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The revolution that never arrived: Clinica l and genetic paradigms in bio-medical discovery and the R&D productivity paradox. Michelle Gittelman Rutgers Business School “New Frontiers in the Economics and Management of Innovation” KiTeS - Knowledge, Internationalization and Technology Studies
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The revolution that never arrived: Clinical and genetic paradigms in bio-medical discovery and the R&D productivity paradox Michelle Gittelman Rutgers Business School “New Frontiers in the Economics and Management of Innovation” KiTeS- Knowledge, Internationalization and Technology Studies Bocconi University, Milan March 22-23, 2012
A puzzle The pharmaceutical industry is having trouble filling its pipeline with new drugs – despite doing many things “right” • Consistent increase in R&D expenditures • Much more basic science and genetics in drug discovery • Increased use of analytical informatics • Deeper division of innovative labor and active markets for technology, fueled by entrepreneurial firms spun off from universities
A puzzle The pharmaceutical industry is having trouble filling its pipeline with new drugs – despite doing many things “right” ent increase in R&D expenditures Much more basic science and genetics in drug discovery Increased use of analytical informatics eper division of innovative laboranmarkets for technology, fueled by entrepreneurial firms spun off from universities “The industry is doomed if we don’t change” - Chairman of Eli Lilly, 2007
This paper Describe research paradigms and historical shifts in the institutional landscape of bio-medical research We study biotech as a “revolutionary” paradigm. What did it replace? Genomics (1980s-present) – locus of discovery is the lab Patient-Oriented Clinical Research (POR) (1940s-1970s) – locus of discovery is the hospital
Genomics discovery paradigm: Bench to bedside DNA mutation associated with pathology Design a drug to bind to target Targets in cells Clinical discovery paradigm: Bedside to bench Test treatment experimentally Theorize disease mechanisms Observe perturbations in humans (e.g. unexpected reaction to a drug)
Competing discovery paradigm Experiential vs. theory-driven search • Rooted in ancient debates between Plato and Aristotle on the role of pure reason and experiential learning in advancing knowledge • Key issue in current debates in medical policy on translational research and systems biology • A central question in organizational learning literature (Arora& Gambardella, 1994; Gavetti and Levinthal, 2000; Nelson, 2003, Fleming and Sorenson, 2004)
This paper: framing the issue • Compare two research paradigms as different epistemologies of discovery – different beliefs about the best way to find new medicines • Describe the institutional history of clinical research in biomedicine in the USA • Review the secondary evidence on discovery outcomes • Suggest that productivity is linked to search paradigms – much more research needed here!
1980s/1990s: genomics was presented as a “silver bullet” in drug discovery • In 1990, Congress approved ~$3 billion funding of the Human Genome Project to sequence the entire human genome with the promise that the knowledge would translate to a wave of new “rationally designed” drugs • Genomics firms were founded to turn genetic information into drugs (Human Genome Sciences, Celera, Millenium, Incyte) • Scientific entrepreneurship by “star scientists” core to the model (Zucker and Darby, 1998). • The model attracted billions in funding from private investors and Wall Street hoping to capitalize on the promise of rational drug discovery
Incyte is based in Palo Alto, Calif., deep in Silicon Valley, and it is no coincidence that the heart of its headquarters is a vast, glass-enclosed room full of powerful computers. ''At the end of the day, it's the information that matters,'' said Randy Scott, the president and chief scientific officer. ''We are all about the application of Moore's Law to biology,'' he said -- a reference to the observation that computer processing power doubles every 18 months. Applying that exponential growth to genomics should produce similar gains for drug discovery, Dr. Scott said.
Incyte is based in Palo Alto, Calif., deep in Silicon Valley, and it is no coincidence that the heart of its headquarters is a vast, glass-enclosed room full of powerful computers. ''At the end of the day, it's the information that matters,'' said Randy Scott, the president and chief scientific officer. ''We are all about the application of Moore's Law to biology,'' he said -- a reference to the observation that computer processing power doubles every 18 months. Applying that exponential growth to genomics should produce similar gains for drug discovery, Dr. Scott said. “Death is a series of preventable diseases” William Haseltine, Founder, Human Genome Sciences
Patient-oriented clinical research • “Research performed by a scientist and a human subject working together, both being warm and alive” (Schechter, 1998) • Rejects the idea of disease causality as a useful starting point for drug discovery • Causal understanding is not useful in finding treatments. • A dominant paradigm in bio-medicine in post-War USA, spurred by the federalization of research (NIH)
Different predictive logics in science [T]here remains a real problem about the relevance of many model systems, and the inability of many to understand that in biology, unlike physics, we don’t have great general laws or large forces operating that allow us to work from the bottom up in terms of clinical prediction Rees, Jonathan. 2002. “Two Cultures?” J Am AcadDermatol, 46:313-6.
Different predictive logics in science The great physicist-turned biologist Leo Szilard said that once he changed fields (no pun intended) he couldn’t enjoy a long bath as he could when he could dream abstract physics in the bath. As a biologist he was always having to get out to check on some annoying little fact. It is the problem of predicting across several levels of biologic explanation, and the absence of the all encompassing general laws in biology, that accounts for the fact that most clinically relevant discoveries come from the clinic rather than the laboratory and not, contrary to what many believe, vice versa. Rees, Jonathan. 2002. “Two Cultures?”
Bedside-to-bench discoveries in medicine • The link between cholesterol and heart disease, which culminated in the development of statins in the 1980s, originated in experiments conducted in 1913, when the Russian scientist Nikolai Anichkov unexpectedly observed that rabbits fed high-fat diets developed atherosclerosis. • The treatment for pernicious anemia was discovered from the mechanistic insight that feeding patients liver cured them – the underlying vitamin deficiency (b12), identified decades later, was one of many complex causes
Bedside-to-bench discoveries in medicine • Observations of surgical patients receiving a new sedative resulted in the unexpected finding of marked decreases in hallucinations and delusions among psychotic patients. The discovery of an effective treatment for psychosis subsequently facilitated new theories of brain activity associated with schizophrenia. • Fundamental discoveries for the treatment of sickle-cell anemia were triggered by the bedside observations of clinical researchers, who noticed that some populations (infants and certain ethnic groups) showed irregularities in disease rates. Later discoveries of the underlying genetic manifestations of the disease were motivated by models developed through earlier clinical research.
Early history: application of scientific principles to medicine • In the 19th century, medical education carried out in for-profit schools taught by practicing doctors with no scientific training • 1910: Two landmarks • Flexner report - teaching-oriented medical schools, housed in universities, full-time university faculty. Medical education based on the European model. • Rockefeller Institute Hospital founded to foster clinically-driven medical discovery
Organizing POR: Rockefeller Institute (1901) and Rockefeller Hospital (1910) • First institution to combine laboratory and clinical work to find treatments for major infectious diseases of the day. • Cosmopolitan, open culture, attracted top scientists from Europe • Unique scientific climate: Diverse specialization; transdisciplinary - no departmental divisions; minimal control by administrators
Bedside to bench learning at Rockefeller “Simon Flexner, the first director of the Rockefeller Institute, conceived of the Rockefeller Hospital as a test site for the bright ideas generated in the Institute’s laboratories. In fact, this has happened only rarely. During my 40 years at Rockefeller Hospital, I recall only one instance in which a laboratory observation by biochemists was turned into a testable hypothesis in patients. Indeed, the traffic of ideas often runs the other way” Ahrens, Crisis in Clinical Research
Organizing POR: The NIH Clinical Center (1955) and GCRC network • Modeled on the Rockefeller Institute – 10x larger: 500 beds • Victory over science policy czar Vannevar Bush, who promoted government funding of basic research, not medical resaerch • A model for a network of clinical sites in AMCs
The bedside as the locus of discovery Scientifically, the most important asset of a POR facility is the golden opportunity it provides for medical investigators and their staffs to watch carefully and to think deeply about the medical challenges posed by their patients; this forces them to formulate new hypotheses and to devise new stratagems for attacking unsolved problems. There is time to ponder an unexpected event – an unexplained turn in the course of the disease or a puzzling response to a medication – and thus to obtain fresh insights into a disease or a manipulation under study. Ahrens, “The Crisis in Clinical Research”
Two (three) factors accounting for the decline in POR Declining career opportunities for young PIs Eroding institutional and financial support for POR Emergence of genomics as a dominant discovery paradigm
The Promise, ca. 2012 Biotech Big Pharma
Genomics and basic science in medicine • Now acknowledged that genomics has been a bonanza for science but not for medicine. Few new drugs have emerged from the paradigm. • Recent study at Brigham: 101 genetic markers that have been statistically linked to heart disease were shown to have no value in forecasting disease among 19,000 subjects followed for 12 years; a more valid predictor was the old-fashioned method of a family history
Basic science and firm-level innovation • Genomics as a business model has failed – major firms sell diagnostics tests and home kits • $100+ billion invested in the biotech industry –never made money • Growing empirical evidence that “star” scientists have a negative impact on firm-level innovation: nonstars and scientists in applied fields have a positive impact on performance in biopharmaceuticals (Baba et al, 2009; Breschi and Catalini, 2010, Gittelman and Kogut, 2003, Rothermel and Hess, 2007, Toole and Czarnitzky, 2009, Zucker and Darby, 2001)
Concluding remarks • Science is not homogenous – enormous variation in search logics • Experimentation vs. theory-driven logics • Real world vs. reductionist methods • We need to pay more attention to field-specific differences in explaining science-technology links • Experiential learning important for complex problems • Almost NO empirical research on the clinical paradigm in medical discovery – a rich terrain for future research
1. Identify and define medical needs • Research on disease mechanisms 3. Identify and validate targets involved in disease processes • Search for lead compounds that interact with target • Optimize the properties of the lead compounds to generate drug molecules • Drug development and pre-clinical studies (in vitro and in vivo)
Genomics as a rational approach to drug discovery Rational design of molecules is gradually replacing random, trial-and-error experiments. . . Growth of scientific understanding in molecular biology and genetic engineering has clarified important aspects of human metabolism and the chemical and biological action of drugs. . . By studying the structure of receptors, scientists can design (typically on computer) a theoretical compound that matches a given receptor site, and is expected to counter a certain pathology. Arora and Gambardella, The changing technology of technological change, Research Policy, 23 (1994)
