[Disclaimer: I am an employee of Celgene. The views expressed here are my own.]
Human genetics offers the potential to identify drug targets and to inform decision-making on the journey to an approved drug. A recent study by Ference et al in the New England Journal of Medicine (NEJM) provides an example of human genetics in action. While most of the study focuses on Mendelian randomization to establish a relation among ACLY genetic variation, LDL cholesterol levels, and cardiovascular events, in this blog I focus on a topic highlighted in the companion NEJM editorial: human genetics to predict on-target adverse drug events (see NEJM editorial here).
First, what is the framework for the application of human genetics to predict on-target adverse drug events (ADEs)? Briefly, human genetics can predict on-target toxicity if the following criteria are met: (1) unambiguous association of genetic variant to a clinical phenotype that is a surrogate for drug efficacy and toxicity; (2) unambiguous relationship between disease-associated variant and implicated gene that is the target of the therapeutic intervention; (3) quantitative assessment of gene function and clinical phenotypes of efficacy and toxicity to estimate a “genotype-phenotype dose-response” relationship; and (4) confidence that the therapeutic intervention mimics the mechanism of action of the disease-associated variant.…
[I am an employee of Celgene. All views expressed here are my own.]
At the 2018 Annual Atlas Ventures Retreat (AVR), I participated in a panel on Digital Health (along with David Schenkhein, John Reed, Scott Brun). The panel discussion was led by Michael Ringel, who also provide an excellent introduction to Digital Health (his slides here). While there are many aspects to digital health, we focused on the application to drug discovery and development. In this blog, the main point I want to emphasize is that I believe that the digital health tipping point will occur when products that benefit patients (e.g., therapeutics) facilitate the integration of digital health initiatives that currently reside in silos.
What is digital health in relation to drug discovery & development? There are many different definitions with many different components, and this, in essence, is part of the challenge (see Figure below). In early discovery biology, digital health represents various data types (e.g., human genetics, ‘omics data, cell models) and analytical methods (e.g., simple regression, machine learning, artificial intelligence). In late discovery biology, digital health includes sophisticated analytical methods for in silico drug design and organoid models to recapitulate the human system for pre-clinical testing.…
[I am an employee of Celgene. All opinions expressed here are my own.]
A meeting was recently convened to discuss a roadmap for understanding the genetics of common diseases (search Twitter for #cdcoxf18). I presented my vision of a genetics dose-response portal (slides here; link to related 2018 ASHG talk here). The organizers (@RachelGLiao, @markmccarthyoxf, @ceclindgren, Rory Collins [Oxford], Judy Cho [New York], @NancyGenetics, @dalygene, @eric_lander) asked participants to share their vision. I thought I would blog about my mine.
You’ll notice my vision is ambitious. Nonetheless, I believe these objectives are feasible to accomplish within a 3-year (Phase 1) and 7-year (Phase 2) time frame. Phase 1 would start immediately and would guide projects for Phase 2. In reality, many aspects of Phase 1 are already underway today (e.g., GWAS catalogue at EBI; Global Alliance for Genomics and Health [GA4GH] data sharing methods). Phase 2 consists of two parts: federation of global biobanks and experimental validation of variants, genes and pathways. Some components of Phase 2 could start today (e.g., exome sequencing in >100,000 cases selected from existing case-control cohorts and biobanks; human knockout project). As with Phase 1, many components of Phase 2 are already underway (federation of existing biobanks [e.g.,…
[Disclaimer: I am an employee of Celgene. The views reported here are my own.]
I presented at the PharmacoGenomics Research Network (PGRN) portion of the 2018 ASHG meeting (link to my slides here). A major theme from my talk was that precision medicine holds promise for advancing novel therapies, but that implementation of pharmacogenomics (PGx) will happen by design not by accident. Here is what I mean – and why our health care systems need to build for this future state today.
PGx by design – PGx by design starts at the very beginning of the drug discovery journey, when the choice is made to develop a therapeutic molecule against a target or a pathway. A precision medicine hypothesis is carried forward into the design of a therapeutic molecule (“matching modality with mechanism”), pre-clinical biomarkers to measure pharmacodynamic responses, and early proof-of-concept clinical studies in defined patient subsets. Late-stage clinical development is performed in these patient subsets, and regulatory approval is obtained with a label that defines this patient subset. Health care systems will essentially be required to incorporate precision medicine into patient care.
There are emerging examples of PGx by design. Indeed, there are an increasing number of FDA approvals that fit with the PGx by design model (see figure below).…
[Disclaimer: I am an employee of Celgene. The views reported here are my own.]
I recently participated in a Harvard Medical School Executive Education course on human genetics and drug discovery (link here, slides here and here). My presentation concluded with a short discussion on emerging resources such as Phenome-Wide Association Studies (PheWAS) to predict adverse drug events and guide indication selection, and protein quantitative trait loci (pQTLs) for Mendelian randomization. In this blog, I highlight briefly our recent Nature publication on pQTLs, “Genomic atlas of the human plasma proteome” (here), which represents a new public resource for drug discovery.
Human genetic targets are endowed with favorable properties, one of which is the ability to use genetic tools for nature’s randomized control trial. Central to this concept is Mendelian randomization, a method that uses human genetic variants as an instrument to examine the causal effect of a modifiable exposure (e.g., protein biomarker) on disease in observational studies (reviewed here and recent Nature Reviews Geneticshere).
Proteins provide an ideal paradigm for Mendelian randomization analysis for drug discovery, as proteins are under proximal genetic control and represent the targets of most approved drugs.…
A new genetics initiative was announced today: the creation of FinnGen (press release here). FinnGen’s goal is to generate sequence and GWAS data on up to 500,000 individuals with linked clinical data and consented for recall. There are many applications for such a resource, including drug discovery and development. In this blog, I want to first describe the application of PheWAS for drug discovery and development, and then introduce FinnGen as a new PheWAS resource (see FinnGen slide deck here).
[Disclaimer: I am an employee of Celgene. The views expressed here are my own.]
PheWAS turns GWAS on its head. While GWAS tests millions of genetic variants for association to a single trait, PheWAS does the opposite: tests hundreds (if not thousands) of traits for association with a single genetic variant. This approach is primarily relevant for those genetic variants with an unambiguous functional consequence – for example, a variant associated with disease risk or a variant that completely abrogates gene function. There are useful online resources (see here), as well as several nice recent reviews by Josh Denny and colleagues, which provide additional background on PheWAS (see here, here).
Work that originated from my academic lab represents the first example of PheWAS for drug discovery – in particular, how to use PheWAS to predict on-target adverse drug events (ADEs) and to select indications for clinical trials (see 2015 PLoS One publication here).…
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).…
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.…
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.]…
Welcome to our first blog of 2015 on genetics/genomics for drug discovery. After a nice vacation in sunny Arizona flying drones (here), I am back soliciting ideas from our Merck Genetic & Pharmacogenomics (GpGx) team. This week’s pick riffs off the events at J.P.Morgan 2015, where there were a number of interesting deals made by pharmaceutical companies and genetic companies (see here, here, here).
With all of this interest in human genetics, it raises the question about how genetics can be used to develop new drugs. The first step is to go from “genes to screens”. That is, the first step is to progress from a human genetic variant associated with a clinical trait of interest to an actual drug screen. This week’s article, published in Nature Chemical Biology, describes one example (see here, here).
Summary of the manuscript: Deleterious mutations in the ABHD12 gene cause a rare neuroinflammatory-neurodegenerative disorder named polyneuropathy, hearing loss, ataxia, retinitis pigmentosa and cataract (PHARC, see here). A similar phenotype is observed in ABHD12-deficient mice. ABHD12 is an enzyme degrading lysophosphatidylserine (lyso-PS), a signaling lipid known to regulate macrophage activation. The Nature Chemical Biology study by Kamat and colleagues describes the chemical proteomic identification of a related enzyme, ABHD16A, which synthesizes the terminal step leading to lyso-PS generation.…
Bill James developed the “Keltner list” to serve as a series of gut-check questions to test a baseball player’s suitability for the Hall of Fame (see here). The list comprises 15 questions designed to aid in the thought process, where each question is designed to be relatively easy to answer. As a subjective method, the Keltner list is not designed to yield an undeniable answer about a player’s worthiness. Says James: “You can’t total up the score and say that everybody who is at eight or above should be in, or anything like that.”
The Keltner list concept has been adapted to address to serve as a common sense assessment of non-baseball events, including political scandals (see here) and rock bands like Devo (see here).
Here, I try out this concept for genetics and drug discovery. That is, I ask a series of question designed to answer the question: “Would a drug against the product of this gene be a useful drug?” I use PCSK9 as one of the best examples (see brief PCSK9 slide deck here). I also used in on our recent study of CD40 in rheumatoid arthritis, published in PLoS Genetics (see here).…
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.…
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). …