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Measuring the knowledge translation and convergence in pharmaceutical innovation by funding-science-technology-innovation linkages analysis

机译:通过资金-科学-技术-创新联系分析来衡量药物创新中的知识转化和融合

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We propose a backward tracking model for measuring knowledge transfer in the whole translational research spectrum. Using the drugs-patents-papers-grants backward linkages, we try to figure out the funding-science-technology-innovation translational pattern and ponder some policy implications on e.g., which priority areas and knowledge convergence level are more likely to generate new drugs. The drug-patent linkage data was accessed through the USFDA Orange Book, covering a drug's active ingredient, formulation, or methods of use for approved indications. It will take about 10 years from the application of earliest patent to the approval of the new drug. Also such high-value patents in FDA Orange Book tend to cite scientific knowledge published on average 10-15 years ago. The technology linkage of new drugs was relatively stable while the science linkage of technology inventions increased rapidly. Among the scientific papers cited by drug patents, private-institution originated papers are only a quarter of the public. By linking theses scientific papers with funding sources, we found a large majority (90%) are public-funded and only a very small part are private-funded or public-private joint-funded. Our study also indicates the importance of research on such fields as pharmacology, chemistry (including medicinal chemistry, biochemistry, and organic chemistry), molecular biology, neurosciences, and immunology on new drugs innovation. There is no obvious relationship between "basicness" and linkages to the resulting patents' impact and to drugs innovation. A balanced basic research and applied research maybe essential for fostering drug innovation because it is a complete chain translating from basic discovery to clinical evidence then to clinical practice. In order to achieve successful pharmaceutical innovation, rather than focusing on only technology, convergence with science at moderate levels (maybe 1/3) is suggested. (C) 2018 The Authors. Published by Elsevier Ltd.
机译:我们提出了一个反向跟踪模型,用于测量整个翻译研究领域中的知识转移。利用药品专利,论文,赠款的反向联系,我们试图找出资金,科学,技术,创新的转换模式,并思考一些政策含义,例如哪些优先领域和知识融合水平更可能产生新药。可以通过USFDA橙皮书访问药物专利链接数据,其中涵盖了药物的活性成分,制剂或批准适应症的使用方法。从最早的专利申请到新药的批准,大约需要10年的时间。 FDA Orange Book中的此类高价值专利也倾向于引用平均10到15年前发布的科学知识。新药的技术联系相对稳定,而技术发明的科学联系迅速增长。在药品专利引用的科学论文中,私人机构起源的论文仅占公共领域的四分之一。通过将这些科学论文与资金来源联系起来,我们发现绝大多数(90%)是公共资金,只有一小部分是私人资金或公私联合资金。我们的研究还表明,药理学,化学(包括药物化学,生物化学和有机化学),分子生物学,神经科学和免疫学等领域的研究对于新药创新的重要性。 “基本”与所产生的专利影响和药物创新之间的联系之间没有明显的关系。平衡的基础研究和应用研究可能对促进药物创新至关重要,因为这是一条从基础发现到临床证据再到临床实践的完整链。为了实现成功的药物创新,而不是只关注技术,建议在中等水平(可能为1/3)与科学融合。 (C)2018作者。由Elsevier Ltd.发布

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