Comparative bioavailability study with two sodium valproate tablet formulations administered under fasting conditions in healthy subjects.

AIM: To assess the bioequivalence of two sodium valproate formulations in healthy subjects of both sexes. MATERIALS AND METHODS: The study was conducted using an open, randomized, two-period crossover design with a 2-week washout interval. Plasma samples were obtained over a 96-hour period. Plasma concentrations of valproate were analyzed by liquid chromatography coupled to tandem mass spectrometry (LC/MS) with negative ion electrospray ionization. From the sodium valproate plasma concentration vs. time curves, the following pharmacokinetic parameters were obtained Cmax, AUC, tmax, Ke, and T1/2. RESULTS: The geometric mean with corresponding 90% confidence interval for test/reference percent ratios were 104.43% (90% CI 100.42 - 108.61%) for Cmax, 98.11% (90% CI = 94.66 - 101.70%) for AUClast, and 96.71% (90% CI = 92.97 - 100.60%) for AUC0-inf. CONCLUSION: Since the 90% CI for Cmax and AUClast ratios were all inside the 80 - 125% interval proposed by the US Food and Drug Administration Agency (FDA), it was concluded that the new sodium valproate formulation (epilenil 500-mg coated tablet) without food elaborated by Biolab Sanus Farmaceutica Ltda is bioequivalent to depakene formulation for both the rate and the extent of absorption.

Fatal Outcome of Chikungunya Virus Infection in Brazil.

BACKGROUND: Chikungunya virus (CHIKV) emerged in the Americas in 2013 and has caused approximately 2.1 million cases and >600 deaths. A retrospective investigation was undertaken to describe clinical, epidemiological, and viral genomic features associated with deaths caused by CHIKV in Ceará state, northeast Brazil. METHODS: Sera, cerebrospinal fluid (CSF), and tissue samples from 100 fatal cases with suspected arbovirus infection were tested for CHIKV, dengue virus (DENV), and Zika virus (ZIKV). Clinical, epidemiological, and death reports were obtained for patients with confirmed CHIKV infection. Logistic regression analysis was undertaken to identify independent factors associated with risk of death during CHIKV infection. Phylogenetic analysis was conducted using whole genomes from a subset of cases. RESULTS: Sixty-eight fatal cases had CHIKV infection confirmed by reverse-transcription quantitative polymerase chain reaction (52.9%), viral antigen (41.1%), and/or specific immunoglobulin M (63.2%). Co-detection of CHIKV with DENV was found in 22% of fatal cases, ZIKV in 2.9%, and DENV and ZIKV in 1.5%. A total of 39 CHIKV deaths presented with neurological signs and symptoms, and CHIKV-RNA was found in the CSF of 92.3% of these patients. Fatal outcomes were associated with irreversible multiple organ dysfunction syndrome. Patients with diabetes appear to die at a higher frequency during the subacute phase. Genetic analysis showed circulation of 2 CHIKV East-Central-South African (ECSA) lineages in Ceará and revealed no unique virus genomic mutation associated with fatal outcome. CONCLUSIONS: The investigation of the largest cross-sectional cohort of CHIKV deaths to date reveals that CHIKV-ECSA strains can cause death in individuals from both risk and nonrisk groups, including young adults.

A Machine Learning Application Based in Random Forest for Integrating Mass Spectrometry-Based Metabolomic Data: A Simple Screening Method for Patients With Zika Virus.

Recent Zika outbreaks in South America, accompanied by unexpectedly severe clinical complications have brought much interest in fast and reliable screening methods for ZIKV (Zika virus) identification. Reverse-transcriptase polymerase chain reaction (RT-PCR) is currently the method of choice to detect ZIKV in biological samples. This approach, nonetheless, demands a considerable amount of time and resources such as kits and reagents that, in endemic areas, may result in a substantial financial burden over affected individuals and health services veering away from RT-PCR analysis. This study presents a powerful combination of high-resolution mass spectrometry and a machine-learning prediction model for data analysis to assess the existence of ZIKV infection across a series of patients that bear similar symptomatic conditions, but not necessarily are infected with the disease. By using mass spectrometric data that are inputted with the developed decision-making algorithm, we were able to provide a set of features that work as a "fingerprint" for this specific pathophysiological condition, even after the acute phase of infection. Since both mass spectrometry and machine learning approaches are well-established and have largely utilized tools within their respective fields, this combination of methods emerges as a distinct alternative for clinical applications, providing a diagnostic screening-faster and more accurate-with improved cost-effectiveness when compared to existing technologies.