Delineating Translational Innovation Pathways for Nanomedical Research Using Tech Mining

Clinical translation of technological discoveries from bench to bedside has been a slow and incremental process. Capturing early events in technology development can provide key insights into the nature of biomedical innovation and its bottlenecks. The sheer volume of the available information, however, presents a significant barrier to a systematic assessment of current capabilities. Nanomedicine, which deals with medical applications of nanotechnology, perfectly exemplifies the challenges facing translational research. In this study, we have explored the feasibility of using a streamlined tech mining approach for identification of translational innovation pathways using observable markers found in research literature. The framework contains three sections: 1) extraction of feature terms from titles and abstracts; 2) tagging research articles with translational stages and application markers; and 3) analysis of topical changes, translational phases, and innovation pathways. We applied this strategy to analyze a set of 23,982 PubMed records that involved gold nanostructures (GNSs), which have been extensively studied in a wide range of biomedical applications. The studies were classified based on their intended clinical application, research field, disease, and translational stage. Our results have identified a significant increase in GNSs studies in the areas of cancer, therapeutic applications, and animal testing. Additionally, the tags along with feature terms were used to build innovation pathway maps for three types of biomedical applications: treatment, in vitro detection, or imaging. The framework described in this paper can be useful for academic researchers, funding agencies, as well as pharmaceutical and medical device companies to facilitate assessment of translational readiness and future research planning