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.
Like many, I waited with bated breath for results of the anti-PCSK9 (evolocumab) FOURIER cardiovascular outcome study last week. There have been many interesting commentaries written on the findings.A few of my favorites are listed here (Matthew Herper), here (David Grainger), here (Derek Lowe), and here (Larry Husten), amongst others, with summaries provided at the end of this blog.Most of these articles focused on clinical risk reduction vs. what was predicted for cardiovascular outcome, as well as whether payers will cover the cost of the drugs.These are incredibly important topics, and I won’t comment on them further here, other than to say that the debate is now about who should get the drug and how much it should cost.
In this blog, I want to emphasize key points that pertain to human genetics and drug discovery.And make no mistake: the anti-PCSK9 story and FOURIER clinical trial outcome is a triumph for genetics and drug discovery. This message seems to be getting muddled, however, given the current cost of evolocumab and the observation that cardiovascular risk reduction was less than expected, based on predictions from a 2005 study published by Cholesterol Treatment Trialists (CTT) (see Lancet study here).…
Yesterday I participated in the National Academy workshop, “Enabling Precision Medicine: The Role of Genetics in Clinical Drug Development” (link here). There were a number of great talks from leaders across academics, industry and government (agenda here).
I was struck, however, by a consistent theme: most think that “precision medicine” will improve delivery of approved therapies or those that are currently being developed, whether or not the therapies were developed originally with precision medicine explicitly in mind. Many assume that the observation that ~90% medicines are effective in only 30% to 50% is the result of biological differences in people across populations (see recent Forbes blog here). This hypothesis is very appealing, as there are many unique features to each of us.
An alternative explanation is that most medicines developed without precision medicine from the beginning only work in ~30% patients because the medicines don’t target the biological pathways that make each of us unique.
I believe the most likely application is in the discovery and development of new therapies. That is, I believe that the greatest impact will come when precision medicine strategies are incorporated into the very beginning of drug discovery, and will only rarely have an impact on therapies that were not developed with precision medicine in mind from the start.…
At the Harvard-Partners Personalized Medicine Conference last week I participated in a panel discussion on complex traits. When asked about where personalized medicine for complex traits will be in the future, I answered that I envision two major categories for personalized therapies.
(1)Development of drugs based on genetic targets will lead to personalized medicine; and
(2)Large effect size variants will be detected in clinical trials or in post-approval studies and will lead to personalized medicine.
This answer, I said, was based in part on current categories of FDA pharmacogenetic labels and in part on how I see new drug discovery occurring in the future. But did the current FDA labels really support this view?
The answer is “yes”. In reviewing the 158 FDA labels (Excel spreadsheet here), my crude analysis found that 31% of labels fall into the “genetic target” category (most from oncology – 26% of total) and 65% fall into the “large effect” category (most from drug metabolism [42% of total], HLA or G6PD [15% of total]).
A subtle but important point is that I predict that category #2 (PGx markers for non-oncology “genetic targets”) will grow in the future. In other words, development of non-oncology drugs will riff-off the success of drugs developed based on somatic cell genetics in oncology. …
So, you have a target and want to start a drug discovery program, do ya? How would you do it?
When I was at Brigham and Women’s Hospital, Harvard Medical School and the Broad Institute, I presented an idea from an early GWAS of rheumatoid arthritis (RA, see here) to Ed Scolnick (former president of Merck Research Labs, now founding director of the Stanely Center at the Broad Institute, see here). In this study, we found evidence that a non-coding variant at the CD40 gene locus increased risk of RA. The first questions he asked: How does the genetic mutation alter CD40 function? Is it gain-of-function or loss-of-function? What assay would you use for a high-throughput small molecule screen to recapitulate the genetic finding?
I was caught off-guard. Sadly, I had never really thought about all of the details. At the time, I knew enough as a clinician, biologist and a geneticist to appreciate that CD40 was an attractive drug target for RA. However, I was quite naïve to the steps required to take a target into a drug screen. That simple conversation led to several years worth of work, which ultimately led to a proof-of-concept phenotypic screen published in PLoS Genetics five years later (see here).…