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WARFARIN DOSE PREDICTION THROUGH A USER INTERFACE USING CLINICAL, DEMOGRAPHIC AND PHARMACOGENETIC DATA

WARFARIN DOSE PREDICTION THROUGH A USER INTERFACE USING CLINICAL, DEMOGRAPHIC AND PHARMACOGENETIC DATA

Jennifer Eliana Montoya Neyra, Júlia Soler and Paulo Santos

We work on a user interface (UI) designed to assist in warfarin therapy by predicting a best therapeutic dose, calculated from the data entered into the UI. It will be able to predict more accurate doses for patients diagnosed with atrial fibrillation, stroke, thrombosis or heart valve prosthesis in whom it is desired to maintain an international normalized ratio (INR) between two and three, using their clinical, demographic and pharmacogenetic data. The prediction models that were considered for the construction of this prediction interface were the International Warfarin Pharmacogenetics Consortium (IWPC), multiple linear regression, regression using regularizers (Lasso regression, Ridge regression), Elastic net regression, regression of selected variables by AIC, Ridge Regression with Variable Selection (foba package in R) and a simple neural network model that consists of 3 hidden layers of 100 neurons each, using data from patients of the Brazilian Heart Institute (InCor – USP). This data include clinical, demographic, and pharmaceutical factors and and risk genotypes of the cytochrome P450 2C9 (CYP2C9), vitamin K epoxy reductase (VKORC1), leukotriene B(4) omega-hydroxylase 1 (CYP4F2) and NAD(P)H dehydrogenase (quinone) 1 (NQO1) genes. The models were trained with the information of 614 individuals, which reached INR values between 2 and 3 when receiving a maintenance dose of warfarin, and tested in a subset of 152 patients. To evaluate the accuracy of the models, the mean absolute error (MAE), root-mean-square error (RMSE) and R-squared were calculated. The best adjusted model was the Ridge Regression with Variable Selection, which obtained the best performance when analyzing both the training group (MAE = 7.54, RMSE = 0.993, R-squared = 0.296) and the evaluation group (MAE = 0.766, RMSE = 1.07, R-squared = 0.282). This tool is still under development, but we have great expectations about its applicability and usefulness for patients who require it.

Posted by rsg2sec on

NAMPT SNPs ASSOCIATED WITH VISFATIN/NAMPT LEVELS LOCATED NEARBY A PUTATIVE ENHANCER REGION ACTIVATED BY METFORMIN

NAMPT SNPs ASSOCIATED WITH VISFATIN/NAMPT LEVELS LOCATED NEARBY A PUTATIVE ENHANCER REGION ACTIVATED BY METFORMIN

Daniela Pereira, Lídia Coura and Marcelo Luizon

Nicotinamide phosphoribosyltransferase (NAMPT) is a potential therapeutic biomarker or target for several diseases. NAMPT is activated by Metformin, the first-line therapy for type 2 diabetes, and it is also used as a treatment for other diseases. Moreover, the single nucleotide polymorphism (SNP) rs1319501 in NAMPT promoter region were found to be associated with plasma NAMPT levels, and tightly linked with the SNPs rs9770242 and rs61330082, which are located ~1,500bp upstream from the NAMPT transcription start site. However, these noncoding SNPs may overlap with functional regulatory elements, such as enhancers. Thus, we searched for metformin-responsive regulatory elements in the NAMPT locus, and linked SNPs within them which may be associated with NAMPT levels. First, we examined publicly available ChIP-seq data for active (H3K27ac) and silenced (H3K27me3) histone marks on human hepatocytes treated with metformin, GeneHancer to identify active regulatory elements (enhancers and promoters), and several cis-regulatory elements assignment tools from the Encyclopedia of DNA Elements (ENCODE) to identify enhancers around the NAMPT locus. Next, we performed the functional annotation of noncoding SNPs located in the NAMPT locus using the Genotype-Tissue Expression (GTEx) project data for SNPs linked to NAMPT expression. The SNPs rs1319501, rs9770242 and rs61330082 overlap with a metformin-responsive region enriched for the active histone mark H3K27ac upon metformin treatment, which is located nearby an enhancer element according to GeneHancer (GH07J106288). Interestingly, rs61330082 and rs11977021 were in perfect linkage disequilibrium in a cohort of severely obese children and are associated with visfatin level and adverse cardiometabolic parameters. According to GTEx, these SNPs are eQTLs for NAMPT expression in heart tissue. These data support that noncoding variation within a metformin-activated enhancer may increase NAMPT expression. The perspectives are to functionally characterize these noncoding NAMPT SNPs, which could help to predict NAMPT levels in patients with type 2 diabetes treated with Metformin.