Deep learning for extracting protein-protein interactions from biomedical literature

State-of-the-art methods for protein-protein interaction (PPI) extraction are primarily feature-based or kernel-based by leveraging lexical and syntactic information. But how to incorporate such knowledge in the recent deep learning methods remains an open question. In this paper, we propose a multichannel dependency-based convolutional neural network model (McDepCNN). It applies one channel to the embedding vector of each word in the sentence, and another channel to the embedding vector of the head of the corresponding word. Therefore, the model can use richer information obtained from different channels. Experiments on two public benchmarking datasets, AIMed and BioInfer, demonstrate that McDepCNN compares favorably to the state-of-the-art rich-feature and single-kernel based methods. In addition, McDepCNN achieves 24.4% relative improvement in F1-score over the state-of-the-art methods on cross-corpus evaluation and 12% improvement in F1-score over kernel-based methods on "difficult" instances. These results suggest that McDepCNN generalizes more easily over different corpora, and is capable of capturing long distance features in the sentences.Comment: Accepted for publication in Proceedings of the 2017 Workshop on Biomedical Natural Language Processing, 10 pages, 2 figures, 6 table

Fliposomes with a pH-sensitive conformational switch for anticancer drug delivery against triple negative breast cancer

Cancer is the second leading cause of death in the US and worldwide, accounting for 16% of deaths worldwide in 2015. Of more than 100 types of cancers affecting humans, breast cancer is the most common cancer among women and is the second leading cause of death in women. Triple negative breast cancer (TNBC) is a subtype of breast carcinomas defined by the lack of the expression of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor (HER2 /neu). The prognosis and survival of TNBC patients remains the poor due to the lack of effective targeted therapy. Nanotechnology-based drug delivery systems, such as liposomes, are widely investigated to enhance anticancer efficacy by concentrating the drug molecules in the tissues of interest and by altering the pharmacokinetic profile. Taking advantage of the pH gradient in the tumor microenvironment, pH-triggered release is a promising strategy to enhance the anticancer efficacy of drug delivery systems against TNBC. Previously, a strategy in our lab has been developed to render saturated and pegylated liposomes pH-sensitive: protonation-induced conformational switch of lipid tails, using trans-2-aminocyclohexanol lipids (TACH, flipids) as a molecular trigger. Based on previous work in our lab, pH-sensitive liposomes (fliposomes) composed of C-16 flipids with amine group of morpholine (MOR) and azetidine (AZE) demonstrated optimized triggered release in response to the tumor’s low pH microenvironment. In this study, different preparation methods were developed and optimized to produce viable fliposomes with high doxorubicin (DOX) encapsulation efficiency. In vitro release assays were established and validated to accurately reflect pH-triggered release of fliposomes. The physicochemical properties of DOX-loaded fliposomes were characterized and their pH-dependent release were investigated. Factors influencing the desirable attributes of liposomes, such as size, pH-sensitivity, stability and drug-loading capacity were explored. Based on these characterizations, central composite design (CCD) was utilized to optimize the formulation of fliposome with two critical factors, flipids and cholesterol. Cell viability assays on traditional monolayer and innovative three-dimensional multicellular spheroids (3D MCS) of TNBC cell lines were conducted to evaluate the anticancer efficacy of the resultant fliposomes in vitro. The constructed 3D MCS carried heterogeneously distributed live and apoptotic cells, as well as acidity inside the 3D MCS based on confocal microscopic imaging studies. The distribution and penetration of DOX-loaded fliposomes into 3D MCS was imaged by confocal microscopy in comparison to DOX-loaded non pH-sensitive liposomes and free DOX. As a result, fliposome manifested superior anticancer activity against TNBC 3D MCS by efficient penetration into 3D MCS, followed by tuning up the release rate of the anticancer agent DOX. A TNBC orthotopic xenograft model was established by transplanting TNBC into the murine mammalian fat pad, which maintains the organ-specific tumor microenvironment of the original organ . A pilot pharmacokinetic study was conducted in order to correlate the pH response and stability properties with the in vivo stability of the optimized AZE-C16 fliposome. The antitumor efficacy was comparable between free DOX and DOX-loaded stealth liposome with tumor volumes of ~ 80-90% of the control treatment 32 days post first dose. In contrast, the DOX-loaded fliposome, especially MOR-C16 fliposome, exhibited a significantly higher antitumor efficacy and delayed progression compared to free DOX and stealth liposome treatments. Taken together, DOX-loaded fliposomes were successfully prepared and optimized for in vivo application. They were able to achieve superior activity against TNBC in vitro and in vivo, facilitated by enhanced release of the anticancer drug DOX after penetration inside TNBC tumor