Identification of non-adherence to adjuvant letrozole using a population pharmacokinetics approach in hormone receptor-positive breast cancer patients.

BACKGROUND: Letrozole, an aromatase inhibitor metabolised via CYP2A6 and CYP3A4/5 enzymes, is used as adjuvant therapy for women with hormone receptor (HR)-positive early breast cancer. The objective of this study was to quantify the impact of CYP2A6 genotype on letrozole pharmacokinetics (PK), to identify non-adherent patients using a population approach and explore the possibility of a relationship between non-adherence and early relapse. METHODS: Breast cancer patients enrolled in the prospective PHACS study (ClinicalTrials.gov NCT01127295) and treated with adjuvant letrozole 2.5 mg/day were included. Trough letrozole concentrations (Css,trough) were measured every 6 months for 3 years by a validated LC-MS/MS method. Concentration-time data were analysed using non-linear mixed effects modelling. Three methods were evaluated for identification of non-adherent subjects using the base PK model. RESULTS: 617 patients contributing 2534 plasma concentrations were included and led to a one-compartment PK model with linear absorption and elimination. Model-based methods identified 28 % of patients as non-adherent based on high fluctuations of their Css,trough compared to 3 % based on patient declarations. The covariate analysis performed in adherent subjects revealed that CYP2A6 intermediate (IM) and slow metabolisers (SM) had 21 % (CI95 % = 12 - 30 %) and 46 % (CI95 % = 41 - 51 %) lower apparent clearance, respectively, compared to normal and ultrarapid metabolisers (NM+UM). Early relapse (19 patients) was not associated with model-estimated, concentration-based or declared adherence in the total population (p = 0.41, p = 0.37 and p = 0.45, respectively). CONCLUSIONS: These findings will help future investigations focusing on the exposure-efficacy relationship for letrozole in adjuvant setting.

Nonlinear Mixed-Effects Model of Z-Endoxifen Concentrations in Tamoxifen-Treated Patients from the CEPAM Cohort.

Tamoxifen is widely used in patients with hormone receptor-positive breast cancer. The polymorphic enzyme CYP2D6 is primarily responsible for metabolic activation of tamoxifen, resulting in substantial interindividual variability of plasma concentrations of its most important metabolite, Z-endoxifen. The Z-endoxifen concentration thresholds below which tamoxifen treatment is less efficacious have been proposed but not validated, and prospective trials of individualized tamoxifen treatment to achieve Z-endoxifen concentration thresholds are considered infeasible. Therefore, we aim to validate the association between Z-endoxifen concentration and tamoxifen treatment outcomes, and identify a Z-endoxifen concentration threshold of tamoxifen efficacy, using pharmacometric modeling and simulation. As a first step, the CYP2D6 Endoxifen Percentage Activity Model (CEPAM) cohort was created by pooling data from 28 clinical studies (> 7,000 patients) with measured endoxifen plasma concentrations. After cleaning, data from 6,083 patients were used to develop a nonlinear mixed-effect (NLME) model for tamoxifen and Z-endoxifen pharmacokinetics that includes a conversion factor to allow inclusion of studies that measured total endoxifen but not Z-endoxifen. The final parent-metabolite NLME model confirmed the primary role of CYP2D6, and contributions from body weight, CYP2C9 phenotype, and co-medication with CYP2D6 inhibitors, on Z-endoxifen pharmacokinetics. Future work will use the model to simulate Z-endoxifen concentrations in patients receiving single agent tamoxifen treatment within large prospective clinical trials with long-term survival to identify the Z-endoxifen concentration threshold below which tamoxifen is less efficacious. Identification of this concentration threshold would allow personalized tamoxifen treatment to improve outcomes in patients with hormone receptor-positive breast cancer.

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.

Limited Sampling Strategy for Determination of Ibrutinib Plasma Exposure: Joint Analyses with Metabolite Data.

Therapeutic drug monitoring of ibrutinib is based on the area under the curve of concentration vs. time (AUCIBRU) instead of trough concentration (Cmin,ss) because of a limited accumulation in plasma. Our objective was to identify a limited sampling strategy (LSS) to estimate AUCIBRU associated with Bayesian estimation. The actual AUCIBRU of 85 patients was determined by the Bayesian analysis of the full pharmacokinetic profile of ibrutinib concentrations (pre-dose T0 and 0.5, 1, 2, 4 and 6 h post-dose) and experimental AUCIBRU were derived considering combinations of one to four sampling times. The T0-1-2-4 design was the most accurate LSS (root-mean-square error RMSE = 11.0%), and three-point strategies removing the 1 h or 2 h points (RMSE = 22.7% and 14.5%, respectively) also showed good accuracy. The correlation between the actual AUCIBRU and Cmin,ss was poor (r2 = 0.25). The joint analysis of dihydrodiol-ibrutinib metabolite concentrations did not improve the predictive performance of AUCIBRU. These results were confirmed in a prospective validation cohort (n = 27 patients). At least three samples, within the pre-dose and 4 h post-dose period, are necessary to estimate ibrutinib exposure accurately.

Population PK-PD Modeling of Circulating Lymphocyte Dynamics in Chronic Lymphocytic Leukemia Patients Under Ibrutinib Treatment.

Ibrutinib is indicated for the treatment of chronic lymphocytic leukemia (CLL). Absolute lymphocyte count (ALC) is a clinical criterion used for the monitoring of CLL. Ibrutinib has several effects on lymphocytes, and has highly variable pharmacokinetics (PK). The objective of this work was to build a PK-pharmacodynamic (PD) model describing ALC dynamics under ibrutinib treatment in patients with CLL. ALC observations before and after ibrutinib treatment initiation in patients with CLL were included in the analysis. A population PK-PD model was developed based on physio-pharmacological knowledge. Individual PK concentrations at each hospital visit were included in the model. The association between PD parameters and lymphocytosis, and between PD parameters and response to treatment were assessed. A total of 94 patients, 658 ALC and 1,501 PK observations were included in model development. The final PK-PD model accurately described ALC dynamics for different patient profiles. It consisted in two compartments (tissues and blood circulation) with ibrutinib plasmatic concentration inducing two drug effects: stimulation of lymphocyte redistribution and death. Patients with hyperlymphocytosis had significantly higher tissues to circulation baseline lymphocyte count ratio, and lower death effect. Patients who progressed under ibrutinib had significantly lower baseline lymphocyte counts in tissues (2-fold lower) and blood (3-fold lower). The first PK-PD model for ALC in patients with CLL under ibrutinib treatment was developed. This model suggests that estimated lymphocyte counts in tissues and blood could be used as an early predictor of response in patients with CLL.

Plasma cystatin C is a marker of renal glomerular injury in children treated with cisplatin or ifosfamide.

BACKGROUND: Plasma cystatin C is a potential marker of the glomerular filtration rate (GFR), and urinary cystatin C has been proposed as a marker of tubular dysfunction. PROCEDURE: A prospective study (NCT02822404) was conducted to assess the benefit of considering cystatin C plasma and urinary levels to better evaluate cisplatin and/or ifosfamide renal toxicity in children with cancer. Plasma 51 Cr-EDTA clearance as a marker of GFR and urinary markers of tubular toxicity were monitored in 40 children treated by cisplatin and/or ifosfamide. Several equations previously proposed to estimate GFR, with or without inclusion of plasma cystatin C level, were compared. A population pharmacokinetic approach was also used to analyze plasma 51 Cr-EDTA data, and evaluate the relationship between patient covariates (including plasma cystatin C level) and GFR during the course of chemotherapy treatment. RESULTS: Equations including plasma cystatin C described GFR changes during chemotherapy better than those without this variable. An equation based on plasma cystatin C, serum creatinine, and body weight enabled us to accurately describe the evolution of GFR during chemotherapy. The urinary cystatin C/creatinine ratio was compared between children with or without tubular toxicity, according to a standard assessment of tubular dysfunction. However, although the urinary cystatin C/creatinine ratio was increased in children with tubular toxicity, this marker does not provide additional information to the well-known markers of tubulopathy. CONCLUSIONS: Monitoring of plasma cystatin C may be substituted to radionucleide glomerular exploration in children treated by cisplatin and/or ifosfamide.

Model-Based Quantification of Impact of Genetic Polymorphisms and Co-Medications on Pharmacokinetics of Tamoxifen and Six Metabolites in Breast Cancer.

Variations in clinical response to tamoxifen (TAM) may be related to polymorphic cytochromes P450 (CYPs) involved in forming its active metabolite endoxifen (ENDO). We developed a population pharmacokinetic (PopPK) model for tamoxifen and six metabolites to determine clinically relevant factors of ENDO exposure. Concentration-time data for TAM and 6 metabolites come from a prospective, multicenter, 3-year follow-up study of adjuvant TAM (20 mg/day) in patients with breast cancer, with plasma samples drawn every 6 months, and genotypes for 63 genetic polymorphisms (PHACS study, NCT01127295). Concentration data for TAM and 6 metabolites from 928 patients (n = 27,433 concentrations) were analyzed simultaneously with a 7-compartment PopPK model. CYP2D6 phenotype (poor metabolizer (PM), intermediate metabolizer (IM), normal metabolizer (NM), and ultra-rapid metabolizer (UM)), CYP3A4*22, CYP2C19*2, and CYP2B6*6 genotypes, concomitant CYP2D6 inhibitors, age, and body weight had a significant impact on TAM metabolism. Formation of ENDO from N-desmethyltamoxifen was decreased by 84% (relative standard error (RSE) = 14%) in PM patients and by 47% (RSE = 9%) in IM patients and increased in UM patients by 27% (RSE = 12%) compared with NM patients. Dose-adjustment simulations support an increase from 20 mg/day to 40 and 80 mg/day in IM patients and PM patients, respectively, to reach ENDO levels similar to those in NM patients. However, when considering Antiestrogenic Activity Score (AAS), a dose increase to 60 mg/day in PM patients seems sufficient. This PopPK model can be used as a tool to predict ENDO levels or AAS according to the patient's CYP2D6 phenotype for TAM dose adaptation.

Preclinical Pharmacokinetics and Dosimetry of an 89Zr Labelled Anti-PDL1 in an Orthotopic Lung Cancer Murine Model.

Anti-PDL1 is a monoclonal antibody targeting the programmed death-cell ligand (PD-L1) by blocking the programmed death-cell (PD-1)/PD-L1 axis. It restores the immune system response in several tumours, such as non-small cell lung cancer (NSCLC). Anti-PDL1 or anti-PD1 treatments rely on PD-L1 tumoural expression assessed by immunohistochemistry on biopsy tissue. However, depending on the biopsy extraction site, PD-L1 expression can vary greatly. Non-invasive imaging enables whole-body mapping of PD-L1 sites and could improve the assessment of tumoural PD-L1 expression. METHODS: Pharmacokinetics (PK), biodistribution and dosimetry of a murine anti-PDL1 radiolabelled with zirconium-89, were evaluated in both healthy mice and immunocompetent mice with lung cancer. Preclinical PET (µPET) imaging was used to analyse [89Zr]DFO-Anti-PDL1 distribution in both groups of mice. Non-compartmental (NCA) and compartmental (CA) PK analyses were performed in order to describe PK parameters and assess area under the concentration-time curve (AUC) for dosimetry evaluation in humans. RESULTS: Organ distribution was correctly estimated using PK modelling in both healthy mice and mice with lung cancer. Tumoural uptake occurred within 24 h post-injection of [89Zr]DFO-Anti-PDL1, and the best imaging time was at 48 h according to the signal-to-noise ratio (SNR) and image quality. An in vivo blocking study confirmed that [89Zr]DFO-anti-PDL1 specifically targeted PD-L1 in CMT167 lung tumours in mice. AUC in organs was estimated using a 1-compartment PK model and extrapolated to human (using allometric scaling) in order to estimate the radiation exposure in human. Human-estimated effective dose was 131 µSv/MBq. CONCLUSION: The predicted dosimetry was similar or lower than other antibodies radiolabelled with zirconium-89 for immunoPET imaging.

Population Pharmacokinetics of Ibrutinib and Its Dihydrodiol Metabolite in Patients with Lymphoid Malignancies.

BACKGROUND AND OBJECTIVE: Ibrutinib is used for the treatment of chronic lymphocytic leukemia and other lymphoid malignancies. The aim of this work is to develop a population pharmacokinetic model for ibrutinib and its dihydrodiol metabolite to quantify pharmacokinetic inter- and intra-individual variability, to evaluate the impact of several covariates on ibrutinib pharmacokinetic parameters, and to examine the relationship between exposure and clinical outcome. METHODS: Patients treated with ibrutinib were included in the study and followed up for 2 years. Pharmacokinetic blood samples were taken from months 1 to 12 after inclusion. Ibrutinib and dihydrodiol-ibrutinib concentrations were assessed using ultra-performance liquid chromatography tandem mass spectrometry. A population pharmacokinetic model was developed using NONMEM version 7.4. RESULTS: A total of 89 patients and 1501 plasma concentrations were included in the pharmacokinetic analysis. The best model consisted in two compartments for each molecule. Absorption was described by a sequential zero first-order process and a lag time. Ibrutinib was either metabolised into dihydrodiol-ibrutinib or excreted through other elimination routes. A link between the dosing compartment and the dihydrodiol-ibrutinib central compartment was added to assess for high first-pass hepatic metabolism. Ibrutinib clearance had 67% and 47% inter- and intra-individual variability, respectively, while dihydrodiol-ibrutinib clearance had 51% and 26% inter- and intra-individual variability, respectively. Observed ibrutinib exposure is significantly higher in patients carrying one copy of the cytochrome P450 3A4*22 variant (1167 ng.h/mL vs 743 ng.h/mL, respectively, p = 0.024). However, no covariates with a clinically relevant effect on ibrutinib or dihydrodiol-ibrutinib exposure were identified in the PK model. An external evaluation of the model was performed. Clinical outcome was expressed as the continuation or discontinuation of ibrutinib therapy 1 year after treatment initiation. Patients who had treatment discontinuation because of toxicity had significantly higher ibrutinib area under the curve (p = 0.047). No association was found between cessation of therapy due to disease progression and ibrutinib area under the curve in patients with chronic lymphocytic leukemia. For the seven patients with mantle cell lymphoma studied, an association trend was observed between disease progression and low exposure to ibrutinib. CONCLUSIONS: We present the first population pharmacokinetic model describing ibrutinib and dihydrodiol-ibrutinib concentrations simultaneously. Large inter-individual variability and substantial intra-individual variability were estimated and could not be explained by any covariate. Higher plasma exposure to ibrutinib is associated with cessation of therapy due to the occurrence of adverse events within the first year of treatment. The association between disease progression and ibrutinib exposure in patients with mantle cell lymphoma should be further investigated. TRIAL REGISTRATION: ClinicalTrials.gov no. NCT02824159.

Formulae recently proposed to estimate renal glomerular filtration rate improve the prediction of carboplatin clearance.

PURPOSE: While doses of carboplatin are mostly individualized according to the Calvert equation based on estimated Glomerular Filtration Rate (eGFR), there is still uncertainty regarding the best formula to predict GFR. Since Janowitz et al. recently proposed a new equation predicting GFR in cancer patients, we aimed to compare this equation to other carboplatin clearance (carboCL) predicting formulae. METHODS: The actual carboCL of 491 patients was compared to predicted carboCL according to the Calvert formula using several equations to predict GFR (Janowitz, Cockcroft-Gault, MDRD, CKD-EPI, CKD-EPI with cystatin C (CKD-EPI-cysC)); and according to two others that directly predict carboCL (Chatelut and Thomas). The formulae were compared on Mean Percentage Error (MPE), Mean Absolute Percentage Error (MAPE) and percentage of patients with a prediction error above 20% (P20). RESULTS: The MPE, MAPE and P20 were, respectively, within the ranges - 5.2 to + 5.9%; 14.0-21.2% and 23-46%. The MAPE and P20 of Calvert-CKD-EPI-cysC were the lowest. The performance of Calvert-CKD-EPI was better than that of other creatinine-based formulae although not significantly different from the Calvert-Janowitz formula. Among formulae based on creatinine only, Calvert-CKD-EPI and Calvert-Janowitz are the least influenced by patient characteristics. CONCLUSION: Whereas CysC improves carboplatin CL prediction, the Calvert-CKD-EPI equation seems the most suitable creatinine-based formula to predict carboCL homogeneously in all subgroups of patients.

Pharmacokinetics and Pharmacodynamics of Once-daily Prolonged-release Tacrolimus in Liver Transplant Recipients.

PURPOSE: Limited published data are available regarding the pharmacokinetic (PK) and pharmacodynamic (PD) variables of prolonged-release tacrolimus (PRT) after liver transplantations. The goal of this study was to compare the PK and PD profiles of PRT in early and stable liver transplant recipients by developing a population PK model of PRT and investigating the profile of calcineurin activity (CNA) in the peripheral blood mononuclear cells. METHODS: A conversion from BID immediate-release tacrolimus (IRT) to once-daily PRT based on a one-to-one daily dose was performed at day 7 (D7) and D90 posttransplantation in groups A (n = 12) and B (n = 12), respectively. Extensive PK samplings, including whole-blood tacrolimus (TAC) concentration, and CNA assessments were performed at D14 and D104 in groups A and B, respectively. TAC concentration-time data (N = 221) were analyzed by using nonlinear mixed effects modeling. FINDINGS: A 2-compartment model with linear elimination and a delayed first-order absorption characterized by 2 transit compartments best described the PK data. Model-predicted dose-normalized (6.0 mg/d) area under the TAC concentration-time curve over the dosing interval in groups A and B was similar (geometric mean, 235.6 ng/mL · h [95% CI, 139.6-598.7] vs 224.6 ng/mL · h [95% CI, 117.6-421.5], respectively; P = 0.94). Area under the CNA versus time curve over the dosing interval did not differ between groups (4897 [3437] and 4079 [1008] pmol/min/106 cells; P = 0.50). In group A, trough CNA at D14 posttransplantation was statistically higher than that measured just before the switch to PRT (ie, D7 posttransplantation) (198 [92] vs 124 [72] pmol/min/106cells, n = 8; P = 0.048); no statistical difference in TAC concentration was observed (P = 0.11). In group B, no statistical difference between D90 and D104 was observed in either trough CNA (149 [78] vs 172 [82] pmol/min/106 cells, n = 6; P = 0.18) or TAC (P = 0.17) concentration. No graft rejection was observed in either of the groups. IMPLICATIONS: This study suggests that one-to-one dosage conversion to once-daily PRT during the early posttransplantation period could result in significant CNA variations but without causing graft rejection. Further investigations in larger cohorts are warranted to confirm these results. ClinicalTrials.gov identifier: NCT02105155.

Factors Affecting Tamoxifen Metabolism in Patients With Breast Cancer: Preliminary Results of the French PHACS Study.

In addition to the effect of cytochrome P450 (CYP) 2D6 genetic polymorphisms, the metabolism of tamoxifen may be impacted by other factors with possible consequences on therapeutic outcome (efficacy and toxicity). This analysis focused on the pharmacokinetic (PK)-pharmacogenetic evaluation of tamoxifen in 730 patients with adjuvant breast cancer included in a prospective multicenter study. Plasma concentrations of tamoxifen and six major metabolites, the genotype for 63 single-nucleotide polymorphisms, and comedications were obtained 6 months after treatment initiation. Plasma concentrations of endoxifen were significantly associated with CYP2D6 diplotype (P < 0.0001), CYP3A4*22 genotype (P = 0.0003), and concomitant intake of potent CYP2D6 inhibitors (P < 0.001). Comparison of endoxifen levels showed that the CYP2D6 phenotype classification could be improved by grouping intermediate metabolizer (IM)/IM and IM/poor metabolizer diplotype into IM phenotype for future use in tamoxifen therapy optimization. Finally, the multivariable regression analysis showed that formation of tamoxifen metabolites was independently impacted by CYP2D6 diplotype and CYP3A4*22, CYP2C19*2, and CYP2B6*6 genetic polymorphisms.

Investigating the potential impact of dose banding for systemic anti-cancer therapy in the paediatric setting based on pharmacokinetic evidence.

BACKGROUND: To make systemic anti-cancer therapy (SACT) preparation more practicable, dose-banding approaches are currently being introduced in many clinical centres. The present study aimed to determine the potential impact of using recently developed National Health Service in England (NHSE) dose-banding tables in a paediatric setting. METHODS: Using pharmacokinetic parameters obtained from 385 drug administrations in 352 children aged from 1 month to 18 years, treated with five drugs (dactinomycin, busulfan, carboplatin, cyclophosphamide and etoposide), individual exposures (area under the plasma drug concentration versus time curve; AUC) obtained using doses rounded according to the published NHSE tables were calculated and compared with those obtained by standard dose calculation methods. RESULTS: For all five drugs, the relative variation between the NHSE dose and the recommended dose (RecDose) (standard individually calculated dose) was between -6% and +5% as expected. In terms of AUC, there was no statistically significant difference in precision between exposures obtained by the RecDose and those obtained with dose banding (absolute value of relative difference 15-34%). CONCLUSION: Based on pharmacokinetic data for these five drugs, the results generated support the implementation of NHSE dose-banding tables. Indeed, inter-patient variability in drug clearance and exposure far outweighs the impact of relatively small drug dose changes associated with dose banding.

Plasma vemurafenib exposure and pre-treatment hepatocyte growth factor level are two factors contributing to the early peripheral lymphocytes depletion in BRAF-mutated melanoma patients.

The therapeutic response to vemurafenib, a BRAF serine-threonine kinase inhibitor, exhibits large variations between patients. Evaluation of factors predicting the clinical efficacy of vemurafenib may help to identify patients at high risk of non-response in the early phase of treatment. The aim of this study was to analyze the pharmacokinetics of vemurafenib by a population approach and to evaluate the relationship between plasma drug exposure and pre-treatment plasma hepatocyte growth factor (HGF) levels with clinical effects (progression-free survival (PFS), peripheral lymphocytes depletion) in patients with metastatic BRAFV600 mutated melanoma treated with single agent vemurafenib. Concentration-time data (n=332) obtained in 44 patients were analyzed using the NONMEM program. Pre-treatment plasma levels of HGF (n=36) were assayed by ELISA method. A Cox model was used to identify prognostic factors associated with progression-free survival (PFS), and a linear regression to identify factors contributing to the depletion of peripheral lymphocytes at day 15. Steady-state pharmacokinetics of vemurafenib was described by a one compartment model with first order absorption and first order elimination. None of the tested covariates explained the inter-patient variability in CL/F. A significant decrease in total lymphocytes count was observed within the first 15days (median ratio Day15/Day0=0.66, p<0.0001). Patients with Day15/Day0 ratio below 0.66 had longer PFS (14 vs 4 months, HR=0.41, CI95%=[0.15-0.77], p=0.0095). In the multivariate Cox model analysis, ECOG PS was the only parameter independently associated with PFS (grade 1 vs 0, HR=3.26, CI95%=[1.29-8.22], p=0.01 and grade ≥2 vs 0, HR=4.77, CI95%=[1.52-14.95], p=0.007). Plasma vemurafenib exposure (p=0.046) and pre-treatment HGF levels (p=0.003) were independently associated with the total lymphocyte ratio Day15/Day0. These findings show that plasma vemurafenib exposure and pre-treatment HGF levels are two factors contributing to the early peripheral lymphocytes depletion which itself is associated with PFS.

Determination of unbound fraction of pazopanib in vitro and in cancer patients reveals albumin as the main binding site.

INTRODUCTION: Pazopanib exhibits wide inter-patient pharmacokinetic variability which may contribute to differences in treatment outcome. Unbound drug concentrations are believed to be more relevant to pharmacological responses than total concentrations. Thus it is desirable to evaluate pazopanib binding on plasma proteins and different factors potentially affecting this process. METHODS: An equilibrium dialysis method coupled with UPLC-MS/MS assay has been optimized and validated for the determination of pazopanib unbound fraction (fu%) in human plasma. Pazopanib binding in the plasma of healthy volunteers and in isolated protein solutions was investigated. The unbound fraction was determined for 24 cancer patients treated daily with pazopanib. RESULTS: We found that pazopanib was extensively bound in human plasma (>99.9 %) with a mean fu% value of 0.0106 ± 0.0013 % at 40 µg/mL. Protein binding was concentration independent over a clinically relevant range of concentrations. In isolated protein solutions, pazopanib at 40 µg/mL was mainly bound to albumin (40 g/L) and to a lesser extent to α1-acid glycoprotein (1 g/L) and low density lipoproteins (1.2 g/L), with a mean fu% of 0.0073 ± 0.0022 %, 0.992 ± 0.44 % and 7.4 ± 1.7 % respectively. Inter-patient variability (CV%) of fu% in cancer patients was limited (27.2 %). A correlation was observed between individual unbound fraction values and albuminemia. CONCLUSIONS: Pazopanib exhibits extensive binding to plasma proteins in human plasma. Variable albumin concentrations, frequently observed in cancer patients, may affect pazopanib unbound fraction with implications for inter-patient variability in drug efficacy and toxicity.

Population pharmacokinetics of peritoneal, plasma ultrafiltrated and protein-bound oxaliplatin concentrations in patients with disseminated peritoneal cancer after intraperitoneal hyperthermic chemoperfusion of oxaliplatin following cytoreductive surgery: correlation between oxaliplatin exposure and thrombocytopenia.

PURPOSE: First, to evaluate the peritoneal (IP), plasma ultrafiltrated (UF) and protein-bound (B) pharmacokinetics (PK) of oxaliplatin after intraperitoneal hyperthermic chemoperfusion (HIPEC) following cytoreductive surgery. Second, to evaluate the relationship between oxaliplatin exposure and observed toxicity. METHODS: IP, UF, and B concentrations from 75 patients treated by 30-min oxaliplatin-based HIPEC procedures were analysed according to a pharmacokinetic modelling approach using NONMEM. Oxaliplatin was administered in a 5 % dextrose solution (2 L/m(2)) at 360 (n = 58) or 460 mg/m(2) (n = 17). The most frequently observed toxicities were related to the peritoneal, systemic exposures and to the parameters corresponding to the oxaliplatin absorption from peritoneal cavity into plasma. RESULTS: IP (n = 536), UF (n = 669) and B (n = 661) concentrations were simultaneously described according to a five-compartment PK model with irreversible nonlinear binding from UF to B according to a Michaelis-Menten equation. The mean (±SD) maximum fraction of dose absorbed and elimination half-life from the peritoneum was 53.7 % (±8.5) and 0.49 h (±0.1), respectively. The mean (±SD) ratio AUC(IP)/AUC(UF) was 5.3 (±2) confirming the pharmacokinetic advantage of the procedure. Haemoperitoneum (22.7 %), neuropathy (18.7 %), grade 3/4 thrombocytopenia (13.3 %) were the most frequently reported toxicities. AUC(UF) accounts for approximately 12 % of the variation in the maximum percentage of platelet decrease (r = 0.35, p = 0.002). Thrombocytopenia was correlated with higher AUCUF, partly dependent on the extent and rate of oxaliplatin absorption. CONCLUSIONS: Despite a common dose administered, variability in peritoneal and systemic oxaliplatin exposures are observed, leading to differences in haematological toxicity between patients.

Pharmacokinetic/Pharmacodynamic modeling of abexinostat-induced thrombocytopenia across different patient populations: application for the determination of the maximum tolerated doses in both lymphoma and solid tumour patients.

BACKGROUND: In the clinical development of oncology drugs, the recommended dose is usually determined using a 3 + 3 dose-escalation study design. However, this phase I design does not always adequately describe dose-toxicity relationships. METHODS: 125 patients, with either solid tumours or lymphoma, were included in the study and 1217 platelet counts were available over three treatment cycles. The data was used to build a population pharmacokinetic/pharmacodynamic (PKPD) model using a sequential modeling approach. Model-derived Recommended Doses (MDRD) of abexinostat (a Histone Deacetylase Inhibitor) were determined from simulations of different administration schedules, and the higher bound for the probability of reaching these MDRD with a 3 + 3 design were obtained. RESULTS: The PKPD model developed adequately described platelet kinetics in both patient populations with the inclusion of two platelet baseline counts and a disease progression component for patients with lymphoma. Simulation results demonstrated that abexinostat administration during the first 4 days of each week in a 3-week cycle led to a higher MDRD compared to the other administration schedules tested, with a maximum probability of 40 % of reaching these MDRDs using a 3 + 3 design. CONCLUSIONS: The PKPD model was able to predict thrombocytopenia following abexinostat administration in both patient populations. A model-based approach to determine the recommended dose in phase I trials is preferable due to the imprecision of the 3 + 3 design.

Population pharmacokinetics of rituximab with or without plasmapheresis in kidney patients with antibody-mediated disease.

AIMS: Both rituximab and plasmapheresis can be associated in the treatment of immune-mediated kidney diseases. The real impact of plasmapheresis on rituximab pharmacokinetics is unknown. The aim of this study was to compare rituximab pharmacokinetics between patients requiring plasmapheresis and others without plasmapheresis. METHODS: The study included 20 patients receiving one or several infusions of rituximab. In 10 patients, plasmapheresis sessions were also performed (between two and six sessions per patient). Rituximab concentrations were measured in blood samples in all patients and in discarded plasma obtained by plasmapheresis using an enzyme-linked immunosorbent assay method. Data were analysed according to a population pharmacokinetic approach. RESULTS: The mean percentage of rituximab removed during the first plasmapheresis session ranged between 47 and 54% when plasmapheresis was performed between 24 and 72 h after rituximab infusion. Rituximab pharmacokinetics was adequately described by a two-compartment model with first-order elimination. Plasmapheresis had a significant impact on rituximab pharmacokinetics, with an increase of rituximab clearance by a factor of 261 (95% confidence interval 146-376), i.e. from 6.64 to 1733 ml h(-1) . Plasmapheresis performed 24 h after rituximab infusion decreased the rituximab area under the curve by 26%. CONCLUSIONS: Plasmapheresis removed an important amount of rituximab when performed less than 3 days after infusion. The removal of rituximab led to a significant decrease of the area under the curve. This pharmacokinetic observation should be taken into account for rituximab dosing, e.g. an additional third rituximab infusion may be recommended when three plasmapheresis sessions are performed after the first rituximab infusion.

Population analysis of erlotinib in adults and children reveals pharmacokinetic characteristics as the main factor explaining tolerance particularities in children.

PURPOSE: The aim of this pharmacokinetic-pharmacodynamic (PK-PD) analysis was to evaluate the pharmacologic characteristics of erlotinib and its main metabolite (OSI-420) in pediatric patients compared with those in adult patients. EXPERIMENTAL DESIGN: Plasma concentrations of erlotinib and OSI-420 of 46 children with malignant brain tumors included in a phase I study and 42 adults with head and neck carcinoma were analyzed by a population-pharmacokinetic method (NONMEM). The effect of several covariates and single nucleotide polymorphisms (SNP) in ABCB1, ABCG2, and CYP3A5 on pharmacokinetic parameters was evaluated. PK/PD relationships between plasma drug exposure Area Under the Curve (AUC) at day 1 and skin toxicity were studied in children and compared with the relationship observed in adults. RESULTS: A significant difference in erlotinib clearance (P = 0.0001), when expressed in L·h(-1)·kg(-1), was observed between children and adults with mean values of 0.146 and 0.095, respectively (mean difference = 0.051 L·h(-1)·kg(-1), SD = 0.0594). However, a common covariate model was obtained describing erlotinib clearance according to body weight, alanine aminotransferase, ABCB1, and CYP3A5 polymorphisms (2677G > T/A and 6986G > A) for both children and adult patients. The PK-PD relationship was very consistent between the children and adult groups with risk of skin toxicity rising with increasing erlotinib AUC. CONCLUSIONS: The nonlinear population approach applied to pharmacokinetic data combined with a pharmacokinetic-pharmacodynamic analysis revealed that the higher recommended dose in children (125 mg/m(2)/day) compared with adults (90 mg/m(2)/day) is mainly due to pharmacokinetic rather than pharmacodynamic particularities.