Population pharmacokinetics of tacrolimus in adult kidney transplant recipients

Staatz, Christine E.

doi:10.1067/mcp.2002.129304

Cited by 133 publications

(171 citation statements)

References 26 publications

Supporting

Mentioning

135

Contrasting

Unclassified

Order By: Relevance

“…This decrease in CL/F is consistent with previous findings [10,26]. A decrease in CL/F may be attributable to an increase in tacrolimus bioavailability over time as the patients clinical status improves.…”

Section: Figuresupporting

confidence: 82%

“…Goodness-of-fit plots for the final tacrolimus model: IPRED, individual predicted concentrations (ng ml -1 ); WRES, weighted residuals Tacrolimus dosing equation recipient age and the use of a CCB.The population CL/F was 38.4 l h -1 and is close to estimates from other pharmacokinetic studies (ranging from 21 to 35 l h -1 ) [10,23,24]. Our estimate of CL/F is slightly larger than previously reported studies.This is expected because, as stated earlier, we modelled (CL/F)*, an approximation to the tacrolimus apparent clearance.…”

Section: Figuresupporting

confidence: 36%

“…Days post transplant was the most significant clinical factor affecting tacrolimus CL/F and has been previously shown to be associated with CL/F [9,10,[25][26][27].Tacrolimus CL/F decreased by 14% in days 6-10 post transplant and by 29% in days 11-180 post transplant, relative to the immediate post-transplant period (days 3-5). This decrease in CL/F is consistent with previous findings [10,26].…”

Section: Figurementioning

confidence: 99%

“…However, the data for this analysis came from an observational study where supervision of trough measurements was conducted by the clinical teams. Haematocrit and aspartate aminotransferase (AST) have been previously shown to be important clinical factors [10] and neither was collected in our study. Future studies should also consider concomitant administration of anti-fungals given the potential for drug interactions [51,52].…”

Section: Figurementioning

confidence: 99%

“…Two recent studies showed that with contemporary immunosuppressive regimens (tacrolimus, mycophenolate and steroids Ϯ antibody induction) low tacrolimus troughs in the first week post transplant were associated with a greater risk of acute rejection [5,6].To tailor therapy better, multiple clinical factors have been explored to determine their effects on tacrolimus pharmacokinetics. It is generally acknowledged that drug interactions, haematocrit, corticosteroid therapy, days post transplant, and race affect tacrolimus pharmacokinetics [3, [7][8][9][10]. It is also established that the cytochrome P4503A5 (CYP3A5)*1 allele is associated with significantly higher tacrolimus CL and lower systemic exposure [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Dosing equation for tacrolimus using genetic variants and clinical factors

Passey

Birnbaum

Brundage

et al. 2011

Brit J Clinical Pharma

151

163

View full text Add to dashboard Cite

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT• Patients with low tacrolimus troughs are at a higher risk of rejection while those with high troughs are at an increased risk for toxicity. Therefore, achieving the therapeutic range is important.• CYP3A5 genotype and days post transplant have been previously shown individually to be associated with tacrolimus troughs.WHAT THIS STUDY ADDS• This paper presents the first dosing model for tacrolimus using a combination of genetic and clinical factors in adult kidney transplant recipients. It was developed from one of the largest tacrolimus pharmacogenetic studies conducted to date (681 subjects and 11 823 trough concentrations).• We found that CL/F was significantly influenced by days post transplant, CYP3A5 genotype, transplantation at a steroid sparing centre, recipient age and the use of a calcium channel blocker.• Our large sample size enabled us to define the distinct differences in tacrolimus CL/F between three CYP3A5 genotype groups (*1/*1, *1/*3 and *3/*3).• This study is an important step towards using pharmacogenetic information in the clinical setting.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.

show abstract

Section: Figuresupporting

confidence: 82%

Section: Figuresupporting

confidence: 36%

Section: Figurementioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dosing equation for tacrolimus using genetic variants and clinical factors

Passey

Birnbaum

Brundage

et al. 2011

Brit J Clinical Pharma

151

163

View full text Add to dashboard Cite

show abstract

Machine Learning: A New Approach for Dose Individualization

Li,

Tang,

et al. 2023

Clin Pharma and Therapeutics

View full text Add to dashboard Cite

The application of machine learning (ML) has shown promising results in precision medicine due to its exceptional performance in dealing with complex multidimensional data. However, using ML for individualized dosing of medicines is still in its early stage, meriting further exploration. A systematic review of study designs and modeling details of using ML for individualized dosing of different drugs was performed. We have summarized the status of the study populations, predictive targets, and data sources for ML modeling, the selection of ML algorithms and features, and the evaluation and validation of their predictive performance. We also used the Prediction model Risk of Bias Assessment Tool (PROBAST) to assess the risk of bias of included studies. Currently, ML can be used for both a priori and a posteriori dose selection and optimization, and it can also assist the implementation of therapeutic drug monitoring. However, studies are mainly focused on drugs with narrow therapeutic windows, predominantly immunosuppressants (N=23, 35.9%) and anti‐infectives (N=21, 32.8%), and there is currently only very limited attention for special populations such as children (N=22, 34.4%). Most studies showed poor methodological quality and a high risk of bias. The lack of external validation and clinical utility evaluation currently limits the further clinical implementation of ML for dose individualization. We therefore have proposed several ways to improve the clinical relevance of the studies and facilitate the translation of ML models into clinical practice.

show abstract

Tacrolimus Population Pharmacokinetics and Multiple CYP3A5 Genotypes in Black and White Renal Transplant Recipients

Campagne

Mager

Brazeau

et al. 2018

The Journal of Clinical Pharma

View full text Add to dashboard Cite

Tacrolimus exhibits inter-patient pharmacokinetic variability attributed to CYP3A5 isoenzymes and the efflux transporter, P-glycoprotein. Most black renal transplant recipients require higher tacrolimus doses compared to whites to achieve similar troughs when race-adjusted recommendations are used. An established guideline provides tacrolimus genotype dosing recommendations based on CYP3A5*1(W/T) and loss of protein function variants: CYP3A5*3 (rs776746), CYP3A5*6 (rs10264272), CYP3A5*7 (rs41303343) and may provide more comprehensive race-adjusted dosing recommendations. Our objective was to develop a tacrolimus population pharmacokinetic model evaluating demographic, clinical, and genomic factors in stable black and white renal transplant recipients. A secondary objective investigated race-based tacrolimus regimens and genotype-specific dosing. Sixty-seven recipients receiving oral tacrolimus and mycophenolic acid ≥6 months completed a 12-hour pharmacokinetic study. CYP3A5*3,*6,*7 and ABCB1 1236C>T, 2677G>T/A, 3435C>T polymorphisms were characterized. Patients were classified as extensive, intermediate, and poor metabolizers using a novel CYP3A5*3*6*7 metabolic composite. Modeling and simulation was performed with computer software (NONMEM 7.3, ICON Development Solutions; Ellicott City, Maryland). A 2-compartment model with first-order elimination and absorption with lag time best described the data. The CYP3A5*3*6*7 metabolic composite was significantly associated with tacrolimus clearance (P value < .05), which was faster in extensive (mean: 45.0 L/hr) and intermediate (29.5 L/hr) metabolizers than poor metabolizers (19.8 L/hr). Simulations support CYP3A5*3*6*7 genotype-based tacrolimus dosing to enhance general race-adjusted regimens, with dose increases of 1.5-fold and 2-fold, respectively, in intermediate and extensive metabolizers for comparable exposures to poor metabolizers. This model offers a novel approach to determine tacrolimus dosing adjustments that maintain comparable therapeutic exposure between black and white recipients with different CYP3A5 genotypes.

show abstract

Population pharmacokinetics of tacrolimus in adult kidney transplant recipients

Cited by 133 publications

References 26 publications

Dosing equation for tacrolimus using genetic variants and clinical factors

Dosing equation for tacrolimus using genetic variants and clinical factors

Machine Learning: A New Approach for Dose Individualization

Tacrolimus Population Pharmacokinetics and Multiple CYP3A5 Genotypes in Black and White Renal Transplant Recipients

Contact Info

Product

Resources

About