eGFR decrease during antiviral C therapy with first generation protease inhibitors: a clinical significance?

BACKGROUND & AIMS: Renal toxicity of first generation protease inhibitors (PIs) was not a safety signal in phase III clinical trials, but was recently reported in recent studies. It appeared important to determine the clinical significance of these findings. METHODS: We retrospectively analysed 101 HCV patients receiving triple therapy with telaprevir (n = 36) or boceprevir (n = 26) or double therapy (n = 39) with peginterferon and ribavirin and having a close monitoring of eGFR (MDRD formula) during and after treatment. EGFR decline over time was assessed by a linear mixed-effects model (LMEM) with search for possible explanatory covariates. RESULTS: Patients treated with telaprevir presented a significant decrease of eGFR with the same kinetics: initial decrease at W (week) 4, nadir at W8 (mean decrease 17.0 ± 18.9 ml/min/1.73 m(2)) and return to baseline at W16. The W8 eGFR was correlated with the D0 eGFR (R(2) = 0.49). The LMEM showed that interindividual variability in the slope of eGFR vs time between D0 and W8 was non-significant and eGFR nadir could be predicted from eGFR obtained at D0. In multivariate analysis, eGFR intercept (i.e. baseline value) was associated with older age and male sex. CONCLUSION: The eGFR significantly varied in telaprevir group only. Our model showed that eGFR nadir mainly depended on initial eGFR. As telaprevir has been shown to inhibit mostly the drug transporter OCT2 which interacts with creatinine transport, the early decrease of eGFR observed could be a benign phenomenon. However, as unpredictable true renal toxicity may occur during therapy, we recommend a thorough follow-up of eGFR.

Ciclosporin population pharmacokinetics and Bayesian estimation in thoracic transplant recipients.

BACKGROUND AND OBJECTIVES: Therapeutic drug monitoring of ciclosporin has been recognized as an essential tool in the management of allograft transplant recipients, as it could help improve their outcome. However, there is still no consensus about the optimal method for monitoring ciclosporin after thoracic transplantation. Better knowledge of the pharmacokinetics of ciclosporin in thoracic transplant patients and design of tools dedicated to ciclosporin monitoring could help its practice and its outcome in this population of patients. The aims of this study were to (i) investigate the population pharmacokinetics of ciclosporin in thoracic (heart or lung) transplant patients and study the influence of a range of potential covariates, including demographic, clinical and genetic factors, on pharmacokinetic parameters; and (ii) develop a Bayesian estimator able to predict the individual pharmacokinetic parameters and exposures indices in this population of patients. METHODS: The analysis was performed with 187 full pharmacokinetic profiles obtained in 57 lung and 19 heart transplant patients within the first year post-transplantation. A population pharmacokinetic model was developed by non-linear mixed-effects modelling using NONMEM(®) (version 7.1) from an index dataset (118 profiles). On the basis of this population model and a limited number of blood samples, a Bayesian estimator able to determine ciclosporin area under the blood concentration-time curve (AUC) during a dosage interval was built and evaluated in the validation dataset (69 profiles). RESULTS: Ciclosporin pharmacokinetics were described using a two-compartment model with time-lagged first order absorption and first-order elimination. The final population model included sex as a covariate: ciclosporin apparent oral clearance was on average 37 % faster in male than in female patients (34.8 vs. 25.4 L/h, p < 0.001). Good predictive performance of the Bayesian estimator was obtained using three blood concentrations measured at 40 min, 2 h and 4 h post-dose, with a non-significant bias of -5 % between the estimated and the reference trapezoidal AUC and a good precision (relative mean square error = 13 %). CONCLUSION: Ciclosporin population pharmacokinetic analysis in thoracic transplant patients (including patients with cystic fibrosis) showed a significant influence of sex on apparent clearance. The Bayesian estimator developed in this study yielded accurate prediction of ciclosporin exposure in this population throughout the first year post-transplantation. This tool may allow routine ciclosporin dose individualization.

Population pharmacokinetic modelling and design of a Bayesian estimator for therapeutic drug monitoring of tacrolimus in lung transplantation.

BACKGROUND: Therapeutic drug monitoring of tacrolimus is a major support to patient management and could help improve the outcome of lung transplant recipients, by minimizing the risk of rejections and infections. However, despite the wide use of tacrolimus as part of maintenance immunosuppressive regimens after lung transplantation, little is known about its pharmacokinetics in this population. Better knowledge of the pharmacokinetics of tacrolimus in lung transplant recipients, and the development of tools dedicated to its therapeutic drug monitoring, could thus help improve their outcome. OBJECTIVES: The aims of this study were (i) to characterize the population pharmacokinetics of tacrolimus in lung transplant recipients, including the influence of biological and pharmacogenetic covariates; and (ii) to develop a Bayesian estimator of the tacrolimus area under the blood concentration-time curve from time zero to 12 hours (AUC(12)) for its therapeutic drug monitoring in lung transplant recipients. METHODS: A population pharmacokinetic model was developed by nonlinear mixed-effects modelling using NONMEM® version VI, from 182 tacrolimus full concentration-time profiles collected in 78 lung transplant recipients within the first year post-transplantation. Patient genotypes for the cytochrome P450 3A5 (CYP3A5) A6986G single nucleotide polymorphism (SNP) were characterized by TaqMan allelic discrimination. Patients were divided into an index dataset (n = 125 profiles) and a validation dataset (n = 57 profiles). A Bayesian estimator was derived from the final model using the index dataset, in order to determine the tacrolimus AUC(12) on the basis of a limited number of samples. The predictive performance of the Bayesian estimator was evaluated in the validation dataset by comparing the estimated AUC(12) with the trapezoidal AUC(12). RESULTS: Tacrolimus pharmacokinetics were described using a two-compartment model with Erlang absorption and first-order elimination. The model included cystic fibrosis (CF) and CYP3A5 polymorphism as covariates. The relative bioavailability in patients with CF was approximately 60% of the relative bioavailability observed in patients without CF, and the transfer rate constant between the transit compartments was 2-fold smaller in patients with CF than in those without CF (3.32 vs 7.06 h-1). The apparent clearance was 40% faster in CYP3A5 expressers than in non-expressers (24.5 vs 17.5 L/h). Good predictive performance was obtained with the Bayesian estimator developed using the final model and concentrations measured at 40 minutes and at 2 and 4 hours post-dose, as shown by the mean bias (1.1%, 95% CI -1.4, 3.7) and imprecision (9.8%) between the estimated and the trapezoidal AUC(12). The bias was >20% in 1.8% of patients. CONCLUSION: Population pharmacokinetic analysis showed that lung transplant patients with CF displayed lower bioavailability and a smaller transfer rate constant between transit compartments than those without CF, while the apparent clearance was faster in CYP3A5 expressers than in non-expressers. The Bayesian estimator developed in this study provides an accurate prediction of tacrolimus exposure in lung transplant patients, with and without CF, throughout the first year post-transplantation. This tool may allow routine tacrolimus dose individualization and may be used to conduct clinical trials on therapeutic drug monitoring of tacrolimus after lung transplantation.

Bayesian estimation of mycophenolate mofetil in lung transplantation, using a population pharmacokinetic model developed in kidney and lung transplant recipients.

BACKGROUND AND OBJECTIVES: The immunosuppressive drug mycophenolate mofetil is used to prevent rejection after organ transplantation. In kidney transplant recipients, it has been demonstrated that adjustment of the mycophenolate mofetil dose on the basis of the area under the concentration-time curve (AUC) of mycophenolic acid (MPA), the active moiety of mycophenolate mofetil, improves the clinical outcome. Because of the high risks of rejections and infections in lung transplant recipients, therapeutic drug monitoring of the MPA AUC might be even more useful in these patients. The aims of this study were to characterize the pharmacokinetics of MPA in lung and kidney transplant recipients, describe the differences between the two populations and develop a Bayesian estimator of the MPA AUC in lung transplant recipients. METHODS: In total, 460 MPA concentration-time profiles from 41 lung transplant recipients and 116 kidney transplant recipients were included. Nonlinear mixed-effects modelling was used to develop a population pharmacokinetic model. Patients were divided into an index dataset and a validation dataset. The pharmacokinetic model derived from the index dataset was used to develop a Bayesian estimator, which was validated using the 35 lung transplant recipients' profiles from the validation dataset. RESULTS: MPA pharmacokinetics were described using a two-compartment model with lag time, first-order absorption and first-order elimination. The influence of ciclosporin co-treatment and the changes over time post-transplantation were included in the model. Lung transplant recipients had, on average, a 53% slower absorption rate and 50% faster MPA apparent oral clearance than kidney transplant recipients (p < 0.001). In lung transplant recipients, the bioavailability was, on average, 31% lower in patients with cystic fibrosis than in patients without cystic fibrosis (p < 0.001). The Bayesian estimator developed using the population pharmacokinetic model--and taking into account ciclosporin co-treatment, cystic fibrosis and time post-transplantation, with concentrations measured at 0, 1 and 4 hours after mycophenolate mofetil dose administration--resulted in a non-significant bias and mean imprecision of 5.8 mg · h/L. This higher imprecision compared with those of similar estimators that have previously been developed in kidney transplantation might have been caused by the high MPA pharmacokinetic variability seen in the lung transplant recipients and by the fact that a large proportion of the patients did not receive ciclosporin, which reduces variability in the elimination phase of MPA by blocking its enterohepatic cycling. CONCLUSION: Lung transplant recipients have a slower MPA absorption rate and faster apparent oral clearance than kidney transplant recipients, while cystic fibrosis results in lower MPA bioavailability. A Bayesian estimator using MPA concentration-time samples at 0, 1 and 4 hours post-dose had the best predictive performance.

Non-invasive detection of hepatic amyloidosis: FibroScan, a new tool.

INTRODUCTION: FibroScan, a non-invasive tool for measuring liver stiffness (LS), is not specific to liver fibrosis. Other extra-hepatic conditions may modify the LS value. OBJECTIVES: Our aim was to examine whether amyloid deposition in the liver may modify LS. METHODS: LS was measured prospectively in 41 patients with systemic AL amyloidosis (AL) in the French AL Reference Center, comprising: 5 patients with liver involvement (LI) and no cardiac involvement (CI), 11 with CI and no LI, 12 with both LI and CI and 13 with neither (2005 consensus criteria); 26 negative controls, 50 patients infected with Hepatitis C virus (HCV)-infected and 18 AL-free patients with right-sided heart disease ('cardiac controls') were also examined. RESULTS: Median LS was significantly higher in patients with AL with liver involvement [27.4 (10.3-75) kPa] than in negative controls [4.8 (2.8-11.9) kPa] (p < 0.0001), and patients infected with HCV [(6.8 (2.9-69.1) kPa] (p = 0.001), and tended to be higher than in the 'cardiac controls' [11 (4.1-75) kPa] (p = 0.08). A cut-off value of 17.3 kPa, prioritising specificity, is proposed for routine diagnosis of significant AL liver infiltration. CONCLUSION: LS > 17.3 kPa is suggestive of AL hepatic disease in patients with non-fibrotic liver changes, and may have diagnostic value in patients with known AL.

Post-transplant lymphoproliferative disease (PTLD): Pharmacological, virological and other determinants.

Post-transplant lymphoproliferative disorders (PTLDs) represent a serious complication in solid organ transplantation and are the first cause of cancer related mortality in this population. Pre-transplant Epstein Barr Virus seronegativity and receipt of T cell depleting agents for induction or severe/refractory rejection are known risk factors, but they primarily impact early occurring disease. On the other hand, late occurring disease, which has typically not correlated with the above or other specific risk factors, has recently been shown to be associated with older recipient age and prolonged receipt of calcineurin inhibitors. Furthermore, recent data has contributed to and, in some instances shed light on, previous debate concerning the role of viruses other than EBV and the level of HLA mismatches as risk factors for PTLD. Gene association studies focusing on key cytokines and their receptors have identified several polymorphisms that may prove useful to identify patients at risk, with distinction for early and late occurring disease. Determining the influence of individual maintenance immunosuppressive agents on lymphomagenesis has been limited by the complexity of the multi-drug regimens used and absence of measures of drug exposure and time-dependent covariates in multivariable analyses. Biomarkers that measure the extent of immunosupression may have a role in avoiding PTLD, and other post-transplant complications.

Polymorphisms in type I and II inosine monophosphate dehydrogenase genes and association with clinical outcome in patients on mycophenolate mofetil.

BACKGROUND: Type I and II inosine monophosphate dehydrogenases (IMPDH) are the targets of mycophenolic acid (MPA), a widely used immunosuppressant. The aims of this study were: to check the presence of controversial polymorphisms in the IMPDH II gene; to look for new ones; and to investigate potential associations between the most frequent SNPs in both IMPDH genes and clinical outcome in renal transplant recipients. METHODS: The DNA and clinical data of 456 patients from two clinical trials were collected. We sequenced the IMPDH II gene in 80 patients and we genotyped the 456 patients' DNA for the IMPDH II rs4974081, rs11706052, 787C>T and the IMPDH I rs2278293 and rs2278294 SNPs, all of which were earlier reported to be potentially involved in MPA treatment related outcome. We investigated the associations of biopsy proven acute rejection (BPAR), leucopenia, cytomegalovirus infections and other infections with these IMPDH polymorphisms, as well as with demographic, biological and treatment data using multivariate analysis. RESULTS: Many IMPDH II variant alleles referenced in Genbank were not detected and no new polymorphisms were identified. In the whole group of 456 patients, the IMPDH I rs2278294 SNP was associated with a lower risk of BPAR and a higher risk of leucopenia over the first year post-transplantation. No other IMPDH I or IMPDH II polymorphism was significantly associated with any clinical outcome. Interestingly, calcineurin inhibitor and MPA exposures below the therapeutic range increased the risk of BPAR. Cytomegalovirus infection was the factor most closely linked with leucopenia, whereas tacrolimus was associated with fewer infections than cyclosporine. CONCLUSION: IMPDH II genotyping may not improve MPA treatment outcome over the first year post-transplantation, in contrast to MPA and calcineurine inhibitor therapeutic drug monitoring and IMPDH I genotyping.

Cost-effectiveness analysis of individualized mycophenolate mofetil dosing in kidney transplant patients in the APOMYGRE trial.

BACKGROUND: In the prospective, randomized, multicenter APOMYGRE trial conducted in France, concentration-controlled mycophenolate mofetil (MMF) dosing based on mycophenolic acid (MPA) exposure significantly reduced the treatment failure and acute rejection during the first posttransplantation year compared with fixed-dose MMF. This analysis investigated the cost effectiveness of dose individualization. METHOD: The study included 65 patients per group (intent-to-treat population). Treatment failure (primary efficacy endpoint) was defined as death, graft loss, acute rejection, or MMF discontinuation because of adverse effects. Data on hospitalizations, drugs prescribed, physicians' fees, laboratory expenses, ambulatory visits, and transportation were retrieved. Costs were calculated from the French National Health System perspective. RESULTS: The mean (95% confidence interval) total yearly cost per patient was Euro 47,477 (Euro 43,933; Euro 51,020) in the concentration-controlled group and Euro 46,783 ( Euro 44,152; Euro 49,414) in the fixed-dose group (P=0.7). The observed incremental cost-effectiveness ratio was Euro 3757 per treatment failure (Purchasing Power Parities United States/France: $4129). Hospitalization and drug costs accounted for approximately 50% and 25% of total costs, respectively. The cost for MPA area under the concentration-time curve and dose calculation was Euro 452 per patient, less than 1% of the total cost. CONCLUSION: In the APOMYGRE trial, therapeutic MPA monitoring using a limited sampling strategy reduced the risk of treatment failure and acute rejection in renal allograft recipients during the first 12 months posttransplantation, at neutral cost.

[Pharmacokinetics and therapeutic drug monitoring of anticancer agents]. / Pharmacocinétique et adaptation de posologie des agents anticancéreux.

Treatment individualisation, an old concept renovated by the progress of analytical techniques and the advent of pharmacogenetics, aims at optimizing the usage of existing drugs by adjusting the nature and dose of anticancer agents to each patient, based on genetic, physiological and pathological criteria, on tumour nature, on associated drugs or previous treatment lines, on treatment efficacy and toxicity or on patient's exposure to the active drug form. This article exposes the pre-requisites and clinical trials necessary to demonstrate the usefulness of therapeutic drug monitoring (TDM) for drugs in general and anticancer agents in particular. It also presents the different TDM approaches used in oncology and reviews the current situation of anticancer drugs' TDM. When TDM cannot be expected to optimize efficacy, it may at least contribute to secure high dose regimens in intensification protocols. TDM in oncology is a discipline in progress. Proofs of efficacy are both scarce are difficult to obtain. However, taking this limitation into account and using the benefits of technical progress could lead to better cognitive research on the predictive response factors for a larger number of anticancer agents in the future. When the usefulness of TDM has been clinically demonstrated or strongly suggested, it is essential that as many patients as possible benefit from it. This "technological transfer" is a continuous process that requires periodical reviews of research results for TDM specialists and clinicians.

Patient characteristics influencing ciclosporin pharmacokinetics and accurate Bayesian estimation of ciclosporin exposure in heart, lung and kidney transplant patients.

BACKGROUND AND OBJECTIVES: Population pharmacokinetic studies of ciclosporin microemulsion are needed to identify the individual factors influencing ciclosporin pharmacokinetic variability in transplant patients and to design efficient tools for the accurate estimation of ciclosporin overall exposure (area under the plasma concentration-time curve from 0 to 12 hours [AUC12]). In the present retrospective study, a large database of heart, lung (with or without cystic fibrosis) and kidney (both adult and paediatric) transplant patients receiving ciclosporin microemulsion was analysed with the aims of (i) building a population pharmacokinetic model and finding the main covariates linked with ciclosporin microemulsion pharmacokinetic parameters; and (ii) developing a maximum a posteriori probability Bayesian estimator (MAP-BE) to estimate ciclosporin microemulsion pharmacokinetic parameters using a limited-sampling strategy. METHODS: 3,072 concentration data from 147 patients (i.e. 309 full pharmacokinetic profiles) were analysed using the nonlinear mixed-effects model program NONMEM. The influence of numerous covariates was tested, and the final model was validated by data splitting. For Bayesian estimation, the best limited-sampling strategy was determined based on the D-optimality criterion, and validation performed in an independent group of 60 patients. RESULTS: The pharmacokinetics of ciclosporin microemulsion were accurately described by a two-compartment model with Erlang distribution for the absorption process. The type of graft and post-transplantation period were identified as significant sources of variability of the absorption parameter. Both apparent volume of the central compartment after oral administration (V1/F) and apparent oral clearance (CL/F) increased with bodyweight. The best limited-sampling strategy for Bayesian estimation was 0 hour, 1 hour and 3 hour post-dose, providing accurate estimation of ciclosporin microemulsion AUC12 in all patients of the test group, with a mean bias of 2.0 +/- 10.5% (range: -19.1% to -21.4% and 95% CI -0.6, +4.7). CONCLUSION: Population pharmacokinetic analysis of ciclosporin microemulsion in allograft transplants resulted in the design of a new pharmacokinetic model for ciclosporin microemulsion, identification of significant covariates and the design of an accurate MAP-BE based on three blood concentrations and these covariates.

Pharmacokinetic study of tacrolimus in cystic fibrosis and non-cystic fibrosis lung transplant patients and design of Bayesian estimators using limited sampling strategies.

OBJECTIVES: To: (i) test different pharmacokinetic models to fit full tacrolimus concentration-time profiles; (ii) estimate the tacrolimus pharmacokinetic characteristics in stable lung transplant patients with or without cystic fibrosis (CF); (iii) compare the pharmacokinetic parameters between these two patient groups; and (iv) design maximum a posteriori Bayesian estimators (MAP-BE) for pharmacokinetic forecasting in these patients using a limited sampling strategy. METHODS: Tacrolimus blood concentration-time profiles obtained on three occasions within a 5-day period in 22 adult lung transplant recipients (11 with CF and 11 without CF) were retrospectively studied. Three different one-compartment models with first-order elimination were tested to fit the data: one with first-order absorption, one convoluted with a gamma distribution to describe the absorption phase, and one convoluted with a double gamma distribution able to describe secondary concentration peaks. Finally, Bayesian estimation using the best model and a limited sampling strategy was tested in the two groups of patients for its ability to provide accurate estimates of the main tacrolimus pharmacokinetic parameters and exposure indices. RESULTS: The one-compartment model with first-order elimination convoluted with a double gamma distribution gave the best results in both CF and non-CF lung transplant recipients. The patients with CF required higher doses of tacrolimus than those without CF to achieve similar drug exposure, and population modelling had to be performed in CF and non-CF patients separately. Accurate Bayesian estimates of area under the blood concentration-time curve from 0 to 12 hours (AUC12), AUC from 0 to 4 hours, peak blood concentration (Cmax) and time to reach Cmax were obtained using three blood samples collected at 0, 1 and 3 hours in non-CF patients (correlation coefficient between observed and estimated AUC12, R2 = 0.96), and at 0, 1.5 and 4 hours in CF patients (R2 = 0.91). CONCLUSION: A particular pharmacokinetic model was designed to fit the complex and highly variable tacrolimus blood concentration-time profiles. Moreover, MAP-BE allowing tacrolimus therapeutic drug monitoring based on AUC12 were developed.

Tacrolimus pharmacokinetics and dose monitoring after lung transplantation for cystic fibrosis and other conditions.

In cystic fibrosis (CF), absorption of tacrolimus through the gastrointestinal tract may be impaired due to fat malabsorption. The aim of this pilot study was to compare tacrolimus pharmacokinetics and inter- and intrasubject variability of exposure in stable lung transplant recipients with and without CF, and to determine the best single-time predictors of exposure. The study included 11 lung transplant recipients with CF and 11 without CF who received tacrolimus twice daily. Blood samples were obtained predose and at 0.5, 1, 1.5, 2, 2.5, 3, 4, 5, 6, 8 and 12 h postdose on 3 separate days within 1 week. Tacrolimus pharmacokinetics and inter- and intrasubject variability of exposure were similar in the two groups, though exposure-per-milligram-dose was approximately 50% lower in CF patients. Tacrolimus trough concentration did not accurately predict the area under the concentration curve (AUC(0-12)), but the concentration measured 3 h postdose (C(3)) was tightly correlated with the AUC(0-12) in both CF (r(2)= 0.86) and non-CF (r(2)= 0.92) patients. In summary, patients with CF have a higher tacrolimus oral clearance, but nonsignificant differences in short-term inter- and intrasubject variability of exposure compared to patients without CF. C(3) is tightly correlated with AUC(0-12) in lung transplant recipients with and without CF.

Cyclosporine pharmacokinetics and dose monitoring after lung transplantation: comparison between cystic fibrosis and other conditions.

BACKGROUND: In cystic fibrosis (CF), absorption of cyclosporine A (CsA) through the gastrointestinal tract is often impaired because of fat malabsorption. The aim of this study was to compare the steady-state pharmacokinetics of CsA and the inter- and intrasubject variability of CsA exposure in stable lung transplant recipients with and without CF and to determine the best single-time predictors of the area under the curve (AUC). METHODS: Ten lung transplant recipients without CF and 10 lung transplant recipients with CF were studied. All patients received Neoral twice daily. Blood samples were obtained predose and at 0.5, 1, 1.5, 2, 2.5, 3, 4, 5, 6, 8, and 12 h postdose on three separate days within a 5-day period. RESULTS: CsA exposure and pharmacokinetic variables were similar in the two groups, although exposure-per-milligram-per-dose was approximately 25% lower in CF patients. Coefficients of intersubject variability were numerically higher in CF patients, but the difference between groups did not reach significance. On the other hand, the maximum concentration (Cmax), the concentration 2 hours after administration (C2), AUC0-12, and AUC0-4 showed a twofold greater intrasubject variability in CF patients. CsA trough concentration did not predict accurately the AUC, but C2 was a good predictor of the AUC0-4 in both CF (r2=0.90) and non-CF (r2=0.78) patients. CONCLUSION: Compared to patients without CF, patients with CF show a lower bioavailability of CsA and a greater intrasubject variability of Cmax, C2, and AUC. C2 is the best single-point predictor of the AUC0-4 in lung transplant recipients with and without CF.

An animal model for the study of chronopharmacokinetics of drugs and application to methotrexate and vinorelbine.

An animal model was designed to study the chronopharmacokinetics of intravenous drugs and applied to anticancer agents vinorelbine (VNB) and methotrexate (MTX). Each experiment was performed on four pigs housed in a standardized light-dark cycle (12:12). Four pigs received a 0.16-mg bolus of VNB, followed by a 60-h continuous infusion at 0.48 mg/h. After hydration and urine alkalinization, four other pigs received a 2 mg/kg bolus of MTX, followed by two concomitant 60-h continuous infusions, one with MTX (8 mg/kg/h) and the other for hydration and folinic acid rescue (1.5 mg/kg/24 h). Serum cortisol was determined in each blood sample collected in these pigs. Blood samples were collected each hour for 60 h. The infusion flow rates and drug solution concentrations were controlled throughout the experiments. Analysis of VNB serum concentrations did not show any circadian rhythm of VNB serum concentrations. One pig administered MTX exhibited severe toxicity. Interestingly, no circadian rhythm of serum cortisol concentration was observed in this pig, whereas the three others exhibited a statistically significant cortisol circadian rhythm with a peak secretion in the morning. Two of these three pigs showed a significant 24-h rhythm of MTX with acrophase occurring at approximately 1:00 PM in both. The maximal concentration was found at 12:00 AM in the third pig. After the data were pooled, a highly significant (P < 0.01) circadian rhythm in MTX serum concentrations (57%) was found, with acrophase at midday. The pig represents a useful model for the study of chronopharmacokinetics of drugs given intravenously in human. The MTX chronokinetic variation found herein may be of interest for the improvement of chemotherapy in cancer patients.

Bayesian estimation of methotrexate pharmacokinetic parameters and area under the curve in children and young adults with localised osteosarcoma.

BACKGROUND: Methotrexate is the most efficient anticancer drug in osteosarcoma. It requires individual exposure monitoring because of the high doses used, its wide interpatient pharmacokinetic variability and the existence of demonstrated relationships between efficacy, toxicity and serum drug concentrations. OBJECTIVE: To develop a maximum a posteriori (MAP) Bayesian estimator able to predict individual pharmacokinetic parameters and exposure indices such as area under the curve (AUC) for methotrexate from a few blood samples, in order to prevent toxicity and facilitate further studies of the relationships between efficacy and exposure. METHODS: Methotrexate population pharmacokinetics were estimated by a retrospective analysis of concentration data from 40 children and young adults by using the nonparametric expectation maximisation method NPEM. A linear two-compartment model with elimination from the central compartment was assumed. Individual pharmacokinetic parameters and AUC were subsequently estimated in 30 other young patients, using MAP Bayesian estimation as implemented in two programs, ADAPT II and an inhouse program Winphar((R)). RESULTS: The pharmacokinetic parameters used in the model were the volume of the central compartment (V(1)) and the transfer constants (k(10), k(12) and k(21)). The mean values (with percentage coefficient of variation) obtained were: 18.24L (54.1%) and 0.41 (42.3%), 0.0168 (68.7%), and 0.1069 (61.3%) h(-1), respectively. Bayesian forecasting enabled nonbiased estimation of AUC and systemic clearance using a schedule with two sampling times (6 and 24 hours after the beginning of the infusion) and either program. Collection of a third sample at 4 hours improved the precision. CONCLUSION: The Bayesian adaptive method developed herein allows accurate estimation of individual exposure to methotrexate and can easily be used in clinical practice.

Application of pharmacokinetic modelling to the routine therapeutic drug monitoring of anticancer drugs.

Over the last 10 years, proofs of the clinical interest of therapeutic drug monitoring (TDM) of certain anticancer drugs have been established. Numerous studies have shown that TDM is an efficient tool for controlling the toxicity of therapeutic drugs, and a few trials have even demonstrated that it can improve their efficacy. This article critically reviews TDM tools based on pharmacokinetic modelling of anticancer drugs. The administered dose of anticancer drugs is sometimes adjusted individually using either a priori or a posteriori methods. The most frequent clinical application of a priori formulae concerns carboplatin and allows the computation of the first dose based on biometrical and biological data such as weight, age, gender, creatinine clearance and glomerular filtration rate. A posteriori methods use drug plasma concentrations to adjust the subsequent dose(s). Thus, nomograms allowing dose adjustment on the basis of blood concentration are routinely used for 5-fluorouracil given as long continuous infusions. Multilinear regression models have been developed, for example for etoposide, doxorubicin. carboplatin, cyclophosphamide and irinotecan, to predict a single exposure variable [such as area under concentration-time curve (AUC)] from a small number of plasma concentrations obtained at predetermined times after a standard dose. These models can only be applied by using the same dose and schedule as the original study. Bayesian estimation offers more flexibility in blood sampling times and, owing to its precision and to the amount of information provided, is the method of choice for ensuring that a given patient benefits from the desired systemic exposure. Unlike the other a posteriori methods, Bayesian estimation is based on population pharmacokinetic studies and can take into account the effects of different individual factors on the pharmacokinetics of the drug. Bayesian estimators have been used to determine maximum tolerated systemic exposure thresholds (e.g. for topotecan or teniposide) as well as for the routine monitoring of drugs characterized by a very high interindividual pharmacokinetic variability such as methotrexate or carboplatin. The development of these methods has contributed to improving cancer chemotherapy in terms of patient outcome and survival and should be pursued.