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 alternative approaches for tacrolimus TDM is presented including dried blood spots, volumetric absorptive microsampling, and the development of intracellular measurements of tacrolimus. The association betweenCYP3A5genotype and tacrolimus dose requirement is consistent (Grading A I). So far, pharmacodynamic and immunologic biomarkers have not entered routine monitoring, but determination of residual nuclear factor of activated T cells–regulated gene expression supports the identification of renal transplant recipients at risk of rejection, infections, and malignancy (B II). In addition, monitoring intracellular T-cell IFN-g production can help to identify kidney and liver transplant recipients at high risk of acute rejection (B II) and select good candidates for immunosuppression minimization (B II). Although cell-free DNA seems a promising biomarker of acute donor injury and to assess the minimally effective C₀of tacrolimus, multicenter prospective interventional studies are required to better evaluate its clinical utility in solid organ transplantation. Population PK models includingCYP3A5andCYP3A4genotypes will be considered to guide initial tacrolimus dosing. Future studies should investigate the clinical benefit of time-to-event models to better evaluate biomarkers as predictive of personal response, the risk of rejection, and graft outcome. The Expert Committee concludes that considerable advances in the different fields of tacrolimus monitoring have been achieved during this last decade. Continued efforts should focus on the opportunities to implement in clinical routine the combination of new standardized PK approaches with PG, and valid biomarkers to further personalize tacrolimus therapy and to improve long-term outcomes for treated patients.

When mycophenolic acid (MPA) was originally marketed for immunosuppressive therapy, fixed doses were recommended by the manufacturer. Awareness of the potential for a more personalized dosing has led to development of methods to estimate MPA area under the curve based on the measurement of drug concentrations in only a few samples. This approach is feasible in the clinical routine and has proven successful in terms of correlation with outcome. However, the search for superior correlates has continued, and numerous studies in search of biomarkers that could better predict the perfect dosage for the individual patient have been published. As it was considered timely for an updated and comprehensive presentation of consensus on the status for personalized treatment with MPA, this report was prepared following an initiative from members of the International Association of Therapeutic Drug Monitoring and Clinical Toxicology (IATDMCT). Topics included are the criteria for analytics, methods to estimate exposure including pharmacometrics, the potential influence of pharmacogenetics, development of biomarkers, and the practical aspects of implementation of target concentration intervention. For selected topics with sufficient evidence, such as the application of limited sampling strategies for MPA area under the curve, graded recommendations on target ranges are presented. To provide a comprehensive review, this report also includes updates on the status of potential biomarkers including those which may be promising but with a low level of evidence. In view of the fact that there are very few new immunosuppressive drugs under development for the transplant field, it is likely that MPA will continue to be prescribed on a large scale in the upcoming years. Discontinuation of therapy due to adverse effects is relatively common, increasing the risk for late rejections, which may contribute to graft loss. Therefore, the continued search for innovative methods to better personalize MPA dosage is warranted.

Multidrug and toxin extrusion 1 (MATE1/SLC47A1) is important for excretion of organic cations in the kidney and liver, where it is located on the luminal side. Although its functional and regulatory characteristics have been clarified, its pharmacokinetic roles in vivo have yet to be elucidated. In the present study, to clarify the relevance of MATE1 in vivo, targeted disruption of the murine Mate1 gene was carried out. The lack of Mate1 expression in the kidney and liver was confirmed by reverse transcription-polymerase chain reaction and Western blot analysis. The mRNA levels of other organic cation transporters such as Octs did not differ significantly between wild-type [Mate1(+/+)] and Mate1 knockout [Mate1(-/-)] mice. It is noteworthy that the Mate1(-/-) mice were viable and fertile. Pharmacokinetic characterization was carried out using metformin, a typical substrate of MATE1. After a single intravenous administration of metformin (5 mg/kg), a 2-fold increase in the area under the blood concentration-time curve for 60 min (AUC_0-60) of metformin in Mate1(-/-) mice was observed. Urinary excretion of metformin for 60 min after the intravenous administration was significantly decreased in Mate1(-/-) mice compared with Mate1(+/+) mice. The renal clearance (CL_ren) and renal secretory clearance (CL_sec) of metformin in Mate1(-/-) mice were approximately 18 and 14% of those in Mate1(+/+) mice, respectively. This is the first report to demonstrate an essential role of MATE1 in systemic clearance of metformin.

Background: Therapeutic drug monitoring (TDM) and model-informed precision dosing (MIPD) have greatly benefitted from computational and mathematical advances over the past 60 years. Furthermore, the use of artificial intelligence (AI) and machine learning (ML) approaches for supporting clinical research and support is increasing. However, AI and ML applications for precision dosing have been evaluated only recently. Given the capability of ML to handle multidimensional data, such as from electronic health records, opportunities for AI and ML applications to facilitate TDM and MIPD may be advantageous. Methods: This review summarizes relevant AI and ML approaches to support TDM and MIPD, with a specific focus on recent applications. The opportunities and challenges associated with this integration are also discussed. Results: Various AI and ML applications have been evaluated for precision dosing, including those related to concentration or exposure prediction, dose optimization, population pharmacokinetics and pharmacodynamics, quantitative systems pharmacology, and MIPD system development and support. These applications provide an opportunity for ML and pharmacometrics to operate in an integrated manner to provide clinical decision support for precision dosing. Conclusions: Although the integration of AI with precision dosing is still in its early stages and is evolving, AI and ML have the potential to work harmoniously and synergistically with pharmacometric approaches to support TDM and MIPD. Because data are increasingly shared between institutions and clinical networks and aggregated into large databases, these applications will continue to grow. The successful implementation of these approaches will depend on cross-field collaborations among clinicians and experts in informatics, ML, pharmacometrics, clinical pharmacology, and TDM.

To elucidate the impact of genetic variations in breast cancer resistance protein (BCRP/ABCG2) and P-glycoprotein (MDR1/ABCB1) on the pharmacokinetics of sunitinib, we carried out a pharmacogenetic study in a clinical setting and pharmacokinetic analysis using Abcg2(-/-), Abcb1a/1b(-/-) and Abcb1a/1b;Abcg2(-/-) mice. Nineteen renal cell carcinoma patients were enrolled in this study. The plasma concentrations of sunitinib and its active metabolite were determined and the area under the concentration-time curve (AUC) was calculated. Genetic polymorphisms in ABCG2 (421C>A) and ABCB1 (1236C>T, 2677G>T/A and 3435C>T) were examined. The dose-adjusted AUC(0-24) of sunitinib was significantly higher in patients with a heterozygous variant for ABCG2 421C>A than in wild-type patients (p = 0.02), and one homozygous patient showed the highest dose-adjusted AUC(0-24). The ABCB1 polymorphisms were not associated with the dose-adjusted AUC(0-24). The maximum concentration and AUC(0-4) of sunitinib were significantly higher in Abcg2(-/-), Abcb1a/1b(-/-) and Abcb1a/1b;Abcg2(-/-) mice than wild-type mice when sunitinib was given orally but not intraperitoneally. Incidence of thrombocytopenia and hypertension and poor compliance were associated with the systemic exposure to sunitinib and its active metabolite. These results suggest that the loss of protein expression of ABCG2 by genetic polymorphism is associated with an increase in the systemic exposure to sunitinib and sunitinib-induced toxicity.