Plenge Lab
Date posted: August 28, 2018 | Author: | No Comments »

Categories: Embedded Genomics Human Genetics Precision Medicine

[I am an employee of Celgene. All views expressed here are my own.]

What is the clinical significance of residing within the tail of a distribution for disease risk? A new study published in Nature Genetics uses a composite polygenic score to measure extremes of genetic risk(see original article here). The authors make the bold statement: “it is time to contemplate the inclusion of polygenic risk prediction in clinical care”. In this plengegen.com blog, I briefly review the paper, frame the impact of the study in terms of “long tails”, and propose how genetic tails may be used as part of a healthcare system reimagined.

The premise of the paper is that a genome-wide polygenic score (GPS) – a composite genetic test that includes thousands and sometimes millions of genetic variants – can identify a small number of individuals from the general population that have an elevated risk.  The study applies polygenic risk scores to five common diseases but spends most attention to coronary artery disease (CAD). For each disease, the increase in risk is approximately 3- to 5-fold higher among individuals at the extreme of the polygenic tail compared to those in the general population – see Figure 2a (and below) for CAD, where ~8% of the general population is at a 3-fold increase in risk based on a polygenic risk score.…

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Date posted: December 28, 2017 | Author: | No Comments »

Categories: Drug Discovery Embedded Genomics Human Genetics

Over the holidays my family participated in an Escape Room, a live puzzle adventure game. We worked as a team to solve riddles, find clues and, over the course of 60-minutes, complete an old town bank heist. Many of the successful clues came from unexpected places – coordinates on maps, numbers inscribed in hidden places, and physical features of the room itself. Other clues seemed promising, but ultimately led to dead ends. In the end, everything came together and we escaped with only seconds to spare.

And so it goes with the invention of new medicines. The approval of a new medicine is an Escape Room of sorts, but over the course of decades not minutes. And like an Escape Room, clues can come from unexpected places, with some leading to new insights and others leading to dead ends.

I was in an Escape Room state-of-mind as I read a Science Translational Medicine article that developed a system to differentiate blood cells into microglia-like cells to study gene variants implicated in neurodegenerative disorders (here). In this blog, I provide a brief summary of the study, and then describe the potentially interesting phenomenon of genetically driven tissue-specific pathogenicity.…

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Date posted: August 12, 2016 | Author: | No Comments »

Categories: Uncategorized

I recently listened to a TED Radio Hour podcast on failure (link here). Casey Gerald reminded listeners that while his biographical narrative is impressive (here), there have been many failures along the way.

This got me thinking…why don’t more people write their personal biographies in such a way that highlights their successes and failures? I have not seen one, and LinkedIn certainly does not encourage us to log our failures publicly. (Maybe an entrepreneur should start a site called LinkedOut, where you endorse people for failures and bios must be written with failures included!)

So I thought I would give it a try.

I will start with my conventional “success” bio (which can also be found here) to provide context. Then, I will provide my new “failure” bio (also here). At the end, I provide a brief “lessons learned” on how failures have shaped my professional life.  I conclude with a shout out to Charles Darwin, one of history’s most famous observer of failure in nature.

[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.]

Here is my conventional “successful” professional biography.

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Date posted: July 11, 2015 | Author: | No Comments »

Categories: Drug Discovery Embedded Genomics Human Genetics Immunogenomics

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.

1.

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The primary purpose of this blog is to recruit clinical scientists into our new Translational Medicine department at Merck (job postings at the end). However, I hope that the content goes beyond a marketing trick and provides substance as to why translational medicine is crucial in drug discovery and development. Moreover, I have embedded recent examples of translational medicine in action, so read on!

[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.]

There is a strong need to recruit clinical scientists into an ecosystem to develop innovative therapies that make a genuine difference in patients. This ecosystem requires those willing to toil away at fundamental biological problems; those committed to converting biological observations into testable therapeutic hypotheses in humans; and those who develop therapies and gain approval from regulatory agencies throughout the world.   The first step is largely done in academic settings, and the other two steps largely done in the biopharmaceutical industry…although I am sure there are many who would disagree with this gross generalization!

The term “Translational Medicine” has been broadly used to describe the second step, thereby bridging the Valley of Death between the first and third steps.…

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There was an eruption in Iceland last week. No, this was not another volcanic eruption. Rather, there was a seismic release of human genetic data that provides a glimpse into the future of drug discovery. The studies were published in Nature Genetics (the issue’s Table of Contents can be found here), with insightful commentary from Carl Zimmer / New York Times (here), Matthew Herper / Forbes (here), and others (here, here).

[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.]

As I have commented before, human genetics represent a very powerful approach to identify new drug targets (see here, here). I have articulated a 4-step process (see slide #5 from this deck): (1) select a phenotype that is relevant for drug discovery; (2) identify a series of genetic variants (or “alleles”) that is associated with the phenotype; (3) assess the biological function of phenotype-associated alleles; and (4) determine if those same alleles are associated with other phenotypes that may be considered adverse drug events.

There is an important assumption about this model: genes with an “allelic series” will be identified from large-scale genetic studies, and these phenotype-associated alleles will serve as an estimate of function-phenotype dose-response curves.…

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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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