AstraZeneca taps CRISPR to extract value from genome data

AstraZeneca ($AZN) has outlined how it is using the white-hot CRISPR genome editing technology to put its repositories of sequencing data to work in its R&D programs. The initiative is one of the many ways in which AstraZeneca is applying the technology at its drug discovery and development units.

Martin Main, director of reagents and assay development at AstraZeneca, told GenomeWeb about the CRISPR-driven programs at an event co-hosted with the Wellcome Trust Sanger Institute in the United Kingdom. "[We're applying CRISPR] in as many areas as we can," Main said. CRISPR-enabled areas include target identification and validation, lead optimization and translational research, each of which AstraZeneca thinks can benefit from the ease and precision offered by CRISPR. The belief is that CRISPR will enable companies to build upon the technological advancements of recent years.

Cheap genome sequencing, and specifically the data it generates, is core to this hypothesis. "In this era of genomics, we have vast amounts of genome sequencing data, and now with CRISPR, we have the ability to use that information, to reconstitute the genomic setting in our cellular models," Main said. AstraZeneca has moved to secure access to technology to facilitate these projects by teaming up with organizations including the Sanger Institute, Thermo Fisher Scientific, the Broad Institute and the Whitehead Institute.

The use of CRISPR and sequencing data to build genomic details into cellular models is part of one of two ways of working with the technology highlighted by Main at the event. Main framed the cellular model work as part of efforts to build CRISPR into target validation programs, which also draws on the company's experience with induced pluripotent stem (iPS) cells. The second case study covered the use of CRISPR to refine models of disease. Data and computational capabilities are central to multiple aspects of work to incorporate CRISPR into R&D.

"Before, if you wanted to make a cellular model, you had to go out and find a patient that had that mutation and make iPS cells from them. That's tough, especially if there are a whole bunch of different mutations. Now, once you observe it computationally, you can make it rather than having to go out and find it. The ability to make patient mutations that have been observed in large datasets on the individual lab scale is totally transformative," the University of California, Berkeley's Jacob Corn said.

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