhea were not associated [29]; thus, exposure to the parent compound may be related to higher grade (grade ≥ 2) diarrhea. The potential mechanism responsible for both diarrhea and creatinine increase may be the activation of chloride secretion as a result of EGFR inhibition [36]. The increased activation of chloride secretion by increased parent compound and AZ5104 exposures can enhance passive water movement through the gastrointestinal lumen, causing high-grade diarrhea and dehydration, which can lead to renal failure [36,37,38]. Assessment of the causal association for the exposure–kidney failure relationship is difficult because kidney failure can influence drug excretion. On the one hand, only 1.7% of the dose is excreted in the urine as osimertinib, AZ5104, and AZ7550 [39] (oral bioavailability of osimertinib: 69.8% [10]), and changes in renal clearance are unlikely to affect drug exposure [40]. On the other hand, because the influence of renal impairment on CYP3A enzyme function has been reported [41], the potential impact on total clearance cannot be ignored, and further studies are warranted to confirm this influence.
Overall, exposure to AZ5104 was significantly associated with any grade ≥ 2 AEs; the exposure to AZ5104 showed a higher area under the curve in ROC analysis than that to osimertinib parent compound and AZ7550. Likewise, an earlier study suggested an absence of a relationship between the trough concentration of the osimertinib parent compound and any toxicity [42]; thus, monitoring AZ5104 may be more beneficial than monitoring the parent compound for the management of any AEs. Additionally, the results of ROC analysis predicting any grade ≥ 2 AEs using AUC0–24 of the parent compound, AZ5104, and AZ7550, showed low sensitivity but high specificity. Therefore, the AUC0–24 levels may not be an absolute index for any AE prediction, but it may become a useful index to decide whether a patient experiencing AEs needs dose reduction. Since several reports suggested that there is no relationship between exposure to osimertinib and efficacy, dose reduction up to 50% of mean exposure may be considered for patients experiencing AEs and high AUC0–24 levels; however, further validation is needed [11, 21, 40, 42].
The two intron variants in EGFR, rs2293348 C > T (C/T genotype) and rs4947492 G > A (A/A genotype), were associated with severe AEs. According to earlier studies, EGFR rs2293348 is related to erlotinib-induced rash, and rs4947492 is related to gefitinib-induced diarrhea [13, 19]. The role of these genetic variants has not been fully investigated; however, since the intron sequence is involved in the regulation of expression, these variants may cause alterations in EGFR expression [43]. Thus, EGFR rs2293348 and rs4947492 may influence sensitivity to EGFR inhibition and severity of AEs.
The germline polymorphisms in ABCG2 rs2231137 G > A (G/A and A/A genotypes) and ABCB1 rs1128503 C > T (C/T and T/T genotypes) were significantly associated with any grade ≥ 2 AEs, and ABCB1 rs2032582 G > T/A polymorphism (A/T and G/T genotypes) was significantly associated with any grade ≥ 2 skin disorders. Osimertinib is a substrate of ABCG2 and ABCB1; thus, polymorphisms in these genes may influence the distribution or PK of this drug [8, 44]. ABCG2 rs2231137 G > A (G/A and A/A genotypes) results in Val12Met substitution and is partly responsible for the functional impairment of ABCG2. The association of this variant with gefitinib-induced rash and the PK of gefitinib has been reported [18, 45], which is consistent with our results for osimertinib. The two ABCB1 polymorphisms (rs1128503 and rs2032582) were associated with grade ≥ 2 AEs but not with the serum concentration of osimertinib. We hypothesized that ABCB1 polymorphisms may influence the tissue distribution and accumulation of osimertinib and its active metabolites, as an in vivo study suggested the involvement of ABCB1 and ABCG2 in tissue accumulation of other TKIs [46].
Taken together, our results suggested that monitoring exposure to the parent compound, AZ5104, and AZ7550 and genotyping polymorphisms in EGFR, ABCG2, and ABCB1 are potential new approaches for AE management. AEs can be effectively managed without dose interruption by adjusting the dose and increasing awareness about the need for AE management. The AEs we focused on in this study, such as paronychia, anorexia, and diarrhea, are not fatal but need to be managed to improve quality of life and encourage patients to continue osimertinib therapy for a long period.
This study has several limitations to consider when interpreting the results. Because of the small sample size, we were unable to perform a multivariable analysis to compare the impact of multiple risk factors or check for potential confounding variables. A larger sample size is also required to evaluate the relationship between fatal or severe AEs (grade ≥ 3) and exposure to osimertinib or its active metabolites. Opportunistic collection of serum samples reduced the burden on patients but led to there being fewer concentration data points around the time of peak serum concentration (Tmax) for PopPK analysis. Additional blood sampling is required to improve the accuracy of the volume of distribution estimate, but this was not essential for the purpose of this study. The exposure–efficacy relationship was not analyzed in this study because progression-free survival was not reached for many of the participants at the time of data cut-off. Although many reports have suggested that there is no relationship between osimertinib exposure and efficacy, further studies are needed to identify the optimal therapeutic window for osimertinib treatment.
Conclusion
Our findings demonstrated for the first time that exposures to the two active metabolites of osimertinib (AZ5104 and AZ7550) were associated with AEs and may have a different impact than exposure to the osimertinib parent compound. Therefore, monitoring not only the parent compound but also the active metabolites is a potential approach for osimertinib AE management. Germline polymorphisms in EGFR (rs2293348 and rs4947492), ABCG2 (rs2231137), and ABCB1 (rs1128503 and rs2032582) were identified as potential biomarkers for predicting the severity of AEs and may help increase awareness of the importance of AE management for patients at higher risk.
Data Availability
All analyzed data are shown in the published article or in the Online Resource.
References
Ramalingam SS, Vansteenkiste J, Planchard D et al (2020) Overall survival with osimertinib in untreated, EGFR-mutated advanced NSCLC. N Engl J Med 382:41–50. https://doi.org/10.1056/nejmoa1913662
Soria JC, Ohe Y, Vansteenkiste J et al (2018) Osimertinib in untreated EGFR-mutated advanced non–small-cell lung cancer. N Engl J Med 378:113–125. https://doi.org/10.1056/nejmoa1713137
AstraZeneca (2015) TAGRISSO™ (AZD9291) approved by the US FDA for patients with EGFR T790M mutation-positive metastatic non-small cell lung cancer. https://www.astrazeneca.com/media-centre/press-releases/2015/TAGRISSO-AZD9291-approved-by-the-US-FDA-for-patients-with-EGFR-T790M-mutation-positive-metastatic-non-small-cell-lung-cancer-13112015.html#. Accessed 17 Oct 2022
AstraZeneca (2018) US FDA approves Tagrisso as 1st-line treatment for EGFR-mutated non-small cell lung cancer. https://www.astrazeneca.com/media-centre/press-releases/2018/us-fda-approves-tagrisso-as-1st-line-treatment-for-EGFR-mutated-non-small-cell-lung-cancer.html#. Accessed 17 Oct 2022
Food US, Administration D (2020) FDA approves osimertinib as adjuvant therapy for non-small cell lung cancer with EGFR mutations. https://www.fda.gov/drugs/resources-information-approved-drugs/fda-approves-osimertinib-adjuvant-therapy-non-small-cell-lung-cancer-egfr-mutations. Accessed 24 Sep 2022
Wu Y-L, Tsuboi M, He J et al (2020) Osimertinib in resected EGFR-mutated non–small-cell lung cancer. N Engl J Med 383:1711–1723. https://doi.org/10.1056/nejmoa2027071
Yu H, Steeghs N, Nijenhuis CM, Schellens JH, Beijnen JH, Huitema AD (2014) Practical guidelines for therapeutic drug monitoring of anticancer tyrosine kinase inhibitors: focus on the pharmacokinetic targets. Clin Pharmacokinet 53:305–325. https://doi.org/10.1007/s40262-014-0137-2
Food US, Administration D (2022) HIGHLIGHTS OF PRESCRIBING INFORMATION - TAGRISSO. https://www.accessdata.fda.gov/drugsatfda_docs/label/2022/208065s025lbl.pdf. Accessed 24 Sep 2022
Ishikawa E, Yokoyama Y, Chishima H et al (2022) Development and validation of a new liquid chromatography-tandem mass spectrometry assay for the simultaneous quantification of afatinib, dacomitinib, osimertinib, and the active metabolites of osimertinib in human serum. J Chromatogr B Anal Technol Biomed Life Sci 1199:123245. https://doi.org/10.1016/j.jchromb.2022.123245
Vishwanathan K, So K, Thomas K, Bramley A, English S, Collier J (2019) Absolute bioavailability of osimertinib in healthy adults. Clin Pharmacol Drug Dev 8:198–207. https://doi.org/10.1002/cpdd.467
Brown K, Comisar C, Witjes H et al (2017) Population pharmacokinetics and exposure-response of osimertinib in patients with non-small cell lung cancer. Br J Clin Pharmacol 83:1216–1226. https://doi.org/10.1111/bcp.13223
Abourehab MAS, Alqahtani AM, Youssif BGM, Gouda AM (2021) Globally approved EGFR inhibitors : insights into their syntheses, target kinases, biological activities, receptor interactions, and metabolism. Molecules 26:6677. https://doi.org/10.3390/molecules26216677
Ma Y, Xin S, Huang M et al (2017) Determinants of Gefitinib toxicity in advanced non-small cell lung cancer (NSCLC): a pharmacogenomic study of metabolic enzymes and transporters. Pharmacogenomics J 17:325–330. https://doi.org/10.1038/tpj.2016.31
Endo-tsukude C, Sasaki J, Saeki S et al (2018) Population pharmacokinetics and adverse events of Erlotinib in Japanese patients with non-small-cell lung cancer: impact of genetic polymorphisms in metabolizing enzymes and transporters. Biol Pharm Bull 41:47–56. https://doi.org/10.1248/bpb.b17-00521
Galvani E, Peters GJ, Giovannetti E (2012) EGF receptor-targeted therapy in non-small-cell lung cancer: role of germline polymorphisms in outcome and toxicity. Futur Oncol 8:1015–1030. https://doi.org/10.2217/FON.12.89
Sogawa R, Nakashima C, Nakamura T et al (2020) Association of genetic polymorphisms with Afatinib-induced diarrhoea. Vivo (Brooklyn) 34:1415–1419. https://doi.org/10.21873/invivo.11922
Hayashi H, Iihara H, Hirose C et al (2019) Effects of pharmacokinetics-related genetic polymorphisms on the side effect profile of afatinib in patients with non-small cell lung cancer. Lung Cancer 134:1–6. https://doi.org/10.1016/j.lungcan.2019.05.013
Tamura M, Kondo M, Horio M et al (2012) Genetic polymorphisms of the adenosine triphosphate-binding cassette transporters (Abcg2, Abcb1) and Gefitinib toxicity. Nagoya J Med Sci 74:133–140
Hasheminasab S-M, Tzvetkov MV, Schumann C et al (2015) High-throughput screening identified inherited genetic variations in the EGFR. Pharmacogenomics 16:1605–1619. https://doi.org/10.2217/pgs.15.97
Ruan Y, Jiang J, Guo L et al (2016) Genetic association of curative and adverse reactions to tyrosine kinase inhibitors in chinese advanced non-small cell lung cancer patients. Sci Rep 6:23368. https://doi.org/10.1038/srep23368
Agema BC, Veerman GDM, Steendam CMJ et al (2022) Improving the tolerability of osimertinib by identifying its toxic limit. Ther Adv Med Oncol 14:1–10. https://doi.org/10.1177/17588359221103212
Chu C-Y, Choi J, Eaby-Sandy B, Langer CJ, Lacouture ME (2018) Osimertinib: a novel dermatologic adverse event profile in patients with lung cancer. Oncologist 23:891–899. https://doi.org/10.1634/theoncologist.2017-0582
Komada F (2018) [Analysis of time-to-onset of interstitial lung disease after the administration of small molecule molecularly-targeted drugs]. Yakugaku Zasshi 138:229–235. https://doi.org/10.1248/yakushi.17-00194[in Japanese]
Aw DC, Tan EH, Chin TM, Lim HL, Lee HY, Soo RA (2018) Management of epidermal growth factor receptor tyrosine kinase inhibitor-related cutaneous and gastrointestinal toxicities. Asia Pac J Clin Oncol 14:23–31. https://doi.org/10.1111/ajco.12687
Common terminology criteria for adverse events (CTCAE). Version 5.0 ed. National Cancer Institute,
Togo, Var [internet] (2018). In: Tokyo National Bioscience Database Center, Japan Science and Technology Agency. https://togovar.biosciencedbc.jp. Accessed 3 Oct 2022
Zhao H, Cao J, Chang J et al (2018) Pharmacokinetics of osimertinib in chinese patients with advanced NSCLC: a phase 1 study. J Clin Pharmacol 58:504–513. https://doi.org/10.1002/jcph.1042
US Food and Drug Administration. Osimertinib (Tagrisso) Clinical Pharmacology and Biopharmaceutics Review. https://www.accessdata.fda.gov/drugsatfda_docs/nda/2015/208065orig1s000clinpharmr.pdf. Accessed 9 Oct 2022
Yokota H, Sato K, Sakamoto S et al (2022) Effects of CYP3A4/5 and ABC transporter polymorphisms on osimertinib plasma concentrations in japanese patients with non-small cell lung cancer. Invest New Drugs. https://doi.org/10.1007/s10637-022-01304-9
Soeters PB, Wolfe RR, Shenkin A (2019) Hypoalbuminemia: pathogenesis and clinical significance. J Parenter Enter Nutr 43:181–193. https://doi.org/10.1002/jpen.1451
Rivory LP, Slaviero KA, Clarke SJ (2002) Hepatic cytochrome P450 3A drug metabolism is reduced in cancer patients who have an acute-phase response. Br J Cancer 87:277–280. https://doi.org/10.1038/sj.bjc.6600448
Fernandez-Teruel C, Gonzalez I, Trocóniz IF, Lubomirov R, Soto A, Fudio S (2019) Population-pharmacokinetic and covariate analysis of Lurbinectedin (PM01183), a new RNA polymerase II inhibitor, in pooled phase I/II trials in patients with cancer. Clin Pharmacokinet 58:363–374. https://doi.org/10.1007/s40262-018-0701-2
Joly-Tonetti N, Ondet T, Monshouwer M, Stamatas GN (2021) EGFR inhibitors switch keratinocytes from a proliferative to a differentiative phenotype affecting epidermal development and barrier function. BMC Cancer 21:1–10. https://doi.org/10.1186/s12885-020-07685-5
Yano S, Kondo K, Yamaguchi M et al (2003) Distribution and function of EGFR in human tissue and the effect of EGFR tyrosine kinase inhibition. Anticancer Res 23:3639–3650
Tanigawa T, Higuchi K, Arakawa T (2001) [Mechanism and management of drug-induced eating disorder]. Nihon Rinsho 59:521–527 [in Japanese]
Crosnier A, Abbara C, Cellier M, Lagarce L, Babin M, Bourneau-Martin D, Briet M (2021) Renal safety profile of egfr targeted therapies: a study from vigibase® the who global database of individual case safety reports. Cancers (Basel) 13:1–10. https://doi.org/10.3390/cancers13235907
Harada Y, Sekine H, Kubota K, Sadatomi D, Iizuka S, Fujitsuka N (2021) Calcium-activated chloride channel is involved in the onset of diarrhea triggered by EGFR tyrosine kinase inhibitor treatment in rats. Biomed Pharmacother 141:111860. https://doi.org/10.1016/j.biopha.2021.111860
Duan T, Cil O, Thiagarajah JR, Verkman AS (2019) Intestinal epithelial potassium channels and CFTR chloride channels activated in ErbB tyrosine kinase inhibitor diarrhea. JCI insight 4:e126444. https://doi.org/10.1172/jci.insight.126444
Dickinson PA, Cantarini MV, Collier J, Frewer P, Martin S, Pickup K, Ballard P (2016) Metabolic disposition of osimertinib in rats, dogs, and humans: insights into a drug designed to bind covalently to a cysteine residue of epidermal growth factor receptor. Drug Metab Dispos 44:1201–1212. https://doi.org/10.1124/dmd.115.069203
Vishwanathan K, Sanchez-Simon I, Keam B et al (2020) A multicenter, phase I, pharmacokinetic study of osimertinib in cancer patients with normal renal function or severe renal impairment. Pharmacol Res Perspect 8:1–10. https://doi.org/10.1002/prp2.613
Sun H, Frassetto LA, Huang Y, Benet LZ (2010) Hepatic clearance, but not gut availability, of erythromycin is altered in patients with end-stage renal disease. Clin Pharmacol Ther 87:465–472. https://doi.org/10.1038/clpt.2009.247
Boosman RJ, Jebbink M, Veldhuis WB et al (2022) Exposure–response analysis of osimertinib in EGFR mutation positive non–small cell lung cancer patients in a real-life setting. Pharm Res 39:2507–2514. https://doi.org/10.1007/s11095-022-03355-2
Rose AB (2019) Introns as gene regulators: a brick on the accelerator. Front Genet 10:1–6. https://doi.org/10.3389/fgene.2018.00672
van Hoppe S, Jamalpoor A, Rood JJM, Wagenaar E, Sparidans RW, Beijnen JH, Schinkel AH (2019) Brain accumulation of osimertinib and its active metabolite AZ5104 is restricted by ABCB1 (P-glycoprotein) and ABCG2 (breast cancer resistance protein). Pharmacol Res 146:104297. https://doi.org/10.1016/j.phrs.2019.104297
Tang LN, Zhang CL, He HR et al (2018) Associations between ABCG2 gene polymorphisms and gefitinib toxicity in non-small cell lung cancer: a meta-analysis. Onco Targets Ther 11:665–675. https://doi.org/10.2147/OTT.S154244
Al-Shammari AH, Masuo Y, Fujita K, ichi et al (2019) Influx and efflux transporters contribute to the increased dermal exposure to active metabolite of Regorafenib after repeated oral administration in mice. J Pharm Sci 108:2173–2179. https://doi.org/10.1016/j.xphs.2019.01.018
Acknowledgements
The authors thank Dr. Itaru Sato for his support in data collection at the Ageo Central General Hospital.
Funding
This work was supported by the Research Foundation for Pharmaceutical Sciences, the Japan Research Foundation for Clinical Pharmacology (grant number 2018A20), JST SPRING (grant number JPMJSP2123), and the Keio University Doctorate Student Grant-in-Aid Program from the Ushioda Memorial Fund.
Author information
Authors and Affiliations
Contributions
All the authors contributed to the conception and design of the study. Material preparation and data collection were performed by Emi Ishikawa, Yuta Yokoyama, Haruna Chishima, Ouki Kuniyoshi, Naoki Nakaya, Hideo Nakajima, Motonori Kimura, Jun Hakamata, Naoya Suehiro, Hideo Nakada, Shinnosuke Ikemura, Kenzo Soejima, Ichiro Kawada, Hiroyuki Yasuda, and Hideki Terai. The data were analyzed and interpreted by Emi Ishikawa, Yuta Yokoyama, Hidefumi Kasai, Sayo Suzuki, and Tomonori Nakamura. Emi Ishikawa wrote the first draft of the manuscript, and previous versions were critically revised by Yuta Yokoyama, Hidefumi Kasai, Ouki Kuniyoshi, Aya Jibiki, Hitoshi Kawazoe, Hiroshi Muramatsu, Sayo Suzuki, and Tomonori Nakamura. All authors have read and approved the final manuscript.
Corresponding author
Ethics declarations
Ethics approval
This study was conducted in compliance with the principles of the Declaration of Helsinki. The study protocol was reviewed and approved by the ethics committees of the Ageo Central General Hospital (Approval No. 564), Keio University School of Medicine (Approval No. 20200098), and Keio University Faculty of Pharmacy (Approval No. 210118–3 and 200710–1).
Informed consent
Written informed consent for the publication of their data was obtained from all the patients who participated in this study.
Competing interests
Dr. Hiroyuki Yasuda has received speaker honoraria from AstraZeneca, Brisol Meyers, and Ono, outside of the submitted work. Dr. Hitoshi Kawazoe has obtained a grant from Eli Lilly, outside of the submitted work. Dr. Kenzo Soejima has received grants from Taiho Pharmaceutical, Boehringer Ingelheim, and AstraZeneca, and speaker honoraria from Chugai, Ono Pharmaceutical, AstraZeneca, BMS, MSD, Takeda Pharmaceutical, Kyorin Pharmaceutical, Boehringer Ingelheim, Sanofi, Novartis, Eli Lilly, and Taiho Pharmaceutical, outside of the submitted work. Dr. Tomonori Nakamura has obtained grants from Astellas Pharma, Chugai, Daiichi Sankyo, Kyowa Kirin, Otsuka Pharmaceutical, Sanofi, Shionogi, and Sato Pharmaceutical, outside of the submitted work. The other authors have no relevant financial or non-financial interests to disclose.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Ishikawa, E., Yokoyama, Y., Chishima, H. et al. 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. Invest New Drugs 41, 122–133 (2023). https://doi.org/10.1007/s10637-023-01328-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10637-023-01328-9