Yasushi Okuno, Ph.D., Professor
In addition to experimentally-driven and theoretically-driven science, two new waves of science are globally emerging that focus on simulation science and data-centric science. Our laboratory is developing new methods for medical big data analysis and medical simulation using actual clinical data from the Kyoto University Hospital, as well as new methods for simulation drug discovery and big data drug discovery by using the K supercomputer. Through these developments we aim to open the way to simulation science and data science for practical medical care and applied drug discovery.
Research and EducationMedical big data analysis and simulation:
In collaboration with the Kyoto University Hospital Cancer Center Biobank & Informatics for Cancer Project, which has established a protocol for longitudinal recording of clinical data as well as collection and storage of blood samples, the laboratory aims to develop new methods for integration and analysis of various types of biological data, and in doing such, provide frameworks that enable clinical data to spur the creation of translational clinical informatics, data-driven personalized medicine, and data-driven predictive medicine. More specifically, we are creating new methods for systematic, rational inference about each individual cancer patient’s physical state, efficacy of treatment, and prediction of drug adverse events. In relation to such data, we are additionally developing methods for novel biomarker identification and drug target discovery.
Simulation and big data drug discovery:
In recent years, the pharmaceutical industry has been faced with a crucial problem of how to efficiently develop new drugs while reducing development costs, and there is high expectation for the use of computers to perform “in-silico drug discovery”. Notably, with the new availability of the K supercomputer has come the blossoming of computational science and informatics for drug discovery. Our laboratory has established a consortium of pharmaceutical companies, IT companies, and academic labs that is developing drug discovery computational technologies based on the K supercomputer.
- Nakaoku, T., Kohno T., Araki M., Niho, S., Chauhan, R., Knowles, P.P., Tsuchihara, K., Matsumoto, S., Shimada, Y., Mimaki, S., Ishii, G., Ichikawa, H., Nagatoishi, S., Tsumoto, K., Okuno, Y., Yoh, K., McDonald, N.Q., Goto, K. “A secondary RET mutation in the activation loop conferring resistance to vandetanib” Nature Communications, 9: 625, 2018. doi: 10.1038/s41467-018-02994-7
- Uneno, Y., Taneishi, K., Kanai, M., Okamoto, K., Yamamoto, Y., Yoshioka, A., Hiramoto, S., Nozaki, A., Nishikawa, Y., Yamaguchi, D., Tomono, T., Nakatsui, M., Baba, M., Morita, T., Mataumoto, S., Kuroda, T., Okuno, Y., Muto, M. “Development and validation of a set of six adaptable prognosis prediction (SAP) models based on time-series real-world big data analysis for patients with cancer receiving chemotherapy: A multicenter case crossover study” PLoS ONE, 12(8), 2017. e0183291. doi: 10.1371/journal.pone.0183291
- Uchibori, K., Inase, N., Araki, M., Kamada, M., Sato, S., Okuno, Y., Fujita, N., Katayama, R. “Brigatinib combined with anti-EGFR antibody overcomes osimertinib resistance in EGFR-mutated non-small-cell lung cancer” Nature Communications, 8:14768, 2017. doi: 10.1038/ncomms14768
- Hamanaka, M., Taneishi, K., Iwata, H., Ye, J., Pei, J., Hou, J., Okuno, Y. “CGBVS-DNN: Prediction of Compound-protein Interactions Based on Deep Learning” Molecular Informatics, 36:1600045, 2017. doi: 10.1002/minf.201600045
- Yabuuchi, H., Niijima, S., Takematsu, H., Ida, T., Hirokawa, T., Hara, T., Ogawa, T., Minowa, Y., Tsujimoto, G., Okuno, Y. “Analysis of multiple compound-protein interactions reveals novel bioactive molecules” Molecular Systems Biology, 7:472, 2011. doi: 10.1038/msb.2011.5
Clinical Systems Onco-InformaticsProfessor：
Mayumi KAMADA, Masahiko NAKATSUI, Mitsugu ARAKI, Yoshinori TAMADA,Rika OKAMOTO
Ryosuke KOJIMA, Kei TERAYAMA, Hiroaki IWATA, Eiichiro UCHINO, Noriaki SATO