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Basic science as a prescription for breakthrough inventions in the pharmaceutical industry

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Abstract

This analysis contributes to the understanding of the role of basic science in generating breakthrough inventions in the pharmaceutical industry. Recognizing the within-firm heterogeneity of inventive activities, we look not only at the firm level, but also at the firm-technology level for characteristics determining breakthroughs. A key finding is that firms pursuing basic science are more likely to produce breakthrough inventions. At the same time, doing more basic science in science disciplines that are closely linked to a given technology domain does not increase the likelihood of BTs in that particular technology. The insignificance of basic science intensity at the technology level, coupled to the significance at the firm level, suggests that the breakthrough rewards from science capacity are not reaped in the technology areas immediately involved in basic science, but in other areas of the technology portfolio of the firm. Our findings are consistent with the view of science as a map to span processes of local search and the wider applicability of scientific insights.

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Notes

  1. As breakthrough inventions do not necessarily make it into commercial breakthroughs, our analysis cannot be extended into determinants of commercial breakthroughs. For an analysis of commercial breakthroughs in the Pharmaceutical industry, see also Cockburn (2007).

  2. Throughout the text we refer to “Science” and “Basic Science” interchangeably as the arguments advanced in this work mostly relate to the process of developing knowledge to understand and explain natural phenomena, in contrast to “Applied Science”. The latter is mostly geared towards the application of scientific principles to practical solutions (compounds in our case). In the context of Pharmaceuticals “applied science” is highly relevant too (e.g. clinical trials), but this type is not the main focus of attention in our analysis.

  3. The original database contains information on patenting activity of the sampled firms as of 1995. The first year is used to construct some of the control variables used in this study.

  4. For a more elaborate discussion of the ‘disintegration’ trend in the pharmaceutical industry, see Cockburn (2007).

  5. The patent applications considered in the analysis have memberships in 108 3-digit IPC patent classes (out of 129 3-digit IPC classes). With the 1 % coverage restriction, 83 technology classes are excluded.

  6. The share of generic firms is negligible, around 1 % of the sample; they are not found to produce any breakthrough inventions, according to the definition of Sect. 3.2. We therefore exclude this dimension from the analysis.

  7. As an alternative to patent-citations, some scholars (e.g. Fontana et al. 2012) used data on R&D awards to identify breakthrough inventions. Comparing the number of patents received by award winning inventions and a control group, Carpenter et al. (1981) found that award winning inventions receive significantly more citations. This shows the close correspondence between citations and data on R&D awards to identify breakthrough inventions.

  8. For an analysis of commercial breakthroughs in Pharma, see also Cockburn (2007).

  9. Our results are robust to adding a control for the applied science activities of firms (publications in levels 1–3).

  10. Journal issues (and the articles they contain) are classified into one of 222 ISI Subject Categories. To increase the robustness of the concordance, the Expertise Centre for R&D Monitoring (ECOOM, KU Leuven) aggregated these detailed categories into 68 scientific disciplines.

  11. We have multiplied these probabilities by the overall technology intensiveness of science fields (i.e. the average number of times that publications in the field are cited in patents). We have also conducted analyses whereby we didn’t apply this correction; this analysis gives very similar results.

  12. See Van Looy et al. (2004, 2006) for more details on the methodology to construct the ST concordance.

  13. A disadvantage of the use of patents as indicator of the scale of firms’ inventive activities is that patents are an output indicator, which do not only reflect differences in inputs, but are also affected by patent propensities. However the propensity to patent is high in the pharmaceutical industry (Arundel and Kabla 1998), making R&D expenditures and patent counts close substitutes for the scale of inventive activities in this particular industry.

  14. In 2011, North America accounted for 41.8 % of world pharmaceutical sales compared with 26.8 % for Europe (Efpia 2012).

  15. Note that LDPFs exhibit a lower average value of Tech specialization than DBFs but greater Tech concentration. This stems from the fact that the technology class distribution for LDPFs is characterized by a relatively long right tail of technology classes with little activity, driving down average specialization (LDPFs are active in 8.4 technology classes on average versus 4.5 for DBFs). Combined with the relatively strong dominance of the A61 and C07 technology classes, which typically account for more than half of patents (see Table 5), this implies a high concentration for LDPFs.

  16. We thank the editor for having spotted this point.

  17. Two-paired t-tests with unequal variance suggest that this difference is more striking the stricter is the criterion for breakthrough patents (the higher number of standard deviations to qualify for breakthroughs).

  18. We have also experimented with the inclusion of the share of publications in other technology fields than the focal one to the current specification and the substitution of Basic Science Intensity with the share of publications in other technology fields than the focal one. The results confirm the intuition that it is the involvement in science outside the focal technology which is associated with the generation of BTs. These additional results are available from the authors upon request.

  19. Further note that our sample consists only of firms with (relatively) high R&D expenditures. Our results may therefore be less representative for the population of very small DBFs in the pharmaceutical industry.

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Acknowledgments

We thank seminar participants at the Competition and Innovation Summer School 2012, in particular Adam Jaffe and Francesco Lissoni, participants at the EuSPRI Roundtable on “Organizing Radical Innovation”, and participants at the T2S conference 2013, New York for helpful comments. Data on scientific production was kindly provided by Bart Thijs at ECOOM – KULeuven. Financial support from the European Union (PIEP-2011-302034), KULeuven (GOA/12/003) and FWO Flanders (G.0825.12) is gratefully acknowledged.

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Authors and Affiliations

  1. Department of Managerial Economics, Strategy and Innovation, University of Leuven, Naamsestraat 69, 3000, Louvain, Belgium

    Antonio Della Malva, Stijn Kelchtermans, Bart Leten & Reinhilde Veugelers

  2. Vlerick Business School, Ledeberg, Belgium

    Bart Leten

  3. Hogeschool-Universiteit Brussel, Brussels, Belgium

    Stijn Kelchtermans

  4. ECOOM, Louvain, Belgium

    Reinhilde Veugelers

  5. Bruegel, Brussels, Belgium

    Reinhilde Veugelers

  6. CEPR, London, Belgium

    Reinhilde Veugelers

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  1. Antonio Della Malva
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  2. Stijn Kelchtermans
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Correspondence to Antonio Della Malva.

Appendix

Appendix

See Table 9.

Table 9 List of firms employed in the study, their typology and their geographical origins
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Malva, A.D., Kelchtermans, S., Leten, B. et al. Basic science as a prescription for breakthrough inventions in the pharmaceutical industry. J Technol Transf 40, 670–695 (2015). https://doi.org/10.1007/s10961-014-9362-y

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