Everolimus in Patients With Advanced Follicular-Derived Thyroid Cancer: Results of a Phase II Clinical Trial.

Background: Mammalian target of rapamycin (mTOR) upregulation has been reported to be involved in the pathogenesis of thyroid tumors, and treatment with the mTOR inhibitor everolimus has shown promising results in endocrine tumors. We conducted a prospective phase II clinical trial to determine the efficacy and safety of everolimus in patients with advanced follicular-derived thyroid cancer. Patients and Methods: Twenty-eight patients with progressive metastatic or locally advanced radioactive refractory differentiated thyroid cancer and 7 patients with anaplastic thyroid cancer were included and received everolimus 10 mg orally once daily. The primary endpoint was disease control rate [complete (CR) + partial response (PR) + stable disease (SD) > 24 weeks]. Secondary endpoints included progression-free survival (PFS), overall survival (OS), toxicity, and mutational and pharmacokinetic-related outcomes. Results: Median follow-up duration was 38 months (2-64). Seventeen patients (65%) showed SD, of which 15 (58%) showed SD >24 weeks. No CR or PR was observed. Median PFS and OS were 9 [95% confidence interval (CI): 4 to 14] and 18 (95% CI: 7 to 29) months, respectively. Survival was negatively influenced by the presence of bone metastases. Toxicity was predominantly grade 1/2 and included anemia (64%), cough (64%), stomatitis (61%), and hyperglycemia (61%). Duration of SD was related to everolimus exposure. The presence of somatic gene variants related to mTOR signaling did not clearly stratify for responses. Conclusion: Everolimus has clinically relevant antitumor activity in patients with advanced differentiated thyroid cancer. Given the observed disease control rate and the relatively low toxicity profile, further investigation of everolimus in sequential or combination therapy in these patients is warranted.

A Semi-Physiological Population Model to Quantify the Effect of Hematocrit on Everolimus Pharmacokinetics and Pharmacodynamics in Cancer Patients.

INTRODUCTION AND OBJECTIVE: Everolimus (a drug from the class of mammalian target of rapamycin [mTOR] inhibitors) is associated with frequent toxicity-related dose reductions. Everolimus accumulates in erythrocytes, but the extent to which hematocrit affects everolimus plasma pharmacokinetics and pharmacodynamics is unknown. We aimed to investigate the everolimus pharmacokinetics/pharmacodynamics and the influence of hematocrit in cancer patients. METHODS: A semi-physiological pharmacokinetic model for everolimus was developed from pharmacokinetic data from 73 patients by non-linear mixed-effects modeling. Using a simulation study with a known pharmacodynamic model describing S6K1 (a downstream mTOR effector) inhibition, we investigated the impact of hematocrit. RESULTS: The apparent volume of distribution of the central and peripheral compartment were estimated to be 207 L with a relative standard error (RSE) of 5.0 % and 485 L (RSE 4.2 %), respectively, with an inter-compartmental clearance of 72.1 L/h (RSE 3.2 %). The apparent intrinsic clearance was 198 L/h (RSE 4.3 %). A decrease in hematocrit from 45 % to 20 % resulted in a predicted reduction in whole-blood exposure of ~50 %, but everolimus plasma pharmacokinetics and pharmacodynamics were not affected. The predicted S6K1 inhibition was at a plateau level in the approved dose of 10 mg once daily. CONCLUSIONS: A population pharmacokinetic model was developed for everolimus in cancer patients. Hematocrit influenced whole-blood pharmacokinetics, but not plasma pharmacokinetics or pharmacodynamics. Everolimus whole-blood concentrations should always be corrected for hematocrit. Since predicted mTOR inhibition was at a plateau level in the approved dose, dose reductions may have only a limited impact on mTOR inhibition.

Dried blood spot analysis for therapeutic drug monitoring of pazopanib.

Dried blood spot (DBS) sampling is potentially a more patient-friendly and flexible alternative to venous sampling of pazopanib. This study determined the agreement between pazopanib DBS and plasma concentrations to facilitate implementation of pazopanib DBS sampling into clinical practice. Paired DBS and plasma samples were collected in 12 patients. Pazopanib plasma concentrations were calculated from DBS concentrations using the formula: plasma concentration = DBSconcentration /(1 - hematocrit). Passing-Bablok and Bland-Altman analyses were used to determine the agreement between calculated and measured plasma concentrations. We predefined a clinical acceptance limit of 25% for the Bland-Altman analysis. Passing-Bablok analysis showed a small constant (intercept estimate, -8.53 [95%CI, -12.22 to -4.41]) and slightly proportional (slope estimate, 1.15 [95%CI, 1.04-1.24]) bias between calculated and measured concentrations. This bias was clinically nonrelevant, as shown by Bland-Altman analysis; the mean ratio of calculated to measured concentrations was 0.94 (95%CI, 0.65-1.23). The clinical acceptance limits were well within these 95% limits of agreement. More specifically, 92.6% of the data points were within the predefined acceptance limits. Pazopanib plasma concentrations can be accurately calculated from DBS concentrations. Although validation of DBS cards prepared by patients themselves is required, these results show that DBS sampling can be used to monitor pazopanib therapy in clinical practice.

Therapeutic drug monitoring to individualize the dosing of pazopanib: a pharmacokinetic feasibility study.

BACKGROUND: Patients treated with the standard dose of pazopanib show a large interpatient variability in drug exposure defined as the area under the plasma concentration-time curve (AUC0-24h). The primary objective of this study was to evaluate the feasibility of pharmacokinetics (PK)-guided individualized dosing to reduce the interpatient variability in pazopanib exposure. METHODS: Thirteen patients were treated with pazopanib for 3 consecutive periods of 2 weeks. During the first period, all patients received 800 mg of pazopanib once daily to reach steady-state exposure. During the second period, the patients either received a PK-guided individualized pazopanib dose or the registered fixed 800-mg dose. During the third period, these 2 dosing regimens were switched. RESULTS: The interpatient variability in pazopanib AUC0-24h during fixed dosing (27.3 coefficient of variation) was not significantly different when compared with the variability in AUC0-24h during PK-guided dosing (24.8 coefficient of variation). The percentage of patients within the target window during PK-guided dosing (53.9%) was not significantly different from the percentage during fixed dosing (46.2%). Both Ctrough and C24 were significantly (P < 0.001) correlated to pazopanib AUC0-24h (R = 0.596 and R = 0.940, respectively). Pazopanib AUC0-24h decreased 17% over time. CONCLUSIONS: PK-guided dosing did not reduce the interpatient variability in pazopanib exposure. In this study, the intrapatient variability in pazopanib exposure was relatively large compared with interpatient variability. This makes it challenging to achieve a target exposure within a predefined window. The causes of intrapatient variability must first be better understood and controlled, before PK-guided dosing can reduce the interpatient variability.

Individualized dosing of tyrosine kinase inhibitors: are we there yet?

Tyrosine kinase inhibitors (TKIs) are registered at a fixed oral dose, despite their large variability in pharmacokinetics (PK). Given that the evidence for a relation between drug exposure and treatment outcome is growing, this one-dose-fits-all approach can unintentionally lead to under- and overexposure. Dose individualization could lower this variability and thereby beneficially effect treatment outcome. In this article, we explore whether TKIs used for solid tumors meet the criteria for dose individualization. Despite limitations such as retrospective analysis, current data suggest that the following Ctrough levels could be used: imatinib 1100ng/ml, sunitinib when continuously dosed 37.5ng/ml, intermittent 50ng/ml and pazopanib 20µg/ml. A comprehensive review of the literature also shows that prospective trials investigating the influence of dose individualization on treatment outcome are warranted.

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.

Effect of gastrointestinal resection on sunitinib exposure in patients with GIST.

BACKGROUND: GIST patients often undergo GI-surgery. Previous studies have shown that imatinib and nilotinib exposures were decreased in GIST patients with prior major gastrectomy. We investigated whether major gastrectomy influences the exposure to sunitinib and its active metabolite SU12662. METHODS: Pharmacokinetic data from 305 GIST patients included in 4 phase I-III trials were analyzed. Patients were subdivided into 6 groups according to their prior GI-surgery. Apparent clearance (CL/F) and dose-corrected steady-state plasma exposures (AUC24,ss) of sunitinib and SU12662 were estimated using a population PK approach. ANCOVA was performed to test for differences in AUC24,ss and CL/F between each surgery subgroup and controls. RESULTS: Major gastrectomy did not influence sunitinib or SU12662 exposure. The geometric mean of sunitinib and SU12662 AUC24,ss was decreased by 21% and 28% in patients with both gastrectomy and small bowel resection (n = 8) compared to controls (n = 63) for sunitinib (931 ng hr/mL (95%-CI; 676-1283) versus 1177 ng hr/mL (95%-CI; 1097-1263); p < 0.05) and SU12662 (354 ng hr/mL (95%-CI; 174-720) versus 492 ng hr/mL (95%-CI; 435-555); p < 0.05). No significant differences in exposure were observed in each of the other subgroups versus controls. CONCLUSION: In contrast to previous results for imatinib and nilotinib, gastrectomy alone does not influence sunitinib or SU12662 exposure. This should be taken into account for the treatment of gastrectomized GIST patients with TKIs. In patients who had undergone both gastrectomy and small bowel resection, sunitinib and SU12662 exposures are significantly, although clinically not relevantly, decreased.

A validated assay for the simultaneous quantification of six tyrosine kinase inhibitors and two active metabolites in human serum using liquid chromatography coupled with tandem mass spectrometry.

A sensitive, sophisticated and practical bioanalytical assay for the simultaneous determination of six tyrosine kinase inhibitors (imatinib, sunitinib, nilotinib, dasatinib, pazopanib, regorafenib) and two active metabolites (N-desmethyl imatinib and N-desethyl sunitinib) was developed and validated. For the quantitative assay, a mixture of three stable isotopes as internal standards was added to human serum, standards and controls. Thereafter, samples were pre-treated using protein precipitation with methanol. The supernatant was diluted with water and injected into an ultra pressure liquid chromatographic system with an Acquity TQ tandem mass spectrometry detector. The compounds were separated on an Acquity BEH C18 analytical column (100mm×2.1mm ID, 1.7µm particle size) and eluted with a linear gradient system. The ions were detected in the multiple reaction monitoring mode. The lower limit of quantification and the linearity of all compounds generously met with the concentrations that are to be expected in clinical practice. The developed bioanalytical assay can be used for guiding TKI therapy in daily clinical practice as well as for investigator-initiated research.

[Neonatal abstinence syndrome after maternal use of tramadol]. / Neonataal abstinentiesyndroom na maternaal tramadolgebruik.

BACKGROUND: Tramadol is an opioid with lipophilic characteristics that freely crosses the placenta. Due to the placental transfer, there is a risk of neonatal withdrawal symptoms. Therefore, chronic use during pregnancy is not advised. CASE DESCRIPTION: A 25-year-old pregnant woman, gravida 3, para 1, used tramadol 100 mg 3 times a day for chronic headache and migraine during this pregnancy. Because of the risk for neonatal abstinence syndrome, delivery took place in the hospital and the neonate had to stay at least 3 days for observation. The newborn developed neonatal withdrawal symptoms 36 hours after delivery. Treatment with phenobarbital was effective; 9 days after delivery the newborn was discharged without further treatment. CONCLUSION: When tramadol is used during pregnancy, there is a serious risk for neonatal abstinence syndrome. Phenobarbital proved to be effective for the treatment of neonatal tramadol withdrawal.