Combined Effect of Inter- and Intrapatient Variability in Tacrolimus Exposure on Graft Impairment Within a 3-Year Period Following Kidney Transplantation: A Single-Center Experience

Impact of IL-6 and IL-10 genotypes on tacrolimus dose requirements in kidney transplant recipients: Monte Carlo analysis

Introduction: IL-6 and IL-10 may affect the activity of cytochrome P450 (CYP) 3A enzymes involved in tacrolimus (Tac) metabolism. Moreover, the effect of IL-6 and IL-10 on Tac pharmacokinetics may differ with respect to the genetic variations in their genes. Aim: To examine the influence of IL-6 and IL-10 gene polymorphisms on Tac dose requirements and exposure over a 5-year period following kidney transplantation. Univariate and standard multivariate linear regression and Monte Carlo analysis were performed to investigate potential covariates influencing Tac dose-adjusted trough concentration (C0/D) in various post-transplantation periods. Materials & methods: IL-6 (-174G > C), IL-10 (-1082G > A, -819C > T and -592C > A) genotype, Tac daily dose, C0, C0/D and intrapatient variability data were collected from 113 patients. Results: Multivariate regression analysis and accompanied Monte Carlo simulation underscore the importance of considering IL-6 -174G > C and IL-10 -1082G > A gene polymorphisms, alongside Tac metabolic phenotype and post-transplantation period, when tailoring Tac dosage regimen. Conclusion: This study provides valuable insights regarding the individualized adjustment of Tac treatment in various post-transplantation periods.

Tacrolimus intra-patient variability measures and its associations with allograft clinical outcomes in kidney transplantation

AIMS

Tacrolimus (Tac) is commonly prescribed in solid organ transplantation to prevent immune-mediated damage to the graft. However, its pharmacokinetics show substantial variability between and within patients. Intra-patient variability of tacrolimus (Tac-IPV) has emerged as a novel marker to predict transplant outcomes. Numerous studies report varying associations between Tac-IPV and clinical outcomes, with Tac-IPV measures showing wide discrepancies among these studies. This inconsistency could be a significant factor that influences the various outcomes reported in different studies. Our review comprehensively assesses the relationship between various Tac-IPV measures and their associations with clinical outcomes in transplant patients.

METHODS

A comprehensive literature search was conducted using the PubMed and Embase databases, covering the period from 2004 to March 31, 2023. The search focused on studies that examined the relationship between Tac-IPV and clinical outcomes in kidney transplantation (KT). The inclusion criteria were specific to studies addressing Tac-IPV, including measures such as standard deviation (SD), coefficient of variation (CV), time-weighted coefficient of variability (CV), mean absolute deviation (MAD), and Tac variability score (TVS). Clinical outcomes included the development of de novo donor-specific antibodies (dnDSA), rejection episodes, graft loss, and graft failure.

RESULTS

Among the 33 studies that met the inclusion criteria, a notable proportion presented conflicting findings in their assessment of various Tac-IPV measures regarding dnDSA, rejection episodes, graft loss, and graft failure.

CONCLUSIONS

Most studies have identified a correlation between high Tac-IPV and poor clinical outcomes; however, this relationship is multifactorial. Influencing factors include the metabolic status of KT patients, the timing of Tac-IPV calculations, and the criteria for defining high and low Tac-IPV thresholds, including the size and selection method. CV, MAD, and TWCV are the metrics that are most frequently used to determine Tac-IPV. Additionally, most of the methods for establishing Tac-IPV thresholds typically employ receiver operating characteristic (ROC) curves and median values. It is also notable that studies examining the clinical significance of Tac-IPV often include tacrolimus levels measured six months after kidney transplantation.

Exploratory associations of tacrolimus exposure and clinical outcomes after lung transplantation: A retrospective, single center experience

PURPOSE

This study aimed to investigate the potential impact of tacrolimus (TAC) exposure on clinical outcomes after lung transplantation.

METHODS

This retrospective observational study enrolled a total of 228 lung transplant recipients. TAC trough levels (C0) were collected for 3 intervals: 0-3 months, 3-12 months, and 12-24 months. The intra-patient variability (IPV) was calculated using coefficient of variation. Genotyping of CYP3A5*3 (rs776746) was performed. Patients were further divided into groups based on the C0 cut-off value of 8 ng/mL and IPV cut-off value of 30%. Cox proportional hazards regression models were used to explore the potential impact of C0 and IPV on outcomes of interests, including de-novo donor-specific antibodies (dnDSA), chronic lung allograft dysfunction (CLAD) and mortality.

RESULTS

The influence of CYP3A5*3 polymorphism was only significant for C0 and IPV during the first 3 months. Low C0 (< 8 ng/mL) at 3-12 months increased the risk of dnDSA (hazard ratio [HR] 2.696, 95% confidence interval [CI] 1.046-6.953) and mortality (HR 2.531, 95% CI 1.368-4.685), while High IPV (≥ 30%) during this period was associated with an increased risk of mortality (HR 2.543, 95% CI 1.336-4.839). Patients with Low C0/High IPV combination had significantly higher risks for dnDSA (HR 4.381, 95% CI 1.279-15.008) and survival (HR 6.179, 95% CI 2.598-14.698), surpassing the predictive power provided by C0 or IPV alone.

CONCLUSION

A combination of Low C0/High IPV might be considered in categorizing patients towards risk of adverse clinical outcomes following lung transplantation.

Impact of tacrolimus intra-patient variability in adverse outcomes after organ transplantation

Tacrolimus (Tac) is currently the most common calcineurin-inhibitor employed in solid organ transplantation. High intra-patient variability (IPV) of Tac (Tac IPV) has been associated with an increased risk of immune-mediated rejection and poor outcomes after kidney transplantation. Few data are available concerning the impact of high Tac IPV in non-kidney transplants. However, even in kidney transplantation, there is still a controversy whether high Tac IPV is indeed detrimental in respect to graft and/or patient survival. This may be due to different methods employed to evaluate IPV and distinct time frames adopted to assess graft and patient survival in those reports published up to now in the literature. Little is also known about the influence of high Tac IPV in the development of other untoward adverse events, update of the current knowledge regarding the impact of Tac IPV in different outcomes following kidney, liver, heart, lung, and pancreas tran splantation to better evaluate its use in clinical practice.

Combined impact of the inter and intra-patient variability of tacrolimus blood level on allograft outcomes in kidney transplantation

INTRODUCTION

Tacrolimus (TAC) has been widely used as an immunosuppressant after kidney transplantation (KT); however, the combined effects of intra-patient variability (IPV) and inter-patient variability of TAC-trough level (C0) in blood remain controversial. This study aimed to determine the combined impact of TAC-IPV and TAC inter-patient variability on allograft outcomes of KT.

METHODS

In total, 1,080 immunologically low-risk patients who were not sensitized to donor human leukocyte antigen (HLA) were enrolled. TAC-IPV was calculated using the time-weighted coefficient variation (TWCV) of TAC-C0, and values > 30% were classified as high IPV. Concentration-to-dose ratio (CDR) was used for calculating TAC inter-patient variability, and CDR < 1.05 ng•mg/mL was classified as rapid metabolizers (RM). TWCV was calculated based on TAC-C0 up to 1 year after KT, and CDR was calculated based on TAC-C0 up to 3 months after KT. Patients were classified into four groups according to TWCV and CDR: low IPV/non-rapid metabolizer (NRM), high IPV/NRM, low IPV/RM, and high IPV/RM. Subgroup analysis was performed for pre-transplant panel reactive antibody (PRA)-positive and -negative patients (presence or absence of non-donor-specific HLA-antibodies). Allograft outcomes, including deathcensored graft loss (DCGL) and biopsy-proven allograft rejection (BPAR), were compared.

RESULTS

The incidences of DCGL, BPAR, and overall graft loss were the highest in the high-IPV/RM group. In addition, a high IPV/RM was identified as an independent risk factor for DCGL. The hazard ratio of high IPV/RM for DCGL and the incidence of active antibody-mediated rejection were considerably increased in the PRA-positive subgroup.

DISCUSSION

High IPV combined with RM (inter-patient variability) was closely related to adverse allograft outcomes, and hence, more attention must be given to pre-transplant PRA-positive 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.

Effect of the Interrelation between CYP3A5 Genotype, Concentration/Dose Ratio and Intrapatient Variability of Tacrolimus on Kidney Graft Function: Monte Carlo Simulation Approach

Background: Tacrolimus (Tac) is characterized by large between- and within-patient (IPV) variability in pharmacokinetics and exposure. Aim: This study aimed to assess and validate the effect of Tac IPV and trough concentration-to-dose ratio (C₀/D) over 6–12 months on reduced estimated glomerular filtration rate (eGFR) values in the late period after kidney transplantation (Tx), applying Monte Carlo (MC) simulation. Methods: The previously published linear regression was the basis for MC simulation, performed to determine how variations in significant predictors affect the distribution of eGFR from 13 to 36 months post-transplantation. The input C₀/D values were derived from CYP3A5 genotype subgroups. Results: Patients characterized by high Tac IPV and low mean C₀/D over 6–12 months could have been at greater risk of lower eGFR values in a three-year period following Tx compared to the other patient groups. This effect was more pronounced in patients with a lower eGFR at the 6th month and a history of acute rejection. The proven contribution of CYP3A5 expresser genotype to low C₀/D values may suggest its indirect effect on long-term graft function. Conclusion: The findings indicate that simultaneous assessment of Tac IPV, C₀/D, and CYP3A5 genotype may identify patients at risk of deterioration of graft function in the long-term post-transplantation period.