Molecular Assays To Predict Targeted Cancer Treatment Response

Once identified, the signatures can then be used to pre-select patients who are good candidates for the targeted therapy.

GE Global Research and GE Healthcare (Niskayuna, NY, USA) have announced a three-year collaborative agreement with Eli Lilly and Company (Indianapolis, IN, USA) to discover and develop these advanced IVD assays. The agreement between GE and Lilly will provide GE with access to clinical tissue samples from unidentified patients enrolled in Lilly's clinical trials. In turn, Lilly will have access to GE's advanced technologies in automated tissue-based image analysis and molecular reagents. These tools can be used during drug development to aid Lilly in evaluating the effectiveness of their drug candidates and potentially select patients for future trials. GE is providing diagnostic tools that have the potential to greatly reduce the time and cost of cancer drug development.

In addition to Lilly's existing chemotherapy agents, Lilly is developing targeted cancer therapeutics, which are now in both early and late stage clinical development. GE is developing advanced multiplexed tissue-based assays and image analysis tools that can measure multiple biologic pathways.

The collaboration with Lilly is consistent with GE Healthcare's Early Health Vision, which is about transforming healthcare delivery from a focus on treating late disease to a focus on adopting an Early Health model-of-care--where prevention, pre-disease detection, and early diagnosis are the key aims. GE Healthcare has a range of in vivo diagnostic imaging technologies and molecular contrast agents to assist with the detection and diagnosis of cancer, and expanding this strength toward in vitro diagnostics is a natural extension of this strategy.

The healthcare industry is experiencing a growing convergence of therapeutics and diagnostics. The use of molecular diagnostic tools that can discover key protein or gene signatures will enable pharmaceutical companies to determine those patients most likely to respond to a particular medication based on their particular genetic makeup.


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