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Drug discovery effectiveness from the standpoint of therapeutic mechanisms and indications

Nature Reviews Drug Discovery volume 17, pages 19–33 (2018)Cite this article

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A Corrigendum to this article was published on 15 December 2017

This article has been updated

Key Points

  • We analysed the past 20 years of drug project history with the aim of understanding more about how the pharmaceutical industry has been performing with regard to therapeutic mechanisms and their intended indications.

  • The analysis suggests that industry output in terms of successful projects in this period has come primarily from a limited set of well-validated therapeutic mechanisms.

  • The analysis highlights inefficiencies in the industry due to continued investment in frequently discontinued therapeutic mechanisms, indicating that the industry could benefit from paying more attention to lessons learned from other projects and avoiding initiating projects for previously studied failed therapeutic mechanisms without rigorous and independent validation.

  • The analysis indicates that the majority of ongoing projects are pursuing novel mechanism–indication pairs, even in the indications with existing therapeutics, which is highly encouraging.

Abstract

The productivity of the pharmaceutical industry has been widely discussed in recent years, particularly with regard to concerns that substantial expenditures on research and development have failed to translate into approved drugs. Various analyses of this productivity challenge have focused on aspects such as attrition rates at particular clinical phases or the physicochemical properties of drug candidates, but relatively little attention has been paid to how the industry has performed from the standpoint of the choice of therapeutic mechanisms and their intended indications. This article examines what the pharmaceutical industry has achieved in this respect by analysing comprehensive industry-wide data on the mechanism–indication pairs that have been investigated during the past 20 years. Our findings indicate several points and trends that we hope will be useful in understanding and improving the productivity of the industry, including areas in which the industry has had substantial success or failure and the relative extent of novelty in completed and ongoing projects.

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Figure 1: Categorization of mechanism–indication pairs.
Figure 2: Overview of completed projects.
Figure 3: Combined drug discovery effectiveness heatmap for the most tested mechanism–indication pairs.
Figure 4: Success rate distribution for validated mechanisms in selected indications.
Figure 5: Comparison of success rates for rare and non-rare diseases.
Figure 6: Ongoing projects for the repeatedly validated and continually unvalidated mechanism–indication pairs.
Figure 7: Target class distribution for completed versus emerging ongoing projects.

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Change history

  • 15 December 2017

    In Figure 3c of this article, the labels for the mechanisms were misplaced. The article has been corrected in the print and online version.

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Acknowledgements

Information reported in this article is derived from Cortellis Competitive Intelligence, a database produced and owned by Clarivate Analytics. For further information about Cortellis and Clarivate Analytics, please use the following link: https://clarivate.com/products/cortellis/cortellis-competitive-intelligence/. Clarivate Analytics will not be liable for any inaccuracy in the information provided or the way in which it is used by any reader of this article. The authors gratefully thank G. McGaughey and J. Come for helpful comments in the course of manuscript preparation. This work is not funded by Clarivate Analytics, and the authors are solely responsible for the analysis and conclusions put forward in it.

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  1. Global Informatics Group, Modelling & Informatics, Vertex Pharmaceuticals Inc., 50 Northern Avenue, Boston, 02210, Massachusetts, USA

    Hsin-Pei Shih & Alex M. Aronov

  2. Global Information Services, Vertex Pharmaceuticals Inc., 50 Northern Avenue, Boston, 02210, Massachusetts, USA

    Xiaodan Zhang

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Glossary

Pharmaceutical pipeline database

A pharmaceutical pipeline database contains extensive information on drug development projects from discovery through to launch, including molecular structures, origins, therapeutic rationales, biological targets, drug properties, indications, licensing details, development history, trial outcomes and scientific references. The information comes from a variety of sources, including press releases, newsletters, conferences, scientific literature and other databases such as clinical data and patents.

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Shih, HP., Zhang, X. & Aronov, A. Drug discovery effectiveness from the standpoint of therapeutic mechanisms and indications. Nat Rev Drug Discov 17, 19–33 (2018). https://doi.org/10.1038/nrd.2017.194

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