Effects of diltiazem on pharmacokinetics of tacrolimus in relation to CYP3A5 genotype status in renal recipients: from retrospective to prospective

Comparison and development of machine learning for thalidomide-induced peripheral neuropathy prediction of refractory Crohn’s disease in Chinese population

BACKGROUND

Thalidomide is an effective treatment for refractory Crohn’s disease (CD). However, thalidomide-induced peripheral neuropathy (TiPN), which has a large individual variation, is a major cause of treatment failure. TiPN is rarely predictable and recognized, especially in CD. It is necessary to develop a risk model to predict TiPN occurrence.

AIM

To develop and compare a predictive model of TiPN using machine learning based on comprehensive clinical and genetic variables.

METHODS

A retrospective cohort of 164 CD patients from January 2016 to June 2022 was used to establish the model. The National Cancer Institute Common Toxicity Criteria Sensory Scale (version 4.0) was used to assess TiPN. With 18 clinical features and 150 genetic variables, five predictive models were established and evaluated by the confusion matrix receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), specificity, sensitivity (recall rate), precision, accuracy, and F1 score.

RESULTS

The top-ranking five risk variables associated with TiPN were interleukin-12 rs1353248 [P = 0.0004, odds ratio (OR): 8.983, 95% confidence interval (CI): 2.497-30.90], dose (mg/d, P = 0.002), brain-derived neurotrophic factor (BDNF) rs2030324 (P = 0.001, OR: 3.164, 95%CI: 1.561-6.434), BDNF rs6265 (P = 0.001, OR: 3.150, 95%CI: 1.546-6.073) and BDNF rs11030104 (P = 0.001, OR: 3.091, 95%CI: 1.525-5.960). In the training set, gradient boosting decision tree (GBDT), extremely random trees (ET), random forest, logistic regression and extreme gradient boosting (XGBoost) obtained AUROC values > 0.90 and AUPRC > 0.87. Among these models, XGBoost and GBDT obtained the first two highest AUROC (0.90 and 1), AUPRC (0.98 and 1), accuracy (0.96 and 0.98), precision (0.90 and 0.95), F1 score (0.95 and 0.98), specificity (0.94 and 0.97), and sensitivity (1). In the validation set, XGBoost algorithm exhibited the best predictive performance with the highest specificity (0.857), accuracy (0.818), AUPRC (0.86) and AUROC (0.89). ET and GBDT obtained the highest sensitivity (1) and F1 score (0.8). Overall, compared with other state-of-the-art classifiers such as ET, GBDT and RF, XGBoost algorithm not only showed a more stable performance, but also yielded higher ROC-AUC and PRC-AUC scores, demonstrating its high accuracy in prediction of TiPN occurrence.

CONCLUSION

The powerful XGBoost algorithm accurately predicts TiPN using 18 clinical features and 14 genetic variables. With the ability to identify high-risk patients using single nucleotide polymorphisms, it offers a feasible option for improving thalidomide efficacy in CD patients.

Prediction of tacrolimus and Wuzhi tablet pharmacokinetic interaction magnitude in renal transplant recipients

AIM

Wuzhi tablets are a dose-sparing agent for tacrolimus (TAC) in China and increase the bioavailability of TAC. The current study aimed to evaluate the pharmacokinetic interaction magnitude of Wuzhi and TAC and explore the potential determinants of this interaction.

METHODS

This study performed a retrospective, self-controlled study of 138 renal transplant recipients who were co-administered TAC and Wuzhi. The trough concentration (C0) of TAC at baseline and 3, 7, 14 and 21 days after Wuzhi co-therapy initiation was measured, and the CYP3A5 polymorphism was genotyped. The corresponding clinical factors were recorded. The ratio of dose-adjusted C0 (C0/D) post- and pre-combination therapy (ΔC0/D) indicates the interaction magnitude. Univariate and multivariate analyses were used to identify determinants and establish the prediction model.

RESULTS

ΔC0/D reached a steady state within 14 days. The geometrical mean ΔC0/D was 2.91 (range 1.02-9.49, IQR 2.13-3.80). ΔC0/D was blunted in CYP3A5 expressers (estimated effect: -39.8%, P = .001) and affected by hematocrit (Hct) (+24.0% per 10% increase, P = .005) and baseline C0/D (-31.9% per 1 ng·ml^-1 ·mg^-1 increase, P < .001). The prediction model was ΔC0/D = .319baseline C0/D × 1.398CYP3A5 (expressers = 0/non-expressers = 1) × 1.024Hct × 1.744, and it explained 28.1% of the variability.

CONCLUSION

Our study is the first attempt to date to give an assessment of the magnitude of pharmacokinetic interaction between TAC and Wuzhi in a cohort of renal transplant recipients, and CYP3A5 genotypes, baseline C0/D and Hct were identified as determinants of this interaction.

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.

The effect of the very low dosage diltiazem on tacrolimus exposure very early after kidney transplantation: a randomized controlled trial

The objective of this study was to assess the effect of the very low dosage of diltiazem on tacrolimus exposure during the first week post-kidney transplantation, among cytochrome P450 (CYP) 3A5 expressers who did not receive diltiazem (EXplb), CYP3A5 expressers who received the very low dose diltiazem (EXdtz), CYP3A5 nonexpressers who did not receive diltiazem (NEplb), and CYP3A5 nonexpressers who received the very low dose diltiazem (NEdtz). Forty kidney recipients who receive tacrolimus-based immunosuppressive regimen were randomly assigned, with stratification on the CYP3A5 genotypes, to receive either diltiazem 30 mg every 12 h or a matched placebo. The observed median dose-adjusted area under the 12-h curve of tacrolimus concentration (AUC/D) at day 7 post-transplantation was lowest in the EXplb group followed by EXdtz, NEplb, and NEdtz at 34.9, 43.6, 49.4, and 71.1 ng*h/mL per mg, respectively. A Kruskal–Wallis test showed a significant difference in the mean ranks of AUC/D among groups. Significant differences between EXplb and NEplb, and between EXplb and NEdtz were demonstrated, whereas no sufficient evidence of significant differences was detected between the other pairs. In conclusion, coadministration of diltiazem 30 mg twice daily may be advantageous for increasing tacrolimus exposure early after kidney transplantation among CYP3A5 expressers.

Early Prediction of Tacrolimus-Induced Tubular Toxicity in Pediatric Refractory Nephrotic Syndrome Using Machine Learning

Background and Aims: Tacrolimus(TAC)-induced nephrotoxicity, which has a large individual variation, may lead to treatment failure or even the end-stage renal disease. However, there is still a lack of effective models for the early prediction of TAC-induced nephrotoxicity, especially in nephrotic syndrome(NS). We aimed to develop and validate a predictive model of TAC-induced tubular toxicity in children with NS using machine learning based on comprehensive clinical and genetic variables. Materials and Methods: A retrospective cohort of 218 children with NS admitted between June 2013 and December 2018 was used to establish the models, and 11 children were prospectively enrolled for external validation. We screened 47 clinical features and 244 genetic variables. The changes in urine N- acetyl- β-D- glucosaminidase(NAG) levels before and after administration was used as an indicator of renal tubular toxicity. Results: Five machine learning algorithms, including extreme gradient boosting (XGBoost), gradient boosting decision tree (GBDT), extremely random trees (ET), random forest (RF), and logistic regression (LR) were used for model generation and validation. Four genetic variables, including TRPC6 rs3824934_GG, HSD11B1 rs846910_AG, MAP2K6 rs17823202_GG, and SCARB2 rs6823680_CC were incorporated into the final model. The XGBoost model has the best performance: sensitivity 75%, specificity 77.8%, accuracy 77.3%, and AUC 78.9%. Conclusion: A pre-administration model with good performance for predicting TAC-induced nephrotoxicity in NS was developed and validated using machine learning based on genetic factors. Physicians can estimate the possibility of nephrotoxicity in NS patients using this simple and accurate model to optimize treatment regimen before administration or to intervene in time after administration to avoid kidney damage.

Precision Dosing for Tacrolimus Using Genotypes and Clinical Factors in Kidney Transplant Recipients of European Ancestry

Genetic variation in the CYP3A4 and CYP3A5 (CYP3A4/5) genes, which encode the key enzymes in tacrolimus metabolism, is associated with tacrolimus clearance and dose requirements. Tacrolimus has a narrow therapeutic index with high intra- and intersubject variability, in part because of genetic variation. High tacrolimus clearance and low trough concentration are associated with a greater risk for rejection, whereas high troughs are associated with calcineurin-induced toxicity. The objective of this study was to develop a model of tacrolimus clearance with a dosing equation accounting for genotypes and clinical factors in adult kidney transplant recipients of European ancestry that could preemptively guide dosing. Recipients receiving immediate-release tacrolimus for maintenance immunosuppression from 2 multicenter studies were included. Participants in the GEN03 study were used for tacrolimus model development (n = 608 recipients) and was validated by prediction performance in the DeKAF Genomics study (n = 1361 recipients). Nonlinear mixed-effects modeling was used to develop the apparent oral tacrolimus clearance (CL/F) model. CYP3A4/5 genotypes and clinical covariates were tested for their influence on CL/F. The predictive performance of the model was determined by assessing the bias (median prediction error [ME] and median percentage error [MPE]) and the precision (root median squared error [RMSE]) of the model. CYP3A5*3, CYP3A4*22, corticosteroids, calcium channel blocker and antiviral drug use, age, and diabetes significantly contributed to the interindividual variability of oral tacrolimus apparent clearance. The bias (ME, MPE) and precision (RMSE) of the final model was good, 0.49 ng/mL, 6.5%, and 3.09 ng/mL, respectively. Prospective testing of this equation is warranted.

Dose tailoring of tacrolimus based on a non-linear pharmacokinetic model in children with refractory nephrotic syndrome

The population pharmacokinetics (PPK) of tacrolimus (TAC) in children with refractory nephrotic syndrome (RNS) have not been well-characterized. This study aimed to investigate the significant factors affecting the TAC PPK characteristics of children with RNS and to optimize the dosing regimen. A total of 494 concentrations from 108 children were obtained from routine therapeutic drug monitoring between 2016 and 2018. Information regarding the demographic features, laboratory test results, genetic polymorphisms of CYP3A5 (rs776746) and co-therapy medications were collected. PPK analysis was performed using the nonlinear mixed-effects modelling (NONMEM) software and two modelling strategies (the linear one-compartment model and nonlinear Michaelis-Menten model) were evaluated and compared. CYP3A5 genotype, weight, daily dose of TAC and daily dose of diltiazem were retained in the final linear model. The absorption rate constant (Ka) was set at 4.48 h^-1 in the linear model, and the apparent clearance (CL/F) and volume of distribution (V/F) in the final linear model were 14.2 L/h and 172 L, respectively. CYP3A5 genotype, weight and daily dose of diltiazem were the significant factors retained in the final nonlinear model. The maximal dose rate (V_max) and the average steady-state concentration at half-Vmax (K_m) in the final nonlinear model were 2.15 mg/day and 0.845 ng/ml, respectively. The nonlinear model described the pharmacokinetic data of TAC better than the linear model in children with RNS. A dosing regimen was proposed based on weight, CYP3A5 genotype and daily dose of diltiazem according to the final nonlinear PK model, which may facilitate individualized drug therapy with TAC.

Predictors of tacrolimus pharmacokinetic variability: current evidences and future perspectives

INTRODUCTION

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

AREAS COVERED

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

EXPERT OPINION

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

Population pharmacokinetics and dosing regimen optimization of tacrolimus in Chinese lung transplant recipients

We aimed to (i) develop a population pharmacokinetic model of tacrolimus in Chinese lung transplant recipients and (ii) propose model-based dosing regimens for individualized treatment. We obtained 807 tacrolimus steady-state whole blood concentrations from 52 lung transplant patients and genotyped CYP3A5*3. Population pharmacokinetic analysis was performed using nonlinear mixed-effects modeling. Monte Carlo simulations were employed to determine the initial dosing regimens. Tacrolimus pharmacokinetics was described by a one-compartment model with first-order absorption and elimination processes. In CYP3A5*3/*3 70-kg patients with 30% hematocrit and voriconazole-free therapy, the mean estimated apparent clearance was 13.1 l h^-1 with 20.1% between-subject variability, which was lower than that in Caucasian lung transplant patients (17.5-36.5 l h^-1). Hematocrit, postoperative days, tacrolimus daily dose, voriconazole concomitant therapy, and CYP3A5*3 genotype were identified as significant covariates for tacrolimus clearance. To achieve target trough concentration (10-15 ng ml^-1) on the 8th day post-transplant, a higher initial dosage than the current regimen of 0.04 mg kg^-1 every 12 h is recommended for CYP3A5*1/*3 patients without voriconazole concomitant therapy. Given the nonlinear kinetics of tacrolimus and large variability, population pharmacokinetic model should be combined with therapeutic drug monitoring to optimize individualized therapy.

Diltiazem on tacrolimus exposure and dose sparing in Chinese pediatric primary nephrotic syndrome: impact of CYP3A4, CYP3A5, ABCB1, and SLCO1B3 polymorphisms

PURPOSE

To evaluate the role of diltiazem on tacrolimus sparing in pediatric primary nephrotic syndrome (PNS) and its relation to CYP3A4, CYP3A5, ABCB1, and SLCO1B3 polymorphisms.

METHODS

The PNS children treated with tacrolimus and with steady-state trough concentration (C₀) were retrospectively collected. The impacts of diltiazem on tacrolimus dose-adjusted C₀ (C₀/D), target concentration achievement, and required dose were evaluated. Meanwhile, the relationship between the polymorphisms (including CYP3A4*1G, CYP3A5*3, ABCB1-C3435T, and SCLO1B3) and dose-sparing effect were investigated.

RESULTS

A total of 71 children with 535 concentrations, including 16 children with concomitant diltiazem, were involved. Significantly increased C₀/D (94.0 vs 83.8 ng/mL per mg/kg, p = 0.038) and lower required daily dose of tacrolimus (0.056 vs 0.064 mg/kg, p = 0.003) were observed in patients co-administered with diltiazem. Subpopulation carrying CYP3A4*1G, CYP3A5*1, ABCB1-3435TT, or SLCO1B3-699AA was presented with enhanced increment in tacrolimus C₀/D by 38.8-102.9%.

CONCLUSION

Moderate effect of diltiazem on tacrolimus sparing, which might relate to the polymorphisms of CYP3A4, CYP3A5, ABCB1, and SLCO1B3, was documented.

Kidney podocyte-associated gene polymorphisms affect tacrolimus concentration in pediatric patients with refractory nephrotic syndrome

Few studies have investigated the correlation between pharmacogenomics and tacrolimus pharmacokinetics in patients with nephrotic syndrome (NS). This study evaluated the influences of genetic polymorphisms of metabolic enzymes, transporters, and podocyte-associated proteins on tacrolimus concentration in Chinese pediatric patients with refractory NS. A total of 167 pediatric patients with refractory NS were included from July 2013 to December 2017. Age of onset was restricted to <14 years of age. Dose-adjusted tacrolimus trough concentration (C₀/D) on the third month was calculated, and 20 single-nucleotide polymorphisms in sixteen genes were genotyped. Age was correlated with tacrolimus C₀/D (p = 0.006, r = 0.213). Tacrolimus C₀/D was higher in CYP3A5 nonexpressers than in CYP3A5 expressers (p = 0.003). ACTN4 rs62121818, MYH9 rs2239781, CYP3A5*3, and age explained 20.5% interindividual variability of tacrolimus concentration in the total cohort. In CYP3A5 nonexpressers, ACTN4 rs62121818 and MYH9 rs2239781 together explained 14.6% variation of tacrolimus C₀/D. MYH9 rs2239781, LAMB2 rs62119873 and age together explained 22.3% variability of tacrolimus level in CYP3A5 expressers. CYP3A5*3 was still an important factor affecting tacrolimus concentration in patients with NS. Podocyte-associated gene polymorphisms, especially ACTN4 rs62121818 and MYH9 rs2239781, were the other most important biomarkers for tacrolimus whole blood levels. Genotyping of CYP3A5, ACTN4, and MYH9 polymorphisms may be helpful for better guiding tacrolimus dosing in pediatric patients with refractory NS.

Atrial Flutter and Fibrillation Following Lung Transplantation: Incidence, Associations and a Suggested Therapeutic Algorithm

BACKGROUND

Atrial arrhythmias are relatively common following lung transplantation and confer considerable perioperative risk, specifically haemodynamic instability, pulmonary congestion, dyspnoea, and can mask other post-transplant complications such as infection or acute rejection. However, for most patients, arrhythmias are limited to the short-term perioperative period.

METHODS

We present a retrospective case-control analysis of 200 lung transplant recipients and using multivariate regression analysis, document the present incidence, risk factors, and outcomes between the two groups.

RESULTS

Twenty-five per cent (25%) of lung transplantation patients developed atrial flutter or fibrillation, most frequently at day 5-7 post lung transplantation, and more commonly present in older recipients and those with underlying chronic obstructive pulmonary disease (COPD), but not in those with previously noted structural heart disease, or in those undergoing single rather than double lung transplants. Atrial arrhythmias were associated with increased intensive care unit and overall length of stay, but were not associated with increased risk of in-hospital stroke, or mortality. Based on our experience, we propose a suggested management algorithm for pharmacological and mechanical rate/rhythm control strategies, for anticoagulation, and discuss the appropriate duration of treatment.

CONCLUSIONS

Atrial arrhythmias are relatively common post lung transplantation. Carefully managed, the associated risk of perioperative morbidity and mortality can be mitigated. Further prospective studies are required to validate these strategies.

Conversion from Twice-Daily Prograf® to Once-Daily Advagraf® in Multi-ethnic Asian Adult Renal Transplant Recipients With or Without Concomitant Use of Diltiazem: Impact of CYP3A5 and MDR1 Genetic Polymorphisms on Tacrolimus Exposure

BACKGROUND AND OBJECTIVES

Tacrolimus is the mainstay of immunosuppression in renal transplantation. Given that once-daily administration improves patient compliance, 1:1 dose conversion from twice-daily Prograf^® to once-daily Advagraf^® is recommended. Although cytochrome P450 (CYP) 3A5 and multi-drug resistance 1 (MDR1) polymorphisms influence tacrolimus concentrations, it is unknown if these impact on conversion. This study investigated the change in the pharmacokinetics of tacrolimus after conversion from Prograf^® to Advagraf^® and examined the impact of CYP3A5 and MDR1 C3435T polymorphisms on those pharmacokinetics.

METHODS

A prospective open-label pharmacokinetic study of 1:1 conversion from Prograf^® to Advagraf^® with or without diltiazem was conducted on 26 stable renal transplant recipients. Blood samples were collected over 24 h during each phase, tacrolimus concentrations were assayed, and noncompartmental pharmacokinetic analysis was performed. All participants were genotyped for CYP3A5*3 and MDR1 C3435T.

RESULTS

After conversion, without diltiazem, the area under the concentration-time curve at steady state from 0 to 24 h after dose administration (AUC_{ss, 0-24}) was significantly reduced [median 224 (range 172-366) vs. 184 (104-347) ng·h/mL, p = 0.006, n = 26]. A decrease in tacrolimus exposure (median 21%) was only evident among CYP3A5 expressors [227 (172-366) vs. 180 (104-347) ng·h/mL, p = 0.014, n = 18], not among non-expressors [215 (197-290) vs. 217 (129-281) ng·h/mL, p = 0.263, n = 8]. In contrast, among CYP3A5 expressors receiving diltiazem, AUC_{ss, 0-24} did not change significantly upon conversion [229 (170-296) vs. 221 (123-342) ng·h/mL, p = 0.575, n = 10]. An independent effect was not evident for MDR1 C3435T polymorphism.

CONCLUSION

The high prevalence of CYP3A5 polymorphism among Asians may lead to a significant reduction in tacrolimus exposure with 1:1 dose conversion of Prograf^® to Advagraf^®. These results advocate for CYP3A5 determination prior to conversion, and suggest that 1:1.25 conversion should be used for CYP3A5 expressors and 1:1 conversion for other patients.

Pharmacokinetics, Pharmacodynamics and Pharmacogenetics of Tacrolimus in Kidney Transplantation

Background:

Tacrolimus (Tac, or FK506), a calcineurin inhibitor (CNI), is the first-line immu-nosuppressant which consists of the footstone as immunosuppressive regimens in kidney transplantation. However, the drug toxicity and the significant differences of pharmacokinetics (PK) and pharmacodynam-ics (PD) among individuals are hidden troubles for clinical application. Recently, emerging evidences of Tac pharmacogenetics (PG) regarding drug absorption, metabolism, disposition, excretion and response are discovered for better understanding of this drug.

Method:

We reviewed the published articles regarding the Tac PG and its effects on PK and PD in kidney transplantation. In addition, we summarized information on polygenic algorithms.

Results:

The polymorphism of genes encoding metabolic enzymes and transporters related to Tac were largely investigated, but the results were inconsistent. In addition to CYP3A4, CYP3A5 and P-gp (also known as ABCB1), single nucleotide polymorphisms (SNPs) might also affect the PK and PD parameters of Tac.

Conclusion:

The correlation between Tac PK, PD and PG is very complex. Although many factors need to be verified, it is envisaged that thorough understanding of PG may assist clinicians to predict the optimal starting dosage, help adjust the maintenance regimen, as well as identify high risk patients for adverse ef-fects or drug inefficacy

Evaluating tacrolimus pharmacokinetic models in adult renal transplant recipients with different CYP3A5 genotypes

PURPOSE

Numerous studies have been conducted on the population pharmacokinetics of tacrolimus in adult renal transplant recipients. It has been reported that the cytochrome P450 (CYP) 3A5 genotype is an important cause of variability in tacrolimus pharmacokinetics. However, the predictive performance of population pharmacokinetic (PK) models of tacrolimus should be evaluated prior to their implementation in clinical practice. The aim of the study reported here was to test the predictive performance of these published PK models of tacrolimus.

METHODS

A literature search of the PubMed and Web of Science databases ultimately led to the inclusion of eight one-compartment models in our analysis. We collected a total of 1715 trough concentrations from 174 patients. Predictive performance was assessed based on visual and numerical comparison bias and imprecision and by the use of simulation-based diagnostics and Bayesian forecasting.

RESULTS

Of the eight one-compartment models assessed, seven showed better predictive performance in CYP3A5 extensive metabolizers in terms of bias and imprecision. Results of the simulation-based diagnostics also supported the findings. The model based on a Chinese population in 2013 (model 3) showed the best and most stable predictive performance in all the tests and was more informative in CYP3A5 extensive metabolizers. As expected, Bayesian forecasting improved model predictability. Diversity among models and between different CYP3A5 genotypes of the same model was also narrowed by Bayesian forecasting.

CONCLUSIONS

Based on our results, we recommend using model 3 in CYP3A5 extensive metabolizers in clinical practice. All models had a poor predictive performance in CYP3A5 poor metabolizers, and they should be used with caution in this patient population. However, Bayesian forecasting improved the predictability and reduced differences, and thus the models could be applied in this latter patient population for the design of maintenance dose.

Artificial neural network model for predicting the bioavailability of tacrolimus in patients with renal transplantation

The objective of the current study was to explore the role of ABCB1 and CYP3A5 genetic polymorphisms in predicting the bioavailability of tacrolimus and the risk for post-transplant diabetes. Artificial neural network (ANN) and logistic regression (LR) models were used to predict the bioavailability of tacrolimus and risk for post-transplant diabetes, respectively. The five-fold cross-validation of ANN model showed good correlation with the experimental data of bioavailability (r2 = 0.93-0.96). Younger age, male gender, optimal body mass index were shown to exhibit lower bioavailability of tacrolimus. ABCB1 1236 C>T and 2677G>T/A showed inverse association while CYP3A5*3 showed a positive association with the bioavailability of tacrolimus. Gender bias was observed in the association with ABCB1 3435 C>T polymorphism. CYP3A5*3 was shown to interact synergistically in increasing the bioavailability in combination with ABCB1 1236 TT or 2677GG genotypes. LR model showed an independent association of ABCB1 2677 G>T/A with post transplant diabetes (OR: 4.83, 95% CI: 1.22-19.03). Multifactor dimensionality reduction analysis (MDR) revealed that synergistic interactions between CYP3A5*3 and ABCB1 2677 G>T/A as the determinants of risk for post-transplant diabetes. To conclude, the ANN and MDR models explore both individual and synergistic effects of variables in modulating the bioavailability of tacrolimus and risk for post-transplant diabetes.

Practical Recommendations for Long-term Management of Modifiable Risks in Kidney and Liver Transplant Recipients: A Guidance Report and Clinical Checklist by the Consensus on Managing Modifiable Risk in Transplantation (COMMIT) Group

Short-term patient and graft outcomes continue to improve after kidney and liver transplantation, with 1-year survival rates over 80%; however, improving longer-term outcomes remains a challenge. Improving the function of grafts and health of recipients would not only enhance quality and length of life, but would also reduce the need for retransplantation, and thus increase the number of organs available for transplant. The clinical transplant community needs to identify and manage those patient modifiable factors, to decrease the risk of graft failure, and improve longer-term outcomes.COMMIT was formed in 2015 and is composed of 20 leading kidney and liver transplant specialists from 9 countries across Europe. The group's remit is to provide expert guidance for the long-term management of kidney and liver transplant patients, with the aim of improving outcomes by minimizing modifiable risks associated with poor graft and patient survival posttransplant.The objective of this supplement is to provide specific, practical recommendations, through the discussion of current evidence and best practice, for the management of modifiable risks in those kidney and liver transplant patients who have survived the first postoperative year. In addition, the provision of a checklist increases the clinical utility and accessibility of these recommendations, by offering a systematic and efficient way to implement screening and monitoring of modifiable risks in the clinical setting.

Application of Machine-Learning Models to Predict Tacrolimus Stable Dose in Renal Transplant Recipients

Tacrolimus has a narrow therapeutic window and considerable variability in clinical use. Our goal was to compare the performance of multiple linear regression (MLR) and eight machine learning techniques in pharmacogenetic algorithm-based prediction of tacrolimus stable dose (TSD) in a large Chinese cohort. A total of 1,045 renal transplant patients were recruited, 80% of which were randomly selected as the “derivation cohort” to develop dose-prediction algorithm, while the remaining 20% constituted the “validation cohort” to test the final selected algorithm. MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied and their performances were compared in this work. Among all the machine learning models, RT performed best in both derivation [0.71 (0.67–0.76)] and validation cohorts [0.73 (0.63–0.82)]. In addition, the ideal rate of RT was 4% higher than that of MLR. To our knowledge, this is the first study to use machine learning models to predict TSD, which will further facilitate personalized medicine in tacrolimus administration in the future.

Comparison of tacrolimus and cyclosporin A in CYP3A5 expressing Chinese de novo kidney transplant recipients: a 2-year prospective study

AIMS

To assess the efficacy and safety of tacrolimus and cyclosporin A (CsA)-based immunosuppressive regimens in Chinese de novo kidney transplant recipients who are CYP3A5 expressers.

METHODS

The CYP3A5 (6986 A>G, rs776746) polymorphism of eligible patients was determined before transplantation. De novo kidney transplant recipients enrolled in this study were assigned to tacrolimus (Tac group) or CsA (CsA group) based therapy. The follow-up period was 2 years. The incidence of acute rejection, patient and graft survival rates, renal allograft function and post-transplant complications were compared. The intra-individual variability (IIV) of Tac and CsA blood concentrations was analysed. Medication costs were also compared. The analysis was conducted on the intention-to-treat principle.

RESULTS

A total of 72 CYP3A5 expressers were enrolled, with 36 patients in each group. AR incidence was higher in the Tac group (11.1% vs. 5.6%), but there was no significant difference (p > 0.05). The 2-year patient and graft survival was comparable, and renal function was comparable in the two groups. Notably, the Tac group presented a significantly higher incidence of BK viremia (22.2% vs. 5.6%, p < 0.05) and BK viruria (38.9% vs. 16.7%, p < 0.05) than the CsA group. The CsA IIV at 1 and 3 months post-transplant was significantly lower than the Tac IIV (p < 0.05). The medical costs of both immunosuppressive drugs and management of complications was significantly lower in the CsA group.

CONCLUSIONS

Cyclosporin A-based maintenance therapy is safe for Chinese de novo kidney transplant recipients who are CYP3A5 expressers. CsA significantly reduced medication costs and decreased BKV infection, suggesting that it is more beneficial for this specific population.

The POR rs1057868-rs2868177 GC-GT diplotype is associated with high tacrolimus concentrations in early post-renal transplant recipients

AIM

Cytochrome P450 oxidoreductase (POR) is the only flavoprotein that donates electrons to all microsomal P450 enzymes (CYP), and several POR SNPs have been shown to be important contributors to altered CYP activity or CYP-mediated drug metabolism. In this study we examined the association between 6 POR SNPs and tacrolimus concentrations in Chinese renal transplant recipients.

METHODS

A total of 154 renal transplant recipients were enrolled. Genotyping of CYP3A5*3 and 6 POR SNPs was performed. All patients received a triple immunosuppressive regimen comprising tacrolimus, mycophenolate mofetil and prednisone. Dose-adjusted tacrolimus trough concentrations were obtained on d 7 (C0D7/D) after transplantation when steady-state concentration of tacrolimus was achieved (dosage had been unchanged for more than 3 d).

RESULTS

Tacrolimus C0D7/D in CYP3A5*3/*3/ POR rs1057868-rs2868177 GC-GT diplotype carriers was 1.62- and 2.72-fold higher than those in CYP3A5*3/*3/ POR rs1057868-rs2868177 GC-GT diplotype non-carriers and CYP3A5*1 carriers (220.17±48.09 vs 135.69±6.86 and 80.84±5.27 ng/mL/mg/kg, respectively, P<0.0001). Of CYP3A5*3/*3/ POR rs1057868-rs2868177GC-GT diplotype carriers, 85.71% exceeded the upper limit of the target range (8 ng/mL), which was also significantly higher compared with the latter two groups (14.29% and 0.00%, respectively, P<0.0001). The CYP3A5*3 and POR rs1057868-rs2868177 GC-GT diplotype explained 31.7% and 5.7%, respectively, of the inter-individual variability of tacrolimus C0D7/D, whereas the POR rs1057868-rs2868177 GC-GT diplotype could explain 10.9% of the inter-individual variability of tacrolimus C0D7/D in CYP3A5 non-expressers.

CONCLUSION

The CYP3A5*3 and POR rs1057868-rs2868177 GC-GT diplotype accounted for the inter-individual variation of tacrolimus C0D7/D. Genotyping of POR rs1057868-rs2868177 diplotypes would help to differentiate initial tacrolimus dose requirements and to achieve early target C0 ranges in Chinese renal transplant recipients.

Interactive effects of CYP3A4, CYP3A5, MDR1 and NR1I2 polymorphisms on tracrolimus trough concentrations in early postrenal transplant recipients

Aims: To evaluate the influences of CYP3A4, CYP3A5, MDR1 and NR1I2 polymorphisms on tacrolimus concentration in early postrenal transplant recipients. Patients & methods: A total of 159 patients were included, dose-adjusted tacrolimus trough concentration on day 7 after transplantation (C0D7/D) was calculated and 10 SNPs in four genes were genotyped. Results: CYP3A5*3 explained 32.8% of variability of tacrolimus C0D7/D. CYP3A4*1G, MDR1 1236–2677–3435 diplotype and NR1I2 -25385C > T explained 21.4% of variability of tacrolimus C0D7/D in CYP3A5 nonexpressers. Conclusion: CYP3A5*3 was the predominant determinant affecting tacrolimus concentration. Genotyping of CYP3A4/MDR1/NR1I2 polymorphisms may be helpful for better guiding tacrolimus dosing in CYP3A5 nonexpressers.

Associations of HSD11B1 polymorphisms with tacrolimus concentrations in Chinese renal transplant recipients with prednisone combined therapy

Tacrolimus requires close therapeutic drug monitoring because of its narrow therapeutic index and marked interindividual pharmacokinetic variation. In this study, we investigated the associations of polymorphisms in the gene encoding 11β-hydroxysteroid dehydrogenase type 1 (HSD11B1) with tacrolimus concentrations in Chinese renal transplant recipients during the early posttransplantation stage. A total of 258 renal transplant recipients receiving tacrolimus with prednisone (30 mg) combined therapy were genotyped for HSD11B1 rs846908, rs846910, rs4844880, and CYP3A5*3 polymorphisms. Tacrolimus trough concentrations were determined on days 6-9 after transplantation, measured by a chemiluminescent microparticle immunoassay. Among the CYP3A5 expressers, the dose-adjusted trough concentration (C0/D) of tacrolimus in HSD11B1 rs846908 AA homozygous individuals was considerably lower than found in GG+GA carriers [56.2 (23.9-86.6) versus 76.7 (12.6-220.0) (ng/ml)/(mg/kg), P = 0.0204]; HSD11B1 rs846910 AA homozygotes had a lower tacrolimus C0/D compared with GG+GA carriers [51.2 (23.9-86.6) versus 76.3 (12.6-220.0) (ng/ml)/(mg/kg), P = 0.0367]; carriers with the HSD11B1 rs4844880 AA genotype had a significantly lower tacrolimus C0/D with respect to carriers of TT+TA genotypes [61.3 (23.9-97.5) versus 77.2 (12.6-220.0) (ng/ml)/(mg/kg), P = 0.0002]; the HSD11B1 AA-AA-AA haplotype carriers had a lower tacrolimus C0/D than noncarriers [51.2 (23.9-86.6) versus 76.3 (12.6-220.0) (ng/ml)/(mg/kg), P = 0.0367]. These findings illustrate that the HSD11B1 genotypes are closely correlated with tacrolimus trough concentrations, suggesting that these polymorphisms may be useful for safer dosing of tacrolimus.

Effect of CYP3A5*3 on kidney transplant recipients treated with tacrolimus: a systematic review and meta-analysis of observational studies

The highly variable pharmacokinetics of tacrolimus can hamper the optimal management of kidney transplant patients. This variability has been attributed to the genetic polymorphism of CYP3A5 6986A>G, but the evidence is not clear. We conducted a meta-analysis of studies evaluating the effect of CYP3A5 polymorphism on kidney transplant recipients with tacrolimus plasma concentration divided by daily dose per body weight (C/D) and clinical outcomes. We searched in MEDLINE and EMBASE. We found evidence suggesting a significantly lower C/D among CYP3A5*1 allele carriers compared with carriers of the CYP3A5*3/*3 genotype at weeks 1 and 2, and months 1, 3, 6 and 12. We demonstrated that the expresser genotype might have higher risk of acute rejection and chronic nephrotoxicity. In conclusion, CYP3A5 6986A>G polymorphism can affect tacrolimus pharmacokinetics and the incidence of acute rejection and chronic nephrotoxicity on kidney transplant recipients. Patients at high risk of developing tacrolimus-related complications could be detected even before their kidney transplant.

Effect of MDR1 polymorphisms on the blood concentrations of tacrolimus in Turkish renal transplant patients

BACKGROUND

Tacrolimus, a calcineurin inhibitör, is prescribed to prevent allograft rejection in renal transplantation. Tacrolimus not only has a narrow therapeutic index, but also shows significant interindividual differences. The absorption and metabolism of this drug are affected by multidrug resistance (MDR) 1 gene polymorphisms that correlated with single-nucleotide polymorphisms (SNPs) affecting in vivo P-glycoprotein activity. This study investigated associations of MDR1 gene C3435T polymorphism with tacrolimus blood concentrations and dose requirements as well as acute rejection episodes among Turkish renal transplant patients.

METHODS

One hundred living-donor transplant recipients and 150 healthy control subjects underwent C3435T genotyping using polymerase chain reaction-restriction fragment length polymorphism. Blood concentrations of tacrolimus were determined with the cloned enzyme donor immunoassay.

RESULTS

The CC, CT, and TT genotype frequencies among patients were, respectively, 44.0%, 33.0%, and 23.0% versus 36.7%, 43.3%, and 20.0% among control subjects. There was no significant difference between (P = .061; P = .102; P = .211; respectively). The ratio of blood concentration to dose of tacrolimus for patients with mutant homozygous 3435 TT genotype was higher than that of wild-type 3435 CC genotype homozygous individuals. The doses for these patients were lower at 1, 3, and 12 months (P = .048; P = .03; P = .041, respectively). There were no significant differences between the groups regarding coprescription of drugs that affect tacrolimus concentrations, such as diltiazem. Acute rejection episodes were not associated with the CC vs CT or TT genotypes: odds ratio (OR), 0.517 (95% confidence interval [CI], 0.190-1.407; P = .192); OR 1.558 (95% CI, 0.587-4.136; P = .372); OR 1.346; (95% CI, 0.456-3.968; P = .590), respectively.

CONCLUSIONS

Determination of MDR1 polymorphism may help to achieve target of tacrolimus blood concentrations.

Effects of CYP3A4 and CYP3A5 polymorphisms on tacrolimus pharmacokinetics in Chinese adult renal transplant recipients: a population pharmacokinetic analysis

OBJECTIVE

Tacrolimus is used clinically for the long-term treatment of antirejection of transplanted organs in liver and kidney transplant recipients, although dose optimization is poorly managed. The aim of this study was to examine the association between tacrolimus pharmacokinetic variability and CYP3A4 and CYP3A5 genotypes by a population pharmacokinetic analysis based on routine drug monitoring data in adult renal transplant recipients.

MATERIALS AND METHODS

Trough tacrolimus concentrations were obtained from 161 adult kidney transplant recipients after transplantation. The population pharmacokinetic analysis was carried out using the nonlinear mixed-effect modeling software NONMEM version 7.2. The CYP3A4*1G and CYP3A5*3 genetic polymorphisms from the patients studied were determined by direct sequencing using a validated automated genetic analyzer.

RESULTS

A one-compartment model with first-order absorption and elimination adequately described the pharmacokinetics of tacrolimus. Covariates including CYP3A5*3 and CYP3A4*1G alleles and hematocrit were retained in the final model. The apparent clearance of tacrolimus was about two-fold higher in kidney transplant patients with higher enzymatic activity of CYP3A5*1 and CYP3A4*1G (with the CYP3A5*1/*1 or *1/*3 and CYP3A4*1/*1G or CYP3A4*1G/*1G) compared with those with lower enzymatic activity (CYP3A5*3/*3 and CYP3A4*1/*1).

CONCLUSION

This is the first study to extensively determine the effect of CYP3A4*1G and CYP3A5*3 genetic polymorphisms and hematocrit value on tacrolimus pharmacokinetics in Chinese renal transplant recipients. The findings suggest that CYP3A5*3 and CYP3A4*1G polymorphisms and hematocrit are determinant factors in the apparent clearance of tacrolimus. The initial dose design is mainly based on CYP3A5 and CYP3A4 genotypes as well as hematocrit. This result may also be useful for maintenance tacrolimus dose optimization and may help to avoid fluctuating tacrolimus levels and improve the efficacy and tolerability of tacrolimus in kidney transplant recipients.

Individualization of tacrolimus dosage basing on cytochrome P450 3A5 polymorphism--a prospective, randomized, controlled study

We investigated how cytochrome P450 (CYP) 3A5 polymorphism affects pharmacokinetics of tacrolimus and its interaction with diltiazem in Chinese kidney transplant recipients. Sixty-two CYP3A5 expressers and 58 non-expressers were, respectively, randomized to receive diltiazem supplement or not. Their pharmacokinetic profiles were acquired on 14th day, sixth month, and 18th month post-transplant and compared among groups. A dosing equation was fit based on above data with CYP3A5 genotype and diltiazem co-administration as variables. Then, necessary initial doses with or without diltiazem were calculated and used in 11 CYP3A5 expressers, respectively, when another 11 expressers received routine doses as control. Trough concentration was measured on the third-day post-transplant and patients failed to reach target range were presented in percentage. These two parameters were compared among three groups. Patients were followed up until June 2010, kidney function, biopsy-proved acute rejection, and other adverse events were monitored. Results showed that CYP3A5 expressers needed more tacrolimus to reach therapeutic concentration window and were more susceptible to diltiazem-induced concentration increase than CYP3A5 non-expressers. CYP3A5 polymorphism-guided dosing equation helped to determine appropriate initial doses of tacrolimus in individuals. In conclusion, CYP3A5 polymorphism profoundly influences pharmacokinetics of tacrolimus and helps to individualize tacrolimus dose.

Dosing equation for tacrolimus using genetic variants and clinical factors

AIM

To develop a dosing equation for tacrolimus, using genetic and clinical factors from a large cohort of kidney transplant recipients. Clinical factors and six genetic variants were screened for importance towards tacrolimus clearance (CL/F).

METHODS

Clinical data, tacrolimus troughs and corresponding doses were collected from 681 kidney transplant recipients in a multicentre observational study in the USA and Canada for the first 6 months post transplant. The patients were genotyped for 2,724 single nucleotide polymorphisms using a customized Affymetrix SNP chip. Clinical factors and the most important SNPs (rs776746, rs12114000, rs3734354, rs4926, rs3135506 and rs2608555) were analysed for their influence on tacrolimus CL/F.

RESULTS

The CYP3A5*1 genotype, days post transplant, age, transplant at a steroid sparing centre and calcium channel blocker (CCB) use significantly influenced tacrolimus CL/F. The final model describing CL/F (l h(-1)) was: 38.4 ×[(0.86, if days 6-10) or (0.71, if days 11-180)]×[(1.69, if CYP3A5*1/*3 genotype) or (2.00, if CYP3A5*1/*1 genotype)]× (0.70, if receiving a transplant at a steroid sparing centre) × ([age in years/50](-0.4)) × (0.94, if CCB is present). The dose to achieve the desired trough is then prospectively determined using the individuals CL/F estimate.

CONCLUSIONS

The CYP3A5*1 genotype and four clinical factors were important for tacrolimus CL/F. An individualized dose is easily determined from the predicted CL/F. This study is important towards individualization of dosing in the clinical setting and may increase the number of patients achieving the target concentration. This equation requires validation in an independent cohort of kidney transplant recipients.