Tacrolimus-Induced Neurotoxicity in Early Post-Liver Transplant Saudi Patients: Incidence and Risk Factors

Alissa, Dema A.; Alkortas, Delal; Al-Sebayel, Mohammed; Almasuood, Rawan A.; Aburas, Wejdan; Altamimi, Tahani; DeVol, Edward; Al‐Jedai, Ahmed

doi:10.12659/aot.935938

Cited by 6 publications

(1 citation statement)

References 17 publications

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“…This variability affords higher risk for poor long-term outcomes, such as late allograft rejection, graft loss, adverse effects (e.g., nephrotoxicity and hypertension), and death despite transplant function ( Lieber and Volk, 2013 ; Alissa et al, 2022 ). To optimize therapeutic efficacy and minimize tacrolimus-induced toxicity, therapeutic drug monitoring (TDM) and population pharmacokinetic (popPK) models are widely used to guide personalized tacrolimus dosing and ensure target whole-blood trough concentrations ( C 0 ), especially early post-transplantation ( De Jonge et al, 2009 ; Campagne et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

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

Cai

Li²,

Li³

et al. 2022

Front. Pharmacol.

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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.

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