(Illustration by Joelle Bolt)
Biotech’s U.S. Birth
It is often taken for granted that the United States is where biotechnology was born and where most of the industry’s successful companies are located. This success was obviously not preordained and raises the question: Why the U.S.? Examining the factors contributing to the early development of biotechnology, and to the continued success in its commercialization, yields insights on how a nation can enable innovation and make it flourish.
Biotechnology is based in innovative science and when examining the key scientific discoveries that laid the technological foundation for the biotechnology industry it is readily apparent that many of them were produced by Americans. For example, Brooklyn-born Arthur Kornberg earned a share of the 1959 Nobel Prize in Physiology or Medicine for his work on the synthesis of DNA and RNA. And Americans continue to push ahead biotech tools, as shown by the 2006 Nobel Prize in Physiology or Medicine going to Americans Andrew Fire and Craig Mello for work on RNA interference. The common national origin of these scientific leaders suggests that the United States likely had many other prolific scientists, and that there were ample opportunities for local collaboration, facilitating spill-over from the laboratories of these Nobel laureates and other productive scientists.
To answer the question of how the United States was able to cultivate this group of outstanding scientists, one can look at research-and-development (R&D) funding. According to figures from the Organisation for Economic Co-operation and Development (OECD), in 2006 the United States accounted for 42 percent of R&D funding by OECD member nations. This leadership has persisted for decades. In 1973 when Stanley Cohen and Herbert Boyer developed methods for gene splicing, the United States’ share of OECD R&D funding was 55 percent, meaning that the United States spent more on R&D than all other OECD members combined.
Although the United States currently has many supportive regulatory policies and opportunities for funding, this was not always the case. Shortly after Cohen and Boyer’s demonstration of gene splicing, for example, the technology was banned by an international moratorium. Moreover, the popular Bayh-Dole Act (which encourages biotechnology by giving inventors the right to patent discoveries made when using Federal funding) and the Small Business Innovation Research (SBIR) funding program were not implemented until the first biotechnology companies were well on their way to commercialization. This indicates that other elements—beyond supportive policies and funding opportunities—must play an important role in the development and growth of the biotechnology industry.
Building U.S. Hubs
Creating hubs for the biotechnology industry makes up one of those other elements. In 1978, for example, Ivor Royston (an untenured professor at the University of California at San Diego) and his research assistant Howard Birndorf formed Hybritech with the objective of commercializing monoclonal antibodies. Hybritech was one of the first biotechnology firms, and it is credited with helping to lay the foundation for San Diego’s biotechnology cluster.
Why did Hybritech form in San Diego? The simplest explanation is that Royston was a UCSD professor and started his company close to home. With $300,000 in seed funding, Hybritech set its focus on diagnostic tests and went public in 1981. As the company grew, it started to attract attention from large pharmaceutical companies. Following the 1985 development of a test for prostate cancer, Eli Lilly purchased Hybritech for nearly $500 million. A collision of cultures quickly ensued between Hybritech, whose managers and scientists were accustomed to a very casual corporate structure, and Eli Lilly, where policies exist for seemingly everything. Within a year of the acquisition, most of Hybritech’s key talent had left the firm. Many of these individuals, rich in experience and cash, went on to start new biotechnology ventures or became venture capitalists, funding other interesting opportunities themselves. With the support of community organizations and an entrepreneurial environment, San Diego has grown to become the third-largest biotechnology cluster in the United States, after the San Francisco Bay area and Boston. More than 100 San Diego companies can trace their history to Hybritech.
While biotechnology clusters may form organically in the vicinity of strong research bases, government actions can also facilitate their development. A prime example of a planned biotechnology cluster is Research Triangle Park, N.C. In the 1950s North Carolina suffered from a brain drain and a declining economy. Markets for the state’s primary industries—textiles, furniture and tobacco—were declining and college graduates typically left the state to find career opportunities elsewhere. Seeking to capitalize on the three nearby universities—Duke, North Carolina State and the University of North Carolina at Chapel Hill—local politicians and businesspeople sought to create homegrown opportunities to stem the mass exodus of talent. Research Triangle Park was founded in 1959 on 4,400 acres of worn-out farmland.
Following a slow start, years of lobbying government and private institutions began to pay off in the late 1960s and 1970s as industry leaders and government agencies decided to establish laboratories and offices in Research Triangle Park. Common draws to locate in Research Triangle Park were the quality of life and modest costs. Today Research Triangle Park encompasses nearly 7,000 acres, is host to more than 150 organizations and its 39,000 employees draw more than $2.7 billion in salaries, making it the largest planned research center in the world. Roughly one-third of the resident firms and organizations are biotechnology and pharmaceutical companies.
Building New Benchmarks
Many countries benchmark their local biotechnology industry against the United States. Some even recommend domestic innovation reforms to match current U.S. laws and incentives. But such comparisons and development plans need to be viewed with respect to the size of the U.S. economy, and the distribution and history of the U.S. biotechnology industry.
First, the U.S. is home to the world’s largest economy and the world’s largest pharmaceutical market—one without price controls. In addition, the relatively consistent legal and political frameworks across states facilitate early stage investments, mergers and acquisitions and market access. Multinational companies also tend to locate their operations closest to their largest markets, which is a strong continuing driver for U.S. growth. Consequently, it’s unfair to expect a country with a smaller economy to have a biotechnology industry that is comparable in size to that of the U.S.
And not all of the United States is necessarily conducive to thriving biotechnology, as evidenced by the uneven distribution of the U.S. biotechnology industry. While there are several very successful regional concentrations, such as San Diego and Research Triangle Park, a majority of U.S. states are struggling to build their local biotechnology industries—a challenge shared by many countries. Given that all states adhere to the same basic federal laws supporting biotechnology but differ in the size of their biotechnology industries, the solution to developing a local biotechnology industry cannot simply be to adopt U.S. federal laws and incentives. In short, local conditions play a significant role in the health of biotech. Additionally, the current set of U.S. federal regulations and incentives helped the biotechnology industry grow to its current state, but that does not necessarily mean that the same conditions would work as well in different political systems and economies. In the end, every company and each country must find their own systems of success in biotech.
Methods
That none of the existing biotechnology datasets are ideal presented a central challenge in this project. Definitions of biotechnology vary between studies and many datasets focus on specific regions, excluding countries outside of their scope. Complicating matters further, individual countries vary in the rigor of their statistical measures and in their transparency. We found that countries that are not part of the European Union (EU), Organisation for Economic Co-operation and Development (OECD) or the Group of Twenty Finance Ministers and Central Bank Governors (G-20) tend to be excluded from global comparative surveys. To overcome these issues, we employed a careful selection of broad data sets to avoid bias from any single source and to ensure global coverage. In certain limited cases, selected measurements for individual countries were added to ensure their representation. China was counted as three independent entities: Mainland China (measured only as Shanghai in some cases), Taiwan and Hong Kong.
Metrics
The following is an inventory and description of the metrics used in this survey.
Public Biotechnology Company Data (not used to calculate the Innovation Score)
Public biotechnology company revenues and Number of public biotechnology companies were derived from: Lawrence, S., Lähteenmäki, R. 2008. Public biotech 2007—the numbers. Nature Biotechnology 26(7):753–762. In selecting public companies, the authors selectively included "companies whose primary commercial activity depends on the application of biological organisms, systems or processes, or on the provision of specialist services to facilitate the understanding thereof." They also excluded pharmaceutical companies, medical-device companies and contract-research organizations. The increased transparency of public companies, relative to private companies, enables a more objective comparison of biotechnology activities. Market capitalization was derived from company disclosures.
Efficiency (not used to calculate the Innovation Score)
The public-company efficiencies were calculated using company data acquired as described above.
Intellectual Property
Patent strength was derived from: Park, W.G. 2008. International patent protection: 1960–2005. Research Policy 37(4):761–766. This index is the unweighted sum of five separate measures: patentable inventions, membership in international treaties, duration of protection, enforcement mechanisms and restrictions (e.g., compulsory licensing).
Intensity
Public biotechnology companies per capita was derived by dividing the public-company count, as described above, by the 2007 mid-year population as sourced from the U.S. Census Bureau International Database. Public biotechnology company employees per capita was derived by dividing the public-company employee count, as described above, by the 2007 mid-year population as sourced from the U.S. Census Bureau International Database. Public biotechnology company revenues per GDP was derived by dividing the public biotechnology company employee count, as described above, by the 2007 GDP as sourced from the IMF World Economic Outlook Database. Biotech patents / total patents filed with PCT (Patent Cooperation Treaty), Biotechnology VC per GDP and Biotechnology R&D per total R&D were derived from the OECD. The measure of Biotechnology R&D per total R&D for Singapore was derived from the Singapore Economic Development Board (SEDB).
Enterprise Support
The Business Friendly Environment metric was derived from: The World Bank Group. 2008. Doing Business 2009: Measuring Business Regulations (www.doingbusiness.org). This index is constructed by surveying local experts on a synthetic business case. Potential limitations of this index are that it is based on a specific business form of a specified size, and refers to conducting business in a country's largest city (with the exception of certain countries such as China). Biotechnology venture capital was derived from the OECD measures of venture capital activity from 2001–2003. Venture capital availability was derived from: Porter, M., Schwab, K. 2008. The Global Competitiveness Report, 2008-2009. World Economic Forum. This source employs an international survey to produce its index. Capital availability was derived from: Milken Institute Capital Access Index, 2007. This data set is an important complement to Biotechnology venture capital and Biotechnology venture capital availability because venture capital is neither necessary nor generally independently sufficient to support nascent biotechnology ventures to financial independence; other forms of capital can play important roles.
Education / Workforce
Post-secondary science graduates per capita were derived from UNESCO figures, divided by 2007 mid-year population as sourced from the U.S. Census Bureau International Database. PhD graduates per capita, R&D personnel per total employment, Biotechnology workers, and Scientific papers per capita were derived from the OECD.
Singapore figures for Post-secondary science graduates per capita, PhD graduates per capita, R&D personnel per total employment, and Biotechnology workers are from the SEDB. The Biotechnology workers figure for China measures Shanghai only. Post-secondary science graduates per capita, PhD graduates per capita, R&D personnel per total employment and Biotechnology workers serve to provide as comprehensive a picture as reasonably possible of the biotechnology workforce. Measuring Ph.D. graduation alone would exclude individuals who obtain education at foreign schools and repatriate, and those who emigrate following doctoral research (although it is important to note that knowledge spillovers from doctoral research are captured by the country of education). Furthermore, Ph.D. graduates are not the only employees of biotechnology companies. Post-secondary science graduation was included as a crude measure of science literacy among post-secondary graduates who might obtain subsequent Masters degrees in science or degrees in fields such as law, business and medicine. More direct measures of biotechnology workforce were also available for many countries and are counted by measuring R&D personnel per total employment and Biotechnology workers. Measuring Biotechnology workers in absolute numbers also provides a strong measure of actual biotechnology-workforce strength to complement the relative measures of worker availability.
Foundations
Business R&D expenditures per GDP and Government R&D support per GDP were derived from the OECD. For Singapore these measures were derived from the SEDB. Infrastructure quality was derived from: Porter, M., Schwab, K. 2008. The Global Competitiveness Report, 2008-2009. World Economic Forum. This utilizes an international survey to produce its index.
Activity Areas (not used to calculate the Innovation Score)
The Allocation of firms by activity areas was derived from the OECD. The figures for China measure Shanghai only.
Market Size (not used to calculate the Innovation Score)
Hectares of biotech crops planted were derived from: James, C. 2007. Global Status of Commercialized Biotech/GM Crops: 2007. ISAAA Briefs No. 37-2007. Therapeutics market were derived from OECD Health Data pharmaceutical sales per capita, multiplied by the mid-year population as sourced from the U.S. Census Bureau International Database. Industrial-enzyme production was derived from Zika, E., Papatryfon, I., Wolf, O., Gómez-Barbero, M., Stein, A.J., Bock, A-K. 2007. Consequences, Opportunities and Challenges of Modern Biotechnology for Europe. European Commission Joint Research Center, Institute for Prospective Technological Studies.
Scoring Methodology
There are two basic types of metrics in the Worldview Scorecard: Those which were used to compute the innovation score, and those which were not. Public Company data were not used to calculate the innovation score because they are too polarizing. They clearly show that the United States is the leader in biotechnology, with the most public companies and greatest revenues, but they are largely—and unfairly—uninformative of countries with few or no public companies. The Efficiency data, which were derived from the Public Company data, were likewise not used to calculate the innovation score.
The Allocation of Firms by Activity Area and Market Size were also included as an accessory; they provide information on biotechnology activities, market size and market access in various countries. Because regulatory burdens, price controls and bans on products directly affect sales and the capacity to produce biotechnology products, their impact is reflected in the Market Size data. To calculate the final innovation score, we first ranked the individual metrics on a scale from 0-5, with the most-favored country (e.g., most patents, greatest capital availability, highest rank in Business Friendly Environment, etcetera) receiving a score of 5 and the least-favored country receiving a score of 0. Countries for which no data were available received no score, which enabled the averaging process to not penalize them for not being included in the data sources used. The average score of each country in each category was calculated, and the average of these category scores was used to determine the overall innovation score. Averaging the scores within each category was necessary to resolve data gaps. Summing the scores in each category would have unfairly penalized countries that are not included in specific measures, and employing a single average across all categories would bias the results in cases where some countries were included in more measures of one category than another (e.g., if a country was poorly represented in the education metrics, but well represented in the education/workforce metrics, the overall innovation score would be biased by this altered representation).
