Statins Prevent the Deleterious Consequences of Placental Chemerin Upregulation in Preeclampsia.

BACKGROUND: Chemerin, an inflammatory adipokine, is upregulated in preeclampsia, and its placental overexpression results in preeclampsia-like symptoms in mice. Statins may lower chemerin. METHODS: Chemerin was determined in a prospective cohort study in women suspected of preeclampsia and evaluated as a predictor versus the sFlt-1 (soluble fms-like tyrosine kinase-1)/PlGF (placental growth factor) ratio. Chemerin release was studied in perfused placentas and placental explants with or without the statins pravastatin and fluvastatin. We also addressed statin placental passage and the effects of chemerin in chorionic plate arteries. RESULTS: Serum chemerin was elevated in women with preeclampsia, and its addition to a predictive model yielded significant effects on top of the sFlt-1/PlGF ratio to predict preeclampsia and its fetal complications. Perfused placentas and explants of preeclamptic women released more chemerin and sFlt-1 and less PlGF than those of healthy pregnant women. Statins reversed this. Both statins entered the fetal compartment, and the fetal/maternal concentration ratio of pravastatin was twice that of fluvastatin. Chemerin constricted plate arteries, and this was blocked by a chemerin receptor antagonist and pravastatin. Chemerin did not potentiate endothelin-1 in chorionic plate arteries. In explants, statins upregulated low-density lipoprotein receptor expression, which relies on the same transcription factor as chemerin, and NO release. CONCLUSIONS: Chemerin is a biomarker for preeclampsia, and statins both prevent its placental upregulation and effects, in an NO and low-density lipoprotein receptor-dependent manner. Combined with their capacity to improve the sFlt-1/PlGF ratio, this offers an attractive mechanism by which statins may prevent or treat preeclampsia.

A Decade of Experience With Alemtuzumab Therapy for Severe or Glucocorticoid-Resistant Kidney Transplant Rejection.

Alemtuzumab is used as lymphocyte-depleting therapy for severe or glucocorticoid-resistant kidney transplant rejection. However, the long-term efficacy and toxicity of alemtuzumab therapy are unclear. Therefore, all cases of alemtuzumab anti-rejection therapy between 2012 and 2022 in our institution were investigated. Graft survival, graft function, lymphocyte depletion, serious infections, malignancies, and patient survival were analyzed and compared with a reference cohort of transplanted patients who did not require alemtuzumab anti-rejection therapy. A total of 225 patients treated with alemtuzumab were identified and compared with a reference cohort of 1,668 patients. Over 60% of grafts was salvaged with alemtuzumab therapy, but graft survival was significantly poorer compared to the reference cohort. The median time of profound T- and B lymphocyte depletion was 272 and 344 days, respectively. Serious infection rate after alemtuzumab therapy was 54.1/100 person-years. The risk of death (hazard ratio 1.75, 95%-CI 1.28-2.39) and infection-related death (hazard ratio 2.36, 95%-CI 1.35-4.11) were higher in the alemtuzumab-treated cohort. In conclusion, alemtuzumab is an effective treatment for severe kidney transplant rejection, but causes long-lasting lymphocyte depletion and is associated with frequent infections and worse patient survival outcomes.

Clinical pharmacokinetics of antipsychotics in pediatric populations: a scoping review focusing on dosing regimen.

INTRODUCTION: Achieving optimal clinical responses and minimizing side effects through precision dosing of antipsychotics in children and adolescents with psychiatric disorders remains a challenge. Identifying patient characteristics (covariates) that affect pharmacokinetics can inform more effective dosing strategies and ultimately improve patient outcomes. This review aims to provide greater insight into the impact of covariates on the clinical pharmacokinetics of antipsychotics in pediatric populations. AREAS COVERED: A comprehensive literature search was conducted, and the main findings regarding the effects of the covariates on the pharmacokinetics of antipsychotics in children and adolescents are presented. EXPERT OPINION: Our study highlights significant covariates, including age, sex, weight, CYP2D6 phenotype, co-medication, and smoking habits, which affect the pharmacokinetics of antipsychotics. However, the findings were generally limited by the small sample sizes of naturalistic, open-label, observational studies, and the homogeneous subgroups. Dosing based on weight and preemptive genotyping could prove beneficial for optimizing the dosing regimen in pediatric populations. Future research is needed to refine dosing recommendations and establish therapeutic reference ranges critical for precision dosing and Therapeutic Drug Monitoring (TDM). The integration of individual patient characteristics with TDM can further optimize the efficacy and safety of antipsychotics for each patient.

Association Between the Intracellular Tacrolimus Concentration in CD3 + T Lymphocytes and CD14 + Monocytes and Acute Kidney Transplant Rejection.

BACKGROUND: Intracellular tacrolimus concentration in peripheral blood mononuclear cells (PBMCs) (TAC [PBMC] ) has been proposed to better represent its active concentration than its whole blood concentration. As tacrolimus acts on T lymphocytes and other white blood cells, including monocytes, we investigated the association of tacrolimus concentration in CD3 + T lymphocytes (TAC [CD3] ) and CD14 + monocytes (TAC [CD14] ) with acute rejection after kidney transplantation. METHODS: From a total of 61 samples in this case-control study, 28 samples were obtained during biopsy-proven acute rejection (rejection group), and 33 samples were obtained in the absence of rejection (control group). PBMCs were collected from both cryopreserved (retrospectively) and freshly obtained (prospectively) samples. CD3 + T lymphocytes and CD14 + monocytes were isolated from PBMCs, and their intracellular tacrolimus concentrations were measured. RESULTS: The correlation between tacrolimus whole-blood and intracellular concentrations was poor. TAC [CD3] was significantly lower than TAC [CD14] (median 12.8 versus 81.6 pg/million cells; P < 0.001). No difference in TAC [PBMC] (48.5 versus 44.4 pg/million cells; P = 0.82), TAC [CD3] (13.4 versus 12.5 pg/million cells; P = 0.28), and TAC [CD14] (90.0 versus 72.8 pg/million cells; P = 0.27) was found between the rejection and control groups. However, freshly isolated PBMCs showed significantly higher TAC [PBMC] than PBMCs from cryopreserved samples. Subgroup analysis of intracellular tacrolimus concentrations from freshly isolated cells did not show a difference between rejectors and nonrejectors. CONCLUSIONS: Differences in TAC [CD3] and TAC [CD14] between patients with and without rejection could not be demonstrated. However, further optimization of the cell isolation process is required because a difference in TAC [PBMC] between fresh and cryopreserved cells was observed. These results need to be confirmed in a study with a larger number of patients.

Monitoring intracellular tacrolimus concentrations and its relationship with rejection in the early phase after renal transplantation.

INTRODUCTION: After kidney transplantation, rejection and drug-related toxicity occur despite tacrolimus whole-blood pre-dose concentrations ([Tac]blood) being within the target range. The tacrolimus concentration within peripheral blood mononuclear cells ([Tac]cells) might correlate better with clinical outcomes. The aim of this study was to investigate the correlation between [Tac]blood and [Tac]cells, the evolution of [Tac]cells and the [Tac]cells/[Tac]blood ratio, and to assess the relationship between tacrolimus concentrations and the occurrence of rejection. METHODS: In this prospective study, samples for the measurement of [Tac]blood and [Tac]cells were collected on days 3 and 10 after kidney transplantation, and on the morning of a for-cause kidney transplant biopsy. Biopsies were reviewed according to the Banff 2019 update. RESULTS: Eighty-three [Tac]cells samples were measured of 44 kidney transplant recipients. The correlation between [Tac]cells and [Tac]blood was poor (Pearson's r = 0.56 (day 3); r = 0.20 (day 10)). Both the dose-corrected [Tac]cells and the [Tac]cells/[Tac]blood ratio were not significantly different between days 3 and 10, and the median inter-occasion variability of the dose-corrected [Tac]cells and the [Tac]cells/[Tac]blood ratio were 19.4% and 23.4%, respectively (n = 24). Neither [Tac]cells, [Tac]blood, nor the [Tac]cells/[Tac]blood ratio were significantly different between patients with biopsy-proven acute rejection (n = 4) and patients with acute tubular necrosis (n = 4) or a cancelled biopsy (n = 9; p > 0.05). CONCLUSION: Tacrolimus exposure and distribution appeared stable in the early phase after transplantation. [Tac]cells was not significantly associated with the occurrence of rejection. A possible explanation for these results might be related to the low number of patients included in this study and also due to the fact that PBMCs are not a specific enough matrix to monitor tacrolimus concentrations.

Avoiding Tacrolimus Underexposure and Overexposure with a Dosing Algorithm for Renal Transplant Recipients: A Single Arm Prospective Intervention Trial.

Bodyweight-based tacrolimus dosing followed by therapeutic drug monitoring is standard clinical care after renal transplantation. However, after transplantation, a meager 38% of patients are on target at first steady-state and it can take up to 3 weeks to reach the target tacrolimus predose concentration (C0 ). Tacrolimus underexposure and overexposure is associated with an increased risk of rejection and drug-related toxicity, respectively. To minimize subtherapeutic and supratherapeutic tacrolimus exposure in the immediate post-transplant phase, a previously developed dosing algorithm to predict an individual's tacrolimus starting dose was tested prospectively. In this single-arm, prospective, therapeutic intervention trial, 60 de novo kidney transplant recipients received a tacrolimus starting dose based on a dosing algorithm instead of a standard, bodyweight-based dose. The algorithm included cytochrome P450 (CYP)3A4 and CYP3A5 genotype, body surface area, and age as covariates. The target tacrolimus C0 , measured for the first time at day 3, was 7.5-12.5 ng/mL. Between February 23, 2019, and July 7, 2020, 60 patients were included. One patient was excluded because of a protocol violation. On day 3 post-transplantation, 34 of 59 patients (58%, 90% CI 47-68%) had a tacrolimus C0 within the therapeutic range. Markedly subtherapeutic (< 5.0 ng/mL) and supratherapeutic (> 20 ng/mL) tacrolimus concentrations were observed in 7% and 3% of the patients, respectively. Biopsy-proven acute rejection occurred in three patients (5%). In conclusion, algorithm-based tacrolimus dosing leads to the achievement of the tacrolimus target C0 in as many as 58% of the patients on day 3 after kidney transplantation.

Pharmacokinetics and pharmacogenetics of high-dose methotrexate in Chinese adult patients with non-Hodgkin lymphoma: a population analysis.

PURPOSE: High-dose methotrexate (HD-MTX) is widely used in the treatment of non-Hodgkin lymphoma (NHL), but the pharmacokinetic properties of HD-MTX in Chinese adult patients with NHL have not yet been established through an approach that integrates genetic covariates. The purposes of this study were to identify both physiological and pharmacogenomic covariates that can explain the inter- and intraindividual pharmacokinetic variability of MTX in Chinese adult patients with NHL and to explore a new sampling strategy for predicting delayed MTX elimination. METHODS: A total of 852 MTX concentrations from 91 adult patients with NHL were analyzed using the nonlinear mixed-effects modeling method. FPGS, GGH, SLCO1B1, ABCB1 and MTHFR were genotyped using the Sequenom MassARRAY technology platform and were screened as covariates. The ability of different sampling strategies to predict the MTX concentration at 72 h was assessed through maximum a posteriori Bayesian forecasting using a validation dataset (18 patients). RESULTS: A two-compartment model adequately described the data, and the estimated mean MTX clearance (CL) was 6.03 L/h (9%). Creatinine clearance (CrCL) was identified as a covariate for CL, whereas the intercompartmental clearance (Q) was significantly affected by the body surface area (BSA). However, none of the genotypes exerted a significant effect on the pharmacokinetic properties of MTX. The percentage of patients with concentrations below 0.2 µmol/L at 72 h decreased from 65.6 to 42.6% when the CrCL decreased from 90 to 60 ml/min/1.73 m2 with a scheduled dosing of 3 g/m2, and the same trend was observed with dose regimens of 1 g/m2 and 2 g/m2. Bayesian forecasting using the MTX concentrations at 24 and 42 h provided the best predictive performance for estimating the MTX concentration at 72 h after dosing. CONCLUSIONS: The MTX population pharmacokinetic model developed in this study might provide useful information for establishing personalized therapy involving MTX for the treatment of adult patients with NHL.

Population pharmacokinetics and target attainment of ciprofloxacin in critically ill patients.

PURPOSE: To develop and validate a population pharmacokinetic model of ciprofloxacin intravenously in critically ill patients, and determine target attainment to provide guidance for more effective regimens. METHODS: Non-linear mixed-effects modelling was used for the model development and covariate analysis. Target attainment of an ƒAUC0-24/MIC ≥ 100 for different MICs was calculated for standard dosing regimens. Monte Carlo simulations were performed to define the probability of target attainment (PTA) of several dosing regimens. RESULTS: A total of 204 blood samples were collected from 42 ICU patients treated with ciprofloxacin 400-1200 mg/day, with median values for age of 66 years, APACHE II score of 22, BMI of 26 kg/m2, and eGFR of 58.5 mL/min/1.73 m2. The median ƒAUC0-24 and ƒCmax were 29.9 mg•h/L and 3.1 mg/L, respectively. Ciprofloxacin pharmacokinetics were best described by a two-compartment model. We did not find any significant covariate to add to the structural model. The proportion of patients achieving the target ƒAUC0-24/MIC ≥ 100 were 61.9% and 16.7% with MICs of 0.25 and 0.5 mg/L, respectively. Results of the PTA simulations suggest that a dose of ≥ 1200 mg/day is needed to achieve sufficient ƒAUC0-24/MIC ratios. CONCLUSIONS: The model described the pharmacokinetics of ciprofloxacin in ICU patients adequately. No significant covariates were found and high inter-individual variability of ciprofloxacin pharmacokinetics in ICU patients was observed. The poor target attainment supports the use of higher doses such as 1200 mg/day in critically ill patients, while the variability of inter-individual pharmacokinetics parameters emphasizes the need for therapeutic drug monitoring to ensure optimal exposure.

Population Pharmacokinetics of Imipenem in Critically Ill Patients: A Parametric and Nonparametric Model Converge on CKD-EPI Estimated Glomerular Filtration Rate as an Impactful Covariate.

BACKGROUND: Population pharmacokinetic (popPK) models for antibiotics are used to improve dosing strategies and individualize dosing by therapeutic drug monitoring. Little is known about the differences in results of parametric versus nonparametric popPK models and their potential consequences in clinical practice. We developed both parametric and nonparametric models of imipenem using data from critically ill patients and compared their results. METHODS: Twenty-six critically ill patients treated with intravenous imipenem/cilastatin were included in this study. Median estimated glomerular filtration rate (eGFR) measured by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation was 116 mL/min/1.73 m2 (interquartile range 104-124) at inclusion. The usual dosing regimen was 500 mg/500 mg four times daily. On average, five imipenem levels per patient (138 levels in total) were drawn as peak, intermediate, and trough levels. Imipenem concentration-time profiles were analyzed using parametric (NONMEM 7.2) and nonparametric (Pmetrics 1.5.2) popPK software. RESULTS: For both methods, data were best described by a model with two distribution compartments and the CKD-EPI eGFR equation unadjusted for body surface area as a covariate on the elimination rate constant (Ke). The parametric population parameter estimates were Ke 0.637 h-1 (between-subject variability [BSV]: 19.0% coefficient of variation [CV]) and central distribution volume (Vc) 29.6 L (without BSV). The nonparametric values were Ke 0.681 h-1 (34.0% CV) and Vc 31.1 L (42.6% CV). CONCLUSIONS: Both models described imipenem popPK well; the parameter estimates were comparable and the included covariate was identical. However, estimated BSV was higher in the nonparametric model. This may have consequences for estimated exposure during dosing simulations and should be further investigated in simulation studies.

Immunomonitoring of Tacrolimus in Healthy Volunteers: The First Step from PK- to PD-Based Therapeutic Drug Monitoring?

Therapeutic drug monitoring is routinely performed to maintain optimal tacrolimus concentrations in kidney transplant recipients. Nonetheless, toxicity and rejection still occur within an acceptable concentration-range. To have a better understanding of the relationship between tacrolimus dose, tacrolimus concentration, and its effect on the target cell, we developed functional immune tests for the quantification of the tacrolimus effect. Twelve healthy volunteers received a single dose of tacrolimus, after which intracellular and whole blood tacrolimus concentrations were measured and were related to T cell functionality. A significant correlation was found between tacrolimus concentrations in T cells and whole blood concentrations (r = 0.71, p = 0.009), while no correlation was found between tacrolimus concentrations in peripheral blood mononuclear cells (PBMCs) and whole blood (r = 0.35, p = 0.27). Phytohemagglutinin (PHA) induced the production of IL-2 and IFNγ, as well as the inhibition of CD71 and CD154 expression on T cells at 1.5 h post-dose, when maximum tacrolimus levels were observed. Moreover, the in vitro tacrolimus effect of the mentioned markers corresponded with the ex vivo effect after dosing. In conclusion, our results showed that intracellular tacrolimus concentrations mimic whole blood concentrations, and that PHA-induced cytokine production (IL-2 and IFNγ) and activation marker expression (CD71 and CD154) are suitable readout measures to measure the immunosuppressive effect of tacrolimus on the T cell.

Therapeutic Drug Monitoring of Tacrolimus-Personalized Therapy: Second Consensus Report.

Ten years ago, a consensus report on the optimization of tacrolimus was published in this journal. In 2017, the Immunosuppressive Drugs Scientific Committee of the International Association of Therapeutic Drug Monitoring and Clinical Toxicity (IATDMCT) decided to issue an updated consensus report considering the most relevant advances in tacrolimus pharmacokinetics (PK), pharmacogenetics (PG), pharmacodynamics, and immunologic biomarkers, with the aim to provide analytical and drug-exposure recommendations to assist TDM professionals and clinicians to individualize tacrolimus TDM and treatment. The consensus is based on in-depth literature searches regarding each topic that is addressed in this document. Thirty-seven international experts in the field of TDM of tacrolimus as well as its PG and biomarkers contributed to the drafting of sections most relevant for their expertise. Whenever applicable, the quality of evidence and the strength of recommendations were graded according to a published grading guide. After iterated editing, the final version of the complete document was approved by all authors. For each category of solid organ and stem cell transplantation, the current state of PK monitoring is discussed and the specific targets of tacrolimus trough concentrations (predose sample C0) are presented for subgroups of patients along with the grading of these recommendations. In addition, tacrolimus area under the concentration-time curve determination is proposed as the best TDM option early after transplantation, at the time of immunosuppression minimization, for special populations, and specific clinical situations. For indications other than transplantation, the potentially effective tacrolimus concentrations in systemic treatment are discussed without formal grading. The importance of consistency, calibration, proficiency testing, and the requirement for standardization and need for traceability and reference materials is highlighted. The status for alternative approaches for tacrolimus TDM is presented including dried blood spots, volumetric absorptive microsampling, and the development of intracellular measurements of tacrolimus. The association between CYP3A5 genotype and tacrolimus dose requirement is consistent (Grading A I). So far, pharmacodynamic and immunologic biomarkers have not entered routine monitoring, but determination of residual nuclear factor of activated T cells-regulated gene expression supports the identification of renal transplant recipients at risk of rejection, infections, and malignancy (B II). In addition, monitoring intracellular T-cell IFN-g production can help to identify kidney and liver transplant recipients at high risk of acute rejection (B II) and select good candidates for immunosuppression minimization (B II). Although cell-free DNA seems a promising biomarker of acute donor injury and to assess the minimally effective C0 of tacrolimus, multicenter prospective interventional studies are required to better evaluate its clinical utility in solid organ transplantation. Population PK models including CYP3A5 and CYP3A4 genotypes will be considered to guide initial tacrolimus dosing. Future studies should investigate the clinical benefit of time-to-event models to better evaluate biomarkers as predictive of personal response, the risk of rejection, and graft outcome. The Expert Committee concludes that considerable advances in the different fields of tacrolimus monitoring have been achieved during this last decade. Continued efforts should focus on the opportunities to implement in clinical routine the combination of new standardized PK approaches with PG, and valid biomarkers to further personalize tacrolimus therapy and to improve long-term outcomes for treated patients.

Converting cyclosporine A from intravenous to oral administration in hematopoietic stem cell transplant recipients and the role of azole antifungals.

PURPOSE: Cyclosporine A (CsA) is the most widely used immunosuppressive agent after a hematopoietic stem cell transplantation (HSCT). Although recommendations for CsA dose conversion from intravenous to oral administration differ from 1:1 to 1:3, most studies did not consider the role of azole antifungals as an important confounder. Therefore, we assess the optimal conversion rate of CsA from intravenous to oral administration in HSCT recipients, taking into account the concomitant use of azole antifungals. METHODS: We retrospectively included patients from a large database of 483 patients who underwent a HSCT and received intravenous CsA as part of the conditioning regimen and peritransplant immunosuppression. All patients were converted from intravenous to oral administration in a 1:1 conversion rate. We collected for each patient three CsA trough concentrations during intravenous and oral administration, directly before and after conversion to oral administration. RESULTS: We included 71 patients; 50 patients co-treated with fluconazole, 10 with voriconazole, and 11 without azole co-medication. In patients with voriconazole, the dose-corrected CsA concentration (CsA concentration divided by CsA dosage) was not different between intravenous and oral administration (2.6% difference, p = 0.754), suggesting a CsA oral bioavailability of nearly 100%. In patients with fluconazole and without azole co-medication, the dose-corrected CsA concentration was respectively 21.5% (p < 0.001) and 25.2% (p = 0.069) lower during oral administration. CONCLUSIONS: In patients with voriconazole, CsA should be converted 1:1 from intravenous to oral administration. In patients with fluconazole and without azole co-medication, a 1:1.3 substitution is advised to prevent subtherapeutic CsA concentrations.

Dosing ribavirin in hepatitis E-infected solid organ transplant recipients.

Hepatitis E virus (HEV) is the most common cause of viral hepatitis worldwide. Genotypes 1 and 2 (GT1 and GT2) are mainly present in developing countries, while GT3 and GT4 are prevalent in developed and high-income countries. In the majority of cases, HEV causes a self-limiting hepatitis. GT3 and GT4 can be responsible for a chronic hepatitis that can lead to cirrhosis in immunocompromized patients, i.e. solid-organ- and stem-cell-transplant-patients, human immunodeficiency virus-infected patients, and patients receiving chemotherapy or immunotherapy. HEV has also been associated with extra-hepatic manifestations such as neurologic disorders (Guillain-Barré Syndrome and neuralgic amyotrophy) and kidney disease. In patients with chronic hepatitis, reduction of immunosuppression, when possible, is the first therapeutic option. In the remaining patients, ribavirin therapy has been shown to very efficient for treating HEV infection leading to a sustained virological response in nearly 80-85% of patients. However, the mechanism of action of ribavirin in this setting is still unknown, as is the impact of HEV RNA polymerase mutations. There are unmet needs with regard to the treatment of chronic HEV with ribavirin. These include the optimal dosing and duration of treatment, and the potential beneficial effects of therapeutic drug monitoring on the virological response and the incidence of side effects. In the present review, we will provide an overview of HEV epidemiology, its mode of transmission and clinical manifestations, as well as its treatment by ribavirin with a focus on the drug's pharmacokinetics and dosing.

Hypoalbuminaemia and decreased midazolam clearance in terminally ill adult patients, an inflammatory effect?

AIMS: Midazolam is the drug of choice for palliative sedation and is titrated to achieve the desired level of sedation. Because of large inter-individual variability (IIV), however, the time it takes to achieve adequate sedation varies widely. It would therefore greatly improve clinical care if an individualized dose could be determined beforehand. To find clinically relevant parameters for dose individualization, we performed a pharmacokinetic study on midazolam, 1OH-midazolam (1-OH-M) and 1OH-midazolam-glucuronide (1-OH-MG) in terminally ill patients. METHODS: Using nonlinear mixed effects modelling (NONMEM 7.2), a population pharmacokinetic analysis was conducted with 192 samples from 45 terminally ill patients who received midazolam either orally or subcutaneously. The covariates analysed were patient characteristics, co-medication and blood chemistry levels. RESULTS: The data were accurately described by a one compartment model for midazolam, 1-OH-M and 1-OH-MG. The population mean estimates for midazolam, 1-OH-M and 1-OH-MG clearance were 8.4 l h-1 (RSE 9%, IIV 49%), 45.4 l h-1 (RSE 12%, IIV 60.5%) and 5.1 l h-1 (RSE 11%, IIV 49.9%), respectively. 1-OH-MG clearance was correlated with the estimated glomular filtration rate (eGFR) explaining 28.4% of the IIV in 1-OH-MG clearance. In addition, low albumin levels were associated with decreased midazolam clearance, explaining 18.2% of the IIV. CONCLUSION: Our study indicates albumin levels and eGFR as relevant clinical parameters to optimize midazolam dosing in terminally ill patients. The correlation between low albumin levels and decreased midazolam clearance is probably a result of inflammatory response as high CRP levels were correlated in a similar way.

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.

Differences in clearance of mycophenolic acid among renal transplant recipients, hematopoietic stem cell transplant recipients, and patients with autoimmune disease.

For more than a decade, mycophenolate mofetil (MMF) has been used as an immunosuppressive drug in solid organ transplant recipients to prevent graft rejection. After oral administration, the prodrug MMF is rapidly hydrolyzed to the active metabolite mycophenolic acid (MPA). MMF is being used increasingly in hematopoietic stem cell transplantation (HSCTx) and autoimmune diseases (AID). The pharmacokinetics of MPA are markedly different in these patients. In comparison with renal transplant recipients (RTx), MPA clearance is increased in HSCTx patients and decreased in AIDS. The aim of this study was to characterize MPA clearance in RTx, HSCTx, and AID patients and to test whether the differences in clearance can be described by clinical chemical parameters. MPA concentration-time profiles from 19 RTx patients coprescribed cyclosporine, 17 RTx patients coprescribed tacrolimus, 38 HSCTx patients coprescribed cyclosporine, and 36 patients with AID were analyzed retrospectively with nonlinear mixed effects modeling (first-order conditional estimate). The following covariates were tested: indication for MMF treatment, sex, age, weight, plasma albumin, cyclosporine cotreatment, dose and predose blood concentration, creatinine clearance, and hemoglobin. Pharmacokinetics of MPA were described by a two-compartment model with time-lagged first-order absorption. MPA clearance was correlated in univariate analysis with plasma albumin, cyclosporine dose and predose blood concentration, creatinine clearance, hemoglobin, and indication for MMF treatment (RTx, HSCTx, or AID) (P < 0.001). All significant covariates were combined in an intermediate multivariate model followed by backward elimination. The indication for MMF treatment could be removed from the intermediate model without compromising the fit. The correlation between clearance and cyclosporine predose concentrations and plasma albumin remained significant in the final model and could describe the difference in clearance between the different indications for MMF treatment. Median clearance was 30.2, 45.6, and 10.7 L/h in RTx, HSCTx, and AID patients, respectively. In conclusion, plasma albumin concentrations and cyclosporine predose concentrations are able to describe the difference in MPA clearance among RTx, HSCTx, and AID patients.

Pharmacokinetics of mycophenolate mofetil in hematopoietic stem cell transplant recipients.

Mycophenolate mofetil (MMF), a prodrug of mycophenolic acid (MPA), is increasingly used in the prophylaxis of graft-versus-host disease (GVHD) after hematopoietic stem cell transplantation (HCT). Few pharmacokinetic data are available about the use of MMF for this indication. This case series aimed at analyzing the pharmacokinetics of MMF in a population of HCT recipients representative for everyday practice. From 15 HCT recipients, serial plasma samples were taken after twice-daily oral intake of MMF. Plasma concentrations of total MPA and its glucuronide metabolites, as well as free MPA, were quantified. Median apparent oral MPA clearance (CL/F), apparent half-life, and total MPA area under the curve for hours 0 to 12 (AUC0-12, normalized to 1000 mg MMF) were, respectively, 56 L/h (range: 29-98 L/h), 2.3 hours (range: 0.8-5.7 hours), and 18.0 mg*h/L (range: 10-35 mg*h/L). Total MPA concentrations were below 2 mg/L 8 hours after MMF administration, indicating reduced enterohepatic recirculation. Median free MPA AUC0-12 (normalized to 1000 mg MMF) was 224 microg*h/L (range: 56-411 microg*h/L). Because of high CL/F, total MPA exposure in HCT recipients is low and apparent half-life is short in comparison with reference values from renal transplantation. Exposure may be improved in HCT recipients by higher or more frequent MMF dosing.