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Learning from successes and failures in pharmaceutical R&D

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Abstract

In this paper, we build a cumulative innovation model to understand the role of both success and failure in the learning dynamics that characterize pharmaceutical R&D. We test the prediction of our model by means of a unique dataset that combines patent information with R&D projects, thus distinguishing patents related to successfully marketed products from those covering candidate drugs that failed in clinical trials. Results confirm model predictions showing that patents associated with successfully completed projects receive more citations than those associated with failed projects. However, we also show that failed projects can be in turn cited more often than patents lacking clinical or preclinical information. We further explore the ‘black box’ of innovation, providing evidence that both successes and failures contribute to R&D investment decisions and knowledge dynamics in science-driven sectors.

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Notes

  1. This type of research is “applied” research in the sense that the experiment is conducted only on one line of research within one paradigm, despite its informational externality, to be shown below. On the other hand, “basic” or “fundamental” research can be thought of as experiments delivering direct results about several lines of research, within or across paradigms, according to how “fundamental” the research could be. This distinction between basic and applied research stresses not the timing of invention, but the contribution to the knowledge accumulation process. Another type of research is research tools, which can be modeled as an invention that increases the precision of applied research, i.e., a better research tool increases β 1 and β 0.

  2. US patents are selected in the database on the basis of the International Patent Classification (IPC) and US classification. Pharmaceutical patents are defined as those in IPC classes A61K and A01N (Lanjouw and Cockburn 2001) and we further include patents in US classes 424, 435, 514, and 800.

  3. The matching of the different sets of data proved to be a formidable, large-scale task, that tied up a great deal of our research efforts for a long time, providing us a unique dataset that monitors R&D activities of pharmaceutical and biotechnology firms from patenting to commercialization (if any) of the protected compound.

  4. What is relevant about uninformative patented compounds is that no information about their therapeutic (lack of) effectiveness has been made available. In a sense, they can also be considered as early failures to be compared to informative later failures and successes.

  5. Besides grant year and application year, by matching patents on the basis of IPC class, we are able to control for the technological field. We have built three different “matched” samples in order to check the robustness of our results. Estimated coefficients across the three samples do not change substantially.

  6. On the contrary, self-citations are considered to be indicators of the cumulative nature of the technology and a measure of the extent to which innovators are able to reap the benefits of their own research (Hall et al. 2001). A known source of noise in citation studies comes from the fact that citations in the final patent document are not only those declared by the inventors, but also added by the examiner. Recent literature shows that analysis based on pooled sets of citations may suffer from bias (Alcácer and Gittelman 2006). In the analysis, we will make relative comparison across the citations to different groups of patents (patents with outcome equal to f, s, n.) As long as the number of citations added by the examiner is unrelated to the outcome of the associated R&D project (which is unknown when the patent is granted), our relative comparison is unaffected by the examiner-citation issue.

  7. This is actually a few months longer for marketed compounds, being equal to 7.8 years for failed R&D projects and to 8.3 years for marketed R&D projects. The value is consistent with previous studies analyzing the average duration of the drug development process (Abrantes-Metz et al. 2004).

  8. As in previous empirical literature dealing with this model, convergence problem forbids the estimation of the model where all the cited-year effects are considered. The problem is solved by introducing the cited-year effects defined on the basis of 5-year time periods.

  9. Both in the case of observed and estimated citation lag distributions, weighted averages are considered, where the weights are the same as the ones used in the estimation process.

  10. See, e.g., Jaffe and Trajtenberg (2002).

  11. Also note that the larger departures between the estimated and observed citation lag distribution in the case of failed patents is registered right after the average time when the project is stopped. This might point to the fact that the termination of the research around a compound/mechanism of action is a major signal for rival firms. Nonetheless failed patents regain interest after a few years from the time of discontinuation and their citation intensity is still higher than the citation intensity of uninformative patents, also many years after discontinuation. On this issue, we asked a pharmacologist to inspect extensively the patents citing failed projects in search of a reason for the citation, finding no instance of “negative” citations. Rather citations refer to pharmacological action or the structure of the compound (i.e., to the research line of the original innovator).

  12. Our estimates are coherent with the estimates of the Drugs and Medical sector presented by Hall et al. (2001), with the exception of the estimated δ 2, which is lower. This might be explained by the fact that we only consider citations by institutions other than the original assignee, which can require a longer time span with respect to self-citations. Moreover, an interesting pattern emerges in their results when comparing Drugs and Medical to other sectors. The citation lag distribution for this sector is flatter, whereas the citation lag distribution functions for the sectors of Computers and Communications, Electrical and Electronics, Chemical, and Mechanical have higher peaks earlier in time. Knowledge in the Drugs and Medical sector diffuses less rapidly and takes a longer time to become obsolete. Important information about the protected compounds in terms of toxicological effects and effectiveness are revealed over time, leading to a lengthier process of citation within this industry.

  13. The index is computed as an Herfindahl index of diversification, considering the share of backward citation in each IPC class. The closer orig is to one, the broader are the technological roots of the underlying research, i.e. they span many different IPC classes. The index is zero when all backward citations contained in the patent are classified within the same IPC class.

  14. The higher the value of i m p o r t b, the higher the number of backward citations contained in the patent and the citations they receive.

  15. As compared with the descriptive statistics reported in Trajtenberg et al. (1997), no difference emerges with respect to the value of s e l f c. On the contrary, the average value of t i m e b in our sample is lower, indicating younger sources for our sample of patents, whereas the values of o r i g, s c i e n c e, and i m p o r t b are higher. One important difference with the sample in Trajtenberg et al. (1997) is that we only consider pharmaceutical patents, and citations are counted only within the pharmaceutical technological classes.

  16. See Scotchmer (1991) for a detailed discussion of the optimal patent scheme in the case of cumulative knowledge.

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Authors and Affiliations

  1. Department of Economics, National Taipei University, Taipei, Taiwan

    Jing-Yuan Chiou

  2. Department of Economics, University of Verona, Verona, Italy

    Laura Magazzini

  3. IMT Institute for Advanced Studies, Lucca, Italy

    Fabio Pammolli & Massimo Riccaboni

  4. Department of Managerial Economics, Strategy and Innovation, K.U. Leuven, Leuven, Belgium

    Massimo Riccaboni

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  1. Jing-Yuan Chiou
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  2. Laura Magazzini
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  4. Massimo Riccaboni
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Correspondence to Massimo Riccaboni.

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Chiou, JY., Magazzini, L., Pammolli, F. et al. Learning from successes and failures in pharmaceutical R&D. J Evol Econ 26, 271–290 (2016). https://doi.org/10.1007/s00191-015-0439-z

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