Correlation of Hematocrit and Tacrolimus Level in Liver Transplant Recipients

Limsrichamrern, Somchai; Chanapul, C.; Mahawithitwong, Prawej; Sirivatanauksorn, Yongyut; Kositamongkol, Prawat; Asavakarn, S.; Tovikkai, Chutwichai; Dumronggittigule, Wethit

doi:10.1016/j.transproceed.2015.12.096

Cited by 20 publications

(17 citation statements)

References 14 publications

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“…This can be explained by the pharmacokinetic parameters of tacrolimus. Tacrolimus is strongly bound to red blood cells . Several studies showed that plasma tacrolimus concentrations change with altering hematocrit levels .…”

Section: Discussionmentioning

confidence: 99%

“…Tacrolimus is strongly bound to red blood cells. [18][19][20] Several studies showed that plasma tacrolimus concentrations change with altering hematocrit levels. [20][21][22] However, Storset et al suggested that this influence might not always be clinically relevant because hematocrit is not expected to influence the unbound, therapeutically active fraction of tacrolimus.…”

Section: Discussionmentioning

confidence: 99%

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Intra‐patient variability in tacrolimus exposure in pediatric liver transplant recipients: Evolution, risk factors, and impact on patient outcomes

Defrancq

Wilde

Raes

et al. 2019

Pediatric Transplantation

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Background This study aims to investigate the evolution and factors associated with TAC IPV and its impact on patient outcomes in pediatric LT recipients. Methods This is a retrospective study including 41 children. The TAC IPV was expressed as the coefficient of variation and was calculated for years 1‐5 following LT. The number of missed clinic appointments was used as a surrogate marker for therapy adherence. Results We identified a decrease in the TAC IPV during the first 3 years after LT (P < 0.01). Serum albumin in the first year (P = 0.03), hematocrit (P = 0.02) and total bilirubin (P = 0.04) in the third year, and therapy adherence (P < 0.01) in the fifth year were associated with TAC IPV. High TAC IPV was associated with biopsy‐proven acute allograft rejection (P = 0.04) and the need for biopsy during the first year (P = 0.02). There was a borderline association between TAC IPV and donor‐specific antibodies (P = 0.08) and CMV viremia (P = 0.07). High TAC IPV was a predictor of need for liver biopsy and AR with an odds ratio of 1.04 (95% CI 1.0‐1.1; P = 0.03) and 1.04 (95% CI 1.0‐1.1; P = 0.05), respectively. Conclusions Our results highlight the impact of biological factors on TAC IPV during the early LT follow‐up and later also therapy adherence. High TAC IPV may be associated with adverse patient outcomes.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Intra‐patient variability in tacrolimus exposure in pediatric liver transplant recipients: Evolution, risk factors, and impact on patient outcomes

Defrancq

Wilde

Raes

et al. 2019

Pediatric Transplantation

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“…Linear mixed effects (LME) analysis (lme4 ::lmer) was used to identify potential genetic predictors of log‐transformed TAC C 0 /D. Haematocrit, age and sex have been reported to significantly impact TAC C 0 ; therefore, genotypes/haplotypes were treated as fixed effects adjusted by age, sex and haematocrit whilst patient and time (1 or 3 months) were treated as random effects on intercept. Diagnostic plot (stats ::resid) was applied to check the normality and homoscedasticity of model residuals.…”

Section: Methodsmentioning

confidence: 99%

CYP3A53* and ABCB1 61A>G Significantly Influence Dose‐adjusted Trough Blood Tacrolimus Concentrations in the First Three Months Post‐Kidney Transplantation

Barratt

Coller

et al. 2018

Basic Clin Pharma Tox

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Tacrolimus (TAC) is a first-line immunosuppressant used to prevent organ rejection after kidney transplantation. There is large inter-individual variability in its pharmacokinetics. Single nucleotide polymorphisms (SNPs) in genes encoding TAC metabolizing enzymes cytochromes P450 3A4/5 (CYP3A4/5), P-glycoprotein efflux transporter (ABCB1), their expression regulator pregnane X receptor (NR1I2) and CYP3A co-factor cytochrome P450 reductase (POR) have been studied for their effects on tacrolimus disposition. However, except for CYP3A5*3, controversies remain about their roles in predicting dose-adjusted trough blood TAC concentrations (C /D). This study aimed to investigate the effects of ABCB1 (61A>G, 1199G>A, 1236C>T, 2677G>T and 3435C>T), CYP3A4*22, CYP3A5*3, NR1I2 (8055C>T, 63396C>T and -25385C>T) and POR*28 SNPs on TAC C /D. In total, 165 kidney transplant recipients were included in this study. SNPs were genotyped by probe-based real-time polymerase chain reaction. Associations between log-transformed whole blood TAC C /D (measured at 1 and 3 months post-transplant) and genotypes/haplotypes were assessed by linear mixed effects analysis, controlling for age, sex and haematocrit. It was observed that CYP3A5 expressors (*1/*1 + *1/*3) (p = 5.5 × 10 ) and ABCB1 61G allele carriers (p = 0.001) had lower log-transformed TAC C /D (56% and 26% lower geometric mean TAC C /D, respectively) and accounted for approximately 30% and 4%, respectively, of log-transformed TAC C /D variability in the first 3 months post-transplant. In conclusion, CYP3A5*3 is a major, and ABCB1 61A>G is a novel, although minor, genetic factor affecting TAC C /D in kidney transplant recipients.

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“…For tacrolimus, which displays similar erythrocyte partitioning as everolimus, various authors have advocated for the implementation of TDM based on haematocrit-normalised whole-blood concentrations across different fields of transplantation [39][40][41][42][43]. Additionally, a recent consensus paper on tacrolimus TDM included recommendations for further investigation of alternative TDM strategies, including haematocrit normalisation, to explain and resolve the highly variable tacrolimus efficacy in patient subpopulations [37].…”

Section: Discussionmentioning

confidence: 99%

Model-Informed Precision Dosing of Everolimus: External Validation in Adult Renal Transplant Recipients

et al. 2020

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Background and Objective The immunosuppressant everolimus is increasingly applied in renal transplantation. Its extensive pharmacokinetic variability necessitates therapeutic drug monitoring, typically based on whole-blood trough concentrations (C 0). Unfortunately, therapeutic drug monitoring target attainment rates are often unsatisfactory and patients with on-target exposure may still develop organ rejection. As everolimus displays erythrocyte partitioning, haematocrit-normalised wholeblood exposure has been suggested as a more informative therapeutic drug monitoring marker. Furthermore, model-informed precision dosing has introduced options for more sophisticated dose adaptation. We have previously developed a mechanistic population pharmacokinetic model, which described everolimus plasma pharmacokinetics and enabled estimation of haematocrit-normalised whole-blood exposure. Here, we externally evaluated this model for its utility for model-informed precision dosing. Methods The retrospective dataset included 4123 pharmacokinetic observations from routine clinical therapeutic drug monitoring in 173 renal transplant recipients. Model appropriateness was confirmed with a visual predictive check. A fit-forpurpose analysis was conducted to evaluate whether the model accurately and precisely predicted a future C 0 or area under the concentration-time curve (AUC) from prior pharmacokinetic observations. Bias and imprecision were expressed as the mean percentage prediction error (MPPE) and mean absolute percentage prediction error (MAPE), stratified on 6 months post-transplant. Additionally, we compared dose adaptation recommendations of conventional C 0-based therapeutic drug monitoring and C 0-or AUC-based model-informed precision dosing, and assessed the percentage of differences between observed and haematocrit-normalised C 0 (∆C 0) and AUC (∆AUC) exceeding ± 20%. Results The model showed adequate accuracy and precision for C 0 and AUC prediction at ≤ 6 months (

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Correlation of Hematocrit and Tacrolimus Level in Liver Transplant Recipients

Cited by 20 publications

References 14 publications

Intra‐patient variability in tacrolimus exposure in pediatric liver transplant recipients: Evolution, risk factors, and impact on patient outcomes

Intra‐patient variability in tacrolimus exposure in pediatric liver transplant recipients: Evolution, risk factors, and impact on patient outcomes

CYP3A53* and ABCB1 61A>G Significantly Influence Dose‐adjusted Trough Blood Tacrolimus Concentrations in the First Three Months Post‐Kidney Transplantation

Model-Informed Precision Dosing of Everolimus: External Validation in Adult Renal Transplant Recipients

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Correlation of Hematocrit and Tacrolimus Level in Liver Transplant Recipients

Cited by 20 publications

References 14 publications

Intra‐patient variability in tacrolimus exposure in pediatric liver transplant recipients: Evolution, risk factors, and impact on patient outcomes

Intra‐patient variability in tacrolimus exposure in pediatric liver transplant recipients: Evolution, risk factors, and impact on patient outcomes

CYP3A5*3 and ABCB1 61A>G Significantly Influence Dose‐adjusted Trough Blood Tacrolimus Concentrations in the First Three Months Post‐Kidney Transplantation

Model-Informed Precision Dosing of Everolimus: External Validation in Adult Renal Transplant Recipients

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CYP3A53* and ABCB1 61A>G Significantly Influence Dose‐adjusted Trough Blood Tacrolimus Concentrations in the First Three Months Post‐Kidney Transplantation