Plenge Lab
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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