Plenge Lab

I admit upfront that this is a self-serving blog, as it promotes a manuscript for which I was directly involved. But I do think it represents a very nice example of the role of human genetics for drug discovery. The concept, which I have discussed before (including my last blog), is that there is a four-step process for progressing from a human genetic discovery to a new target for a drug screen. A slide deck describing these steps and applying them to the findings from the PLoS One manuscript can be found here, which I hope is valuable for those interested in the topic of genetics and drug discovery.

[Disclaimer: I am a Merck/MSD employee. The opinions I am expressing are my own and do not necessarily represent the position of my employer. However, the PLoS One study was performed while I was still in academics at BWH/Harvard/Broad.]

Before I provide a summary of the study, I would like to highlight a few recent news stories that highlight that the world thinks this type of information is valuable. First, the state of California is investing US $3-million in a precision medicine project that links genetics and medical records to develop new therapies and diagnostics (here, here).…

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Date posted: September 8, 2014 | Author: | No Comments »

Categories: Drug Discovery Human Genetics

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).…

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Date posted: June 4, 2013 | Author: | No Comments »

Categories: Drug Discovery Human Genetics Precision Medicine

As I sought advice from colleagues about my career, I was frequently asked if I would prefer to work in academics or industry (emphasis on the word “or”).  The standard discussion went something like this:

ACADEMICS – you are your own boss and you are free to chose your own scientific direction; funding is tight, but good science still gets funded by the NIH, foundations and other organizations (including industry); the team unit centers around individuals (graduate students, post-docs, etc), which favors innovative science but sometimes makes large, multi-disciplinary projects challenging; there is long-term stability, including control over where you want to work and live, assuming funding is procured and good ideas continue; your base salary will be less than in industry, but you still make a good living and there are opportunities to consult – and maybe even start your own company – to supplement income.  Bottom line: if you want to do innovative science under your own control, work in academics – as that is where most fundamental discoveries are made.

INDUSTRY – there are more resources, but those resources are not necessarily under your control (depending upon your seniority); the company may change direction quickly, which changes what you are able to work on; while drug development takes 10-plus years, many goals are short-term (several years), which limits long-term investment in projects that are risky and require years to develop; the team unit centers around projects (e.g., making drugs), so there is less individual glory but more opportunities to do multi-disciplinary projects; there is more turn-over in industry, which means you may need to switch jobs (including location – where you want to live) in several years. …

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Date posted: April 14, 2013 | Author: | No Comments »

Categories: Drug Discovery Human Genetics Immunogenomics

I prepared a lecture for immunology graduate students at Harvard Medical School on clinical features of rheumatoid arthritis (RA) for the G1 IMM302qc class. 

The slide deck can be found here

A brief summary:

•Clinical characteristics and pathophysiology
•Differential diagnosis
•Exam and laboratory studies
•Treatment strategy
•Research opportunities
•Case presentations
The future research opportunities include using human genetics as an anchor for drug discovery in RA.  I briefly go over three strategies:

(1) “look-up” method – simple and suggestive but undisciplined (examples in RA: IL6R/tocilizumab, CTLA4/abatacept)

(2) “Allelic series” method – powerful but likely infrequent (example in other disease: PCSK9)

(3) “pathway” method – powerful and comprehensive but target ID difficult (example in RA: CD40 signaling; Gang Li et al, in press PLoS Genetics)

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I read with interest a recent publication by Khandpur et al in Science Translational Medicine on NETosis in the pathogenesis of rheumatoid arthritis (download PDF here).  It made me think about “cause vs consequence” in scientific discovery.  That is, how does one determine whether a biological process observed in patients with active disease is a cause of disease rather than a consequence of disease?

In reading the article, I learned about how neutrophils cause tissue damage and promote autoimmunity through the aberrant formation of neutrophil extracellular traps (NETs).  Released via a novel form of cell death called NETosis, NETs consist of a chromatin meshwork decorated with antimicrobial peptides typically present in neutrophil granules.  (Read more about NETs on Wikipedia here.) 

Mendelian randomization is a method of using measured variation in genes of known function to examine the causal effect of a modifiable exposure on disease in non-experimental studies (read more here).  It is a powerful to determine if an observation in patients is causal.  For example, if autoantibodies are pathogenic in RA, then DNA variants that influence the formation of autoantibodies should also be associated with risk of RA.  This is indeed the case, as exemplified by variants in a gene, PADI4, the codes for an enzyme involved in peptide citrullination (see here). …

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