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.
|Original language||English (US)|
|Number of pages||43|
|State||Published - Nov 1 2017|
|Name||NBER Working Paper|