Big data analytics bolsters hunt for elusive Huntington's drugs
The CHDI Foundation has put faith in the power of big-data analytics to address the lack of good drugs for Huntington's disease. The group has formed an alliance with GNS Healthcare to build computer models of the genetic brain disease for use in labs to discover potential new therapies.
While researchers have understood the genetic cause of Huntington's for years, the underlying mechanisms that drive the neurodegenerative disorder aren't as well known. And though labs have generated loads of data from DNA sequencing, cell-signaling and gene-expression experiments, the tricky part has been pulling all those findings together to understand what's really happening in the disorder. The foundation hopes that GNS's big-data analytics platform, which involves reverse engineering and forward simulation, can integrate the stockpiles of data and generate some computer-enabled hypotheses on how all the many parts fit together in a computer model.
The alliance calls on Cambridge, MA-based GNS to produce predictive models of disease progression and drug response. With these tools, researchers could better understand the disease and find desperately needed treatments for the 30,000 patients with the genetic disorder in the U.S. And the collaboration builds on GNS's previous alliances with drug research groups at Johnson & Johnson ($JNJ) and Biogen Idec ($BIIB) in building models for cancer and other diseases.
"[T]here are currently no effective treatments for this devastating disorder. CHDI's research efforts focus on understanding the underlying biology of HD in order to discover treatments that slow disease progression," said Keith Elliston, vice president of systems biology at CHDI, said in a statement. "The HD model that GNS is building will allow researchers to perform simulations that generate novel hypotheses to help drive this understanding. As such, this collaboration between CHDI and GNS represents an innovative, complementary approach to advancing discoveries that we hope will lead to novel therapies."
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