ALBERT

Description: Mycophenolate mofetil (MMF) is commonly used to provide host immunosuppression for hematopoietic cell engraftment and prevention of acute graft vs host disease (GVHD) in recipients of nonmyeloablative allogeneic HCT. There is wide variability in plasma concentrations of mycophenolic acid (MPA), the active metabolite of mycophenolate. Low MPA exposure is associated with poorer rates of engraftment and development of acute GVHD. This variability is not satisfactorily explained by hepatic or renal function suggesting variability may be due to genetic factors involved in drug metabolism or transport. MPA is glucuronidated by UDP glucuronosyltransferase enzymes UGT1A8, 1A9, and 1A10 to the primary metabolite, mycophenolic acid glucuronide (MPAG). MPAG is excreted into the urine and bile through multidrug resistant protein (MRP2) transporters. Several genetic variants of UGT enzymes and MRP2 are known to modify MPA pharmacokinetic parameters in kidney transplant recipients but have not been evaluated in HCT. We hypothesized that these variants are associated with systemic MPA and MPAG concentrations in HCT recipients. Recipient pretransplant DNA was obtained in 133 patients and evaluated for UGT1A8*2, UGT1A8*3, UGT1A9*2, UGT1A9*3, UGT1A9 T-275A, UGT1A9 C-2152T, UGT1A10*2, UGT2B7*2, MRP2 C-24T, MRP2 C-3972T, and MRP2 G-1249A. Patients received MMF at 1–1.5 gm IV or PO every 8–12 hours along with cyclosporine. Each subject had steady-state pharmacokinetics studied once or twice within the first 15 days of transplant for a total of 118 pharmacokinetic profiles. Frequencies of all variants were consistent with published data. Thirty three (24.8%) subjects were heterozygous (CT) and 3 (2.26%) were homozygous (TT) for the MRP2 C-24T variant. MRP2 C-3972T was heterozygous (CT) and homozygous (TT) in 56(42.2%) and 7(5.3%) individuals, respectively. In univariate analysis, there was no association between UGT variants and MPA IV or PO pharmacokinetics. Patients receiving oral MMF and carrying at least one MRP2 C-24T variant had significantly higher median (range) total dose adjusted MPAG Cmax 90.8ug/ml(40.0–164.8) vs 63.3(30.8–202.7), p= 0.02, trough 54.5ug/ml(16.1–99.6) vs 39.7(11.9–173.5), p=0.01 and MPAG Css 71.5ug/ml(28.9–123.0) vs 51.0(19.9–175.6), p=0.01. Similar results were observed for the C-3972T. These data suggest that single UGT variants do not significantly alter MPA exposure but MRP2 variants influence MPAG exposure. Reasons for observed UGT effects on MPA exposure in kidney transplant and not HCT remain unclear but may be due to bioavailability, lack of enterohepatic recycling due to gut decontamination, chemotherapy-induced mucosal and subclinical hepatic damage, and unidentified drug interactions. MRP2 variants may have an impact on other MRP2 substrates such as cyclophosphamide or inhibitors such as cyclosporine. A single UGT genotype to assist in dose selection is unlikely to be of clinical benefit; therefore the effect of combined variants should be explored.

Description: Background There is a growing awareness that interaction between multiple genes play an important role in the risk of common, complex multi-factorial diseases. Many common diseases are affected by certain genotype combinations (associated with some genes and their interactions). The identification and characterization of these susceptibility genes and gene-gene interaction have been limited by small sample size and large number of potential interactions between genes. Several methods have been proposed to detect gene-gene interaction in a case control study. The penalized logistic regression (PLR), a variant of logistic regression with L 2 regularization, is a parametric approach to detect gene-gene interaction. On the other hand, the Multifactor Dimensionality Reduction (MDR) is a nonparametric and genetic model-free approach to detect genotype combinations associated with disease risk. Methods We compared the power of MDR and PLR for detecting two-way and three-way interactions in a case-control study through extensive simulations. We generated several interaction models with different magnitudes of interaction effect. For each model, we simulated 100 datasets, each with 200 cases and 200 controls and 20 SNPs. We considered a wide variety of models such as models with just main effects, models with only interaction effects or models with both main and interaction effects. We also compared the performance of MDR and PLR to detect gene-gene interaction associated with acute rejection(AR) in kidney transplant patients. Results In this paper, we have studied the power of MDR and PLR for detecting gene-gene interaction in a case-control study through extensive simulation. We have compared their performances for different two-way and three-way interaction models. We have studied the effect of different allele frequencies on these methods. We have also implemented their performance on a real dataset. As expected, none of these methods were consistently better for all data scenarios, but, generally MDR outperformed PLR for more complex models. The ROC analysis on the real dataset suggests that MDR outperforms PLR in detecting gene-gene interaction on the real dataset. Conclusion As one might expect, the relative success of each method is context dependent. This study demonstrates the strengths and weaknesses of the methods to detect gene-gene interaction.