[I am an employee of Celgene. The views expressed here are my own.]
In the Wizard of Oz, Dorothy clicks her heels and hopes for re-entry from her dream world by repeating, “There’s no place like home…there’s no place like home…” I often feel that many in the genetics community look at their human genetics data with the same youthful optimism as Dorothy – clicking their genetic heels and wishing “my genetic discovery will become a drug…my genetic discovery will become a drug…” But without rigor and discipline, such heel-clicking won’t overcome many of the challenges that face drug hunters along the tortuous journey from a genetic idea to a new medicine.
In this blog, I discuss a recent study on the genetics of multiple sclerosis (MS) published in Science (see here). This is a beautiful study that substantially advances the genetic landscape of patients with a devastating disease. However, the study falls short in terms of the application of human genetics to drug discovery. To chart a course for the future, I introduce the concept of mechanism, magnitude and markers (oh my!), which I refer to as the three M’s. …
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).…
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.
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.…
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.…
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.]…
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.…
In this post I will build on previous blogs (here, here, here) about genetics for target ID and validation (TIDVAL). Here, I argue that new targets with unambiguous promotable advantage will emerge from studies that focus on genetic pathways rather than single genes.
This is not meant to contradict my previous post about the importance of genetic studies of single genes to identify new targets. However, there are important assumptions about the single gene “allelic series” approach that remain unknown, which ultimately may limit its application. In particular, how many genes exist in the human genome have a series of disease-associated alleles? There are enough examples today to keep biopharma busy. Moreover, I am quite confident that with deep sequencing in extremely large sample sizes (>100,000 patients) such genes will be discovered (see PNAS article by Eric Lander here). Given the explosion of efforts such as Genomics England, Sequencing Initiative Suomi (SISu) in Finland, Geisinger Health Systems, and Accelerating Medicines Partnership, I am sure that more detailed genotype-phenotype maps will be generated in the near future.
[Note: Sisu is a Finnish word meaning determination, bravery, and resilience; it is about taking action against the odds and displaying courage and resoluteness in the face of adversity. …
This blog post pertains to the Systems Immunology graduate course at Harvard Medical School (Immunology 306qc; see here), which is led by Drs. Christophe Benoist, Nick Haining and Nir Hacohen. My lecture is on the role of human genetics as a tool for understanding the human immune system in health and disease. What follows is an informal description of my lecture. The slide deck for the lecture can be downloaded here. Throughout, I have added key references, with links to the manuscripts and other web-based resources embedded within the blog (and also listed at the end). I highlight five key manuscripts (#1,#2, #3, #4, and #5), which should be reviewed prior to the lecture; the other references, while interesting, are optional.
It is increasingly clear that humans serve as the best model organism for understanding human health and disease. One reason for this paradigm shift is the lack of fidelity of most animal models to human disease. For systems immunology, the mouse is a powerful model organism to understand fundamental mechanisms of the immune system. However, studies in humans are required to understand how these mechanisms can be translated into new biomarkers and drugs.…