Skip to main content Accessibility help
×
Hostname: page-component-8448b6f56d-wq2xx Total loading time: 0 Render date: 2024-04-23T18:20:04.380Z Has data issue: false hasContentIssue false

3 - The evolution of pharmaceutical innovation

Published online by Cambridge University Press:  22 September 2009

Paul Nightingale
Affiliation:
Science Policy Research Unit, University of Sussex, Brighton, United Kingdom
Surya Mahdi
Affiliation:
Science Policy Research Unit, University of Sussex, Brighton, United Kingdom
Mariana Mazzucato
Affiliation:
The Open University, Milton Keynes
Giovanni Dosi
Affiliation:
Sant'Anna School of Advanced Studies, Pisa
Get access

Summary

Introduction

The aim of this chapter is to explain and highlight a consistent pattern of changes within pharmaceutical R&D processes that produce “small molecule” therapeutics. In doing so, it aims to provide an alternative to the traditional “biotechnology revolution” conception of these changes, whereby a search regime focused on chemistry and on large, integrated pharmaceutical firms is being displaced by a new search regime focused on molecular biology and networks of smaller, dedicated biotech firms. It instead suggests that the changes are better understood within a Chandlerian framework in terms of an “industrialization of R&D,” whereby firms adapt their technologies and organizational structures to exploit the economies of scale and scope that are made possible by economic interdependencies within knowledge production (see Chandler, 1990; Rothwell, 1992; Houston and Banks, 1997; Nightingale, 1998, 2000). These economically important interdependencies are a consequence of knowledge having value only in context (Nightingale, 2004), and generate scale economies because information that is integrated into a coherent whole can have more economic value than the same information divided into its component parts and distributed between economic agents who fail to realize its full value.

The chapter will suggest that changes within pharmaceutical innovation processes over the last twenty years have been driven largely by a shift from a trial and error approach to drug discovery to one that attempts to use scientific understanding of the biology of disease to find drugs for particular diseases and markets (see Schwartzman, 1976; McKelvey, 1995).

Type
Chapter
Information
Knowledge Accumulation and Industry Evolution
The Case of Pharma-Biotech
, pp. 73 - 111
Publisher: Cambridge University Press
Print publication year: 2006

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Adams, M. D., Kelly, J. M., and Gocayne, J. D. (1991), “Complementary DNA sequencing: expressed sequence tags and the Human Genome Project,” Science, 252, 1651–6.CrossRefGoogle ScholarPubMed
Andrade, M. A., and Sander, C. (1997), “Bioinformatics: from genome data to biological knowledge,” Current Opinion in Biotechnology, 8, 675–83.CrossRefGoogle ScholarPubMed
Beattie, J., and Ghazal, P. (2003), “Post-genomics technologies: thinking beyond the hype,” Drug Discovery Today, 8 (20), 909–10.CrossRefGoogle Scholar
Bork, P., and Bairoch, A. (1996), “Go hunting in sequence databases but watch out for the traps,” Trends in Genetics, 12 (10), 425–7.CrossRefGoogle ScholarPubMed
Botstein, D., Ehite, R. L., Skolnick, M., and Davis, R. W. (1980), “Construction of a genetic linkage map in man using restriction fragment length polymorphism,” American Journal of Human Genetics, 32, 314–31.Google Scholar
Bradshaw, J. (1995), Pitfalls in Creating a Chemically Diverse Compound Screening Library, unpublished document, Glaxo, London.
Butte, A. (2002), “The use and analysis of micro-array data,” Nature Reviews Drug Discovery, 1, 951–60.CrossRefGoogle Scholar
Cargill, M., Altshuler, D., and Ireland, J. (1999), “Characterization of single nucleotide polymorphisms in coding regions of human genes,” Nature Genetics, 22 (3), 231–8.CrossRefGoogle ScholarPubMed
Chakravarti, A. (1999), “Population genetics – making sense out of sequence,” Nature Genetics, 21 (1 Supplement), 56–60.CrossRefGoogle ScholarPubMed
Chandler, A. D. Jr. (1990), Scale and Scope: The Dynamics of Industrial Capitalism, Belknap Press, Cambridge, MA.Google Scholar
Chumakov, I., Rigault, P., Guillou, S., Ougen, P., Billaut, A., Guasconi, G., Gervy, P., LeGall, I., Soularue, P., Grinas, L., Bougueleret, L., Chantelot, C. B., Lacroix, B., Barillot, E., Gesnouin, P., Pook, S., Vaysseix, G., Frélat, G., Schmitz, A., Sambucy, J. L., Bosch, A., Estivill, X., Weissenbach, J., Vignal, A., Riethman, H., Cox, D., Patterson, D., Gardinar, K., Hattori, M., Sakaki, Y., Ichikawa, H., Ohki, M., Paslier, D. L., Heilig, R., Antonarakis, S., and Cohen, D. (1992), “Continuum of overlapping clones spanning the entire human chromosome 21q,” Nature, 359, 380–7.CrossRefGoogle ScholarPubMed
Cockburn, I. M., and Henderson, R. (1998), “Absorptive capacity, co-authoring behaviour, and the organization of research in drug discovery,” Journal of Industrial Economics, 46 (2), 157–82.CrossRefGoogle Scholar
Cocket, M., Dracopoli, N., and Sigal, E. (2000), “Applied genomics: integration of the technology within pharmaceutical research and development,” Current Opinion in Biotechnology, 11, 602–9.CrossRefGoogle Scholar
Collins, F. S., and Galas, D. (1993), “A new five-year plan for the U.S. Human Genome Project,” Science, 262, 11.CrossRefGoogle ScholarPubMed
Collins, F. S., and Guyer, M. S. (1995), “How is the Human Genome Project doing, and what have we learned so far?,” Proceedings of the National Academy of Sciences, 92 (24), 10841–8.Google Scholar
Collins, F. S., and McKusick, V. A. (2001), “Implications of the Human Genome Project for medical science,” Journal of the American Medical Association, 285, 540–4.CrossRefGoogle ScholarPubMed
Collins, F. S., Patrinos, A., Jordan, E., Gesteland, R., Walters, L., and members of DOE and NIH planning groups (1998), “New goals for the US Human Genome Project: 1998–2003,” Science, 282, 682–9.CrossRefGoogle Scholar
David, P. A. (1990), “The dynamo and the computer: a historical perspective on a modern productivity paradox,” American Economic Review, 80, 355–61.Google Scholar
David, P. A.(1997), “Knowledge, property and the system dynamics of technological change,” in Summers, L. and Shah, S. (eds.), Proceedings of the World Bank Conference on Development Economics, World Bank, New York, 215–48.Google Scholar
David, P. A.(2004), “Understanding the emergence of open science institutions: functionalist economics in historical context,” Industrial and Corporate Change, 13 (3), 571–89.CrossRefGoogle Scholar
Deloukas, P., Schuler, G. D., Gyapay, G., Beasley, E. M., Soderlund, C., and Rodriguez-Tome, P. (1998), “A physical map of 30,000 human genes,” Science, 282, 744–6.CrossRefGoogle ScholarPubMed
DiMasi, J. A. (1995), “Trends in drug development costs, times and risks,” Drug Information Journal, 29, 375–84.CrossRefGoogle Scholar
Donis-Keller, H., Green, P., Helms, C., Cartinhour, S., Weiffenbach, B., Stephens, K., Keith, T. P., Bowden, D. W., Smith, D. R., and Lander, E. S. (1987), “A genetic linkage map of the human genome,” Cell, 23 (51), 319–37.CrossRefGoogle Scholar
Dosi, G., P. Llerena, and M. Sylos Labini (2005), Science–Technology–Industry Links and the “European Paradox”: Some Notes on the Dynamics of Scientific and Technological Research in Europe, Working Paper no. 2005:2, Laboratory of Economics and Management, Sant'Anna School for Advanced Studies, Pisa.
Dosi, G. (1982), “Technological paradigms and technological trajectories: a suggested interpretation,” Research Policy, 11, 147–62.CrossRefGoogle Scholar
Drews, J. (2000), The Impact of Cost Containment Initiatives on Pharmaceutical R&D, Annual Lecture, Centre for Medicines Research, London.Google Scholar
Duggan, D. J., Bittner, M., Chen, Y., Meltzer, P., and Trent, J. M. (1999), “Expression profiling using cDNA microarrays,” Nature Genetics, 21 (1 Supplement), 10–14.CrossRefGoogle ScholarPubMed
Freeman, C. (1982), The Economics of Innovation, 3rd edn., Pinter, Cheltenham.Google Scholar
Freeman, C., and Louca, F. (2001), As Time Goes By: The Information and Industrial Revolutions in Historical Perspective, Oxford University Press, Oxford.Google Scholar
Gelbart, W. M. (1998), “Databases in genomic research,” Science, 282, 659–661.CrossRefGoogle ScholarPubMed
Gelbert, L. M., and Gregg, R. E. (1997), “Will genetics really revolutionise the drug discovery process?,” Current Opinion in Biotechnology, 8, 669–74.CrossRefGoogle Scholar
Geuna, A., Salter, A., and Steinmueller, W. E. (2003), Science and Innovation: Rethinking the Rationale for Funding and Governance, Edward Elgar, Cheltenham.CrossRefGoogle Scholar
Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Scott, P., and Trow, M. (1994), The New Production of Knowledge: The Dynamics of Science and Research in Contemporary Societies, Sage Publications, London.Google Scholar
Gilbert, W. (1991), “Towards a paradigm shift in biology,” Science, 249, 99.Google Scholar
Grunstein, M., and Hogness, D. S. (1975), “Colony hybridisation: a method for the isolation of cloned DNAs that contain a specific gene,” Proceedings of the National Academy of Sciences, 72, 3961–5.CrossRefGoogle ScholarPubMed
GSK (2004), “Briefing to industrial analysts,” Glaxo SmithKline, London.
Henderson, R. M., L. Orsenigo, and G. Pisano (1999), “The pharmaceutical industry and the revolution in molecular biology: exploring the interactions between scientific, institutional, and organizational change,” in Mowery, D. C. and Nelson, R. R. (eds.), Sources of Industrial Leadership: Studies of Seven Industries, Cambridge University Press, Cambridge, 267–311.CrossRefGoogle Scholar
Holzman, N. A., and Marteau, T. M. (2000), “Will genetics revolutionize medicine?,” New England Journal of Medicine, 343, 141–4.CrossRefGoogle Scholar
Hopkins, M. (1998), An Examination of Technology Strategies for the Integration of Bioinformatics in Pharmaceutical R&D Processes, Masters dissertation, Science and Technology Policy Research Unit, University of Sussex, Brighton.Google Scholar
Horrobin, D. F. (2001), “Realism in drug discovery – could Cassandra be right?,” Nature Biotech, 19, 1099–100.CrossRefGoogle ScholarPubMed
Horrobin, D. F.(2003), “Modern biomedical research: an internally self-consistent universe with little contact with medical reality?,” Nature Review Drug Discovery, 2, 151–4.CrossRefGoogle ScholarPubMed
Houston, J. G., and Banks, M. (1997), “The chemical-biological interface: developments in automated and miniaturised screening technology,” Current Opinion in Biotechnology, 8, 734–40.CrossRefGoogle ScholarPubMed
Hudson, T. J., Stein, L. D., Gerety, S. S., Ma, J., Castle, A. B., Silva, J., Slonim, D. K., Baptista, R., Kruglyak, L., and Xu, S. H. (1995), “An STS-based map of the human genome,” Science, 270, 1919–20.CrossRefGoogle ScholarPubMed
Hughes, T. (1983), Networks of Power: Electrification in Western Society, 1880–1930, Johns Hopkins Press, Baltimore.Google Scholar
Lahana, R. (1999), “How many leads from HTS?,” Drug Discovery Today, 4 (10), 447–8.CrossRefGoogle ScholarPubMed
Lander, E. S. (1996), “The new genomics: global views of biology,” Science, 274, 536–9.CrossRefGoogle ScholarPubMed
Lander, E. S., and Schork, N. (1994), “Genetic dissection of complex diseases,” Science, 265, 2037–48.CrossRefGoogle Scholar
Lewontin, R. C. (2000), It Ain't Necessarily So: The Dream of the Human Genome and Other Illusions, New York Review of Books, New York.Google Scholar
Lipshutz, R. J., Fodor, S. P. A., Gingeras, T. R., and Lockhart, D. J. (1999), “High-density synthetic oligo-nucleotide arrays,” Nature Genetics, 21 (1 Supplement), 20–4.CrossRefGoogle Scholar
Mahdi, S. (2003), “Search strategy in product innovation process: theory and evidence from the evolution of agrochemical lead discovery process,” Industrial and Corporate Change, 12 (2), 235–70.CrossRefGoogle Scholar
Martin, P. (1998), From Eugenics to Therapeutics: Science and the Social Shaping of Gene Therapy, D.Phil. thesis, Science and Technology Policy Research Unit, University of Sussex, Brighton.Google Scholar
McKelvey, M. (1995), Evolutionary Innovation: The Business of Biotechnology, Oxford University Press, Oxford.Google Scholar
Meldrum, D. R. (1995), “The interdisciplinary nature of genomics,” IEEE Engineering in Medicine and Biology, 14, 443–8.CrossRefGoogle Scholar
Meldrum, D. R.(2000a), “Automation for genomics: part 1, preparation for sequencing,” Genome Research, 10 (8), 1081–92.CrossRefGoogle Scholar
Meldrum, D. R.(2000b), “Automation for genomics: part 2, sequencers, micro-arrays, and future trends,” Genome Research, 10 (9), 1288–1303.CrossRefGoogle Scholar
Mowery, D. C. (1983), “The relationship between intrafirm and contractual forms of industrial research in American manufacturing, 1900–1940,” Explorations in Economic History, 20, 351–74.CrossRefGoogle Scholar
Murray Rust, P. (1994), “Bioinformatics and drug discovery,” Current Opinion in Biotechnology, 5, 648–53.CrossRefGoogle ScholarPubMed
Murray, J. C., Buetow, K. H., Weber, J. L.Ludwigsen, S.Shirpbier-Meddem, T. and Manion, F. (1994), “A comprehensive human linkage map with centimorgan density,” Science, 265, 2049–54.CrossRefGoogle ScholarPubMed
Nature Genetics (1999), “The chipping forecast,” Nature Genetics, 21 (1 Supplement), 1–60.
Nature Genetics(2002), “The chipping forecast II,” Nature Genetics, 32 (4 Supplement), 461–552.
Nelson, R. R. (1991), “Why do firms differ, and how does it matter?,” Strategic Management Journal, 12 (1), 61–74.CrossRefGoogle Scholar
Nelson, R. R.(2004), “The market economy, and the scientific commons,” Research Policy, 33 (3), 455–71.CrossRefGoogle Scholar
Nelson, R. R., and Winter, S. G. (1982), An Evolutionary Theory of Economic Change, Belknap Press, Cambridge, MA.Google Scholar
Nightingale, P. (1998), “A cognitive theory of innovation,” Research Policy, 27 (7), 689–709.CrossRefGoogle Scholar
Nightingale, P.(2000), “Economies of scale in experimentation: knowledge and technology in pharmaceutical R&D,” Industrial and Corporate Change, 9 (2), 315–59.CrossRefGoogle Scholar
Nightingale, P.(2004), “Technological capabilities, invisible infrastructure and the un-social construction of predictability: the overlooked fixed costs of useful research,” Research Policy, 33 (9), 1259–84.CrossRefGoogle Scholar
Nightingale, P., Brady, T., Davies, A., and Hall, J. (2003), “Capacity utilisation revisited: software, control & the growth of large technical systems,” Industrial and Corporate Change, 12 (3), 477–517.CrossRefGoogle Scholar
Nightingale, P., and Martin, P. (2004), “The myth of the biotech revolution,” Trends in Biotechnology, 22 (11), 564–9.CrossRefGoogle ScholarPubMed
Pavitt, K. (1984), “Sectoral patterns of technological change: towards a taxonomy and a theory,” Research Policy, 13, 343–74.CrossRefGoogle Scholar
Pavitt, K.(1998), “Technologies, products and organisation in the innovating firms: what Adam Smith tells us and Schumpeter doesn't,” Industrial and Corporate Change, 7 (3), 433–52.CrossRefGoogle Scholar
Pavitt, K.(1999), Technology, Management and Systems of Innovation, Edward Elgar, Cheltenham.Google Scholar
Pavitt, K.(2001), “Public policies to support basic research: what can the rest of the world learn from US theory and practice? (And what they should not learn),” Industrial and Corporate Change, 10 (3), 761–79.CrossRefGoogle Scholar
Pennisi, E. (1998), “A closer look at SNPs suggests difficulties,” Science, 281, 1787–9.CrossRefGoogle Scholar
Penrose, E. (1960), “The growth of the firm: a case study of the Hercules Powder Company,” Business History Review, 43, 1–23.CrossRefGoogle Scholar
Penrose, E. 1959, The Theory of the Growth of the Firm, Basil Blackwell, Oxford.Google Scholar
Polanyi, M. (1966), The Tacit Dimension, Doubleday, New York.Google Scholar
Pollack, A. (2002), “Despite billions for discoveries, pipeline of drugs is far from full,” New York Times, C1.
Risch, N. J. (2000), “Searching for genetic determinants in the new millennium,” Nature, 405, 847–56.CrossRefGoogle ScholarPubMed
Risch, N. J., and Merikangas, K. (1996), “The future of genetic studies of complex human diseases,” Science, 273, 1516–17.CrossRefGoogle ScholarPubMed
Rommens, J. M., Januzzi, M. C., and Kerem, B.-S. (1989), “Identification of the cystic fibrosis gene: chromosome walking and jumping,” Science, 245, 1059–65.CrossRefGoogle ScholarPubMed
Rosenberg, N. (1974), “Science innovation and economic growth,” Economic Journal, 84, 90–108.CrossRefGoogle Scholar
Rosenberg, N.(1976), Perspectives on Technology, Cambridge University Press, Cambridge.CrossRefGoogle Scholar
Rosenberg, N.(1979), “Technological interdependence in the American economy,” Technology and Culture, 20 (1), 25–40.CrossRefGoogle Scholar
Roses, A. D. (2000), “Pharmacogenetics and the practice of medicine,” Nature, 405, 857–65.CrossRefGoogle Scholar
Roses, A. D.(2001), “Pharmacogenetics,” Human Molecular Genetics, 10 (20), 2261–7.CrossRefGoogle ScholarPubMed
Rothwell, R. (1992), “Successful industrial innovation: critical factors for the 1990s,” R&D Management, 22 (3), 221–39.Google Scholar
Scherer, M. F. (1993), “Pricing, profits and technological progress in the pharmaceutical industry,” Journal of Economic Perspective, 7, 97–115.CrossRefGoogle Scholar
Schwartzman, D. (1976), Innovation in the Pharmaceutical Industry, Johns Hopkins University Press, Baltimore.Google Scholar
Smith, L. M., Fung, S., Hunkapiller, M. W., Hunkapiller, T. J., and Hood, L. E. (1985), “The synthesis of oligonucleotides containing an aliphatic amino group at the 5' terminus: synthesis of fluorescent DNA primers for use in DNA sequence analysis,” Nucleic Acids Research, 13, 2399–412.CrossRefGoogle ScholarPubMed
Smith, L. M., Sanders, J. Z., Kaiser, R. J., Hughes, P., Dodd, C., Connell, C. R., Heiner, C., Kent, S. B. H., and Hood, L. E. (1986), “Fluorescence detection in automated DNA sequence analysis,” Nature, 321, 674–9.CrossRefGoogle ScholarPubMed
Southern, E. M. (1975), “Detection of specific sequences among DNA fragments separated by gel electrophoresis,” Journal of Molecular Biology, 98, 503–17.CrossRefGoogle ScholarPubMed
Southern, E. M., Mir, K., and Shchepinov, M. (1999), “Molecular interactions on microarrays,” Nature Genetics, 21 (1 Supplement), 5–9.CrossRefGoogle ScholarPubMed
Strachan, T., and Read, A. P. (2000), Human Molecular Genetics 3, Garland Press, Oxford.Google Scholar
Sudbury, P. (1999), Human Molecular Genetics, Cell and Molecular Biology in Action Series, Addison-Wesley, London.Google Scholar
Terwilliger, J. D., Haghighi, F., Hiekkalinna, T. S., and Goring, H. H. (2002), “A bias-ed assessment of the use of SNPs in human complex traits,” Current Opinion in Genetics and Development, 12, 726–34.CrossRefGoogle ScholarPubMed
Venter, C. J., Levy, S., Stockwell, T., Remington, K., and Halpern, A. (2003), “Massive parallelism, randomness and genomic advances,” Nature Genetics, 33 (3 Supplement), 219–27.CrossRefGoogle ScholarPubMed
Vincenti, W. G. (1990), What Engineers Know and How They Know It, Johns Hopkins University Press, Baltimore.Google Scholar
Wang, D. G., Fan, J. B., and Siao, C. J. (1998), “Large-scale identification, mapping and genotyping of single nucleotide polymorphisms in the human genome,” Science, 280, 1077–82.CrossRefGoogle ScholarPubMed
Weiss, K. M. (1993), Genetic Variation and Human Disease: Principles and Evolutionary Approaches, Studies in Biological Anthropology no. 11, Cambridge University Press, Cambridge.CrossRefGoogle Scholar
Weiss, K. M., and Terwilliger, J. D. (2000), “How many diseases does it take to map a gene with SNPs?,” Nature Genetics, 26 (2), 151–7.CrossRefGoogle ScholarPubMed
Weissenbach, J., Gyapay, G., Dib, C., Vignal, A., Morissette, J., Millas-Seav, P., Vaysseix, G., and Lathrop, M. (1992), “A second-generation linkage map of the human genome,” Nature, 359, 794–801.CrossRefGoogle ScholarPubMed
Wodicka, L., Dong, H., Mittmann, M., Ho, M. H., and Lockhart, D. J. (1997), “Genome-wide expression monitoring in Saccharomyces cerevisiae,” Nature Biotechnology, 15 (13), 1359–67.CrossRefGoogle ScholarPubMed

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×