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The purine belly syndrome
European Journal of Clinical Pharmacology https://doi.org/10.1007/s00228-018-2440-6
PHARMACOKINETICS AND DISPOSITION
Effect of CYP2C19, UGT1A8, and UGT2B7 on valproic acid clearance in children with epilepsy: a population pharmacokinetic model Shenghui Mei 1,2 & Weixing Feng 3,4 & Leting Zhu 1 & Xingang Li 1 & Yazhen Yu 4 & Weili Yang 4 & Baoqin Gao 4 & Xiaojuan Wu 4 & Fang Fang 3 & Zhigang Zhao 1,2 Received: 7 December 2017 / Accepted: 2 March 2018 # Springer-Verlag GmbH Germany, part of Springer Nature 2018
Abstract Purpose Valproic acid (VPA) is an important drug in seizure control with great inter-individual differences in metabolism and treatment effect. This study aims to identify the effects of genetic variants on VPA clearance in a population pharmacokinetic (popPK) model in children with epilepsy. Methods A total of 325 VPA plasma concentrations from 290 children with epilepsy were used to develop the popPK model by using the nonlinear mixed-effects modeling method. The one-compartment model was established to describe the pharmacokinetics of VPA. Twelve single nucleotide polymorphisms involved in the pharmacokinetics of VPA were identified by MassARRAY system and their effects on VPA clearance were evaluated. Results In the two final popPK models, inclusion of a combined genotype of four variants (rs1042597, rs28365062, rs4986893, and rs4244285), total daily dose (TDD), and body surface area (BSA) significantly reduced inter-individual variability for clearance over the base model. The inter-individual clearance equals to 0.73 × (TDD/628.92)0.59 × eUGT-CYP for TDD included model and 0.70 × (BSA/0.99)0.57 × eUGT-CYP for BSA included model. The precision of all parameters were acceptable (relative standard error < 32.81%). Bootstrap and visual predictive check results indicated that both two final popPK models were stable with acceptable predictive ability. Conclusion TDD, BSA, and genotype might affect VPA clearance in children. The popPK models may be useful for dosing adjustment in children on VPA therapy. Keywords Valproic acid . Uridine diphosphate glucuronosyltransferase . Cytochrome P450 family 2 subfamily C member 19 . Clearance . Children . Population pharmacokinetic model . Nonlinear mixed-effects modeling
Introduction Epilepsy is a global health problem with particularly high morbidity in children. The total direct healthcare costs per
person ranged from $10,192 to $47,862 for general epilepsy populations in the USA [1]. Valproic acid (VPA) is a first-line drug in treating patients with various kinds of seizures [2]. Due to its narrow therapeutic range and wide inter- and
Shenghui Mei and Weixing Feng are equal first authors. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00228-018-2440-6) contains supplementary material, which is available to authorized users. * Fang Fang [email protected] * Zhigang Zhao [email protected] 1
Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, 6 Tiantan Xili, Dongcheng District, Beijing 100050, People’s Republic of China
2
Department of Clinical Pharmacology, College of Pharmaceutical Sciences, Capital Medical University, Beijing 100045, People’s Republic of China
3
Department of Neurology, Beijing Children’s Hospital, Capital Medical University, 56 Nanlishi Road, Xicheng District, Beijing 100045, People’s Republic of China
4
Department of Pediatrics, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China
Eur J Clin Pharmacol
intra-individual pharmacokinetic variability, the dosage adjustment is a challenge for clinicians because under dosing might evoke epilepsy, whereas overdoing increased side effects, especially in pediatric patients [3]. Various factors involved in VPA absorption (diet, soybean intake, and dosage form) [4], distribution [body weight (BW), age, total daily dose (TDD), and protein binding] [5–11], and metabolism (gender, age, hepatic function, TDD, genotype of enzymes related to VPA glucuronidation and oxidation, coadministered enzyme inducers of antiepileptic drugs such as phenobarbital, phenytoin, and carbamazepine) have significant influence on VPA plasma concentration [3, 5, 6, 8, 10–24]. For example, cytochrome P450 family 2 subfamily C member 9*3 (CYP2C9*3, rs1057910) was associated with a decreased enzyme activity [25] and an increased VPA concentration and clearance [3, 12, 18, 19]. The quantitative relation between VPA plasma concentration and these influence factors should be identified prior to the individualized therapy. Therefore, various population pharmacokinetic (popPK) models were constructed by using the nonlinear mixed-effects modeling method (NONMEM) [5–16, 22]. The pharmacokinetics of VPA could be best estimated by one-compartment model with first-order absorption and elimination [5, 6, 8–16]. Only two studies used twocompartment model for data fitting [7, 22]. In previous published studies, BW, age, and TDD could affect VPA distribution volume [6–11], while BW, age, gender, TDD, and comedications had significant influence on VPA clearance [5–16]. Although various genetic polymorphisms were associated with the increase or decrease of VPA plasma concentration [3, 12, 17–21, 23, 24], only one study identified the quantitative relationship between genetic polymorphisms [cytochrome P450 family 2 subfamily C member 19 (CYP2C19) and CYP2C9 genotype] and VPA clearance [12]. The aim of this study is to identify the influence of metabolic enzyme genotypes and patients’ characteristics on VPA pharmacokinetic parameters in a popPK model, which might be useful for VPA dose adjustment in clinical practice.
data [gender, age, BW, and body surface area (BSA)], dosage regimen (dose, dosing time, and frequency), and coadministered antiepileptic drugs were recorded. After regularly taking VPA for at least 6 days, the blood samples were collected and the sampling time was recorded. The blood plasma was used for VPA concentration analysis while the white blood cells were used for genotyping [26].
Plasma concentration of VPA The total plasma concentrations of VPA were evaluated by a fluorescence polarization immunoassay (TDx, ABBOTT, USA). Calibrators and quality control samples were routinely performed according to the manufacturer’s instructions for quality control.
Genotype identification Based on previous studies and the PharmGKB database, 12 genetic variants were selected [3, 12, 17–21, 23, 24]. Patients’ DNA were purified by QIAamp DNA purification kit (Qiagen, Hilden, Germany). Genotyping was identified in Bio Miao Biological Technology (Beijing) by MassArray method (Sequenom, USA) [26]. To control the quality of genotyping, 5% of the whole patients were measured repeatedly, and the results were acceptable.
Grouping and combination of variants Each of the selected variants was divided into three or two groups by genotype. The group number of variants increased with the decrease of enzyme activity or VPA clearance (Table 1). Any two of the variants that were divided into three groups were combined into a new variant by summation of their group numbers. Then, the combined new variant was separated into three or two groups according to the combination rules (Table 2) and the new group number increased with the decrease of enzyme activity and VPA clearance.
Statistical analysis
Methods Study design This study was approved by the Ethics Committee of Beijing Tiantan Hospital, Capital Medical University, Beijing, China. Consent from the parents and assent from children were obtained. All patients were Chinese origin. The inclusion and exclusion criteria were the same as that mentioned in our previous study [23]. A total of 290 children (325 plasma concentrations) on VPA treatment in Beijing Tiantan Hospital were enrolled according to the criteria described above. Patients’ demographic
Statistical analysis was carried out by PLINK software (version 1.07, Shaun Purcell, Boston, USA). For all selected alleles, the minor allele frequency, genotype, and HardyWeinberg Equilibrium (P value, chi-square test) were calculated.
Population pharmacokinetic model development To develop the popPK model, a nonlinear mixed-effects modeling approach was performed by using the Phoenix® NLME™ 7.0 (Certara, St. Louis, MO) software. The firstorder conditional estimation-extended least squares method,
Eur J Clin Pharmacol Table 1 Grouping rules for variants
Group type
Enzyme activity changing by variants
Wild-type homozygote
Heterozygote
Variant homozygote
Three groups
Increased Decreased
3 1
2 2
1 3
Two groups, rule 1
Increased Decreased
2 1
1 2
1 2
Two groups, rule 2
Increased Decreased
2 1
2 1
1 2
which was equivalent to the NONMEM first-order conditional estimation methodology with interaction, was used for model construction [27]. VPC and bootstrap were performed to test the predictive ability and stability of the final model, respectively.
Base model The one-compartment model with first-order absorption and first-order elimination was used to describe the time course of VPA in human plasma (Appendix 1) [5, 6, 8–16, 22]. The model was parameterized by using the absorption rate constant (Ka), apparent volume of central compartment (Vc/F), and apparent total clearance (CL/F). Very few samples were at the absorption phase, and the VPA formulations were the same to a published study [6]. Therefore, Ka was fixed at 2.64 and 0.46 h−1 for immediate release tablets/solutions and controlled release tablets, respectively [6]. The residual error was characterized by the multiple model. The following equations are used to describe the model: dAa =dt ¼ −K a Aa
ð1Þ
dAc =dt ¼ K a Aa −CLc C c
ð2Þ
C c ¼ Ac =V c
ð3Þ
Ka and CLc/F represent the absorption rate and clearance of VPA, respectively. Aa and Ac represent the amount of VPA in
Table 2
Combination rules for any two of variants
Combined new group type
Summation of two group numbers
Combined new group number
Three groups
2 3 4, 5 2 3, 4, 5 2, 3 4, 5
1 2 3 1 2 1 2
Two groups, rule 1 Two groups, rule 2
the absorption site and central compartment, respectively. Cc represents the VPA concentration in the central compartment.
Population pharmacokinetic model After the construction of the base model, the influence of various covariates on VPA pharmacokinetic parameters was evaluated. All of the continuous covariates were centered at their mean or median values. Between- and intrasubject variability (η and ε) of pharmacokinetic parameters were assumed to follow a normal distribution with a mean of zero and a variance of ω2 and σ2, respectively. In the present study, most patients had only one observation, which might be associated with a higher value of shrinkage [28]. Therefore, the differences in the objective function value (OFV) were used for model comparison [6]. In covariate selection, compared to its previous model, an intermediate model with an OFV decrease > 6.635 (P < 0.01, df = 1) was considered to be superior for forward addition. After all covariates were added, the full model was refined by removing the added covariates using a tougher criterion: an increase in the OFV of > 10.828 (P < 0.001, df = 1) [5–7, 10–12, 29].
Goodness-of-fit and model evaluation OFV and the scatter plots including observed concentrations versus population predicted concentrations, conditional weighted residuals (CWRES) versus population predicted concentrations, CWRES versus time after the last dose, and CWRES versus standard normal quantiles were used to evaluate the goodness-of-fit between base model and final model. A total of 1000 bootstrap and 2000 VPC were performed to evaluate the stability and predictive ability of the final model, respectively [30]. The median, the 2.5–97.5% intervals of estimated parameters, and the 5–95% prediction intervals of the simulated data were calculated. The prediction ability of the final popPK model is acceptable when 90% of the measured VPA concentrations are within the 90% prediction interval.
Eur J Clin Pharmacol
Results Demographic data and genotyping of enrolled patients A total of 325 VPA plasma concentrations were obtained from 290 children (108 females and 182 males). A total of 196 patients were treated by controlled release tablets of VPA, while 94 children were treated by immediate release tablets or solutions. Patients’ characteristics and dosage regimens are shown in Appendix 2 (detail in Appendix 6). The base information of the 12 identified variants is listed in Appendix 3. The frequency of selected gene locus all conformed to the Hardy-Weinberg equilibrium (P > 0.05).
compared to the base model. The success rate (successful in minimization) of both bootstrap analyses was 100%. The median value and the distribution of the popPK parameters obtained from bootstrap were similar to the values observed in the final popPK model, which indicated that the final popPK model was robust (Table 3). For popPK models with TDD or BSA as covariates, 89.2% (35/325) and 90.4% (31/325) of the measured VPA plasma concentrations fell inside the 90% prediction interval (Appendix 5). Stratified VPC analysis with genotype indicated that both two popPK models did a better job for genotype 2 versus genotype 1. Moreover, the predictive ability of BSA-included popPK model was superior than the TDD-included model.
Model construction and final popPK model
Discussion
The covariates were added in descending order of reduction in the OFV value (Appendix 4). Two final popPK models were obtained by using TDD or BSA as covariates. TDD, BSA, and uridine diphosphate glucuronosyltransferase and cytochrome P450 (UGT-CYP) genotype have significant influence on VPA clearance. Their quantitative relationships are listed below: For model with TDD,
Model development
CL=F ¼ 0:73 ðTDD=628:92Þ0:59 eUGT−CYP eηCL ð4Þ V c = F ¼ 22:12 eηV c
ð5Þ
For model with BSA, CL=F ¼ 0:70 ðBSA=0:99Þ0:57 eUGT−CYP eηCL V c = F ¼ 18:36 e
ηV c
ð6Þ ð7Þ
where 0.73 and 0.70 (L/h) are the typical value of CL/F (L/h), and 22.12 and 18.36 (L) are typical value of Vc/F (L). The mean value of TDD is 628.92 mg/day, and the median value of BSA is 0.99 m2. The estimated coefficients representing the relationship between clearance and TDD or BSA are 0.59 and 0.57, respectively. In Eqs. 4 and 6, UGT-CYP is the genotype of four combined variants (rs1042597, rs28365062, rs4986893, and rs4244285), and UGT-CYP = 0 for patients with group 1 genotype, otherwise UGT-CYP = − 0.22 for Eq. 4 and − 0.19 for Eq. 6. Table 3 lists the estimate, standard error, 95% confidence interval, and inter-individual variability of the parameters for the base model, two final models, and bootstrap.
Goodness-of-fit and model evaluation Four pairs of scatter plots are shown in Fig. 1 to evaluate the goodness-of-fit of the final popPK models. In general, the two final popPK models obviously improved data fitting
The one-compartment popPK model, which has been widely used in previous studies [5, 6, 8–16], was employed to describe the pharmacokinetic property of VPA in our patients. In one of the two final popPK models, TDD has an extremely significant influence on VPA clearance, which has been reported in four published popPK models [5, 10, 11, 13], and it could be partly explained by the fact that patients with higher VPA clearance need a higher dose to ensure the VPA concentration within the therapeutic window (also known as therapeutic drug monitoring effect) [6]. The mechanism for this phenomenon is due to protein binding being saturable in the therapeutic range, increasing doses result in increased free fraction and therefore higher clearance [5, 6]. TDD has a significant influence on Vc/F in two popPK studies [10, 11]. In the present study, TDD alone had a significant influence on Vc/F, but this effect was removed when it was added after the addition of TDD on clearance. BSA, an important indicator, was used widely in population pharmacokinetic analyses to describe inter-individual variance in drug clearance [31]. Two VPA popPK studies chose BSA as covariates, but neither of them successfully added BSA on any of the parameters [13, 16]. In the final popPK model without TDD, BSA increased with the increase of VPA clearance, which could be explained by the following reasons: VPA is excreted partly via kidney, and higher BSA is related to a higher basal metabolic rate and glomerular filtration rate [27]; in children, VPA dose was mainly depended on the BW, which was highly correlated with BSA [31]. Glucuronidation and mitochondrial β-oxidation are two major pathways for VPA metabolism in adults. About 30– 50% of VPA was metabolized into VPA-glucuronide conjugate by uridine diphosphate glucuronyl transferase family members including uridine diphosphate glucuronosyltransferase family 1 member A3/4/6/8/9/10 (UGT1A3/4/6/8/9/10)
Eur J Clin Pharmacol Table 3
Parameter estimates and bootstrap results of valproic acid population pharmacokinetic model in children with epilepsy
Parameters
Ka (h 1) ˉ
CL (L/h) V (L) TDD on CL (L/h) UGT-CYP genotype on CL (L/h) σ (multiple, mg/L)
Estimate (% SE) 2.64 or 0.46*
Base model 95% CI
0.61 (0.021) 24.93 (4.14) -
0.57 to 0.65 16.78 to 33.08 -
0.35 (0.023)
0.30 to 0.39
IIV (CV%)
38.11 6.42
Final model-TDD Estimate 95% CI (% SE) 2.64 or 0.46* 0.73 (0.038) 0.66 to 0.81 22.12 (7.26) 7.84 to 36.40 0.59 (0.072) 0.45 to 0.73 ˉ0.22 ˉ0.33 to ˉ (0.056) 0.11 0.33 (0.017)
IIV (CV%)
25.86 6.47
0.29 to 0.36
Bootstrap-TDD Median 95% CI (% SE) 2.64 or 0.46* 0.73 (5.56) 0.65 to 0.81 21.83 (7.25) 13.30 to 41.75 0.60 (0.072) 0.46 to 0.75 ˉ0.22 ˉ0.33 to ˉ (0.056) 0.11 0.32 (0.020)
Final model-BSA Estimate 95% CI (% SE) 2.64 or 0.46* 0.70 (0.039) 0.63 to 0.78 18.36 (4.15) 10.20 to 26.52 0.57 (0.084) 0.41 to 0.74 ˉ0.19 ˉ0.31 to ˉ (0.061) 0.072
IIV (CV%)
32.84 6.49
Bootstrap-BSA Median 95% CI (% SE) 2.64 or 0.46* 0.70 (0.037) 0.63 to 0.78 17.46 (4.22) 12.36 to 28.91 0.57 (0.083) 0.42 to 0.73 ˉ0.19 ˉ0.32 to ˉ (0.061) 0.077
0.28 to 0.36 0.32 (0.026)
0.27 to 0.37
0.31 (0.039)
0.22 to 0.35
TDD total daily dose, BSA body surface area, RSE relative standard error, IIV inter-individual variability, CV coefficient of variation; 95%CI 95% confidence interval, Ka absorption rate, CL clearance, V distribution volume, UGT-CYP uridine diphosphate glucuronosyltransferase and cytochrome P450 family, σ coefficient variation of intra-individual variability *Ka was fixed at 2.64 h−1 for immediate release tablets/solutions, and 0.46 h−1 for controlled release tablets
and uridine diphosphate glucuronosyltransferase family 2 member B7/15 (UGT2B7/15) [32]. The influence of 12 selected variants on VPA clearance was evaluated. However, none of them had significant influence (P < 0.001) on VPA clearance after the addition of TDD or BSA on clearance. This could be explained by two reasons: the extensive metabolism of VPA via various kinds of enzymes, and the limited effect of a single nucleotide polymorphism on enzyme activity and VPA metabolism. Therefore, combination of the variants was performed. In our two final popPK models, the combined genotype (rs1042597, rs28365062, rs4986893, and rs4244285) has significant influence on VPA clearance, and it was partly in agreement with the results in the published popPK model, which indicated that rs4986893, rs4244285, and rs1057910 had significant influence on VPA clearance [12]. Interestingly, when
we combined rs1057910 with the combined variant of the four variants, the OFV value increased by 4.56. The effect of rs4986893 and rs4244285 on VPA clearance could be explained by the fact that both CYP2C19*2 (rs4244285) and CYP2C19*3 (rs4986893) resulted in nonfunctional protein expression [33, 34] and subsequently reduced VPA metabolism. When added on CL/F alone, rs1042597 (C > G, UGT1A8) increased VPA clearance, this finding was in accordance with its effect on raloxifene glucuronidation [35]. rs28365062 (A > G, UGT2B7) decreased the VPA clearance, and the result demonstrated that this variant was associated with a decreased enzyme activity, which was in agreement with its effect on efavirenz metabolism [36]. Cytochrome P450 family 2 subfamily C member 9 (CYP2C9), cytochrome P450 family 2 subfamily A member 6 (CYP2A6), and cytochrome P450 family 2
Fig. 1 Goodness-of-fit plot for the base model and the final model. a The observed concentrations versus population predicted concentrations. b The conditional weighted residuals (CWRES) versus population predicted concentrations. c The conditional weighted residuals versus
time after the last dose. d The conditional weighted residuals versus standard normal quantiles. Red lines represent LOESS smoothing. TDD total daily dose, BSA body surface area
Eur J Clin Pharmacol
subfamily B member 6 (CYP2B6) account for 15–20% of VPA metabolism [19, 37]. However, the lower expression of uridine 5′-diphosphate-glucuronyl transferases [38, 39] and the higher cytochrome P450 enzyme activities in children as compared to adults [40, 41], and the inhibition of mitochondrial β-oxidation by VPA and its metabolites [42, 43] indicated that cytochrome P450 enzymes might be more important for children than for adults in VPA metabolism [3]. CYP2C9*3 (1075 A > C, rs1057910) was associated with a decreased enzyme activity [25] and an increased VPA plasma concentrations [3, 18, 19], and it has been used to adjust VPA clearance in the popPK model [12]. Moreover, CYP2C9*3 was used in clinical practice for dose adjustment to avoid adverse drug reactions [3]. However, the influence of CYP2C9*3 on VPA clearance was not observed in our study due to its extremely low frequency (0.038 in our patients). The enzyme activity (represented by the bupropion clearance) in CYP2B6*4 (rs2279343) carriers was 1.66-fold higher than wild-type carriers [44], while CYP2A6*9 (rs28399433) resulted in a reduced level of mRNA and protein expression [45, 46]. However, neither CYP2B6*4 nor CYP2A6*9 has significant influence on VPA clearance in our final popPK model. In three popPK models of children, age had significant influence on VPA clearance and Vc/F [6–8]. The effect of age on VPA clearance could be explained by its effect on glucuronidation, a major pathway in VPA metabolism. Generally, the hepatic glucuronidation activities are low in infants, particularly in children younger than 2 years old, and reach the adult levels after 10–15 years old [38, 39]. The increase of Vc/F with age could be understood as the increase of BW with age [7]. In our popPK model, age had significant influence on VPA clearance and Vc/F when they were added alone, but both of the two effects were gone when they were added after the addition of TDD or BSA on clearance [5, 10, 13]. BW has significant influence on VPA clearance and Vc/ F in four popPK models in children [6–8, 13]. The influence of BW on Vc/F is easy to understand when patients with a larger BW have a higher distribution volume, and the effect of BW on VPA clearance in children may be explained by the correlation between BW and age, which was associated with VPA clearance in two popPK studies in children [6, 8]. The influence of BW on VPA clearance and Vc/F was observed in our model when each of them was added alone. However, after the addition of TDD on clearance, these effects were gone. The TDD of VPA usually increases with BW. In our children, TDD, BW, and age were collinear. TDD was correlated with BW (r = 0.49) and age (r = 0.53), and BW was strongly correlated with age (r = 0.906). Moreover, clearance and distribution volume has mathematical relationship (CL = kV). When TDD was
added on clearance with a reduction of OFV value by 87.2, the influence of age and BW on clearance or Vc/F was disappeared. Enzyme inducers of co-medicated antiepileptic drugs could increase VPA clearance [5, 6, 8, 10, 11, 13, 14, 16]. In our patients, only 12 patients were co-medicated with at least one of the three enzyme inducers (carbamazepine, phenobarbital, and phenytoin), and their influences on VPA clearance were not observed in our study as well as in other models [7, 9, 12, 15]. Females have lower VPA clearance than males in three VPA popPK models [10, 11, 14]; however, this finding was neither confirmed by other VPA popPK models [5–9, 12, 13, 15, 16] nor by ours. VPA clearance increased with TDD:BW ratio in two published popPK models [6, 16]; however, it was not confirmed in our models.
Deficiencies of the study Limitations of the study are listed below: (1) the sample size was relatively small (290 patients with 325 observations); (2) the Ka was fixed because very few samples were in the absorption phase [6]; (3) despite their influence on VPA metabolism, variants such as CYP2C9*2 were not chosen for analysis for their low frequency (minor allele frequency < 0.05) [12, 17, 47]; (4) the external evaluation of the model was not performed because data with genetic information could not be obtained from other study groups; (5) the influence of food on VPA metabolism was not considered [4]. However, this influence might be very limited because most of the observations were trough concentrations; (6) the analytical method used in the present study could not measure 5 of active VPA metabolites; therefore, the influence of these genetic polymorphisms on the concentration of VPA metabolites could not be evaluated; (7) the developed popPK model was not performed in actual clinical practice. However, Lin et al. (2015) used their popPK model in case examples for VPA dose adjustment with satisfactory results [5]. Moreover, Budi et al. (2015) nicely present real utility of CYP2C9 genotype guided VPA dosing to avoid the misdosing-induced side effects [3].
Conclusion Two popPK models for VPA in children with epilepsy have been successfully developed. TDD, BSA, and UGT-CYP genotype have significant influence on VPA clearance. Bootstrap and VPC results indicated that the two final popPK models were stable with acceptable predictive ability. The popPK models may be useful for dosing adjustment in children on VPA therapy. Further studies are warranted to confirm the results.
Eur J Clin Pharmacol Acknowledgements Thanks are given to our patients. Funding Author Weixing Feng was supported by the National Natural Science Foundation of China (No. 81301118).
Compliance with ethical standards All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Conflict of interest The authors declare that they have no conflict of interest.
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PHARMACOKINETICS AND DISPOSITION
Effect of CYP2C19, UGT1A8, and UGT2B7 on valproic acid clearance in children with epilepsy: a population pharmacokinetic model Shenghui Mei 1,2 & Weixing Feng 3,4 & Leting Zhu 1 & Xingang Li 1 & Yazhen Yu 4 & Weili Yang 4 & Baoqin Gao 4 & Xiaojuan Wu 4 & Fang Fang 3 & Zhigang Zhao 1,2 Received: 7 December 2017 / Accepted: 2 March 2018 # Springer-Verlag GmbH Germany, part of Springer Nature 2018
Abstract Purpose Valproic acid (VPA) is an important drug in seizure control with great inter-individual differences in metabolism and treatment effect. This study aims to identify the effects of genetic variants on VPA clearance in a population pharmacokinetic (popPK) model in children with epilepsy. Methods A total of 325 VPA plasma concentrations from 290 children with epilepsy were used to develop the popPK model by using the nonlinear mixed-effects modeling method. The one-compartment model was established to describe the pharmacokinetics of VPA. Twelve single nucleotide polymorphisms involved in the pharmacokinetics of VPA were identified by MassARRAY system and their effects on VPA clearance were evaluated. Results In the two final popPK models, inclusion of a combined genotype of four variants (rs1042597, rs28365062, rs4986893, and rs4244285), total daily dose (TDD), and body surface area (BSA) significantly reduced inter-individual variability for clearance over the base model. The inter-individual clearance equals to 0.73 × (TDD/628.92)0.59 × eUGT-CYP for TDD included model and 0.70 × (BSA/0.99)0.57 × eUGT-CYP for BSA included model. The precision of all parameters were acceptable (relative standard error < 32.81%). Bootstrap and visual predictive check results indicated that both two final popPK models were stable with acceptable predictive ability. Conclusion TDD, BSA, and genotype might affect VPA clearance in children. The popPK models may be useful for dosing adjustment in children on VPA therapy. Keywords Valproic acid . Uridine diphosphate glucuronosyltransferase . Cytochrome P450 family 2 subfamily C member 19 . Clearance . Children . Population pharmacokinetic model . Nonlinear mixed-effects modeling
Introduction Epilepsy is a global health problem with particularly high morbidity in children. The total direct healthcare costs per
person ranged from $10,192 to $47,862 for general epilepsy populations in the USA [1]. Valproic acid (VPA) is a first-line drug in treating patients with various kinds of seizures [2]. Due to its narrow therapeutic range and wide inter- and
Shenghui Mei and Weixing Feng are equal first authors. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00228-018-2440-6) contains supplementary material, which is available to authorized users. * Fang Fang [email protected] * Zhigang Zhao [email protected] 1
Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, 6 Tiantan Xili, Dongcheng District, Beijing 100050, People’s Republic of China
2
Department of Clinical Pharmacology, College of Pharmaceutical Sciences, Capital Medical University, Beijing 100045, People’s Republic of China
3
Department of Neurology, Beijing Children’s Hospital, Capital Medical University, 56 Nanlishi Road, Xicheng District, Beijing 100045, People’s Republic of China
4
Department of Pediatrics, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China
Eur J Clin Pharmacol
intra-individual pharmacokinetic variability, the dosage adjustment is a challenge for clinicians because under dosing might evoke epilepsy, whereas overdoing increased side effects, especially in pediatric patients [3]. Various factors involved in VPA absorption (diet, soybean intake, and dosage form) [4], distribution [body weight (BW), age, total daily dose (TDD), and protein binding] [5–11], and metabolism (gender, age, hepatic function, TDD, genotype of enzymes related to VPA glucuronidation and oxidation, coadministered enzyme inducers of antiepileptic drugs such as phenobarbital, phenytoin, and carbamazepine) have significant influence on VPA plasma concentration [3, 5, 6, 8, 10–24]. For example, cytochrome P450 family 2 subfamily C member 9*3 (CYP2C9*3, rs1057910) was associated with a decreased enzyme activity [25] and an increased VPA concentration and clearance [3, 12, 18, 19]. The quantitative relation between VPA plasma concentration and these influence factors should be identified prior to the individualized therapy. Therefore, various population pharmacokinetic (popPK) models were constructed by using the nonlinear mixed-effects modeling method (NONMEM) [5–16, 22]. The pharmacokinetics of VPA could be best estimated by one-compartment model with first-order absorption and elimination [5, 6, 8–16]. Only two studies used twocompartment model for data fitting [7, 22]. In previous published studies, BW, age, and TDD could affect VPA distribution volume [6–11], while BW, age, gender, TDD, and comedications had significant influence on VPA clearance [5–16]. Although various genetic polymorphisms were associated with the increase or decrease of VPA plasma concentration [3, 12, 17–21, 23, 24], only one study identified the quantitative relationship between genetic polymorphisms [cytochrome P450 family 2 subfamily C member 19 (CYP2C19) and CYP2C9 genotype] and VPA clearance [12]. The aim of this study is to identify the influence of metabolic enzyme genotypes and patients’ characteristics on VPA pharmacokinetic parameters in a popPK model, which might be useful for VPA dose adjustment in clinical practice.
data [gender, age, BW, and body surface area (BSA)], dosage regimen (dose, dosing time, and frequency), and coadministered antiepileptic drugs were recorded. After regularly taking VPA for at least 6 days, the blood samples were collected and the sampling time was recorded. The blood plasma was used for VPA concentration analysis while the white blood cells were used for genotyping [26].
Plasma concentration of VPA The total plasma concentrations of VPA were evaluated by a fluorescence polarization immunoassay (TDx, ABBOTT, USA). Calibrators and quality control samples were routinely performed according to the manufacturer’s instructions for quality control.
Genotype identification Based on previous studies and the PharmGKB database, 12 genetic variants were selected [3, 12, 17–21, 23, 24]. Patients’ DNA were purified by QIAamp DNA purification kit (Qiagen, Hilden, Germany). Genotyping was identified in Bio Miao Biological Technology (Beijing) by MassArray method (Sequenom, USA) [26]. To control the quality of genotyping, 5% of the whole patients were measured repeatedly, and the results were acceptable.
Grouping and combination of variants Each of the selected variants was divided into three or two groups by genotype. The group number of variants increased with the decrease of enzyme activity or VPA clearance (Table 1). Any two of the variants that were divided into three groups were combined into a new variant by summation of their group numbers. Then, the combined new variant was separated into three or two groups according to the combination rules (Table 2) and the new group number increased with the decrease of enzyme activity and VPA clearance.
Statistical analysis
Methods Study design This study was approved by the Ethics Committee of Beijing Tiantan Hospital, Capital Medical University, Beijing, China. Consent from the parents and assent from children were obtained. All patients were Chinese origin. The inclusion and exclusion criteria were the same as that mentioned in our previous study [23]. A total of 290 children (325 plasma concentrations) on VPA treatment in Beijing Tiantan Hospital were enrolled according to the criteria described above. Patients’ demographic
Statistical analysis was carried out by PLINK software (version 1.07, Shaun Purcell, Boston, USA). For all selected alleles, the minor allele frequency, genotype, and HardyWeinberg Equilibrium (P value, chi-square test) were calculated.
Population pharmacokinetic model development To develop the popPK model, a nonlinear mixed-effects modeling approach was performed by using the Phoenix® NLME™ 7.0 (Certara, St. Louis, MO) software. The firstorder conditional estimation-extended least squares method,
Eur J Clin Pharmacol Table 1 Grouping rules for variants
Group type
Enzyme activity changing by variants
Wild-type homozygote
Heterozygote
Variant homozygote
Three groups
Increased Decreased
3 1
2 2
1 3
Two groups, rule 1
Increased Decreased
2 1
1 2
1 2
Two groups, rule 2
Increased Decreased
2 1
2 1
1 2
which was equivalent to the NONMEM first-order conditional estimation methodology with interaction, was used for model construction [27]. VPC and bootstrap were performed to test the predictive ability and stability of the final model, respectively.
Base model The one-compartment model with first-order absorption and first-order elimination was used to describe the time course of VPA in human plasma (Appendix 1) [5, 6, 8–16, 22]. The model was parameterized by using the absorption rate constant (Ka), apparent volume of central compartment (Vc/F), and apparent total clearance (CL/F). Very few samples were at the absorption phase, and the VPA formulations were the same to a published study [6]. Therefore, Ka was fixed at 2.64 and 0.46 h−1 for immediate release tablets/solutions and controlled release tablets, respectively [6]. The residual error was characterized by the multiple model. The following equations are used to describe the model: dAa =dt ¼ −K a Aa
ð1Þ
dAc =dt ¼ K a Aa −CLc C c
ð2Þ
C c ¼ Ac =V c
ð3Þ
Ka and CLc/F represent the absorption rate and clearance of VPA, respectively. Aa and Ac represent the amount of VPA in
Table 2
Combination rules for any two of variants
Combined new group type
Summation of two group numbers
Combined new group number
Three groups
2 3 4, 5 2 3, 4, 5 2, 3 4, 5
1 2 3 1 2 1 2
Two groups, rule 1 Two groups, rule 2
the absorption site and central compartment, respectively. Cc represents the VPA concentration in the central compartment.
Population pharmacokinetic model After the construction of the base model, the influence of various covariates on VPA pharmacokinetic parameters was evaluated. All of the continuous covariates were centered at their mean or median values. Between- and intrasubject variability (η and ε) of pharmacokinetic parameters were assumed to follow a normal distribution with a mean of zero and a variance of ω2 and σ2, respectively. In the present study, most patients had only one observation, which might be associated with a higher value of shrinkage [28]. Therefore, the differences in the objective function value (OFV) were used for model comparison [6]. In covariate selection, compared to its previous model, an intermediate model with an OFV decrease > 6.635 (P < 0.01, df = 1) was considered to be superior for forward addition. After all covariates were added, the full model was refined by removing the added covariates using a tougher criterion: an increase in the OFV of > 10.828 (P < 0.001, df = 1) [5–7, 10–12, 29].
Goodness-of-fit and model evaluation OFV and the scatter plots including observed concentrations versus population predicted concentrations, conditional weighted residuals (CWRES) versus population predicted concentrations, CWRES versus time after the last dose, and CWRES versus standard normal quantiles were used to evaluate the goodness-of-fit between base model and final model. A total of 1000 bootstrap and 2000 VPC were performed to evaluate the stability and predictive ability of the final model, respectively [30]. The median, the 2.5–97.5% intervals of estimated parameters, and the 5–95% prediction intervals of the simulated data were calculated. The prediction ability of the final popPK model is acceptable when 90% of the measured VPA concentrations are within the 90% prediction interval.
Eur J Clin Pharmacol
Results Demographic data and genotyping of enrolled patients A total of 325 VPA plasma concentrations were obtained from 290 children (108 females and 182 males). A total of 196 patients were treated by controlled release tablets of VPA, while 94 children were treated by immediate release tablets or solutions. Patients’ characteristics and dosage regimens are shown in Appendix 2 (detail in Appendix 6). The base information of the 12 identified variants is listed in Appendix 3. The frequency of selected gene locus all conformed to the Hardy-Weinberg equilibrium (P > 0.05).
compared to the base model. The success rate (successful in minimization) of both bootstrap analyses was 100%. The median value and the distribution of the popPK parameters obtained from bootstrap were similar to the values observed in the final popPK model, which indicated that the final popPK model was robust (Table 3). For popPK models with TDD or BSA as covariates, 89.2% (35/325) and 90.4% (31/325) of the measured VPA plasma concentrations fell inside the 90% prediction interval (Appendix 5). Stratified VPC analysis with genotype indicated that both two popPK models did a better job for genotype 2 versus genotype 1. Moreover, the predictive ability of BSA-included popPK model was superior than the TDD-included model.
Model construction and final popPK model
Discussion
The covariates were added in descending order of reduction in the OFV value (Appendix 4). Two final popPK models were obtained by using TDD or BSA as covariates. TDD, BSA, and uridine diphosphate glucuronosyltransferase and cytochrome P450 (UGT-CYP) genotype have significant influence on VPA clearance. Their quantitative relationships are listed below: For model with TDD,
Model development
CL=F ¼ 0:73 ðTDD=628:92Þ0:59 eUGT−CYP eηCL ð4Þ V c = F ¼ 22:12 eηV c
ð5Þ
For model with BSA, CL=F ¼ 0:70 ðBSA=0:99Þ0:57 eUGT−CYP eηCL V c = F ¼ 18:36 e
ηV c
ð6Þ ð7Þ
where 0.73 and 0.70 (L/h) are the typical value of CL/F (L/h), and 22.12 and 18.36 (L) are typical value of Vc/F (L). The mean value of TDD is 628.92 mg/day, and the median value of BSA is 0.99 m2. The estimated coefficients representing the relationship between clearance and TDD or BSA are 0.59 and 0.57, respectively. In Eqs. 4 and 6, UGT-CYP is the genotype of four combined variants (rs1042597, rs28365062, rs4986893, and rs4244285), and UGT-CYP = 0 for patients with group 1 genotype, otherwise UGT-CYP = − 0.22 for Eq. 4 and − 0.19 for Eq. 6. Table 3 lists the estimate, standard error, 95% confidence interval, and inter-individual variability of the parameters for the base model, two final models, and bootstrap.
Goodness-of-fit and model evaluation Four pairs of scatter plots are shown in Fig. 1 to evaluate the goodness-of-fit of the final popPK models. In general, the two final popPK models obviously improved data fitting
The one-compartment popPK model, which has been widely used in previous studies [5, 6, 8–16], was employed to describe the pharmacokinetic property of VPA in our patients. In one of the two final popPK models, TDD has an extremely significant influence on VPA clearance, which has been reported in four published popPK models [5, 10, 11, 13], and it could be partly explained by the fact that patients with higher VPA clearance need a higher dose to ensure the VPA concentration within the therapeutic window (also known as therapeutic drug monitoring effect) [6]. The mechanism for this phenomenon is due to protein binding being saturable in the therapeutic range, increasing doses result in increased free fraction and therefore higher clearance [5, 6]. TDD has a significant influence on Vc/F in two popPK studies [10, 11]. In the present study, TDD alone had a significant influence on Vc/F, but this effect was removed when it was added after the addition of TDD on clearance. BSA, an important indicator, was used widely in population pharmacokinetic analyses to describe inter-individual variance in drug clearance [31]. Two VPA popPK studies chose BSA as covariates, but neither of them successfully added BSA on any of the parameters [13, 16]. In the final popPK model without TDD, BSA increased with the increase of VPA clearance, which could be explained by the following reasons: VPA is excreted partly via kidney, and higher BSA is related to a higher basal metabolic rate and glomerular filtration rate [27]; in children, VPA dose was mainly depended on the BW, which was highly correlated with BSA [31]. Glucuronidation and mitochondrial β-oxidation are two major pathways for VPA metabolism in adults. About 30– 50% of VPA was metabolized into VPA-glucuronide conjugate by uridine diphosphate glucuronyl transferase family members including uridine diphosphate glucuronosyltransferase family 1 member A3/4/6/8/9/10 (UGT1A3/4/6/8/9/10)
Eur J Clin Pharmacol Table 3
Parameter estimates and bootstrap results of valproic acid population pharmacokinetic model in children with epilepsy
Parameters
Ka (h 1) ˉ
CL (L/h) V (L) TDD on CL (L/h) UGT-CYP genotype on CL (L/h) σ (multiple, mg/L)
Estimate (% SE) 2.64 or 0.46*
Base model 95% CI
0.61 (0.021) 24.93 (4.14) -
0.57 to 0.65 16.78 to 33.08 -
0.35 (0.023)
0.30 to 0.39
IIV (CV%)
38.11 6.42
Final model-TDD Estimate 95% CI (% SE) 2.64 or 0.46* 0.73 (0.038) 0.66 to 0.81 22.12 (7.26) 7.84 to 36.40 0.59 (0.072) 0.45 to 0.73 ˉ0.22 ˉ0.33 to ˉ (0.056) 0.11 0.33 (0.017)
IIV (CV%)
25.86 6.47
0.29 to 0.36
Bootstrap-TDD Median 95% CI (% SE) 2.64 or 0.46* 0.73 (5.56) 0.65 to 0.81 21.83 (7.25) 13.30 to 41.75 0.60 (0.072) 0.46 to 0.75 ˉ0.22 ˉ0.33 to ˉ (0.056) 0.11 0.32 (0.020)
Final model-BSA Estimate 95% CI (% SE) 2.64 or 0.46* 0.70 (0.039) 0.63 to 0.78 18.36 (4.15) 10.20 to 26.52 0.57 (0.084) 0.41 to 0.74 ˉ0.19 ˉ0.31 to ˉ (0.061) 0.072
IIV (CV%)
32.84 6.49
Bootstrap-BSA Median 95% CI (% SE) 2.64 or 0.46* 0.70 (0.037) 0.63 to 0.78 17.46 (4.22) 12.36 to 28.91 0.57 (0.083) 0.42 to 0.73 ˉ0.19 ˉ0.32 to ˉ (0.061) 0.077
0.28 to 0.36 0.32 (0.026)
0.27 to 0.37
0.31 (0.039)
0.22 to 0.35
TDD total daily dose, BSA body surface area, RSE relative standard error, IIV inter-individual variability, CV coefficient of variation; 95%CI 95% confidence interval, Ka absorption rate, CL clearance, V distribution volume, UGT-CYP uridine diphosphate glucuronosyltransferase and cytochrome P450 family, σ coefficient variation of intra-individual variability *Ka was fixed at 2.64 h−1 for immediate release tablets/solutions, and 0.46 h−1 for controlled release tablets
and uridine diphosphate glucuronosyltransferase family 2 member B7/15 (UGT2B7/15) [32]. The influence of 12 selected variants on VPA clearance was evaluated. However, none of them had significant influence (P < 0.001) on VPA clearance after the addition of TDD or BSA on clearance. This could be explained by two reasons: the extensive metabolism of VPA via various kinds of enzymes, and the limited effect of a single nucleotide polymorphism on enzyme activity and VPA metabolism. Therefore, combination of the variants was performed. In our two final popPK models, the combined genotype (rs1042597, rs28365062, rs4986893, and rs4244285) has significant influence on VPA clearance, and it was partly in agreement with the results in the published popPK model, which indicated that rs4986893, rs4244285, and rs1057910 had significant influence on VPA clearance [12]. Interestingly, when
we combined rs1057910 with the combined variant of the four variants, the OFV value increased by 4.56. The effect of rs4986893 and rs4244285 on VPA clearance could be explained by the fact that both CYP2C19*2 (rs4244285) and CYP2C19*3 (rs4986893) resulted in nonfunctional protein expression [33, 34] and subsequently reduced VPA metabolism. When added on CL/F alone, rs1042597 (C > G, UGT1A8) increased VPA clearance, this finding was in accordance with its effect on raloxifene glucuronidation [35]. rs28365062 (A > G, UGT2B7) decreased the VPA clearance, and the result demonstrated that this variant was associated with a decreased enzyme activity, which was in agreement with its effect on efavirenz metabolism [36]. Cytochrome P450 family 2 subfamily C member 9 (CYP2C9), cytochrome P450 family 2 subfamily A member 6 (CYP2A6), and cytochrome P450 family 2
Fig. 1 Goodness-of-fit plot for the base model and the final model. a The observed concentrations versus population predicted concentrations. b The conditional weighted residuals (CWRES) versus population predicted concentrations. c The conditional weighted residuals versus
time after the last dose. d The conditional weighted residuals versus standard normal quantiles. Red lines represent LOESS smoothing. TDD total daily dose, BSA body surface area
Eur J Clin Pharmacol
subfamily B member 6 (CYP2B6) account for 15–20% of VPA metabolism [19, 37]. However, the lower expression of uridine 5′-diphosphate-glucuronyl transferases [38, 39] and the higher cytochrome P450 enzyme activities in children as compared to adults [40, 41], and the inhibition of mitochondrial β-oxidation by VPA and its metabolites [42, 43] indicated that cytochrome P450 enzymes might be more important for children than for adults in VPA metabolism [3]. CYP2C9*3 (1075 A > C, rs1057910) was associated with a decreased enzyme activity [25] and an increased VPA plasma concentrations [3, 18, 19], and it has been used to adjust VPA clearance in the popPK model [12]. Moreover, CYP2C9*3 was used in clinical practice for dose adjustment to avoid adverse drug reactions [3]. However, the influence of CYP2C9*3 on VPA clearance was not observed in our study due to its extremely low frequency (0.038 in our patients). The enzyme activity (represented by the bupropion clearance) in CYP2B6*4 (rs2279343) carriers was 1.66-fold higher than wild-type carriers [44], while CYP2A6*9 (rs28399433) resulted in a reduced level of mRNA and protein expression [45, 46]. However, neither CYP2B6*4 nor CYP2A6*9 has significant influence on VPA clearance in our final popPK model. In three popPK models of children, age had significant influence on VPA clearance and Vc/F [6–8]. The effect of age on VPA clearance could be explained by its effect on glucuronidation, a major pathway in VPA metabolism. Generally, the hepatic glucuronidation activities are low in infants, particularly in children younger than 2 years old, and reach the adult levels after 10–15 years old [38, 39]. The increase of Vc/F with age could be understood as the increase of BW with age [7]. In our popPK model, age had significant influence on VPA clearance and Vc/F when they were added alone, but both of the two effects were gone when they were added after the addition of TDD or BSA on clearance [5, 10, 13]. BW has significant influence on VPA clearance and Vc/ F in four popPK models in children [6–8, 13]. The influence of BW on Vc/F is easy to understand when patients with a larger BW have a higher distribution volume, and the effect of BW on VPA clearance in children may be explained by the correlation between BW and age, which was associated with VPA clearance in two popPK studies in children [6, 8]. The influence of BW on VPA clearance and Vc/F was observed in our model when each of them was added alone. However, after the addition of TDD on clearance, these effects were gone. The TDD of VPA usually increases with BW. In our children, TDD, BW, and age were collinear. TDD was correlated with BW (r = 0.49) and age (r = 0.53), and BW was strongly correlated with age (r = 0.906). Moreover, clearance and distribution volume has mathematical relationship (CL = kV). When TDD was
added on clearance with a reduction of OFV value by 87.2, the influence of age and BW on clearance or Vc/F was disappeared. Enzyme inducers of co-medicated antiepileptic drugs could increase VPA clearance [5, 6, 8, 10, 11, 13, 14, 16]. In our patients, only 12 patients were co-medicated with at least one of the three enzyme inducers (carbamazepine, phenobarbital, and phenytoin), and their influences on VPA clearance were not observed in our study as well as in other models [7, 9, 12, 15]. Females have lower VPA clearance than males in three VPA popPK models [10, 11, 14]; however, this finding was neither confirmed by other VPA popPK models [5–9, 12, 13, 15, 16] nor by ours. VPA clearance increased with TDD:BW ratio in two published popPK models [6, 16]; however, it was not confirmed in our models.
Deficiencies of the study Limitations of the study are listed below: (1) the sample size was relatively small (290 patients with 325 observations); (2) the Ka was fixed because very few samples were in the absorption phase [6]; (3) despite their influence on VPA metabolism, variants such as CYP2C9*2 were not chosen for analysis for their low frequency (minor allele frequency < 0.05) [12, 17, 47]; (4) the external evaluation of the model was not performed because data with genetic information could not be obtained from other study groups; (5) the influence of food on VPA metabolism was not considered [4]. However, this influence might be very limited because most of the observations were trough concentrations; (6) the analytical method used in the present study could not measure 5 of active VPA metabolites; therefore, the influence of these genetic polymorphisms on the concentration of VPA metabolites could not be evaluated; (7) the developed popPK model was not performed in actual clinical practice. However, Lin et al. (2015) used their popPK model in case examples for VPA dose adjustment with satisfactory results [5]. Moreover, Budi et al. (2015) nicely present real utility of CYP2C9 genotype guided VPA dosing to avoid the misdosing-induced side effects [3].
Conclusion Two popPK models for VPA in children with epilepsy have been successfully developed. TDD, BSA, and UGT-CYP genotype have significant influence on VPA clearance. Bootstrap and VPC results indicated that the two final popPK models were stable with acceptable predictive ability. The popPK models may be useful for dosing adjustment in children on VPA therapy. Further studies are warranted to confirm the results.
Eur J Clin Pharmacol Acknowledgements Thanks are given to our patients. Funding Author Weixing Feng was supported by the National Natural Science Foundation of China (No. 81301118).
Compliance with ethical standards All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Conflict of interest The authors declare that they have no conflict of interest.
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