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The handle http://hdl.handle.net/1887/62361 holds various files of this Leiden University dissertation
Author: Liu, Xiaoyan
Title: Optimization of sunitinib treatment in metastatic renal cell carcinoma : pharmacogenetic evidence and challenges
Date: 2018-05-15
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CHAPTER 5
Effect of CYP3A4 rs4646437 on clearance of sunitinib and its active metabolite SU12662
Xiaoyan Liu, Dirk Jan A.R. Moes, Jesse J. Swen, Meta H.M. Diekstra, Heinz-Josef Klümpen, Martijn P.J.K. Lolkema, Jacqueline S.L. Kloth,
Huixin Yu, Hans Gelderblom, Ron H.N. van Schaik, Howard Gurney,
Alwin D.R. Huitema, Neeltje Steeghs, Megan Crumbaker, Ron H.J. Mathijssen and Henk-Jan Guchelaar
Manuscript in preparation
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ABSTRACT
Background It was reported that A-allele carriers of CYP3A4 rs4646437 had an increased risk of sunitinib-induced hypertension in patients with metastatic renal cell carcinoma.
However, it is yet uncertain whether the above-mentioned finding is due to the increase or decrease CYP3A4 activity by CYP3A4 rs4646437. The aim of the present study was to answer this question by investigating the effect of CYP3A4 rs4646437 on the clearance of both sunitinib and its metabolite SU12662.
Methods 144 sunitinib-treated patients were included in this study, of whom the plasma concentration of sunitinib and SU12662 and DNA for CYP3A4 rs4646437 genotyping were obtained. A well-established integrated semi-physiological model was used for the calculation of pharmacokinetic parameters of sunitinib and SU12662. The clearance of both sunitinib and SU12662 were compared between A-allele carriers and patients with wild- type genotype.
Results CYP3A4 rs4646437 showed significant associations with clearance of both sunitinib and SU12662. The A-allele carriers had an average 29.3% and 18.9 % higher clearance of sunitinib and SU12662, respectively, compared to patients with wild-type genotype. 5.5% of variance in sunitinib clearance and 3.9% in SU12662 clearance could be explained by CYP3A4 rs4646437.
Conclusion Our results suggest that A-allele of CYP3A4 rs4646437 is related to an increased activity of CYP3A4 expression, leading to an accumulation of SU12662, which might contribute to the higher risk of sunitinib-induced hypertension. However, its clinical use is limited because CYP3A4 rs464643 in the current population only explains a small fraction of the total variability of sunitinib and SU12662 clearance.
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INTRODUCTION
Sunitinib has been approved for the treatment of metastatic renal cell carcinoma (mRCC), imatinib refractory or intolerant gastrointestinal stromal tumor and pancreatic neuroendocrine tumor and also been suggested as treatment option for other solid tumor types.1-3 It has been reported repeatedly that the inter-individual variabilities of sunitinib pharmacokinetics (PK) and pharmacodynamics (PD) are quite large.4 The genetic diversities in genes encoding sunitinib-metabolizing enzymes as well as sunitinib-targeted receptors could be one reason.
Sunitinib is predominately metabolized by the cytochrome P450 enzyme CYP3A4 to its active metabolite SU12662, which could be further metabolized to inactive metabolite SU14335 by CYP3A4 (Figure 1).1 In a cohort including 159 sunitinib-treated mRCC patients, A-allele carriers of CYP3A4 rs4646437 G>A showed a four times lower risk of grades 3–4 toxicity (odds ratio (OR)=0.27, 95% CI: 0.08–0.88, P=0.03) compared with patients with wild-type.5, 6 No genetic association with hypertension was observed.5, 6 In a validation study consisting of 287 mRCC patients, we identified that the A-allele of CYP3A4 rs4646437 was associated with an increased risk for sunitinib-induced hypertension (OR=2.4, 95% CI: 1.1–5.2, P=0.021).7 A meta-analysis showed the relationship between the increased exposure of sunitinib with the improved time to tumor progression and overall survival as well as the higher incidence of adverse events.8 Therefore, it is hypothesised that CYP3A4 rs4646437 might affect the activity of CYP3A4 enzyme, resulting in an increase or decrease of sunitinib exposure, and subsequently associated with sunitinib-induced toxicity.
Located in intron 7, CYP3A4 rs4646437 is not a coding region of known CYP3A4 transcripts. To date, the functionality of this variant has not been tested mechanistically. In the published studies, the impact of this genetic variant on CYP3A4 activity was conducted by the evaluation of PK parameters of a CYP3A4-metabolized drug, but not a specific CYP3A4-substrate.9-11 Moreover, the interpretations of whether A-allele is associated with increased or decreased activity of CYP3A4 from these studies were conflicting.9-11 Thus, it is not sensible to utilize these result to explain the association of CYP3A4 rs4646437 with sunitinib-induced hypertension.
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Therefore, we perform the present study to elucidate the effect of CYP3A4 rs4646437 on clearance of sunitinib and SU12662, in order to explain the previous observed association of CYP3A4 rs4646437 with sunitinib-induced hypertension.
Figure 1 Metabolism process of sunitinib and its active metabolite SU12662
METHODS Study population
A total of 146 sunitinib-treated patients who participated in previous study in The Netherlands and Australia were taken into consideration in the present study (Supplement Table 1).12-16 All patients received sunitinib in a 4-week on/2-week off schedule or a continuous dosing regimen with a daily oral dose ranging from 25.0 to 62.5mg. One trough sample or multiple samples were collected from each patient for the concentration of sunitinib and SU12662. Genomic DNA was isolated from whole blood for genotyping. This study was carried out in accordance with the Declaration of Helsinki and approved by the local medical ethics review board of all participating medical centers. All patients provided written informed consent.
Genotyping assay
CYP3A4 rs4646437 was determined with TaqMan probes (Applied Biosystems, Nieuwerkerk aan den IJssel, The Netherlands) using the LightCycler480 (LC480) Real- Time PCR Instrument (Roche Applied Science, Almere, The Netherlands).7 Genotyping was performed for all the patients in Leiden University Medical Center. 10% of samples were carried out in duplicate, and no inconsistency was observed.
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Pharmacokinetic and statistical analysis
An integrated dataset consisting of concentration of sunitinib and SU12662 and genotype data from each of the subjects was created. The PK parameters for both sunitinib and SU12662 were estimated using a previously published integrated semi-physiological PK model.17 An one-compartment model for sunitinib and two-compartment for SU12662 were described in the base model. NONMEM (v7.2.1, Icon Development Solutions, Ellicott City, MD) was used for modeling, using PsN toolkit 3.4.2 and Piranã version 2.8.0 (ref. 49) as modeling environment. A posterior power calculation using the Stochastic Simulation and Estimation (SSE) tool of the PsN toolkit was performed to determine the power of our study.
Diagnostic plot was constructed of the random effects of apparent oral clearance (CL/F) versus the pharmacogenetic covariate (CYP3A4 rs4646437). Based on this plot, further test in the pharmacostatistical model was performed. Clearance of sunitinib and SU12662 were statistically compared between patients with GG-genotype and A-allele carriers of CYP3A4 rs4646437. The genotype effect was statistically significant if the difference in the objective function values (OFV) between the base model and the final covariate model was > 6.63 (p
< 0.01, with 1 degree of freedom, assuming chi-squared [χ2] distribution). Evaluation of the precision of the PK parameters was performed with 1000 bootstrap replicates. Plotting of the NONMEM results was performed using statistical software package R (v2.15.2) and RStudio (v0.97.248; Boston, MA).
RESULTS
Patient characteristics and genotype result
A total of 144 patients were included in the statistical analysis and two were excluded due to the failure of genotyping. Patient characteristics are showed in Table 1. Patients were diagnosed with RCC (n=96), neuroendocrine tumor of the pancreas (n=13), gastrointestinal stromal tumor (n=8) or other solid tumor type (n=27). 61 patients had complete PK sampling (580 samples in total) and 83 patients had one random sample or multiple trough levels available (255 samples in total, Supplementary Table 1). The genotype call rate was 99%. The allele frequency was in Hardy-Weinberg equilibrium.
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Table 1 Patient Characteristics (n=144)
Power analysis
The power of our study to be able to identify a clinically relevant genetic effect (≥ 25% ) was calculated using the SSE tool by 300 simulations. We found a power (95% confidence) of 91% (α=0.05) in detecting a clinically relevant genetic effect with the allele frequency of this particular variant.
Effect of CYP3A4 rs4646437 on clearance of sunitinib and SU12662
The population PK parameters obtained from the base and final model are presented in Table 2. The evaluation of the precision of the PK parameters was performed with 1000 bootstrap replicates. CYP3A4 rs4646437 showed significant associations with clearance of both sunitinib and SU12662. The A-allele carriers had an average 29.3% higher clearance of sunitinib and 18.9% higher clearance of SU12662, compared to patients with GG- genotype (p=0.006, Figure 2). CYP3A4 rs4646437 explained 5.5% of variance in sunitinib clearance and 3.9% of variance in SU12662 clearance.
Demographic n (%) or mean (range)
Age (years) 59 (27-81)
Male 95 (66.0%)
Caucasian 134 (93.7%)
Tumor type
Renal cell carcinoma
Neuroendocrine tumors of the pancreas Gastrointestinal stromal tumor
Others
96 (66.7%) 13 (9.0%) 8 (5.6%) 27 (18.8%) Sunitinib daily dose
25mg 37.5mg 50mg 62.5mg
10 (6.9%) 65 (45.1%) 67 (46.5%) 2 (1.4%) CYP3A4 rs4646437
GG GA AA
113 (78.5%) 29 (20.1%) 2 (1.4%)
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Table 2 Pharmacokinetic parameter estimates from the base model, final model and 1000 bootstrap replicates with 95% confidence interval
Parameters
Base model Final model 1000 bootstrap Estimate (RES%) Estimate (RES%) Median (95%CI)
Ka (h-1) 0.36 (12%) 0.36 (12%) 0.36 (0.29-0.45)
CLp (L/h) 40.1 (3%) 37.9 (3%) 37.9 (35.4-40.5)
Vcp (L) 1580 (6%) 1590 (5%) 1597 (1419-1782)
Qh (L/h) 80 fix 80 fix 80 fix
CLm (L/h) 17.9 (4%) 17.2 (5%) 17.1 (15.7-18.7)
Vcm (L) 859 (13%) 860 (12%) 856 (626-1072)
CYP3A4 rs4646437 on CLp*
+29.3% (35%) +29.5% (8.5%-53.4%) CYP3A4 rs4646437 on CLm* +18.9% (56%) +19.9% (-1.1%-23.9%) IIV CLp (CV%) 35.7% (9.7%) 30.1% (9%) 29.9% (24.5%-35.5%) IIV Vcp (CV%) 32.1% (10%) 37.4% (17%) 35.5% (24.7%-48.9%) IIV CLm (CV%) 43.7% (6%) 43.0% (6%) 42.7% (37.7%-48.3%) IIV Vcm (CV%) 52.8% (10%) 52.7% (10%) 53.2% (42.3%-65.0%)
*Patients with GG-genotype are reference group; Ka, absorption rate constant; CLp, clearance of sunitinib; CLm, clearance of SU12662, Vcp, central volume of distribution of sunitinib; Vcm, central volume of distribution of SU12662; Qh, hepatic blood flow; IIV, inter-individual variability; CV%, percentage of coefficient variation; RES, relative standard error.
Figure 2 Sunitinib and SU12662 clearance plotted again CYP3A4 rs4646437 genotypes
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DISCUSSIONS
Our previous study observed a significant association between the presence of A-allele in CYP3A4 rs4646437 and an increased risk of sunitinib-induced hypertension in a cohort of 287 mRCC patients.7 The relationship between sunitinib exposure and its clinical outcomes has been established.8 Therefore, we hypothesized that A-allele of CYP3A4 rs4646437 might increase or decrease the activity of CYP3A4 enzyme, resulting in the increased exposure of either sunitinib or SU12662. Because SU12662 has a similar inhibitory activity on VEGFR and an even longer half-life (80 hours) compared to sunitinib,1 both hypotheses seem logical to interpret the observation. Even though the function of the CYP3A4 rs4646437 has been suggested previously in several CYP3A4-metabolized drugs,9-11 results were inconsistent. Therefore, the present study was conducted to provide a conclusive answer by investigating the effect of CYP3A4 rs4646437 on the clearance of sunitinib and SU12662 in a cohort of 144 sunitinib-treated patients.
In the present study, CYP3A4 rs4646437 showed significant associations with clearance of both sunitinib and SU12662. The A-allele carriers had an average 29.3% higher clearance of sunitinib and 18.9% higher clearance of SU12662, compared to patients with GG genotype. The findings suggest that the A-allele of CYP3A4 rs4646437 should be associated with an increased CYP3A4 enzyme activity. Due to the longer half-life of SU1266218 as well as the relatively smaller effect of CYP3A4 rs4646437 on clearance of SU12662, A-allele carriers rather than patients with GG-genotype had a higher accumulation of SU12662, which contributed to the increased risk of sunitinib-induced hypertension. However, due to its limited ability to explain the variability in clearance of sunitinib and SU12662, it is not worthy to implement genotyping of CYP3A4 rs4646437 in clinical practice.
Even though sunitinib was mainly metabolized by CYP3A4, CYP3A5 and CYP1A1 were also involved in the metabolism process. Besides, ABCB1 and ABCG2 together with P450 regulators (encoded by NR1I2, NR1I3 and POR*28) give their contribution to the exposure of sunitinib as well. In 2014, 14 single-nucleotide polymorphisms (SNPs) from genes encoding the aforementioned enzymes have been investigated with clearance of sunitinib and SU12662 in a similar cohort including 114 sunitinib-treated patients.4 Although no significant results were observed, A-allele carriers of CYP3A5 rs776746 showed a 22.5%
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increase in the clearance of sunitinib.4 It has been suggested that CYP3A4 rs4646437 is in high linkage disequilibrium (LD) to CYP3A5 rs776746 (correlation from 0.781 to 0.913).19 Thus, the effect of CYP3A4 rs4646437 on clearance might be actually due to CYP3A5 rs776746. However, Li et al.11 and Liu et al.20 have individually reported conflicting results that the correlation between CYP3A5 rs776746 and CYP3A4 rs4646437 was quite low in Chinese population (r2=0.244 and r2=0.22, respectively, no Dˈ data were given). In a post hoc analysis, we calculated the LD between CYP3A4 rs4646437 and CYP3A5 rs776746 within the present dataset (Dˈ=0.859, r2=0.542) and compared the genotyping distribution of both SNPs (Results are shown in Supplementary Table 2). Of 23 A-allele carriers of CYP3A5 rs776746, 21 patients also carried A-allele of CYP3A4 rs4646437. But in 31 A- allele carriers of CYP3A4 rs4646437, 21 carried CYP3A5 rs776746. Together with the LD results, it could be hypothesized that the effect of CYP3A4 rs4646437 on clearance might be driven by the effect CYP3A5 rs776746.
There are several limitations in the present study. Due to the fact that the clinical data (hypertension) of the current cohort is not available, we cannot establish a full picture of relationships between CYP3A4 rs4646437 and sunitinib-induced hypertension as well as PK parameters of sunitinib and SU12662. We only focused on one SNP in this analysis without consideration of other genetic or non-genetic variants. This is because the research question of current study is to identify the relationship between CYP3A4 rs4646437 and clearance of sunitinib and SU12662 in order to explain our previous finding in the association of CYP3A4 rs4646437 with sunitinib-induced hypertension. Owing to the fact that the low minor allele frequency of CYP3A4 rs4646437 (0.09-0.13) in the Caucasian population,21 the explanatory capability of CYP3A4 rs4646437 on clearance is probably higher and therefore it is worthwhile to investigate its effect in other ethnicities with a high A-allele frequency, such as African-American.
In conclusion, our results indicate that A-allele of CYP3A4 rs4646437 is related to an increased activity of CYP3A4 expression, leading to an accumulation of SU12662, which might contribute to the higher risk of sunitinib-induced hypertension. However, its clinical utility might be limited because of the small explanatory capability of inter-individual difference in clearance and the strong linkage between CYP3A4 rs4646437 and CYP3A5 rs776746 in the Caucasian population.
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8. Houk BE, Bello CL, Poland B, Rosen LS, Demetri GD, Motzer RJ. Relationship between exposure to sunitinib and efficacy and tolerability endpoints in patients with cancer: results of a pharmacokinetic/pharmacodynamic meta-analysis. Cancer Chemother Pharmacol 2010; 66(2): 357-371.
9. Crettol S, Venetz JP, Fontana M, Aubert JD, Pascual M, Eap CB. CYP3A7, CYP3A5, CYP3A4, and ABCB1 genetic polymorphisms, cyclosporine concentration, and dose requirement in transplant recipients. Ther Drug Monit 2008; 30(6): 689-699.
10. He HR, Sun JY, Ren XD, Wang TT, Zhai YJ, Chen SY, et al. Effects of CYP3A4 polymorphisms on the plasma concentration of voriconazole. Eur J Clin Microbiol Infect Dis 2015; 34(4): 811-819.
11. Li CJ, Li L, Lin L, Jiang HX, Zhong ZY, Li WM, et al. Impact of the CYP3A5, CYP3A4, COMT, IL-10 and POR genetic polymorphisms on tacrolimus metabolism in Chinese renal transplant recipients. PLoS One 2014; 9(1): e86206.
12.Lankheet NAG, Kloth JSL, Gadellaa-van Hooijdonk CGM, Cirkel GA, Mathijssen RHJ, Lolkema MPJK, et al. Individual PK-guided sunitinib dosing: A feasibility study in patients with advanced solid tumors. Journal of Clinical Oncology 2012; 30(15_suppl):
2596-2596.
13. Lankheet NA, Knapen LM, Schellens JH, Beijnen JH, Steeghs N, Huitema AD. Plasma concentrations of tyrosine kinase inhibitors imatinib, erlotinib, and sunitinib in routine clinical outpatient cancer care. Ther Drug Monit 2014; 36(3): 326-334.
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14. Kloth JS, Klumpen HJ, Yu H, Eechoute K, Samer CF, Kam BL, et al. Predictive value of CYP3A and ABCB1 phenotyping probes for the pharmacokinetics of sunitinib: the ClearSun study. Clinical pharmacokinetics 2014; 53(3): 261-269.
15. de Wit D, Gelderblom H, Sparreboom A, den Hartigh J, den Hollander M, Konig- Quartel JM, et al. Midazolam as a phenotyping probe to predict sunitinib exposure in patients with cancer. Cancer chemotherapy and pharmacology 2014; 73(1): 87-96.
16. Kloth JS, Binkhorst L, de Bruijn P. Effect of dosing time on sunitinib pharmacokinetics.
Eur J Cancer 2013; 49(#692).
17. Yu H, Steeghs N, Kloth JS, de WD, van Hasselt JG, van Erp NP, et al. Integrated semi- physiological pharmacokinetic model for both sunitinib and its active metabolite SU12662. Br J Clin Pharmacol 2015; 79(5): 809-819.
18. Houk BE, Bello CL, Kang D, Amantea M. A population pharmacokinetic meta-analysis of sunitinib malate (SU11248) and its primary metabolite (SU12662) in healthy volunteers and oncology patients. Clin Cancer Res 2009; 15(7): 2497-2506.
19. Wei C, Elston RC, Lu Q. A weighted U statistic for association analyses considering genetic heterogeneity. Statistics in medicine 2016; 35(16): 2802-2814.
20. Liu MZ, He HY, Zhang YL, Hu YF, He FZ, Luo JQ, et al. IL-3 and CTLA4 gene polymorphisms may influence the tacrolimus dose requirement in Chinese kidney transplant recipients. Acta pharmacologica Sinica 2017; 38(3): 415-423.
21. Database of Single Nucleotide Polymorphisms (dbSNP). Bethesda (MD): National Center for Biotechnology Information, National Library of Medicine.
https://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=4646437.
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Supplementary Table 1 Time points of blood sampling for included PK studies
Study acronym
Number of patient
Clinical trial
registration number Time points
NIB12 21 - 0 and one random sampling time
point after start of sunitinib
M10PKS13 34 NCT01286896 0 and 24 hour(s)
Clearsun14 45 NCT01098903 0, 4, 8, and 24 hour(s)
Chrono15 7 NTR3526 0, 1, 2, 4, 6, 8, 12 and 24 hour(s) Phenotyping
study16 11 NCT01743300 0, 10, 30, 40 minutes and 1, 2, 3, 4, 5, 6, 7, 8, 10, 12 and 24 hour(s) CRESTO 28 NCT01711268 Multiple trough levels after start
sunitinib
Supplementary Table 2 The genotype distribution of CYP3A5 rs776746 and CYP3A4 rs4646437
CYP3A4 rs4646437
GG GA/AA
CYP3A5 rs776746
GG 110 10
GA/AA 2 21
