Multilayer network analysis of the drugs development cycle in the global pharmaceutical industry

Drug development is a time-consuming process from the start of research to obtaining approval, and the probability of success with a candidate compound is extremely low. We aim to understand the characteristics of the flow and localization of knowledge during drug development in the global pharmaceutical industry. We analyze the multilayer network constructed with the drug pipeline layer, global supply chain layer, and global ownership layer. First, we identify the bow-tie structure and the community structure of each network layer. The obtained bow-tie structure shows the large strongly connected component and suggests that the knowledge flow in drug pipelines has similar characteristics as the supply chain network. The communities in each layer are characterized by country, category of the company, and bow tie component. We then study the multilayer network’s knowledge flow, conduct a statistical test, and verify the significance of the overlapping links between the drug pipeline and supply chain layers. Our results suggest a strong connection between open innovation in the pharmaceutical industry and firms’ economic activities in the supply chain.

[1]  B. Munos.  Lessons from 60 years of pharmaceutical innovation , 2009, Nature Reviews Drug Discovery.

[2]  F. Scherer.  Chapter 12 – Pharmaceutical Innovation , 2010 .

[3]  R. K. Pati,et al.  Managerial research on the pharmaceutical supply chain - A critical review and some insights for future directions , 2014 .

[4]  Martin Rosvall,et al.  Multilevel Compression of Random Walks on Networks Reveals Hierarchical Organization in Large Integrated Systems , 2010, PloS one.

[5]  F. Scherer,et al.  Mergers and innovation in the pharmaceutical industry. , 2013, Journal of health economics.

[6]  眞里 治部,et al.  日本版NIH創設に向けた新しい指標の開発（4） パイプラインにつながる特許の判別指標 , 2014 .

[7]  眞里 治部,et al.  AMED（日本版NIH）創設に向けた新しい指標の開発（8） 医薬品研究開発における知識の流れ , 2014 .

[8]  Andrea Lancichinetti,et al.  Community detection algorithms: a comparative analysis: invited presentation, extended abstract , 2009, VALUETOOLS.

[9]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..

[10]  Guanrong Chen,et al.  A two-level complex network model and its application , 2009 .

[11]  Nic Fleming,et al.  How artificial intelligence is changing drug discovery , 2018, Nature.

[12]  青山 秀明,et al.  Econophysics and companies : statistical life and death in complex business networks , 2011 .

[13]  Martin Rosvall,et al.  Maps of random walks on complex networks reveal community structure , 2007, Proceedings of the National Academy of Sciences.

[14]  眞里 治部,et al.  日本版NIH創設に向けた新しい指標の開発（2） テクノロジー別にみた医薬品開発の現状俯瞰・将来予測 , 2013 .

[15]  眞里 治部,et al.  AMED（日本版NIH）創設に向けた新しい指標の開発（7） 米国のファンディング動向 , 2014 .

[16]  Jürgen Kurths,et al.  Investigating the topology of interacting networks , 2011, 1102.3067.

[17]  眞里 治部,et al.  AMED（日本版NIH）創設に向けた新しい指標の開発（6） 疾病別にみた医薬品開発の現状俯瞰・将来予測 , 2014 .

[18]  眞里 治部,et al.  日本版NIH創設に向けた新しい指標の開発（5） パイプラインにつながる特許判別指標の応用 , 2014 .

[19]  M. Bruccoleri,et al.  Supply chain of innovation and new product development , 2015 .

[20]  Patrick Thiran,et al.  Layered complex networks. , 2006, Physical review letters.

[21]  Yoshi Fujiwara,et al.  Econophysics and Companies: Contents , 2010 .

[22]  眞里 治部,et al.  日本版NIH創設に向けた新しい指標の開発（1） 新しい指標に基づいた医薬品産業の現状俯瞰・将来予測 , 2013 .

[23]  Yoshi Fujiwara,et al.  Econophysics and Companies: List of figures , 2010 .

[24]  D. Hand.  Econophysics and Companies: Statistical Life and Death in Complex Business Networks by Hideaki Aoyama, Yoshi Fujiwara, Yuichi Ikeda, Hiroshi Iyetomi, Wataru Souma , 2010 .

[25]  G. Bianconi.  Statistical mechanics of multiplex networks: entropy and overlap. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[26]  D. He,et al.  Topological relation of layered complex networks , 2010 .

[27]  Harry Eugene Stanley,et al.  Catastrophic cascade of failures in interdependent networks , 2009, Nature.

[28]  Andrei Z. Broder,et al.  Graph structure in the Web , 2000, Comput. Networks.