Predictive Modeling of Tacrolimus Dose Requirement Based on High-Throughput Genetic Screening

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.

Insights into the Pharmacogenetics of Tacrolimus Pharmacokinetics and Pharmacodynamics

The influence of pharmacogenetics in tacrolimus pharmacokinetics and pharmacodynamics needs further investigation, considering its potential in assisting clinicians to predict the optimal starting dosage and the need for a personalized adjustment of the dose, as well as to identify patients at a high risk of rejection, drug-related adverse effects, or poor outcomes. In the past decade, new pharmacokinetic strategies have been developed to improve personalized tacrolimus treatment. Several studies have shown that patients with tacrolimus doses C0/D < 1 ng/mL/mg may demonstrate a greater incidence of drug-related adverse events and infections. In addition, C0 tacrolimus intrapatient variability (IPV) has been identified as a potential biomarker to predict poor outcomes related to drug over- and under-exposure. With regard to tacrolimus pharmacodynamics, inconsistent genotype-phenotype relationships have been identified. The aim of this review is to provide a concise summary of currently available data regarding the influence of pharmacogenetics on the clinical outcome of patients with high intrapatient variability and/or a fast metabolizer phenotype. Moreover, the role of membrane transporters in the interindividual variability of responses to tacrolimus is critically discussed from a transporter scientist’s perspective. Indeed, the relationship between transporter polymorphisms and intracellular tacrolimus concentrations will help to elucidate the interplay between the biological mechanisms underlying genetic variations impacting drug concentrations and clinical effects.

The influence of recipient SLCO1B1 rs2291075 polymorphism on tacrolimus dose-corrected trough concentration in the early period after liver transplantation

Purpose

To explore the relationship between rs2291075 polymorphism in SLCO1B1 gene, which encodes an influx transmembrane protein transporter, and tacrolimus dose–corrected trough concentration (C/D, ng ml⁻¹ mg⁻¹ kg⁻¹) in the early period after liver transplantation.

Methods

CYP3A5 rs776746 and SLCO1B1 rs2291075 polymorphisms of 210 liver transplantation patients and their corresponding donor livers were assessed by PCR amplification and DNA sequencing. The influence of gene polymorphisms on C/D values of tacrolimus was analyzed. The early postoperative period after liver transplantation was divided into the convalescence phase (1–14 days) and stationary phase (15–28 days) according to the change of liver function and tacrolimus C/D values.

Results

The combined analysis of donor and recipient CYP3A5 rs776746 could distinguish the metabolic phenotype of tacrolimus into three groups: fast elimination (FE), intermediate elimination (IE), and slow elimination (SE), which was entitled the FIS classification system. Tacrolimus C/D ratios of recipient SLCO1B1 rs2291075 CT and TT carriers were very close and were significantly lower than those of recipient SLCO1B1 rs2291075 CC genotype carriers in convalescence phase (p = 0.0195) and in stationary phase (p = 0.0152). There were no statistically significant differences between tacrolimus C/D ratios of patients carried with SLCO1B1 rs2291075 CT, TT genotype donors, and those carried with SLCO1B1 rs2291075 CC genotype donors. A model consisting of tacrolimus daily dose, total bilirubin, FIS classification, and recipient SLCO1B1 rs2291075 could predict tacrolimus C/D ratios in the convalescence phase by multivariate analysis. However, recipient SLCO1B1 rs2291075 genotype failed to enter forecast model for C/D ratios in stationary phase. Recipient SLCO1B1 rs2291075 genotype had significant effect on tacrolimus C/D ratios in convalescence phase (p = 0.0300) and stationary phase (p = 0.0400) in subgroup, which excluded the interference come from donor and recipient CYP3A5 rs776746.

Conclusion

SLCO1B1 rs2291075 could be a novel genetic locus associated with tacrolimus metabolism. The combined analysis of donor and recipient CYP3A5 rs776746, recipient SLCO1B1 rs2291075 genotypes, could be helpful to guide the personalized administration of tacrolimus in early period after liver transplantation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00228-020-03058-w.

A Novel, Dose-Adjusted Tacrolimus Trough-Concentration Model for Predicting and Estimating Variance After Kidney Transplantation

Background and Objective

Given that a high intrapatient variability (IPV) of tacrolimus whole blood concentration increases the risk for a poor kidney transplant outcome, some experts advocate routine IPV monitoring for detection of high-risk patients. However, attempts to estimate the variance of tacrolimus trough concentrations (TTC) are limited by the need for patients to receive a fixed dose over time and/or the use of linear statistical models. A goal of this study is to overcome the current limitations through the novel application of statistical methodology generalizing the relationship between TTC and dose through the use of nonparametric functional regression modeling.

Methods

With TTC as a response and dose as a covariate, the model employs an unknown bivariate function, allowing for the potentially complex, nonlinear relationship between the two parameters. A dose-adjusted variance of TTC is then derived based on standard functional principal component analysis (FPCA). To assess the model, it was compared against an FPCA-based model and linear mixed-effects models using prediction error, bias, and coverage probabilities for simulated data as well as phase III data from the Astellas new drug application studies for extended-release tacrolimus.

Results

Our numerical investigation indicates that the new model better predicts dose-adjusted TTCs compared with the prediction of linear mixed effects models. Estimated coverage probabilities also indicate that the new model accurately accounts for the variance of TTC during the periods of large fluctuation in dose, whereas the linear mixed effects model consistently underestimates the coverage probabilities because of the inaccurate characterization of TTC fluctuation.

Conclusion

This is the first known application of a functional regression model to assess complex relationships between TTC and dose in a real clinical setting. This new method has applicability in future clinical trials including real-world data sets due to flexibility of the nonparametric modeling approach.

Electronic supplementary material

The online version of this article (10.1007/s40268-019-0271-2) contains supplementary material, which is available to authorized users.

Tacrolimus diffusion across the peripheral mononuclear blood cell membrane: impact of drug transporters

Measuring tacrolimus (TAC) concentration in peripheral blood mononuclear cells (PBMCs) could better reflect the drug effect on its target (calcineurin (CaN) in lymphocytes) than whole blood concentrations. Mechanisms influencing TAC diffusion into PBMC are not well characterized. This work aimed at describing, ex vivo, TAC diffusion kinetics into PBMC and investigating the contribution of membrane transporters to regulate TAC intracellular concentration as well as the impact on CaN activity. PBMCs were incubated with TAC for 5 min to 4 h and under several experimental conditions: 37 °C (physiological conditions), 4 °C (inhibition of influx and efflux active transport), 37 °C + transporter inhibitors (verapamil, carvedilol, and probenecid and bromosulfophthalein, respectively, inhibitors of P-gp, OAT, and OATP). TAC concentration and CaN activity were measured in PBMC using liquid chromatography coupled with mass spectrometry. TAC intra-PBMC concentration was maximal after 1 h of incubation. Mean TAC PMBC concentrations were significantly lower in samples incubated at 4 °C compared to the 37 °C groups. Addition of verapamil slightly increased TAC accumulation in PBMC while other inhibitors had no effect. A significant correlation was found between TAC intra-PBMC concentration and the level of inhibition of CaN. Using an ex vivo cellular model, these results suggest that P-gp is involved in the drug efflux from PBMC while influx active transporters likely to regulate TAC intra-PBMC disposition remain to be identified. TAC concentration in PBMC is correlated with its pharmacodynamic effect. Then, TAC intra-PBMC concentration appears to be a promising biomarker to refine TAC therapeutic drug monitoring.

Attempted validation of 44 reported SNPs associated with tacrolimus troughs in a cohort of kidney allograft recipients

AIM

Multiple genetic variants have been associated with variation in tacrolimus (TAC) trough concentrations. Unfortunately, additional studies do not confirm these associations, leading one to question if a reported association is accurate and reliable. We attempted to validate 44 published variants associated with TAC trough concentrations.

MATERIALS & METHODS

Genotypes of the variants in our cohort of 1923 kidney allograft recipients were associated with TAC trough concentrations.

RESULTS

Only variants in CYP3A4 and CYP3A5 were significantly associated with variation in TAC trough concentrations in our validation.

CONCLUSION

There is no evidence that common variants outside the CYP3A4 and CYP3A5 loci are associated with variation in TAC trough concentrations. In the future rare variants may be important and identified using DNA sequencing.

Genome-wide association study identifies the common variants in CYP3A4 and CYP3A5 responsible for variation in tacrolimus trough concentration in Caucasian kidney transplant recipients

The immunosuppressant tacrolimus (TAC) is metabolized by both cytochrome P450 3A4 (CYP3A4) and CYP3A5 enzymes. It is common for European Americans (EA) to carry two CYP3A5 loss-of-function (LoF) variants that profoundly reduces TAC metabolism. Despite having two LoF alleles, there is still considerable variability in TAC troughs and identifying additional variants in genes outside of the CYP3A5 gene could provide insight into this variability. We analyzed TAC trough concentrations in 1345 adult EA recipients with two CYP3A5 LoF alleles in a genome-wide association study. Only CYP3A4*22 was identified and no additional variants were genome-wide significant. Additional high allele frequency genetic variants with strong genetic effects associated with TAC trough variability are unlikely to be associated with TAC variation in the EA population. These data suggest that low allele frequency variants, identified by DNA sequencing, should be evaluated and may identify additional variants that contribute to TAC pharmacokinetic variability.

SLC28A3 rs7853758 as a new biomarker of tacrolimus elimination and new-onset hypertension in Chinese liver transplantation patients

AIM

The effect of SLC28A3 on tacrolimus disposition and new-onset hypertension (NOHP) after liver transplantation (LT) remains unclear. Methodology & results: A total of 169 patients in two cohorts from the China Liver Transplant Registry database were included. Rs7853758 in recipients'SLC28A3 could predict tacrolimus pharmacokinetics in two sets. The model of donors' CYP3A5 rs776746 and recipients' CYP3A4 rs2242480 could predict tacrolimus metabolism at week 1 and the model of donors' CYP3A5 rs776746, recipients' CYP3A4 rs2242480, recipients' SLC28A3 rs7853758 and hemoglobin could predict tacrolimus disposition at weeks 2, 3 and 4. Besides, recipients' SLC28A3 rs7853758 was a new risk factor of NOHP after LT.

CONCLUSION

Rs7853758 in recipients' SLC28A3 has a correlation with tacrolimus pharmacokinetics and the risk of NOHP in Chinese LT patients.