At the Harvard-Partners Personalized Medicine Conference last week I participated in a panel discussion on complex traits. When asked about where personalized medicine for complex traits will be in the future, I answered that I envision two major categories for personalized therapies.
(1)Development of drugs based on genetic targets will lead to personalized medicine; and
(2)Large effect size variants will be detected in clinical trials or in post-approval studies and will lead to personalized medicine.
This answer, I said, was based in part on current categories of FDA pharmacogenetic labels and in part on how I see new drug discovery occurring in the future. But did the current FDA labels really support this view?
The answer is “yes”. In reviewing the 158 FDA labels (Excel spreadsheet here), my crude analysis found that 31% of labels fall into the “genetic target” category (most from oncology – 26% of total) and 65% fall into the “large effect” category (most from drug metabolism [42% of total], HLA or G6PD [15% of total]).
A subtle but important point is that I predict that category #2 (PGx markers for non-oncology “genetic targets”) will grow in the future. In other words, development of non-oncology drugs will riff-off the success of drugs developed based on somatic cell genetics in oncology. …
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). …
So, you have a target and want to start a drug discovery program, do ya? How would you do it?
When I was at Brigham and Women’s Hospital, Harvard Medical School and the Broad Institute, I presented an idea from an early GWAS of rheumatoid arthritis (RA, see here) to Ed Scolnick (former president of Merck Research Labs, now founding director of the Stanely Center at the Broad Institute, see here). In this study, we found evidence that a non-coding variant at the CD40 gene locus increased risk of RA. The first questions he asked: How does the genetic mutation alter CD40 function? Is it gain-of-function or loss-of-function? What assay would you use for a high-throughput small molecule screen to recapitulate the genetic finding?
I was caught off-guard. Sadly, I had never really thought about all of the details. At the time, I knew enough as a clinician, biologist and a geneticist to appreciate that CD40 was an attractive drug target for RA. However, I was quite naïve to the steps required to take a target into a drug screen. That simple conversation led to several years worth of work, which ultimately led to a proof-of-concept phenotypic screen published in PLoS Genetics five years later (see here).…
In my previous blog series I talked about why genetics is important in drug discovery: human genetics takes you to a target, informs on mechanism of action (MOA) for therapeutic perturbation, provides guidance for pre-clinical assays of target engagement, and facilitates indication selection for clinical trials.
Here, I provide an overview of a new blog series on how genetics influences decision-making during drug discovery. The key principle: human genetics establishes a disciplined mindset and a firm foundation – anchoring points – for advancing targets through the complicated process of drug discovery. [For those less familiar with drug discovery, the end of this blog provides a brief primer on the stages of drug discovery.]
I highlight three areas: establishing a balanced portfolio, identifying targets with novel MOA, and creating a framework for objective decision-making. In subsequent posts, I will focus primarily on how human genetics informs on the latter (decision-making), with blogs pertaining to designing assays for screens and target engagement, utilizing pre-clinical animal models, predicting on-target adverse drug events, and selecting indications for clinical trials.
1. Establish a balanced portfolio
Whether in academic research, a small biotech company (see here) or a large pharmaceutical company (such as Merck, where I work), a balanced portfolio of projects is very important.…
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. …
A key learning from my time in academia was the value of collaborations. Much of my most enjoyable and productive research was conducted in collaboration with fellow scientists across the globe.
I am pleased to report that industry is no different. After one year working for Merck, I have found that in addition to collaborations across the company ties with external scientific experts focused on advancing programs of interest are actively encouraged.
It is heartening to see how some recent progress in several notable drug development programs is leading to increased excitement around the application of human genetics in identifying human drug targets. As I have previously noted, human genetics can also provide insights to identifying pathways enriched for approved drugs (see Nature article here), which indicates that novel pathways may provide an important foundation for novel drug discovery programs. Indeed, the use of pathway-based approaches, including phenotypic screens, can provide a powerful way to make complex genetic pathways actionable for drug discovery.
Today, I am excited to note that Merck has launched a Merck Innovation Network (MINt) Request for Proposals to identify collaborations with academic scientists to evaluate genetic targets or genetic pathways for their potential to become drug discovery programs. …
Question: What can we learn from Sputnik (see here), DARPA (see here) and disruptive innovation (see here) to invent new drugs?
Answer: The best way to prevent surprise is to create it. And if you don’t create the surprise, someone else will. (This is a cryptic answer, I know, but I hope the answer will become clearer by the end of the blog.)
My previous blogs highlighted (1) the pressing need to match an innovative R&D culture with an innovative R&D strategy rooted in basic science (see here), and (2) the importance of phenotype in target ID and validation (TIDVAL) efforts anchored in human genetics (see here). Now, I want to flesh out more of the scientific strategy around human genetics – with a focus on single genes and single drug targets.
To start, I want to frame the problem using an unexpected source of innovation: the US government.
There is an interesting article in Harvard Business Review on DARPA and “Pasteur’s Quadrant” – use-inspired, basic-science research (see here and here). This theme is critically important for drug discovery, as the biopharma industry has a profound responsibility to identify new targets with increased probability-of-success and unambiguous promotable advantage (see here). …
1.The observable physical or biochemical characteristics of an organism, as determined by both genetic makeup and environmental influences.
2. An individual or group of organisms exhibiting a particular phenotype.
There are many different phenotypes: strength in the face of adversity (see here); self-reflection in a time of uncertainty (see here); and creativity amidst a sea of sameness (see here).
Phenotypes also refer to disease states such as risk of disease, response to therapy, a quantitative biomarker of medical relevance, or a physical trait such as height (as in the figure above).
For drug discovery, I have put forth the premise that human genetics is a useful tool to uncover novel drug targets that are likely to have unambiguous promotable advantage (see here). The starting point in a genetic study is to pick the right phenotype, one that is an appropriate surrogate for drug efficacy.
And phenotype matters!
Two illustrative examples are the autoimmune diseases type 1 diabetes and rheumatoid arthritis. In type 1 diabetes the immune system destroys the pancreas, thereby preventing insulin secretion and the control of blood glucose levels.
Human genetics has identified many alleles associated with the risk of type 1 diabetes, nearly all of which act on the immune system (see here). …
The key is to find targets with novel mechanism of action (MOA) and an increased probability of success to differentiate in the clinic.
The pharmaceutical industry is in desperate need of new therapies with “unambiguous promotable advantage” that address unmet clinical need (see here, here and here). Of course, this is a laudable goal in drug development. In fact, given the current health care climate, we have no other choice (see here). If we are to have a sustainable industry, we must change the way we do discovery science. According to the Bernstein Report on BioBusiness: “Differentiation or Bust: Drug companies must start creating the case for value differentiation in discovery and then steadily build a body of evidence throughout the product development process.”
This means that dedicated drug hunters have a steep challenge ahead: to identify targets with novel mechanisms of action that have an increased probability to differentiate in the clinic. This will take creativity, hard work and innovation.
There is a lot written about “innovation”. [See here for a collection of articles from the HBR Insight Center; you can test your “innovation quotient” here.] Most comments about innovation involve creating a climate of risk taking balanced with accountability. …
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).…
In science, a pendulum swings as new discoveries are made and old hypotheses proven false. Unfortunately, the arc of this swing is often unrelated to the facts, but more tied to the prevailing views of what is and what should be. With incomplete information, the pendulum may swing too far in one direction – for example, towards the view that genome-wide association studies (GWAS) will identify the vast majority of genetic risk for complex traits in relatively small cohorts (now defined humbly as tens-of-thousands of case-control samples). After an initial wave of discoveries – or lack thereof – the pendulum swung too far in the other direction: disease-associated variants from GWAS cannot explain most of the estimated heritability in complex traits, therefore rare variants of large effect must be the root genetic cause of complex traits.
Too often, science creates an artificial mirror image of data interpretation. If one hypothesis is not true, then the opposite must be true. If it is not common variants, then it must be rare variants; if it is not genetics, then it must be epigenetics; if it is not the host, then it must be the microbiome; and so forth. Too often, incomplete data to support one model results in a knee-jerk reaction towards an orthogonal model, even if there is little evidence to support the model. …
As I sought advice from colleagues about my career, I was frequently asked if I would prefer to work in academics or industry (emphasis on the word “or”). The standard discussion went something like this:
ACADEMICS – you are your own boss and you are free to chose your own scientific direction; funding is tight, but good science still gets funded by the NIH, foundations and other organizations (including industry); the team unit centers around individuals (graduate students, post-docs, etc), which favors innovative science but sometimes makes large, multi-disciplinary projects challenging; there is long-term stability, including control over where you want to work and live, assuming funding is procured and good ideas continue; your base salary will be less than in industry, but you still make a good living and there are opportunities to consult – and maybe even start your own company – to supplement income. Bottom line: if you want to do innovative science under your own control, work in academics – as that is where most fundamental discoveries are made.
INDUSTRY – there are more resources, but those resources are not necessarily under your control (depending upon your seniority); the company may change direction quickly, which changes what you are able to work on; while drug development takes 10-plus years, many goals are short-term (several years), which limits long-term investment in projects that are risky and require years to develop; the team unit centers around projects (e.g., making drugs), so there is less individual glory but more opportunities to do multi-disciplinary projects; there is more turn-over in industry, which means you may need to switch jobs (including location – where you want to live) in several years. …
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).…
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.…
While most consider pharmacogenomics (PGx) the study of response to drugs in the clinic (e.g., efficacy and toxicity), PGx is also an amazing tool to understand fundamental biology of human disease. Drugs perturb human physiology in a way that cannot be accomplished in the resting state.
Most would agree that complex traits such as rheumatoid arthritis (RA) are more than just one disease. In fact, some advocate using the term “syndrome” rather than “disease” for RA, as syndrome emphasizes the complex and heterogeneous etiology. However, what are the underlying subsets?
Genomic technology promises to deconstruct complex traits such as RA. The problem that I have seen, however, is how to classify the subsets of disease. On one hand, we could take an unsupervised approach, and allow the data to form phenotypic subclassifications. In the study of RA synovial tissue (the primary site of pathology), data suggest that there are histological categories of disease depending upon the predominant cell type. One the other hand, what is ultimately important is how to translate disease subsets into clinical care. And for this to occur, there must be a correlation with clinical findings.
Here is where drug exposure can help translate an unsupervised approach into a clinically actionable discovery.…
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). …
For our website, we have chosen the term “precision medicine” rather than “personalized medicine”. A recent News article in Nature Medicine reinforces this concept (see here).
I have had many of my non-genetic physician colleagues comment to me: “We practice personalized medicine every day. It’s called basic patient care!” Their point: physicians see patients and make decisions about the best course of treatment based on patient preferences. For example, one RA patient may prefer to have a drug infusion once per month and another patient may prefer to take a pill each day.
The Nature Medicine article emphasizes “the idea that molecular information improves the precision with which patients are categorized and treated“. While personalized medicine might say “patient X with disease Y should get drug Z”, precision medicine says “patient X has a subset of disease Y — actually, disease Y3, not disease Y1, Y2 or Y4 — and patients with disease Y tend to respond more favorably to drug Z”. Said another way bt Charles Sawyers, an oncologist at the Memorial Sloan-Kettering Cancer Center in New York: “we are trying to convey a more precise classification of disease into subgroups that in the past have been lumped together because there wasn’t a clear way to discriminate between them“.…
The value of genetics to clinical prediction depends upon the underlying genetic architecture of complex traits (including disease risk and drug efficacy/toxicity). It is increasingly clear that common variants contribute to common phenotypes, but that extremely large sample sizes are required to tease apart true signal from the noise at a stringent level of statistical significance. Occasionally, common variants have a large effect on common phenotypes (e.g., MHC alleles and risk of autoimmunity; VKORC1 and warfarin metabolism), but this seems to be the exception rather than the rule.
A recent paper published in Nature Genetics explores this concept in more detail (download PDF here). As stated in the manuscript by Chatterjee and colleagues: “The gap between estimates of heritability based on known loci and those estimated owing to the comprehensive set of common susceptibility variants raises the possibility of substantially improving prediction performance of risk models by using a polygenic approach, one that includes many SNPs that do not reach the stringent threshold for genome-wide significance.” They measure the ability of models based on current as well as future GWAS to improve the prediction of individual traits.
The results, which are intriguing, depend not only on the underlying genetic architecture (which is often unknown, especially for PGx traits), but also disease prevalence and familial aggregation: “We observed that for less common, highly familial conditions, such as T1D and Crohn’s disease, risk models that include family history and optimal polygenic scores based on current GWAS can identify a large majority of cases by targeting a small group of high-risk individuals (for example, subjects who fall in the highest quintile of risk).…