Discovering cures for cancer, for Alzheimer’s, for multiple sclerosis, for Parkinson’s, for the halting and reversing of aging itself, may not require the development of new drugs. It may mean discovering properties and therapies in drugs already developed and used for other diseases.
That’s the principle driving bioinformatics start-up , a Baltimore-based company utilizing GPU-accelerated 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.
“We’ve found a way to suture together our data with many other databases,” said Zhavoronkov, “and then it starts making sense. The breakthrough is combining so many pieces of the puzzle in one particular place.”
In February 2015 at the Personalized Medicine World Conference in Mountain View, CA, Insilico was recognized as the “Most Promising Company” in the fields of human genetics and personalized medicine. In March, Insilico was one of 12 finalists selected to present at the Early Stage Challenge at NVIDIA’s 2015 GPU Technology Conference.
Insilico has configured four NVIDIA DevBox desktop supercomputer, using TESLA K80 GPU accelerators and four Titan X graphics cards, for a total of 28TF of processing power.
CEO of Insilico Alex Zhavoronkov said Insilico is experimenting with many flavors of deep neural nets as well as deep learning combined with more traditional research and testing methods. This includes deep feed forward neural nets using different data types as inputs, stacked auto encoders for cross-platform data harmonization, deep belief nets for drug scoring and, ultimately, drug repurposing.
Insilico developed methods to augment its proprietary gene expression and proteomic data using Hadoop and other methods to harmonize and compare data from different sources and turn it into usable pathway activation profiles that can be used by deep learning algorithms. In so doing, the company has created biomarkers for cancer, Alzheimer’s and other diseases.
The results include:
• DeepPharma, a GPU-based visual computing platform for creating virtual cells, tissues, bodies, and even virtual populations. These virtual laboratories are used is to simulate and test tissue-specific pathway activation – also called “net signaling drift” – measuring the effects of millions of compounds on the molecules within diseased or aged cells.
• OncoFinder, a personalized medicine decision-support tool that has been used by physicians, mostly in Europe and Asia, to help identify drug treatments for more than 800 patients.
“When you’re using deep learning in bioinformatics your only option today is GPU computing,” Zhavoronkov said. “Deep neural networks are evolving and revolutionizing many aspects of our daily lives – in pictures in videos in voice. GPU computing is becoming much more available and more databases, with millions of samples, also are becoming available. So success in deep learning is primarily centered around two factors: being able to utilize the full power of GPU computing, and access to huge databases.”
Insilico is not required to undergo FDA or other regulatory approvals because OncoFinder is not used for diagnostics, Zhavoronkov said.
Zhavoronkov said one of his greatest challenges has been assembling a staff combining expertise in machine learning, human genetics and pharmacology – particularly since deep learning is new to genomics research. “Finding talent that is qualified to experiment with deep learning applied to gene expression data is very difficult,” he said, “because you need people who are good with math and programming but also understand the biology. There are few people with this range of skills, so it’s a very precious resource.”
One of Insilico’s first aging-related projects is researching the process of skin aging. Zhavoronkov said Insilico has predicted the first compounds that may ameliorate the skin aging process and will announce its findings next year.
“Our first frontier is human skin,” Zhavoronkov said, “if you can successfully treat skin aging you can basically apply the principle to other tissues.”
Keywords: Drug development; BioInformatics; GPU; Aging; Insilico Medicine.