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

Transporter and metabolizer gene polymorphisms affect fluoroquinolone pharmacokinetic parameters

Tuberculosis (TB) is an infectious disease that occurs globally. Treatment of TB has been hindered by problems with multidrug-resistant strains (MDR-TB). Fluoroquinolones are one of the main drugs used for the treatment of MDR-TB. The success of therapy can be influenced by genetic factors and their impact on pharmacokinetic parameters. This review was conducted by searching the PubMed database with keywords polymorphism and fluoroquinolones. The presence of gene polymorphisms, including UGT1A1, UGT1A9, SLCO1B1, and ABCB1, can affect fluoroquinolones pharmacokinetic parameters such as area under the curve (AUC), creatinine clearance (C_Cr), maximum plasma concentration (C_max), half-life (t_1/2) and peak time (t_max) of fluoroquinolones.

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.

Individualized Management of Blood Concentration in Patients with Gastrointestinal Stromal Tumors

Gastrointestinal stromal tumor (GIST) is the most common mesenchymal tumor, and surgical resection is the first choice for the treatment of the disease, but since the advent of tyrosine kinase inhibitors (TKIs) such as imatinib (IM), the prognosis of the disease has undergone revolutionary changes. According to the current version of the guidelines, most GIST patients receive a fixed dose without taking into account their own individual differences, resulting in a wide difference in blood concentration, adverse reactions and prognosis. With more studies on the relationship between blood drug concentrations and prognosis, the concept of individualized therapy has been paid more attention by researchers. Therapeutic drug monitoring (TDM) has also been made available for the research field of GIST targeted therapy. How to reduce the incidence of drug resistance and adverse reactions in patients with GISTs has become the focus of the current research. This article reviews the common monitoring methods and timing of TKIs blood concentration, the reasonable range of blood drug concentration, the toxic or adverse effects caused by high blood drug concentration, some possible factors affecting blood drug concentration and recent research progress, in order to discuss and summarize the treatment strategy of individual blood drug concentration, improve the prognosis of patients and reduce the adverse effects as much as possible.

Comprehensive Analysis of the ATP-binding Cassette Subfamily B Across Renal Cancers Identifies ABCB8 Overexpression in Phenotypically Aggressive Clear Cell Renal Cell Carcinoma

BACKGROUND

ATP-binding cassette (ABC) transporters play a crucial role in the development of multidrug resistance in diverse cancer entities.

OBJECTIVE

Our study was designed to comprehensively analyze the ABC subfamily B (ABCB) in renal cell carcinoma (RCC) using The Cancer Genome Atlas (TCGA) datasets.

DESIGN, SETTING, AND PARTICIPANTS

We performed systematic survival analyses of ABCB1-10 using the TCGA datasets for clear cell, papillary, and chromophobe RCC.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Results were validated via quantitative polymerase chain reaction in a clear cell RCC (ccRCC) cohort containing 152 samples. Afterward, ABCB8 protein expression was assessed in a tissue microarray RCC cohort (n = 144) by immunohistochemistry with subsequent quantitative image analysis. In vitro, antisense oligonucleotide-induced ABCB8 knockdowns were established in ACHN and CAKI1 following functional analyses.

RESULTS AND LIMITATIONS

Various ABCB members have prognostic value among the three most occurring RCC subtypes. Of note, ABCB8 was identified as the most prognostic ABCB gene in the RCC TCGA cohorts. Further, ABCB8 proved to be an independent predictor of shortened cancer-specific survival in three independent cohorts. In vitro, specific ABCB8 knockdown reduced viability and migration capacity in ACHN and CAKI1.

CONCLUSIONS

ABCB8 was identified as a promising prognostic biomarker. Functional analyses suggest a tumor-promoting role of ABCB8 in ccRCC.

PATIENT SUMMARY

In this study, the transporter gene ABCB8 proved to be a risk predictor of a worse clinical course in clear cell renal cell carcinoma. In the renal cell carcinoma cell culture model, depletion of this gene led to a reduction in the malignant potential, and inhibition of this gene may therefore possess a therapeutic value.

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.

Cytochromes P450 1A2 and 3A4 Catalyze the Metabolic Activation of Sunitinib

Sunitinib is a multitargeted tyrosine kinase inhibitor associated with idiosyncratic hepatotoxicity. The mechanisms of this toxicity are unknown. We hypothesized that sunitinib undergoes metabolic activation to form chemically reactive, potentially toxic metabolites which may contribute to development of sunitinib-induced hepatotoxicity. The purpose of this study was to define the role of cytochrome P450 (P450) enzymes in sunitinib bioactivation. Metabolic incubations were performed using individual recombinant P450s, human liver microsomal fractions, and P450-selective chemical inhibitors. Glutathione (GSH) and dansylated GSH were used as trapping agents to detect reactive metabolite formation. Sunitinib metabolites were analyzed by liquid chromatography-tandem mass spectrometry. A putative quinoneimine-GSH conjugate (M5) of sunitinib was detected from trapping studies with GSH and dansyl-GSH in human liver microsomal incubations, and M5 was formed in an NADPH-dependent manner. Recombinant P450 1A2 generated the highest levels of defluorinated sunitinib (M3) and M5, with less formation by P450 3A4 and 2D6. P450 3A4 was the major enzyme forming the primary active metabolite N-desethylsunitinib (M1). In human liver microsomal incubations, P450 3A inhibitor ketoconazole reduced formation of M1 by 88%, while P450 1A2 inhibitor furafylline decreased generation of M5 by 62% compared to control levels. P450 2D6 and P450 3A inhibition also decreased M5 by 54 and 52%, respectively, compared to control. In kinetic assays, recombinant P450 1A2 showed greater efficiency for generation of M3 and M5 compared to that of P450 3A4 and 2D6. Moreover, M5 formation was 2.7-fold more efficient in human liver microsomal preparations from an individual donor with high P450 1A2 activity compared to a donor with low P450 1A2 activity. Collectively, these data suggest that P450 1A2 and 3A4 contribute to oxidative defluorination of sunitinib to generate a reactive, potentially toxic quinoneimine. Factors that alter P450 1A2 and 3A activity may affect patient risk for sunitinib toxicity.

Association analysis of SNPs present in plasma with adverse events and population pharmacokinetics in Chinese sunitinib treated patients with renal cell carcinoma

Background

Sunitinib is a tyrosine kinase inhibitor with effective therapeutic outcomes in patients with renal-cell carcinoma. The study were to analyze the association of single-nucleotide polymorphisms present in cell-free DNA and pharmacokinetics with sunitinib treatment-emergent adverse events in Chinese patients with renal-cell carcinoma.

Materials and Methods

We genotyped 8 keys SNPs in 6 candidate genes. The plasma concentrations of sunitinib and N-desethyl sunitinib were measured using a high performance liquid chromatography-tandam mass spectrometry method. Correlations between the single-nucleotide polymorphisms and adverse events were investigated by univariate and multivariate logistic regression and we quantitatively evaluated the effect of single-nucleotide polymorphisms on the pharmacokinetics of sunitinib by using a population PK model.

Results

Necessary dose reductions of sunitinib were significantly correlated with SNP rs1933437 in FLT3. A higher severity of AEs were collected with SNP rs2032582 in ABCB1 and rs1800812 in PDGFRα. Thrombocytopenia was collected with rs1800812 in PDGFRα. Our study provides a population PK model of sunitinib with the ABCB1 genotype as a predictive covariate for apparent oral clearance.

Conclusions

Our study preliminarily confirmed the hypothesis that the pharmacokinetics of sunitinib is affected by the SNPs of enzyme in Chinese renal-cell carcinoma patients, and this affects the different distribution and severity of adverse events of sunitinib.