Development of a novel drug information provision system for Kampo medicine using natural language processing technology.

BACKGROUND: Kampo medicine is widely used in Japan; however, most physicians and pharmacists have insufficient knowledge and experience in it. Although a chatbot-style system using machine learning and natural language processing has been used in some clinical settings and proven useful, the system developed specifically for the Japanese language using this method has not been validated by research. The purpose of this study is to develop a novel drug information provision system for Kampo medicines using a natural language classifier® (NLC®) based on IBM Watson. METHODS: The target Kampo formulas were 33 formulas listed in the 17th revision of the Japanese Pharmacopoeia. The information included in the system comes from the package inserts of Kampo medicines, Manuals for Management of Individual Serious Adverse Drug Reactions, and data on off-label usage. The system developed in this study classifies questions about the drug information of Kampo formulas input by natural language into preset questions and outputs preset answers for the questions. The system uses morphological analysis, synonym conversion by thesaurus, and NLC®. We fine-tuned the information registered into NLC® and increased the thesaurus. To validate the system, 900 validation questions were provided by six pharmacists who were classified into high or low levels of knowledge and experience of Kampo medicines and three pharmacy students. RESULTS: The precision, recall, and F-measure of the system performance were 0.986, 0.915, and 0.949, respectively. The results were stable even with differences in the amount of expertise of the question authors. CONCLUSIONS: We developed a system using natural language classification that can give appropriate answers to most of the validation questions.

COL8A1 facilitates the growth of triple-negative breast cancer via FAK/Src activation.

PURPOSE: Triple-negative breast cancer (TNBC) is one of the most aggressive breast cancer subtypes, and treatment options are limited because of the lack of signature molecules and heterogeneous properties of cancer. COL8A1 expression is higher in breast cancer than in normal tissues and is strongly correlated with worse overall survival in patients with breast cancer. However, the biological function of COL8A1 on cancer progression is not fully understood. In this study, we investigated the biological function of COL8A1 on TNBC progression. METHODS: COL8A1-deficient cells were generated using the CRISPR-Cas9 system. The tumor growth and metastasis of TNBC cells were evaluated using three-dimensional culture (3D) methods and xenograft mouse models. The activation of focal adhesion kinase (FAK)/Src by COL8A1 in TNBC cells was evaluated by immunoblotting. RESULTS: COL8A1 expression was primarily distributed into TNBC cell lines. Further, relapse-free survival in TNBC patients with the MSL subtype was strongly associated with the COL8A1 expression. MDA-MB-231 and Hs578T cells, classified as the MSL subtype, strongly express COL8A1, and COL8A1 protein expression was induced by hypoxia in both cell lines. Loss of COL8A1 expression inhibited spheroid /tumor growth and metastasis in vitro and in vivo. Further, exogenous COL8A1 promoted TNBC growth via the FAK/Src activation. Finally, the spheroid growth of MDA-MB-231 and Hs578T cells was inhibited by defactinib, a FAK inhibitor, without cytotoxicity. CONCLUSION: These results indicate that COL8A1-mediated FAK/Src activation produces a more aggressive phenotype in TNBC, and its target inhibition may be an efficacious treatment for TNBC.