Plenge Lab
Date posted: April 30, 2017 | Author: | No Comments »

Categories: Drug Discovery Human Genetics Immunogenomics

A recent study in the New England Journal of Medicine provides genetic support for a pharmacologically validated target, BAFF, in the treatment of systemic lupus erythematosus.  But can human genetics also be used to estimate the target dose and a therapeutic window?

As readers of know, I am constantly on the lookout for published studies that provide insight into the utility of human genetics for drug discovery and development.  This past week there was a great post from Francis Collins on the role of the NIH in the discovery (in part via human genetics) and development of tofacitinib (see here), anakinra and potentially novel targets (e.g., STING) for inflammatory diseases (here).  Nature Reviews Drug Discovery published a News & Analysis on PCSK9 as a “fertile testing ground for new drug modalities including long-acting RNA interference drugs, vaccines against self-antigens, CRISPR therapeutics and small molecules that control ribosomal activity” (here).  New York City released information about a new public health initiative, The NYC Macroscope, which will use electronic health records (EHRs) to track conditions managed by primary care practices that are important to public health..and one day may be linked to genetic data for discovery research (that is me just speculating).…

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Date posted: June 3, 2016 | Author: | No Comments »

Categories: Drug Discovery Human Genetics


One of the biggest challenges of drug discovery is to determine which targets, when perturbed, will have an acceptable efficacy-safety therapeutic window in patients. In fact, the success rate at choosing the right target and developing a safe and effective drug is quite low: less than 10% of drugs that enter Phase I are approved by regulatory authorities (see recent Nature Reviews Drug Discovery article here, Derek Lowe blog here). Most of the failures in Phase II and III are due to lack of efficacy or unexpected toxicity.

Human genetics offers one potential solution to identify new drug targets with an acceptable therapeutic window. A study published this week in Science Translational Medicine (STM) provides genetics support for an established therapeutic target in type 2 diabetes (T2D), glucagon-like peptide-1 receptor, GLP1R (link to STM article here).  What is surprising, however, is that human genetics suggests that GLP1R agonists may also protect from coronary heart disease (CHD).

[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 are three points that I want to make in this blog. First, the STM study provides general support for the model that human genetics is useful to predict efficacy & safety in drug discovery.…

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Date posted: April 16, 2016 | Author: | No Comments »

Categories: Drug Discovery Human Genetics Precision Medicine

Water does not resist. Water flows…But water always goes where it wants to go, and nothing in the end can stand against it.” – Margaret Atwood

The path of least resistance leads to crooked rivers and crooked men.” – Henry David Thoreau

What fraction of potential protein targets is accessible to conventional therapeutic modalities such as small molecules and protein biologics? The “druggable genome”, a term coined by Hopkins and Groom in 2002 (here), provides an estimate: approximately 10% of proteins in the human body are druggable by small molecule therapeutics. Greg Verdine and others estimate that an additional 10% of protein targets – those that are extracellular proteins – are druggable by biologics (here; excellent podcast by Janelle Anderson, humanPoC, here). Derek Lowe, however, has blogged that there is a lot of uncertainty in these estimates (here, here).

But just because a protein is druggable does not necessarily make it a potential drug target, for that honour belongs only to proteins that are also linked to disease”. That is, proteins that are compelling targets based on causal human biology may not be druggable.

These two issues create a natural tension for drug hunters at the start of a drug discovery program: pursue those targets that are druggable or those targets with the most compelling human evidence.

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It has been a good week for human genetics, with high-profile studies published in Science (here) and NEJM (here, here, here), and a summit at the White House on Precision Medicine. Here, I summarize the published studies and put them in context for drug discovery. But first, I want to briefly detour into a story about the Wright Brothers.

[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.]

In 1900, Wilbur and Orville Wright first began experiments with their flying machine. They defined three problems for manned flight: power, wing structure and control. As described beautifully in David McCullough’s book (review here), the brothers focused on the latter, control, which when sufficiently solved led to the first manned flight in 1903. Within ten years of solving the “flying problem”, aviation technology progressed to the point that manned flights were routine.

By analogy, I would argue that there are three key challenges for drug discovery: targets, biomarkers and clinical proof-of-concept studies. The key problem to solve is target selection. Today, we do not know enough about causal human biology to select targets, and as a consequence we have a crisis in cost (drugs are too expensive to develop because of failures at the most costly stage, late development) and innovation (for those drugs that work, there is insufficient differentiation from standard-of-care treatments to change health care outcomes).…

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Date posted: February 14, 2016 | Author: | No Comments »

Categories: Drug Discovery Immunogenomics

Today was the second coldest day of my life. When I woke up in Ludlow, Vermont, it was -20 degrees Fahrenheit; with wind chill it was -45° F. As the kids played downstairs, I caught up on my reading comforted by a raging log fire.

The topic de jour: non-genetic examples of causal human biology for drug discovery.   Here, the experiment of nature was the formation of autoantibodies against a target and pathway implicated in acquired thrombotic thrombocytopenic purpura (TTP), a life-threatening disorder.

The study that caught my interest, “Caplacizumab for Acquired Thrombotic Thrombocytopenic Purpura”, was published last week in the New England Journal of Medicine. I won’t say much about the NEJM article itself, but I will briefly discuss the background leading up to the clinical trial. The key point: autoantibodies against ADAMTS13 pinpointed the target and pathway as causal in the ideal model organism, humans.

The story starts in 1976, when whole blood exchange transfusion resulted in clinical benefit in 8 of 14 patients with TTP. The following year, it was determined that the plasma fraction of the blood was the source of clinical benefit.   It took approximately 20 years, however, to identify the deficient plasma factor as ADAMTS13, with deficiency caused by IgG autoantibodies that inhibit the enzyme.…

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Date posted: February 13, 2016 | Author: | No Comments »

Categories: Drug Discovery Embedded Genomics Human Genetics Precision Medicine

A study published last week in Science described a large-scale genetic association study of Neandertal-derived alleles with clinical phenotypes from electronic health records (EHRs). Here, I focus less on the Neandertal aspect of the study – which to me is really just a gimmick and not medically relevant – and more on the ability to use EHR data for unbiased association studies against a large number of clinical traits captured in real-world datasets. I also provide some thoughts on how this same approach could be used for 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.]

The study used clinical data from the Electronic Medical Records and Genomics (eMERGE) Network, a consortium that unites EHR systems linked to patient genetic data from nine sites across the United States. The clinical data was primarily from ICD9 billing codes, an imperfect but decent way to capture clinical data from EHRs. In total, a set of 28,416 adults of European ancestry from across the eMERGE sites had both genotype data and sufficient EHR data to define clinical phenotypes (n=13,686 in the Discovery set; n=14,730 in the replication set).…

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Date posted: August 21, 2015 | Author: | No Comments »

Categories: Drug Discovery Embedded Genomics Human Genetics

I say article of the week, but I have been lazy this summer (or maybe just consumed by other things).  My last “article of the week” was in May and my last Plengegen blog post was over a month ago!

By now everyone knows the PCSK9 story. Human genetics identified the target; functional work in mouse and human cells led to a mechanistic understanding of PCSK9’s role in LDL receptor recycling; therapeutic modulation was shown to lower LDL cholesterol in clinical trials; and the FDA approved drugs based on LDL lowering, with outcome trials underway to demonstrate (presumably) cardiovascular benefit. What the story highlights is that a mechanistic understanding of causal pathways in human disease is key to the success of translating targets into therapies. Further, the PCSK9 story underscores the importance of a simple biomarker (LDL cholesterol) to measure a complex causal pathway in a clinical trial.

A recent study in the New England Journal of Medicine (NEJM) provides insight into a putative causal pathway in obesity, and thus a potentially a new mechanism for therapeutic modulation. The accompanying Editorial also provides a nice perspective.

[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.

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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.


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Date posted: June 26, 2015 | Author: | No Comments »

Categories: Drug Discovery Human Genetics

I attended the Mendelian randomization meeting in Bristol, UK this past week (link to the program’s oral abstracts here). The meeting was timed with the release of a number of articles in the International Journal of Epidemiology (current issue here, Volume 44, No. 2 April 2015 TOC here). This blog is a brief synopsis of the meeting – with a focus on human genetics and drug discovery. The blog includes links to several slide decks, as well as references to several published reviews and studies.

[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.]

Several speakers, including Lon Cardon from GSK, gave overview talks on how Mendelian randomization can be applied to pharmaceutical development. In my overview, I described important guiding principles for successful drug discovery (link to my slides here), and how Mendelian randomization (MR) is applied within this framework. In particular, I emphasized the role of establishing causality in the human system: MR is a powerful tool to pick targets by estimating safety and efficacy (i.e., genotype-phenotype dose-response curves) at the time of target identification and validation; MR is effective at picking biomarkers for target modulation; and MR provides quantitative modeling of clinical proof-of-concept (POC) studies.…

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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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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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Date posted: August 20, 2014 | Author: | No Comments »

Categories: Drug Discovery Human Genetics Uncategorized

In this post I will build on previous blogs (here, here, here) about genetics for target ID and validation (TIDVAL).  Here, I argue that new targets with unambiguous promotable advantage will emerge from studies that focus on genetic pathways rather than single genes.

This is not meant to contradict my previous post about the importance of genetic studies of single genes to identify new targets.  However, there are important assumptions about the single gene “allelic series” approach that remain unknown, which ultimately may limit its application. In particular, how many genes exist in the human genome have a series of disease-associated alleles?  There are enough examples today to keep biopharma busy.  Moreover, I am quite confident that with deep sequencing in extremely large sample sizes (>100,000 patients) such genes will be discovered (see PNAS article by Eric Lander here).  Given the explosion of efforts such as Genomics England, Sequencing Initiative Suomi (SISu) in Finland, Geisinger Health Systems, and Accelerating Medicines Partnership, I am sure that more detailed genotype-phenotype maps will be generated in the near future.

[Note: Sisu is a Finnish word meaning determination, bravery, and resilience; it is about taking action against the odds and displaying courage and resoluteness in the face of adversity. …

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Date posted: August 16, 2014 | Author: | No Comments »

Categories: Drug Discovery Human Genetics

Question: What can we learn from Sputnik (see here), DARPA (see here) and disruptive innovation (see here) to invent new drugs?

Answer: The best way to prevent surprise is to create it. And if you don’t create the surprise, someone else will. (This is a cryptic answer, I know, but I hope the answer will become clearer by the end of the blog.)

My previous blogs highlighted (1) the pressing need to match an innovative R&D culture with an innovative R&D strategy rooted in basic science (see here), and (2) the importance of phenotype in target ID and validation (TIDVAL) efforts anchored in human genetics (see here).  Now, I want to flesh out more of the scientific strategy around human genetics – with a focus on single genes and single drug targets.

To start, I want to frame the problem using an unexpected source of innovation: the US government.

There is an interesting article in Harvard Business Review on DARPA and “Pasteur’s Quadrant” – use-inspired, basic-science research (see here and here).  This theme is critically important for drug discovery, as the biopharma industry has a profound responsibility to identify new targets with increased probability-of-success and unambiguous promotable advantage (see here).   …

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

Categories: Drug Discovery Human Genetics Uncategorized

phe·no·type  n.

1.The observable physical or biochemical characteristics of an organism, as determined by both genetic makeup and environmental influences.

2. An individual or group of organisms exhibiting a particular phenotype.


There are many different phenotypes: strength in the face of adversity (see here); self-reflection in a time of uncertainty (see here); and creativity amidst a sea of sameness (see here).

Phenotypes also refer to disease states such as risk of disease, response to therapy, a quantitative biomarker of medical relevance, or a physical trait such as height (as in the figure above).

For drug discovery, I have put forth the premise that human genetics is a useful tool to uncover novel drug targets that are likely to have unambiguous promotable advantage (see here).  The starting point in a genetic study is to pick the right phenotype, one that is an appropriate surrogate for drug efficacy.

And phenotype matters!

Two illustrative examples are the autoimmune diseases type 1 diabetes and rheumatoid arthritis. In type 1 diabetes the immune system destroys the pancreas, thereby preventing insulin secretion and the control of blood glucose levels.

Human genetics has identified many alleles associated with the risk of type 1 diabetes, nearly all of which act on the immune system (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: May 17, 2013 | Author: | No Comments »

Categories: Drug Discovery Human Genetics

After all, baseball is a metaphor for life.

Bill James developed the “Keltner list” to serve as a series of gut-check questions to test a baseball player’s suitability for the Hall of Fame (see here).  The list comprises 15 questions designed to aid in the thought process, where each question is designed to be relatively easy to answer.  As a subjective method, the Keltner list is not designed to yield an undeniable answer about a player’s worthiness.  Says James: “You can’t total up the score and say that everybody who is at eight or above should be in, or anything like that.”

The Keltner list concept has been adapted to address to serve as a common sense assessment of non-baseball events, including political scandals (see here) and rock bands like Devo (see here).

Here, I try out this concept for genetics and drug discovery.  That is, I ask a series of question designed to answer the question: “Would a drug against the product of this gene be a useful drug?”  I use PCSK9 as one of the best examples (see brief PCSK9 slide deck here).  I also used in on our recent study of CD40 in rheumatoid arthritis, published in PLoS Genetics (see here).…

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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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Date posted: April 3, 2013 | Author: | No Comments »

Categories: Drug Discovery Human Genetics Immunogenomics

This blog post pertains to the Systems Immunology graduate course at Harvard Medical School (Immunology 306qc; see here), which is led by Drs. Christophe Benoist, Nick Haining and Nir Hacohen.  My lecture is on the role of human genetics as a tool for understanding the human immune system in health and disease.  What follows is an informal description of my lecture.  The slide deck for the lecture can be downloaded here.  Throughout, I have added key references, with links to the manuscripts and other web-based resources embedded within the blog (and also listed at the end).  I highlight five key manuscripts (#1, #2, #3, #4, and #5), which should be reviewed prior to the lecture; the other references, while interesting, are optional.


It is increasingly clear that humans serve as the best model organism for understanding human health and disease.  One reason for this paradigm shift is the lack of fidelity of most animal models to human disease.  For systems immunology, the mouse is a powerful model organism to understand fundamental mechanisms of the immune system.  However, studies in humans are required to understand how these mechanisms can be translated into new biomarkers and drugs.…

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Date posted: February 8, 2013 | Author: | No Comments »

Categories: Drug Discovery Precision Medicine

Genetics can guide the first phase of drug development (identifying drug targets, see here ) as well as late phase clinical trials (e.g., patient segmentation for response/non-responder status, see here ). But is there a convergence between the two areas, or pharmaco-convergence (a term I just made up!)? And are there advantages to a program anchored at both ends in human genetics?


Consider the following two hypothetical examples.

(1) Human genetics identifies loss-of-function (LOF) mutations that protect from disease. The same LOF mutation is associated with an intermediate biomarker, but is not associated with other phenotypes that might be considered adverse drug events. A drug is developed that mimics the effect of the mutation; that is, a drug is developed that inhibits the protein product of the gene. In early mechanistic studies, the drug is shown to influence the intermediate biomarker in a way that is consistent to that predicted by the LOF-protective mutations. Further, because functional studies of the LOF-protective mutations provide insight into relevant biological pathways in humans (e.g., a gene expression signature that correlates with mutation carrier status), additional information is known about genomic signatures of those who carry the LOF-protective mutations (which mimics drug exposure) compared to those who do not carry the LOF-protective mutations (which mimics those who are not exposed to drug).…

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