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. 2019 Dec;7(24):806.
doi: 10.21037/atm.2019.12.60.

The analysis of pharmacokinetic and pharmacogenomic impact on gefitinib efficacy in advanced non-small cell lung cancer patients: results from a prospective cohort study

Yuxiang Ma  1 , Shuang Xin  2   3 , Qingguang Lin  1 , Wei Zhuang  2 , Yuanyuan Zhao  1 , Xia Zhu  2 , Hongyun Zhao  1 , Min Huang  2 , Xu Xun  3 , Yunpeng Yang  1 , Wenfeng Fang  1 , Li Zhang  1 , Xueding Wang  2
Affiliations

The analysis of pharmacokinetic and pharmacogenomic impact on gefitinib efficacy in advanced non-small cell lung cancer patients: results from a prospective cohort study

Yuxiang Ma et al. Ann Transl Med. 2019 Dec.

Abstract

Background: The current study is aimed to examine the impact of pharmacokinetics and gene polymorphisms of enzymes involving in absorption, distribution, metabolism and excretion (ADME) on the efficacy of gefitinib in non-small cell lung cancer (NSCLC) patients.

Methods: Eligible patients with indication of gefitinib treatment were prospectively enrolled in this study. Two peripheral blood samples at baseline and before cycle 2 day 1 were collected for the detection of single nucleotide polymorphisms (SNPs) of drug ADME enzymes and trough drug concentration (Ctrough) at steady state. Thirteen SNPs were genotyped using the Sequenom Massarray system. Ctrough was determined by validated high-performance liquid chromatographic method with tandem mass spectrometric (LC-MS/MS).

Results: Fifty-eight NSCLC patients were enrolled in this study. The median of Ctrough was 175ng/mL (range from 47.8 to 470 ng/mL). The trough concentration was not associated with either objective response or progression free survival (PFS). Ctrough was significantly lower in CYP3A4 rs2242480 CC + CT genotype than in TT genotype (P=0.019) and in ABCG2 rs2231142 AA genotype than in AC + CC genotype (P=0.031). ABCB1 rs2032582 dominant model was significantly correlated with overall response rate (ORR) and patients with GG phenotype respond better than patients with GT + TT phenotypes (84.6% vs. 51.2%, P=0.032). ABCB1 rs10256836 recessive model was significantly correlated with PFS and patients with GG phenotype achieved longer PFS than patients with GC + CC phenotypes (17.40 vs. 10.33 months, P=0.040).

Conclusions: The Ctrough of gefitinib was significantly different between CYP3A4 and ABCG2 genotypes, but not with the efficacy of gefitinib treatment. ABCB1 rs2032582 and rs10256836 polymorphisms were correlated treatment outcome. Polymorphisms analysis of ABCB1 could be a predictive biomarker for gefitinib treatment.

Keywords: ABCB1; Non-small cell lung cancer (NSCLC); gefitinib; pharmacokinetic; single nucleotide polymorphisms (SNPs).

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Conflict of interest statement

Conflicts of Interest: The authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1
The impact of patients’ demographics and clinical characteristics on Gefitinib efficacy (overall response rate and progression free survival). ORR, overall response rate; 95% CI, 95% confidence interval.
Figure 2
Figure 2
Kaplan-Meier curve and log-rank test for progression-free survival in advanced NSCLC patients treated with gefitinib: association with ABCB1 rs10256836 recessive model (GG vs. GC + CC). NSCLC, non-small cell lung cancer.
Figure S1
Figure S1
The distribution of gefitinib trough concentration in all 58 patients.
Figure S2
Figure S2
The distribution of gefitinib trough concentration in CYP3A4 rs2242480 genotype groups. *, P value <0.05.
Figure S3
Figure S3
The distribution of gefitinib trough concentration in ABCG2 rs2231142 genotype groups. *, P value <0.05.
Figure S4
Figure S4
The Kaplan-Meier curve of the PFS in high trough concentration (>200 ng/mL) group compared with that in low trough concentration (<200 ng/mL) group. PFS, progression free survival.
See this image and copyright information in PMC

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