If you could pick three innovations that would revolutionize drug discovery in the next 10-20 years, what would they be?
I found myself thinking about this question during a recent family vacation to Italy. I was visiting the Galileo Museum, marveling at the state of knowledge during the 1400-1600’s. The debate over planetary orbits seem so obvious now, but the disagreement between church and science led to Galileo’s imprisonment in 1633.
So what is it today that will seem so obvious to our children and grandchildren…and generations beyond? Let me offer a few ideas related to drug discovery, and hope that others will add their own. I am not sure if my ideas are grounded in reality, but that is part of the fun of the game. In addition, “The best way to predict the future is to invent it.”
To start, let me remind readers of this blog that I believe that the three major challenges to efficient drug discovery are picking the right targets, developing the right biomarkers to enable proof-of-concept (POC) studies, and testing therapeutic hypotheses in humans as quickly and safely as possible. Thus, the future needs to address these three challenges.
Imagine you live in Boston or New York. It is Monday January 26, 2015. You are watching headlines of an impending blizzard, trying to figure out the truth about the weather for the next day. You find that the National Weather Service has a cool online tool – experimental probabilistic snow forecast (see here). As described in Slate magazine (see here), this tool predicted a 67 percent chance of at least 18 inches in New York City.
Unfortunately, most people interpreted this data that there would be 18 inches of snow, not that there could be (with a certain probability) 18 inches of snow.
It was not until Mother Nature did her experiment that we saw the outcome: not much snow in the Big Apple, more than 2 feet of snow in Boston.
The analogy with human genetics is this: it is possible to forecast the functional consequences of deleterious mutations, but it is not until the experimental snow falls – molecular or cellular experiments revealing the functional consequences of mutations – that the functional consequences are actually known. And without knowing the functional consequences of mutations, it is difficult to determine the association of these mutations with human disease.…
In science, a pendulum swings as new discoveries are made and old hypotheses proven false. Unfortunately, the arc of this swing is often unrelated to the facts, but more tied to the prevailing views of what is and what should be. With incomplete information, the pendulum may swing too far in one direction – for example, towards the view that genome-wide association studies (GWAS) will identify the vast majority of genetic risk for complex traits in relatively small cohorts (now defined humbly as tens-of-thousands of case-control samples). After an initial wave of discoveries – or lack thereof – the pendulum swung too far in the other direction: disease-associated variants from GWAS cannot explain most of the estimated heritability in complex traits, therefore rare variants of large effect must be the root genetic cause of complex traits.
Too often, science creates an artificial mirror image of data interpretation. If one hypothesis is not true, then the opposite must be true. If it is not common variants, then it must be rare variants; if it is not genetics, then it must be epigenetics; if it is not the host, then it must be the microbiome; and so forth. Too often, incomplete data to support one model results in a knee-jerk reaction towards an orthogonal model, even if there is little evidence to support the model. …