生命科学の事業化へのタイミングオプションの応用(<特集>生命科学の事業化)

書誌事項

タイトル別名
  • Application of Timing Option to Commercialization of Life Science(<SPECIAL REPORT>COMMERCIALIZATION OF LIFE SCIENCE)
  • 生命科学の事業化へのタイミングオプションの応用
  • セイメイ カガク ノ ジギョウカ エ ノ タイミングオプション ノ オウヨウ

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抄録

About a quarter of recent new approved medicines are biopharmaceuticals. The increase of this ratio is expected in the future. Large pharmaceutical companies are making more R & D resources concentrate on clinical development rather than basic research, while the weight of basic research investment to life science is very high by the governments of Japan, the U.S.A., and Western Europe. In the meantime, the drug-discovery biotech start-ups are expected to fill this gap by more rapid commercialization than large pharmaceutical companies. But start-ups' bankruptcy rates are very high from resources limitation. Why, as first of research questions, can there be a lot of deficit biotech start-ups in biotech clusters as San Francisco bay area, even if the basic premise is the scrap and build? Why is it basically possible to repeatedly invest the irreversible and huge amount of sunk cost and time in the long term and high risky projects? Otherwise, why is there relatively a less number of biotech start-ups in Japan than in the U.S.A. where a more large number of companies are continuously founded, even the majority are deficits? In addition, can Japan transplant it into the country, if the U.S.A. model of biotech start-up has an economic rationality for a breakthrough? As a methodology, the timing option is focused on as an option to defer investing until the uncertainty declines, among the real options expected for its evaluation function on the economic validity of an innovative, promising, but high risky project. Thus, in this article, we examine the relationship between the symbiotic modeling of biotech start-ups and the useful possibility of real options, the application of real options to a process as the proof of concept, a basic model of the timing option enabling intermittent investment decisions to new projects, and the characteristics and functions of each model under the deterministic and stochastic models of underlying assets. In particular, the optimal timing was forecasted by simulation, when the behavior of the underlying assets was modeled by the stochastic theory.

収録刊行物

  • 研究 技術 計画

    研究 技術 計画 23 (4), 351-362, 2009

    研究・イノベーション学会

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