Therapeutic drug monitoring in kidney and liver transplantation: current advances and future directions.

INTRODUCTION: Immunosuppressive drugs (ISD) present a narrow therapeutic window and extremely high inter- and intra-individual pharmacokinetic variability, which complicates their use in solid organ transplant recipients. In order to find a narrow appropriate equilibrium for each patient with the aim of maintaining clinical efficacy and reducing the risk of adverse drug reactions, a complex both clinical and biological monitoring is required, in particular through the use of therapeutic drug monitoring (TDM). AREA COVERED: This review provides an overview of the available information on the relationship between exposure to immunosuppressive drugs and their efficacy and/or toxicity in kidney and liver transplantation. The aim of the review is to describe the pharmacodynamic/pharmacokinetic relationship that exists for immunosuppressive drugs, to summarize the studies that assess the value of TDM for these drugs in clinical practice, and to present the target and monitoring strategies aimed at optimizing patient immunosuppression, which could help to take a step forward in the field of solid organ transplant patient care. EXPERT OPINION: To improve the care of transplant patients, several TDM innovations can be pursued by investigators. Among these, the development of microsampling methods for TDM or the combination of pharmacodynamic biomarkers with ISD exposure measurements appear to be relevant strategies.

Impact of Switching From Immediate- or Prolonged-Release to Once-Daily Extended-Release Tacrolimus (LCPT) on Tremor in Stable Kidney Transplant Recipients: The Observational ELIT Study.

Once-daily extended-release tacrolimus (LCPT) exhibits increased bioavailability versus immediate-release (IR-TAC) and prolonged release (PR-TAC) tacrolimus. Improvements in tremor were previously reported in a limited number of kidney transplant patients who switched to LCPT. We conducted a non-interventional, non-randomized, uncontrolled, longitudinal, prospective, multicenter study to assess the impact of switching to LCPT on tremor and quality of life (QoL) in a larger population of stable kidney transplant patients. The primary endpoint was change in The Essential Tremor Rating Assessment Scale (TETRAS) score; secondary endpoints included 12-item Short Form Survey (SF-12) scores, tacrolimus trough concentrations, neurologic symptoms, and safety assessments. Subgroup analyses were conducted to assess change in TETRAS score and tacrolimus trough concentration/dose (C0/D) ratio by prior tacrolimus formulation and tacrolimus metabolizer status. Among 221 patients, the mean decrease of TETRAS score after switch to LCPT was statistically significant (p < 0.0001 vs. baseline). There was no statistically significant difference in change in TETRAS score after switch to LCPT between patients who had received IR-TAC and those who had received PR-TAC before switch, or between fast and slow metabolizers of tacrolimus. The overall increase of C0/D ratio post-switch to LCPT was statistically significant (p < 0.0001) and from baseline to either M1 or M3 (both p < 0.0001) in the mITT population and in all subgroups. In the fast metabolizers group, the C0/D ratio crossed over the threshold of 1.05 ng/mL/mg after the switch to LCPT. Other neurologic symptoms tended to improve, and the SF-12 mental component summary score improved significantly. No new safety concerns were evident. In this observational study, all patients had a significant improvement of tremor, QoL and C0/D ratio post-switch to LCPT irrespective of the previous tacrolimus formulation administered (IR-TAC or PR-TAC) and irrespective from their metabolism status (fast or slow metabolizers).

Follow the Area Under the Curve Not the Trough Concentration: A Case Study of Tacrolimus Monitoring in a Kidney Transplant Recipient Cotreated With Phenobarbital.

ABSTRACT: The authors described tacrolimus dosing in a kidney transplant patient concurrently treated with phenobarbital, where measuring the tacrolimus area under the curve was necessary to achieve adequate drug exposure and improve kidney function.

Tacrolimus Variability and Clinical Outcomes in the Early Post-lung Transplantation Period: Oral Versus Continuous Intravenous Administration.

BACKGROUND AND OBJECTIVE: High variability in tacrolimus pharmacokinetics directly after lung transplantation (LuTx) may increase the risk for acute kidney injury (AKI) and transplant rejection. The primary objective was to compare pharmacokinetic variability in patients receiving tacrolimus orally versus intravenously early after LuTx. METHODS: Pharmacokinetic and clinical data from 522 LuTx patients transplanted between 2010 and 2020 in two university hospitals were collected to compare orally administered tacrolimus to intravenous tacrolimus early post-transplantation. Tacrolimus blood concentration variability, measured as intrapatient variability (IPV%) and  percentage of time within the therapeutic range (TTR%), was analyzed within the first 14 days after LuTx. Secondary outcomes were AKI, acute rejection, length of stay in the intensive care unit (ICU), and mortality in the ICU and during hospital admission. RESULTS: We included 224 patients in the oral and 298 in the intravenous group. The mean adjusted IPV% was 10.8% (95% confidence interval [CI] 6.9-14.6; p < 0.001) higher in the oral group (27.2%) than the intravenous group (16.4%). The mean TTR% was 7.3% (95% CI - 11.3 to - 3.4; p < 0.001) lower in the oral group (39.6%) than in the intravenous group (46.9%). The incidence of AKI was 46.0% for oral and 42.6% for intravenous administration (adjusted odds ratio [OR] 1.2; 95% CI 0.8-1.8; p = 0.451). The frequencies of clinically diagnosed acute rejection in the oral and intravenous groups were nonsignificant (24.6% vs 17.8%; OR 1.5 [95% CI 1.0-2.3; p = 0.059]). ICU and hospital mortality rate and ICU length of stay were similar. CONCLUSIONS: Administering tacrolimus orally directly after LuTx leads to a higher variability in blood concentrations compared to intravenous administration. There was no difference in the occurrence of AKI or transplant rejection.

Association of CYP3A4-392A/G, CYP3A5-6986A/G, and ABCB1-3435C/T Polymorphisms with Tacrolimus Dose, Serum Concentration, and Biochemical Parameters in Mexican Patients with Kidney Transplant.

Tacrolimus (TAC) is an immunosuppressant drug that prevents organ rejection after transplantation. This drug is transported from cells via P-glycoprotein (ABCB1) and is a metabolic substrate for cytochrome P450 (CYP) 3A enzymes, particularly CYP3A4 and CYP3A5. Several single-nucleotide polymorphisms (SNPs) have been identified in the genes encoding CYP3A4, CYP3A5, and ABCB1, including CYP3A4-392A/G (rs2740574), CYP3A5 6986A/G (rs776746), and ABCB1 3435C/T (rs1045642). This study aims to evaluate the association among CYP3A4-392A/G, CYP3A5-6986A/G, and ABCB1-3435C/T polymorphisms and TAC, serum concentration, and biochemical parameters that may affect TAC pharmacokinetics in Mexican kidney transplant (KT) patients. METHODS: Forty-six kidney transplant recipients (KTR) receiving immunosuppressive treatment with TAC in different combinations were included. CYP3A4, CYP3A5, and ABCB1 gene polymorphisms were genotyped using qPCR TaqMan. Serum TAC concentration (as measured) and intervening variables were assessed. Logistic regression analyses were performed at baseline and after one month to assess the extent of the association between the polymorphisms, intervening variables, and TAC concentration. RESULTS: The GG genotype of CYP3A5-6986 A/G polymorphism is associated with TAC pharmacokinetic variability OR 4.35 (95%CI: 1.13-21.9; p = 0.0458) at one month of evolution; in multivariate logistic regression, CYP3A5-6986GG genotype OR 9.32 (95%CI: 1.54-93.08; p = 0.028) and the use of medications or drugs that increase serum TAC concentration OR 9.52 (95%CI: 1.79-88.23; p = 0.018) were strongly associated with TAC pharmacokinetic variability. CONCLUSION: The findings of this study of the Mexican population showed that CYP3A5-6986 A/G GG genotype is associated with a four-fold increase in the likelihood of encountering a TAC concentration of more than 15 ng/dL. The co-occurrence of the CYP3A5-6986GG genotype and the use of drugs that increase TAC concentration correlates with a nine-fold increased risk of experiencing a TAC at a level above 15 ng/mL. Therefore, these patients have an increased susceptibility to TAC-associated toxicity.

Tools for a personalized tacrolimus dose adjustment in the follow-up of renal transplant recipients. Metabolizing phenotype according to CYP3A genetic polymorphisms versus concentration-dose ratio.

BACKGROUND AND JUSTIFICATION: The strategy of the concentration-dose (C/D) approach and the different profiles of tacrolimus (Tac) according to the cytochrome P450 polymorphisms (CYPs) focus on the metabolism of Tac and are proposed as tools for the follow-up of transplant patients. The objective of this study is to analyse both strategies to confirm whether the stratification of patients according to the pharmacokinetic behaviour of C/D corresponds to the classification according to their CYP3A4/5 cluster metabolizer profile. MATERIALS AND METHODS: 425 kidney transplant patients who received Tac as immunosuppressive treatment have been included. The concentration/dose ratio (C/D) was used to divide patients in terciles and classify them according to their Tac metabolism rate (fast, intermediate, and slow). Based on CYP3A4 and A5 polymorphisms, patients were classified into 3 metabolizer groups: fast (CYP3A5*1 carriers and CYP34A*1/*1), intermediate (CYP3A5*3/3 and CYP3A4*1/*1) and slow (CYP3A5*3/*3 and CYP3A4*22 carriers). RESULTS: When comparing patients included in each metabolizer group according to C/D ratio, 47% (65/139) of the fast metabolizers, 85% (125/146) of the intermediate and only 12% (17/140) of the slow also fitted in the homonym genotype group. Statistically lower Tac concentrations were observed in the fast metabolizers group and higher Tac concentrations in the slow metabolizers when compared with the intermediate group both in C/D ratio and polymorphisms criteria. High metabolizers required approximately 60% more Tac doses than intermediates throughout follow-up, while poor metabolizers required approximately 20% fewer doses than intermediates. Fast metabolizers classified by both criteria presented a higher percentage of times with sub-therapeutic blood Tac concentration values. CONCLUSION: Determination of the metabolizer phenotype according to CYP polymorphisms or the C/D ratio allows patients to be distinguished according to their exposure to Tac. Probably the combination of both classification criteria would be a good tool for managing Tac dosage for transplant patients.

Topical Tacrolimus and Mycophenolic Acid Therapy Synergizes with Low Dose Systemic Immunosuppression to Sustain Vascularized Composite Allograft Survival.

AIM: The goal of this study was to evaluate whether topical administration of tacrolimus (TAC) and mycophenolic acid (MPA) at the transplant site enables vascularized composite allograft (VCA) survival with significant minimization of the dose and adverse effects of systemic TAC (STAC) immunosuppression. MATERIALS AND METHODS: Lewis (Lew) rats received orthotopic hind limb allotransplants from fully mismatched Brown Norway (BN) donors. Group 1 (Controls) received no treatment. Other groups were treated with STAC at a dose of 1 mg/kg/day for 7 days. On post-operative day (POD) 8, the STAC dose was dropped to 0.1 mg/kg/day for Group 2 and maintained at 1 mg/kg for Group 3. Group 4 received topical application of TAC and MPA on the transplanted (Tx) limb starting POD 8 without STAC. Group 5 received topical TAC and MPA on the contralateral non-Tx limb and Group 6 received topical TAC and MPA on the Tx limb starting POD 8 along with low dose STAC (0.1 mg/kg/day). Treatment was continued until the study end point was reached, defined as either grade 3 rejection or allograft survival exceeding 100 days. .We conducted sequential LC-MS/MS measurements to assess TAC and MPA concentrations in both blood/plasma and allograft tissues. Additionally, we evaluated markers indicative of organ toxicity associated with STAC immunosuppression. RESULTS: Compared to controls, topical therapy with TAC+MPA significantly prolonged allograft survival beyond 100 daysat very low dose STAC (0.1 mg/kg/day) (Group 6). The histopathological assessment of the grafts was consistent with the clinical outcomes. .Drug levels in blood/plasma remained low or undetectable, while allograft tissues showed higher drug concentrations compared to contralateral limb tissues (P<0.05). . Urinary creatinine clearance remained within the normal range at 2.5 mL/min. CONCLUSION: Combination therapy with topical TAC and MPA synergizes with a very low dose, corticosteroid- free-STAC regimen and facilitates rejection-free, prolonged VCA survival without morbidity.

Genotype-Guided Model for Prediction of Tacrolimus Initial Dosing After Lung Transplantation.

The determination of the appropriate initial dose for tacrolimus is crucial in achieving the target concentration promptly and avoiding adverse effects and poor prognosis. However, the trial-and-error approach is still common practice. This study aimed to establish a prediction model for an initial dosing algorithm of tacrolimus in patients receiving a lung transplant. A total of 210 lung transplant recipients were enrolled, and 26 single nucleotide polymorphisms (SNP) from 18 genes that could potentially affect tacrolimus pharmacokinetics were genotyped. Associations between SNPs and tacrolimus concentration/dose ratio were analyzed. SNPs that remained significant in pharmacogenomic analysis were further combined with clinical factors to construct a prediction model for tacrolimus initial dose. The dose needed to reach steady state tacrolimus concentrations and achieve the target range was used to validate model prediction efficiency. Our final model consisted of 7 predictors-CYP3A5 rs776746, SLCO1B3 rs4149117, SLC2A2 rs1499821, NFATc4 rs1955915, alanine aminotransferase, direct bilirubin, and hematocrit-and explained 41.4% variance in the tacrolimus concentration/dose ratio. It achieved an area under the receiver operating characteristic curve of 0.804 (95% confidence interval, 0.746-0.861). The Hosmer-Lemeshow test yielded a nonsignificant P value of .790, suggesting good fit of the model. The predicted dose exhibited good correlation with the observed dose in the early postoperative period (r = 0.748, P less than .001). Our study provided a genotype-guided prediction model for tacrolimus initial dose, which may help to guide individualized dosing of tacrolimus in the lung transplant population in clinical practice.

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.

Plasma Concentration of Antifungal Agent Micafungin for Pediatric Living Donor Liver Transplantation.

BACKGROUND: Pediatric living-donor liver transplantation (LDLT) candidates often receive long-term antibiotic treatment. Micafungin has been used as an antifungal agent after LDLT, but the adequate dose after pediatric LDLT was unknown. Here, we report micafungin blood concentrations after pediatric LDLT and discuss its safety and adequate dosing. METHODS: Pediatric patients with data on micafungin concentrations after LDLT were identified. Those with surgical complications were excluded. All patients received standard tacrolimus-based immunosuppression. A micafungin dose of 1 mg/kg was administered once daily for 10 days starting on postoperative day (POD) 1. The trough and peak micafungin blood concentrations were evaluated on PODs 1, 4, 7, and 10. Beta D glucan levels and liver function tests were assessed to determine micafungin effectiveness and safety. RESULTS: Ten patients were enrolled, with a median age of 1.2 years. The median graft vs body weight ratio was 2.7%. The primary diseases were biliary atresia (n = 7), Alagille syndrome (n = 2), and progressive familial intrahepatic cholestasis type 2 (n = 1). Mean peak micafungin levels were 4.47, 6.27, 5.47, and 5.47 µg/mL on PODs 1, 4, 7, and 10, respectively. Mean trough levels were 2.03, 1.88, and 2.66 µg/mL on PODs 4, 7, and 10, respectively. The micafungin half-lives were 13.7, 14.7, and 14.0 hours on PODs 4, 7, and 10, respectively. Beta D glucan levels were 4.4 pg/mL and 3.7 pg/mL before and after transplantation, respectively, indicating no significant difference (P = .3). No clinical fungal infections were observed. CONCLUSION: Micafungin administration is safe and effective after pediatric LDLT.

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.

Pharmacokinetics of Tacrolimus in Pregnant Solid-Organ Transplant Recipients: A Retrospective Study.

Data on the pharmacokinetics of tacrolimus during pregnancy are limited. Therefore, the aim of this retrospective study was to characterize the whole-blood pharmacokinetics of tacrolimus throughout pregnancy. In this single-center retrospective cohort study, whole-blood tacrolimus trough concentrations corrected for the dose (concentration-to-dose [C/D] ratios) were compared before, monthly during, and after pregnancy in kidney, liver, and lung transplant recipients who became pregnant and gave birth between 2000 and 2022. Descriptive statistics and linear mixed models were used to characterize changes in tacrolimus C/D ratios before, during, and after pregnancy. The total study population included 46 pregnancies (31 pregnant women). Nineteen, 21, and 6 pregnancies were following kidney, liver, and lung transplantation, respectively. Immediate-release or extended-release formulations were used in 54.5% and 45.5% of the women, respectively. Tacrolimus C/D ratios significantly (P < .001) decreased (-48%) compared to the prepregnancy state at 7 months of pregnancy. These ratios recovered within 3 months postpartum (P = .002). C/D ratios tended to be lower during treatment with an extended-release formulation than with an immediate-release formulation (P = .071). Transplantation type did not significantly affect C/D ratios during pregnancy (P = .873). In conclusion, we found that tacrolimus whole-blood pharmacokinetics change throughout pregnancy, with the lowest C/D ratios (48% decrease) in the 7th month of pregnancy. In general, the decrease in C/D ratios seems to stabilize from month 4 onward compared to prepregnancy.

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.

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.

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.

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.

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.

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.