Predictive Value of CYP3A and ABCB1 Phenotyping Probes for the Pharmacokinetics of Sunitinib: the ClearSun Study

The Use of Microdosing for In vivo Phenotyping of Cytochrome P450 Enzymes: Where Do We Stand? A Narrative Review

Cytochrome P450 (CYP) enzymes play a central role in the elimination of approximately 80% of all clinically used drugs. Differences in CYP enzyme activity between individuals can contribute to interindividual variability in exposure and, therefore, treatment outcome. In vivo CYP enzyme activity could be determined with phenotyping. Currently, (sub)therapeutic doses are used for in vivo phenotyping, which can lead to side effects. The use of microdoses (100 µg) for in vivo phenotyping for CYP enzymes could overcome the limitations associated with the use of (sub)therapeutic doses of substrates. The aim of this review is to provide a critical overview of the application of microdosing for in vivo phenotyping of CYP enzymes. A literature search was performed to find drug-drug interaction studies of CYP enzyme substrates that used microdoses of the respective substrates. A substrate was deemed sensitive to changes in CYP enzyme activity when the pharmacokinetics of the substrate significantly changed during inhibition and induction of the enzyme. On the basis of the currently available evidence, the use of microdosing for in vivo phenotyping for subtypes CYP1A2, CYP2C9, CYP2D6, and CYP2E1 is not recommended. Microdosing can be used for the in vivo phenotyping of CYP2C19 and CYP3A. The recommended microdose phenotyping test for CYP2C19 is measuring the omeprazole area-under-the-concentration-time curve over 24 h (AUC0-24) after administration of a single 100 µg dose. CYP3A activity could be best determined with a 0.1-75 µg dose of midazolam, and subsequently measuring AUC extrapolated to infinity (AUC∞) or clearance. Moreover, there are two metrics available for midazolam using a limited sampling strategy: AUC over 10 h (AUC0-10) and AUC from 2 to 4 h (AUC2-4).

Dietary sodium restriction prevents vascular endothelial growth factor inhibitor-induced hypertension

BACKGROUND

Vascular endothelial growth factor inhibitors (VEGFIs) are effective anticancer agents which often induce hypertension. VEGFI-induced hypertension is sodium-sensitive in animal studies. Therefore, the efficacy of dietary sodium restriction (DSR) to prevent VEGFI-induced hypertension in cancer patients was studied.

METHODS

Cancer patients with VEGFI-induced hypertension (day mean >135/85 mmHg or a rise in systolic and/or diastolic BP ≥ 20 mmHg) were treated with DSR (aiming at <4 g salt/day). The primary endpoint was the difference in daytime mean arterial blood pressure (MAP) increase between the treatment cycle with and without DSR.

RESULTS

During the first VEGFI treatment cycle without DSR, mean daytime MAP increased from 95 to 110 mmHg. During the subsequent treatment cycle with DSR, mean daytime MAP increased from 94 to 102 mmHg. Therefore, DSR attenuated the increase in mean daytime MAP by 7 mmHg (95% CI 1.3-12.0, P = 0.009). DSR prevented the rise in the endothelin-1/renin ratio that normally accompanies VEGFI-induced hypertension (P = 0.020) and prevented the onset of proteinuria: 0.15 (0.10-0.25) g/24 h with DSR versus 0.19 (0.11-0.32) g/24 h without DSR; P = 0.005.

DISCUSSION

DSR significantly attenuated VEGFI induced BP rise and proteinuria and thus is an effective non-pharmacological intervention.

Interindividual Variability in Cytochrome P450 3A and 1A Activity Influences Sunitinib Metabolism and Bioactivation

Sunitinib is an orally administered tyrosine kinase inhibitor associated with idiosyncratic hepatotoxicity; however, the mechanisms of this toxicity remain unclear. We have previously shown that cytochromes P450 1A2 and 3A4 catalyze sunitinib metabolic activation via oxidative defluorination leading to a chemically reactive, potentially toxic quinoneimine, trapped as a glutathione (GSH) conjugate (M5). The goals of this study were to determine the impact of interindividual variability in P450 1A and 3A activity on sunitinib bioactivation to the reactive quinoneimine and sunitinib N-dealkylation to the primary active metabolite N-desethylsunitinib (M1). Experiments were conducted in vitro using single-donor human liver microsomes and human hepatocytes. Relative sunitinib metabolite levels were measured by liquid chromatography-tandem mass spectrometry. In human liver microsomes, the P450 3A inhibitor ketoconazole significantly reduced M1 formation compared to the control. The P450 1A2 inhibitor furafylline significantly reduced defluorosunitinib (M3) and M5 formation compared to the control but had minimal effect on M1. In CYP3A5-genotyped human liver microsomes from 12 individual donors, M1 formation was highly correlated with P450 3A activity measured by midazolam 1'-hydroxylation, and M3 and M5 formation was correlated with P450 1A2 activity estimated by phenacetin O-deethylation. M3 and M5 formation was also associated with P450 3A5-selective activity. In sandwich-cultured human hepatocytes, the P450 3A inducer rifampicin significantly increased M1 levels. P450 1A induction by omeprazole markedly increased M3 formation and the generation of a quinoneimine-cysteine conjugate (M6) identified as a downstream metabolite of M5. The nonselective P450 inhibitor 1-aminobenzotriazole reduced each of these metabolites (M1, M3, and M6). Collectively, these findings indicate that P450 3A activity is a key determinant of sunitinib N-dealkylation to the active metabolite M1, and P450 1A (and potentially 3A5) activity influences sunitinib bioactivation to the reactive quinoneimine metabolite. Accordingly, modulation of P450 activity due to genetic and/or nongenetic factors may impact the risk of sunitinib-associated toxicities.

Sensitive UHPLC-MS/MS quantification method for 4β- and 4α-hydroxycholesterol in human plasma for accurate CYP3A phenotyping

4β-Hydroxycholesterol (4β-OHC) is formed by CYP3A4 and CYP3A5 and has drawn attention as an endogenous phenotyping probe for CYP3A activity. However, 4β-OHC is also increased by cholesterol autooxidation occurring in vitro due to dysregulated storage and in vivo by oxidative stress or inflammation, independent of CYP3A activity. 4α-hydroxycholesterol (4α-OHC), a stereoisomer of 4β-OHC, is also formed via autooxidation of cholesterol, not by CYP3A, and thus may have clinical potential in reflecting the state of cholesterol autooxidation. In this study, we establish a sensitive method for simultaneous quantification of 4β-OHC and 4α-OHC in human plasma using ultra-high performance liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS). Plasma samples were prepared by saponification, two-step liquid-liquid extraction, and derivatization using picolinic acid. Intense [M+H]+ signals for 4β-OHC and 4α-OHC di-picolinyl esters were monitored using electrospray ionization. The assay fulfilled the requirements of the US Food and Drug Administration guidance for bioanalytical method validation, with a lower limit of quantification of 0.5 ng/mL for both 4β-OHC and 4α-OHC. Apparent recovery rates from human plasma ranged from 88.2% to 101.5% for 4β-OHC, and 91.8% to 114.9% for 4α-OHC. Additionally, matrix effects varied between 86.2% and 117.6% for 4β-OHC, and between 89.5% and 116.9% for 4α-OHC. Plasma 4β-OHC and 4α-OHC concentrations in healthy volunteers, stage 3-5 chronic kidney disease (CKD) patients, and stage 5D CKD patients as measured by the validated assay were within the calibration ranges in all samples. We propose this novel quantification method may contribute to accurate evaluation of in vivo CYP3A activity.

Therapeutic Drug Monitoring of Sunitinib in Gastrointestinal Stromal Tumors and Metastatic Renal Cell Carcinoma in Adults-A Review

BACKGROUND

Sunitinib is an inhibitor of multiple receptor tyrosine kinases and is a standard-of-care treatment for advanced and metastatic renal cell carcinoma and a second-line treatment in locally advanced inoperable and metastatic gastrointestinal stromal tumors. A fixed dose of the drug, however, does not produce a uniform therapeutic outcome in all patients, and many face adverse effects and/or toxicity. One of the possible causes of the interindividual variability in the efficacy and toxicity response is the highly variable systemic exposure to sunitinib and its active metabolite. This review aims to summarize all available clinical evidence of the treatment of adult patients using sunitinib in approved indications, addressing the necessity to introduce proper and robust therapeutic drug monitoring (TDM) of sunitinib and its major metabolite, N-desethylsunitinib.

METHODS

The authors performed a systematic search of the available scientific literature using the PubMed online database. The search terms were "sunitinib" AND "therapeutic drug monitoring" OR "TDM" OR "plasma levels" OR "concentration" OR "exposure." The search yielded 520 journal articles. In total, 447 publications were excluded because they lacked sufficient relevance to the reviewed topic. The remaining 73 articles were, together with currently valid guidelines, thoroughly reviewed.

RESULTS

There is sufficient evidence confirming the concentration-efficacy and concentration-toxicity relationship in the indications of gastrointestinal stromal tumors and metastatic renal clear-cell carcinoma. For optimal therapeutic response, total (sunitinib + N-desethylsunitinib) trough levels of 50-100 ng/mL serve as a reasonable target therapeutic range. To avoid toxicity, the total trough levels should not exceed 100 ng/mL.

CONCLUSIONS

According to the current evidence presented in this review, a TDM-guided dose modification of sunitinib in selected groups of patients could provide a better treatment outcome while simultaneously preventing sunitinib toxicity.

Feasibility of Extrapolating Randomly Taken Plasma Samples to Trough Levels for Therapeutic Drug Monitoring Purposes of Small Molecule Kinase Inhibitors

Small molecule kinase inhibitors (SMKIs) are widely used in oncology. Therapeutic drug monitoring (TDM) for SMKIs could reduce underexposure or overexposure. However, logistical issues such as timing of blood withdrawals hamper its implementation into clinical practice. Extrapolating a random concentration to a trough concentration using the elimination half-life could be a simple and easy way to overcome this problem. In our study plasma concentrations observed during 24 h blood sampling were used for extrapolation to trough levels. The objective was to demonstrate that extrapolation of randomly taken blood samples will lead to equivalent estimated trough samples compared to measured C_min values. In total 2241 blood samples were analyzed. The estimated C_trough levels of afatinib and sunitinib fulfilled the equivalence criteria if the samples were drawn after T_max. The calculated C_trough levels of erlotinib, imatinib and sorafenib met the equivalence criteria if they were taken, respectively, 12 h, 3 h and 10 h after drug intake. For regorafenib extrapolation was not feasible. In conclusion, extrapolation of randomly taken drug concentrations to a trough concentration using the mean elimination half-life is feasible for multiple SMKIs. Therefore, this simple method could positively contribute to the implementation of TDM in oncology.

Imatinib, sunitinib and pazopanib: From flat-fixed dosing towards a pharmacokinetically guided personalized dose

Tyrosine kinase inhibitors (TKIs) are anti‐cancer drugs that target tyrosine kinases, enzymes that are involved in multiple cellular processes. Currently, multiple oral TKIs have been introduced in the treatment of solid tumours, all administered in a fixed dose, although large interpatient pharmacokinetic (PK) variability is described. For imatinib, sunitinib and pazopanib exposure‐treatment outcome (efficacy and toxicity) relationships have been established and therapeutic windows have been defined, therefore dose optimization based on the measured blood concentration, called therapeutic drug monitoring (TDM), can be valuable in increasing efficacy and reducing the toxicity of these drugs.

In this review, an overview of the current knowledge on TDM guided individualized dosing of imatinib, sunitinib and pazopanib for the treatment of solid tumours is presented. We summarize preclinical and clinical data that have defined thresholds for efficacy and toxicity. Furthermore, PK models and factors that influence the PK of these drugs which partly explain the interpatient PK variability are summarized. Finally, pharmacological interventions that have been performed to optimize plasma concentrations are described. Based on current literature, we advise which methods should be used to optimize exposure to imatinib, sunitinib and pazopanib.

Development of a Pharmacokinetic Model to Describe the Complex Pharmacokinetics of Pazopanib in Cancer Patients

BACKGROUND AND OBJECTIVE

Pazopanib is a multi-targeted anticancer tyrosine kinase inhibitor. This study was conducted to develop a population pharmacokinetic (popPK) model describing the complex pharmacokinetics of pazopanib in cancer patients.

METHODS

Pharmacokinetic data were available from 96 patients from three clinical studies. A multi-compartment model including (i) a complex absorption profile, (ii) the potential non-linear dose-concentration relationship and (iii) the potential long-term decrease in exposure was developed.

RESULTS

A two-compartment model best described pazopanib pharmacokinetics. The absorption phase was modelled by two first-order processes: 36 % (relative standard error [RSE] 34 %) of the administered dose was absorbed with a relatively fast rate (0.4 h^-1 [RSE 31 %]); after a lag time of 1.0 h (RSE 6 %), the remaining dose was absorbed at a slower rate (0.1 h^-1 [RSE 28 %]). The relative bioavailability (rF) at a dose of 200 mg was fixed to 1. With an increasing dose, the rF was strongly reduced, which was modelled with an E _max (maximum effect) model (E _max was fixed to 1, the dose at half of maximum effect was estimated as 480 mg [RSE 23 %]). Interestingly, the plasma exposure to pazopanib also decreased over time, modelled on rF with a maximum magnitude of 50 % (RSE 27 %) and a first-order decay constant of 0.15 day^-1 (RSE 43 %). The inter-patient and intra-patient variability on rF were estimated as 36 % (RSE 16 %) and 75 % (RSE 22 %), respectively.

CONCLUSION

A popPK model for pazopanib was developed that illustrated the complex absorption process, the non-linear dose-concentration relationship, the high inter-patient and intra-patient variability, and the first-order decay of pazopanib concentration over time. The developed popPK model can be used in clinical practice to screen covariates and guide therapeutic drug monitoring.

Drug monitoring of sunitinib in patients with advanced solid tumors: a monocentric observational French study

Therapeutic drug monitoring (TDM) could be helpful in oral targeted therapies. Data are sparse to evaluate its impact on treatment management. This study aimed to determine a threshold value of plasma drug exposure associated with the occurrence of grade 3-4 toxicity, then the potential impact of TDM on clinical decision. Consecutive outpatients treated with sunitinib were prospectively monitored between days 21 and 28 of the first cycle, then monthly until disease progression. At each consultation, the composite AUC_Ƭ,ss (sunitinib + active metabolite SU12662) was assayed. The decisions taken during each consultation were matched with AUC_Ƭ,ss and compared to the decisional algorithm based on TDM. A total of 105 cancer patients and 288 consultations were matched with the closest AUC_Ƭ,ss measurement. The majority (60%) of the patients had metastatic renal clear-cell carcinoma (mRCC). Fifty-five (52%) patients experienced grade 3-4 toxicity. Multivariate analysis identified composite AUC_Ƭ,ss as a parameter independently associated with grade 3-4 toxicity (P < 0.0001). Using the ROC curve, the threshold value of composite AUC_Ƭ,ss predicting grade ≥3 toxicity was 2150 ng/mL/h (CI 95%, 0.6-0.79%; P < 0.0001). At disease progression in patients with mRCC, AUC_Ƭ,ss tended to be lower than the one assayed during the first cycle (1678 vs. 2004 ng/mL/h, respectively, P = 0.072). TDM could have changed the medical decision for sunitinib dosing in 30% of patients at the first cycle of treatment, and in 46% of the patients over the whole treatment course. TDM is routinely feasible and may both contribute to improve toxicity management and to identify sunitinib underexposure at the time of disease progression.

Overcoming ABC transporter-mediated multidrug resistance: The dual role of tyrosine kinase inhibitors as multitargeting agents

Resistance to conventional and target specific antitumor drugs still remains one of the major cause of treatment failure and patience death. This condition often involves ATP-binding cassette (ABC) transporters that, by pumping the drugs outside from cancer cells, attenuate the potency of chemotherapeutics and negatively impact on the fate of anticancer therapy. In recent years, several tyrosine kinase inhibitors (TKIs) (e.g., imatinib, nilotinib, dasatinib, ponatinib, gefitinib, erlotinib, lapatinib, vandetanib, sunitinib, sorafenib) have been reported to interact with ABC transporters (e.g., ABCB1, ABCC1, ABCG2, ABCC10). This finding disclosed a very complex scenario in which TKIs may behave as substrates or inhibitors depending on the expression of specific pumps, drug concentration, affinity for transporters and types of co-administered agents. In this context, in-depth investigation on TKI chemosensitizing functions might provide a strong rationale for combining TKIs and conventional therapeutics in specific malignancies. The reposition of TKIs as antagonists of ABC transporters opens a new way towards anticancer therapy and clinical strategies aimed at counteracting drug resistance. This review will focus on some paradigmatic examples of the complex and not yet fully elucidated interaction between clinical available TKIs (e.g. BCR-ABL, EGFR, VEGFR inhibitors) with the main ABC transporters implicated in multidrug resistance.

Influence of OATP1B1 Function on the Disposition of Sorafenib-β-D-Glucuronide

The oral multikinase inhibitor sorafenib undergoes extensive UGT1A9-mediated formation of sorafenib-β-D-glucuronide (SG). Using transporter-deficient mouse models, it was previously established that SG can be extruded into bile by ABCC2 or follow a liver-to-blood shuttling loop via ABCC3-mediated efflux into the systemic circulation, and subsequent uptake in neighboring hepatocytes by OATP1B-type transporters. Here we evaluated the possibility that this unusual process, called hepatocyte hopping, is also operational in humans and can be modulated through pharmacological inhibition. We found that SG transport by OATP1B1 or murine Oatp1b2 was effectively inhibited by rifampin, and that this agent can significantly increase plasma levels of SG in wildtype mice, but not in Oatp1b2-deficient animals. In human subjects receiving sorafenib, rifampin acutely increased the systemic exposure to SG. Our study emphasizes the need to consider hepatic handling of xenobiotic glucuronides in the design of drug-drug interaction studies of agents that undergo extensive phase II conjugation.

BSA and ABCB1 polymorphism affect the pharmacokinetics of sunitinib and its active metabolite in Asian mRCC patients receiving an attenuated sunitinib dosing regimen

PURPOSE

An attenuated dosing (AD) sunitinib regimen of 37.5 mg daily has been suggested to reduce the toxicity reported with the standard dosing regimen to metastatic renal cell carcinoma (mRCC) patients. The aim of this study was to characterize the population pharmacokinetic (PK) properties of sunitinib and SU12662, the active metabolite, in patients receiving the AD regimen and to ascertain significant covariates influencing PK parameters.

METHODS

Thirty-one mRCC patients receiving AD sunitinib regimen were included. Plasma samples were collected on day 29 of each treatment cycle after the start of the therapy. Nonlinear mixed-effects modeling was applied to estimate the population PK properties of sunitinib and SU12662 as well as the effect of covariates on PK parameters. Monte Carlo simulation was also performed to predict the total trough level (TTL) of sunitinib and SU12662.

RESULTS

Sunitinib population means for CL/F and V d /F central were 13.8 L/h and 1720 L, respectively. SU12662 population means for CL/F and V d /F were 42.1 L/h and 1410 L, respectively. Body surface area (BSA) and ABCB1 polymorphism significantly influenced the CL/F variability of sunitinib: CL/F parent = 13.8 × exp((BSA - 1.75) × 2.08 + (ABCB1 genotype - 0.67) × 0.61), ABCB1-0: wild genotype, 1: mutant genotype. The effect size of ABCB1 mutant genotype and BSA greater than 1.75 m(2) in relation to sunitinib clearance was 31.14 % (p = 0.006) and 22.11 % (p = 0.011), respectively, relative to the reference group.

CONCLUSIONS

Adjusting doses of sunitinib according to BSA and ABCB1 polymorphism in Asian mRCC patients may be recommended for sufficient attainment of a target TTL of sunitinib and its metabolite.

CYP3A activity: towards dose adaptation to the individual

INTRODUCTION

Co-medication, gene polymorphisms and co-morbidity are main causes for high variability in expression and function of the CYP3A isoenzymes. Pharmacokinetic variability is a major source of interindividual variability of drug effect and response of CYP3A substrates. While CYP3A genotyping is of limited use, direct testing of enzyme function ('phenotyping') may be more promising to achieve individualized dosing of CYP3A substrates.

AREAS COVERED

We will discuss available phenotyping strategies for CYP3A isoenzymes and causes of intra- and interindividual variability of CYP3A. The impact of phenotyping on the dose selection and pharmacokinetics of CYP3A substrates (docetaxel, irinotecan, tyrosine kinase inhibitors, ciclosporin, tacrolimus) are reviewed. Pubmed searches were conducted during March-November 2015 to retrieve articles related to CYP3A enzyme, phenotyping, drug interactions with CYP3A probe substrates, and phenotyping-guided dosing algorithms.

EXPERT OPINION

While ample data is available on the choice appropriate phenotyping drugs (midazolam, alfentanil, aplrazolam, buspirone, triazolam), less clinical trial data is available concerning strategies to usefully guide dosing in the clinical practice. Implementation into the clinical routine necessitates further research to identify (1) an easy-to-use and cheap test for CYP3A activity that (2) adequately predicts drug exposure to (3) allow a sound decision on dose adaptation and hence (4) improve clinical outcome and/or reduce the intensity or frequency of adverse drug effects.

Integrated semi-physiological pharmacokinetic model for both sunitinib and its active metabolite SU12662

AIMS

Previously published pharmacokinetic (PK) models for sunitinib and its active metabolite SU12662 were based on a limited dataset or lacked important elements such as correlations between sunitinib and its metabolite. The current study aimed to develop an improved PK model that circumvented these limitations and to prove the utility of the PK model in treatment optimization in clinical practice.

METHODS

One thousand two hundred and five plasma samples from 70 cancer patients were collected from three PK studies with sunitinib and SU12662. A semi-physiological PK model for sunitinib and SU12662 was developed incorporating pre-systemic metabolism using non-linear mixed effects modelling (nonmem). Allometric scaling based on body weight was applied. The final model was used for simulation of the PK of different treatment regimens.

RESULTS

Sunitinib and SU12662 PK were best described by a one and two compartment model, respectively. Introduction of pre-systemic formation of SU12662 strongly improved model fit, compared with solely systemic metabolism. The clearance of sunitinib and SU12662 was estimated at 35.7 (relative standard error (RSE) 5.7%) l h(-1) and 17.1 (RSE 7.4%) l h(-1), respectively for 70 kg patients. Correlation coefficients were estimated between inter-individual variability of both clearances, both volumes of distribution and between clearance and volume of distribution of SU12662 as 0.53, 0.48 and 0.45, respectively. Simulation of the PK model predicted correctly the ratio of patients who did not reach proposed PK targets for efficacy.

CONCLUSIONS

A semi-physiological PK model for sunitinib and SU12662 in cancer patients was presented including pre-systemic metabolism. The model was superior to previous PK models in many aspects.

Pharmacokinetically guided sunitinib dosing: a feasibility study in patients with advanced solid tumours

Background:

Plasma exposure of sunitinib shows large inter-individual variation. Therefore, a pharmacokinetic (PK) study was performed to determine safety and feasibility of sunitinib dosing based on PK levels.

Methods:

Patients were treated with sunitinib 37.5 mg once daily. At days 15 and 29 of treatment, plasma trough levels of sunitinib and N-desethyl sunitinib were measured. If the total trough level (TTL) was <50 ng ml⁻¹ and the patient did not show any grade ⩾3 toxicity, the daily sunitinib dose was increased by 12.5 mg. If the patient suffered from grade ⩾3 toxicity, the sunitinib dose was lowered by 12.5 mg.

Results:

Twenty-nine out of 43 patients were evaluable for PK assessments. Grade ⩾3 adverse events were experienced in seven patients (24%) at the starting dose and in nine patients (31%) after dose escalation. TTLs were below target in 15 patients (52%) at the starting dose. Of these, five patients (17%) reached target TTL after dose escalation without additional toxicity.

Conclusions:

In a third of the patients that were below target TTL at standard dose, the sunitinib dose could be increased without additional toxicities. This could be the basis for future studies and the implementation of a PK-guided dosing strategy in clinical practice.

Association analysis of genetic polymorphisms in genes related to sunitinib pharmacokinetics, specifically clearance of sunitinib and SU12662

Interpatient variability in the pharmacokinetics (PK) of sunitinib is high. Single nucleotide polymorphisms (SNPs) in PK candidate genes have been associated with the efficacy and toxicity of sunitinib, but whether these SNPs truly affect the PK of sunitinib remains to be elucidated. This multicenter study involving 114 patients investigated whether these SNPs and haplotypes in genes encoding metabolizing enzymes or efflux transporters are associated with the clearance of sunitinib and its active metabolite SU12662. SNPs were tested as covariates in a population PK model. From univariate analysis, we found that the SNPs in CYP3A4, CYP3A5, and ABCB1 were associated with the clearance of both sunitinib and SU12662. In multivariate analysis, CYP3A4*22 was found to be eliminated last with an effect size of -22.5% on clearance. Observed effect sizes are below the interindividual variability in clearance and are therefore too limited to directly guide individual dosing of sunitinib.