Posted on Jan 21, 2019, 6 p.m.
Novartis is partnering up with the University of Oxford to use their AI to improve drug development; collaboration will analyze MRI data from multiple sclerosis patients and results from IL-17 inhibitor trials to gain better understandings of diseases important to research prospects.
The pharmaceutical seems to be looking to tie its future to digital technologies and data science to set itself apart from peers. In theory combination of data sets and computers could potentially unlock development insights, however in practice it may take awhile before the techno optimists vision of R&D comes to pass.
Collaboration with the Big Data Institute over 5 years is hoped to move the tech vision closer to fruition, with ambitions on changing how large data sets are merged/analyzed with views on deriving insights to improve drug development and patient care. Technologies such as this have ability to spot disease patterns/signals earlier in their manifestations; having ability to spot patterns within disease, and detect disease commonalities may help to predict how patients will respond to medicines earlier.
This initiative is believed to be transformative in the long term on how trials are designed and conducted which may uncover new paths, targets, and opportunities of development; early gains are expected to center around speed and accuracy with which disease are diagnosed. Initial efforts will be focussed on two programs: 1) data on upwards of 35,000 MS patients has been gathered with hopes to finding patterns and timing of disease progression; equipped with a more clear picture of MS disease continuum the company believes it can design more efficient clinical trials. 2) Then clinical data from 11,000 participants in IL-17 development trials will be looked at to develop IL-17C inhibitor MOR106.
The scientists will work in collaboration to apply deep learning algorithms to genomic, imaging, proteomic, and other types of data generated by development teams which may be hiding insights to enable predictions of early responses, and improve outcomes across a range of diseases.
As with much of the AI field it is unclear whether the optimistic approach will live up to its advocates hopes, regardless having access to extensive data, the university, plus neighboring collaborators possessing leading edge data skills, puts the partnership well placed at pushing the limits of science.
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