Plenge Lab
Date posted: June 19, 2017 | Author: | No Comments »

Categories: Drug Discovery Human Genetics

A new manuscript by Jonathan Pritchard and colleagues published in Cell (see here) has garnered a lot of attention from the genetics community (see here, here, here, here, here). In this blog, I add to the ongoing commentary. I first summarize the main conclusions of the manuscript, and then I discuss the implications for drug discovery and development. For the latter, the three main points are: (1) “core genes” represent good drug targets, especially if they harbor a series of alleles that link function to phenotype; (2) regulatory networks identified by “peripheral genes” point to specific cell types and mechanism that can be used for phenotypic screens; and (3) new approaches are needed to drug cellular networks – what I will refer to as “circuit pharmacology” – as the bulk of drug discovery today is an attempt to reduce complex mechanisms to individual drug targets.

Here is a brief summary of the main conclusions of the manuscript.

  1. There is a small number of “core genes” that “provide mechanistic insights into disease biology and may suggest druggable targets.” How these core genes are defined, however, remains to be determined. The manuscript suggests a few approaches, including: genes with large effect size variants from GWAS and genes with an allelic series, especially those with lower-frequency variants of larger effects.

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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 plengegen.com 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: April 23, 2017 | Author: | No Comments »

Categories: Drug Discovery Embedded Genomics Human Genetics

In response to an original research article published in Nature by Sekar Kathiresan and colleagues (see here), I penned a News & Views piece for Nature (here), a blog for the Timmerman Report (here, here), and a podcast for BBC Inside Science (here). An important theme for drug discovery & development is that human knockouts can rule-in and to rule-out drug targets.  For human knock-out data, the key concept is to understand the effect of maximum genetic perturbation on human physiology.

  1. Rule-in drug targets: As has been described by Matt Nelson and colleagues from GlaxoSmithKline (see 2015 Nature Genetics), and David Cook and colleagues from AstraZeneca (see 2014 Nature Reviews Drug Discovery), therapeutic molecules developed against targets with human genetic data are more likely to lead to regulatory approval than those without.  PCSK9 represents the poster child for human genetic knockouts in drug discovery & development (see my plengegen.com blog here).  But there are many other examples, too.
  1. Rule-out drug targets: But human genetics can also rule-out drug targets or mechanisms that are nominated through animal models, human epidemiology or other approaches.  A prominent example is related to raising HDL cholesterol, the so-called “good cholesterol”.

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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 15, 2017 | Author: | No Comments »

Categories: Drug Discovery Human Genetics

There were so many good articles and news reports this week on genetics/genomics and drug discovery & development.  A few examples include: article in Nature Communications on gene therapy via CRISPR/Cas9 for retinitis pigments (here); a partnership between Editas and Allergan (Matthew Herper story here); Nature Reviews Genetics article by Khera and Kathiresan on genetics of coronary heart disease (here); Genome Magazine article on the importance of pharmacogenetics across ethnic groups to prevent severe adverse events (here); and a victory for pre-prints in challenging the statistical robustness of a publication in Nature Genetics (here).

I decided to focus on a study that provides a mechanistic link between a genetic mutation and a therapeutic hypothesis in Parkinson’s disease. The reason I chose this article is that it highlights the challenges of going from a robust genetic association to a biology hypothesis, and ultimately how to gain confidence in a therapeutic hypothesis with pre-clinical models.  As you will see at the end, a clinical trial is now underway to test the therapeutic hypothesis in humans.

The manuscript was published March 7 in PNAS, “Glucosylceramide synthase inhibition alleviates aberrations in synucleinopathy models” (see 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: October 5, 2016 | Author: | No Comments »

Categories: Drug Discovery Human Genetics

I have many fears, both professional and personal. When I decided to leave academics for a job in industry in 2013, my biggest fear about making the transition was scientific. In my mind, I had a model of how human genetics might transform drug discovery and development. There were anecdotes (e.g., PCSK9 inhibitors) and a few systematic studies in specific diseases (e.g., genetics of rheumatoid arthritis), but there were many holes to the model. Over the last couple of years, additional anecdotes and systematic analyses have emerged (e.g., Matt Nelson, et al. Nature Genetics), which helps to soothe my fears…but I still have concerns.

[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 blogged about previously, I see two primary routes to go from human genetics to new drug discovery programs (see here, here). The first requires that there are genes with a series of disease-associated alleles with a range of biological effects, ideally from gain- to loss-of-function (allelic series model). The second requires disease-associated genes to aggregate within specific biological pathways, which can then be turned into assays for disease-relevant pathway-based screens such as phenotypic screens.…

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Date posted: September 15, 2016 | Author: | No Comments »

Categories: Drug Discovery

The partnership between Academic-industry is one of my favorite topics. As someone who has spent most of his time in academics, and only recently joined industry (well, three years ago), I still feel as if I have a good perspective on both sides. I have written about my personal perspective about making the switch from academics into industry (blog post here), and I have referenced articles related to different perspectives on academics and industry (here, here).

A recent perspective in Cell Metabolism by Morris Birnbaum, Chief Scientific Officer of Cardiovascular and Metabolic Diseases at Pfizer, adds important points to this conversation (links here, here). Specifically, Birnbaum discusses obstacles to relationships between academics and industry, and what can be done to make the relationships optimally productive.

[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.]

Birnbaum starts with an important premise:

Let’s start with something on which everyone agrees: the best hope for the development of novel therapeutics is through effective collaboration between the academic and private sectors, the latter including the biotechnology and pharmaceutical industries.” 

I certainly agree with this point, but not everyone does.…

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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 28, 2016 | Author: | No Comments »

Categories: Drug Discovery

I know it may seem strange that I am commenting on my own commentary. While writing the Science Translational Medicine (STM) article (here, here), I wanted to focus on a clear vision for improving R&D productivity via integrating human biology, therapeutic modulation, pharmacodynamics biomarkers, and proof-of-concept clinical trials (see Figure 1 of the manuscript, also above). Too often, these four key concepts get lost amongst the many steps in drug development.  I won’t revisit these concepts here, as I encourage you to read the article itself.

However, there is much more to R&D productivity than just these four concepts, and the purpose of this blog is to broaden the perspective piece a bit.

[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.]

1. Drug discovery should always start with the patient. Every drug discovery journey must begin with a clear definition of the unmet medical need. As a former practicing rheumatologist, I always try to keep in mind the questions that a patient would ask me: “Why did I develop this disease?” “Will I respond to this medication?” “What is my prognosis?” “Will I ever be cured?“

These questions are also important in the context of the translational medicine model proposed in the STM article.…

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Date posted: July 24, 2016 | Author: | No Comments »

Categories: Drug Discovery Embedded Genomics

Here are my thoughts on the Discussion Paper by Bernard H. Munos and John J. Orloff, “Disruptive Innovation and Transformation of the Drug Discovery and Development Enterprise” (download pdf here). This blog won’t make much sense if read out-of-context. Thus, I recommend reading the Discussion Paper itself, and using this blog as a companion guide at the completion of each section.

[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.]

STRENGTHS AND WEAKNESSES OF THE CURRENT INDUSTRY MODEL

In the near-term (10-years), I suspect that the pharma model of late development and commercialization will likely persist, as the cost and complexity of getting a drug approved is difficult by other mechanisms. Over time, however, new ways of performing late-stage trials will likely evolve. Drugs that are today in the early R&D pipeline will drive this evolution. If drugs look like they do today, dominated by small molecules and biologics with high probability of failure in Phase II/III, then the current model will likely continue with incremental improvements in efficiency. However, if new therapeutic modalities emerge (CRISPR, mRNA, microbiome, etc) and/or the probability of success in Phase II/III improves substantially, then the model of late development and commercialization will be forced to evolve, too.…

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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 24, 2016 | Author: | No Comments »

Categories: Drug Discovery Human Genetics Immunogenomics

Inevitably when I post a blog on “human biology” I get a series of comments about the importance of non-human model organisms in drug discovery and development. My position is clear: pick targets based on causal human biology, and then use whatever means necessary to advance a drug discovery program to the clinic.

Very often, non-human model organisms are the “whatever means necessary” to understand mechanism of action. For example, while human genetic studies identified PCSK9 as an important regulator of LDL cholesterol, mouse studies were critical to understand that PCSK9 acts via binding to LDL receptor (LDLR) on the surface of cells (see here). As a consequence, therapeutic antibodies were designed to block circulating PCSK9 from the blood and increase LDLR-mediated removal of circulating LDL (and hopefully to protect from cardiovascular disease).

Moreover, non-human animal models are necessary to understand in vivo pharmacology and safety of therapeutic molecules before advancing into human clinical trials.

Beyond drug discovery, of course, studies from non-human animal models provide fundamental biological insights. Without studies of prokaryotic organisms, for example, we would not have powerful genome-editing tools such as CRISPR-Cas9. Without decades of work on mouse embryonic stem cells, we would not have human induced pluripotent stem cells (iPSCs).…

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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: December 8, 2015 | Author: | No Comments »

Categories: Uncategorized

My daughter, on a team of fifth grade all-girls from Wellesley MA, recently competed in a First Lego League (FLL) robotics competition. My wife and I served as coaches, which was a demanding but thoroughly rewarding experience. This year’s team got me thinking about design principles for complex systems, as the goal of the annual Challenge is to build from simple (individual Lego pieces) to complex (navigating a robot built from those Lego pieces around a field with missions created from the same Lego pieces) with efficiency and precision.

For those not familiar with FLL, a video link to our team’s performance can be found here. A graphic from Google trends (link here) and the number of views on YouTube (link here) gives you a sense of the magnitude of participation across the world. Overall, FLL is a wonderful example of STEM (Science, Technology, Engineering, Math) in action. The FLL event also fits very well with evolving views on our educational system, as described in a new documentary “Most Likely To Succeed”.

[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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