A new manuscript by Jonathan Pritchard and colleagues published in Cell (see here) has garnered a lot of attention from the genetics community (see here, here, here, here, here). In this blog, I add to the ongoing commentary. I first summarize the main conclusions of the manuscript, and then I discuss the implications for drug discovery and development. For the latter, the three main points are: (1) “core genes” represent good drug targets, especially if they harbor a series of alleles that link function to phenotype; (2) regulatory networks identified by “peripheral genes” point to specific cell types and mechanism that can be used for phenotypic screens; and (3) new approaches are needed to drug cellular networks – what I will refer to as “circuit pharmacology” – as the bulk of drug discovery today is an attempt to reduce complex mechanisms to individual drug targets.
Here is a brief summary of the main conclusions of the manuscript.
There is a small number of “core genes” that “provide mechanistic insights into disease biology and may suggest druggable targets.” How these core genes are defined, however, remains to be determined. The manuscript suggests a few approaches, including: genes with large effect size variants from GWAS and genes with an allelic series, especially those with lower-frequency variants of larger effects.
As readers of my blog know, I am a strong supporter of a disciplined R&D model that focuses on: picking targets based on causal human biology (e.g., genetics); developing molecules that therapeutically recapitulate causal human biology; deploying pharmacodynamic biomarkers that also recapitulate causal human biology; and conducting small clinical proof-of-concept studies to quickly test therapeutic hypotheses (see Figure below).As such, I am constantly on the look-out for literature or news reports to support / refute this model.Each week, I cryptically tweet these reports, and occasionally – like this week – I have the time and energy to write-up the reports in a coherent framework.
Inevitably when I post a blog on “human biology” I get a series of comments about the importance of non-human model organisms in drug discovery and development. My position is clear: pick targets based on causal human biology, and then use whatever means necessary to advance a drug discovery program to the clinic.
Very often, non-human model organisms are the “whatever means necessary” to understand mechanism of action. For example, while human genetic studies identified PCSK9 as an important regulator of LDL cholesterol, mouse studies were critical to understand that PCSK9 acts via binding to LDL receptor (LDLR) on the surface of cells (see here). As a consequence, therapeutic antibodies were designed to block circulating PCSK9 from the blood and increase LDLR-mediated removal of circulating LDL (and hopefully to protect from cardiovascular disease).
Moreover, non-human animal models are necessary to understand in vivo pharmacology and safety of therapeutic molecules before advancing into human clinical trials.
Beyond drug discovery, of course, studies from non-human animal models provide fundamental biological insights. Without studies of prokaryotic organisms, for example, we would not have powerful genome-editing tools such as CRISPR-Cas9. Without decades of work on mouse embryonic stem cells, we would not have human induced pluripotent stem cells (iPSCs).…
Oliver Sacks has terminal cancer. If you have not yet read his heart-warming Op-Ed piece in the New York Times and if you only have five-minutes to spare, then I suggest you read his essay rather than this blog about “experiments of nature” in drug discovery. In his essay, Dr. Sacks concludes with the poignant sentence: “Above all, I have been a sentient being, a thinking animal, on this beautiful planet, and that in itself has been an enormous privilege and adventure.”
So why do I blog, tweet, etc. given the potential risk? I enjoy the public exchange of ideas because, as Dr. Sacks write, that is the essence of our “sentient being”. I enjoy a network of inter-related ideas for which I can create unique connections.…