Predictors of Severe Thrombocytopenia Secondary to Peginterferon Alfa-2a Treatment in Subjects With Hepatitis C Virus Infection

Misunderstandings Between Platelets and Neutrophils Build in Chronic Inflammation

Regulated hemostasis, inflammation and innate immunity entail extensive interactions between platelets and neutrophils. Under physiological conditions, vascular inflammation offers a template for the establishment of effective intravascular immunity, with platelets providing neutrophils with an array of signals that increase their activation threshold, thus limiting collateral damage to tissues and promoting termination of the inflammatory response. By contrast, persistent systemic inflammation as observed in immune-mediated diseases, such as systemic vasculitides, systemic sclerosis, systemic lupus erythematosus or rheumatoid arthritis is characterized by platelet and neutrophil reciprocal activation, which ultimately culminates in the generation of thrombo-inflammatory lesions, fostering vascular injury and organ damage. Here, we discuss recent evidence regarding the multifaceted aspects of platelet-neutrophil interactions from bone marrow precursors to shed microparticles. Moreover, we analyse shared and disease-specific events due to an aberrant deployment of these interactions in human diseases. To restore communications between the pillars of the immune-hemostatic continuum constitutes a fascinating challenge for the near future.

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.