Plenge Lab

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.

Of course, this model is not so easy to follow in the real-world as has been pointed out nicely by Derek Lowe and others (see here).  A nice blog this week by Keith Robison (Warp Drive Bio) highlights why drug R&D is so hard.

Here are the studies or news reports from this week that support this model. 

(1) Picking targets based on causal human biology:  I am a proponent of an “allelic series” model for target identification.  Here are a couple of published reports that fit with this model.

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Date posted: March 24, 2017 | Author: | No Comments »

Categories: Drug Discovery Human Genetics Precision Medicine

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).

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Date posted: March 9, 2017 | Author: | No Comments »

Categories: Drug Discovery Human Genetics Precision Medicine

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.…

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A new sickle cell anemia gene therapy study published in the New England Journal of Medicine (see here, here) gives hope to patients and the concept of rapidly programmable therapeutics based on causal human biology. But how close are we really?

It takes approximately 5-7 years to advance from a therapeutic hypothesis to an early stage clinical trial, and an additional 4-7 years of late stage clinical studies to advance to regulatory approval. This is simply too long, too inefficient and too expensive.

But how can timelines be shortened?

In the current regulatory environment, it is difficult to compress late stage development timelines. This leaves the time between target selection (or “discovery”) and early clinical trials (ideally clinical proof-of-concept, or “PoC”) as an important time to gain efficiencies. Further, discovery to PoC is an important juncture for minimizing failure rates in late development and delivering value to patients in the real world (see here).

Here, I argue that rapidly programmable therapeutics based a molecular understanding of the causal disease process is key to compressing the discovery to PoC timeline.

Imagine a world where the molecular basis of disease is completely understood. For common diseases, germline genetics contributes approximately two-thirds of risk; for rare diseases, germline genetics contributes nearly 100% of risk.…

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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 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: May 26, 2015 | Author: | No Comments »

Categories: Drug Discovery Human Genetics Precision Medicine

This week I want to focus on the role of biomarkers in drug discovery and development, which is one of the three pillars of a successful translational medicine program (see slide deck here). The focus is on Alzheimer’s disease, based on recent articles published in JAMA. At the end of the blog you will find postings for new biomarker positions in Merck’s Translational Medicine Department.

[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.]

Before I start, I want to point to a few blogs that provide counterarguments to some of the optimistic opinions expressed in this blog. The first is David Dobb’s negative view on big data (here); the second on Larry Husten’s concerns about conflicts of interest between academics and industry, as it relates to a recent NEJM series (here). I will not comment further, but it is worth pointing readers to these blogs and related blogs for a balanced view on complicated topics.

I have expressed the strong opinion that what ails drug discovery and development is that we pick the wrong targets, don’t develop robust biomarkers, and we don’t test therapeutic hypotheses quickly enough in clinical trials.

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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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Many of you are probably fully aware of how immuno-oncology is changing cancer treatment. Ken Burns highlighted immunotherapy in his recent PBS series, “Cancer: The Emperor of All Maladies” (video link here). Forbes’ Matthew Herper, BBC and others have written extensively about it, too (here, here). More recently, Genome Magazine had a feature article on the history of immunotherapy (here). As the article states: “The promise of immunotherapy is startling in its simplicity: With a little help from cancer doctors, the patients will cure themselves.

The key word here is “cure”. Cure!

The purpose of this blog is two-fold: (1) introduce geneticists and genomicists to cancer immunotherapy, if they have not thought about it before, and (2) highlight a recent Science publication by Elaine Mardis, Gerald Linette, and colleagues at WashU (here), with an accompanying News & Views article in Nature (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.]

Cancer immunotherapy is really cool! As a former practicing rheumatologist at Brigham and Women’s Hospital, I had thought about the role of neoantigens in autoimmunity for many years.…

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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: March 9, 2015 | Author: | No Comments »

Categories: Drug Discovery Human Genetics Precision Medicine

My overly simplistic vision of the way to transform drug discovery is to (1) pick targets based on causal human biology (e.g., experiments of nature, especially human genetics), (2) develop drugs that recapitulate the biology of the human experiments of nature (e.g., therapeutic inhibitors of proteins), (3) develop biomarkers that measure target modulation in humans, and (4) test therapeutic hypotheses in humans as safely and efficiently as possible.

Thus, one of my favorite themes is “causal human biology”. The word “causal” is key: it means that there is clear evidence between the cause-effect relationship of target perturbation in humans and a desired effect on human physiology. Human genetics represent one way to get at causal human biology, and in my last blog I highlighted recent examples outside of human genetics.

I am constantly scanning the literature to find examples that support or refute this model, as I predict that a discipline portfolio of projects based on causal human biology will be more successful than past efforts by the pharmaceutical industry.

This week I have selected two articles on genetics/genomics in drug discovery that provide further support of this model. [Disclaimer: the first study was funded by Merck, my employer.]

Clinical improvement in psoriasis with specific targeting of interleukin-23, Kopp et al Nature (March 2015).

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Date posted: January 25, 2015 | Author: | No Comments »

Categories: Drug Discovery Human Genetics Precision Medicine

Dear Mr. President,

I was very pleased to listen to your State of the Union address and learn of your interest in Precision Medicine. As I am sure you know, this has led to a number of commentaries about what this term actually means (here, here, here). I would like to provide yet another perspective, this time from someone who has practiced clinical medicine, led academic research teams and currently works in the pharmaceutical industry.

Let me start by acknowledging that I know very little about your plan, but that is because no plan has been announced. However, that inconvenient fact should not prevent me from forming a very strong opinion about what you should do. Similar behavior is observed in politics (which you know well) and sports radio (see for example “Deflate-gate”). So here it goes…

I want to clarify my definition of “precision medicine” (see here for my previous blog on how this is different from “personalized medicine”). In the simplest of terms, precision medicine refers to the ability to classify individuals into subpopulations based on a deep understanding of disease biology. Note that this is different than what clinicians normally practice, which is to classify patients based on signs and symptoms (which can be measured by clinicians as part of routine clinical appointments).…

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Date posted: November 15, 2014 | Author: | No Comments »

Categories: Drug Discovery Human Genetics Precision Medicine

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. …

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Date posted: November 12, 2014 | Author: | No Comments »

Categories: Drug Discovery Embedded Genomics Human Genetics Precision Medicine

I have come across three reports in the last few days that help me think about the question: How many genomes is enough?  My conclusion – we need a lot!  Here are some thoughts and objective data that support this conclusion.

(1) Clinical sequencing for rare disease – JAMA reported compelling evidence that exome sequencing identified a molecular diagnosis for patients (Editorial here).  One study investigated 2000 consecutive patients who had exome sequencing at one academic medical center over 2 years (here).  Another study investigated 814 consecutive pediatric patients over 2.5 years (here).  Both groups report that ~25% of patients were “solved” by exome sequencing.  All patients had a rare clinical presentation that strongly suggested a genetic etiology.

(2) Inactivating NPC1L1 mutations protect from coronary heart diease – NEJM reported an exome sequencing study in ~22,000 case-control samples to search for coronary heart disease (CHD) genes, with follow-up of a specific inactivating mutation (p.Arg406X in the gene NPC1L1) in ~91,000 case-control samples (here).  The data suggest that naturally occurring mutations that disrupt NPC1L1 function are associated with reduced LDL cholesterol levels and reduced risk of CHD.  The statistics were not overwhelming despite the large sample size (P=0.008, OR=0.47). …

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I believe that humans represent the ideal model organism for the development of innovative therapies to improve human health.  Experiments of nature (e.g., human genetics) and longitudinal observations in patients with disease can differentiate between cause and consequence, and therefore can overcome fundamental challenges of drug development (e.g., target identification, biomarkers of drug efficacy).  Using my Twitter account (@rplenge), this blog (www.plengegen.com/blog), and other forms of social media, I provide compelling examples that illustrate key concepts of “humans as the ideal model organism” (#himo) for drug development.

Why do drugs fail (#whydrugsfail)? This simple question is at the center of problems facing the pharmaceutical industry.  In short, drugs fail in early development because of unresolved safety signals or lack of biomarkers for target engagement, and drugs fail in late development because of lack of efficacy or excess toxicity.  This leads to a costly system for bringing new drugs to market – not because of the successes, but because >95% of drug programs ultimately fail.  Without improvements in rates of success in drug development, the sustainability of the pharmaceutical industry as we know it is in trouble (see here). Not surprisingly, much has been written about this topic, including analyses of development strategies (Forbes blog, Drug Baron), company pipelines (Nature Reviews Drug Discovery manuscript from AstraZeneca) and FDA approvals (here and 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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At the Spring PGRN meeting last week, there were a number of interesting talks about the need for new databases to foster genetics research.  One talk was from Scott Weiss on Gene Insight (see here).  I gave a talk about our “RA Responder” Crowdsourcing Challenge (complete slide deck here).  Here are a few general thoughts about the databases we need for genomics research.

(1) Silo’s are so last year

Too often, data from one interesting pharmacogenomic study (e.g., GWAS data on treatment response) are completely separate from another dataset that can be used to interpret the data (e.g., RNA-sequencing). Yes, specialized labs that generated the data can integrate the data for their own analysis. And yes, they can release individual datasets into the public for others to stitch together. But is this really what we need? Somehow, we need to make data available in a manner that is fully integrated and interoperable. One simple example of this is GWAS for autoimmune diseases. Since 2006, a large number of genetic data have been published. Still, there is no single place to go see results for all autoimmune diseases, despite the fact that there is tremendous shared overlap among the genetic basis for these diseases.…

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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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