The effect of genetic variants in the transcription factor TSPYL family on the CYP3A4 mediated cyclosporine metabolism in kidney transplant patients.

CYP3A4 activity shows considerable interindividual variability. Although studies indicate 60%-80% is heritable, common single nucleotide variants (SNVs) in CYP3A4 together only explain ~10%. Transcriptional factors, such as the testis-specific Y-encoded-like proteins (TSPYLs) family, have been reported to regulate the expression of CYP enzymes including CYP3A4 in vitro. Here, we investigated the effect of genetic variants in TSPYL on CYP3A4 activity using data from a clinical study and a human liver bank. Five SNVs (rs3828743, rs10223646, rs6909133, rs1204807, and rs1204811) in TSPYL were selected because of a reported effect on CYP3A4 expression in vitro or suggested clinical effect. For the clinical study, whole blood concentrations, clinical data, and DNA were available from 295 kidney transplant recipients participating in the prospective MECANO study. A multivariate pharmacokinetic model adjusted for body weight, steroid treatment, and CYP3A4 genotype was used to assess the effect of the genetic variants on cyclosporine clearance. In multivariate analysis, homozygous carriers of rs3828743 had a 18% lower cyclosporin clearance compared to the wild-type and heterozygous patients (28.72 vs. 35.03 L/h, p = 0.018) indicating a lower CYP3A4 activity and an opposite direction of effect compared to the previously reported increased CYP3A4 expression. To validate, we tested associations between rs3828743 and CYP3A4 mRNA and protein expression as well as enzyme activity with data from a liver bank (n = 150). No association with any of these end points was observed. In conclusion, the totality of evidence is not in support of a significant role for TSPYL SNV rs3828743 in explaining variability in CYP3A4 activity.

Population pharmacokinetics of cyclosporine A in pediatric patients with thalassemia undergoing allogeneic hematopoietic stem cell transplantation.

PURPOSE: To establish the population pharmacokinetics (PPK) model of cyclosporine A(CsA) in pediatric patients with thalassemia undergoing allogeneic hematopoietic stem cell transplantation (HSCT), aiming at providing a reference for clinical dose individualization of CsA. METHODS: Children with thalassemia who underwent allogeneic HSCT were enrolled retrospectively. The PPK structural model and the random variable model of CsA were established on NONMEN. And goodness of fit plots (GOFs), visual predictive check (VPC), and bootstrap and normalized prediction distribution errors (NPDE) were used to evaluate the final model. RESULTS: A one-compartment model with first-order absorption was employed to fit the base model. A total of 74 pediatric patients and 600 observations of whole blood concentration were included. The final model included weight (WT) in clearance (CL), alongside post-operative day (POD), fluconazole (FLUC), voriconazole (VORI), posaconazole (POSA), and red blood cell count (RBC) significantly. All the model evaluations were passed. CONCLUSION: In the PPK model based on the pediatric cohort on CsA with thalassemia undergoing allogeneic HSCT, WT, POD, FLUC, VORI, POSA, and RBC were found to be the significant factors influencing CL of CsA. The reliability and robustness of the final model were excellent. It is expected that the PPK model can assist in individualizing dosing strategy clinically.

Limited sampling strategy and population pharmacokinetic model of mycophenolic acid in pediatric patients with systemic lupus erythematosus: application of a double gamma absorption model with SAEM algorithm.

INTRODUCTION: Mycophenolic acid (MPA), the active metabolite of mycophenolate mofetil (MMF), is widely used in the treatment of systemic lupus erythematosus (SLE). It has been shown that its therapeutic drug monitoring based on the area under the curve (AUC) improves treatment efficacy. MPA exhibits a complex bimodal absorption, and a double gamma distribution model has been already proposed in the past to accurately describe this phenomenon. These previous population pharmacokinetics models (POPPK) have been developed using iterative two stage Bayesian (IT2B) or non-parametric adaptive grid (NPAG) methods. However, non-linear mixed effect (NLME) approaches based on stochastic approximation expectation-maximization (SAEM) algorithms have never been published so far for this particular model. The objectives of this study were (i) to implement the double absorption gamma model in Monolix, (ii) to compare different absorption models to describe the pharmacokinetics of MMF, and (iii) to develop a limited sampling strategy (LSS) to estimate AUC in pediatric SLE patients. MATERIAL AND METHODS: A data splitting of full pharmacokinetic profiles sampled in 67 children extracted either from the expert system ISBA (n = 34) or the hospital Saint Louis (n = 33) was performed into train (75%) and test (25%) sets. A POPPK was developed for MPA in the train set using a NLME and the SAEM algorithm and different absorption models were implemented and compared (first order, transit, or simple and double gamma). The best limited sampling strategy was then determined in the test set using a maximum-a-posteriori Bayesian method to estimate individual PK parameters and AUC based on three blood samples compared to the reference AUC calculated using the trapezoidal rule applied on all samples and performances were assessed in the test set. RESULTS: Mean patient age and dose was 13 years old (5-18) and 18.1 mg/kg (7.9-47.6), respectively. MPA concentrations (764) from 107 occasions were included in the analysis. A double gamma absorption with a first-order elimination from the central compartment best fitted the data. The optimal LSS with samples at 30 min, 2 h, and 3 h post-dose exhibited good performances in the test set (mean bias - 0.32% and RMSE 21.0%). CONCLUSION: The POPPK developed in this study adequately estimated the MPA AUC in pediatric patients with SLE based on three samples. The double absorption gamma model developed with the SAEM algorithm showed very accurate fit and reduced computation time.

A Population Pharmacokinetic Model to Predict the Individual Starting Dose of Tacrolimus for Tunisian Adults after Renal Transplantation.

BACKGROUND: Tacrolimus is the most frequently used immunosuppressive drug for preventing renal rejection. However, its use is hampered by its narrow therapeutic index and large intra and interpatient variability in pharmacokinetics. The objective of this study was to externally validate a tacrolimus population pharmacokinetic model developed for the Dutch population and adjust the model for the Tunisian population for use in predicting the starting dose requirement after kidney transplantation. METHODS: Data on tacrolimus exposure were obtained from kidney transplant recipients (KTRs) during the first 3 months post-transplantation. External validation of the Dutch model and its adjustment for the Tunisian population was performed using nonlinear mixed-effects modeling. RESULTS: In total, 1901 whole-blood predose tacrolimus concentrations from 196 adult KTRs were analyzed. According to a visual predictive check, the Dutch model underestimated the starting dose for the Tunisian adult population. The effects of age, together with the CYP3A5*3 and CYP3A4*22 genotypes on tacrolimus clearance were significantly different in the Tunisian population than in the Dutch population. Based on a bodyweight-based dosing, only 21.9% of tacrolimus concentrations were within the target range, whereas this was estimated to be 54.0% with the newly developed model-based dosing. After adjustment, the model was successfully validated internally in a Tunisian population. CONCLUSIONS: A starting-dose population pharmacokinetic model of tacrolimus for Tunisian KTRs was developed based on a previously published Dutch model. Using this starting dose could potentially increase the percentage of patients achieving target tacrolimus concentrations after the initial starting dose.

Tacrolimus Pharmacokinetics is Associated with Gut Microbiota Diversity in Kidney Transplant Patients: Results from a Pilot Cross-Sectional Study.

Clinical use of tacrolimus (TAC), an essential immunosuppressant following transplantation, is complexified by its high pharmacokinetic (PK) variability. The gut microbiota gains growing interest but limited investigations have evaluated its contribution to TAC PKs. Here, we explore the associations between the gut microbiota composition and TAC PKs. In this pilot cross-sectional study (Clinicaltrial.gov NCT04360031), we recruited 93 CYP3A5 non-expressers stabilized kidney transplant recipients. Gut microbiota composition was characterized by 16S rRNA gene sequencing, TAC PK parameters were computed, and additional demographic and medical covariates were collected. Associations between PK parameters or diabetic status and the gut microbiota composition, as reflected by α- and ß-diversity metrics, were evaluated. Patients with higher TAC area under the curve AUC/(dose/kg) had higher bacterial richness, and TAC PK parameters were associated with specific bacterial taxa (e.g., Bilophila) and amplicon sequence variant (ASV; e.g., ASV 1508 and ASV 1982 (Veillonella/unclassified Sporomusaceae); ASV 664 (unclassified Oscillospiraceae)). Building a multiple linear regression model showed that ASV 1508 (co-abundant with ASV 1982) and ASV 664 explained, respectively, 16.0% and 4.6% of the interindividual variability in TAC AUC/(dose/kg) in CYP3A5 non-expresser patients, when adjusting for hematocrit and age. Anaerostipes relative abundance was decreased in patients with diabetes. Altogether, this pilot study revealed unprecedented links between the gut microbiota composition and diversity and TAC PKs in stable kidney transplant recipients. It supports the relevance of studying the gut microbiota as an important contributor to TAC PK variability. Elucidating the causal relationship will offer new perspectives to predict TAC inter- and intra-PK variability.

Modelling changes in the pharmacokinetics of tacrolimus during pregnancy after kidney transplantation: A retrospective cohort study.

AIMS: Pregnancy after kidney transplantation is realistic but immunosuppressants should be continued to prevent rejection. Tacrolimus is safe during pregnancy and is routinely dosed based on whole-blood predose concentrations. However, maintaining these concentrations is complicated as physiological changes during pregnancy affect tacrolimus pharmacokinetics. The aim of this study was to describe tacrolimus pharmacokinetics throughout pregnancy and explain the changes by investigating covariates in a population pharmacokinetic model. METHODS: Data of pregnant women using a twice-daily tacrolimus formulation following kidney transplantation were retrospectively collected from 6 months before conception, throughout gestation and up to 6 months postpartum. Pharmacokinetic analysis was performed using nonlinear mixed effects modelling. Demographic, clinical and genetic parameters were evaluated as covariates. The final model was evaluated using goodness-of-fit plots, visual predictive checks and a bootstrap analysis. RESULTS: A total of 260 whole-blood tacrolimus predose concentrations from 14 pregnant kidney transplant recipients were included. Clearance increased during pregnancy from 34.5 to 41.7 L/h, by 15, 19 and 21% in the first, second and third trimester, respectively, compared to prior to pregnancy. This indicates a required increase in the tacrolimus dose by the same percentage to maintain the prepregnancy concentration. Haematocrit and gestational age were negatively correlated with tacrolimus clearance (P ≤ 0.01), explaining 18% of interindividual and 85% of interoccasion variability in oral clearance. CONCLUSIONS: Tacrolimus clearance increases during pregnancy, resulting in decreased exposure to tacrolimus, which is explained by gestational age and haematocrit. To maintain prepregnancy target whole-blood tacrolimus predose concentrations during pregnancy, increasing the dose is required.

Population pharmacokinetic models of tacrolimus in paediatric solid organ transplant recipients: A systematic review.

AIMS: This study aimed to provide up-to-date information on paediatric population pharmacokinetic models of tacrolimus and to identify factors influencing tacrolimus pharmacokinetic variability. METHODS: Systematic searches in the Web of Science, PubMed, Scopus, Science Direct, Cochrane, EMBASE databases and reference lists of articles were conducted from inception to March 2023. All population pharmacokinetic studies of tacrolimus using nonlinear mixed-effect modelling in paediatric solid organ transplant patients were included. RESULTS: Of the 21 studies reviewed, 62% developed from liver transplant recipients and 33% from kidney transplant recipients. Most studies used a 1-compartment model to describe tacrolimus pharmacokinetics. Body weight was a significant predictor for tacrolimus volume of distribution (Vd/F). The estimated Vd/F for 1-compartment models ranged from 20 to 1890 L, whereas the peripheral volume of distribution (Vp/F) for 2-compartment models was between 290 and 1520 L. Body weight, days post-transplant, CYP3A5 genotype or haematocrit were frequently reported as significant predictors of tacrolimus clearance. The estimated apparent clearance values range between 0.12 and 2.18 L/h/kg, with inter-individual variability from 13.5 to 110.0%. Only 29% of the studies assessed the generalizability of the models with external validation. CONCLUSION: This review highlights the potential factors, modelling approaches and validation methods that impact tacrolimus pharmacokinetics in a paediatric population. The clinician could predict tacrolimus clearance based on body weight, CYP3A5 genotype, days post-transplant or haematocrit. Further research is required to determine the relationship between pharmacogenetics and tacrolimus pharmacodynamics in paediatric patients and confirm the applicability of nonlinear kinetics in this population.

A Comprehensive Mixed-Method Approach to Characterize the Source of Diurnal Tacrolimus Exposure Variability in Children: Systematic Review, Meta-analysis, and Application to an Existing Data Set.

Tacrolimus is widely reported to display diurnal variation in pharmacokinetic parameters with twice-daily dosing. However, the contribution of chronopharmacokinetics versus food intake is unclear, with even less evidence in the pediatric population. The objectives of this study were to summarize the existing literature by meta-analysis and evaluate the impact of food composition on 24-hour pharmacokinetics in pediatric kidney transplant recipients. For the meta-analysis, 10 studies involving 253 individuals were included. The pooled effect sizes demonstrated significant differences in area under the concentration-time curve from time 0 to 12 hours (standardized mean difference [SMD], 0.27; 95% confidence interval [CI], 0.03-0.52) and maximum concentration (SMD, 0.75; 95% CI, 0.35-1.15) between morning and evening dose administration. However, there was significant between-study heterogeneity that was explained by food exposure. The effect size for minimum concentration was not significantly different overall (SMD, -0.09; 95% CI, -0.27 to 0.09) or across the food exposure subgroups. A 2-compartment model with a lag time, linear clearance, and first-order absorption best characterized the tacrolimus pharmacokinetics in pediatric participants. As expected, adding the time of administration and food composition covariates reduced the unexplained within-subject variability for the first-order absorption rate constant, but only caloric composition significantly reduced variability for lag time. The available data suggest food intake is the major driver of diurnal variation in tacrolimus exposure, but the associated changes are not reflected by trough concentrations alone.

Pharmacokinetics of Imatinib Mesylate and Development of Limited Sampling Strategies for Estimating the Area under the Concentration-Time Curve of Imatinib Mesylate in Palestinian Patients with Chronic Myeloid Leukemia.

BACKGROUND AND OBJECTIVE: Imatinib is a tyrosine kinase inhibitor used in the treatment of chronic myeloid leukemia (CML). The area under the concentration-time curve (AUC) is a pharmacokinetic parameter that symbolizes overall exposure to a drug, which is correlated with complete cytogenetic and treatment responses to imatinib, as well as its side effects in patients with CML. The limited sampling strategy (LSS) is considered a sufficiently precise and practical method that can be used to estimate pharmacokinetic parameters such as AUC, without the need for frequent, costly, and inconvenient blood sampling. This study aims to investigate the pharmacokinetic parameters of imatinib, develop and validate a reliable and practical LSS for estimating imatinib AUC0-24, and determine the optimum sampling points for predicting the imatinib AUC after the administration of once-daily imatinib in Palestinian patients with CML. METHOD: Pharmacokinetic profiles, involving six blood samples collected during a 24-h dosing interval, were obtained from 25 Palestinian patients diagnosed with CML who had been receiving imatinib for at least 7 days and had reached a steady-state level. Imatinib AUC0-24 was calculated using the trapezoidal rule, and linear regression analysis was performed to assess the relationship between measured AUC0-24 and concentrations at each sampling time. All developed models were analyzed to determine their effectiveness in predicting AUC0-24 and to identify the optimal sampling time. To evaluate predictive performance, two error indices were employed: the percentage of root mean squared error (% RMSE) and the mean predictive error (% MPE). Bland and Altman plots, along with mountain plots, were utilized to assess the agreement between measured and predicted AUC. RESULTS: Among the one-timepoint estimations, predicted AUC0-24 based on concentration of imatinib at the eighth hour after administration (C8-predicted AUC0-24) demonstrated the highest correlation with the measured AUC (r2 = 0.97, % RMSE = 6.3). In two-timepoint estimations, the model consisting of C0 and C8 yielded the highest correlation between predicted and measured imatinib AUC (r2 = 0.993 and % RMSE = 3.0). In three-timepoint estimations, the combination of C0, C1, and C8 provided the most robust multilinear regression for predicting imatinib AUC0-24 (r2 = 0.996, % RMSE = 2.2). This combination also outperformed all other models in predicting AUC. The use of a two-timepoint limited sampling strategy (LSS) for predicting AUC was found to be reliable and practical. While C0/C8 exhibited the highest correlation, the use of C0/C4 could be a more practical and equally accurate choice. Therapeutic drug monitoring of imatinib based on C0 can also be employed in routine clinical practice owing to its reliability and practicality. CONCLUSION: The LSS using one timepoint, especially C0, can effectively predict imatinib AUC. This approach offers practical benefits in optimizing dose regimens and improving adherence. However, for more precise estimation of imatinib AUC, utilizing two- or three-timepoint concentrations is recommended over relying on a single point.

Population pharmacokinetics of mycophenolic acid and dose optimisation in adult Chinese kidney transplant recipients.

1. This study aimed to establish a population pharmacokinetic (PPK) model of mycophenolic acid (MPA), quantify the effect of clinical factors and pharmacogenomics of MPA, and optimise the dosage for adult kidney transplant recipients.2. One-hundred and four adult renal transplant patients were enrolled. The PPK model was established using the Phoenix® NMLE software and the stepwise methods were filtered for significant covariates. Monte Carlo simulations were performed to optimise the dosage regimen.3. A two-compartment model with first-order absorption and elimination (including lag time) provided a more accurate description of MPA pharmacokinetics. Serum albumin (ALB) significantly affected the central apparent clearance (CL/F), whereas post-transplant time and creatinine clearance were associated with a central apparent volume of distribution (V/F). The estimated population values obtained by the final model were 17.5 L/h and 93.97 L for CL/F and V/F, respectively. Simulation results revealed that larger mycophenolate mofetil doses are required as the ALB concentration decreases. This study established a PPK model of MPA and validated it using various methods. ALB significantly affected CL/F and recommended optimal dose strategies were given based on the final model. These results provide a reference for the personalised therapy of MPA for kidney transplant patients.

Area under the curve of tacrolimus using microsampling devices: towards precision medicine in solid organ transplantation?

PURPOSE: Therapeutic drug monitoring of tacrolimus using trough concentration (Cmin) is mandatory to ensure drug efficacy and safety in solid organ transplantation. However, Cmin is just a proxy for the area under the curve of drug concentrations (AUC) which is the best pharmacokinetic parameter for exposure evaluation. Some studies suggest that patients may present discrepancies between these two parameters. AUC is now easily available through mini-invasive microsampling approach. The aim of this study is to evaluate the relationship between AUC and Cmin in patients benefiting from a complete pharmacokinetic profile using a microsampling approach. METHODS: Fifty-one transplant recipients benefited from a complete pharmacokinetic profile using a microsampling approach, and their 24-h AUC were calculated using the trapezoidal method. The correlation with Cmin was then explored. In parallel, we estimated AUC using the sole Cmin and regression equations according to the post-transplantation days and the galenic form. RESULTS: Weak correlations were found between 24-h AUC observed and the corresponding Cmin (R2 = 0.60) and between AUC observed and expected using the sole Cmin (R2 = 0.62). Therapeutic drug monitoring of tacrolimus using Cmin leads to over- or under-estimate drug exposure in 40.3% of patients. CONCLUSION: Tacrolimus Cmin appears to be an imperfect reflection of drug exposure. Evaluating AUC using a microsampling approach offers a mini-invasive strategy to monitor tacrolimus treatment in transplant recipients.

Effectiveness and safety of once-daily tacrolimus formulations in de novo liver transplant recipients: The PRETHI study.

Data comparing long-term effectiveness and safety of once-daily tacrolimus formulations in de novo liver transplantation are scarce. We compared the effectiveness, pharmacokinetic profile, and safety of LCPT (Envarsus) and PR-Tac (Advagraf) for up to 12 months post-transplant. Adult de novo liver transplant recipients who started IR-Tac (Prograf) and were converted to LCPT or PR-Tac 3-5 days post-transplant were included. Data from 163 patients were analyzed, 87 treated with LCPT and 76 with PR-Tac. The incidence of treatment failure was 30.5% in the LCPT group versus 23.0% in the PR-Tac group (p = .291). Biopsy-proven acute rejection (BPAR) was reported in 26.8% of patients in the LCPT group and 17.6% in the PR-Tac group (p = .166). Graft loss was experienced in one patient (1.2%) in the LCPT group and three patients (4.1%) in the PR-Tac group (p = .346). Death was registered in three patients (3.7%) in the LCPT group and three patients (4.1%) in the PR-Tac group (p > .999). Patients in the LCPT group showed 45.7% higher relative bioavailability (Cmin /total daily dose [TDD]; p < .01) with similar Cmin and 33.3% lower TDD versus PR-Tac (p < .01). The evolution of renal function, safety profile, and the incidence of post-transplant renal failure, dyslipidemia, obesity, hypertension, and diabetes mellitus were similar in patients treated with LCPT and PR-Tac. In de novo liver transplant patients, LCPT and PR-Tac showed comparable effectiveness with higher relative bioavailability, similar Cmin and lower TDD in the LCPT group. Renal function, safety, and post-transplant complications were comparable in LCPT and PR-Tac groups.

Mycophenolic acid therapeutic drug monitoring using area under the curve in pediatric heart transplant recipients.

INTRODUCTION: Pharmacokinetics of mycophenolic acid (MPA) display substantial interpatient variability, with up to 10-fold difference of exposure in individual patients under a fixed-dose regimen. MPA trough level (C0) monitoring is common in clinical practice but has not proven sufficiently informative in predicting MPA exposure or patient outcomes, especially in children. No limited sampling strategies (LSSs) have been generated from pediatric heart transplant (HTx) recipients to estimate MPA AUC. METHODS: Single-center, observational analysis of 135 de novo pediatric HTx recipients ≤21 years old who underwent MPA AUC between 2011 and 2021. RESULTS: Median age was 4 years (IQR .6-12.1). Median time from transplant to MPA AUC sampling was 15 days (IQR 11-19). MMF doses (mg or mg/day) had low, negative Pearson correlation coefficients (r) while doses adjusted for weight or body surface area had low correlation with Trapezoidal MPA AUC0-24 h (r = .3 and .383, respectively). MPA C0 had weak association (r = .451) with Trapezoidal MPA AUC0-24 h . LSS with two pharmacokinetic sampling time points at 90 (C3 ) and 360 (C5 ) min after MMF administration (estimated AUC0-24 h  = 32.82 + 4.12 × C3  + 11.53 × C5 ) showed strong correlation with Trapezoidal MPA AUC0-24 h (r = .87). CONCLUSION: MMF at fixed or weight-adjusted doses, as well as MPA trough levels, correlate poorly with MPA AUC0-24 h . We developed novel LSSs to estimate Trapezoidal MPA AUC from a large cohort of pediatric HTx recipients. Validation of our LSSs should be completed in a separate cohort of pediatric HTx recipients.

Influence of genetic variants on the pharmacokinetics and pharmacodynamics of sirolimus: a systematic review.

Sirolimus is an antiproliferative and immunosuppressive compound inhibiting the mTOR pathway, which is often activated in congenital low-flow vascular malformations. Studies have demonstrated the efficacy of sirolimus for this disease. Studies in kidney transplant patients suggest that genetic variants can influence these pharmacokinetic parameters. Therefore, a systematic literature search was performed to gain insight into pharmacogenetic studies with sirolimus. Most studies investigated CYP3A4 and CYP3A5, with inconsistent results. No pharmacogenetic studies focusing on sirolimus have been performed for low-flow vascular malformations. We analyzed two common variants of CYP3A4 and CYP3A5 (CYP3A4*22 and CYP3A5*3, respectively) in patients (n = 59) with congenital low-flow vascular malformations treated with sirolimus. No association with treatment outcome was identified in this small cohort of patients.

Significant Effects of Renal Function on Mycophenolic Acid Total Clearance in Pediatric Kidney Transplant Recipients with Population Pharmacokinetic Modeling.

BACKGROUND AND OBJECTIVES: Mycophenolic acid (MPA) is an immunosuppressant commonly prescribed in pediatric kidney transplantation to prevent graft rejection. Large variabilities in MPA plasma exposures have been observed in this population, which could result in severe adverse effects. The majority of the MPA pharmacokinetic data have been reported in adult populations, whereas information in pediatric patients is still very limited. The objective of this study was to establish a novel, nonlinear mixed-effects model for MPA and investigate the clinical variables affecting MPA population pharmacokinetics in pediatric kidney transplant recipients. METHODS: Data were collected retrospectively from pediatric kidney transplant patients (≤ 18 years when MPA concentrations were initially collected; on oral administration of mycophenolate mofetil) in Calgary, Alberta, Canada. Nonlinear mixed-effect modeling was conducted using stochastic approximation expectation-maximization in Monolix 2021R2 (Lixoft SAS, France) to determine population pharmacokinetic estimates, interindividual variabilities, and interoccasional variabilities. Covariate models were constructed using the Model Proposal function in Monolix in conjunction with a systematic stepwise inclusion/elimination protocol. The best model was selected based on objective function values, relative standard errors, goodness-of-fit plots, prediction-corrected visual predictive checks, and numerical predictive checks. RESULTS: A total of 50 pediatric kidney transplant patients (25 female) with 219 MPA plasma concentration-time profiles were included. The average age (± standard deviation) and posttransplant time for the sample population were 12.8 ± 4.8 years and 762 ± 1160 days, respectively. The majority of study subjects (i.e., > 85% based on all occasions) were co-administered tacrolimus. A two-compartment, first-order absorption with lag time and linear elimination structural model with lognormal distributed proportional residual errors best described the MPA concentration-time data. The absorption rate constant (2.52 h-1 or 0.042 min-1), lag time (0.166 h or 9.96 min), volumes of distributions of the central (22.8 L) and peripheral (216 L) compartments, and intercompartment clearance (17.6 L h-1 or 0.293 L min-1) were consistent with literature values; whereas total MPA clearance (0.72 L h-1 or 0.012 L min-1) was relatively reduced, likely due to the general lack of cyclosporine interactions and the stabilized graft functions from significantly longer posttransplant time in our sample population. Of the clinical variables tested, only estimated glomerular filtration rate (eGFR) was identified a significant covariate affecting total MPA clearance with a positive, exponential relationship. The final population pharmacokinetic model was successfully evaluated/validated using a variety of complementary methods. CONCLUSION: We have successfully constructed and validated a novel population pharmacokinetic model of MPA in pediatric kidney transplant patients. A positive, nonlinear relationship between eGFR and total MPA clearance identified in our model is likely attributed to multiple concurrent mechanisms, which warrant further systematic investigations.

Gut microbiome modulates tacrolimus pharmacokinetics through the transcriptional regulation of ABCB1.

BACKGROUND: Following solid organ transplantation, tacrolimus (TAC) is an essential drug in the immunosuppressive strategy. Its use constitutes a challenge due to its narrow therapeutic index and its high inter- and intra-pharmacokinetic (PK) variability. As the contribution of the gut microbiota to drug metabolism is now emerging, it might be explored as one of the factors explaining TAC PK variability. Herein, we explored the consequences of TAC administration on the gut microbiota composition. Reciprocally, we studied the contribution of the gut microbiota to TAC PK, using a combination of in vivo and in vitro models. RESULTS: TAC oral administration in mice resulted in compositional alterations of the gut microbiota, namely lower evenness and disturbance in the relative abundance of specific bacterial taxa. Compared to controls, mice with a lower intestinal microbial load due to antibiotics administration exhibit a 33% reduction in TAC whole blood exposure and a lower inter-individual variability. This reduction in TAC levels was strongly correlated with higher expression of the efflux transporter ABCB1 (also known as the p-glycoprotein (P-gp) or the multidrug resistance protein 1 (MDR1)) in the small intestine. Conventionalization of germ-free mice confirmed the ability of the gut microbiota to downregulate ABCB1 expression in a site-specific fashion. The functional inhibition of ABCB1 in vivo by zosuquidar formally established the implication of this efflux transporter in the modulation of TAC PK by the gut microbiota. Furthermore, we showed that polar bacterial metabolites could recapitulate the transcriptional regulation of ABCB1 by the gut microbiota, without affecting its functionality. Finally, whole transcriptome analyses pinpointed, among others, the Constitutive Androstane Receptor (CAR) as a transcription factor likely to mediate the impact of the gut microbiota on ABCB1 transcriptional regulation. CONCLUSIONS: We highlight for the first time how the modulation of ABCB1 expression by bacterial metabolites results in changes in TAC PK, affecting not only blood levels but also the inter-individual variability. More broadly, considering the high number of drugs with unexplained PK variability transported by ABCB1, our work is of clinical importance and paves the way for incorporating the gut microbiota in prediction algorithms for dosage of such drugs. Video Abstract.

Tacrolimus population pharmacokinetics in adult heart transplant patients.

INTRODUCTION: Tacrolimus is an immunosuppressant largely used in heart transplantation. However, the calculation of its exposure based on the area under the curve (AUC) requires the use of a population pharmacokinetic (PK) model. The aims of this work were (i) to develop a population PK model for tacrolimus in heart transplant patients, (ii) to derive a maximum a posteriori Bayesian estimator (MAP-BE) based on a limited sampling strategy (LSS) and (iii) to estimate probabilities of target attainment (PTAs) for AUC and trough concentration (C0). MATERIAL AND METHODS: Forty-seven PK profiles (546 concentrations) of 18 heart transplant patients of the Pharmacocinétique des Immunosuppresseurs chez les patients GREffés Cardiaques study receiving tacrolimus (Prograf®) were included. The database was split into a development (80%) and a validation (20%) set. PK parameters were estimated in MONOLIX® and based on this model a Bayesian estimator using an LSS was built. Simulations were performed to calculate the PTA for AUC and C0. RESULTS: The best model to describe the tacrolimus PK was a two-compartment model with a transit absorption and a linear elimination. Only the CYP3A5 covariate was kept in the final model. The derived MAP-BE based on the LSS (0-1-2 h postdose) yielded an AUC bias ± SD = 2.7 ± 10.2% and an imprecision of 9.9% in comparison to the reference AUC calculated using the trapezoidal rule. PTAs based on AUC or C0 allowed new recommendations to be proposed for starting doses (0.11 mg·kg-1 ·12 h-1 for the CYP3A5 nonexpressor and 0.22 mg·kg1 ·12 h-1 for the CYP3A5 expressor). CONCLUSION: The MAP-BE developed should facilitate estimation of tacrolimus AUC in heart transplant patients.

A Joint Pharmacokinetic Model for the Simultaneous Description of Plasma and Whole Blood Tacrolimus Concentrations in Kidney and Lung Transplant Recipients.

BACKGROUND AND OBJECTIVE: Historically, dosing of tacrolimus is guided by therapeutic drug monitoring (TDM) of the whole blood concentration, which is strongly influenced by haematocrit. The therapeutic and adverse effects are however expected to be driven by the unbound exposure, which could be better represented by measuring plasma concentrations. OBJECTIVE: We aimed to establish plasma concentration ranges reflecting whole blood concentrations within currently used target ranges. METHODS: Plasma and whole blood tacrolimus concentrations were determined in samples of transplant recipients included in the TransplantLines Biobank and Cohort Study. Targeted whole blood trough concentrations are 4-6 ng/mL and 7-10 ng/mL for kidney and lung transplant recipients, respectively. A population pharmacokinetic model was developed using non-linear mixed-effects modelling. Simulations were performed to infer plasma concentration ranges corresponding to whole blood target ranges. RESULTS: Plasma (n = 1973) and whole blood (n = 1961) tacrolimus concentrations were determined in 1060 transplant recipients. A one-compartment model with fixed first-order absorption and estimated first-order elimination characterised observed plasma concentrations. Plasma was linked to whole blood using a saturable binding equation (maximum binding 35.7 ng/mL, 95% confidence interval (CI) 31.0-40.4 ng/mL; dissociation constant 0.24 ng/mL, 95% CI 0.19-0.29 ng/mL). Model simulations indicate that patients within the whole blood target range are expected to have plasma concentrations (95% prediction interval) of 0.06-0.26 ng/mL and 0.10-0.93 ng/mL for kidney and lung transplant recipients, respectively. CONCLUSION: Whole blood tacrolimus target ranges, currently used to guide TDM, were translated to plasma concentration ranges of 0.06-0.26 ng/mL and 0.10-0.93 ng/mL for kidney and lung transplant recipients, respectively.

Using Prior Knowledge on Systems Through PBPK to Gain Further Insight into Routine Clinical Data on Trough Concentrations: The Case of Tacrolimus in Chronic Kidney Disease.

BACKGROUND: Routine therapeutic drug monitoring (TDM) relies heavily on measuring trough drug concentrations. Trough concentrations are affected not only by drug bioavailability and clearance, but also by various patient and disease factors and the volume of distribution. This often makes interpreting differences in drug exposure from trough data challenging. This study aimed to combine the advantages of top-down analysis of therapeutic drug monitoring data with bottom-up physiologically-based pharmacokinetic (PBPK) modeling to investigate the effect of declining renal function in chronic kidney disease (CKD) on the nonrenal intrinsic metabolic clearance ( CLint ) of tacrolimus as a case example. METHODS: Data on biochemistry, demographics, and kidney function, along with 1167 tacrolimus trough concentrations for 40 renal transplant patients, were collected from the Salford Royal Hospital's database. A reduced PBPK model was developed to estimate CLint for each patient. Personalized unbound fractions, blood-to-plasma ratios, and drug affinities for various tissues were used as priors to estimate the apparent volume of distribution. Kidney function based on the estimated glomerular filtration rate ( eGFR ) was assessed as a covariate for CLint using the stochastic approximation of expectation and maximization method. RESULTS: At baseline, the median (interquartile range) eGFR was 45 (34.5-55.5) mL/min/1.73 m 2 . A significant but weak correlation was observed between tacrolimus CLint and eGFR (r = 0.2, P < 0.001). The CLint declined gradually (up to 36%) with CKD progression. Tacrolimus CLint did not differ significantly between stable and failing transplant patients. CONCLUSIONS: Kidney function deterioration in CKD can affect nonrenal CLint for drugs that undergo extensive hepatic metabolism, such as tacrolimus, with critical implications in clinical practice. This study demonstrates the advantages of combining prior system information (via PBPK) to investigate covariate effects in sparse real-world datasets.

Population pharmacokinetic analyses of tacrolimus in non-transplant patients: a systematic review.

BACKGROUND AND OBJECTIVES: Tacrolimus (TAC) has been increasingly used in patients with non-transplant settings. Because of its large between-subject variability, several population pharmacokinetic (PPK) studies have been performed to facilitate individualized therapy. This review summarized published PPK models of TAC in non-transplant patients, aiming to clarify factors affecting PKs of TAC and identify the knowledge gap that may require further research. METHODS: The PubMed, Embase databases, and Cochrane Library, as well as related references, were searched from the time of inception of the databases to February 2023, to identify TAC population pharmacokinetic studies modeled in non-transplant patients using a non-linear mixed-effects modeling approach. RESULTS: Sixteen studies, all from Asian countries (China and Korea), were included in this study. Of these studies, eleven and four were carried out in pediatric and adult patients, respectively. One-compartment models were the commonly used structural models for TAC. The apparent clearance (CL/F) of TAC ranged from 2.05 to 30.9 L·h-1 (median of 14.9 L·h-1). Coadministered medication, genetic factors, and weight were the most common covariates affecting TAC-CL/F, and variability in the apparent volume of distribution (V/F) was largely explained by weight. Coadministration with Wuzhi capsules reduced CL/F by about 19 to 43%. For patients with CYP3A5*1*1 and *1*3 genotypes, the CL/F was 39-149% higher CL/F than patients with CYP3A5*1*1. CONCLUSION: The optimal TAC dosage should be adjusted based on the patient's co-administration, body weight, and genetic information (especially CYP3A5 genotype). Further studies are needed to assess the generalizability of the published models to other ethnic groups. Moreover, external validation should be frequently performed to improve the clinical practicality of the models.