Big Data, Drugs and Rare Phenotypes

drugs

In the era of Big Data, search for new biologics (therapeutics, such as monoclonal antibodies, recombinant proteins, blood, cells, or tissue components, created via biological processes, rather than chemically synthesized or extracted drugs) and new drug targets not much differ from the search for a terrorist suspect. And if in the latter case, cell phone data are mined; in search for drugs, companies mine rare phenotypes, or individuals, families, groups or communities possessing great self-healing abilities, disease susceptibility, incredible memory, or other extraordinary abilities. Thus, a few dozens of known individuals producing HIV broadly neutralizing antibodies (that are antibodies able to trigger the response of the immune system killing many HIV strains), as their antibodies can be potentially used for HIV therapy; while, still better, the peculiarities of the immune system of those individuals can be deciphered for developing an HIV protective vaccine.

This Forbes’ article discusses three examples of the companies’ approaches at developing drugs via discovering rare phenotypes. The major motivator is a successful (in Phase III trial now) development of lipid-lowering drugs targeting PCSK9, an enzyme discovered from the study of two French families with unusually high cholesterol levels and a strong history of heart disease (when overproduced, PCSK9 increases cholesterol level).

Thus, Amgen acquired deCODE, the Iceland company collected 400,000 blood samples and a large amount of health records. Regeneron partnered with Geisinger Health System to sequence the exome DNA of 100,000 patients (see this article in The New York Times). Belgium-based pharma UCB partnered with Innocentive to crowdsource the search for rare genetic variations associated with extreme phenotypes. The latter overlaps with an approach by PersonalGenomes.org, an online system that matches individuals willing to share their personal health information with researchers seeking such information.

“There are now important opportunities … to collect the dense phenotypic data that, in conjunction with genomic data, biological insight – and yes, muscular,  empirically-driven analytics — will power the medical innovation of the future.” (Forbes)

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