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

Comparative Analysis of Traditional and Pharmacometric-Based Pharmacoeconomic Modeling in the Cost-Utility Evaluation of Sunitinib Therapy

BACKGROUND

Cost-utility analyses (CUAs) increasingly use models to predict long-term outcomes and translate trial data to real-world settings. Model structure uncertainty affects these predictions. This study compares pharmacometric against traditional pharmacoeconomic model evaluations for CUAs of sunitinib in gastrointestinal stromal tumors (GIST).

METHODS

A two-arm trial comparing sunitinib 37.5 mg daily with no treatment was simulated using a pharmacometric-based pharmacoeconomic model framework. Overall, four existing models [time-to-event (TTE) and Markov models] were re-estimated to the survival data and linked to logistic regression models describing the toxicity data [neutropenia, thrombocytopenia, hypertension, fatigue, and hand-foot syndrome (HFS)] to create traditional pharmacoeconomic model frameworks. All five frameworks were used to simulate clinical outcomes and sunitinib treatment costs, including a therapeutic drug monitoring (TDM) scenario.

RESULTS

The pharmacometric model framework predicted that sunitinib treatment costs an additional 142,756 euros per quality adjusted life year (QALY) compared with no treatment, with deviations - 21.2% (discrete Markov), - 15.1% (continuous Markov), + 7.2% (TTE Weibull), and + 39.6% (TTE exponential) from the traditional model frameworks. The pharmacometric framework captured the change in toxicity over treatment cycles (e.g., increased HFS incidence until cycle 4 with a decrease thereafter), a pattern not observed in the pharmacoeconomic frameworks (e.g., stable HFS incidence over all treatment cycles). Furthermore, the pharmacoeconomic frameworks excessively forecasted the percentage of patients encountering subtherapeutic concentrations of sunitinib over the course of time (pharmacoeconomic: 24.6% at cycle 2 to 98.7% at cycle 16, versus pharmacometric: 13.7% at cycle 2 to 34.1% at cycle 16).

CONCLUSIONS

Model structure significantly influences CUA predictions. The pharmacometric-based model framework more closely represented real-world toxicity trends and drug exposure changes. The relevance of these findings depends on the specific question a CUA seeks to address.

Physiologically-based pharmacokinetic models versus allometric scaling for prediction of tyrosine-kinase inhibitor exposure from adults to children

PURPOSE

Model-based methods can predict pediatric exposure and support initial dose selection. The aim of this study was to evaluate the performance of allometric scaling of population pharmacokinetic (popPK) versus physiologically based pharmacokinetic (PBPK) models in predicting the exposure of tyrosine kinase inhibitors (TKIs) for pediatric patients (≥ 2 years), based on adult data. The drugs imatinib, sunitinib and pazopanib were selected as case studies due to their complex PK profiles including high inter-patient variability, active metabolites, time-varying clearances and non-linear absorption.

METHODS

Pediatric concentration measurements and adult popPK models were derived from the literature. Adult PBPK models were generated in PK-Sim® using available physicochemical properties, calibrated to adult data when needed. PBPK and popPK models for the pediatric populations were translated from the models for adults and were used to simulate concentration-time profiles that were compared to the observed values.

RESULTS

Ten pediatric datasets were collected from the literature. While both types of models captured the concentration-time profiles of imatinib, its active metabolite, sunitinib and pazopanib, the PBPK models underestimated sunitinib metabolite concentrations. In contrast, allometrically scaled popPK simulations accurately predicted all concentration-time profiles. Trough concentration (Ctrough) predictions from the popPK model fell within a 2-fold range for all compounds, while 3 out of 5 PBPK predictions exceeded this range for the imatinib and sunitinib metabolite concentrations.

CONCLUSION

Based on the identified case studies it appears that allometric scaling of popPK models is better suited to predict exposure of TKIs in pediatric patients ≥ 2 years. This advantage may be attributed to the stable enzyme expression patterns from 2 years old onwards, which can be easily related to adult levels through allometric scaling. In some instances, both methods performed comparably. Understanding where discrepancies between the model methods arise, can further inform model development and ultimately support pediatric dose selection.

Semi-physiological Pharmacokinetic Model of Clozapine and Norclozapine in Healthy, Non-smoking Volunteers: The Impact of Race and Genetics

BACKGROUND AND OBJECTIVES: Clozapine is the medication of choice for treatment-resistant schizophrenia. However, it has a complex metabolism and unexplained interindividual variability. The current work aims to develop a pharmacokinetic model of clozapine and norclozapine in non-smokers and assess the impact of demographic and genetic predictors.

METHODS

Healthy volunteers were recruited in a population pharmacokinetic study. Blood samples were collected at 30 min and 1, 2, 3, 5 and 8 h following a single flat dose of clozapine (12.5 mg). The clozapine and norclozapine concentrations were measured via high-performance liquid chromatography-ultraviolet method. A semi-physiological pharmacokinetic model of clozapine and norclozapine was developed using nonlinear mixed-effects modeling. Clinical and genetic predictors were evaluated, including CYP1A2 (rs762551) and ABCB1 (rs2032582), using restriction fragment length polymorphism.

RESULTS

A total of 270 samples were collected from 33 participants. The data were best described using a two-compartment model for clozapine and a two-compartment model for norclozapine with first-order absorption and elimination and pre-systemic metabolism. The estimated (relative standard error) clearance of clozapine and norclozapine were 27 L h-1 (31.5 %) and 19.6 L h-1 (30%), respectively. Clozapine clearance was lower in sub-Saharan Africans (n = 4) and higher in Caucasians (n = 9) than Asians (n = 20). Participants with CYP1A2 (rs762551) (n = 18) and ABCB1 (rs2032582) (n = 12) mutant alleles had lower clozapine clearance in the univariate analysis.

CONCLUSIONS

This is the first study to develop a semi-physiological pharmacokinetic model of clozapine and norclozapine accounting for the pre-systemic metabolism. Asians required lower doses of clozapine as compared with Caucasians, while clozapine pharmacokinetics in sub-Saharan Africans should be further investigated in larger trials.

A Randomized, Double-Blind, Placebo-Controlled Trial Evaluating the Effect of Topical Urea for Secondary Prophylaxis of Hand Foot Skin Reaction in Renal Cell Cancer Patients on Sunitinib Therapy

INTRODUCTION

Hand foot skin reaction (HFSR) is a common dose-limiting adverse effect of multi kinase inhibitors (MKI) whose mechanism is not fully understood, and the prophylaxis is inadequate.

OBJECTIVE

In this pilot study, a double-blind, randomized placebo-controlled trial was conducted to evaluate the effect of topical urea in secondary prevention of sunitinib-induced HFSR in renal cell cancer patients.

METHODS

Out of 55 screened patients, 14 were randomized to receive topical urea or placebo for four weeks. The association of HFSR with drug levels of sunitinib and its metabolite (n-desethyl sunitinib), genetic polymorphism of VEGFR2 gene, quality of life (QOL) and biochemical markers was also assessed.

RESULTS

The results showed that urea-based cream was not superior to placebo (P = .075). There was no change in the QOL in both the groups. Single nucleotide polymorphism was checked for two nucleotides rs1870377 and rs2305948 located in VEGFR2 gene on chromosome 4. SNP (variant T > A) at rs1870377 was associated with appearance of new HFSR as compared to the wild type, although the association was not statistically significant (OR 0.714). There was no statistically significant difference between mean plasma levels of sunitinib and N-desethyl sunitinib in urea arm as compared to placebo arm as compared to placebo. The best fit population pharmacokinetic model for sunitinib was one compartment model with first order absorption and linear elimination. The median (IQR) of population parameters calculated from the population pharmacokinetics model for Ka, V and Cl was 0.22 (0.21-0.24) h-1, 4.4 (4.09-4.47) L, 0.049 (0.042-0.12) L/hr, respectively.

CONCLUSION

The study suggested that the urea-based cream was not superior to placebo in decreasing the appearance of new HFSR in renal cancer patients receiving 4:2 regimen of sunitinib.

Integrating Clopidogrel's First-Pass Effect in a Joint Semi-Physiological Population Pharmacokinetic Model of the Drug and Its Inactive Carboxylic Acid Metabolite

Clopidogrel (CLO), a pro-drug for preventing thrombotic events, undergoes rapid absorption and extensive metabolism, with approximately 85-90% converted to an inactive carboxylic acid metabolite (CLO-CA) and the remaining to an active thiol (CLO-TH). Few pharmacokinetic models for the drug and its metabolites exist, with most focusing on CLO-TH. Although CLO-CA is inactive, its predominant (compared to its parent drug and metabolites) presence in plasma underscores the importance of characterizing its formation and pharmacokinetic profile. This study aimed to characterize the process of the absorption of CLO and its conversion to CLO-CA via developing a population pharmacokinetic model. Individual participants' data from two bioequivalence studies were utilized. Extensive blood samples were collected at predetermined intervals, including 841 concentrations of CLO and 1149 of CLO-CA. A nonlinear, mixed-effects modelling approach using NONMEM® software (v 7.5) was applied. A one-compartment model was chosen for CLO, while a two-compartment proved optimal for CLO-CA. Absorption from the depot compartment was modeled via two transit compartments, incorporating transit rate constants (Ktr). A semi-physiological model explained the first-pass effect of CLO, integrating a liver compartment. The estimated mean transit times (MTTs) for the studies were 0.470 and 0.410 h, respectively. The relative bioavailability for each study's generic medicine compared to the reference were 1.08 and 0.960, respectively. Based on the estimated parameters, the fractions metabolized to inactive metabolites (FiaM_st1 and FiaM_st2) were determined to be 87.27% and 86.87% for the two studies, respectively. The appropriateness of the final model was confirmed. Our model offers a robust framework for elucidating the pharmacokinetic profiles of CLO and CLO-CA.

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.

Dose Individualization of Oral Multi-Kinase Inhibitors for the Implementation of Therapeutic Drug Monitoring

Oral multi-kinase inhibitors have transformed the treatment landscape for various cancer types and provided significant improvements in clinical outcomes. These agents are mainly approved at fixed doses, but the large inter-individual variability in pharmacokinetics and pharmacodynamics (efficacy and safety) has been an unsolved clinical issue. For example, certain patients treated with oral multi-kinase inhibitors at standard doses have severe adverse effects and require dose reduction and discontinuation, yet other patients have a suboptimal response to these drugs. Consequently, optimizing the dosing of oral multi-kinase inhibitors is important to prevent over-dosing or under-dosing. To date, multiple studies on the exposure-efficacy/toxicity relationship of molecular targeted therapy have been attempted for the implementation of therapeutic drug monitoring (TDM) strategies. In this milieu, we recently conducted research on several multi-kinase inhibitors, such as sunitinib, pazopanib, sorafenib, and lenvatinib, with the aim to optimize their treatment efficacy using a pharmacokinetic/pharmacodynamic approach. Among them, sunitinib use is an example of successful TDM implementation. Sunitinib demonstrated a significant correlation between drug exposure and treatment efficacy or toxicities. As a result, TDM services for sunitinib has been covered by the National Health Insurance program in Japan since April 2018. Additionally, other multi-kinase targeted anticancer drugs have promising data regarding the exposure-efficacy/toxicity relationship, suggesting the possibility of personalization of drug dosage. In this review, we provide a comprehensive summary of the clinical evidence for dose individualization of multi-kinase inhibitors and discuss the utility of TDM of multi-kinase inhibitors, especially sunitinib, pazopanib, sorafenib, and lenvatinib.

Easy and reliable maximum a posteriori Bayesian estimation of pharmacokinetic parameters with the open-source R package mapbayr

Pharmacokinetic (PK) parameter estimation is a critical and complex step in the model‐informed precision dosing (MIPD) approach. The mapbayr package was developed to perform maximum a posteriori Bayesian estimation (MAP‐BE) in R from any population PK model coded in mrgsolve. The performances of mapbayr were assessed using two approaches. First, “test” models with different features were coded, for example, first‐order and zero‐order absorption, lag time, time‐varying covariates, Michaelis–Menten elimination, combined and exponential residual error, parent drug and metabolite, and small or large inter‐individual variability (IIV). A total of 4000 PK profiles (combining single/multiple dosing and rich/sparse sampling) were simulated from each test model, and MAP‐BE of parameters was performed in both mapbayr and NONMEM. Second, a similar procedure was conducted with seven “real” previously published models to compare mapbayr and NONMEM on a PK outcome used in MIPD. For the test models, 98% of mapbayr estimations were identical to those given by NONMEM. Some discordances could be observed when dose‐related parameters were estimated or when models with large IIV were used. The exploration of objective function values suggested that mapbayr might outdo NONMEM in specific cases. For the real models, a concordance close to 100% on PK outcomes was observed. The mapbayr package provides a reliable solution to perform MAP‐BE of PK parameters in R. It also includes functions dedicated to data formatting and reporting and enables the creation of standalone Shiny web applications dedicated to MIPD, whatever the model or the clinical protocol and without additional software other than R.

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.

Association of lenvatinib plasma concentration with clinical efficacy and adverse events in patients with hepatocellular carcinoma

PURPOSE

This study aimed to examine the association between the trough plasma concentration of lenvatinib with the objective response rate (ORR) and adverse events in patients with hepatocellular carcinoma (HCC).

METHODS

Twenty-one patients with HCC who received lenvatinib were enrolled. We examined the median trough concentration (C_{trough median}) of plasma lenvatinib until the first clinical response evaluation. The receiver-operating characteristic curve was drawn to show the discrimination potential of the C_{trough median} for the ORR, using the modified Response Evaluation Criteria in Solid Tumors. Adverse events were graded based on the Common Terminology Criteria for Adverse Events (ver. 5.0).

RESULTS

The C_{trough median} values in the complete response and partial response group were significantly higher than those in the stable disease and progressive disease groups. The ORR was significantly higher in the high-C_{trough median} group (≥ 42.68 ng/mL) than in the low-C_{trough median} group (< 42.68 ng/mL) (80.0% vs. 18.2%; p = 0.0089). Although there was no difference in the occurrence of most adverse events between the high- and low-C_{trough median} groups, the occurrence of any grade anorexia (100.0% vs. 45.5%; p = 0.0124) and grade 3 serious hypertension (70.0% vs. 18.2%; p = 0.0300) was significantly higher in the high-C_{trough median} group than in the low-C_{trough median} group. Multivariate analysis showed that high-C_{trough median} was significantly associated with ORR development (odds ratio, 15.00; 95% confidence interval, 1.63-138.16; p = 0.0168).

CONCLUSION

Maintaining C_{trough median} above 42.68 ng/mL was crucial for achieving the ORR in patients with HCC.

Model-Based Biomarker Selection for Dose Individualization of Tyrosine-Kinase Inhibitors

Tyrosine-kinase inhibitors (TKIs) demonstrate high inter-individual variability with respect to safety and efficacy and would therefore benefit from dose or schedule adjustments. This study investigated the efficacy, safety, and economical aspects of alternative dosing options for sunitinib in gastro-intestinal stromal tumors (GIST) and axitinib in metastatic renal cell carcinoma (mRCC). Dose individualization based on drug concentration, adverse effects, and sVEGFR-3 was explored using a modeling framework connecting pharmacokinetic and pharmacodynamic models, as well as overall survival. Model-based simulations were performed to investigate four different scenarios: (I) the predicted value of high-dose pulsatile schedules to improve clinical outcomes as compared to regular daily dosing, (II) the potential of biomarkers for dose individualizations, such as drug concentrations, toxicity measurements, and the biomarker sVEGFR-3, (III) the cost-effectiveness of biomarker-guided dose-individualizations, and (IV) model-based dosing approaches versus standard sample-based methods to guide dose adjustments in clinical practice. Simulations from the axitinib and sunitinib frameworks suggest that weekly or once every two weeks high-dosing result in lower overall survival in patients with mRCC and GIST, compared to continuous daily dosing. Moreover, sVEGFR-3 appears a safe and cost-effective biomarker to guide dose adjustments and improve overall survival (€36 784.- per QALY). Model-based estimations were for biomarkers in general found to correctly predict dose adjustments similar to or more accurately than single clinical measurements and might therefore guide dose adjustments. A simulation framework represents a rapid and resource saving method to explore various propositions for dose and schedule adjustments of TKIs, while accounting for complicating factors such as circulating biomarker dynamics and inter-or intra-individual variability.

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.

A new high-performance liquid chromatography-tandem mass spectrometry method for the determination of sunitinib and N-desethyl sunitinib in human plasma: Light-induced isomerism overtaking towards therapeutic drug monitoring in clinical routine

Sunitinib is approved for advanced renal cell cancer, imatinib-resistant or -intolerant gastrointestinal stromal tumors and pancreatic neuroendocrine cancers. It is prescribed at a fixed dose but its plasma exposure shows large inter-individual variations. Taking into account the narrow therapeutic window and the positive exposure-efficacy relationship, there is a robust rationale for its therapeutic drug monitoring. In fact, a target plasma concentration of sunitinib plus its active metabolite, N-desethyl sunitinib, ≥50 ng/mL was suggested. In order to quantify sunitinib and N-desethyl sunitinib in patients' plasma, we developed and validated a new LC-MS/MS method applicable to clinical routine. In solution, sunitinib and N-desethyl sunitinib undergo to photo-isomerization and many published methods overcome this problem by conducting the entire procedures of samples collection and handling under strictly light-protection. Our method is based on a simple and fast procedure that quantitatively reconverts the E-isomer of both analytes, obtained during sample draw and processing without light-protection, into their Z-forms. Moreover, our method uses a small plasma volume (30 μL) and the analytes are extracted by a rapid protein precipitation. It was validated according to EMA-FDA guidelines. The calibration curves resulted linear (R² always >0.993) over the concentration ranges (0.1-500 ng/mL for sunitinib, 0.1-250 ng/mL for N-desethyl sunitinib) with a good precision (within 7.7 % for sunitinib and 10.8% for N- desethyl sunitinib) and accuracy (range 95.8-102.9% for sunitinib and 92.3-106.2% for N-desethyl sunitinib). This method was applied to a pharmacokinetic study in one patient treated with sunitinib. Moreover, as incurred samples reanalysis is an established part of the bioanalytical process to support clinical studies, its assessment was performed early in order to assure that any reproducibility issues was detected as soon as possible. The percentage difference between the two runs resulted within ±20% in all the re-analysed samples for both sunitinib and N- desethyl sunitinib.

Magnusson N. Drug-Induced Hypertension Caused by Multikinase Inhibitors (Sorafenib, Sunitinib, Lenvatinib and Axitinib) in Renal Cell Carcinoma Treatment

This paper reviews current treatments for renal cell carcinoma/cancer (RCC) with the multikinase inhibitors (MKIs) sorafenib, sunitinib, lenvatinib and axitinib. Furthermore, it compares these drugs regarding progression-free survival, overall survival and adverse effects (AE), with a focus on hypertension. Sorafenib and sunitinib, which are included in international clinical guidelines as first- and second-line therapy in metastatic RCC, are now being challenged by new-generation drugs like lenvatinib and axitinib. These drugs have shown significant clinical benefits for patients with RCC, but all four induce a variety of AEs. Hypertension is one of the most common AEs related to MKI treatment. Comparing sorafenib, sunitinib and lenvatinib revealed that sorafenib and sunitinib had the same efficacy, but sorafenib was safer to use. Lenvatinib showed better efficacy than sorafenib but worse safety. No trials have yet been completed that compare lenvatinib with sunitinib. Although axitinib promotes slightly higher hypertension rates compared to sunitinib, the overall discontinuation rate and cardiovascular complications are favourable. Although the mean rate of patients who develop hypertension is similar for each drug, some trials have shown large differences, which could indicate that lifestyle and/or genetic factors play an additional role.

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.

Clinical trial simulations in paediatric oncology: A feasibility study from the Innovative Therapies for Children with Cancer Consortium

INTRODUCTION

Paediatric dose-finding studies are challenging to perform due to ethical reasons, the limited number of available patients and restricted number of blood samples. In certain cases, the adult pharmacokinetic (PK) exposure can be used as target for dose finding in paediatrics. The aim of this study was to investigate the performance of a paediatric phase I dose-finding clinical trial in silico.

METHODS

Using an adult pharmacokinetic model, clinical trial simulations were performed to determine the power of a proposed clinical trial design. Power was defined as the fraction of 1000 trials with an area under the plasma concentration-time curve at steady-state (AUC_0-24,SS) within ±20% of the adult geometric mean AUC_0-24,SS. Different scenarios were compared to optimise the design of the trial. To show the potential of this framework for similar compounds, the current simulation method was also evaluated with adult and paediatric data from literature on sunitinib.

RESULTS

At the starting dose of 300 mg/m², the power of the trial design was 66.9%. Power did not improve by dose escalation to 350 mg/m² (65.3%). Power increased to 78.9% with inclusion of 10 patients per trial. Paediatric sunitinib PK data were adequately predicted from adult data with a mean prediction error of 1.80%.

CONCLUSION

The performance of PK-based clinical trials in paediatrics can be predicted and optimised through PK modelling and simulation. Application of this approach enables clinical trials in paediatrics to be performed as efficiently as possible while protecting the child from unnecessary harm.

Practical Recommendations for Therapeutic Drug Monitoring of Kinase Inhibitors in Oncology

Despite the fact that pharmacokinetic exposure of kinase inhibitors (KIs) is highly variable and clear relationships exist between exposure and treatment outcomes, fixed dosing is still standard practice. This review aims to summarize the available clinical pharmacokinetic and pharmacodynamic data into practical guidelines for individualized dosing of KIs through therapeutic drug monitoring (TDM). Additionally, we provide an overview of prospective TDM trials and discuss the future steps needed for further implementation of TDM of KIs.

The addition of abemaciclib to sunitinib induces regression of renal cell carcinoma xenograft tumors

Multiple therapies currently exist for renal cell carcinoma, however, most do not result in cure and the development of acquired resistance is the rule rather than the exception. CDK4/6 and PIM1 kinases are potential new therapeutic targets in RCC. Abemaciclib is a potent CDK4/6 and PIM1 kinase inhibitor, thus we evaluated the effects of abemaciclib on renal cell carcinoma. In vitro, abemaciclib causes decreased cellular viability, increased apoptosis, and alterations in autophagy in renal cell carcinoma cell lines. A pre-clinical mouse model of RCC shows abemaciclib in combination with sunitinib to cause dramatic reduction in tumor sizes without overt toxicity. Thus abemaciclib is active in renal cell carcinoma and should be evaluated in a clinical trial in combination with sunitinib. Additionally, CDK4/6 and PIM1 kinase appear to be viable clinical targets in renal cell carcinoma.

Population Modeling Integrating Pharmacokinetics, Pharmacodynamics, Pharmacogenetics, and Clinical Outcome in Patients With Sunitinib-Treated Cancer

The tyrosine kinase inhibitor sunitinib is used as first‐line therapy in patients with metastasized renal cell carcinoma (mRCC), given in fixed‐dose regimens despite its high variability in pharmacokinetics (PKs). Interindividual variability of drug exposure may be responsible for differences in response. Therefore, dosing strategies based on pharmacokinetic/pharmacodynamic (PK/PD) models may be useful to optimize treatment. Plasma concentrations of sunitinib, its active metabolite SU12662, and the soluble vascular endothelial growth factor receptors sVEGFR‐2 and sVEGFR‐3, were measured in 26 patients with mRCC within the EuroTARGET project and 21 patients with metastasized colorectal cancer (mCRC) from the C‐II‐005 study. Based on these observations, PK/PD models with potential influence of genetic predictors were developed and linked to time‐to‐event (TTE) models. Baseline sVEGFR‐2 levels were associated with clinical outcome in patients with mRCC, whereas active drug PKs seemed to be more predictive in patients with mCRC. The models provide the basis of PK/PD‐guided strategies for the individualization of anti‐angiogenic therapies.

Optimizing the dose in cancer patients treated with imatinib, sunitinib and pazopanib

AIM

Fixed dose oral tyrosine kinase inhibitors imatinib, sunitinib and pazopanib show a high interpatient variability in plasma exposure. A relationship between plasma exposure and treatment outcome has been established, which supports the rationale for dose optimization of these drugs. The aim of this study was to monitor how many patients reached adequate trough levels after therapeutic drug monitoring-based dose optimization in daily practice.

METHODS

A cohort study was performed in patients treated with imatinib, sunitinib or pazopanib of whom follow-up drug levels were measured between August 2012 and April 2016. Patients' characteristics were collected by reviewing electronic patient records. Drug levels were measured using high-performance liquid chromatography coupled with tandem mass spectrometry and trough levels were estimated using a predefined algorithm. Dose interventions were proposed based on trough levels.

RESULTS

In total, 396 trough levels were determined in 109 patients. Median sample frequency per patient was 3. During the first measurement only 38% of patients showed trough levels within the predefined target ranges despite standard dosing; 52% of the patients showed drug levels below and 10% above the target range. In 35 out of 41 patients (85%) dose interventions led to adequate trough levels. Eventually, 64% of the total cohort reached adequate trough levels.

CONCLUSIONS

Dose optimization proved an effective tool to reach adequate trough levels in patients treated with imatinib, sunitinib and pazopanib. The percentage of patients with adequate trough levels increased from 38 to 64%. Therapeutic drug monitoring may add to the improvement of efficacy and reduction of toxicity and costs of these treatments.

Monte Carlo simulations of the clinical benefits from therapeutic drug monitoring of sunitinib in patients with gastrointestinal stromal tumours

PURPOSE

Therapeutic drug monitoring (TDM) is being considered as a tool to individualise sunitinib treatment of gastrointestinal stromal tumours (GIST). Here, we used computer simulations to assess the expected impact of sunitinib TDM on the clinical outcome of patients with GIST.

METHODS

Monte Carlo simulations were performed in R, based on previously published pharmacokinetic-pharmacodynamic models. Clinical trials with dose-limiting toxicity and patient dropout were simulated to establish the study size required to obtain sufficient statistical power for comparison of TDM-guided and fixed dosing.

RESULTS

The simulations revealed that TDM might increase time to tumour progression by about 1-2 months (15-31 %) in eligible patients. However, the number of subjects required for a sufficient statistical power to quantify clinical benefit of TDM guided is likely to be prohibitively high (>1000).

CONCLUSION

Although data from randomised clinical trials on the clinical impact of sunitinib TDM are lacking, our findings support implementation of sunitinib TDM in clinical practice. For rare cancers with well-defined exposure-response relationships, modelling and simulation might allow the optimisation of dosing strategies when clinical trials cannot be performed due to low number of patients.

Greater Sensitivity of Blood Pressure Than Renal Toxicity to Tyrosine Kinase Receptor Inhibition With Sunitinib

Hypertension and renal injury are off-target effects of sunitinib, a tyrosine kinase receptor inhibitor used for the treatment of various tumor types. Importantly, these untoward effects are accompanied by activation of the endothelin system. Here, we set up a study to explore the dose dependency of these side effects. Normotensive Wistar Kyoto rats were exposed to 3 different doses of sunitinib or vehicle. After 8 days, rats were euthanized. Telemetrically measured blood pressure rose dose dependently, from 13 to 30 mm Hg. Proteinuria was present at all doses, but a rise in cystatin C occurred only at the intermediate and high doses. Compared with vehicle circulating endothelin-1 increased dose dependently, whereas 24-hour urinary endothelin excretion decreased. Light and electron microscopy revealed glomerular endotheliosis and ischemia with the intermediate and high doses of sunitinib but completely absent histological abnormalities with the low dose. Podocyte number per glomerular circumference did not change. Glomerular nephrin, Neph1, podocin, and endothelin-converting enzyme gene expression were downregulated in a dose-dependent manner. We conclude that the sunitinib-induced rise in blood pressure requires lower doses than its induction of renal function impairment and that functional changes in glomerular filtration barrier contribute to the occurrence of proteinuria, given the lack of histopathologic changes with the low dose of sunitinib.