Body weight‐based initial dosing of tacrolimus in renal transplantation: Is this an ideal approach?

Chinnadurai, Rajkumar; Ibrahim, Sara T.; Tay, Tricia; Bhutani, Shiv; Kalra, Philip A.

doi:10.1111/jorc.12339

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…Patients with obesity often exhibit a body composition with higher adipose content, potentially leading to increased distribution of lipophilic drugs, such as tacrolimus. [27][28][29] In fact, overexposure from initial doses of tacrolimus has been observed in several studies of patients with obesity, [30][31][32][33][34] suggesting that dosing of tacrolimus in transplant recipients is not one size fits all and that body weight may play a significant role in initial doses and correlation to tacrolimus levels.…”

Section: Discussionmentioning

confidence: 99%

Retrospective evaluation of LCP‐tacrolimus (Envarsus XR) dosing in de novo kidney transplant

Alzahrani,

Belcher,

Benken

et al. 2023

Clinical Transplantation

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IntroductionTacrolimus is known to exhibit significant inter‐ and intra‐patient pharmacokinetic (PK) and pharmacodynamic (PD) variability regarding therapeutic response. LCP‐tacrolimus (LCPT—Envarsus XR) was approved in 2018 for use as a de novo immunosuppressive agent in kidney transplants, but there is limited evidence to guide de novo dosing of LCPT in patients with obesity. The primary objective of this study was to evaluate the impact of different calculated weight‐based de novo LCPT dosing on early transplant outcomes.MethodsThis was a retrospective study of patients with obesity (BMI ≥ 30 kg/m2) who received a kidney transplant at the University of Illinois Hospital and Health System (UIH), between March 2019 and March 2021. Subjects were included if were age 18 years or older and received de novo LCPT throughout index hospitalization. The primary endpoint of this study was to compare correlations between the first tacrolimus trough level and dosing weight strategy (e.g., TBW, AdjBW, IBW).ResultsThere was a statistically significant, though modest, correlation between all three dosing strategies and the first tacrolimus trough level (TBW correlation coefficient = .431, p < .001; AdjBW correlation coefficient = .455, p < .001; IBW correlation coefficient = .465; p < .001). In regression modeling for supratherapeutic levels each additional .01 mg/kg increase in dose by TBW, AdjBW, and IBW increased the odds of a supratherapeutic level by 1.46, 1.34, and 1.24, respectively (p < .001).ConclusionsThe use of LCPT in kidney transplant recipients with obesity dosed using TBW demonstrated the strongest correlation with initial supratherapeutic tacrolimus levels. Larger prospective studies are needed to investigate the further impact of body weight on dosing regimens in the obese population.

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Section: Discussionmentioning

confidence: 99%

Retrospective evaluation of LCP‐tacrolimus (Envarsus XR) dosing in de novo kidney transplant

Alzahrani,

Belcher,

Benken

et al. 2023

Clinical Transplantation

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“…Therefore, such patients will require a lower tacrolimus dose per kilogram of body weight. Further, a recent study 20 questioned that weight‐based initial administration of tacrolimus in kidney transplantation is not an ideal regimen. Chinnadurai et al 20 found that patients with higher BMI required a significantly lower tacrolimus dose per kilogram of body weight when dosing was based on tacrolimus C 0 at months 1 and 6 after transplantation.…”

Section: Discussionmentioning

confidence: 99%

Compare the performance of multiple machine learning models in predicting tacrolimus concentration for infant patients with living donor liver transplantation

Song

Liu

Gao

et al. 2022

Pediatric Transplantation

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Background: This study aims to establish multiple ML models and compare their performance in predicting tacrolimus concentration for infant patients who received LDLT within 3 months after transplantation.Methods: Retrospectively collected basic information and relevant biochemical indicators of included infant patients. CMIA was used to determine tacrolimus C 0 . PCR was used to determine the donors' and recipients' CYP3A5 genotypes. Multivariate stepwise regression analysis and stepwise elimination covariates were used for covariates selection. Thirteen machine learning algorithms were applied for the development of prediction models. APE, the ratio of the APE ≤3 ng ml −1 and ideal rate (the proportion of the predicted value with a relative error of 30% or less) were used to evaluate the predictive performance of the model. Results:A total of 163 infant patients were included in this study. In the case of the optimal combination of covariates, the Ridge model had the lowest APE, 2.01 (0.85, 3.35 ng ml −1 ). The highest ratio of the APE ≤3 ng ml −1 was the LAR model (71.77%).And the Ridge model showed the highest ideal rate (55.05%). For the Ridge model, GRWR was the most important predictor. Conclusions:Compared with other ML models, the Ridge model had good predictive performance and potential clinical application.

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“… 26 , 27 Further, in patients receiving kidney transplantation, the concentration of TAC among obese patients (BMI>30 kg/m 2 ) was higher than that in non-obese patients, and obese patients were also subject to the risk of overexposure when the same dose of 0.15 mg/kg/day was administered. 28 …”

Section: Discussionmentioning

confidence: 99%

Influential Factors and Efficacy Analysis of Tacrolimus Concentration After Allogeneic Hematopoietic Stem Cell Transplantation in Children with β-Thalassemia Major

Li¹,

Lu²,

Zhou³

et al. 2021

PGPM

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Purpose To analyze factors influencing tacrolimus (TAC) trough concentration (C 0 ) in β-thalassemia major (β-TM) pediatric patients after allogeneic hematopoietic stem cell transplantation (Allo-HSCT) and to investigate the effects of genotype polymorphism and drug-drug interactions on TAC trough concentration in children with β-TM. Furthermore, to analyze the correlation between TAC C 0 and efficacy and adverse reactions. Patients and Methods Prospectively collection of demographic information and details of combined treatment of patients with β-TM receiving HSCT, and genotypes of CYP3A4, CYP3A5, and ABCB1 (rs1045642, rs1128503, rs2032582) were obtained for each patient. Univariate analysis and multiple linear regression analysis were used to investigate influencing factors on TAC C 0 . The impact of different genotypes and the co-administration of azole antifungal drugs on β-TM patients receiving TAC were evaluated, together with the correlation between acute graft-versus-host disease (aGVHD), infection, and liver injury of TAC C 0 . Results A total of 46 patients with 587 concentration data were included. The multiple linear regression results showed that the patient’s sex, weight, postoperative time, hemoglobin, platelet count, serum cystatin C, and combined voriconazole were independent influencing factors of the infusion trough concentration/daily dose, C 0 /D iv . Age, body surface area, postoperative time, co-administration of voriconazole, and CYP3A4*18B are independent influencing factors of C 0 /D po . Group comparisons showed that voriconazole can affect TAC C 0 administered intravenously (IV) and orally in β-TM pediatric patients, while patient genotype can affect TAC C 0 during oral administration. TAC C 0 does not correlate with aGVHD or liver injury, but infection may be associated with TAC C 0 . Conclusion The concentration of TAC should be closely monitored when co-administered with voriconazole. It is worth considering that the influence of genotype on the trough concentration of oral TAC and individualized drug administration warrant investigation. Finally, this study indicated that C 0 is not suitable as an indicator of the efficacy of TAC.

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Body weight‐based initial dosing of tacrolimus in renal transplantation: Is this an ideal approach?

Cited by 8 publications

References 19 publications

Retrospective evaluation of LCP‐tacrolimus (Envarsus XR) dosing in de novo kidney transplant

Retrospective evaluation of LCP‐tacrolimus (Envarsus XR) dosing in de novo kidney transplant

Compare the performance of multiple machine learning models in predicting tacrolimus concentration for infant patients with living donor liver transplantation

Influential Factors and Efficacy Analysis of Tacrolimus Concentration After Allogeneic Hematopoietic Stem Cell Transplantation in Children with β-Thalassemia Major

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