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).…
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.…
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).…
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.…
It is not uncommon that I am asked the following question during public talks: “Does innovation happen in large pharmaceutical companies?” Sometimes, the question is just a critical comment, disguised as a question: “Large pharma does not innovate, they just conduct clinical trials and drive up the cost of drugs. Right?” Other times the questions are more thoughtful: “As an academic, I don’t see what happens in industry. Can you describe examples of innovation driven out of large pharma?”
[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.]
At the risk of sounding defensive, here are some answers to the “pharma innovation” question. I know there are many more, and I invite readers to share their examples. Admittedly, the examples are biased towards examples at Merck, but that is just because I know these examples better.
First, the past couple of weeks have been particularly good for industry scientists. These recent examples provide objective evidence to answer the pharma innovation question.
(a) 2015 Nobel Prizein Physiology or Medicine. Former Merck scientist Dr. William Campbell was awarded the Nobel Prize for the discovery of an antiparasitic agent used to treat river blindness in places like Latin America, Africa and Yemen.…
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.
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.
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.…
We held our second annual GpGx retreat last week at the Dolce Norwalk Conference Center in Norwalk, CT. The theme was “integrate and elevate”: integrate across Translational Medicine and elevate our mission to infuse cutting-edge genetics and genomics into Merck’s pipeline. What follows is a brief recount of the event.
For those who don’t like looking at photos of someone else’s family vacation, this blog post might not be for you. However, for those curious about life within pharma – read on! You might be surprised that the basic principles that create a strong community within academics or a small biotechnology company are at play within a large company like Merck. I also provide examples of Translational Medicine in action: picking the right target based on causal human biology; developing the right biomarkers based on mechanistic insight of the target; selecting therapeutic molecules (e.g., biologics) in a modality independent manner; and testing clinical proof-of-concept (POC) in small Phase Ib/IIa clinical 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.]
At the start of the retreat, I provided an overview on our theme: “integrate and elevate” (see slides here).…
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.…
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.…
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.…
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).…
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.…
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.]…
Oliver Sacks has terminal cancer. If you have not yet read his heart-warming Op-Ed piece in the New York Times and if you only have five-minutes to spare, then I suggest you read his essay rather than this blog about “experiments of nature” in drug discovery. In his essay, Dr. Sacks concludes with the poignant sentence: “Above all, I have been a sentient being, a thinking animal, on this beautiful planet, and that in itself has been an enormous privilege and adventure.”
So why do I blog, tweet, etc. given the potential risk? I enjoy the public exchange of ideas because, as Dr. Sacks write, that is the essence of our “sentient being”. I enjoy a network of inter-related ideas for which I can create unique connections.…
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.…
Imagine you live in Boston or New York. It is Monday January 26, 2015. You are watching headlines of an impending blizzard, trying to figure out the truth about the weather for the next day. You find that the National Weather Service has a cool online tool – experimental probabilistic snow forecast (see here). As described in Slate magazine (see here), this tool predicted a 67 percent chance of at least 18 inches in New York City.
Unfortunately, most people interpreted this data that there would be 18 inches of snow, not that there could be (with a certain probability) 18 inches of snow.
It was not until Mother Nature did her experiment that we saw the outcome: not much snow in the Big Apple, more than 2 feet of snow in Boston.
The analogy with human genetics is this: it is possible to forecast the functional consequences of deleterious mutations, but it is not until the experimental snow falls – molecular or cellular experiments revealing the functional consequences of mutations – that the functional consequences are actually known. And without knowing the functional consequences of mutations, it is difficult to determine the association of these mutations with human disease.…
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).…
Recently I was asked by the American Society of Human Genetics (ASHG) to provide my perspective on career and professional development in genetics (see here). At about the same time, I read the book “How Google Works”, by Google Executive Chairman and ex-CEO Eric Schmidt and former SVP of Products Jonathan Rosenberg. A very creative slide deck accompanies the book, which is definitely worth a few minutes of your time (here).
Both got me thinking about opportunities in the pharmaceutical industry for genetic graduate students. Here are a few thoughts based on the outline from the Google slide deck.
What is different now?
In human genetics, large-scale genotyping and sequencing is unlocking the inherited basis of most complex and rare traits in the ideal model organism, humans. This is very different than it was just a few years ago. But there is more: this is happening at a scale that will not likely stop until most humans on the planet have their genome sequenced. Like the “Internet of things”, there will soon be a “Genomes of things”, in which our genomes will be connected to all sorts of data – electronic health records, wearable technology, portable blood monitoring, etc.…
