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Lessons learned from the fate of AstraZeneca's drug pipeline: a five-dimensional framework

Abstract

Maintaining research and development (R&D) productivity at a sustainable level is one of the main challenges currently facing the pharmaceutical industry. In this article, we discuss the results of a comprehensive longitudinal review of AstraZeneca's small-molecule drug projects from 2005 to 2010. The analysis allowed us to establish a framework based on the five most important technical determinants of project success and pipeline quality, which we describe as the five 'R's: the right target, the right patient, the right tissue, the right safety and the right commercial potential. A sixth factor — the right culture — is also crucial in encouraging effective decision-making based on these technical determinants. AstraZeneca is currently applying this framework to guide its R&D teams, and although it is too early to demonstrate whether this has improved the company's R&D productivity, we present our data and analysis here in the hope that it may assist the industry overall in addressing this key challenge.

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Figure 1: Overview of project success rates and reasons for closure.
Figure 2: Analysis of project closures due to safety issues.
Figure 3: Analysis of project closures due to efficacy issues.
Figure 4: Analysis of patient selection and commercial positioning.
Figure 5: The 5R framework.
Figure 6: Analysis of back-up candidate drugs.

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References

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  3. Herper, M. The truly staggering cost of inventing new drugs. Forbes [online], (10 Feb 2012).

    Google Scholar 

  4. Munros, B. Lessons from 60 years of pharmaceutical innovation. Nature Rev. Drug Discov. 8, 959–968 (2009).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  6. Handen, J. S. The industrialization of drug discovery. Drug Discov. Today 7, 83–85 (2002).

    Article  Google Scholar 

  7. Linder, M. D. Clinical attrition due to biased preclinical assessments of potential efficacy. Pharmacol. Ther. 115, 148–175 (2007).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  10. Redfern, W. et al. Impact and frequency of different toxicities throughout the pharmaceutical life cycle. The Toxicologist 114, 231 (2010).

    Google Scholar 

  11. Kola, I. & Landis, J. Can the pharmaceutical industry reduce attrition rates? Nature Rev. Drug Discov. 3, 711–716 (2004).

    Article  CAS  Google Scholar 

  12. Picker, S. M. In-vitro assessment of platelet function. Transfus. Apher. Sci. 44, 305–319 (2011).

    Article  Google Scholar 

  13. Seok, J. et al. Genomic responses in mouse models poorly mimic human inflammatory diseases. Proc. Natl Acad. Sci. USA 110, 3507–3512 (2013).

    Article  CAS  Google Scholar 

  14. de Jong, M. & Maina, T. Of mice and humans: are they the same? — Implications in cancer translational research. J. Nucl. Med. 51, 501–504 (2010).

    Article  Google Scholar 

  15. Wendler, A. & Wehling, M. The translatability of animal models for clinical development: biomarkers and disease models. Curr. Opin. Pharmacol. 10, 601–606 (2010).

    Article  CAS  Google Scholar 

  16. Jucker, M. The benefits and limitations of animal models for translational research in neurodegenerative diseases. Nature Med. 16, 1210–1214 (2010).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  18. Prahalad, S. Negative association between the chemokine receptor CCR5-Δ32 polymorphism and rheumatoid arthritis: a meta-analysis. Genes Immun. 7, 264–268 (2006).

    Article  CAS  Google Scholar 

  19. Vierboom, M. P. et al. Inhibition of the development of collagen-induced arthritis in rhesus monkeys by a small molecular weight antagonist of CCR5. Arthritis Rheum. 52, 627–636 (2005).

    Article  CAS  Google Scholar 

  20. Okamoto, H. & Kamatani, N. CCR-5 antagonist inhibits the development of adjuvant arthritis in rats. Rheumatology 45, 230–232 (2006).

    Article  CAS  Google Scholar 

  21. Fleishaker, D. L. et al. Maraviroc, a chemokine receptor-5 antagonist, fails to demonstrate efficacy in the treatment of patients with rheumatoid arthritis in a randomized, double-blind placebo-controlled trial. Arthritis Res. Ther. 14, R11 (2012).

    Article  CAS  Google Scholar 

  22. van Kuijk, A. W. et al. CCR5 blockade in rheumatoid arthritis: a randomised, double-blind, placebo-controlled clinical trial. Ann. Rheum. Dis. 69, 2013–2016 (2010).

    Article  CAS  Google Scholar 

  23. Gerlag, D. M. et al. Preclinical and clinical investigation of a CCR5 antagonist, AZD5672, in patients with rheumatoid arthritis receiving methotrexate. Arthritis Rheum. 62, 3154–3160 (2010).

    Article  CAS  Google Scholar 

  24. Wehling, M. Assessing the translatability of drug projects: what needs to be scored to predict success? Nature Rev. Drug Discov. 8, 541–546 (2009).

    Article  CAS  Google Scholar 

  25. Morgan, P. et al. Can the flow of medicines be improved? Fundamental pharmacokinetic and pharmacological principles toward improving Phase II survival. Drug Discov. Today 17, 419–424 (2012).

    Article  CAS  Google Scholar 

  26. Berman, R. M. et al. Antidepressant effects of ketamine in depressed patients. Biol. Psychiatry 47, 351–354 (2000).

    Article  CAS  Google Scholar 

  27. aan het Rot, M. et al. Safety and efficacy of repeated-dose intravenous ketamine for treatment-resistant depression. Biol. Psychiatry 67, 139–145 (2010).

    Article  CAS  Google Scholar 

  28. Quirk, M. et al. Abstract P-09-045. Effects of low-trapping NMDA channel blocker AZD6765 on gamma-band EEG and psychotomimetic liability: a comparison to ketamine in freely behaving rats. Int. J. Neuropsychopharmacol. 15 (Suppl. S1), 153 (2012).

    Google Scholar 

  29. Skolnick, P., Popik, P. & Trullas, R. Glutamate-based antidepressants: 20 years on. Trends Pharmacol. Sci. 30, 563–569 (2009).

    Article  CAS  Google Scholar 

  30. Mealing, G., Lanthorn, T. H., Murray, C. L., Small, D. L. & Morley, P. Differences in degree of trapping of low-affinity uncompetitve N-methyl-d-aspartic acid receptor antagonists with similar kinetics of block. J. Pharmacol. Exp. Ther. 288, 204–210 (1999).

    CAS  PubMed  Google Scholar 

  31. Zarate, C. A. et al. Replication of ketamine's antidepressant efficacy in bipolar depression: a randomized controlled add-on trial. Biol. Psychiatry 71, 939–946 (2012).

    Article  CAS  Google Scholar 

  32. Lip, G. Y. et al. Oral direct thrombin inhibitor AZD0837 for the prevention of stroke and systemic embolism in patients with non-valvular atrial fibrillation: a randomised doseguiding, safety and tolerability study of four doses of AZD0837 versus vitamin K antagonists. Eur. Heart J. 30, 2897–2907 (2009).

    Article  CAS  Google Scholar 

  33. Olsson, S. B. et al. Safety and tolerability of an immediate-release formulation of the oral direct thrombin inhibitor AZD0837 in the prevention of stroke and systemic embolism in patients with atrial fibrillation. Thromb. Haemost. 103, 604–612 (2010).

    Article  CAS  Google Scholar 

  34. Pehrsson, S., Johansson K., Kjaer, M. & Elg, M. Evaluation of AR-H067637, the active metabolite of the new direct thrombin inhibitor AZD0837, in models of venous and arterial thrombosis and bleeding in anaesthetised rats. Thromb. Haemost. 104, 1242–1249 (2010).

    Article  CAS  Google Scholar 

  35. Wolzt, M. et al. Effect on perfusion chamber thrombus size in patients with atrial fibrillation during anticoagulant treatment with oral direct thrombin inhibitors, AZD0837 or ximelagatran, or with vitamin K antagonists. Thromb. Res. 129, e83–e91 (2012).

    Article  CAS  Google Scholar 

  36. Olsson, S. et al. Stroke preventions with the oral direct thrombin inhibitor ximelagatran compared with warfarin in patients with non-valvular atrial fibrillation (SPORTIF III): randomised clinical trial. Lancet 362, 1691–1698 (2003).

    Article  CAS  Google Scholar 

  37. SPORTIF Executive Steering Committee for the SPORTIF V Investigators. Ximelagatran versus warfarin for stroke prevention in patients with nonvalvular atrial fibrillation: a randomized trial. JAMA 293, 690–698 (2005).

  38. Uppoor, R. S. et al. The use of imaging in the early development of neuropharmacological drugs: a survey of approved NDAs. Clin. Pharmacol. Ther. 84, 69–74 (2008).

    Article  CAS  Google Scholar 

  39. Freedman, N. M. et al. In vivo measurement of brain monoamine oxidase B occupancy by rasagiline, using 11C-L-Deprenyl and PET. J. Nucl. Med. 46, 1618–1624 (2005).

    CAS  PubMed  Google Scholar 

  40. Thebault, J. J., Guillaume, M. & Levy, R. Tolerability, safety and pharmacodynamics and pharmacokinetics of rasagiline: a potent, selective and irreversible monoamine oxidase type B inhibitor. Pharmacotherapy 24, 1295–1305 (2004).

    Article  CAS  Google Scholar 

  41. Ramsey, S. J., Attkins, N. J., Fish, R. & van der Graaf, P. H. Quantitative pharmacological analysis of antagonist binding kinetics at CRF1 receptors in vitro and in vivo. Br. J. Pharmacol. 164, 992–1007 (2011).

    Article  CAS  Google Scholar 

  42. Qureshi, Z. P., Seoane-Vazquez, E., Rodriguez-Monguio, R., Stevenson, K. B. & Szeinbach, S. L. Market withdrawal of new molecular entities approved in the United States from 1980 to 2009. Pharmacoepidemiol. Drug Safety 20, 772–777 (2011).

    Article  Google Scholar 

  43. King, A. Prevention: neuropsychiatric adverse effects signal the end of the line for rimonabant. Nature Rev. Cardiol. 7, 602 (2010).

    Article  Google Scholar 

  44. Tralau, T. & Luch, A. Drug-mediated toxicity: illuminating the 'bad' in the test tube by means of cellular assays? Trends Pharmacol. Sci. 33, 353–364 (2012).

    Article  CAS  Google Scholar 

  45. Farmer, H. et al. Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy. Nature. 434, 917–921 (2005).

    Article  CAS  Google Scholar 

  46. Fong, P. C. et al. Inhibition of poly(ADP-ribose) polymerase in tumors from BRCA mutation carriers. N. Engl. J. Med. 361, 123–134 (2009).

    Article  CAS  Google Scholar 

  47. Ledermann, J. et al. Olaparib maintenance therapy in patients with platinum-sensitive relapsed serous ovarian cancer (SOC) and a BRCA mutation (BRCAm). J. Clin. Oncol. Abstr. 31, 5505 (2013).

    Google Scholar 

  48. Jack, C. R. et al. Evidence for ordering of Alzheimer disease biomarkers. JAMA Neurol. 68, 1526–1535 (2011).

    Google Scholar 

  49. Poirier, J. et al. Apolipoprotein E polymorphism and Alzheimer's disease. Lancet. 2, 697–699 (1993).

    Article  Google Scholar 

  50. Holgate, S. Pathophysiology of asthma: what has our current understanding taught us about new therapeutic approaches? J. Allergy Clin. Immunol. 128, 495–505 (2011).

    Article  CAS  Google Scholar 

  51. Izuhara, K. et al. [Clarification of the pathogenesis and development of clinical examination for allergic disease]. Rinsho Byori 55, 369–374 (in Japanese) (2007).

    CAS  PubMed  Google Scholar 

  52. Izuhara, K. & Saito, H. Microarray-based identification of novel biomarkers in asthma. Allergol. Int. 55, 361–367 (2006).

    Article  CAS  Google Scholar 

  53. Izuhara, K. et al. IL-13: a promising therapeutic target for bronchial asthma. Curr. Med. Chem. 13, 2291–2298 (2006).

    Article  CAS  Google Scholar 

  54. Woodruff, P. G. et al. Genome-wide profiling identifies epithelial cell genes associated with asthma and with treatment response to corticosteroids. Proc. Natl Acad. Sci. USA. 104, 15858–15863 (2007).

    Article  CAS  Google Scholar 

  55. Corren, J. et al. Lebrikizumab treatment in adults with asthma. N. Engl. J. Med. 365, 1088–1098 (2011).

    Article  CAS  Google Scholar 

  56. Thomson, N. C., Patel, M. & Smith, A. D. Lebrikizumab in the personalized management of asthma. Biologics 6, 329–335 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Wenzel, S. E. et al. Evidence that severe asthma can be divided pathologically into two inflammatory subtypes with distinct physiologic and clinical characteristics. Am. J. Respir. Crit. Care Med. 160, 1001–1008 (1999).

    Article  CAS  Google Scholar 

  58. Pavord, I. D. et al. Mepolizumab for severe eosinophilic asthma (DREAM): a multicentre, double-blind, placebo-controlled trial. Lancet 380, 651–659 (2012).

    Article  CAS  Google Scholar 

  59. Leckie, M. J. et al. Effects of an interleukin-5 blocking monoclonal antibody on eosinophils, airway hyper-responsiveness, and the late asthmatic response. Lancet 356, 2144–2148 (2000).

    Article  CAS  Google Scholar 

  60. Visser, S. et al. Model-based drug discovery: implementation and impact. Drug Discov. Today 18, 746–775 (2013).

    Article  Google Scholar 

  61. Lisman, J. E., Raghavachari, S. & Tsien, R. W. The sequence of events that underlie quantal transmission at central glutamatergic synapses. Nature Rev. Neurosci. 8, 597–609 (2007).

    Article  CAS  Google Scholar 

  62. Moghaddam, B. & Javitt, D. From revolution to evolution: the glutamate hypothesis of schizophrenia and its implication for treatment. Neuropsychopharmacology 37, 4–15 (2012).

    Article  CAS  Google Scholar 

  63. Patil, S. T. et al. Activation of mGlu2/3 receptors as a new approach to treat schizophrenia: a randomized Phase 2 clinical trial. Nature Med. 13, 1102–1107 (2007).

    Article  CAS  Google Scholar 

  64. Geyer, M. A. & Moghaddam, B. in Neuropsychopharmacology: The Fifth Generation of Progress (eds Davies, K., Charney, D., Coyle, J. T. & Nemeroff, C.) 689–701 (Lippincott Williams & Wilkins, 2002).

    Google Scholar 

  65. Matsuoka, T. et al. Prostaglandin D2 as a mediator of allergic astma. Science 287, 2013–2017 (2000).

    Article  CAS  Google Scholar 

  66. Arimura, A. et al. Prevention of allergic inflammation by a novel prostaglandin receptor antagonist, S-5751. J. Pharmacol. Exp. Ther. 298, 411–419 (2001).

    CAS  PubMed  Google Scholar 

  67. Shichijo, M. et al. A prostaglandin D2 receptor antagonist modifies experiemental asthma in sheep. Clin. Exp. Allergy 39, 1404–1414 (2009).

    Article  CAS  Google Scholar 

  68. Lukacs, N. W. et al. CRTH2 antagonism significantly ameliorates airway hyperreactivity and downregulates inflammation-induced genes in a mouse model of airway inflammation. Am. J. Physiol. Lung Cell. Mol. Physiol. 295, L767–L779 (2008).

    Article  CAS  Google Scholar 

  69. Gervais, F. G. et al. Pharmacological characterization of MK-7246, a potent and selective CRTH2 (chemoattractant receptor-homologous molecule expressed on T-helper type 2 cells) antagonist. Mol. Pharmacol. 79, 69–76 (2011).

    Article  CAS  Google Scholar 

  70. Barnes, N. et al. A randomized, double-blind, placebo-controlled strudy of the CRTH2 antagonist OC0000459 in moderate persistent asthma. Clin. Exp. Allergy. 42, 38–48 (2012).

    Article  CAS  Google Scholar 

  71. Busse, W. W. et al. Safety and efficacy of the prostaglandin D2 receptor antagonist AMG 853 in asthmatic patients. J. Allergy Clin. Immunol. 131, 339–345 (2013).

    Article  CAS  Google Scholar 

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Acknowledgements

The authors would like to thank S. Curran, J. Curwen, U. Eriksson, G. Johnston, R. Maciewicz, J. Munroe, M. Needham, P. Newham, W. Redfern, M. Snowden, F. Tierney, J.-P. Valentin and J. Waterton for their help in producing and reviewing this manuscript.

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Correspondence to Menelas N. Pangalos.

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All authors are employees and shareholders of AstraZeneca.

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Cook, D., Brown, D., Alexander, R. et al. Lessons learned from the fate of AstraZeneca's drug pipeline: a five-dimensional framework. Nat Rev Drug Discov 13, 419–431 (2014). https://doi.org/10.1038/nrd4309

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