Machine learning, deep learning, and pre-competitive data analysis will deliver tangible benefits to patients

London, 29 March, 2017 The Pistoia Alliance, a global, not for profit alliance that works to lower barriers to innovation in life sciences R&D, is calling upon the industry to improve collaborative efforts to use patient data to its full effect. In a series of keynote speeches delivered at The Pistoia Alliance’s annual member conference in London, speakers from Amgen, Accenture and AstraZeneca, discussed the need to more closely connect outcomes data with the R&D process – to help pharmaceutical companies focus their research efforts and deliver real benefits to patients. Building machine learning and deep learning systems, and incorporating data from therapeutic interventions or diagnostics into R&D is technologically challenging, and would benefit significantly from industry-wide pre-competitive collaboration.

“Pharmaceutical companies are capturing and storing more data than ever before. But deriving insights from data which translate into R&D outcomes that actually benefit patients, is a huge challenge,” commented Dr Steve Arlington, President, The Pistoia Alliance. “At the same time, today’s healthcare environments demand that pharmaceutical companies prove their therapies work, and that their cost can be justified. These goals can only be achieved if the industry collaborates to build cutting edge data analysis solutions, including deep learning and machine learning systems. Pharmaceutical companies cannot go it alone – unique solutions that are not interoperable or cannot share data are a considerable waste of time and money, which benefit neither patients nor payers in the slightest.”

A key event on the conference agenda was an update on The Pistoia Alliance’s President’s Series Hackathon. The hackathon was a series of five challenges, held over the 25th-26th March 2017, designed to bring the deep and machine learning community together with the life science and healthcare industries, to demonstrate the potential of deep learning to aid drug discovery and bring life-saving treatments to the world. The first challenge was delivered by Elsevier, in conjunction with UK-based charity, Findacure; it sought to accelerate treatment and clinical research for Friedreich’s ataxia (FRDA). The second challenge – the compound prediction challenge – was sponsored by ExCAPE, in conjunction with Janssen and Imec; the hacketeers were tasked with proposing innovative and performant predictive machine learning models for a number of assays.

A third challenge was on the ability of machine and deep learning to gain insights from social media forums into patient experience of a particular disease, such as asthma. The fourth challenge, sponsored by Promeditec, aimed to accelerate early diagnosis of Thoracic Aortic Aneurysm (TAA) through machine learning. Finally, a fifth challenge to predict potential disease-causing DNA mutations from the ClinVar public resource and Ensembl genome browser, was delivered by Microsoft. All of the five hackathon challenges were supported by Microsoft; which provided access to, and support for, its Azure cloud suite, Azure Machine Learning Studio, and Azure notebooks for machine learning and scripting.

The Pistoia Alliance’s European conference was attended by 120 life science professionals, representatives from top 10 pharma companies, leading biotechs and academic medical centres. Speakers and panel attendees converged on a range of topics; including patient data capture, deep data analysis, and ‘future thinking’ – such as, cutting edge therapeutics, companion diagnostics and reality mining. The Pistoia Alliance was created to address the barriers and challenges that companies face during life sciences R&D, and prevent the waste of time and resources resulting from siloed research programmes. Its projects bring together key constituents to identify root causes of R&D inefficiencies; it has also created a proven legal framework for open innovation. For more information about The Pistoia Alliance, please visit:

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