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Ten years ago, a consensus report on the optimization of tacrolimus was published in this journal. In 2017, the Immunosuppressive Drugs Scientific Committee of the International Association of Therapeutic Drug Monitoring and Clinical Toxicity (IATDMCT) decided to issue an updated consensus report considering the most relevant advances in tacrolimus pharmacokinetics (PK), pharmacogenetics (PG), pharmacodynamics, and immunologic biomarkers, with the aim to provide analytical and drug-exposure recommendations to assist TDM professionals and clinicians to individualize tacrolimus TDM and treatment. The consensus is based on in-depth literature searches regarding each topic that is addressed in this document. Thirty-seven international experts in the field of TDM of tacrolimus as well as its PG and biomarkers contributed to the drafting of sections most relevant for their expertise. Whenever applicable, the quality of evidence and the strength of recommendations were graded according to a published grading guide. After iterated editing, the final version of the complete document was approved by all authors. For each category of solid organ and stem cell transplantation, the current state of PK monitoring is discussed and the specific targets of tacrolimus trough concentrations (predose sample C0) are presented for subgroups of patients along with the grading of these recommendations. In addition, tacrolimus area under the concentration-time curve determination is proposed as the best TDM option early after transplantation, at the time of immunosuppression minimization, for special populations, and specific clinical situations. For indications other than transplantation, the potentially effective tacrolimus concentrations in systemic treatment are discussed without formal grading. The importance of consistency, calibration, proficiency testing, and the requirement for standardization and need for traceability and reference materials is highlighted. The status for alternat

A validated CYP3A genotype classification system allows clustering patients into poor, intermediate and extensive metabolizer phenotypes. However, substantial overlap exists between the clusters. A rare CYP3A4 allele, named CYP3A4*20 (rs67666821), has been specifically described in the Spanish population. The authors investigated the relevance of CYP3A4*20 testing to see if the above-mentioned metabolic CYP3A classification system can be improved. In a cohort of 204 kidney transplant recipients, one male patient carrying a CYP3A4*20 allele was detected. This patient was receiving very low doses of tacrolimus to maintain therapeutic levels from day 7 onward when compared with the majority of the patients. These data suggest that this patient should be regarded as a CYP3A-poor metabolizer.

Tacrolimus (Tac) has a narrow therapeutic window and shows large between-patient pharmacokinetic variability. As a result, over-immunosuppression and under-immunosuppression are frequently encountered in daily clinical practice. Unraveling the impact of genetic polymorphisms on Tac pharmacokinetics may help to refine therapy. In this study, the associations of single-nucleotide polymorphisms (SNPs) in drug-metabolizing enzymes (CYP3A) with Tac pharmacokinetics were investigated in renal transplant recipients. In a cohort of 272 kidney transplant recipients, associations between functional genetic variants (CYP3A4*22 and CYP3A5*3) and dose-adjusted predose Tac concentrations (C0) and daily doses of Tac at days 5-7 and 15 and 1, 3, 6 and 12 months after renal transplantation were evaluated. Patients were genotyped and clustered according to both CYP3A4*22 and CYP3A5*3 allelic status: poor (PM) (CYP3A4*22 carriers with CYP3A5*3/*3), intermediate (IM) (CYP3A4*1/*1 with CYP3A5*3/*3 or CYP3A4*22 carriers with CYP3A5*1 carriers) and extensive CYP3A-metabolizers (EM) (CYP3A4*1/*1 and CYP3A5*1 carriers). EM had an 88% lower dose-adjusted C0 compared with IM. PM had a 26% higher dose-adjusted C0 compared with IM. The percentage of patients with supratherapeutic Tac exposure (C0>15 ng/ml) was significantly higher in PM (43.5%) compared with EM (0%) at days 5-7 after transplantation (P=0.01). About 30% of EM had subtherapeutic exposure (C0<5 ng/ml) at days 5-7 after transplantation (P=0.001). The combined CYP3A4 and CYP3A5 genotype of renal transplant recipients has a major influence on the Tac dose required to reach the target exposure.

Single nucleotide polymorphisms (SNPs) in the CYP3A5 and CYP3A4 genes have been reported to be an important cause of variability in the pharmacokinetics of tacrolimus in renal transplant patients. The aim of this study was to merge all of the new genetic information available with tacrolimus pharmacokinetics to generate a more robust population model with data from renal transplant recipients. Tacrolimus exposure data from 304 renal transplant recipients were collected throughout the first year after transplantation and were simultaneously analyzed with a population pharmacokinetic approach using NONMEM® version 7.2. The tacrolimus whole-blood concentration versus time data were best described by a two-open-compartment model with inter-occasion variability assigned to plasma clearance. The following factors led to the final model, which significantly decreased the minimum objective function value (p < 0.001): a new genotype cluster variable combining the CYP3A5*3 and CYP3A4*22 SNPs defined as extensive, intermediate, and poor metabolizers; the standardization of tacrolimus whole blood concentrations to a hematocrit value of 45%; and age included as patients <63 years versus patients ≥63 years. External validation confirmed the prediction ability of the model with median bias and precision values of 1.17 ng/mL (95% confidence interval [CI] -3.68 to 4.50) and 1.64 ng/mL (95% CI 0.11-5.50), respectively. Simulations showed that, for a given age and hematocrit at the same fixed dose, extensive metabolizers required the highest doses followed by intermediate metabolizers and then poor metabolizers. Tacrolimus disposition in renal transplant recipients was described using a new population pharmacokinetic model that included the CYP3A5*3 and CYP3A4*22 genotype, age, and hematocrit.