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Tracing the system transformations and innovation pathways of an emerging technology: Solid lipid nanoparticles

机译:追踪新兴技术的系统转换和创新途径:固体脂质纳米颗粒

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Accurately evaluating opportunities in new and emerging science and technologies is a growing concern. This study proposes an integrated framework for identifying a range of potential innovation pathways and commercial applications for solid lipid nanoparticles - one particularly promising contender within the field of nano-enabled drug delivery. Several text mining techniques - term clumping, SAO technique, and net effect analysis as well as technology roadmapping, are combined with expert judgment to identify the main areas of R&D in this field, and to track their evolution over time. Through analysis, data from multiple sources, including research publications, patents, and commercial press, reveal possible future applications and commercialization opportunities for this emerging technology. We find that research is moving away from materials and delivery outcomes toward clinical applications. The most promising markets are pharmaceuticals and cosmetics; however, the "time-to-market" is much shorter for cosmetics than it is for pharmaceuticals.The most significant contributions of this paper have been highlighted as follows. One innovation is extracting the intelligence from three kinds of data sources after in-depth considering their characteristics and matching with the features of different technology development stages to identify innovative research topics. The second one is combining SAO technique with net effect analysis to identify what the evolutionary links between research topics are, and then to use TRM to visualize the evolution of the main areas of R&D over time.
机译:准确评估新兴科学技术中的机会已成为人们日益关注的问题。这项研究提出了一个综合的框架,用于确定固体脂质纳米颗粒的一系列潜在创新途径和商业应用-纳米赋形药物递送领域中一个特别有前途的竞争者。几种文本挖掘技术-术语集,SAO技术,净效应分析以及技术路线图,与专家判断相结合,以确定该领域R&D的主要领域,并跟踪其随时间的演变。通过分析,来自多种来源的数据,包括研究出版物,专利和商业新闻,揭示了该新兴技术的可能的未来应用和商业化机会。我们发现研究正在从材料和交付成果转向临床应用。最有前途的市场是药品和化妆品。然而,化妆品的“上市时间”比药品的“上市时间”短得多。本文的主要贡献如下。一种创新是在深入考虑三种数据源的特征并与不同技术开发阶段的特征相匹配以识别创新研究主题之后,从三种数据源中提取情报。第二个方法是将SAO技术与净效应分析相结合,以确定研究主题之间的进化联系,然后使用TRM可视化R&D主要领域随时间的演变。

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