A Light Regulatory Touch Helps the NGS Data Revolution

pics-blog-lifesci-02A Light Regulatory Touch Helps the NGS Data Revolution

posted by Mosaic Data Science





About twenty years ago the post-genomic era began to emerge in computation biology disciplines. Rather than information flowing from DNA to RNA to protein sequences, a new central dogma, much broader in scope, began to take shape. Genomes led to gene products, which implied structures and functions, which led to pathways and physiology. In the post-genomic era computational biology would move from single genes and single functions to systems of genes, structures, functions, pathways and behaviors. And when this new approach was applied to the new genomics data the result would be, as Francis Collins put it, personalized medicine. That day is now soon approaching with the development of Next Generation Sequencing and with it the need for new regulations.

Next Generation Sequencing, or NGS, not only allows for very rapid, personalized, genomic sequencing, it also, by its very nature, supports a synoptic view of a patient’s health risks. Rather than the traditional “one test—one disease” paradigm, NGS scans the genome. A patient’s genome may reveal thousands of genetic variants, indicating risks for a wide range of diseases or conditions. And as FDA Commissioner Margaret Hamburg recently pointed out, that raises a host of regulatory and oversight concerns.

In its oversight role, the FDA has avoided burdening industry with heavy-handed, exhaustive testing requirements. The FDA is not slowing progress, but it does want to ensure a certain level of accuracy in the new whole-genome sequencing products. As Hamburg explains:

FDA intends to develop a practical and nimble approach that will allow medical advances to be implemented as soon as possible, using its regulatory flexibility and the power of the information placed into high-quality databases.

That’s good because the rapid advances in NGS—a technology that has easily exceeded the Moore’s law standard of doubling every two years—is producing a data explosion that may revolution both life science research as well as healthcare. Healthcare predictive modeling and healthcare data analysis will not only provide patients and healthcare providers with new insights, but this enormous database of patient data will feedback into the medical sciences in ways we can only imagine right now.

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