Long-term sustained release Poly(lactic-co-glycolic acid) microspheres of asenapine maleate with improved bioavailability for chronic neuropsychiatric diseases

Schizophrenia and bipolar disorder are severe chronic neuropsychiatric diseases, affecting hundreds of millions of people worldwide. Asenapine maleate (ASM) has been demonstrated as a safe and effective therapeutic agent under twice-daily administration. However, lower compliance is observed when patients are treated with ASM, which significantly limits its application in schizophrenia and bipolar disorder. Moreover, the low bioavailability of ASM caused by first-pass metabolism and poor aqueous solubility also impairs the treatment effect. A formulation of ASM with the property of long-term sustained release and improved bioavailability can be a solution to overcome these weaknesses. In this article, we prepared ASM-loaded poly(lactic-co-glycolic acid) (ASM-PLGA) microspheres through different techniques, including emulsification-solvent evaporation (ESE), Shirasu porous glass membrane emulsification (SPG-ME), and microfluidic method. In vitro and in vivo assessments demonstrated that uniform-sized microspheres generated by the microfluidic process sustainably released ASM throughout 40-days, showing low burst release and significantly improved bioavailability. The results suggest that ASM-PLGA microspheres prepared by the microfluidic method provide an efficient strategy to enhance the drug exposure of ASM as the treatment of chronic neuropsychiatric diseases. It is also evident that this microfluidic strategy has the potential to construct with other drugs, establishing long-acting formulations

MeSHLabeler: Improving the accuracy of large-scale MeSH indexing by integrating diverse evidence

Motivation: Medical Subject Headings (MeSHs) are used by National Library of Medicine (NLM) to index almost all citations in MEDLINE, which greatly facilitates the applications of biomedical information retrieval and text mining. To reduce the time and financial cost of manual annotation, NLM has developed a software package, Medical Text Indexer (MTI), for assisting MeSH annotation, which uses k-nearest neighbors (KNN), pattern matching and indexing rules. Other types of information, such as prediction by MeSH classifiers (trained separately), can also be used for automatic MeSH annotation. However, existing methods cannot effectively integrate multiple evidence for MeSH annotation. Methods: We propose a novel framework, MeSHLabeler, to integrate multiple evidence for accurate MeSH annotation by using 'learning to rank'. Evidence includes numerous predictions from MeSH classifiers, KNN, pattern matching, MTI and the correlation between different MeSH terms, etc. Each MeSH classifier is trained independently, and thus prediction scores from different classifiers are incomparable. To address this issue, we have developed an effective score normalization procedure to improve the prediction accuracy. Results: MeSHLabeler won the first place in Task 2A of 2014 BioASQ challenge, achieving the Micro F-measure of 0.6248 for 9, 040 citations provided by the BioASQ challenge. Note that this accuracy is around 9.15% higher than 0.5724, obtained by MTI

Exenatide-loaded inside-porous poly(lactic-co-glycolic acid) microspheres as a long-acting drug delivery system with improved release characteristics

The glucagon-like peptide-1 receptor agonist exenatide (EXT) is an effective treatment for type 2 diabetes. However, this peptide has a short biological half-life and the delayed release characteristic of current formulations limit its clinical application. Herein, we prepared EXT-loaded inside-porous poly(d,l-lactic-co-glycolic acid (PLGA) microspheres with outside layers (EXT-PMS) using a W1/O/W2 emulsion method with a microfluidic technique and its fabrication and formulation conditions were systematically investigated. In vitro dissolution experiments showed that the PLGA concentration, proportion of drug and oil phase, and the number and size of pores strongly affected the release behaviors of EXT-PMS. In vitro, the optimized EXT-PMS with large internal pores exhibited rapid and stable release without a lag phase. In a rat model, subcutaneous administration of the product yielded plasma concentrations of EXT that was sustained for 30 days with low burst and no delayed-release effect. The preparation of inside-porous microspheres is lighting up the development of long-acting drug delivery systems for other drugs with favorable release characteristics

MADE: A Computational Tool for Predicting Vaccine Effectiveness for the Influenza A(H3N2) Virus Adapted to Embryonated Eggs

Seasonal Influenza H3N2 virus poses a great threat to public health, but its vaccine efficacy remains suboptimal. One critical step in influenza vaccine production is the viral passage in embryonated eggs. Recently, the strength of egg passage adaptation was found to be rapidly increasing with time driven by convergent evolution at a set of functionally important codons in the hemagglutinin (HA1). In this study, we aim to take advantage of the negative correlation between egg passage adaptation and vaccine effectiveness (VE) and develop a computational tool for selecting the best candidate vaccine virus (CVV) for vaccine production. Using a probabilistic approach known as mutational mapping, we characterized the pattern of sequence evolution driven by egg passage adaptation and developed a new metric known as the adaptive distance (AD) which measures the overall strength of egg passage adaptation. We found that AD is negatively correlated with the influenza H3N2 vaccine effectiveness (VE) and ~75% of the variability in VE can be explained by AD. Based on these findings, we developed a computational package that can Measure the Adaptive Distance and predict vaccine Effectiveness (MADE). MADE provides a powerful tool for the community to calibrate the effect of egg passage adaptation and select more reliable strains with minimum egg-passaged changes as the seasonal A/H3N2 influenza vaccine