That’s the principle driving bioinformatics start-up Insilico Medicine, a Baltimore-based company utilizing GPU-accelerated NVIDIA advanced scale computing to power deep learning neural nets using massive datasets for drug repurposing research that targets aging and age-related diseases.
Drug re-targeting is not new. One of the best known cases is rapamycin, a drug originally thought to be an antifungal agent before it became widely used in in organ transplantation and then as a cancer fighter. Other companies have pursued drug re-purposing as a development strategy, but Dr. Alex Zhavoronkov, Insilico CEO, said his company using big data analytics to scale the strategy to a level never previously attempted.
Insilico researchers not only generate their own data, they ”scavenge” existing datasets that pharmaceutical companies and research institutions have retired because they were too small, in themselves, to provide much research value. Aggregated and analyzed, the data is providing Insilico, its pharmaceutical partners and physicians with insights into how medications designed and approved for one ailment can be redirected to attack another.
“We’ve found a way to suture together our data with many other databases,” said Zhavoronkov, “and then it starts making sense.” Altogether, Insilico has 3 million gene expression samples amounting to hundreds of terabytes of data. “The breakthrough is combining so many pieces of the puzzle in one particular place,” he said, explaining that Hadoop has been instrumental to harmonizing large amounts of unstructured, weakly related data, and then running Insilico’s drug scoring algorithms against it.
SOURCES -Youtube, insilico medicine, Enterprise Tech