Importance of hematocrit for a tacrolimus target concentration strategy

Exploring the comprehensive factors influencing tacrolimus pharmacokinetics in early renal transplant recipients: A population pharmacokinetic analysis

PURPOSE

To establish a population pharmacokinetic (PopPK) model of tacrolimus in the early stages after renal transplantation and evaluate the model's predictive performance with external data.

METHODS

Intravenous and oral tacrolimus were administered to 302 renal transplant recipients in the early posttransplantation stages. Related data were obtained from the electronic medical records. Single nucleotide polymorphisms in genes associated with tacrolimus pharmacokinetics were tested. The data were analyzed by NONMEM. The external data from 153 patients were subsequently used to evaluate model extrapolation.

RESULTS

A one-compartment model was used to determine tacrolimus pharmacokinetics. The estimated clearance (CL), volume of distribution (V) and bioavailability (F) of tacrolimus were 4.91 L/h, 77 L and 26.5%, respectively. CL and V decreased with increasing hematocrit. CL and F decreased with increasing operation time. Diltiazem and Wuzhi capsule resulted in 28.4% and 43.9% decreases in the CL, respectively. Omeprazole or esomeprazole resulted in a 9% increase in F. The value of F for patients expressing CYP3A5 was 36.6% lower than that for the patients who did not express CYP3A5. The evaluation of external data revealed that the proportion of individual prediction error within 20% of the observed tacrolimus concentration was greater than 77.3%.

CONCLUSIONS

A PopPK model for tacrolimus was established for early renal transplantation. CYP3A5 was a significant covariate for F. Fat-free mass was the best predictor of the influence of body size on CL and V. The model could be extrapolated to stable renal transplant recipients.

Customizing Tacrolimus Dosing in Kidney Transplantation: Focus on Pharmacogenetics

ABSTRACT

Different polymorphisms in genes encoding metabolizing enzymes and drug transporters have been associated with tacrolimus pharmacokinetics. In particular, studies on CYP3A4 and CYP3A5, and their combined cluster have demonstrated their significance in adjusting tacrolimus dosing to minimize under- and overexposure thereby increasing the proportion of patients who achieve tacrolimus therapeutic target. Many factors influence the pharmacokinetics of tacrolimus, contributing to inter-patient variability affecting individual dosing requirements. On the other hand, the growing use of population pharmacokinetic models in solid organ transplantation, including different tacrolimus formulations, has facilitated the integration of pharmacogenetic data and other variables into algorithms to easier implement the personalized dose adjustment in transplant centers. The future of personalized medicine in transplantation lies in implementing these models in clinical practice, with pharmacogenetics as a key factor to account for the high inter-patient variability in tacrolimus exposure. To date, three clinical trials have validated the clinical application of these approaches. The aim of this review is to provide an overview of the current studies regarding the different population pharmacokinetic including pharmacogenetics and those translated to the clinical practice for individualizing tacrolimus dose adjustment in kidney transplantation.

Examining Whole Blood, Total and Free Plasma Tacrolimus in Elderly Kidney Transplant Recipients

BACKGROUND

Therapeutic monitoring is routinely performed to ensure tacrolimus whole-blood concentrations fall within a predefined target. Despite this, patients still experience inefficacy and toxicity that could be related to variability in free (unbound) tacrolimus exposure. Therefore, the aim of this study was to compare tacrolimus-free plasma (C u ), total plasma (C p ), and whole-blood (C wb ) concentrations in adult kidney transplant recipients and to characterize tacrolimus disposition across different matrices.

METHODS

Twelve-hour concentration-time profiling was performed in 15 recipients, allowing simultaneous measurement of C u , C p , and C wb . Pharmacokinetic parameters were estimated using noncompartmental analysis. The relationship between C wb and C p were examined using a capacity-limited binding model, incorporating the hematocrit fraction ( fHCT ) to estimate maximum binding concentration ( Bmax ) and dissociation constant ( Kd ). The relationship between C p and C u was evaluated using a linear binding model to estimate the nonspecific binding parameter ( Nplasma ). Nonlinear regression analysis was used to obtain estimates of Bmax , Kd , and Nplasma .

RESULTS

A total of 195 paired C wb , C p , and C u values were collected. The median ratios of C wb :C p , C p :C u , and C wb :C u were 9:1, 20:1, and 138:1, respectively. Variability in free plasma exposure was large; free trough values ranged from 8 to 51 ng/L and free area-under-the-concentration-time-curve values ranged from 424 to 7160 ng·h/L. Median (range) estimates of Bmax , Kd , and Nplasma were 90.4 µg/L (22.4-752.5 µg/L), 2.36 µg/L (0-69.2 µg/L), and 0.05 (0.035-0.085), respectively. The interindividual variability (CV%) in binding parameters was considerable ( Bmax 117.2%; Nplasma 32.5%).

CONCLUSIONS

Large variability was observed in tacrolimus-free plasma exposure and binding parameters. Future research to characterize the relationship between tacrolimus C u and patient outcomes may be of benefit.

Alternate Sampling Matrices for Therapeutic Drug Monitoring of Immunosuppressants

BACKGROUND

Immunosuppressant (IS) therapeutic drug monitoring (TDM) relies on measuring mostly pharmacologically inactive erythrocyte-bound and/or plasma protein-bound drug levels. Variations in hematocrit and plasma protein levels complicate interpretation of blood calcineurin inhibitor (CNI) and inhibitors of the molecular target of rapamycin (mTORi) concentrations. Variable binding of mycophenolic acid (MPA) to albumin similarly complicates its TDM in plasma. A different matrix may improve IS concentration-response relationships and better reflect exposures at sites of action.

METHODS

This review explores the evidence for IS TDM using peripheral blood mononuclear cell (PBMC), graft tissue, and total or unbound plasma concentrations.

RESULTS

Tandem mass spectrometry provides the sensitivity for assessing these matrices. But several challenges must be addressed, including minimizing hemolysis during blood collection, preventing IS efflux during PBMC preparation, and determining the need for further purification of the PBMC fraction. Assessing and reducing nonspecific binding during separation of unbound IS are also necessary, especially for lipophilic CNIs/mTORi. Although TDM using PBMC or unbound plasma concentrations may not be feasible due to increased costs, plasma CNI/mTORi levels may be more easily integrated into routine TDM. However, no validated TDM targets currently exist, and published models to adjust blood CNI/mTORi concentrations for hematocrit or to predict PBMC, and total and unbound plasma IS concentrations have yet to be validated in terms of measured concentrations or prediction of clinical outcomes.

CONCLUSIONS

Even if CNI/mTORi measurements in novel matrices do not become routine, they may help refine pharmacokinetic-pharmacodynamic relationships and improve mathematical models for TDM using whole blood. Notably, there is evidence to support measuring unbound MPA in patients with severe renal dysfunction, hypoalbuminemia, and hyperbilirubinemia, with some proposed TDM targets.

Effect of Hematopoietic Stem Cell Transplantation Regimen on Tacrolimus Pharmacokinetics

Objectives

Treatment with tacrolimus requires strict control of the whole-blood concentration in patients undergoing allogeneic hematopoietic stem cell transplantation (HSCT). In patients undergoing cord blood transplantation (CBT), there is a negative correlation between volume of distribution of tacrolimus and hemoglobin levels, which reflect the red blood cell (RBC) count. In this study, we evaluated the influence of the conditioning regimen (myeloablative and reduced-intensity conditioning) or donor source (cord blood, bone marrow, and peripheral blood stem cells) on the pharmacokinetics of tacrolimus in patients undergoing HSCT, including those undergoing CBT. We also examined applicability of dosing strategy of tacrolimus considering the RBC count.

Methods

We retrospectively analyzed clinical data-including whole-blood tacrolimus concentrations-from patients with HSCT. The observation period spanned from first continuous intravenous infusions until switch to oral medication, transfer to another hospital, relapse, or death. Population pharmacokinetic analysis was performed on whole-blood tacrolimus concentrations obtained from therapeutic drug monitoring during the observation period. Patient characteristics and laboratory data were evaluated as covariates.

Results

We enrolled 91 patients undergoing HSCT (CBT: n = 56; bone marrow transplantation: n = 22; and peripheral blood stem cell transplantation: n = 13); 58 and 33 patients received myeloablative conditioning and reduced-intensity conditioning, respectively. Whole-blood tacrolimus concentrations were accurately captured (n = 1,658 measurements) using a one-compartment and additive error model. The conditioning regimen and donor source did not have an impact on the pharmacokinetics of tacrolimus. Therefore, these factors were not considered when forming the dosing strategy. Nevertheless, a negative correlation between volume of distribution and hemoglobin level was confirmed, indicating that monitoring the RBC count is useful in assessing the dosing strategy.

Conclusions

A tacrolimus dosing strategy that considers the variability in hemoglobin levels applies to all patients undergoing HSCT.

Patiromer Does Not Alter Tacrolimus Pharmacokinetics in Kidney Transplant Recipients When Administered Three Hours Post-Tacrolimus

Background

Hyperkalemia is common in kidney transplant (KTx) recipients. Patiromer, a potassium-binding polymer used to treat acute and chronic hyperkalemia, has the potential to bind charged particles in the gastrointestinal tract and thereby potentially affect the absorption of coadministered drugs. The immunosuppressive drug tacrolimus (Tac) has a narrow therapeutic window, is susceptible to drug-drug interactions (DDIs), and a potential gastrointestinal interaction with patiromer could elevate the risk of allograft rejection. We aimed to investigate the potential DDI between patiromer and Tac pharmacokinetics in KTx with hyperkalemia by sampling capillary blood using volumetric absorptive microsampling (VAMS).

Methods

Thirteen KTx recipients on Tac twice daily (BID) with plasma potassium levels of >4.6 mmol/L were included. Two 12 h Tac pharmacokinetic investigations were performed with and without 8.4 mg patiromer/d for 1 wk. Oral Tac dose remained unchanged and patiromer was administered 3 h after Tac dose. Tac sampling was self-conducted using VAMS after mastering the technique.

Results

Ten patients provided 2 evaluable pharmacokinetic profiles. The Tac area under the curve (AUC)0-12 ratio (AUCTac+patiromer/AUCTac) was 0.99 (90% confidence interval [CI], 0.86-1.14), and the Cmax ratio was 1.01 (90% CI, 0.86-1.19). Tac C0 and C12 fulfilled the bioequivalence criteria with a ratio of 0.98 (90% CI, 0.90-1.07) and 0.93 (90% CI, 0.83-1.04), respectively.

Conclusions

When administered 3 h after the Tac morning dose, patiromer has no clinically relevant impact on Tac pharmacokinetics. We demonstrate that VAMS is a well-suited sampling method to simplify the execution of DDI studies.

Factors influencing intrapatient variability of tacrolimus and its association with 1-year post-transplant outcomes in pediatric liver transplant recipients

Objective

This study aims to explore the factors influencing tacrolimus intrapatient variability (TAC-IPV) and its association with 1-year post-transplant outcomes in pediatric liver transplant recipients.

Methods

Clinical and biological data of pediatric patients after liver transplantation were collected. The patients were divided into high- and low-IPV groups according to the median TAC-IPV for statistical comparisons. Factors with p < 0.05 in univariate analysis were introduced into binomial logistic regression analysis. Correlation analysis was used to test the connections between the Tac-IPV and outcomes within 1 year after liver transplantation (LT), and Kaplan-Meier was used to draw the survival curves.

Results

A total of 116 children underwent 746 measurements of TAC trough concentrations. The median TAC-IPV was 32.31% (20.81%, 46.77%). Hematocrit (p = 0.017) and concomitant medications (p = 0.001) were identified as independent influencing factors for TAC-IPV. The incidence of transplant rejection (p = 0.008), CMV infection (p < 0.001), and hospital admission due to infection (p = 0.003) were significantly higher in the high-IPV group than in the low-IPV group. Kaplan-Meier survival analysis suggests that after considering the time factor, high IPV (IPV > 32.31%) was still significantly associated with transplant rejection (HR = 3.17 and p = 0.005) and CMV infection (HR = 2.3 and p < 0.001) within 1 year after LT.

Conclusion

The study highlights the significant variation in TAC-IPV among children post-liver transplantation, emphasizing the impact of hematocrit levels and concomitant medications on TAC-IPV. Elevated TAC-IPV is associated with increased risks of transplant rejection, CMV infection, and readmission due to infection in the first year after liver transplantation. Close monitoring of patients with high TAC-IPV is recommended to promptly detect adverse reactions and provide timely intervention and treatment.

Tacrolimus Dose Requirement in De Novo Adult Kidney Transplant Patients Treated With Adoport® Can Be Anticipated

All the factors potentially influencing tacrolimus dose requirement and combinations thereof have never been thoroughly investigated, precluding accurate prediction of tacrolimus starting dose. This prospective, non-interventional, multicenter study in de novo adult kidney transplant recipients over the first year after transplantation aimed to investigate the factors influencing tacrolimus dose-standardized trough blood concentration (C0/D) over the first week post-transplant (D4-D7, primary objective), D8-M3 and M3-M12 (secondary objectives). Statistical analysis employed mixed linear models with repeated measures. Eighteen sites enrolled 440 patients and followed them up for 9.5 ± 4.1 months. Age at baseline (p = 0.0144), end-stage renal disease (p = 0.0092), CYP3A phenotype (p < 0.0001), dyslipidemia at baseline (p = 0.0031), hematocrit (p = 0.0026), total bilirubin (p = 0.0261) and plasma creatinine (p = 0.0484) independently increased with log(C0/D) over D4-D7, explaining together 72.3% of the interindividual variability, and representing a robust model to estimate tacrolimus initial dose. Donor age and CYP3A phenotype were also influential over D8-M3 and M3-12, in addition to recipient age. Corticosteroids, diabetes at baseline, and ASAT yielded inconstant results between D8-M3 and M3-M12. We found no ethnicity effect when CYP3A phenotype was accounted for, and no food effect. Intra-individual variability over M3-M12 was moderate, and significantly lower in patients with chronic hepatic disorder (p = 0.0196) or cancer (p = 0.0132).

Model-Informed Precision Dosing of Tacrolimus: A Systematic Review of Population Pharmacokinetic Models and a Benchmark Study of Software Tools

BACKGROUND AND OBJECTIVE

Tacrolimus is an immunosuppressant commonly administered after solid organ transplantation. It is characterized by a narrow therapeutic window and high variability in exposure, demanding personalized dosing. In recent years, population pharmacokinetic models have been suggested to guide model-informed precision dosing of tacrolimus. We aimed to provide a comprehensive overview of population pharmacokinetic models and model-informed precision dosing software modules of tacrolimus in all solid organ transplant settings, including a simulation-based investigation of the impact of covariates on exposure and target attainment.

METHODS

We performed a systematic literature search to identify population pharmacokinetic models of tacrolimus in solid organ transplant recipients. We integrated selected population pharmacokinetic models into an interactive software tool that allows dosing simulations, Bayesian forecasting, and investigation of the impact of covariates on exposure and target attainment. We conducted a web survey amongst model-informed precision dosing software tool providers and benchmarked publicly available tools in terms of models, target populations, and clinical integration.

RESULTS

We identified 80 population pharmacokinetic models, including 44 one-compartment and 36 two-compartment models. The most frequently retained covariates on clearance and distribution parameters were cytochrome P450 3A5 polymorphisms and body weight, respectively. Our simulation tool, hosted at https://lpmx.shinyapps.io/tacrolimus/ , allows thorough investigation of the impact of covariates on exposure and target attainment. We identified 15 model-informed precision dosing software tool providers, of which ten offer a tacrolimus solution and nine completed the survey.

CONCLUSIONS

Our work provides a comprehensive overview of the landscape of available tacrolimus population pharmacokinetic models and model-informed precision dosing software modules. Our simulation tool allows an interactive thorough exploration of covariates on exposure and target attainment.

Optimizing tacrolimus dosing in Hispanic renal transplant patients: insights from real-world data

Aim

Tacrolimus, an immunosuppressant used to prevent organ rejection in renal transplant patients, exhibits high inter-patient variability, necessitating therapeutic drug monitoring. Early post-transplant tacrolimus exposure in Hispanics is understudied. Although genotypic information is linked to pharmacokinetic differences, its clinical application remains limited. This study aimed to use a real-world data-driven, pharmacokinetic model-based approach for tacrolimus in Hispanics to determine a suitable initial dose and design an optimal dose titration strategy by simulations to achieve plasma trough concentration target levels of 10-12 ng/mL at the earliest.

Methods

Sparse concentration-time data of tacrolimus were obtained from electronic medical records for self-identified Hispanic subjects following renal transplant. Rich pharmacokinetic literature data was leveraged to estimate structural pharmacokinetic model parameters, which were then fixed in the current analysis. Only apparent clearance was estimated with the sparse tacrolimus data and potential covariates were identified. Simulations of various starting doses and different dose titration strategies were then evaluated.

Results

The analysis included 121 renal transplant patients with 2,215 trough tacrolimus concentrations. A two-compartment transit absorption model with allometrically scaled body weight and time-varying hematocrit on apparent clearance adequately described the data. The estimated apparent clearance was 13.7 L/h for a typical patient weighing 70 kg and at 30% hematocrit, demonstrating a 40% decrease in clearance compared to other patient populations. Model based simulations indicated the best initial dose for the Hispanic population is 0.1 mg/kg/day. The proposed titration strategy, with three dose adjustments based on trough levels of tacrolimus, increased the proportion of patients within the target range (10-12 ng/mL) more than 2.5-fold and decreased the proportion of patients outside the therapeutic window by 50% after the first week of treatment.

Conclusion

Hispanic renal transplant population showed an estimated 40% decrease of apparent clearance in the typical patient compared to other populations with similar characteristics. The proposed dose adjustment attained the target range rapidly and safely. This study advocates for tailored tacrolimus dosing regimens based on population pharmacokinetics to optimize therapy in Hispanic renal transplant recipients.

Guiding the starting dose of the once-daily formulation of tacrolimus in "de novo" adult renal transplant patients: a population approach

Aims

The once-daily extended-release tacrolimus formulation (ER-Tac) has demonstrated similar efficacy and safety to the twice-daily immediate-release formulation (IR-Tac), but few population-based pharmacokinetic models have been developed in de novo kidney transplant patients to optimize doses. Therefore, this study aimed i) at developing a population pharmacokinetic model for ER-Tac in de novo adult kidney transplant patients ii) and identifying genetic factors and time-varying covariates predictive of pharmacokinetic variability to guide tacrolimus dosage during the early post-transplant period.

Methods

A total of 1,067 blood tacrolimus concentrations from 138 kidney transplant patients were analyzed. A total of 29 out of 138 patients were intensively sampled for 24 h on the day 5 post-transplantation; meanwhile, for the remaining patients, concentrations were collected on days 5, 10, and 15 after transplantation. Tacrolimus daily doses and genetic and demographic characteristics were retrieved from the medical files. Biochemistry time-varying covariates were obtained on different days over the pharmacokinetic (PK) study. A simultaneous PK analysis of all concentrations was carried out using the non-linear mixed-effects approach with NONMEM 7.5.

Results

A two-compartment model with linear elimination and delayed absorption best described the tacrolimus pharmacokinetics. Between-patient variability was associated with oral blood clearance (CL/F) and the central compartment distribution volume (Vc/F). Tacrolimus concentrations standardized to a hematocrit value of 45% significantly improved the model (p < 0.001). This method outperformed the standard covariate modeling of the hematocrit-blood clearance relationship. The effect of the CYP3A5 genotype was statistically (p < 0.001) and clinically significant on CL/F. The CL/F of patients who were CYP3A5*1 carriers was 51% higher than that of CYP3A5*1 non-carriers. Age also influenced CL/F variability (p < 0.001). Specifically, CL/F declined by 0.0562 units per each increased year from the value estimated in patients who were 60 years and younger.

Conclusion

The 36% between-patient variability in CL/F was explained by CYP3A5 genotype, age, and hematocrit. Hematocrit standardization to 45% explained the variability of tacrolimus whole-blood concentrations, and this was of utmost importance in order to better interpret whole-blood tacrolimus concentrations during therapeutic drug monitoring. The dose requirements of CYP3A5*/1 carriers in patients aged 60 years or younger would be highest, while CYP3A5*/1 non-carriers older than 60 years would require the lowest doses.

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.

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.

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.

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.