Incorporation of Gene-Environment Interaction Terms Improved the Predictive Accuracy of Tacrolimus Stable Dose Algorithms in Chinese Adult Renal Transplant Recipients.

The narrow therapeutic window of tacrolimus necessitates daily monitoring and predictive algorithms based on genetic and nongenetic factors. In this study, we constructed predictive algorithms for tacrolimus stable dose in a retrospective cohort of 1045 Chinese renal transplant recipients. All patients were genotyped for CYP3A4 20230T>C (rs2242480), CYP3A4 T>C (rs4646437), CYP3A5*3 6898A>G (rs776746), ABCB1 129T>C (rs3213619); ABCB1 c.1236C>T (rs01128503), ABCB1 c.2677G>T/A (rs2032582) and ABCB1 c.3435C>T (rs1045642) polymorphisms, and the effects of gene-gene and gene-environment interactions on the predictive accuracy of algorithm were evaluated. In wild-type CYP3A4 rs2242480 (TT) carriers, patients who took calcium channel blockers had lower tacrolimus stable doses than those without the concomitant medications (P < 1 × 10-4 ). In contrast, there was no significant difference in mutant type patients. Similarly, the tacrolimus stable doses in wild-type CYP3A5 rs776746 carriers who had hypertension were higher than those without hypertension (P = 4.10 × 10-3 ). More importantly, dose-predictive algorithms with interaction terms showed higher accuracy and better performance than those without interaction terms. Our finding suggested that wild-type CYP3A4 rs2242480 (TT) carriers should be more cautious to take tacrolimus when they are coadministrated with calcium channel blockers, and CYP3A5 rs776746 (AA) carriers may need higher tacrolimus dosage when they are in combination with hypertension.

Corrigendum: Application of Machine-Learning Models to Predict Tacrolimus Stable Dose in Renal Transplant Recipients.

This corrects the article DOI: 10.1038/srep42192.

Application of Machine-Learning Models to Predict Tacrolimus Stable Dose in Renal Transplant Recipients.

Tacrolimus has a narrow therapeutic window and considerable variability in clinical use. Our goal was to compare the performance of multiple linear regression (MLR) and eight machine learning techniques in pharmacogenetic algorithm-based prediction of tacrolimus stable dose (TSD) in a large Chinese cohort. A total of 1,045 renal transplant patients were recruited, 80% of which were randomly selected as the "derivation cohort" to develop dose-prediction algorithm, while the remaining 20% constituted the "validation cohort" to test the final selected algorithm. MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied and their performances were compared in this work. Among all the machine learning models, RT performed best in both derivation [0.71 (0.67-0.76)] and validation cohorts [0.73 (0.63-0.82)]. In addition, the ideal rate of RT was 4% higher than that of MLR. To our knowledge, this is the first study to use machine learning models to predict TSD, which will further facilitate personalized medicine in tacrolimus administration in the future.

IL-3 and CTLA4 gene polymorphisms may influence the tacrolimus dose requirement in Chinese kidney transplant recipients.

The highly variable pharmacokinetics and narrow therapeutic window of tacrolimus (TAC) has hampered its clinical use. Genetic polymorphisms may contribute to the variable response, but the evidence is not compelling, and the explanation is unclear. In this study we attempted to find previously unknown genetic factors that may influence the TAC dose requirements. The association of 105 pathway-related single nucleotide polymorphisms (SNPs) with TAC dose-adjusted concentrations (C0/D) was examined at 7, 30 and 90 d post-operation in 382 Chinese kidney transplant recipients. In CYP3A5 non-expressers, the patients carrying the IL-3 rs181781 AA genotype showed a significantly higher TAC logC0/D than those with the AG genotype at 30 and 90 d post-operation (AA vs AG, 2.21±0.06 vs 2.01±0.03, P=0.004; and 2.17±0.06 vs 2.03±0.03, P=0.033, respectively), and than those with the GG genotype at 30 d (AA vs GG, 2.21±0.06 vs 2.04±0.03, P =0.011). At 30 d, the TAC logC0/D in the grouped AG+GG genotypes of CTLA4 rs4553808 was significantly lower than that in the AA genotype (P =0.041) in CYP3A5 expressers, but it was higher (P=0.008) in the non-expressers. We further validated the influence of CYP3A5 rs776746, CYP3A4 rs2242480 and rs4646437 on the TAC C0/D; other candidate SNPs were not associated with the differences in TAC C0/D. In conclusion, genetic polymorphisms in the immune genes IL-3 rs181781 and CTLA4 rs4553808 may influence the TAC C0/D. They may, together with CYP3A5 rs776746, CYP3A4 rs2242480 and rs4646437, contribute to the variation in TAC dose requirements. When conducting individualized therapy with tacrolimus, these genetic factors should be taken into account.

Differences in willingness to donate cadaveric organ between young donor families and adult donor families: evidence from the Hunan Province, China.

BACKGROUND: The Red Cross of China and Ministry of Health jointly started a pilot program of organ donation after cardiac death to overcome the shortage of available organs since 2010. The purpose of this qualitative study were to compare the consent rate of organ donation between young donor families and adult donor families; to explore and determine factors associated with differences in willingness to donate organs between them. Research objective was to provide a rationale for further preparation of professionals involved in this sensitive work. METHODS: Between March 2010 and June 2012, 24 young deceased patients including donors and non-donors and 96 potential adult donors were collected, and consent rates of young donors' families and adult donors' families were calculated. A χ(2) test analysis to compare the consent rates of the two groups was conducted. We studied through semistructured interviews 15 parents of young donors and 15 relatives of old donors who were interviewed for petition of consent. Data collection and analysis of the overall study were performed according to the grounded theory methodology. Factors that influenced the families' decisions were identified and classified. We found the differences in willingness to donate organs between the two groups. RESULTS: The consent rate of young donor families was 66.67%, while the consent rate of adult donor families was 26.04%. Young donor families easily consented to organ donation than adult donor families (P < 0.005). The donors' families had been affected by various factors throughout the process of deciding to give consent for donation. The findings led to the formulation of an empirically based model of interlinking categories that influence families' decision-making process in organ donation. These factors are grouped into five main categories: (1) personal factors, (2) conditions of organ request, (3) interpersonal factors, (4) ethical factors, and (5) traditional views. The funeral tradition influenced the young donor parents' consent to donation, but had no relation with family decision of adult donors. And the family members of young donors are relatively less, who are more likely to reach a consensus. CONCLUSIONS: Young donor families influenced by traditional funeral beliefs are easier to consent to organ donation than adult donor families. Family members of young donors are relatively less who are more likely to reach a consensus. Acceptance of the expanded criteria donors may improve the organ donation rates, especially those of the advanced age.

Risk factors for delayed graft function in cardiac death donor renal transplants.

BACKGROUND: Delayed graft function (DGF) is common in kidney transplants from organ donation after cardiac death (DCD) donors. It is associated with various factors. Determination of center-specific risk factors may help to reduce the incidence of DGF and improve the transplantation results. The aim of this study is to define risk factors of DGF after renal transplantation. METHODS: From March 2010 to June 2012, 56 cases of recipients who received DCD kidneys were selected. The subjects were divided into two groups: immediate graft function (IGF) and DGF groups. Transplantation factors of donors and recipients as well as early post-transplant results of recipients were compared between the two groups. RESULTS: On univariate analysis, preoperative dialysis time of recipients (P < 0.001), type of dialysis (P = 0.039), human leucocyte antigen (HLA) mismatch sites (P < 0.001), the cause of brain death (P = 0.027), body mass index (BMI) of donors (P < 0.001), preoperative infection (P = 0.002), preoperative serum creatinine of donors (P < 0.001), norepinephrine used in donors (P < 0.001), cardiopulmonary resuscitation (CPR) of donors (P < 0.001), warm ischemia time (WIT) (P < 0.001) and cold ischemia time (CIT) (P < 0.001) showed significant differences. Recipients who experienced DGF had a longer hospital stay, and higher level of postoperative serum creatinine. CONCLUSION: Multiple risk factors are associated with DGF, which had deleterious effects on the early post-transplant period.

Hepatic veins anatomy and piggy-back liver transplantation.

BACKGROUND: The piggy-back caval anastomosis technique is widely used in orthotopic liver transplantation although it carries an increased risk of complications, including outflow obstruction and Budd-Chiari syndrome. The aim of this study is to clarify the anatomy and variations of hepatic veins (HVs) draining into the inferior vena cava (IVC), and to classify the surgical techniques of piggy-back liver transplantation (PBLT) based on the anatomy of HVs which can reduce the occurrence of complications. METHODS: PBLT was performed in 248 consecutive cases at our hospital from January 2004 to August 2011. The anatomy of recipients' HVs was determined when removing the native diseased livers. Both anatomy of HVs and short HVs draining into the IVC were recorded. These data were collected and analyzed. RESULTS: We classified anatomic variations of HVs in the 248 livers into five types according to the way of drainage into the IVC: type I (trunk type of left and middle HVs), 142 (57.3%) patients; type II (trunk type of right and middle HVs), 54 (21.8%); type III (trunk type of left, middle and right HVs), 14 (5.6%); type IV (non-trunk type of left, middle and right HVs), of which, type IVa, 16 (6.5%), in the same horizontal plane; type IVb, 18 (7.3%), in different horizontal planes; and type V (segment type), 4 (1.6%). The patients whose HVs anatomy belonged to types I, II and III underwent classical piggy-back liver transplantation. Type IVa patients had classical PBLT via HV venoplasty prior to piggy-back anastomosis, while type IVb patients and type V patients could only have modified PBLT. CONCLUSION: This study demonstrates that HVs can be classified according to the anatomy of their drainage into the IVC and we can use this classification to choose the best operative approach to PBLT.