Impact of CYP2D6*2, CYP2D6*35, rs5758550, and related haplotypes on risperidone clearance in vivo.

PURPOSE: The CYP2D6 gene exhibits significant polymorphism, contributing to variability in responses to drugs metabolized by CYP2D6. While CYP2D6*2 and CYP2D6*35 are presently designated as alleles encoding normal metabolism, this classification is based on moderate level evidence. Additionally, the role of the formerly called "enhancer" single nucleotide polymorphism (SNP) rs5758550 is unclear. In this study, the impacts of CYP2D6*2, CYP2D6*35 and rs5758550 on CYP2D6 activity were investigated using risperidone clearance as CYP2D6 activity marker. METHODS: A joint parent-metabolite population pharmacokinetic model was used to describe 1,565 serum concentration measurements of risperidone and 9-hydroxyrisperidone in 512 subjects. Risperidone population clearance was modeled as the sum of a CYP2D6-independent clearance term and the partial clearances contributed from each individually expressed CYP2D6 allele or haplotype. In addition to the well-characterized CYP2D6 alleles (*3-*6, *9, *10 and *41), *2, *35 and two haplotypes assigned as CYP2D6*2-rs5758550G and CYP2D6*2-rs5758550A were evaluated. RESULTS: Each evaluated CYP2D6 allele was associated with significantly lower risperidone clearance than the reference normal function allele CYP2D6*1 (p < 0.001). Further, rs5758550 differentiated the effect of CYP2D6*2 (p = 0.005). The haplotype-specific clearances for CYP2D6*2-rs5758550A, CYP2D6*2-rs5758550G and CYP2D6*35 were estimated to 30%, 66% and 57%, respectively, relative to the clearance for CYP2D6*1. Notably, rs5758550 is in high linkage disequilibrium (R2 > 0.85) with at least 24 other SNPs and cannot be assigned as a functional SNP. CONCLUSION: CYP2D6*2 and CYP2D6*35 encode reduced risperidone clearance, and the extent of reduction for CYP2D6*2 is differentiated by rs5758550. Genotyping of these haplotypes might improve the precision of genotype-guided prediction of CYP2D6-mediated clearance.

Effect of the NFIB rs28379954 T>C polymorphism on CYP2D6-catalyzed metabolism of solanidine.

Cytochrome P450 2D6 (CYP2D6) is important for metabolism of 20%-25% of all clinically used drugs. Many known genetic variants contribute to the large interindividual variability in CYP2D6 metabolism, but much is still unexplained. We recently described that nuclear factor 1B (NFIB) regulates hepatic CYP2D6 expression with the minor allele of NFIB rs28379954 T>C significantly increasing CYP2D6-mediated risperidone metabolism. In this study, we investigated the effect of NFIB T>C on metabolism of solanidine, a dietary CYP2D6 substrate. Analyses of solanidine and metabolites (M414, M416, and M444) were performed by ultra-high performance liquid chromatography-high-resolution mass spectrometry in a cohort of 463 CYP2D6-genotyped patients of which with 58 (12.5%) carried NFIB TC (n = 56) or CC (n = 2). Increased metabolism of solanidine was found in CYP2D6 normal metabolizers (NMs; n = 258, 55.7%) carrying the NFIB C variant (n = 27, 5.8%) with 2.83- and 3.38-fold higher M416-to-solanidine (p = 0.039) and M444-to-solanidine (p = 0.046) ratios, respectively, whereas this effect was not significant among intermediate metabolizers (n = 166, 35.9%) (p ≥ 0.09). Importantly, no effect of the NFIB polymorphism on solanidine metabolism was seen in TC or CC carriers lacking CYP2D6 activity (poor metabolizers, n = 30, 6.5%, p ≥ 0.74). Furthermore, the NFIB polymorphism significantly explained variability in solanidine metabolism (M414 p = 0.013, M416 p = 0.020, and M416 and M444 p = 0.009) in multiple linear regression models for each metabolic ratio in the entire population, correcting for covariates (including CYP2D6 genotypes). Thus, the study confirms the effect of NFIB in regulating CYP2D6 activity, suggesting an about 200% increase in CYP2D6-mediated clearance in NMs being NFIB CT or CC carriers, comprising around 6% of Europeans.

Evidence for solanidine as a dietary CYP2D6 biomarker: Significant correlation with risperidone metabolism.

AIMS: The extensive variability in cytochrome P450 2D6 (CYP2D6) metabolism is mainly caused by genetic polymorphisms. However, there is large, unexplained variability in CYP2D6 metabolism within CYP2D6 genotype subgroups. Solanidine, a dietary compound found in potatoes, is a promising phenotype biomarker predicting individual CYP2D6 metabolism. The aim of this study was to investigate the correlation between solanidine metabolism and the CYP2D6-mediated metabolism of risperidone in patients with known CYP2D6 genotypes. METHODS: The study included therapeutic drug monitoring (TDM) data from CYP2D6-genotyped patients treated with risperidone. Risperidone and 9-hydroxyrisperidone levels were determined during TDM, and reprocessing of the respective TDM full-scan high-resolution mass spectrometry files was applied for semi-quantitative measurements of solanidine and five metabolites (M402, M414, M416, M440 and M444). Spearman's tests determined the correlations between solanidine metabolic ratios (MRs) and the 9-hydroxyrisperidone-to-risperidone ratio. RESULTS: A total of 229 patients were included. Highly significant, positive correlationswere observed between all solanidine MRs and the 9-hydroxyrisperidone-to-risperidone ratio (ρ > 0.6, P < .0001). The strongest correlation was observed for the M444-to-solanidine MR in patients with functional CYP2D6 metabolism, i.e., genotype activity scores of 1 and 1.5 (ρ 0.72-0.77, P < .0001). CONCLUSION: The present study shows strong, positive correlations between solanidine metabolism and CYP2D6-mediated risperidone metabolism. The strong correlation within patients carrying CYP2D6 genotypes encoding functional CYP2D6 metabolism suggests that solanidine metabolism may predict individual CYP2D6 metabolism, and hence potentially improve personalized dosing of drugs metabolized by CYP2D6.

Dopamine agonist serum concentrations and impulse control disorders in Parkinson's disease.

BACKGROUND AND PURPOSE: Impulse control disorders (ICDs) are common among Parkinson's disease patients using dopamine agonists. We wanted to determine whether ICD patients have higher dopamine agonist serum concentrations than those without any sign of ICD. METHODS: Patients who used either pramipexole or ropinirole depot once daily were screened for ICDs using the validated Questionnaire for Impulsive-Compulsive Disorders in Parkinson's Disease-Rating Scale. Those who scored above the cut-off for one or more of the four defined ICDs (gambling, compulsive sexual behavior, compulsive shopping, and binge-eating) were compared in a case-control study to patients who scored zero points (no evidence of ICD) on the same items. They were examined clinically and evaluated using relevant scales. Three blood samples were taken on the same day: before daily dose, and then 6 and 12 h later. RESULTS: Forty-six patients were included: 19 ICD-positive and 27 controls. Ropinirole serum concentrations 6 h after daily intake (Cmax ) were higher in the case group compared to the control group, as was the daily ropinirole dosage. No differences were observed in serum concentrations, dosage or total drug exposure for pramipexole. Disease duration and length of dopamine agonist treatment was significantly longer among ICD patients for ropinirole, but not for pramipexole. CONCLUSIONS: The use of pramipexole may in itself confer high ICD risk, whereas ICDs among ropinirole users depend more on serum concentration and drug exposure. The pharmacokinetic properties of ropinirole make it challenging to predict its effects on patients, which supports the need for therapeutic drug monitoring to reduce risk of ICD.

Pro-arrhythmic effect of escitalopram and citalopram at serum concentrations commonly observed in older patients - a study based on a cohort of 19,742 patients.

BACKGROUND: For a decade, patients have been advised against using high citalopram- and escitalopram-doses due to risk for ventricular arrhythmia and cardiac arrest. Still, these drugs are widely used to treat depression and anxiety especially in older patients. It is unclear why they are cardiotoxic and at what serum concentrations patients are at risk for arrhythmias. Thus, how many patients that are at risk for iatrogenic cardiac arrest is unknown. METHODS: We studied the arrhythmogenic effects of citalopram, escitalopram and their metabolites on human cardiomyocytes. Concentrations showing pro-arrhythmic activity were compared with observed drug and metabolite serum concentrations in a cohort of 19,742 patients (age 12-105 years) using escitalopram or citalopram in Norway (2010-2019). As arrhythmia-risk is related to maximum serum concentration, this was simulated for different age-groups from the escitalopram patient material. FINDINGS: Therapeutic concentrations of both citalopram and escitalopram but not their metabolites showed pro-arrhythmic changes in the human cardiac action potential. Due to age-dependent reduction of drug clearance, the proportion of patients above threshold for arrhythmia-risk increased with age. 20% of patients >65 years were predicted to reach potentially pro-arrhythmic concentrations, following intake of 10 mg escitalopram. INTERPRETATION: All patients that are using escitalopram or citalopram and have genetic disposition for acquired long-QT syndrome, are >65 years, are using additional pro-arrhythmic drugs or have predisposition for arrhythmias, should be monitored with therapeutic drug monitoring (TDM) to avoid exposure to potentially cardiotoxic concentrations. Serum concentrations should be kept below 100 nM, to reduce arrhythmia-risk. FUNDING: This study was funded by The Research Council of Norway (project number: 324062).

Prediction of CYP2D6 poor metabolizers by measurements of solanidine and metabolites-a study in 839 patients with known CYP2D6 genotype.

PURPOSE: Poor metabolizers (PMs) of the highly polymorphic enzyme CYP2D6 are usually at high risk of adverse effects during standard recommended dosing of CYP2D6-metabolized drugs. We studied if the metabolism of solanidine, a dietary compound found in potatoes, could serve as a biomarker predicting the CYP2D6 PM phenotype for precision dosing. METHODS: The study included 839 CYP2D6-genotyped patients who were randomized by a 4:1 ratio into test or validation cohorts. Full-scan high-resolution mass spectrometry data files of previously analyzed serum samples were reprocessed for identification and quantification of solanidine and seven metabolites. Metabolite-to-solanidine ratios (MRs) of the various solanidine metabolites were calculated prior to performing receiver operator characteristic (ROC) and multiple linear regression analyses on the test cohort. The MR thresholds obtained from the ROC analyses were tested for the prediction of CYP2D6 PMs in the validation cohort. RESULTS: In the test cohort, the M414-to-solanidine MR attained the highest sensitivity and specificity parameters from the ROC analyses (0.98 and 1.00) and highest explained variance from the linear models (R2 = 0.68). Below these thresholds, CYP2D6 PM predictions were tested in the validation cohort providing positive and negative predictive values of 100% for the MR of M414, while similar values for the other MRs ranged from 20.5 to 73.3% and 96.7 to 99.3%, respectively. CONCLUSION: The M414-to-solanidine MR is an excellent predictor of the CYP2D6 PM phenotype. By measuring solanidine and metabolites using liquid chromatography-mass spectrometry in patient serum samples, CYP2D6 PMs can easily be identified, hence facilitating the implementation of precision dosing in clinical practice.

Recovery of CYP3A Phenotype after Kidney Transplantation.

End-stage renal disease impairs drug metabolism via cytochrome P450 CYP3A; however, it is unclear whether CYP3A activity recovers after kidney transplantation. Therefore, the aim of this study was to evaluate the change in CYP3A activity measured as 4ß-hydroxycholesterol (4ßOHC) concentration after kidney transplantation. In total, data from 58 renal transplant recipients with 550 prospective 4ßOHC measurements were included in the study. One sample per patient was collected before transplantation, and 2-12 samples per patient were collected 1-82 days after transplantation. The measured pretransplant 4ßOHC concentrations ranged by >7-fold, with a median value of 22.8 ng/ml. Linear mixed-model analysis identified a 0.16-ng/ml increase in 4ßOHC concentration per day after transplantation (P < 0.001), indicating a regain in CYP3A activity. Increasing estimated glomerular filtration rate after transplantation was associated with increasing 4ßOHC concentration (P < 0.001), supporting that CYP3A activity increases with recovering uremia. In conclusion, this study indicates that CYP3A activity is regained subsequent to kidney transplantation.

High Tacrolimus Clearance Is a Risk Factor for Acute Rejection in the Early Phase After Renal Transplantation.

BACKGROUND: Patients with high tacrolimus clearance eliminate more drug within a dose interval compared with those with low clearance. Delays in dosing time will result in transient periods of lower concentrations in high versus low clearance patients. Transient subtherapeutic tacrolimus concentrations may induce acute rejection episodes. METHODS: A retrospective study in all renal transplant patients treated with tacrolimus at our center from 2009 to 2013 was conducted. The association between individually estimated tacrolimus clearance (daily tacrolimus dose [mg]/trough concentration [µg/L]) and biopsy-proven acute rejection (BPAR) the first 90 days posttransplantation was investigated. RESULTS: In total, 638 patients treated with oral tacrolimus were included in the analysis. Eighty-five (13.3%) patients experienced BPAR. Patients were stratified into 4 groups per their estimated clearance. The patients in the high clearance group had significantly higher incidence of BPAR (20.6%) with a hazard ratio of 2.39 (95% confidence interval, 1.30-4.40) compared with the low clearance group. Clearance estimate (as a continuous variable) showed a hazard ratio of 2.25 (95% confidence interval, 1.70-2.99) after adjusting for other risk factors. There were no significant differences in neither trough concentrations the first week after transplantation nor time to target trough concentration between patients later experiencing BPAR or not. CONCLUSIONS: High estimated clearance is significantly associated with increased risk of BPAR the first 90 days posttransplantation and may predict an increased risk of rejection in the early phase after renal transplantation.

The CYP3A biomarker 4ß-hydroxycholesterol does not improve tacrolimus dose predictions early after kidney transplantation.

AIMS: Tacrolimus is a cornerstone in modern immunosuppressive therapy after kidney transplantation. Tacrolimus dosing is challenged by considerable pharmacokinetic variability, both between patients and over time after transplantation, partly due to variability in cytochrome P450 3A (CYP3A) activity. The aim of this study was to assess the value of the endogenous CYP3A marker 4ß-hydroxycholesterol (4ßOHC) for tacrolimus dose individualization early after kidney transplantation. METHODS: Data were obtained from 79 adult kidney transplant recipients who contributed a total of 625 4ßOHC measurements and 1999 tacrolimus whole blood concentrations during the first 2 months after transplantation. The relationships between 4ßOHC levels and individual estimates of tacrolimus apparent plasma clearance (CL/Fplasma ) at different time points after transplantation were investigated using scatterplots and population pharmacokinetic modelling. RESULTS: There was no significant correlation between pre-transplant 4ßOHC levels and tacrolimus CL/Fplasma the first week (r = 0.19 [95% CI -0.03-0.40]) or between 4ßOHC and tacrolimus CL/Fplasma 1 week (r = 0.20 [-0.11-0.47]), 4 weeks (r = 0.21 [-0.07-0.46]) or 2 months (r = 0.24 [-0.03-0.48]) after transplantation (P ≥ 0.06). In the population analysis, time-varying 4ßOHC was not a statistically significant covariate on tacrolimus CL/Fplasma , neither in terms of absolute values (P = 0.11) nor in terms of changes from baseline (P = 0.17). 4ßOHC values increased between 1 week and 2 months after transplantation (median change +57% [IQR +22-83%], P < 0.001), indicating increasing CYP3A activity. Contradictorily, tacrolimus CL/Fplasma decreased over the same period (median change -13% [IQR -3 to -26%], P < 0.001). CONCLUSIONS: 4ßOHC does not appear to have a clinical potential to improve individualization of tacrolimus doses early after kidney transplantation.

Prediction of Fat-Free Mass in Kidney Transplant Recipients.

BACKGROUND: Individualization of drug doses is essential in kidney transplant recipients. For many drugs, the individual dose is better predicted when using fat-free mass (FFM) as a scaling factor. Multiple equations have been developed to predict FFM based on healthy subjects. These equations have not been evaluated in kidney transplant recipients. The objectives of this study were to develop a kidney transplant specific equation for FFM prediction and to evaluate its predictive performance compared with previously published equations. METHODS: Ten weeks after transplantation, FFM was measured by dual-energy X-ray absorptiometry. Data from a consecutive cohort of 369 kidney transplant recipients were randomly assigned to an equation development data set (n = 245) or an evaluation data set (n = 124). Prediction equations were developed using linear and nonlinear regression analysis. The predictive performance of the developed equation and previously published equations in the evaluation data set was assessed. RESULTS: The following equation was developed: FFM (kg) = {FFMmax × body weight (kg)/[81.3 + body weight (kg)]} × [1 + height (cm) × 0.052] × [1-age (years) × 0.0007], where FFMmax was estimated to be 11.4 in males and 10.2 in females. This equation provided an unbiased, precise prediction of FFM in the evaluation data set: mean error (ME) (95% CI), -0.71 kg (-1.60 to 0.19 kg) in males and -0.36 kg (-1.52 to 0.80 kg) in females, root mean squared error 4.21 kg (1.65-6.77 kg) in males and 3.49 kg (1.15-5.84 kg) in females. Using previously published equations, FFM was systematically overpredicted in kidney-transplanted males [ME +1.33 kg (0.40-2.25 kg) to +5.01 kg (4.06-5.95 kg)], but not in females [ME -2.99 kg (-4.07 to -1.90 kg) to +3.45 kg (2.29-4.61) kg]. CONCLUSIONS: A new equation for FFM prediction in kidney transplant recipients has been developed. The equation may be used for population pharmacokinetic modeling and clinical dose selection in kidney transplant recipients.

Low-target tacrolimus in de novo standard risk renal transplant recipients: A single-centre experience.

AIM: Optimal tacrolimus exposure in transplant recipients is not well established. The results from the Symphony study indicated that low-target tacrolimus (trough concentrations 3-7 µg/L) in de novo standard risk renal transplant recipients should be appropriate. The aim of this study was to evaluate real-life outcomes when applying a similar strategy in a clinical setting. METHODS: A single-centre analysis was conducted in standard risk renal transplant recipients receiving low-target tacrolimus, mycophenolate mofetil, glucocorticoids and basiliximab induction. One-year estimated glomerular filtration rate (eGFR, Cockcroft-Gault), one-year biopsy-proven acute rejection rate and graft- and patient survival up to 3 years post-transplant were compared with the outcomes in the Symphony study. RESULTS: From 1 January 2009 to 31 March 2013, we included 406 patients. One year after transplantation, the mean ± SD eGFR was 76.8 ± 28.3 mL/min (Symphony: 65.4 ± 27.0 mL/min, P < 0.001). Biopsy-proven acute rejections were seen in 14.5% of the patients (Symphony: 12.3%, P = 0.35). Kaplan-Meier estimates [95% confidence interval] of three-year death-censored graft- and patient survival were 96.6% [94.2-99.0%] (Symphony: 93%) and 95.0% [92.6-97.3%] (Symphony: 95%), respectively. CONCLUSION: Low-target tacrolimus-based immunosuppression is safe and effective also in a standard clinical setting in de novo standard risk renal transplant recipients.

Improved Tacrolimus Target Concentration Achievement Using Computerized Dosing in Renal Transplant Recipients--A Prospective, Randomized Study.

BACKGROUND: Early after renal transplantation, it is often challenging to achieve and maintain tacrolimus concentrations within the target range. Computerized dose individualization using population pharmacokinetic models may be helpful. The objective of this study was to prospectively evaluate the target concentration achievement of tacrolimus using computerized dosing compared with conventional dosing performed by experienced transplant physicians. METHODS: A single-center, prospective study was conducted. Renal transplant recipients were randomized to receive either computerized or conventional tacrolimus dosing during the first 8 weeks after transplantation. The median proportion of tacrolimus trough concentrations within the target range was compared between the groups. Standard risk (target, 3-7 µg/L) and high-risk (8-12 µg/L) recipients were analyzed separately. RESULTS: Eighty renal transplant recipients were randomized, and 78 were included in the analysis (computerized dosing (n = 39): 32 standard risk/7 high-risk, conventional dosing (n = 39): 35 standard risk/4 high-risk). A total of 1711 tacrolimus whole blood concentrations were evaluated. The proportion of concentrations per patient within the target range was significantly higher with computerized dosing than with conventional dosing, both in standard risk patients (medians, 90% [95% confidence interval {95% CI}, 84-95%] vs 78% [95% CI, 76-82%], respectively, P < 0.001) and in high-risk patients (medians, 77% [95% CI, 71-80%] vs 59% [95% CI, 40-74%], respectively, P = 0.04). CONCLUSIONS: Computerized dose individualization improves target concentration achievement of tacrolimus after renal transplantation. The computer software is applicable as a clinical dosing tool to optimize tacrolimus exposure and may potentially improve long-term outcome.

Improved prediction of tacrolimus concentrations early after kidney transplantation using theory-based pharmacokinetic modelling.

AIMS: The aim was to develop a theory-based population pharmacokinetic model of tacrolimus in adult kidney transplant recipients and to externally evaluate this model and two previous empirical models. METHODS: Data were obtained from 242 patients with 3100 tacrolimus whole blood concentrations. External evaluation was performed by examining model predictive performance using Bayesian forecasting. RESULTS: Pharmacokinetic disposition parameters were estimated based on tacrolimus plasma concentrations, predicted from whole blood concentrations, haematocrit and literature values for tacrolimus binding to red blood cells. Disposition parameters were allometrically scaled to fat free mass. Tacrolimus whole blood clearance/bioavailability standardized to haematocrit of 45% and fat free mass of 60 kg was estimated to be 16.1 l h−1 [95% CI 12.6, 18.0 l h−1]. Tacrolimus clearance was 30% higher (95% CI 13, 46%) and bioavailability 18% lower (95% CI 2, 29%) in CYP3A5 expressers compared with non-expressers. An Emax model described decreasing tacrolimus bioavailability with increasing prednisolone dose. The theory-based model was superior to the empirical models during external evaluation displaying a median prediction error of −1.2% (95% CI −3.0, 0.1%). Based on simulation, Bayesian forecasting led to 65% (95% CI 62, 68%) of patients achieving a tacrolimus average steady-state concentration within a suggested acceptable range. CONCLUSION: A theory-based population pharmacokinetic model was superior to two empirical models for prediction of tacrolimus concentrations and seemed suitable for Bayesian prediction of tacrolimus doses early after kidney transplantation.

Importance of hematocrit for a tacrolimus target concentration strategy.

PURPOSE: To identify patient characteristics that influence tacrolimus individual dose requirement in kidney transplant recipients. METHODS: Data on forty-four 12-h pharmacokinetic profiles from 29 patients and trough concentrations in 44 patients measured during the first 70 days after transplantation (1,546 tacrolimus whole blood concentrations) were analyzed. Population pharmacokinetic modeling was performed using NONMEM 7.2®. RESULTS: Standardization of tacrolimus whole blood concentrations to a hematocrit value of 45 % improved the model fit significantly (p<0.001). Fat-free mass was the best body size metric to predict tacrolimus clearance and volume of distribution. Bioavailability was 49 % lower in expressers of cytochrome P450 3A5 (CYP3A5) than in CYP3A5 nonexpressers. Younger females (<40 years) showed a 35 % lower bioavailability than younger males. Bioavailability increased with age for both males and females towards a common value at age >55 years that was 47 % higher than the male value at age <40 years. Bioavailability was highest immediately after transplantation, decreasing steeply thereafter to reach its nadir at day 5, following which it increased during the next 55 days towards an asymptotic value that was 28 % higher than that on day 5. CONCLUSIONS: Hematocrit predicts variability in tacrolimus whole blood concentrations but is not expected to influence unbound (therapeutically active) concentrations. Fat-free mass, CYP3A5 genotype, sex, age and time after transplant influence the tacrolimus individual dose requirement. Because hematocrit is highly variable in kidney transplant patients and increases substantially after kidney transplantation, hematocrit is a key factor in the interpretation of tacrolimus whole blood concentrations.

Inclusion of CYP3A5 genotyping in a nonparametric population model improves dosing of tacrolimus early after transplantation.

Following organ engraftment, initial dosing of tacrolimus is based on recipient weight and adjusted by measured C(0) concentrations. The bioavailability and elimination of tacrolimus are affected by the patients CYP3A5 genotype. Prospective data of the clinical advantage of knowing patient's CYP3A5 genotype prior to transplantation are lacking. A nonparametric population model was developed for tacrolimus in renal transplant recipients. Data from 99 patients were used for model development and validation. A three-compartment model with first-order absorption and lag time from the dosing compartment described the data well. Clearances and volumes of distribution were allometrically scaled to body size. The final model included fat-free mass, body mass index, hematocrit, time after transplantation, and CYP3A5 genotype as covariates. The bias and imprecision were 0.35 and 1.38, respectively, in the external data set. Patients with functional CYP3A5 had 26% higher clearance and 37% lower bioavailability. Knowledge of CYP3A5 genotype provided an initial advantage, but only until 3-4 tacrolimus concentrations were known. After this, a model without CYP3A5 genotype predicted just as well. The present models seem applicable for clinical individual dose predictions but need a prospective evaluation.