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Yokohama-shi, Japan

Teramoto R.,Forerunner Pharma Research Co. | Kato T.,University of Tokyo | Kato T.,Gunma University
Journal of Bioinformatics and Computational Biology | Year: 2011

In the drug discovery process, the metabolic fate of drugs is crucially important to prevent drugdrug interactions. Therefore, P450 isozyme selectivity prediction is an important task for screening drugs of appropriate metabolism profiles. Recently, large-scale activity data of five P450 isozymes (CYP1A2 CYP2C9, CYP3A4, CYP2D6, and CYP2C19) have been obtained using quantitative high-throughput screening with a bioluminescence assay. Although some isozymes share similar selectivities, conventional supervised learning algorithms independently learn a prediction model from each P450 isozyme. They are unable to exploit the other P450 isozyme activity data to improve the predictive performance of each P450 isozyme's selectivity. To address this issue, we apply transfer learning that uses activity data of the other isozymes to learn a prediction model from multiple P450 isozymes. After using the large-scale P450 isozyme selectivity dataset for five P450 isozymes, we evaluate the model's predictive performance. Experimental results show that, overall, our algorithm outperforms conventional supervised learning algorithms such as support vector machine (SVM), Weighted k-nearest neighbor classifier, Bagging, Adaboost, and latent semantic indexing (LSI). Moreover, our results show that the predictive performance of our algorithm is improved by exploiting the multiple P450 isozyme activity data in the learning process. Our algorithm can be an effective tool for P450 selectivity prediction for new chemical entities using multiple P450 isozyme activity data. © 2011 Imperial College Press. Source

Forerunner Pharma Research Co. and University of Tokyo | Date: 2010-04-15

[Problem to be Solved] An object of the present invention is to provide novel means for the treatment and diagnosis of cancer. [Solution] The present inventors have obtained a monoclonal antibody against TMPRSS11E and found that this antibody binds to a native form of TMPRSS11E, and TMPRSS11E is highly expressed on the cell membranes of cancer cell lines in flow cytometry. This antibody exhibits antibody-dependent cell-mediated cytotoxicity activity (ADCC activity) and antitumor effect based on internalization activity and is promising as a therapeutic target. Moreover, this antibody has neutralization activity against protease activity and is also expected to have effect brought about by the inhibition of TMPRSS11E functions.


The present inventors identified inhibition of a combination of EGFR ligands that serve as targets for inhibition of cancer cell proliferation. More specifically, EREG antagonists and TGF antagonists were found to be useful as inhibitors of cell growth. The present invention relates to pharmaceutical compositions containing EGF family ligand antagonists as components.

University of Tokyo and Forerunner Pharma Research Co. | Date: 2011-01-28

It is intended to disclose an antibody which binds to DLL3 protein. Preferably, the antibody of the present invention recognizes a region from amino acids 216 to 492 in human DLL3 having the amino acid sequence as set forth in SEQ ID NO: 1. The present invention also provides a pharmaceutical composition, for example, an anticancer agent, comprising the antibody of the present invention as an active ingredient. The present invention further discloses a method for diagnosing cancer using the antibody of the present invention and a diagnostic drug for cancer comprising the antibody of the present invention.

Kakiuchi M.,Tokyo University of Science | Kakiuchi M.,University of Tokyo | Nishizawa T.,Forerunner Pharma Research Co. | Ueda H.,Tokyo University of Science | And 24 more authors.
Nature Genetics | Year: 2014

Diffuse-type gastric carcinoma (DGC) is characterized by a highly malignant phenotype with prominent infiltration and stromal induction. We performed whole-exome sequencing on 30 DGC cases and found recurrent RHOA nonsynonymous mutations. With validation sequencing of an additional 57 cases, RHOA mutation was observed in 25.3% (22/87) of DGCs, with mutational hotspots affecting the Tyr42, Arg5 and Gly17 residues in RHOA protein. These positions are highly conserved among RHO family members, and Tyr42 and Arg5 are located outside the guanine nucleotide-binding pocket. Several lines of functional evidence indicated that mutant RHOA works in a gain-of-function manner. Comparison of mutational profiles for the major gastric cancer subtypes showed that RHOA mutations occur specifically in DGCs, the majority of which were histopathologically characterized by the presence of poorly differentiated adenocarcinomas together with more differentiated components in the gastric mucosa. Our findings identify a potential therapeutic target for this poor-prognosis subtype of gastric cancer with no available molecularly targeted drugs. © 2014 Nature America, Inc. Source

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