Midazolam as a phenotyping probe to predict sunitinib exposure in patients with cancer

An Investigation in the Comparability of the Exposure and Recommended Dose of Selected Pfizer Drugs in East Asian Countries: Is Mutual Usage of Clinical Data Among East Asian Countries Feasible?

The current regulatory path for new drug registration in East Asian countries has led to significant delay of the new medicines in these countries. A unified regulatory path and allowance of mutual usage of clinical data in East Asian countries would lead to cost saving in drug development and expedite the new drug registration in these countries. The objectives of the present analysis are to compare the approval dates of a selection of products developed by Pfizer in the United States and East Asian countries (China, Japan, Korea) and compare the pharmacokinetics and recommended doses of these products in East Asian countries. Eighteen products (20 drugs, 2 products with 2 combination drugs) with exposure data available in at least 2 of the 3 East Asian countries across different therapeutic areas were included in the analyses. The results showed that most products had delayed approval in East Asian countries (up to 8 years) after US or EU approval. No distinct differences were observed in the drug exposure and recommended doses for the selected products in East Asian countries. These results together with literature data of genetic similarity of the East Asian populations support the mutual usage of the clinical data in the East Asian countries for expedited regulatory submission and approval.

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).

Therapeutic drug monitoring to personalize dosing of imatinib, sunitinib, and pazopanib: A mixed methods study on barriers and facilitators

BACKGROUND

Personalized dosing based on measurement of individual drug levels and adjusting the dose accordingly can improve efficacy and decrease unnecessary toxicity of oncological treatment. For imatinib, sunitinib, and pazopanib, this therapeutic drug monitoring (TDM)-guided dosing is, however, not routinely used, despite accumulating evidence favoring individualized dosing. Therefore, we aimed to identify and quantify (potential) barriers and facilitators in TDM-guided dosing for imatinib, sunitinib, and pazopanib.

METHODS

We performed a mixed methods study among all stakeholders involved: patients, healthcare professionals (HCPs), pharmaceutical companies, and health insurance companies. During the first qualitative part of this study, we performed semi-structured individual interviews and one focus group interview to identify all (potential) barriers and facilitators, and during the second quantitative part of this study, we used a web-based survey to quantify these findings. The interviews addressed the six domains of the implementation of change model of Grol and Wensing: (1) the innovation itself; (2) the HCP; (3) the patient; (4) social context; (5) organizational context; and (6) finances, law, and governance.

RESULTS

In the qualitative study, we interviewed 20 patients, 18 HCPs and 10 representatives of pharmaceutical and health insurance companies and identified 72 barriers and 90 facilitators. In the quantitative study, the survey was responded by 66 HCPs and 58 patients. Important barriers were on the domain of the HCP, such as a lack of experience with TDM (36.4%), on the domain of the patient, such as lack of awareness of TDM (39.7%), and the processing time for measurement and interpretation of the TDM result (40.9%) (organizational domain). Important facilitators were education of HCPs (95.5%), education of patients (87.9%) and facilitating an overview of when and where TDM measurements are being performed (86.4%).

CONCLUSION

We identified and quantified important barriers and facilitators for the implementation of TDM-guided dosing for imatinib, sunitinib, and pazopanib. Based on our results, the implementation strategy should mainly focus on educating both HCPs and patients and on the organizational aspect of TDM.

Novel Approaches to Characterize Individual Drug Metabolism and Advance Precision Medicine

Interindividual variability in drug metabolism can significantly affect drug concentrations in the body and subsequent drug response. Understanding an individual's drug metabolism capacity is important for predicting drug exposure and developing precision medicine strategies. The goal of precision medicine is to individualize drug treatment for patients to maximize efficacy and minimize drug toxicity. While advances in pharmacogenomics have improved our understanding of how genetic variations in drug-metabolizing enzymes (DMEs) affect drug response, nongenetic factors are also known to influence drug metabolism phenotypes. This minireview discusses approaches beyond pharmacogenetic testing to phenotype DMEs-particularly the cytochrome P450 enzymes-in clinical settings. Several phenotyping approaches have been proposed: traditional approaches include phenotyping with exogenous probe substrates and the use of endogenous biomarkers; newer approaches include evaluating circulating noncoding RNAs and liquid biopsy-derived markers relevant to DME expression and function. The goals of this minireview are to 1) provide a high-level overview of traditional and novel approaches to phenotype individual drug metabolism capacity, 2) describe how these approaches are being applied or can be applied to pharmacokinetic studies, and 3) discuss perspectives on future opportunities to advance precision medicine in diverse populations. SIGNIFICANCE STATEMENT: This minireview provides an overview of recent advances in approaches to characterize individual drug metabolism phenotypes in clinical settings. It highlights the integration of existing pharmacokinetic biomarkers with novel approaches; also discussed are current challenges and existing knowledge gaps. The article concludes with perspectives on the future deployment of a liquid biopsy-informed physiologically based pharmacokinetic strategy for patient characterization and precision dosing.

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.

Evaluating the utility of therapeutic drug monitoring in the clinical use of small molecule kinase inhibitors: a review of the literature

Introduction: Orally administered small molecule kinase inhibitors (KI) are a key class of targeted anti-cancer medicines that have contributed substantially to improved survival outcomes in patients with advanced disease. Since the introduction of KIs in 2001, there has been a building body of evidence that the benefit derived from these drugs may be further enhanced by individualizing dosing on the basis of concentration.Areas covered: This review considers the rationale for individualized KI dosing and the requirements for robust therapeutic drug monitoring (TDM). Current evidence supporting TDM-guided KI dosing is presented and critically evaluated, and finally potential approaches to address translational challenges for TDM-guided KI dosing and alternate approaches to support individualization of KI dosing are discussed.Expert opinion: Intuitively, the individualization of KI dosing through an approach such as TDM-guided dosing has great potential to enhance the effectiveness and tolerability of these drugs. However, based on current literature evidence it is unrealistic to propose that TDM-guided KI dosing should be routinely implemented into clinical practice.

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.

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.

Interindividual Variation in CYP3A Activity Influences Lapatinib Bioactivation

Lapatinib is a dual tyrosine kinase inhibitor associated with rare but potentially severe idiosyncratic hepatotoxicity. We have previously shown that cytochromes P450 CYP3A4 and CYP3A5 quantitatively contribute to lapatinib bioactivation, leading to formation of a reactive, potentially toxic quinone imine. CYP3A5 is highly polymorphic; however, the impact of CYP3A5 polymorphism on lapatinib metabolism has not been fully established. The goal of this study was to determine the effect of CYP3A5 genotype and individual variation in CYP3A activity on the metabolic activation of lapatinib using human-relevant in vitro systems. Lapatinib metabolism was examined using CYP3A5-genotyped human liver microsomes and cryopreserved human hepatocytes. CYP3A and CYP3A5-selective activities were measured in liver tissues using probe substrates midazolam and T-5 (T-1032), respectively, to evaluate the correlation between enzymatic activity and lapatinib metabolite formation. Drug metabolites were measured by high-performance liquid chromatography-tandem mass spectrometry. Further, the relative contributions of CYP3A4 and CYP3A5 to lapatinib O-debenzylation were estimated using selective chemical inhibitors of CYP3A. The results from this study demonstrated that lapatinib O-debenzylation and quinone imine-GSH conjugate formation were highly correlated with hepatic CYP3A activity, as measured by midazolam 1'-hydroxylation. CYP3A4 played a dominant role in lapatinib bioactivation in all liver tissues evaluated. The CYP3A5 contribution to lapatinib bioactivation varied by individual donor and was dependent on CYP3A5 genotype and activity. CYP3A5 contributed approximately 20%-42% to lapatinib O-debenzylation in livers from CYP3A5 expressers. These findings indicate that individual CYP3A activity, not CYP3A5 genotype alone, is a key determinant of lapatinib bioactivation and likely influences exposure to reactive metabolites. SIGNIFICANCE STATEMENT: This study is the first to examine the effect of CYP3A5 genotype, total CYP3A activity, and CYP3A5-selective activity on lapatinib bioactivation in individual human liver tissues. The results of this investigation indicate that lapatinib bioactivation via oxidative O-debenzylation is highly correlated with total hepatic CYP3A activity, and not CYP3A5 genotype alone. These findings provide insight into the individual factors, namely, CYP3A activity, that may affect individual exposure to reactive, potentially toxic metabolites of lapatinib.

Study Protocol for a Pilot, Open-Label, Prospective, and Observational Study to Evaluate the Pharmacokinetics of Drugs Administered to Patients during Extracorporeal Circulation; Potential of In Vivo Cytochrome P450 Phenotyping to Optimise Pharmacotherapy

Pharmacokinetic alterations of medications administered during surgeries involving cardiopulmonary bypass (CPB) and extracorporeal membrane oxygenation (ECMO) have been reported. The impact of CPB on the cytochrome P450 (CYP) enzymes’ activity is the key factor. The metabolic rates of caffeine, dextromethorphan, midazolam, omeprazole, and Losartan to the CYP-specific metabolites are validated measures of in vivo CYP 1A2, 2D6, 3A4, 2C19, and 2C9 activities, respectively. The study aim is to assess the activities of major CYPs in patients on extracorporeal circulation (EC). This is a pilot, prospective, open-label, observational study in patients undergoing surgery using EC and patients undergoing laparoscopic cholecystectomy as a control group. CYP activities will be measured on the day, and 1–2 days pre-surgery/3–4 days post-surgery (cardiac surgery and Laparoscopic cholecystectomy) and 1–2 days after starting ECMO, 1–2 weeks after starting ECMO, and 1–2 days after discontinuation from ECMO. Aforementioned CYP substrates will be administered to the patient and blood samples will be collected at 0, 1, 2, 4, and 6 h post-dose. Major CYP enzymes’ activities will be compared in each participant on the day, and before/after surgery. The CYP activities will be compared in three study groups to investigate the impact of CYPs on EC.

Towards better dose individualisation: metabolic phenotyping to predict cabazitaxel pharmacokinetics in men with prostate cancer

BACKGROUND

Cabazitaxel is approved for treatment of castration-resistant metastatic prostate cancer. The current dosing strategy of cabazitaxel is based on body surface area (BSA). Body surface area is known as a poor predictor for total systemic exposure to drugs, since it does not take into account variability in activity of metabolising enzymes, necessary for clearance of drugs. As exposure to cabazitaxel is related to treatment response, it is essential to develop a better individualised dosing strategy.

METHODS

Ten patients with metastatic castration-resistant prostate cancer, who received cabazitaxel dosed on BSA as a part of routine palliative care, were enrolled in this study. Midazolam was administered as phenotyping probe for cytochrome P450 isoenzyme 3A (CYP3A). The relationship between midazolam and cabazitaxel clearance was investigated using non-linear mixed effects modelling.

RESULTS

The clearance of Midazolam highly correlated with cabazitaxel clearance (R=0.74). Midazolam clearance significantly (P<0.004) explained the majority (∼60%) of the inter-individual variability in cabazitaxel clearance in the studied population.

CONCLUSIONS

Metabolic phenotyping of CYP3A using midazolam is a promising strategy to individualise cabazitaxel dosing. Before clinical application, a randomised study is warranted.

Association of axitinib plasma exposure and genetic polymorphisms of ABC transporters with axitinib-induced toxicities in patients with renal cell carcinoma

PURPOSE

Axitinib is a selective tyrosine kinase inhibitor of VEGF receptors, approved for advanced renal cell carcinoma (RCC). Associations between axitinib plasma exposure, genetic polymorphisms of ABC transporters and axitinib-induced toxicities have not been adequately explored.

METHODS

Twenty RCC patients treated with axitinib were enrolled in this study. Blood samples were collected 0, 0.5, 1, 2, 4, and 6 h after administration of axitinib on day 1 and at steady state. Plasma concentrations of axitinib were analyzed by UPLC-MS/MS. The ABCG2 (421C>A) and ABCB1 (1236C>T, 2677G>T/A, 3435C>T) genetic polymorphisms were determined by real-time PCR.

RESULTS

ABCB1 haplotype was associated with increased dose-adjusted area under the plasma concentration-time curve (AUC) of axitinib at steady state. The incidence of fatigue during therapy was associated with high AUC0-6 of axitinib (P = 0.013). The treatment period without discontinuation or dose reduction due to adverse events in patients with high AUC0-6 of axitinib was significantly shorter than for those with low AUC0-6 (P = 0.024). No significant differences were found in the frequency of adverse events among the ABCG2 genotype and ABCB1 haplotype groups.

CONCLUSIONS

Our results have demonstrated that adverse events leading to discontinuation or dose reduction in axitinib were associated with increased axitinib plasma exposure, but not directly with genetic polymorphisms of ABC transporters. Therefore, measurement of steady state axitinib plasma concentrations may be useful in avoiding adverse events in axitinib therapy.

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.

Sunitinib tissue distribution changes after coadministration with ketoconazole in mice

Sunitinib is a multitargeted tyrosine kinase inhibitor approved for gastrointestinal stromal tumor (GIST), advanced renal cell carcinoma (RCC) and pancreatic neuroendocrine tumors. It is metabolized via CYP3A4 and has low brain penetration due to efflux transporters ABCB1B and ABCG2. We studied the interaction with ketoconazole (50 mg/kg), antifungal drug which shares metabolic pathways and efflux transporters, in ICR female mice after oral coadministration (30 min apart) of 60 mg/kg sunitinib (study group) versus sunitinib alone (control group). Plasma, liver, kidney and brain sunitinib concentrations were measured by HPLC at 2, 5, 10, 20, 40 min, 1, 2, 4, 6, 12 h post-sunitinib administration, and non-compartmental pharmacokinetic parameters estimated. In plasma, ketoconazole coadministration increased plasma maximum concentration (C MAX) 60 %, delayed time to C MAX (T MAX); 1.6-fold greater area under the curve AUC0→∞ (p < 0.001); lower apparent steady-state volume of distribution (V SS/F) and oral clearance (Cl/F) 40 and 61 %, respectively; and shorter elimination half-life (t 1/2). Sunitinib exhibited extensive tissue distribution which increased after ketoconazole coadministration: total area under the curve (AUC0→∞) increased 1.8-, 2.8- and 1.2-fold in kidney, liver and brain, respectively (all p < 0.001). Sunitinib presented high tissue-to-plasma AUC0→∞ ratio in liver (17.8 ± 1.2), kidney (14.6 ± 1.52) and brain (2.25 ± 0.18) which was modified after coadministration: AUC0→∞ ratio increased in liver (31.4 ± 4.7; p < 0.001), kidney (17.1 ± 2.2; p > 0.05) and decreased in brain (1.70 ± 0.23, p > 0.05). The results showed a significant ketoconazole-sunitinib interaction that affected plasma, tissue pharmacokinetics and tissue uptake mechanisms. The study portrays the risk to increase toxicity and potential clinical translatability to treat tumors in tissues.

Assessment of Sunitinib-Induced Toxicities and Clinical Outcomes Based on Therapeutic Drug Monitoring of Sunitinib for Patients With Renal Cell Carcinoma

BACKGROUND

Sunitinib has been approved for the treatment of metastatic renal cell carcinoma (RCC). Sunitinib pharmacokinetics shows a large interpatient variability.

PATIENTS AND METHODS

A retrospective, observational clinical study of 21 patients with RCC was performed. Sunitinib was administered for 4 weeks of a 6-week cycle for the first cycle. We evaluated the association of sunitinib-induced toxicities and clinical outcomes with the trough total sunitinib concentration in a steady state during the first cycle.

RESULTS

The median total sunitinib concentration was 91.8 ng/mL (range, 49.8-205 ng/mL). There was an association between total sunitinib concentration and the severity of thrombocytopenia, anorexia, and fatigue. Patients with ≥ 100 ng/mL total sunitinib (n = 8), compared with patients with < 100 ng/mL (n = 13), had a greater incidence of Grade ≥ 3 toxicities (6 patients [75.0%] vs. 3 patients [23.1%]). Patients with < 100 ng/mL total sunitinib had significantly longer time to treatment failure (TTF) and progression-free survival (PFS) time than patients with ≥ 100 ng/mL (median TTF, 590 vs. 71 days; P = .04; median PFS, 748 vs. 238 days; P = .02).

CONCLUSION

Results of this study suggest that therapeutic drug monitoring of sunitinib could be useful for avoiding severe toxicities. Dose reduction might be needed, especially when the total sunitinib concentration is ≥ 100 ng/mL, to avoid unnecessary early discontinuation of treatment.

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.