Life sciences must collaborate to overcome technology challenges

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

Technology solutions will not work if pharmaceutical companies try to go it alone, say Pistoia Alliance

The Pistoia Alliance, a global, not-for-profit alliance that works to lower barriers to innovation in life sciences R and D, is calling upon the industry to improve collaborative efforts and thus use patient data to full effect.

The Pistoia Alliance projects bring together key constituents to identify root causes of R and D inefficiencies. Its European conference was attended by 120 life science professionals, representatives from top 10 pharma companies, biotechs and academic medical centres.

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 and D process.

Unique solutions that are not interoperable or cannot share data are a considerable waste of time and money.

Building machine learning, deep learning systems and incorporating data from therapeutic interventions or diagnostics into R and D is technologically challenging.

“Pharmaceutical companies are capturing and storing more data than ever before. But deriving insights from data which translate into R and D outcomes that actually benefit patients, is a huge challenge,” said Steve Arlington, President of The Pistoia Alliance.

“The 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.”

Hackathon

A key event on the conference agenda was an update on The Pistoia Alliance’s hackathon.

The hackathon was a series of five challenges 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.

The first challenge was delivered by Elsevier along with Findacure. It sought to accelerate treatment and clinical research for Friedreich’s ataxia (FRDA).

The second, a compound prediction challenge, was sponsored by ExCAPE alongside 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 its Azure cloud suite, Azure Machine Learning Studio and Azure notebooks for machine learning and scripting.

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