Knowledge Flows and Delays in the Pharmaceutical Innovation System

Introduction This paper presents an analysis of knowledge flows in the pharmaceutical innovation process. Backward citations, citations to non-patent literature (NPL), and forward citations that link patents, scientific publications, and pharmaceutical pipelines data on drug developments are analyzed and visualized to provide a more holistic understanding. Results show that patents linked to drugs tend to be technically specialized when compared to patents without linkages to drugs. Moreover, patents linked to drugs tend to cite older patents and scientific publications and impact wider technological and scientific fields than pharmaceutical patents not linked to drugs. Diverse studies have been conducted to study the origin, trajectory, and destination of knowledge flows and the delays in the science and technology system. Patents and citations between patents and to non-patent literature (NPL) are analyzed to understand knowledge spillovers (Lukach & Plasmans, 2002) or to measure patent quality (Squicciarni et al., 2013). The OECD Science, Technology and Industry Scoreboard 2013 (OECD, 2013) uses comprehensive and up-to-date data to report on knowledge flows via collaboration networks (e.g., derived from co-authored publications and co-inventors on patents), international migration of researchers (e.g., estimated from changes in author’s addresses on publications), but also flows of royalty and license fees for technologies. Recently, the OECD introduced a new indicator, called “Patent-Science Link,” that aims to measure knowledge flows between the science base and the innovation system (OECD, 2013). According to this new indicator, patented pharmaceutical inventions account for the majority of citations made from patents to scientific publications. That is, the distance between the science base and the innovation system is much closer in pharmaceutical fields than it is in other technological fields. Pharmaceutical innovation is particularly important for drug discovery, as research and development (R&D) costs are huge and major challenges exist for arriving at costeffective new drugs. In fact, there is a steady decrease in R&D productivity over the last number of years (Booth & Zemmel, 2004). The structure of the paper is as follows: The next Section details data acquisition and preparation. This is followed by a description of the methodology and results. The paper concludes with a discussion of key insights and their comparison to prior work.