Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Drug discovery effectiveness from the standpoint of therapeutic mechanisms and indications

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.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

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.

Similar content being viewed by others

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.

References

  1. Kola, I. & Landis, J. Can the pharmaceutical industry reduce attrition rates? Nat. Rev. Drug Discov. 3, 711–715 (2004).

    Article  CAS  PubMed  Google Scholar 

  2. Paul, S. M. et al. How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nat. Rev. Drug Discov. 9, 203–214 (2010).

    Article  CAS  PubMed  Google Scholar 

  3. Empfield, J. R. & Leeson, P. D. Lessons learned from candidate drug attrition. IDrugs 13, 869–873 (2010).

    PubMed  Google Scholar 

  4. DiMasi, J. A., Feldman, L., Seckler, A. & Wilson, A. Trends in risks associated with new drug development: success rates for investigational drugs. Clin. Pharmacol. Ther. 87, 272–277 (2010).

    Article  CAS  PubMed  Google Scholar 

  5. Pammolli, F., Magazzini, L. & Riccaboni, M. The productivity crisis in pharmaceutical R&D. Nat. Rev. Drug Discov. 10, 428–438 (2011).

    Article  CAS  PubMed  Google Scholar 

  6. Arrowsmith, J. Trial watch: phase II failures: 2008–2010. Nat. Rev. Drug Discov. 10, 328–329 (2011).

    Article  CAS  PubMed  Google Scholar 

  7. Scannell, J. W., Blanckley, A., Boldon, H. & Warrington, B. Diagnosing the decline in pharmaceutical R&D efficiency. Nat. Rev. Drug Discov. 11, 191–200 (2012).

    Article  CAS  PubMed  Google Scholar 

  8. Ringel, M., Tollman, P., Hersch, G. & Schulze, U. Does size matter in R&D productivity? If not, what does? Nat. Rev. Drug Discov. 12, 901–902 (2013).

    Article  CAS  PubMed  Google Scholar 

  9. Arrowsmith, J. & Miller, P. Trial watch: phase II and phase III attrition rates 2011–2012. Nat. Rev. Drug Discov. 12, 569 (2013).

    Article  CAS  PubMed  Google Scholar 

  10. Hay, M., Thomas, D. W., Craighead, J. L., Economides, C. & Rosenthal, J. Clinical development success rates for investigational drugs. Nat. Biotechnol. 32, 40–51 (2014).

    Article  CAS  PubMed  Google Scholar 

  11. Peck, R. W., Lendrem, D. W., Grant, I., Lendrem, B. C. & Isaacs, J. D. Why is it hard to terminate failing projects in pharmaceutical R&D? Nat. Rev. Drug Discov. 14, 663–664 (2015).

    Article  CAS  PubMed  Google Scholar 

  12. Waring, M. J. et al. An analysis of the attrition of drug candidates from four major pharmaceutical companies. Nat. Rev. Drug Discov. 14, 475–486 (2015).

    Article  CAS  PubMed  Google Scholar 

  13. Smietana, K., Siatkowski, M. & Møller, M. Trends in clinical success rates. Nat. Rev. Drug Discov. 15, 379–380 (2016).

    Article  CAS  PubMed  Google Scholar 

  14. Harrison, R. K. Phase II and phase III failures: 2013–2015. Nat. Rev. Drug Discov. 15, 817–818 (2016).

    Article  CAS  PubMed  Google Scholar 

  15. Kassel, D. B. Applications of high-throughput ADME in drug discovery. Curr. Opin. Chem. Biol. 8, 339–345 (2004).

    Article  CAS  PubMed  Google Scholar 

  16. Wang, J. Comprehensive assessment of ADMET risks in drug discovery. Curr. Pharm. Des. 15, 2195–2219 (2009).

    Article  CAS  PubMed  Google Scholar 

  17. Wishart, D. S. Improving early drug discovery through ADME modelling: an overview. Drugs R D 8, 349–362 (2007).

    Article  CAS  PubMed  Google Scholar 

  18. De Buck, S. S. et al. Prediction of human pharmacokinetics using physiologically based modeling: a retrospective analysis of 26 clinically tested drugs. Drug Metab. Dispos. 35, 1766–1780 (2007).

    Article  CAS  PubMed  Google Scholar 

  19. Jones, H. M., Gardner, I. B. & Watson, K. J. Modelling and PBPK simulation in drug discovery. AAPS J. 11, 155–166 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Bowes, J. et al. Reducing safety-related drug attrition: the use of in vitro pharmacological profiling. Nat. Rev. Drug Discov. 11, 909–922 (2012).

    Article  CAS  PubMed  Google Scholar 

  21. Segall, M. D. & Barber, C. Addressing toxicity risk when designing and selecting compounds in early drug discovery. Drug Discov. Today 19, 688–693 (2014).

    Article  CAS  PubMed  Google Scholar 

  22. Blomme, E. A. G. & Will, Y. Toxicology strategies for drug discovery: present and future. Chem. Res. Toxicol. 29, 473–504 (2016).

    Article  CAS  PubMed  Google Scholar 

  23. Prentis, R. A., Lis, Y. & Walker, S. R. Pharmaceutical innovation by the seven UK-owned pharmaceutical companies. Br. J. Clin. Pharmacol. 25, 387–396 (1988).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Bunnage, M. E. Getting pharmaceutical R&D back on target. Nat. Chem. Biol. 7, 335–339 (2011).

    Article  CAS  PubMed  Google Scholar 

  25. Deeks, S. G., Smith, M., Holodniy, M. & Kahn, J. O. HIV-1 protease inhibitors. A review for clinicians. JAMA 277, 145–153 (1997).

    Article  CAS  PubMed  Google Scholar 

  26. Olbe, L., Carlsson, E. & Lindberg, P. A proton-pump inhibitor expedition: the case histories of omeprazole and esomeprazole. Nat. Rev. Drug Discov. 2, 132–139 (2003).

    Article  CAS  PubMed  Google Scholar 

  27. Simons, F. E. R. Advances in H1-antihistamines. N. Engl. J. Med. 351, 2203–2217 (2004).

    Article  CAS  PubMed  Google Scholar 

  28. Agarwal, P., Sanseau, P. & Cardon, L. R. Novelty in the target landscape of the pharmaceutical industry. Nat. Rev. Drug Discov. 12, 575–576 (2013).

    Article  CAS  PubMed  Google Scholar 

  29. Burnett, J. R. & Huff, M. W. Cholesterol absorption inhibitors as a therapeutic option for hypercholesterolaemia. Expert Opin. Investig. Drugs 15, 1337–1351 (2006).

    Article  CAS  PubMed  Google Scholar 

  30. Stoekenbroek, R. M., Kastelein, J. J. P. & Hovingh, G. K. Recent failures in antiatherosclerotic drug development. Curr. Opin. Lipidol. 24, 459–466 (2013).

    Article  CAS  PubMed  Google Scholar 

  31. Ikonomidou, C. & Turski, L. Why did NMDA receptor antagonists fail clinical trials for stroke and traumatic brain injury? Lancet Neurol. 1, 383–386 (2002).

    Article  CAS  PubMed  Google Scholar 

  32. Ho, L.-J. & Lai, J.-H. Small-molecule inhibitors for autoimmune arthritis: success, failure and the future. Eur. J. Pharmacol. 747, 200–205 (2015).

    Article  CAS  PubMed  Google Scholar 

  33. Nissen, S. E. et al. Effect of ACAT inhibition on the progression of coronary atherosclerosis. N. Engl. J. Med. 354, 1253–1263 (2006).

    Article  CAS  PubMed  Google Scholar 

  34. Meuwese, M. C. et al. ACAT inhibition and progression of carotid atherosclerosis in patients with familial hypercholesterolemia: the CAPTIVATE randomized trial. JAMA 301, 1131–1139 (2009).

    Article  CAS  PubMed  Google Scholar 

  35. Damjanov, N., Kauffman, R. S. & Spencer-Green, G. T. Efficacy, pharmacodynamics, and safety of VX-702, a novel p38 MAPK inhibitor, in rheumatoid arthritis: results of two randomized, double-blind, placebo-controlled clinical studies. Arthritis Rheum. 60, 1232–1241 (2009).

    Article  PubMed  Google Scholar 

  36. Cohen, S. B. et al. Evaluation of the efficacy and safety of pamapimod, a p38 MAP kinase inhibitor, in a double-blind, methotrexate-controlled study of patients with active rheumatoid arthritis. Arthritis Rheum. 60, 335–344 (2009).

    Article  CAS  PubMed  Google Scholar 

  37. Davis, S. M. et al. Selfotel in acute ischemic stroke: possible neurotoxic effects of an NMDA antagonist. Stroke 31, 347–354 (2000).

    Article  CAS  PubMed  Google Scholar 

  38. Albers, G. W., Goldstein, L. B., Hall, D. & Lesko, L. M. Aptiganel hydrochloride in acute ischemic stroke: a randomized controlled trial. JAMA 286, 2673–2682 (2001).

    Article  CAS  PubMed  Google Scholar 

  39. Sacco, R. L. et al. Glycine antagonist in neuroprotection for patients with acute stroke: GAIN Americas: a randomized controlled trial. JAMA 285, 1719–1728 (2001).

    Article  CAS  PubMed  Google Scholar 

  40. Heinonen, T. M. Inhibition of acyl coenzyme A-cholesterol acyltransferase: a possible treatment of atherosclerosis? Curr. Atheroscler. Rep. 4, 65–70 (2002).

    Article  PubMed  Google Scholar 

  41. Hoyte, L., Barber, P. A., Buchan, A. M. & Hill, M. D. The rise and fall of NMDA antagonists for ischemic stroke. Curr. Mol. Med. 4, 131–136 (2004).

    Article  CAS  PubMed  Google Scholar 

  42. McNamee, K., Williams, R. & Seed, M. Animal models of rheumatoid arthritis: how informative are they? Eur. J. Pharmacol. 759, 278–286 (2015).

    Article  CAS  PubMed  Google Scholar 

  43. Denayer, T., Stöhr, T. & Van Roy, M. Animal models in translational medicine: validation and prediction. New Horiz. Transl Med. 2, 5–11 (2014).

    Google Scholar 

  44. Karran, E., Mercken, M. & De Strooper, B. The amyloid cascade hypothesis for Alzheimer's disease: an appraisal for the development of therapeutics. Nat. Rev. Drug Discov. 10, 698–712 (2011).

    Article  CAS  PubMed  Google Scholar 

  45. Soejitno, A., Tjan, A. & Purwata, T. E. Alzheimer's disease: lessons learned from amyloidocentric clinical trials. CNS Drugs 29, 487–502 (2015).

    Article  PubMed  Google Scholar 

  46. Le Couteur, D. G., Hunter, S. & Brayne, C. Solanezumab and the amyloid hypothesis for Alzheimer's disease. BMJ 355, i6771 (2016).

    Article  PubMed  Google Scholar 

  47. Selkoe, D. J. The therapeutics of Alzheimer's disease: where we stand and where we are heading. Ann. Neurol. 74, 328–336 (2013).

    Article  CAS  PubMed  Google Scholar 

  48. M. U.I. R., K. Glutamate-based therapeutic approaches: clinical trials with NMDA antagonists. Curr. Opin. Pharmacol. 6, 53–60 (2006).

  49. Langmead, C. J., Watson, J. & Reavill, C. Muscarinic acetylcholine receptors as CNS drug targets. Pharmacol. Ther. 117, 232–243 (2008).

    Article  CAS  PubMed  Google Scholar 

  50. Pal, P., Gandhi, H., Giridhar, R. & Yadav, M. R. ACAT inhibitors: the search for novel cholesterol lowering agents. Mini Rev. Med. Chem. 13, 1195–1219 (2013).

    Article  CAS  PubMed  Google Scholar 

  51. Karran, E. & Hardy, J. A critique of the drug discovery and phase 3 clinical programs targeting the amyloid hypothesis for Alzheimer disease. Ann. Neurol. 76, 185–205 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Pangalos, M. N., Schechter, L. E. & Hurko, O. Drug development for CNS disorders: strategies for balancing risk and reducing attrition. Nat. Rev. Drug Discov. 6, 521–532 (2007).

    Article  CAS  PubMed  Google Scholar 

  53. McGonigle, P. Animal models of CNS disorders. Biochem. Pharmacol. 87, 140–149 (2014).

    Article  CAS  PubMed  Google Scholar 

  54. Pankevich, D. E., Altevogt, B. M., Dunlop, J., Gage, F. H. & Hyman, S. E. Improving and accelerating drug development for nervous system disorders. Neuron 84, 546–553 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Hait, W. N. Anticancer drug development: the grand challenges. Nat. Rev. Drug Discov. 9, 253–254 (2010).

    Article  CAS  PubMed  Google Scholar 

  56. Gould, S. E., Junttila, M. R. & de Sauvage, F. J. Translational value of mouse models in oncology drug development. Nat. Med. 21, 431–439 (2015).

    Article  CAS  PubMed  Google Scholar 

  57. Zitvogel, L., Pitt, J. M., Daillère, R., Smyth, M. J. & Kroemer, G. Mouse models in oncoimmunology. Nat. Rev. Cancer 16, 759–773 (2016).

    Article  CAS  PubMed  Google Scholar 

  58. Cessak, G. et al. TNF inhibitors — mechanisms of action, approved and off-label indications. Pharmacol. Rep. 66, 836–844 (2014).

    Article  CAS  PubMed  Google Scholar 

  59. Traxler, P. Tyrosine kinases as targets in cancer therapy — successes and failures. Expert Opin. Ther. Targets 7, 215–234 (2003).

    Article  CAS  PubMed  Google Scholar 

  60. Gschwind, A., Fischer, O. M. & Ullrich, A. The discovery of receptor tyrosine kinases: targets for cancer therapy. Nat. Rev. Cancer 4, 361–370 (2004).

    Article  CAS  PubMed  Google Scholar 

  61. Gross, S., Rahal, R., Stransky, N., Lengauer, C. & Hoeflich, K. P. Targeting cancer with kinase inhibitors. J. Clin. Invest. 125, 1780–1789 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Pergola, C. & Werz, O. 5-Lipoxygenase inhibitors: a review of recent developments and patents. Expert Opin. Ther. Pat. 20, 355–375 (2010).

    Article  CAS  PubMed  Google Scholar 

  63. Di Gennaro, A. & Haeggström, J. Z. Targeting leukotriene B4 in inflammation. Expert Opin. Ther. Targets 18, 79–93 (2014).

    Article  CAS  PubMed  Google Scholar 

  64. Lee, K. et al. AMPA receptors as therapeutic targets for neurological disorders. Adv. Protein Chem. Struct. Biol. 103, 203–261 (2016).

    Article  CAS  PubMed  Google Scholar 

  65. King, H., Aleksic, T., Haluska, P. & Macaulay, V. M. Can we unlock the potential of IGF-1R inhibition in cancer therapy? Cancer Treat. Rev. 40, 1096–1105 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Goudreau, N. & Llinàs-Brunet, M. The therapeutic potential of NS3 protease inhibitors in HCV infection. Expert Opin. Investig. Drugs 14, 1129–1144 (2005).

    Article  CAS  PubMed  Google Scholar 

  67. Gentile, I., Coppola, N., Buonomo, A. R., Zappulo, E. & Borgia, G. Investigational nucleoside and nucleotide polymerase inhibitors and their use in treating hepatitis C virus. Expert Opin. Investig. Drugs 23, 1211–1223 (2014).

    Article  CAS  PubMed  Google Scholar 

  68. Jadhav, M., Yeola, C., Zope, G. & Nabar, A. Aliskiren, the first direct renin inhibitor for treatment of hypertension: the path of its development. J. Postgrad. Med. 58, 32–37 (2012).

    Article  CAS  PubMed  Google Scholar 

  69. Jensen, C., Herold, P. & Brunner, H. R. Aliskiren: the first renin inhibitor for clinical treatment. Nat. Rev. Drug Discov. 7, 399–410 (2008).

    Article  CAS  PubMed  Google Scholar 

  70. Graham, W. V., Bonito-Oliva, A. & Sakmar, T. P. Update on Alzheimer's disease therapy and prevention strategies. Annu. Rev. Med. 68, 413–430 (2017).

    Article  CAS  PubMed  Google Scholar 

  71. Antonelli, G., Scagnolari, C., Moschella, F. & Proietti, E. Twenty-five years of type I interferon-based treatment: a critical analysis of its therapeutic use. Cytokine Growth Factor Rev. 26, 121–131 (2015).

    Article  CAS  PubMed  Google Scholar 

  72. Plenge, R. M., Scolnick, E. M. & Altshuler, D. Validating therapeutic targets through human genetics. Nat. Rev. Drug Discov. 12, 581–594 (2013).

    Article  CAS  PubMed  Google Scholar 

  73. Nelson, M. R. et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47, 856–860 (2015).

    Article  CAS  PubMed  Google Scholar 

  74. Renwick, M. J., Simpkin, V. & Mossialos, E. Targeting innovation in antibiotic drug discovery and development. The need for a One Health – One Europe – One World Framework (WHO, 2016).

    Google Scholar 

  75. Meekings, K. N., Williams, C. S. M. & Arrowsmith, J. E. Orphan drug development: an economically viable strategy for biopharma R&D. Drug Discov. Today 17, 660–664 (2012).

    Article  PubMed  Google Scholar 

  76. Braun, M. M., Farag-El-Massah, S., Xu, K. & Coté, T. R. Emergence of orphan drugs in the United States: a quantitative assessment of the first 25 years. Nat. Rev. Drug Discov. 9, 519–522 (2010).

    Article  CAS  PubMed  Google Scholar 

  77. Melnikova, I. Rare diseases and orphan drugs. Nat. Rev. Drug Discov. 11, 267–268 (2012).

    Article  CAS  PubMed  Google Scholar 

  78. Santos, R. et al. A comprehensive map of molecular drug targets. Nat. Rev. Drug Discov. 16, 19–34 (2017).

    Article  CAS  PubMed  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Hsin-Pei Shih or Alex M. Aronov.

Ethics declarations

Competing interests

The authors are employees of Vertex Pharmaceuticals Incorporated and receive salaries and stock compensation. This work is not funded by Clarivate Analytics. The authors are solely responsible for the analysis and conclusions put forward in this work.

Supplementary information

Supplementary information

Supplementary information S1 (table) (XLSX 638 kb)

Supplementary information

Supplementary information S2 (figure) (PDF 3368 kb)

PowerPoint slides

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.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrd.2017.194

This article is cited by

Search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research