Plenge Lab
Date posted: December 9, 2018 | Author: | No Comments »

Categories: Drug Discovery Embedded Genomics Human Genetics

[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., UK Biobank with FinnGen], technologies for high-throughput CRISPR mutagenesis and single-cell eQTL analysis).…

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Date posted: October 21, 2018 | Author: | No Comments »

Categories: Drug Discovery Embedded Genomics Human Genetics Precision Medicine

[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).…

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Date posted: June 7, 2018 | Author: | No Comments »

Categories: Drug Discovery Human Genetics

[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 Genetics here).

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.

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Date posted: April 13, 2018 | Author: | No Comments »

Categories: Drug Discovery Embedded Genomics Human Genetics

[Disclaimer: I am an employee of Celgene. The views reported here are my own.]

Drug research and development (R&D) is a slow, arduous process. As readers of this blog know, it takes >10 years and upwards of $2.5 billion dollars to bring new therapies to patients in need. An aspiration of the biopharmaceutical ecosystem is to shorten cycle times and increase probability of success, thereby dramatically improving the efficiency of R&D.

One potential solution is to use human genetics to pick targets, understand molecular mechanism, select pharmacodynamics biomarkers, and identify patients most likely to respond to treatment (see Science Translational Medicine article here). While intuitively appealing and supported by retrospective analyses (here), it is not yet routinely implemented in most R&D organizations (although see Amgen blog here; Regeneron study below). Indeed, human genetics often represents an inconvenient path to a new therapeutic, as it takes substantial effort to understand the molecular mechanism responsible for genetic risk and many such targets are difficult to drug.

But what if…

…it were possible to go from gene variant to therapeutic hypothesis instantly via in silico analysis;

…it were possible to select an “off-the shelf” therapeutic molecule that recapitulates a human genetic mutation, and take this molecule into humans almost immediately, with limited pre-clinical testing;

…it were possible to select pharmacodynamics (PD) biomarkers that capture underlying human physiology, and to measure those PD biomarkers in a small, human proof-of-mechanism clinical trial;

…it were possible to model the magnitude of effect of a therapeutic intervention relative to existing standard-of-care, and thereby to estimate the commercial market of an as-yet-to-be-approved drug?…

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Date posted: December 19, 2017 | Author: | No Comments »

Categories: Drug Discovery Embedded Genomics Human Genetics Precision Medicine

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

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).…

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