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Can literature analysis identify innovation drivers in drug discovery?

Nature Reviews Drug Discovery volume 8, pages 865–878 (2009)Cite this article

Key Points

  • Drug discovery has traditionally responded to the 'pull' of unmet medical need and commercial potential. Here, we evaluate the scientific areas that are providing 'push' in terms of scientific innovation, as measured by publications and patents.

  • The amount of funding from the US National Institutes of Health, the number of doctoral degrees awarded, the number of publications and the number of approvals for new molecular entities issued by the US Food and Drug Administration show a general upward trend from the 1950s to the present. Although explanations can be suggested for some of the short-term ups and downs, these trends are also affected by numerous hidden variables, and well as time lags, feedback loops and other complications.

  • The numbers of publications in various disease areas correlate well with global disease burden.The correlation is greater in the developed world, where the impact of infectious and parasitic diseases, respiratory infections and infant mortality, is considerably lower than in the developing world.

  • A few therapeutic areas stand out when analysing trends in publications, citations, publications in high-impact journals and patents. Oncology is the clearest outlier on almost every metric. Viruses, nutrition and metabolism, and the immune system also show increases in the majority of metrics.

  • In terms of individual diseases, insulin resistance, orthomyxoviridae infections, depression, autism, macular degeneration, inflammation, obesity, cognitive disorders and ventricular dysfunction have strong publication growth in both 2- and 5-year periods. Publications related to the genes encoding forkhead box P3, leucine-rich repeat kinase 2, janus kinase 2, transcription factor 7-like 2, interleukin-17A, toll-like receptor 2 (TLR2), TLR4, FK506 binding protein 12-rapamycin associated protein 1and ADIPOQ (adiponectin, C1Q and collagen domain-containing) show the strongest publication growth in the same periods.

  • Assessing scientific innovation is a complex endeavour; however, the 'bibliome' seems to offer many approaches that could enhance decision-making in drug discovery.

Abstract

Drug discovery must be guided not only by medical need and commercial potential, but also by the areas in which new science is creating therapeutic opportunities, such as target identification and the understanding of disease mechanisms. To systematically identify such areas of high scientific activity, we use bibliometrics and related data-mining methods to analyse over half a terabyte of data, including PubMed abstracts, literature citation data and patent filings. These analyses reveal trends in scientific activity related to disease studied at varying levels, down to individual genes and pathways, and provide methods to monitor areas in which scientific advances are likely to create new therapeutic opportunities.

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Figure 1: NIH funding, US biology and chemistry doctorates awarded, PubMed publications and FDA NME approvals by year.
Figure 2: Rate of scientific publication versus relative disease burden for key therapeutic areas.
Figure 3: Scientific publication over three decades classified by MeSH disease headings.
Figure 4: Rates of change in scientific publication by year for MeSH disease categories.
Figure 5: Representation of MeSH disease categories in high-impact journals.
Figure 6: Highly-cited articles and associations with patent filings by disease area over 5 years.
Figure 7: Recent growth in publications by disease and by individual gene.

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Acknowledgements

We gratefully acknowledge P. Vallance for posing the problem, V. Kumar for implementing the methods to identify disease areas and genes with strong publication growth, and R. Liu for suggesting the use of common authors to map patents to publication, and thereby associate patents with MeSH key words. D. Rajagopalan, K. Kabnick, L. Liu, T. White, W. Reisdorf and Y. Li provided technical assistance and valuable suggestions.

Author information

Authors and Affiliations

  1. Computational Biology Department, GlaxoSmithKline Pharmaceuticals, 709 Swedeland Road, PO BOX 1539, King of Prussia, 19406, Pennsylvania, USA

    Pankaj Agarwal & David B. Searls

Authors
  1. Pankaj Agarwal
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  2. David B. Searls
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Corresponding author

Correspondence to Pankaj Agarwal.

Supplementary information

Supplementary information S1 (table)

The PubMed queries that were used to generate publication counts that corresponded to the WHO disease areas in Figure 2. (PDF 454 kb)

Supplementary information S2 (table)

(a) Overlap in publications between disease areas: the number in each cell is the percentage of publications in the disease area in the specific row that is also contained in the disease area in a specific column. (PDF 194 kb)

Supplementary information S3 (table)

PubMed queries used for each disease area in figures 3, 4, 5 and 6. (PDF 108 kb)

Supplementary information S4 (table)

List of diseases with MeSH History notes dated between 1998 and 2007. (PDF 116 kb)

Supplementary information S5 (table)

List of pathways (of size between 10 and 100 associated genes) from Ingenuity (http://www.ingenuity.com), GeneGO (http://www.genego.com) and Biocarta (http://www.biocarta.com) that have a significant fraction of 'hot' genes (that is, genes that are associated with a significant increase in publication counts). (PDF 99 kb)

Related links

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FURTHER INFORMATION

David B. Searls's homepage

National Science Foundation Science and Engineering Statistics

US FDA Center for Drug Evaluation and Research

World Health Report

Glossary

Impact factor

The average number of citations made in a given year to articles that were published in the journal in question during the two preceding years. Impact factors (and many variants) are used to rank journals for their impact on a field, and are sometimes misused in evaluating individual researchers.

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Agarwal, P., Searls, D. Can literature analysis identify innovation drivers in drug discovery?. Nat Rev Drug Discov 8, 865–878 (2009). https://doi.org/10.1038/nrd2973

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