Pharmacogenomics Variability of Lipid-Lowering Therapies in Familial Hypercholesterolemia.

The exponential expansion of genomic data coupled with the lack of appropriate clinical categorization of the variants is posing a major challenge to conventional medications for many common and rare diseases. To narrow this gap and achieve the goals of personalized medicine, a collaborative effort should be made to characterize the genomic variants functionally and clinically with a massive global genomic sequencing of "healthy" subjects from several ethnicities. Familial-based clustered diseases with homogenous genetic backgrounds are amongst the most beneficial tools to help address this challenge. This review will discuss the diagnosis, management, and clinical monitoring of familial hypercholesterolemia patients from a wide angle to cover both the genetic mutations underlying the phenotype, and the pharmacogenomic traits unveiled by the conventional and novel therapeutic approaches. Achieving a drug-related interactive genomic map will potentially benefit populations at risk across the globe who suffer from dyslipidemia.

A population pharmacodynamic model characterizing neutropenia associated with pegylated interferon alpha 2-a therapy in patients with chronic hepatitis C viral infection.

Neutropenia is a hematologic disorder commonly reported in patients with chronic hepatitis C viral (HCV) infection. The objective of the present analysis is to describe the change in neutrophil count resulting from peglated interferon alpha 2-a (PEG-IFN α-2a) therapy in HCV-infected patients. A population pharmacodynamic model will be developed. We also plan to identify patient characteristics that contribute to the development of PEG-IFN α-2a-induced neutropenia in hepatitis C patients. A population pharmacodynamic modeling approach was applied to a cohort of patients (n = 292) with chronic HCV infection. Modeling was performed using NONMEM 6. Data was obtained from two phases III studies sponsored by Hoffmann-La Roche. Covariate screening was applied to evaluate various demographic and clinical characteristics as possible predictors of pharmacodynamic parameter during model development. A total of 4517 neutrophil counts from 292 subjects were analyzed by the proposed population pharmacodynamic model. A constant residual error model was used to the log-transformed neutrophil count. Platelet baseline count and uric acid level were identified as predictors of neutrophil pharmacodynamic model. Increased baseline platelet count is expected to result in higher neutrophil baseline. A higher neutrophil baseline is also expected in patients with increased uric acid level. In conclusion, a mechanistic pharmacodynamic model was developed. The effect of various covariates was included in the model. This allows the prediction of neutrophil count following antiviral therapy in patients with hepatitis C infection. Clinical studies: NV15942 and NV15801.

Patient characteristics associated with peglyated interferon alfa-2a induced neutropenia in chronic hepatitis C patients.

Neutropenia is a haematologic disorder commonly reported in patients with chronic hepatitis C virus (HCV) infection treated with pegylated interferon alfa-2a (PEG-IFN α-2a). The objective of the present project is to identify patient characteristics associated with neutropenia in hepatitis C patients. Demographic, clinical, and genetic data from 715 patients with chronic HCV infection treated with PEG-IFN α-2a and ribavirin. The outcome variable was the development of grade 3 or 4 neutropenia, defined as the decrease in neutrophil counts below 1 109 /L anytime during study. Predictors of neutropenia were identified using a 2-stage approach. First, univariate analysis was performed to identify possible predictors of neutropenia. T test was used for continuous variables and Fisher's exact test was used for categorical variables. Second, multiple logistic regression with stepwise addition was then performed using predictors identified in the univariate analysis step to produce final model containing independent predictors at P < .05. Logistic regression identified female gender, absolute neutrophils counts, and cholesterol level as the main predictors of neutropenia. Female gender increases the odds of experiencing neutropenia by 86% compared to male gender. A 1 unit (mmol/L) increase in cholesterol level decreases the odds of developing neutropenia by 13%. A 55% reduction in the likelihood of developing neutropenia for a 1 unit (109 /L) increase in the absolute neutrophils counts. Patients with high risk of developing neutropenia can be identified. Identification of this cohort allows early intervention to prevent neutropenia. Possible intervention is to administer drugs that raise neutrophil count such as filgrastim before neutropenia occurs.