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. 2013;8(3):e57755.
doi: 10.1371/journal.pone.0057755. Epub 2013 Mar 20.

Risk in vaccine research and development quantified

Esther S Pronker  1 , Tamar C Weenen, Harry Commandeur, Eric H J H M Claassen, Albertus D M E Osterhaus
Affiliations

Risk in vaccine research and development quantified

Esther S Pronker et al. PLoS One. 2013.

Abstract

To date, vaccination is the most cost-effective strategy to combat infectious diseases. Recently, a productivity gap affects the pharmaceutical industry. The productivity gap describes the situation whereby the invested resources within an industry do not match the expected product turn-over. While risk profiles (combining research and development timelines and transition rates) have been published for new chemical entities (NCE), little is documented on vaccine development. The objective is to calculate risk profiles for vaccines targeting human infectious diseases. A database was actively compiled to include all vaccine projects in development from 1998 to 2009 in the pre-clinical development phase, clinical trials phase I, II and III up to Market Registration. The average vaccine, taken from the preclinical phase, requires a development timeline of 10.71 years and has a market entry probability of 6%. Stratification by disease area reveals pandemic influenza vaccine targets as lucrative. Furthermore, vaccines targeting acute infectious diseases and prophylactic vaccines have shown to have a lower risk profile when compared to vaccines targeting chronic infections and therapeutic applications. In conclusion; these statistics apply to vaccines targeting human infectious diseases. Vaccines targeting cancer, allergy and autoimmune diseases require further analysis. Additionally, this paper does not address orphan vaccines targeting unmet medical needs, whether projects are in-licensed or self-originated and firm size and experience. Therefore, it remains to be investigated how these - and other - variables influence the vaccine risk profile. Although we find huge differences between the risk profiles for vaccine and NCE; vaccines outperform NCE when it comes to development timelines.

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Conflict of interest statement

Competing Interests: Esther S. Pronker is completing her PhD program using funds from the Dutch NGI Valorisation award from 2009. A.D.M.E. Osterhaus wishes to declare, for the avoidance of any misunderstanding on competing interests, that he co-founded and is chief scientific officer of Viroclinics Biosciences BV a spin out of Erasmus MC. However, for clarification, no materials or support were received from the company, and no agreements were in place concerning the execution or publication of this work. There are no patents, products in development or marketed products to declare. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials.

Figures

Figure 1
Figure 1. Vaccine risk profiles for selected disease areas.
Risk profiles for the selected disease areas, combining phase duration with the cumulative transition probabilities as indicated by market entry probabilities. Rank order indicates quantity of projects in data set. Data points are labelled on the All Data curve; this labelling also applies to the other curves.
Figure 2
Figure 2. Vaccine risk profile granulating acute versus chronic infections and prophylactic versus therapeutic vaccines.
Risk profiles for vaccines, granulated to show risk profiles for vaccines targeting acute versus chronic infections and prophylactic versus therapeutic vaccines. Groups are stratified from 100% of the data from the dataset. Percentage per group included.
Figure 3
Figure 3. Combining the cumulative success rate with contextual factors.
Combining the cumulative success rate with the contextual factors of disease burden and size of investment (indicated by the size of the bubble).
See this image and copyright information in PMC

References

    1. Andre F (2001) Short Survey: the Future of Vaccines, Immunization Concepts and Practices. Vaccine 19: 2206–2209. - PubMed
    1. Armstrong G, Conn L, Pinner R (1999) Trends in Infectious Disease Mortality in the US During the 20th Century. Journal of American Medical Association 281: 61–66. - PubMed
    1. Davis M, Butchart A, Coleman M, Singer D, Wheeler J, et al. (2010) The Expanding Vaccine Pipeline, 1995–2008. Vaccine 28: 1353–1356. - PubMed
    1. Ulmer B, Valley U, Rappuoli R (2006) Vaccine manufacturing; Challenges and solutions. Nature Biotechnology 24: 1377–1383. - PubMed
    1. Andre F (2002) How the Research-based Industry Approaches Vaccine Development and Establishes Priorities. Developments in Biology 110: 25–29. - PubMed

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