Plenge Lab
Date posted: October 5, 2016 | Author: | No Comments »

Categories: Drug Discovery Human Genetics

I have many fears, both professional and personal. When I decided to leave academics for a job in industry in 2013, my biggest fear about making the transition was scientific. In my mind, I had a model of how human genetics might transform drug discovery and development. There were anecdotes (e.g., PCSK9 inhibitors) and a few systematic studies in specific diseases (e.g., genetics of rheumatoid arthritis), but there were many holes to the model. Over the last couple of years, additional anecdotes and systematic analyses have emerged (e.g., Matt Nelson, et al. Nature Genetics), which helps to soothe my fears…but I still have concerns.

[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 blogged about previously, I see two primary routes to go from human genetics to new drug discovery programs (see here, here). The first requires that there are genes with a series of disease-associated alleles with a range of biological effects, ideally from gain- to loss-of-function (allelic series model). The second requires disease-associated genes to aggregate within specific biological pathways, which can then be turned into assays for disease-relevant pathway-based screens such as phenotypic screens.…

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Date posted: June 26, 2015 | Author: | No Comments »

Categories: Drug Discovery Human Genetics

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.…

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Date posted: August 20, 2014 | Author: | No Comments »

Categories: Drug Discovery Human Genetics Uncategorized

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. …

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Date posted: April 3, 2013 | Author: | No Comments »

Categories: Drug Discovery Human Genetics Immunogenomics

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.

Overview

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.…

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Date posted: March 13, 2013 | Author: | No Comments »

Categories: Human Genetics Precision Medicine

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).

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