The Pharmacogene Variation Consortium (PharmVar) catalogues star (*) allele nomenclature for the polymorphic human CYP2C19 gene. CYP2C19 genetic variation impacts the metabolism of many drugs and has been associated with both efficacy and safety issues for several commonly prescribed medications. This GeneFocus provides a comprehensive overview and summary of CYP2C19 and describes how haplotype information catalogued by PharmVar is utilized by the Pharmacogenomics Knowledgebase and the Clinical Pharmacogenetics Implementation Consortium (CPIC).

Current Clinical Management Guidelines of Diabetic Peripheral Neuropathy (DPN) are based on adequate glucose control and symptomatic pain relief. However, meticulous glycemic control could delay the onset or slow the progression of diabetic neuropathy in patients with DM type 2, but it does not completely prevent the progression of the disease. Complications of DPN as it continues its natural course, produce increasing pain and discomfort, loss of sensation, ulcers, infections, amputations and even death. In addition to the increased suffering, disability and loss of productivity, there is a very significant economic impact related to the treatment of DPN and its complications. In USA alone, it has been estimated that there are more than 5,000,000 patients suffering from DPN and the total annual cost of treating the disease and its complications is over $10,000 million dollars. In order to be able to reduce complications of DPN, it is crucial to improve or correct the metabolic conditions that lead to the pathology present in this condition. Pathophysiologic mechanisms implicated in diabetic neuropathy include: increased polyol pathway with accumulation of sorbitol and reduced Na+/K+-ATPase activity, microvascular damage and hypoxia due to nitric oxide deficit and increased oxygen free radical activity. Moreover, there is a decrease in glutathione and increase in homocysteine. Clinical trials in the last two decades have demonstrated that the use of specific nutrients can correct some of these metabolic derangements, improving symptom control and providing further benefits such as improved sensorium, blood flow and nerve regeneration. We will discuss the evidence on lipoic acid, acetyl-L-carnitine, benfotiamine and the combination of active B vitamins L-methylfolate, methylcobalamin and piridoxal-6-phosphate. In addition, we discuss the role of metformin, an important drug in the management of diabetes, and the presence of specific polymorphic genes, in the risk of developing DPN and how metabolic correction can reduce these risks.

Although the Hispanic population is continuously growing in the United States, they are underrepresented in pharmacogenetic studies. This review addresses the need for compiling available pharmacogenetic data in US Hispanics, discussing the prevalence of clinically relevant polymorphisms in pharmacogenes encoding for drug-metabolizing enzymes. CYP3A5 *3 (0.245–0.867) showed the largest frequency in a US Hispanic population. A higher prevalence of CYP2C9 *3, CYP2C19 *4, and UGT2B7 IVS1+985 A>G was observed in US Hispanic vs. non-Hispanic populations. We found interethnic and intraethnic variability in frequencies of genetic polymorphisms for metabolizing enzymes, which highlights the need to define the ancestries of participants in pharmacogenetic studies. New approaches should be integrated in experimental designs to gain knowledge about the clinical relevance of the unique combination of genetic variants occurring in this admixed population. Ethnic subgroups in the US Hispanic population may harbor variants that might be part of multiple causative loci or in linkage-disequilibrium with functional variants. Pharmacogenetic studies in Hispanics should not be limited to ascertain commonly studied polymorphisms that were originally identified in their parental populations. The success of the Personalized Medicine paradigm will depend on recognizing genetic diversity between and within US Hispanics and the uniqueness of their genetic backgrounds.

Despite some previous examples of successful application to the field of pharmacogenomics, the utility of machine learning techniques for warfarin dose predictions in Caribbean Hispanic patients has yet to be fully evaluated. This study compares seven machine learning methods to predict warfarin dosing in Caribbean Hispanics. This is a secondary analysis of genetic and non-genetic clinical data from 190 cardiovascular Hispanic patients. Seven Machine Learning (ML) algorithms were applied to the data. Data was divided into 80% and 20% to be used as training and test sets. ML algorithms were trained with the training set to obtain the models. Model performance was determined by computing the corresponding Mean Absolute Error (MAE) and % patients whose predicted optimal dose were within ±20% of the actual stabilization dose, and then compared between groups of patients with ‘Normal’ (i.e., >21 but <49mg/week), low (i.e., ≤21mg/week, ‘Sensitive’) and high (i.e., ≥49 mg/week, ‘Resistant’) dose requirements. RFR significantly outperform all other methods, with a MAE of 4.93 mg/week and 80.56% of cases within ±20% of the actual stabilization dose. Among those with ‘Normal’ dose requirements, RFR performance is also better than the rest of models (MAE=2.91 mg/week). In the ‘Sensitive’ group, SVR shows superiority over the others with lower MAE of 5.58 mg/week. Finally, MARS shows the best performance in the resistant group (MAE=7.22 mg/week) and 66.7% of predictions within ±20%. Models generated by using RFR, MARS and SVR algorithms showed significantly better predictions of weekly warfarin dosing in the studied cohorts than other algorithms. Better performance of the ML models for patients with ‘Normal’, ‘Sensitive’ and ‘Resistant’ to warfarin were obtained when compared to other populations and previous statistical models.

Aim: Variants of the MTHFR gene have been associated with a wide range of diseases. Materials & methods: The present study analyzed data from clinical genotyping of MTHFR 677C>T and 1298A>C in 1405...