The effect of PINK1/Parkin pathway on glucose homeostasis imbalance induced by tacrolimus in mouse livers

Tacrolimus and diabetic rodent models

Tacrolimus (TAC) is an immunosuppressant widely utilized in organ transplantation. One of its primary adverse effects is glucose metabolism disorder, which significantly increases the risk of diabetes. Investigating the molecular mechanisms underlying TAC-induced diabetes is essential for developing effective prevention and treatment strategies for these adverse effects. In addition, TAC can induce cost-effective, non-obese animal models of diabetes, where the metabolic parameter changes closely resemble those observed during the onset and progression of type 2 diabetes (T2DM), post-transplantation diabetes mellitus (PTDM), and associated complications. This review, based on articles indexed in PubMed up to August 19, 2024, identified 48 studies focusing on TAC-induced diabetic rodent models and 22 studies exploring the effects of TAC on diabetic or obese rodent models. These studies were systematically summarized based on TAC dosage, route of administration, duration of administration, and glucose metabolism indices used for evaluation. Additionally, the impact of TAC dose reduction or discontinuation on glucose metabolism was assessed, along with pharmacological agents that modulate TAC-induced diabetes, including anti-diabetic medications, anti-inflammatory and antioxidant compounds, biologics, and antibiotics. Key signaling pathways implicated in TAC-induced diabetes include CaN/NFAT, PI3K/AKT/mTOR, and TGF-β/Smad, all of which impair islet β-cell function, thereby contributing to diabetes development. This review provides a concise summary of the characteristics of relevant murine models, offering valuable guidance for selecting appropriate and economical animal models for future research.

Genotype-Guided Model for Prediction of Tacrolimus Initial Dosing After Lung Transplantation

The determination of the appropriate initial dose for tacrolimus is crucial in achieving the target concentration promptly and avoiding adverse effects and poor prognosis. However, the trial-and-error approach is still common practice. This study aimed to establish a prediction model for an initial dosing algorithm of tacrolimus in patients receiving a lung transplant. A total of 210 lung transplant recipients were enrolled, and 26 single nucleotide polymorphisms (SNP) from 18 genes that could potentially affect tacrolimus pharmacokinetics were genotyped. Associations between SNPs and tacrolimus concentration/dose ratio were analyzed. SNPs that remained significant in pharmacogenomic analysis were further combined with clinical factors to construct a prediction model for tacrolimus initial dose. The dose needed to reach steady state tacrolimus concentrations and achieve the target range was used to validate model prediction efficiency. Our final model consisted of 7 predictors-CYP3A5 rs776746, SLCO1B3 rs4149117, SLC2A2 rs1499821, NFATc4 rs1955915, alanine aminotransferase, direct bilirubin, and hematocrit-and explained 41.4% variance in the tacrolimus concentration/dose ratio. It achieved an area under the receiver operating characteristic curve of 0.804 (95% confidence interval, 0.746-0.861). The Hosmer-Lemeshow test yielded a nonsignificant P value of .790, suggesting good fit of the model. The predicted dose exhibited good correlation with the observed dose in the early postoperative period (r = 0.748, P less than .001). Our study provided a genotype-guided prediction model for tacrolimus initial dose, which may help to guide individualized dosing of tacrolimus in the lung transplant population in clinical practice.