Plenge Lab
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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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: 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.

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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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: February 7, 2015 | Author: | No Comments »

Categories: Drug Discovery Embedded Genomics Human Genetics

ICYMI – the New England Patriots won the Super Bowl. How they did it was remarkable, and improbable. To introduce this week’s articles on human genetics and drug discovery, I want to focus on the interception of Russell Wilson by Malcolm Butler. If the pass is on-target, Seahawks win. By now you know the story: the pass was off-target, and the Seahawks lost.

[A lot has been said about Pete Carroll’s play call (see FiveThirtyEight.com statistical analysis here), but that is irrelevant for this discussion.]

As in football, on-target vs off-target events are highly relevant in drug discovery. Think about what it takes to develop a drug, and how “drug accuracy” (like passing accuracy) can make-or-break a development program. First, you start with a target. Next, you develop a drug against that target. Then, you test the target in pre-clinical models to make sure it is doing what you think it should do. And finally, you take the drug into humans to see if it has an adequate therapeutic index (i.e., is safe and effective).

All along the way you assess whether the therapeutic molecule is selectively engaging and modulating the desired target, and not acting more promiscuously on other targets in the system.…

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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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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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