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The completion of the Human Genome Project ("HGP") led many scientists to predict a swift revolution in human therapeutics. Despite large advances, however, this revolution has been slow to materialize. We investigate the hypothesis that this slow progress may stem from the large amounts of biological complexity unveiled by the Genome. Our test relies on a disease-specific measure of biological complexity, constructed by drawing on insights from Network Medicine (Barabasi et al., 2011). According to this measure, more complex diseases are those associated with a larger number of genetic associations, or with higher centrality in the Human Disease Network (Goh et al., 2007). With this measure in hand, we estimate the rate of translation of new science into early stage drug innovation by focusing on a leading type of genetic epidemiological knowledge (Genome-Wide Association Studies), and employing standard methods for the measurement of R&D productivity. For less complex diseases, we find a strong and positive association between cumulative knowledge and the amount of innovation. This association weakens as complexity increases, becoming statistically insignificant at the extreme. Our results therefore suggest that biological complexity is in part responsible for the slower-than-expected unfolding of the therapeutical revolution set in motion by the HGP.

Zonder onderwerpscode: economie

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Many scientists predicted a swift revolution in human therapeutics after the completion of the Human Genome Project ("HGP"). This revolution, however, has been slow to materialize in spite of the scientific advances. We investigate the role of biological complexity in slowing down this revolution. Our test relies on a disease-specific measure of biological complexity, constructed by drawing on insights from Network Medicine (Barabási et al., 2011). According to our measure, more complex diseases are associated with a larger number of genetic mutations--higher centrality in the Human Disease Network (Goh et al., 2007). With this measure in hand, we estimate the rate of translation of new science into early-stage drug innovation by focusing on a leading type of genetic epidemiological knowledge (Genome-Wide Association Studies), and employing standard methods for the measurement of R&D productivity. For less complex diseases, we find a strong and positive association between cumulative knowledge and the amount of innovation. This association weakens as complexity increases, becoming statistically insignificant at the extreme. Our results suggest that biological complexity is, in part, responsible for the slower-than-expected unfolding of the therapeutical revolution set in motion by the HGP.

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Innovation policy involves trading off monopoly output and pricing in the short run in exchange for incentives for firms to develop new products in the future. While existing research demonstrates that expected profits fuel R&D investments, little is known about the novelty of the projects funded by these investments. Relying on data that describe the scientific approaches used by a large sample of experimental drug projects, we expand on this literature by examining the scientific novelty of pharmaceutical R&D investments following the creation of the Medicare Part D program. We find little evidence that the positive demand shock implied by this program prompted firms to undertake scientifically novel R&D activity, as measured by whether the specific scientific approach had been used before. However, we find some evidence that firms invested in products involving novel combinations of scientific approaches. These estimates can inform economists and policymakers assessing the tradeoffs associated with marginal changes in commercial returns from newly developed pharmaceutical products.