Importance of hematocrit for a tacrolimus target concentration strategy

Effect of early post-hematopoietic stem cell transplant tacrolimus concentration on transplant outcomes in pediatric recipients: One facility's ten-year experience of immunosuppression with tacrolimus

Acute graft-versus-host disease (GVHD) is a common life-threatening complication of allogeneic hematopoietic stem cell transplantation (allo-HSCT), ranking as the second leading cause of death among recipients, surpassed only by disease relapse. Tacrolimus is commonly used for GVHD prophylaxis, but achieving therapeutic blood levels is challenging, particularly in pediatrics, due to the narrow therapeutic window and the high interindividual variability. The retrospective study conducted at IRCCS "Burlo Garofolo" in Italy aimed to assess the impact of early post-HSCT tacrolimus levels on transplant-related outcomes in pediatric recipients. The population pharmacokinetic model (POP/PK) was set up to describe tacrolimus pharmacokinetics. Elevated tacrolimus (>12-15 ng/ml) levels within the initial weeks post-HSCT are associated with reduced post-transplant infections (p < 0.0001) and decreased incidence of early transplant-related events (p < 0.01), including a lower incidence of acute GVHD (p < 0.05 on day 0). High tacrolimus exposure can lead to an increased risk of chronic GVHD (p < 0.0001) and reduced overall survival (p < 0.01). Personalized dosing and therapeutic monitoring of tacrolimus are crucial to ensure optimal outcomes. POP/PK could help achieve this goal, giving us a model by which we can balance immunosuppression while looking at the patient's general well-being and providing the necessary treatment.

A joint population pharmacokinetic model to assess the high variability of whole-blood and intracellular tacrolimus in early adult renal transplant recipients

Tacrolimus (TAC) has high pharmacokinetic (PK) variability during the early transplantation period. The relationships between whole-blood and intracellular TAC concentrations and clinical outcomes remain controversial. This study identifies the factors affecting the PK variability of TAC and characterizes the relationships between whole-blood and intracellular TAC concentrations. Data regarding whole-blood TAC concentrations of 1,787 samples from 215 renal transplant recipients (<90 days postoperative) across two centers and intracellular TAC concentrations (648 samples) digitized from previous studies were analyzed using nonlinear mixed-effects modeling. The effects of potential covariates were screened, and the distribution of whole-blood to intracellular TAC concentration ratios (RWB:IC) was estimated. The final model was evaluated using bootstrap, goodness of fit, and prediction-corrected visual predictive checks. The optimal dosing regimens and target ranges for each type of immune cell subsets were determined using Monte Carlo simulations. A two-compartment model adequately described the data, and the estimated mean TAC CL/F was 23.6 L·h-1 (relative standard error: 11.5 %). The hematocrit level, CYP3A5*3 carrier status, co-administration with Wuzhi capsules, and tapering prednisolone dose may contribute to the high variability of TAC PK variability during the early post-transplant period. The estimated RWB:IC of all TAC concentrations in peripheral blood mononuclear cells (PBMCs) was 4940, and inter-center variability of PBMCs was observed. The simulated TAC target range in PBMCs was 20.2-85.9 pg·million cells-1. Inter-center variability in intracellular concentrations should be taken into account in further analyses. TAC dosage adjustments can be guided based on PK/PD variability and simulated intracellular concentrations.

Evaluation of Published Population Pharmacokinetic Models to Inform Tacrolimus Therapy in Adult Lung Transplant Recipients

BACKGROUND

The applicability of currently available tacrolimus population pharmacokinetic models in guiding dosing for lung transplant recipients is unclear. In this study, the predictive performance of relevant tacrolimus population pharmacokinetic models was evaluated for adult lung transplant recipients.

METHODS

Data from 43 lung transplant recipients (1021 tacrolimus concentrations) administered an immediate-release oral formulation of tacrolimus were used to evaluate the predictive performance of 17 published population pharmacokinetic models for tacrolimus. Data were collected from immediately after transplantation up to 90 days after transplantation. Model performance was evaluated using (1) prediction-based assessments (bias and imprecision) of individual predicted tacrolimus concentrations at the fourth dosing based on 1 to 3 previous dosings and (2) simulation-based assessment (prediction-corrected visual predictive check; pcVPC). Both assessments were stratified based on concomitant azole antifungal use. Model performance was clinically acceptable if the bias was within ±20%, imprecision was ≤20%, and the 95% confidence interval of bias crossed zero.

RESULTS

In the presence of concomitant antifungal therapy, no model showed acceptable performance in predicting tacrolimus concentrations at the fourth dosing (n = 33), and pcVPC plots displayed poor model fit to the data set. However, this fit slightly improved in the absence of azole antifungal use, where 4 models showed acceptable performance in predicting tacrolimus concentrations at the fourth dosing (n = 33).

CONCLUSIONS

Although none of the evaluated models were appropriate in guiding tacrolimus dosing in lung transplant recipients receiving concomitant azole antifungal therapy, 4 of these models displayed potential applicability in guiding dosing in recipients not receiving concomitant azole antifungal therapy. However, further model refinement is required before the widespread implementation of such models in clinical practice.

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.

Single Nucleotide Polymorphisms of CYP3A4 and CYP3A5 in Romanian Kidney Transplant Recipients: Effect on Tacrolimus Pharmacokinetics in a Single-Center Experience

Background: This study examines the impact of CYP3A4 and CYP 3A5 genotypes on tacrolimus (Tac) pharmacokinetics in Romanian kidney transplanted patients. Methods: We included 112 kidney recipients genotyped for CYP3A5*3, CYP3A4*1.001, and CYP3A4*22. Patients were categorized into poor, intermediate, rapid, and ultra-rapid metabolizers based on the functional defects linked to CYP3A variants. Results: Predominantly male (63.4%) with an average age of 40.58 years, the cohort exhibited a high prevalence of the CYP3A4*1/*1 (86.6%) and CYP3A5*3/*3 (77.7%) genotypes. CYP3A4*1.001 and CYP3A5*1 alleles significantly influenced the Tac concentration-to-dose (C0/D) ratio in various post-transplant periods, while the CYP3A4*22 allele showed no such effect (p = 0.016, p < 0.001). Stepwise regression highlighted the CYP3A4*1.001's impact in early post-transplant phases, with hematocrit and age also influencing Tac variability. Conclusions: The study indicates a complex interaction of CYP3A4 and CYP3A5 genotypes on Tac metabolism, suggesting the necessity for personalized medication approaches based on genetic profiling in kidney transplant recipients.

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.

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.

Determining Plasma Tacrolimus Concentrations Using High-Performance LC-MS/MS in Renal Transplant Recipients

BACKGROUND

Whole-blood therapeutic drug monitoring of tacrolimus is conducted to maintain tacrolimus concentrations within a safe and effective range. Changes in hematocrit cause variability in blood concentrations of tacrolimus because it is highly bound to erythrocytes. Measuring plasma concentrations may eliminate this variability; however, current methods have limitations owing to the use of cross-reactive immunoassays, plasma separation at nonbiological temperatures, and lack of clinical validation. This study aimed to develop and validate a clinically applicable method to measure plasma tacrolimus concentrations in renal transplant recipients and to examine the concentration differences between genotypic CYP3A5 expressors and nonexpressors.

METHODS

Plasma tacrolimus concentrations were measured in 9 stable renal transplant recipients who were genotypic CYP3A5 expressors or nonexpressors. Tacrolimus was extracted from plasma using solid-phase extraction, and liquid chromatography-tandem mass spectrometry was used for detection and quantitation.

RESULTS

This assay was sensitive, selective, and linear between 100 and 5000 ng/L, with intraassay and interassay imprecision and inaccuracy <10% and <5% respectively. The extraction recovery of tacrolimus and ascomycin was 74%. Matrix ion suppression effects were 31.5% and 35% with overall recovery of 50.6% and 48.3% for tacrolimus and ascomycin, respectively. Whole-blood concentrations accounted for approximately 46% of the variation in plasma concentrations in CYP3A5 expressors and nonexpressors. No difference in dose-adjusted whole-blood and plasma concentrations was observed between CYP3A5 expressors and nonexpressors.

CONCLUSIONS

This assay is clinically applicable with excellent performance and demonstrated that tacrolimus plasma concentrations highly correlated with whole-blood concentrations.

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 β-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.

Elevation of tacrolimus concentration after administration of methotrexate for treatment of graft-versus-host disease in pediatric patients received allogeneic hematopoietic stem cell transplantation

BACKGROUND

Methotrexate (MTX) is used to treat graft-versus-host disease (GVHD) following allogeneic hematopoietic stem cell transplantation (allo-HSCT). Recently, a case was encountered in which the blood concentration of tacrolimus (TCR) at steady state increased after intravenous MTX administration for GVHD treatment (therapeutic IV-MTX administration). Therefore, this study aimed to investigate the effect of therapeutic IV-MTX administration on the pharmacokinetics of TCR.

METHODS

This single-center, retrospective, observational study included patients who underwent allo-HSCT and received therapeutic IV-MTX administration during immunosuppressive therapy with continuous intravenous infusion (CIV) of TCR from April 2004 to December 2021. Here, each therapeutic IV-MTX administration was defined as a case and independently subjected to subsequent analyses. The blood concentration of TCR at steady state (Css), ratio of Css to daily TCR dose (C/D), and clinical laboratory data were compared before and after therapeutic IV-MTX administration. In addition, dose changes in the TCR after therapeutic IV-MTX administration were evaluated.

RESULTS

Ten patients (23 cases) were included in this study. The C/D value significantly increased after therapeutic IV-MTX administration (median: 697 vs. 771 (ng/mL)/(mg/kg), 1.16-fold increase, P < 0.05), indicating a reduction in the apparent clearance of TCR. Along with the increase in C/D, significant increases were observed in aspartate transaminase level (median: 51.0 vs. 92.9 U/L, P < 0.01) and alanine aminotransferase level (median: 74.5 vs. 99.4 U/L, P < 0.01) indicating that liver injury after therapeutic IV-MTX administration contributes to the observed C/D increase. In addition, the daily dose of TCR was reduced in 11 cases (47.8%) after therapeutic IV-MTX administration, and the relative frequency of dose reduction tended to be higher than that of dose increase (median: 37.5% vs. 0.0%, P = 0.0519, permuted Brunner-Munzel test). The magnitude of dose reduction was 18.8% (7.4-50.0%) in the 11 cases with dose reduction.

CONCLUSIONS

Therapeutic IV-MTX administration cause a significant increase in C/D, which requires TCR dose reduction. Careful therapeutic drug monitoring of TCR is needed after therapeutic IV-MTX administration in patients receiving immunosuppressive therapy with TCR after allo-HSCT.

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.

Dosage optimization of tacrolimus based on the glucocorticoid dose and pharmacogenetics in adult patients with systemic lupus erythematosus

BACKGROUND

The purpose of the study was to develop a genotype-incorporated population pharmacokinetic (PPK) model of tacrolimus (TAC) in adults with systemic lupus erythematosus (SLE) to investigate the factors influencing TAC pharmacokinetics and to develop an individualized dosing regimen based on the model. In addition, a non-genotype-incorporated model was also established to assess its predictive performance compared to the genotype-incorporated model.

METHODS

A total of 365 trough concentrations from 133 adult SLE patients treated with TAC were collected to develop a genotype-incorporated PPK model and a non-genotype-incorporated PPK model of TAC using a nonlinear mixed-effects model (NONMEM). External validation of the two models was performed using data from an additional 29 patients. Goodness-of-fit diagnostic plots, bootstrap method, and normalized predictive distribution error test were used to validate the predictive performance and stability of the final models. The goodness-of-fit of the two final models was compared using the Akaike information criterion (AIC). The dosing regimen was optimized using Monte Carlo simulations based on the developed optimal model.

RESULTS

The typical value of the apparent clearance (CL/F) of TAC estimated in the final genotype-incorporated model was 14.3 L h-1 with inter-individual variability of 27.6%. CYP3A5 polymorphism and coadministered medication were significant factors affecting TAC-CL/F. CYP3A5 rs776746 GG genotype carriers had only 77.3% of the TAC-CL/F of AA or AG genotype carriers. Omeprazole reduced TAC-CL/F by 3.7 L h-1 when combined with TAC, while TAC-CL/F increased nonlinearly as glucocorticoid dose increased. Similar findings were demonstrated in the non-genotype-incorporated PPK model. Comparing these two models, the genotype-incorporated PPK model was superior to the non-genotype-incorporated PPK model (AIC = 643.19 vs. 657.425). Monte Carlo simulation based on the genotype-incorporated PPK model indicated that CYP3A5 rs776746 AA or AG genotype carriers required a 1/2-1 fold higher dose of TAC than GG genotype carriers to achieve the target concentration. And as the daily dose of prednisone increases, the dose of TAC required to reach the target concentration increases appropriately.

CONCLUSIONS

We developed the first pharmacogenetic-based PPK model of TAC in adult patients with SLE and proposed a dosing regimen based on glucocorticoid dose and CYP3A5 genotype according to the model, which could facilitate individualized dosing for TAC.

A prospective controlled, randomized clinical trial of kidney transplant recipients developed personalized tacrolimus dosing using model-based Bayesian Prediction

For three decades, tacrolimus (Tac) dose adjustment in clinical practice has been calculated empirically according to the manufacturer's labeling based on a patient's body weight. Here, we developed and validated a Population pharmacokinetic (PPK) model including pharmacogenetics (cluster CYP3A4/CYP3A5), age, and hematocrit. Our study aimed to assess the clinical applicability of this PPK model in the achievement of Tac Co (therapeutic trough Tac concentration) compared to the manufacturer's labelling dosage. A prospective two-arm, randomized, clinical trial was conducted to determine Tac starting and subsequent dose adjustments in 90 kidney transplant recipients. Patients were randomized to a control group with Tac adjustment according to the manufacturer's labeling or the PPK group adjusted to reach target Co (6-10 ng/ml) after the first steady state (primary endpoint) using a Bayesian prediction model (NONMEM). A significantly higher percentage of patients from the PPK group (54.8%) compared with the control group (20.8%) achieved the therapeutic target fulfilling 30% of the established superiority margin defined. Patients receiving PPK showed significantly less intra-patient variability compared to the control group, reached the Tac Co target sooner (5 days vs 10 days), and required significantly fewer Tac dose modifications compared to the control group within 90 days following kidney transplant. No statistically significant differences occurred in clinical outcomes. Thus, PPK-based Tac dosing offers significant superiority for starting Tac prescription over classical labeling-based dosing according to the body weight, which may optimize Tac-based therapy in the first days following transplantation.

Tacrolimus-why pharmacokinetics matter in the clinic

The calcineurin inhibitor (CNI) Tacrolimus (Tac) is the most prescribed immunosuppressant drug after solid organ transplantation. After renal transplantation (RTx) approximately 95% of recipients are discharged with a Tac-based immunosuppressive regime. Despite the high immunosuppressive efficacy, its adverse effects, narrow therapeutic window and high intra- and interpatient variability (IPV) in pharmacokinetics require therapeutic drug monitoring (TDM), which makes treatment with Tac a major challenge for physicians. The C/D ratio (full blood trough level normalized by daily dose) is able to classify patients receiving Tac into two major metabolism groups, which were significantly associated with the clinical outcomes of patients after renal or liver transplantation. Therefore, the C/D ratio is a simple but effective tool to identify patients at risk of an unfavorable outcome. This review highlights the challenges of Tac-based immunosuppressive therapy faced by transplant physicians in their daily routine, the underlying causes and pharmacokinetics (including genetics, interactions, and differences between available Tac formulations), and the latest data on potential solutions to optimize treatment of high-risk 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.

Importance of Pharmacogenetics and Drug-Drug Interactions in a Kidney Transplanted Patient

Tacrolimus (TAC) is a narrow-therapeutic-range immunosuppressant drug used after organ transplantation. A therapeutic failure is possible if drug levels are not within the therapeutic range after the first year of treatment. Pharmacogenetic variants and drug-drug interactions (DDIs) are involved. We describe a patient case of a young man (16 years old) with a renal transplant receiving therapy including TAC, mycophenolic acid (MFA), prednisone and omeprazole for prophylaxis of gastric and duodenal ulceration. The patient showed great fluctuation in TAC blood concentration/oral dose ratio, as well as pharmacotherapy adverse effects (AEs) and frequent diarrhea episodes. Additionally, decreased kidney function was found. A pharmacotherapeutic follow-up, including pharmacogenetic analysis, was carried out. The selection of the genes studied was based on the previous literature (CYP3A5, CYP3A4, POR, ABCB1, PXR and CYP2C19). A drug interaction with omeprazole was reported and the nephrologist switched to rabeprazole. A lower TAC concentration/dose ratio was achieved, and the patient's condition improved. In addition, the TTT haplotype of ATP Binding Cassette Subfamily B member 1 (ABCB1) and Pregnane X Receptor (PXR) gene variants seemed to affect TAC pharmacotherapy in the studied patient and could explain the occurrence of long-term adverse effects post-transplantation. These findings suggest that polymorphic variants and co-treatments must be considered in order to achieve the effectiveness of the immunosuppressive therapy with TAC, especially when polymedicated patients are involved. Moreover, pharmacogenetics could influence the drug concentration at the cellular level, both in lymphocyte and in renal tissue, and should be explored in future studies.

Individualized dosing algorithms for tacrolimus in kidney transplant recipients: current status and unmet needs

INTRODUCTION

Tacrolimus is a potent immunosuppressive drug with many side effects including nephrotoxicity and post-transplant diabetes mellitus. To limit its toxicity, therapeutic drug monitoring (TDM) is performed. However, tacrolimus' pharmacokinetics are highly variable within and between individuals, which complicates their clinical management. Despite TDM, many kidney transplant recipients will experience under- or overexposure to tacrolimus. Therefore, dosing algorithms have been developed to limit the time a patient is exposed to off-target concentrations.

AREAS COVERED

Tacrolimus starting dose algorithms and models for follow-up doses developed and/or tested since 2015, encompassing both adult and pediatric populations. Literature was searched in different databases, i.e. Embase, PubMed, Web of Science, Cochrane Register, and Google Scholar, from inception to February 2023.

EXPERT OPINION

Many algorithms have been developed, but few have been prospectively evaluated. These performed better than bodyweight-based starting doses, regarding the time a patient is exposed to off-target tacrolimus concentrations. No benefit in reduced tacrolimus toxicity has yet been observed. Most algorithms were developed from small datasets, contained only a few tacrolimus concentrations per person, and were not externally validated. Moreover, other matrices should be considered which might better correlate with tacrolimus toxicity than the whole-blood concentration, e.g. unbound plasma or intra-lymphocytic tacrolimus concentrations.

Factors Affecting Day-to-Day Variations in Tacrolimus Concentration among Children and Young Adults Undergoing Allogeneic Hematopoietic Stem Cell Transplantation

Tacrolimus is widely used as prophylaxis for graft-versus-host disease (GVHD) in allogeneic stem cell transplantation (allo-HSCT). It has a narrow therapeutic index range; high tacrolimus concentrations are associated with toxicity, whereas low concentrations are associated with an increased risk of GVHD. Although dose adjustments based on therapeutic drug monitoring are performed, unexpected large variations in tacrolimus concentration are sometimes encountered. The available evidence suggests that the factors affecting tacrolimus concentration are not fully understood. This study was aimed primarily at investigating the factors affecting day-to-day variations in tacrolimus concentration in children and young adults who received continuous tacrolimus infusion after allo-HSCT. The secondary objective was to identify the factors causing large variations (>20%) in tacrolimus concentrations. This retrospective cohort study comprised 123 consecutive pediatric and young adult patients (age <25 years) who received continuous i.v. tacrolimus infusion after allo-HSCT at Shinshu University Hospital, Matsumoto, Japan, between January 2009 and December 2021. To compare day-to-day variations in tacrolimus concentration without consideration of the tacrolimus dose, 2 consecutive days when the tacrolimus dose was not changed were selected from between the first post-allo-HSCT day of a tacrolimus concentration >7 ng/mL and day 28 post-allo-HSCT. Subsequently, information for the subsequent 24 hours was collected along with the tacrolimus concentrations and hematocrit values. Tacrolimus concentration was determined using whole blood samples. Tacrolimus concentrations were significantly higher in patients who received red blood cell concentrate (RCC) transfusions (P < .0001) and methotrexate (P = .0162), patients with persistent fever (P = .0056), and patients with a decline in fever (P = .0003). In contrast, tacrolimus concentrations were significantly lower in patients who received platelet concentrate (PC) transfusions (P < .0001), who redeveloped fever (P = .0261), and who had a replaced tacrolimus administration route set (P = .0008). Variations in tacrolimus concentration were significantly correlated with variations in hematocrit (r = .556; P < .0001). Body weight (P < .0001), RCC transfusion (P < .0001), methotrexate use (P = .0333), persistent fever (P = .0150), and decline in fever (P = .0073) were associated with a sharp increase in tacrolimus concentration. In contrast, body weight (P < .0001), PC transfusion (P = .0025), and replacement of the tacrolimus administration route set (P = .0025) were associated with a sharp decrease in tacrolimus concentration. RCC and PC transfusions, fever, methotrexate administration, and replacement of the tacrolimus administration route set were independent factors affecting day-to-day variations in tacrolimus concentration. In addition to these factors, low body weight was a risk factor for both sharp increases and decreases in tacrolimus concentration. These findings suggest the need for better control of tacrolimus concentration using whole blood samples.

Considerations for Intravenous Anesthesia Dose in Obese Children: Understanding PKPD

The intravenous induction or loading dose in children is commonly prescribed per kilogram. That dose recognizes the linear relationship between volume of distribution and total body weight. Total body weight comprises both fat and fat-free mass. Fat mass influences the volume of distribution and the use of total body weight fails to recognize the impact of fat mass on pharmacokinetics in children. Size metrics alternative to total body mass (e.g., fat-free and normal fat mass, ideal body weight and lean body weight) have been proposed to scale pharmacokinetic parameters (clearance, volume of distribution) for size. Clearance is the key parameter used to calculate infusion rates or maintenance dosing at steady state. Dosing schedules recognize the curvilinear relationship, described using allometric theory, between clearance and size. Fat mass also has an indirect influence on clearance through both metabolic and renal function that is independent of its effects due to increased body mass. Fat-free mass, lean body mass and ideal body mass are not drug specific and fail to recognize the variable impact of fat mass contributing to body composition in children, both lean and obese. Normal fat mass, used in conjunction with allometry, may prove a useful size metric but computation by clinicians for the individual child is not facile. Dosing is further complicated by the need for multicompartment models to describe intravenous drug pharmacokinetics and the concentration effect relationship, both beneficial and adverse, is often poorly understood. Obesity is also associated with other morbidity that may also influence pharmacokinetics. Dose is best determined using pharmacokinetic-pharmacodynamic (PKPD) models that account for these varied factors. These models, along with covariates (age, weight, body composition), can be incorporated into programmable target-controlled infusion pumps. The use of target-controlled infusion pumps, assuming practitioners have a sound understanding of the PKPD within programs, provide the best available guide to intravenous dose in obese children.

Volumetric Absorptive Microsampling to Enhance the Therapeutic Drug Monitoring of Tacrolimus and Mycophenolic Acid: A Systematic Review and Critical Assessment

BACKGROUND

Volumetric absorptive microsampling (VAMS) is an emerging technique that may support multisample collection to enhance therapeutic drug monitoring in solid organ transplantation. This review aimed to assess whether tacrolimus and mycophenolic acid can be reliably assayed using VAMS and to identify knowledge gaps by providing granularity to existing analytical methods and clinical applications.

METHODS

A systematic literature search was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The PubMed, Embase, and Scopus databases were accessed for records from January 2014 to April 2022 to identify scientific reports on the clinical validation of VAMS for monitoring tacrolimus and mycophenolic acid concentrations. Data on the study population, sample sources, analytical methods, and comparison results were compiled.

RESULTS

Data from 12 studies were collected, including 9 studies pertaining to tacrolimus and 3 studies on the concurrent analysis of tacrolimus and mycophenolic acid. An additional 14 studies that provided information relevant to the secondary objectives (analytical validation and clinical application) were also included. The results of the clinical validation studies generally met the method agreement requirements described by regulatory agencies, but in many cases, it was essential to apply correction factors.

CONCLUSIONSS

Current evidence suggests that the existing analytical methods that use VAMS require additional optimization steps for the analysis of tacrolimus and mycophenolic acid. The recommendations put forth in this review can help guide future studies in achieving the goal of improving the care of transplant recipients by simplifying multisample collection for the dose optimization of these drugs.

Prospective population pharmacokinetic study of tacrolimus in adult recipients early after liver transplantation: A comparison of Michaelis-Menten and theory-based pharmacokinetic models

Background and Objective: Tacrolimus, a calcineurin inhibitor widely used as a potent immunosuppressant to prevent graft rejection, exhibits nonlinear kinetics in patients with kidney transplantation and nephrotic syndrome. However, whether nonlinear drug metabolism occurs in adult patients undergoing liver transplantation remains unclear, as do the main underlying mechanisms. Therefore, here we aimed to further confirm the characteristics of nonlinearity through a large sample size, and determine the potential influence of nonlinearity and its possible mechanisms.

Methods: In total, 906 trough concentrations from 176 adult patients (150 men/26 women; average age: 50.68 ± 9.71 years, average weight: 64.54 ± 11.85 kg after first liver transplantation) were included in this study. Population pharmacokinetic analysis was performed using NONMEM®. Two modeling strategies, theory-based linear compartmental and nonlinear Michaelis–Menten (MM) models, were evaluated and compared. Potential covariates were screened using a stepwise approach. Bootstrap, prediction-, and simulation-based diagnostics (prediction-corrected visual predictive checks) were performed to determine model stability and predictive performance. Finally, Monte Carlo simulations based on the superior model were conducted to design dosing regimens.

Results: Postoperative days (POD), Aspartate aminotransferase (AST), daily tacrolimus dose, triazole antifungal agent (TAF) co-therapy, and recipient CYP3A5*3 genotype constituted the main factors in the theory-based compartmental final model, whereas POD, Total serum bilirubin (TBIL), Haematocrit (HCT), TAF co-therapy, and recipient CYP3A5*3 genotype were important in the nonlinear MM model. The theory-based final model exhibited 234 L h−1 apparent plasma clearance and 11,000 L plasma distribution volume. The maximum dose rate (Vmax) of the nonlinear MM model was 6.62 mg day−1; the average concentration at steady state at half-Vmax (Km) was 6.46 ng ml−1. The nonlinear MM final model was superior to the theory-based final model and used to propose dosing regimens based on simulations.

Conclusion: Our findings demonstrate that saturated tacrolimus concentration-dependent binding to erythrocytes and the influence of daily tacrolimus dose on metabolism may partly contribute to nonlinearity. Further investigation is needed is need to explore the causes of nonlinear pharmacokinetic of tacrolimus. The nonlinear MM model can provide reliable support for tacrolimus dosing optimization and adjustment in adult patients undergoing liver transplantation.

Model-Based Tacrolimus Follow-up Dosing in Adult Renal Transplant Recipients: A Simulation Trial

BACKGROUND

Initial algorithm-based dosing appears to be effective in predicting tacrolimus dose requirement. However, achieving and maintaining the target concentrations is challenging. Model-based follow-up dosing, which considers patient characteristics and pharmacological data, may further personalize treatment. This study investigated whether model-based follow-up dosing could lead to more accurate tacrolimus exposure than standard therapeutic drug monitoring (TDM) in kidney transplant recipients after an initial algorithm-based dose.

METHODS

This simulation trial included patients from a prospective trial that received an algorithm-based tacrolimus starting dose followed by TDM. For every measured tacrolimus predose concentration (C 0,obs ), model-based dosing advice was simulated using the InsightRX software. Based on previous tacrolimus doses and C 0 , age, body surface area, CYP3A4 and CYP3A5 genotypes, hematocrit, albumin, and creatinine, the optimal next dose, and corresponding tacrolimus concentration (C 0,pred ) were predicted.

RESULTS

Of 190 tacrolimus C 0 values measured in 59 patients, 121 (63.7%; 95% CI 56.8-70.5) C 0,obs were within the therapeutic range (7.5-12.5 ng/mL) versus 126 (66.3%, 95% CI 59.6-73.0) for C 0,pred ( P = 0.89). The median absolute difference between the tacrolimus C 0 and the target tacrolimus concentration (10.0 ng/mL) was 1.9 ng/mL for C 0,obs versus 1.6 ng/mL for C 0,pred . In a historical cohort of 114 kidney transplant recipients who received a body weight-based starting dose followed by TDM, 172 of 335 tacrolimus C 0 (51.3%) were within the therapeutic range (10.0-15.0 ng/mL).

CONCLUSIONS

The combination of an algorithm-based tacrolimus starting dose with model-based follow-up dosing has the potential to minimize under- and overexposure to tacrolimus in the early posttransplant phase, although the additional effect of model-based follow-up dosing on initial algorithm-based dosing seems small.

Tacrolimus Population Pharmacokinetic Model in Adult Chinese Patients with Nephrotic Syndrome and Dosing Regimen Identification Using Monte Carlo Simulations

BACKGROUND

The study aimed to establish a population pharmacokinetic (PPK) model of tacrolimus for Chinese patients with nephrotic syndrome using the patient's genotype and Wuzhi capsule dosage as the main test factors.

METHODS

Ninety-six adult patients with nephrotic syndrome, who were receiving tacrolimus treatment, were enrolled. A nonlinear mixed-effects model was used to determine the influencing factors of interindividual tacrolimus metabolism variation and establish a PPK model. To optimize the tacrolimus dosage, 10,000 Monte Carlo simulations were performed.

RESULTS

The 1-chamber model of first-order absorption and elimination was the most suitable model for the data in this study. The typical population tacrolimus clearance (CL/F) value was 16.9 L/h. The percent relative standard error (RSE%) of CL/F was 12%. Increased Wuzhi capsule and albumin doses both decreased the tacrolimus CL/F. In CYP3A5 homozygous mutation carriers, the CL/F was 39% lower than that of carriers of the wild-type and heterozygous mutation. The tacrolimus CL/F in patients who were coadministered glucocorticoids was 1.23-fold higher than that of the control. According to the patient genotype and combined use of glucocorticoids, 26 combinations of Wuzhi capsule and tacrolimus doses were matched. The Monte Carlo simulation identified the most suitable combination scheme.

CONCLUSIONS

An improved tacrolimus PPK model for patients with nephrotic syndrome was established, and the most suitable combination of Wuzhi capsule and tacrolimus doses was identified, thus, facilitating the selection of a more economical and safe administration regimen.

A Prediction Model for Tacrolimus Daily Dose in Kidney Transplant Recipients With Machine Learning and Deep Learning Techniques

Tacrolimus is a major immunosuppressor against post-transplant rejection in kidney transplant recipients. However, the narrow therapeutic index of tacrolimus and considerable variability among individuals are challenges for therapeutic outcomes. The aim of this study was to compare different machine learning and deep learning algorithms and establish individualized dose prediction models by using the best performing algorithm. Therefore, among the 10 commonly used algorithms we compared, the TabNet algorithm outperformed other algorithms with the highest R² (0.824), the lowest prediction error [mean absolute error (MAE) 0.468, mean square error (MSE) 0.558, and root mean square error (RMSE) 0.745], and good performance of overestimated (5.29%) or underestimated dose percentage (8.52%). In the final prediction model, the last tacrolimus daily dose, the last tacrolimus therapeutic drug monitoring value, time after transplantation, hematocrit, serum creatinine, aspartate aminotransferase, weight, CYP3A5, body mass index, and uric acid were the most influential variables on tacrolimus daily dose. Our study provides a reference for the application of deep learning technique in tacrolimus dose estimation, and the TabNet model with desirable predictive performance is expected to be expanded and applied in future clinical practice.

Body composition is associated with tacrolimus pharmacokinetics in kidney transplant recipients

Purpose

A population pharmacokinetic (popPK) model may be used to improve tacrolimus dosing and minimize under- and overexposure in kidney transplant recipients. It is unknown how body composition parameters relate to tacrolimus pharmacokinetics and which parameter correlates best with tacrolimus exposure. The aims of this study were to investigate which body composition parameter has the best association with the pharmacokinetics of tacrolimus and to describe this relationship in a popPK model.

Methods

Body composition was assessed using bio-impedance spectroscopy (BIS). Pharmacokinetic analysis was performed using nonlinear mixed effects modeling (NONMEM). Lean tissue mass, adipose tissue mass, over-hydration, and phase angle were measured with BIS and then evaluated as covariates. The final popPK model was evaluated using goodness-of-fit plots, visual predictive checks, and a bootstrap analysis.

Results

In 46 kidney transplant recipients, 284 tacrolimus concentrations were measured. The base model without body composition parameters included age, plasma albumin, plasma creatinine, CYP3A4 and CYP3A5 genotypes, and hematocrit as covariates. After full forward inclusion and backward elimination, only the effect of the phase angle on clearance (dOFV =  − 13.406; p < 0.01) was included in the final model. Phase angle was positively correlated with tacrolimus clearance. The inter-individual variability decreased from 41.7% in the base model to 34.2% in the final model. The model was successfully validated.

Conclusion

The phase angle is the bio-impedance spectroscopic parameter that correlates best with tacrolimus pharmacokinetics. Incorporation of the phase angle in a popPK model can improve the prediction of an individual’s tacrolimus dose requirement after transplantation.

Supplementary information

The online version contains supplementary material available at 10.1007/s00228-022-03323-0.

Predicting model‐informed precision dosing: A test‐case in tacrolimus dose adaptation for kidney transplant recipients

Before investing resources into the development of a precision dosing (model‐informed precision dosing [MIPD]) tool for tacrolimus, the performance of the tool was evaluated in silico. A retrospective dataset of 315 de novo kidney transplant recipients was first used to identify a one‐compartment pharmacokinetic (PK) model with time‐dependent clearance. MIPD performance was subsequently evaluated by calculating errors to predict future concentrations, which is directly related to dosing precision and probability of target attainment (PTA). Based on the identified model residual error, the theoretical upper limit was 45% PTA for a target of 13.5 ng/ml and an acceptable range of 12–15 ng/ml. Using empirical Bayesian estimation, this limit was reached on day 5 post‐transplant and beyond. By incorporating correlated within‐patient variability when predicting future individual concentrations, PTA improved beyond the theoretical upper limit. This yielded a Bayesian feedback dosing algorithm accurately predicting future trough concentrations and adapting each dose to reach a target concentration. Simulated concentration‐time profiles were then used to quantify MIPD‐based improvement on three end points: average PTA increased from 28% to 39%, median time to three concentrations in target decreased from 10 to 7 days, and mean log‐squared distance to target decreased from 0.080 to 0.055. A study of 200 patients was predicted to have sufficient power to demonstrate these nuanced PK end points reliably. These simulations supported our decision to develop a precision dosing tool for tacrolimus and test it in a prospective trial.

Effect of laparoscopic sleeve gastrectomy on drug pharmacokinetics

INTRODUCTION

Given its feasibility and efficacy, laparoscopic sleeve gastrectomy (LSG) has become a widely accepted bariatric surgery for patients with clinically diagnosed severe obesity. LSG induces anatomical changes and subsequent weight loss which may affect drug pharmacokinetics (PK) and consequently impact dosing regimens. This review aims to examine the effect of LSG on drug PK and identify relevant gastrointestinal physiological alterations.

AREAS COVERED

PubMed, Embase, Scopus, and the Cochrane Library were searched for articles related to drug PK and LSG from inception to July 2021. Moreover, literature concerning postoperative physiological conditions in the gastrointestinal tract, such as gastric pH, gastric emptying, and small bowel transit time, etc., which may affect the PK profile of drug products was also reviewed.

EXPERT OPINION

Although LSG is classified as having restrictive property without malabsorptive bypass, postoperative changes in gastrointestinal physiology and subsequent weight loss may also lead to increased, decreased or unaltered drug exposure levels. General monitoring on drug efficacy or safety using biomarkers is proposed. In addition, therapeutic drug monitoring for those drugs when it is applicable and available is recommended to ensure efficient drug dosing and avoid adverse effects. Further research into many individual drugs are warranted.

Tacrolimus Dose-Conversion Ratios Based on Switching of Formulations for Patients with Solid Organ Transplants

Background

Tacrolimus may be administered during hospitalization as an IV formulation or oral suspension. However, literature suggesting appropriate ratios for conversion from these formulations to capsules is limited.

Objective

To evaluate conversion ratios after a switch in formulation of tacrolimus for solid-organ transplant recipients.

Methods

This single-centre observational longitudinal study involved hospitalized patients who underwent a switch in formulation of tacrolimus according to 1 of 3 possible scenarios: IV to oral suspension, IV to capsule, or oral suspension to capsule. Data were collected from the earliest accessible electronic file (January 2009) to January 1, 2019. Conversion ratios were calculated for each of the 3 groups using data for blood concentrations and doses before and after the switch. The calculated ratios were then compared with recommended conversion ratios: 1:5 (i.e., 1 mg of IV tacrolimus is converted to 5 mg of oral tacrolimus, expressed as "5") for either of the switches involving an IV formulation and 1:1 (i.e., same amount, expressed as "1") for the switch from oral formulation to capsules.

Results

For the group who underwent switching from the IV formulation to oral suspension, the mean calculated conversion ratio was 3.04, which was significantly different from the recommended ratio of 5. For the group who underwent switching from the IV formulation to capsules, the calculated conversion ratio was 5.18, which was not significantly different from the recommended ratio of 5. For the group who underwent switching from oral suspension to capsules, the calculated conversion ratio was 1.17, which was not significantly different from the recommended ratio of 1.

Conclusion

In this small retrospective study of tacrolimus therapy, the calculated conversion ratio was significantly different from the recommended ratio for patients who were switched from IV administration to oral suspension, but not for those switched from IV administration or oral suspension to capsules. Therapeutic drug monitoring therefore appears indispensable, regardless of conversion ratios.

Evaluation of published population pharmacokinetic models to inform tacrolimus dosing in adult heart transplant recipients

BACKGROUND AND AIM

Identification of the most appropriate population pharmacokinetic model-based Bayesian estimation is required prior to its implementation in routine clinical practice to inform tacrolimus dosing decisions. This study aimed to determine the predictive performances of relevant population pharmacokinetic models of tacrolimus developed from various solid organ transplant recipient populations in adult heart transplant recipients, stratified based on concomitant azole antifungal use. Concomitant azole antifungal therapy alters tacrolimus pharmacokinetics substantially, necessitating dose adjustments.

METHODS

Population pharmacokinetic models of tacrolimus were selected (n = 17) for evaluation from a recent systematic review. The models were transcribed and implemented in NONMEM version 7.4.3. Data from 85 heart transplant recipients (2387 tacrolimus concentrations) administered the oral immediate-release formulation of tacrolimus (Prograf) were obtained up to 391 days post-transplant. The performance of each model was evaluated using: (i) prediction-based assessment (bias and imprecision) of the individual predicted tacrolimus concentration of the fourth dosing occasion (MAXEVAL = 0, FOCE-I) from 1-3 prior dosing occasions; and (ii) simulation-based assessment (prediction-corrected visual predictive check). Both assessments were stratified based on concomitant azole antifungal use.

RESULTS

Regardless of the number of prior dosing occasions (1-3) and concomitant azole antifungal use, all models demonstrated unacceptable individual predicted tacrolimus concentration of the fourth dosing occasion (n = 152). The prediction-corrected visual predictive check graphics indicated that these models inadequately predicted observed tacrolimus concentrations.

CONCLUSION

All models evaluated were unable to adequately describe tacrolimus pharmacokinetics in adult heart transplant recipients included in this study. Further work is required to describe tacrolimus pharmacokinetics for our heart transplant recipient cohort.

Population Pharmacokinetic Models of Tacrolimus in Adult Transplant Recipients: A Systematic Review

BACKGROUND AND OBJECTIVES

Numerous population pharmacokinetic (PK) models of tacrolimus in adult transplant recipients have been published to characterize tacrolimus PK and facilitate dose individualization. This study aimed to (1) investigate clinical determinants influencing tacrolimus PK, and (2) identify areas requiring additional research to facilitate the use of population PK models to guide tacrolimus dosing decisions.

METHODS

The MEDLINE and EMBASE databases, as well as the reference lists of all articles, were searched to identify population PK models of tacrolimus developed from adult transplant recipients published from the inception of the databases to 29 February 2020.

RESULTS

Of the 69 studies identified, 55% were developed from kidney transplant recipients and 30% from liver transplant recipients. Most studies (91%) investigated the oral immediate-release formulation of tacrolimus. Few studies (17%) explained the effect of drug-drug interactions on tacrolimus PK. Only 35% of the studies performed an external evaluation to assess the generalizability of the models. Studies related variability in tacrolimus whole blood clearance among transplant recipients to either cytochrome P450 (CYP) 3A5 genotype (41%), days post-transplant (30%), or hematocrit (29%). Variability in the central volume of distribution was mainly explained by body weight (20% of studies).

CONCLUSION

The effect of clinically significant drug-drug interactions and different formulations and brands of tacrolimus should be considered for any future tacrolimus population PK model development. Further work is required to assess the generalizability of existing models and identify key factors that influence both initial and maintenance doses of tacrolimus, particularly in heart and lung transplant recipients.

Unbound Plasma, Total Plasma, and Whole-Blood Tacrolimus Pharmacokinetics Early After Thoracic Organ Transplantation

BACKGROUND AND OBJECTIVE

Therapeutic drug monitoring of tacrolimus whole-blood concentrations is standard care in thoracic organ transplantation. Nevertheless, toxicity may appear with alleged therapeutic concentrations possibly related to variability in unbound concentrations. However, pharmacokinetic data on unbound concentrations are not available. The objective of this study was to quantify the pharmacokinetics of whole-blood, total, and unbound plasma tacrolimus in patients early after heart and lung transplantation.

METHODS

Twelve-hour tacrolimus whole-blood, total, and unbound plasma concentrations of 30 thoracic organ recipients were analyzed with high-performance liquid chromatography-tandem mass spectrometry directly after transplantation. Pharmacokinetic modeling was performed using non-linear mixed-effects modeling.

RESULTS

Plasma concentration was < 1% of the whole-blood concentration. Maximum binding capacity of erythrocytes was directly proportional to hematocrit and estimated at 2700 pg/mL (95% confidence interval 1750-3835) with a dissociation constant of 0.142 pg/mL (95% confidence interval 0.087-0.195). The inter-individual variability in the binding constants was considerable (27% maximum binding capacity, and 29% for the linear binding constant of plasma).

CONCLUSIONS

Tacrolimus association with erythrocytes was high and suggested a non-linear distribution at high concentrations. Monitoring hematocrit-corrected whole-blood tacrolimus concentrations might improve clinical outcomes in clinically unstable thoracic organ transplants.

CLINICAL TRIAL REGISTRATION

NTR 3912/EudraCT 2012-001909-24.

A Population Pharmacokinetic Model Does Not Predict the Optimal Starting Dose of Tacrolimus in Pediatric Renal Transplant Recipients in a Prospective Study: Lessons Learned and Model Improvement

Background and Objective

Bodyweight-based dosing of tacrolimus is considered standard care. Currently, at first steady state, a third of pediatric kidney transplant recipients has a tacrolimus pre-dose concentration within the target range. We investigated whether adaptation of the starting dose according to a validated dosing algorithm could increase this proportion.

Methods

This was a multi-center, single-arm, prospective trial with a planned interim analysis after 16 patients, in which the tacrolimus starting dose was based on bodyweight, cytochrome P450 3A5 genotype, and donor status (living vs. deceased donor).

Results

At the interim analysis, 31% of children had a tacrolimus pre-dose concentration within the target range. As the original dosing algorithm was poorly predictive of tacrolimus exposure, the clinical trial was terminated prematurely. Next, the original model was improved by including the data of the children included in this trial, thereby doubling the number of children in the model building cohort. Data were best described with a two-compartment model with inter-individual variability, allometric scaling, and inter-occasion variability on clearance. Cytochrome P450 3A5 genotype, hematocrit, and creatinine influenced the tacrolimus clearance. A new starting dose model was developed in which the cytochrome P450 3A5 genotype was incorporated. Both models were successfully internally and externally validated.

Conclusions

The weight-normalized starting dose of tacrolimus should be higher in patients with a lower bodyweight and in those who are cytochrome P450 3A5 expressers.

Electronic supplementary material

The online version of this article (10.1007/s40262-019-00831-8) contains supplementary material, which is available to authorized users.

Case Report: Low Hematocrit Leading to Tacrolimus Toxicity

Tacrolimus is a calcineurin inhibitor characterized by a narrow therapeutic index and high intra- and inter-individual pharmacokinetic variability. Therapeutic drug monitoring in whole-blood is the standard monitoring procedure. However, tacrolimus extensively binds to erythrocytes, and tacrolimus whole-blood distribution and whole-blood trough concentrations are strongly affected by hematocrit. High whole-blood tacrolimus concentrations at low hematocrit may result in high unbound plasma concentrations and increased toxicity. We present the case of a 16-year-old girl with kidney and liver transplant in whom low concentrations of tacrolimus in the context of low hematocrit led to significant increase in the dosage of tacrolimus and participate, along with a genetic polymorphism of ABCB1, in nephrotoxicity.

Model-informed precision dosing to optimise immunosuppressive therapy in renal transplantation

Immunosuppressive therapy is pivotal for sustained allograft and patient survival after renal transplantation. However, optimally balanced immunosuppressive therapy is challenged by between-patient and within-patient pharmacokinetic (PK) variability. This could warrant the application of personalised dosing strategies to optimise individual patient outcomes. Pharmacometrics, the science that investigates the xenobiotic-biotic interplay using computer-aided mathematical modelling, provides options to describe and quantify this PK variability and enables identification of patient characteristics affecting immunosuppressant PK and treatment outcomes. Here, we review and critically appraise the available pharmacometric model-informed dosing solutions for the typical immunosuppressants in modern renal transplantation, to guide their initial and subsequent dosing.

Clinical significance of tacrolimus intra-patient variability on kidney transplant outcomes according to pre-transplant immunological risk

High intra-patient variability (IPV) of tacrolimus trough concentrations is increasingly recognized as a predictor of poor long-term outcomes in kidney transplant. However, there is a lack of information regarding the association between tacrolimus IPV and graft outcomes according to immunological risk. We analyzed tacrolimus IPV using the coefficient of variability from months 6-12 after transplantation in 1080 kidney transplant recipients. Patients were divided into two immunological risk groups based on pre-transplant panel reactive antibodies and donor-specific antibodies. High immunological risk was defined as panel reactive antibodies ≥ 20% or the presence of donor-specific antibodies. The effects of tacrolimus IPV on graft outcomes were significantly different between low and high immunological risk patients. A multivariable Cox regression model confirmed that high tacrolimus IPV was an independent risk factor for graft failure in the high risk group (HR, 2.90; 95% CI, 1.42-5.95, P = 0.004). In the high risk group, high tacrolimus IPV was also significantly associated with increased risk of antibody-mediated rejection (P = 0.006). In contrast, death-censored graft survival and antibody-mediated rejection in the low immunological risk group was not significantly different by tacrolimus IPV. High tacrolimus IPV significantly increases the risk of graft failure and antibody-mediated rejection in patients with high immunological risk.

Clinical Pharmacokinetics and Impact of Hematocrit on Monitoring and Dosing of Tacrolimus Early After Heart and Lung Transplantation

The calcineurin inhibitor tacrolimus is an effective immunosuppressant and is extensively used in solid organ transplantation. In the first week after heart and lung transplantation, tacrolimus dosing is difficult due to considerable physiological changes because of clinical instability, and toxicity often occurs, even when tacrolimus concentrations are within the therapeutic range. The physiological and pharmacokinetic changes are outlined. Excessive variability in bioavailability may lead to higher interoccasion (dose-to-dose) variability than interindividual variability of pharmacokinetic parameters. Intravenous tacrolimus dosing may circumvent this high variability in bioavailability. Moreover, the interpretation of whole-blood concentrations is discussed. The unbound concentration is related to hematocrit, and changes in hematocrit may increase toxicity, even within the therapeutic range of whole-blood concentrations. Therefore, in clinically unstable patients with varying hematocrit, aiming at the lower therapeutic level is recommended and tacrolimus personalized dosing based on hematocrit-corrected whole-blood concentrations may be used to control the unbound tacrolimus plasma concentrations and subsequently reduce toxicity.

Tacrolimus Area Under the Concentration Versus Time Curve Monitoring, Using Home-Based Volumetric Absorptive Capillary Microsampling

BACKGROUND

Therapeutic drug monitoring (TDM) of tacrolimus (Tac) is mandatory in renal transplant recipients (RTxR). Area under the concentration versus time curve (AUC) is the preferred measure for Tac exposure; however, for practical purposes, most centers use trough concentrations as a clinical surrogate. Limited sampling strategies in combination with population pharmacokinetic model-derived Bayesian estimators (popPK-BE) may accurately predict individual AUC. The use of self-collected capillary microsamples could simplify this strategy. This study aimed to investigate the potential of AUC-targeted Tac TDM using capillary microsamples in combination with popPK-BE.

METHODS

A single-center prospective pharmacokinetic study was conducted in standard-risk RTxR (n = 27) receiving Tac twice daily. Both venous and capillary microsamples (Mitra; Neoteryx, Torrance, CA) were obtained across 2 separate 12-hour Tac dosing intervals (n = 13 samples/AUC). Using popPK-BE, reference AUC (AUCref) was determined for each patient using all venous samples. Different limited sampling strategies were tested for AUC predictions: (1) the empiric sampling scheme; 0, 1, and 3 hours after dose and (2) 3 sampling times determined by the multiple model optimal sampling time function in Pmetrics. Agreement between the predicted AUCs and AUCref were evaluated using C-statistics. Accepted agreement was defined as a total deviation index ≤±15%.

RESULTS

The AUC from capillary microsamples revealed high accuracy and precision compared with venous AUCref, and 85% of the AUCs had an error within ±11.9%. Applying microsamples at 0, 1, and 3 hours after dose predicted venous AUCref with acceptable agreement. Patients performed self-sampling with acceptable accuracy.

CONCLUSIONS

Capillary microsampling is patient-centered, making AUC-targeted TDM of Tac feasible without extended hospital stays. Samples obtained 0, 1, and 3 hours after dose, combined with popPK-BE, accurately predict venous Tac AUC.

Tacrolimus Exposure in Obese Patients: and A Case-Control Study in Kidney Transplantation

BACKGROUND

Tacrolimus pharmacokinetics in obese (Ob) patients has been poorly studied. In this article, the authors explored the impact of obesity on tacrolimus exposure in kidney transplant recipients (KTRs) and estimated a more suitable initial dosage in this population.

METHODS

A retrospective, observational, monocentric case-control study was performed in obese KTRs (BMI > 30 kg/m2) who received tacrolimus between 2013 and 2017 (initial dose: 0.15 mg/kg/d) (actual weight). Nonobese (Nob) controls (BMI <30 kg/m2) were matched for age and sex. Weekly centralized monitoring of tacrolimus trough levels was performed by liquid chromatography/mass spectrometry until the third month (M3). Target trough levels were set between 8 and 10 ng/mL. All patients received antilymphocyte globulin, corticosteroids, and mycophenolate mofetil.

RESULTS

Of the 541 KTRs, 28 tacrolimus-treated Ob patients were included and compared with 28 NOb-matched controls. With a mean of 22 assays/patient, tacrolimus trough levels were higher in Ob patients (mean 9.9 versus 8.7 ng/mL; P = 0.008); the weight-related dose of Tac was lower at M3 (mean 0.10 versus 0.13 mg/kg/d, P < 0.0001). The tacrolimus concentration to dose (C0/D) was higher in the Ob cohort [mean 116 versus 76 (ng/mL)/(mg/kg/d); P = 0.001]. In Ob patients, a mean decrease of -4.6 mg/d in the 3 months after tacrolimus initiation was required (versus -1.12 in NOb; P = 0.001) to remain within the therapeutic range. Obesity, high mycophenolate mofetil daily dose at M3, and CYP3A5 expression were independently associated with higher tacrolimus exposure. Four dose-adaptation strategies were simulated and compared with the study results.

CONCLUSIONS

An initial dose calculation based on either ideal or lean body weight may allow for faster achievement of tacrolimus trough level targets in Ob KTRs, who are at risk of overexposure when tacrolimus is initiated at 0.15 mg/kg/d. A prospective study is required to validate alternative dose calculation strategies in these patients.

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.

Early prognostic performance of miR155-5p monitoring for the risk of rejection: Logistic regression with a population pharmacokinetic approach in adult kidney transplant patients

Previous results from our group and others have shown that urinary pellet expression of miR155-5p and urinary CXCL-10 production could play a key role in the prognosis and diagnosis of acute rejection (AR) in kidney transplantation patients. Here, a logistic regression model was developed using NONMEM to quantify the relationships of miR155-5p urinary expression, CXCL-10 urinary concentration and tacrolimus and mycophenolic acid (MPA) exposure with the probability of AR in adult kidney transplant patients during the early post-transplant period. Owing to the contribution of therapeutic drug monitoring to achieving target exposure, neither tacrolimus nor MPA cumulative exposure was identified as a predictor of AR in the studied population. Even though CXCL-10 urinary concentration showed a trend, its effect on AR was not significant. In contrast, urinary miR155-5p expression was prognostic of clinical outcome. Monitoring miR155-5p urinary pellet expression together with immunosuppressive drug exposure could be very useful during routine clinical practice to identify patients with a potential high risk of rejection at the early stages of the post-transplant period. This early risk assessment would allow for the optimization of treatment and improved prevention of AR.

High Variability of Whole-Blood Tacrolimus Pharmacokinetics Early After Thoracic Organ Transplantation

Background and Objective

Oral tacrolimus is initiated perioperatively in heart and lung transplantation patients. There have been few studies on oral tacrolimus pharmacokinetics early post-transplantation, even though tacrolimus-related toxicity may occur early, potentially leading to morbidity and mortality. Therefore, we aimed to study the pharmacokinetics of oral tacrolimus in thoracic organ recipients during the first days after transplantation.

Methods

We conducted a pharmacokinetic study in 30 thoracic organ transplants at intensive care at the University Medical Center Utrecht in the first week post-transplantation. Twelve-hour whole-blood tacrolimus profiles were examined using high-performance liquid chromatography tandem mass spectrometry (HPLC–MS/MS) and analysed via population pharmacokinetic modelling.

Results

The concentration–time profiles showed high variability. Concentrations at 12 h were outside the target range in 69% of the cases. A two-compartment model with mixed first-order and zero-order absorption adequately described tacrolimus concentrations. The typical value of the apparent clearance was 19.6 L/h (95% CI 16.2–22.9), and the apparent distribution volumes of central and peripheral compartments, V1 and V2, were 231 L (95% CI 199–267) and 521 L (95% CI 441–634), respectively. Inter-occasion (dose-to-dose) variability far exceeded the interindividual variability (IIV), with an estimated variability in relative bioavailability of 55% (95% CI 48.5–64.4).

Conclusions

The high variability of tacrolimus pharmacokinetics early after thoracic organ transplantation is largely due to excessive variability in bioavailability, making individualised dosing based on measured concentrations futile. To bypass this bioavailability issue, we suggest administering tacrolimus intravenously and aiming below the upper therapeutic range early post-transplantation.

Clinical Trial Registraion: NTR 3912/EudraCT 2012-001909-24.

Electronic supplementary material

The online version of this article (10.1007/s13318-019-00591-7) contains supplementary material, which is available to authorized users.

Predictors of tacrolimus pharmacokinetic variability: current evidences and future perspectives

INTRODUCTION

In kidney transplantation, tacrolimus (TAC) is at the cornerstone of current immunosuppressive strategies. Though because of its narrow therapeutic index, it is critical to ensure that TAC levels are maintained within this sharp window through reactive adjustments. This would allow maximizing efficiency while limiting drug-associated toxicity. However, TAC high intra- and inter-patient pharmacokinetic (PK) variability makes it more laborious to accurately predict the appropriate dosage required for a given patient.

AREAS COVERED

This review summarizes the state-of-the-art knowledge regarding drug interactions, demographic and pharmacogenetics factors as predictors of TAC PK. We provide a scoring index for each association to grade its relevance and we present practical recommendations, when possible for clinical practice.

EXPERT OPINION

The management of TAC concentration in transplanted kidney patients is as critical as it is challenging. Recommendations based on rigorous scientific evidences are lacking as knowledge of potential predictors remains limited outside of DDIs. Awareness of these limitations should pave the way for studies looking at demographic and pharmacogenetic factors as well as gut microbiota composition in order to promote tailored treatment plans. Therapeutic approaches considering patients' clinical singularities may help allowing to maintain appropriate concentration of TAC.

Clinical Factors Affecting the Dose Conversion Ratio from Intravenous to Oral Tacrolimus Formulation among Pediatric Hematopoietic Stem Cell Transplantation Recipients

BACKGROUND

Tacrolimus is converted from intravenous to oral formulation for the prophylaxis of graft-versus-host disease when patients can tolerate oral intake and graft-versus-host disease is under control. Oral tacrolimus formulation presents poor bioavailability with intraindividual and interindividual variations; however, some factors affecting its blood concentration among pediatric hematopoietic stem cell transplantation (HCT) recipients are still unclear. This study aimed to identify the clinical factors affecting tacrolimus blood concentrations after switching its formulation.

METHODS

Changes in the blood concentration/dose ratio (C/D) of tacrolimus in pediatric HCT recipients were analyzed after the switching of tacrolimus from intravenous to oral formulation. Clinical records of 57 pediatric patients who underwent allogenic HCT from January 2006 to April 2019 in our institute were retrospectively reviewed. The C/D of tacrolimus before discontinuation of intravenous infusion (C/Div) was compared with the tacrolimus trough level within 10 days after the initiation of oral administration (C/Dpo). Multiple linear regression analysis was performed to identify factors affecting (C/Dpo)/(C/Div).

RESULTS

The constant coefficient of (C/Dpo)/(C/Div) was 0.1692 [95% confidence interval (CI), 0.137-0.2011]. The concomitant use of voriconazole or itraconazole and female sex were significant variables with a beta coefficient of 0.0974 (95% CI, 0.062-0.133) and -0.0373 (95% CI, -0.072 to -0.002), respectively.

CONCLUSIONS

After switching of tacrolimus formulation, pediatric HCT recipients might need oral tacrolimus dose that is 5-6 and 3.5-4.5 times the intravenous dose to maintain tacrolimus blood concentrations and area under the concentration-time curve, respectively. With the concomitant use of voriconazole or itraconazole, an oral tacrolimus dose of 4-5 times the intravenous dose seemed appropriate to maintain blood tacrolimus concentration.

Fasting Status and Circadian Variation Must be Considered When Performing AUC-based Therapeutic Drug Monitoring of Tacrolimus in Renal Transplant Recipients

Therapeutic drug monitoring (TDM) is mandatory for the immunosuppressive drug tacrolimus (Tac). For clinical applicability, TDM is performed using morning trough concentrations. With recent developments making tacrolimus concentration determination possible in capillary microsamples and Bayesian estimator predicted area under the concentration curve (AUC), AUC‐guided TDM may now be clinically applicable. Tac circadian variation has, however, been reported, with lower systemic exposure following the evening dose. The aim of the present study was to investigate tacrolimus pharmacokinetic (PK) after morning and evening administrations of twice‐daily tacrolimus in a real‐life setting without restrictions regarding food and concomitant drug timing. Two 12 hour tacrolimus investigations were performed; after the morning dose and the following evening dose, respectively, in 31 renal transplant recipients early after transplantation both in a fasting‐state and under real‐life nonfasting conditions (14 patients repeated the investigation). We observed circadian variation under fasting‐conditions: 45% higher peak‐concentration and 20% higher AUC following the morning dose. In the real‐life nonfasting setting, the PK‐profiles were flat but comparable after the morning and evening doses, showing slower absorption rate and lower AUC compared with the fasting‐state. Limited sampling strategies using concentrations at 0, 1, and 3 hours predicted AUC after fasting morning administration, and samples obtained at 1, 3, and 6 hours predicted AUC for the other conditions (evening and real‐life nonfasting). In conclusion, circadian variation of tacrolimus is present when performed in patients who are in the fasting‐state, whereas flatter PK‐profiles and no circadian variation was present in a real‐life, nonfasting setting.

Is There a Temporal Relationship Between Trough Whole Blood Tacrolimus Concentration and Acute Rejection in the First 14 Days After Kidney Transplantation?

BACKGROUND

There are inconsistent findings regarding the relationship between trough whole blood tacrolimus concentration (TAC C0) and acute kidney rejection in recipients undergoing TAC therapeutic drug monitoring (TDM). However, studies have not always assessed TAC C0 at the time of rejection or accounted for variability in hematocrit. Therefore, this study aimed to investigate the temporal relationship between TAC C0 and acute rejection, including when accounting for variation in hematocrit.

METHODS

For 38 recipients who developed biopsy-proven acute rejection (BPAR) in the first 14 days after kidney transplantation, daily TAC C0 from TDM and hematocrit was collected from case notes. Differences in log10-transformed TAC C0 between the day of BPAR (log Cr), 1 day before BPAR (log Cr-1), and 2 days before BPAR (log Cr-2) and the combined median concentrations for the days preceding these (log Cprior) were examined by repeated-measures analysis of variance with Dunnett post hoc testing. Generalized linear mixed-effects regression (glmer) examined the ability of TAC C0 to predict acute rejection episodes with and without controlling for hematocrit.

RESULTS

Log Cr-1 [mean difference (95% confidence interval) = -0.13 (-0.21 to -0.048), post hoc P = 0.002] and log Cr [-0.13 (-0.24 to -0.025), post hoc P = 0.013] were significantly lower than log Cprior. TAC C0 was a significant (P = 0.0078) predictor of rejection episodes (area under the receiver operating characteristic curve = 0.79) only in glmer models accounting for variability in hematocrit.

CONCLUSIONS

In recipients who developed BPAR, there was a significant temporal relationship between TAC C0 and BPAR incidence under TAC TDM that may not be detected in cross-sectional studies, especially if variability in hematocrit is not addressed. This supports a TAC C0-rejection relationship, which differs between recipients, and may explain why some recipients do or do not experience rejection within or below the TDM range, respectively. However, studies with larger sample sizes are needed to confirm this finding.

Clinical Pharmacokinetic and Pharmacodynamic Considerations in the Treatment of Ulcerative Colitis

Ulcerative colitis (UC) is an inflammatory bowel disease (IBD) of unknown etiology, probably caused by a combination of genetic and environmental factors. The treatment of patients with active UC depends on the severity, localization and history of IBD medication. According to the classic step-up approach, treatment with 5-aminosalicylic acid compounds is the first step in the treatment of mild to moderately active UC. Corticosteroids, such as prednisolone are used in UC patients with moderate to severe disease activity, but only for remission induction therapy because of side effects associated with long-term use. Thiopurines are the next step in the treatment of active UC but monotherapy during induction therapy in UC patients is not preferred because of their slow onset. Therapeutic drug monitoring (TDM) of the pharmacologically active metabolites of thiopurines, 6-thioguanine nucleotide (6-TGN), has proven to be beneficial. Thiopurine S-methyltransferase (TMPT) plays a role in the metabolic conversion pathway of thiopurines and exhibits genetic polymorphism; however, the clinical benefit and relevance of TPMT genotyping is not well established. In patients with severely active UC refractory to corticosteroids, calcineurin inhibitors such as ciclosporin A (CsA) and tacrolimus are potential therapeutic options. These agents usually have a rather rapid onset of action. Monoclonal antibodies (anti-tumor necrosis factor [TNF] agents, vedolizumab) are the last pharmacotherapeutic option for UC patients before surgery becomes inevitable. Body weight, albumin status and antidrug antibodies contribute to the variability in the pharmacokinetics of anti-TNF agents. Additionally, the use of concomitant immunomodulators (thiopurines/methotrexate) lowers the rate of immunogenicity, and therefore the concomitant use of anti-TNF therapy with an immunomodulator may confer some advantage compared with monotherapy in certain patients. TDM of anti-TNF agents could be beneficial in patients with primary nonresponse and secondary loss of response. The potential benefit of applying TDM during vedolizumab treatment has yet to be determined.