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Pharma Informatics

Human Genomic Evidence: Revolutionising the identification and prioritisation of targets for better medicines

Nine out of 10 potential drugs that enter clinical trials never make it to the market. Failure often occurs because the biological target chosen is not well understood. However, it is hard to objectively select targets with a high chance of clinical success because the data required to predict efficacy and safety are complex, dispersed and incomplete. To address this challenge, Open Targets was founded in 2014 as a public-private partnership by GSK, EMBL-EBI and the Wellcome Sanger Institute. The consortium has grown since its launch, welcoming new partners Biogen in 2016, Takeda in 2017, and Celgene in 2018.

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Transforming Drug Discovery by Accelerating the Development of More Effective Therapies

This article was originally published by Technology Networks in August 2018 and is published here with permission.

John Baldoni currently heads up a drug discovery unit at GlaxoSmithKline. Since joining GSK in 1989, John has held numerous positions within the company and has led many significant cross-functional strategic initiatives. He has an impressive 37 years’ experience working within the biopharmaceutical industry.

The recently established Accelerating Therapeutics for Opportunities in Medicine consortium, was conceived by John, with a mission “to accelerate the development of more effective therapies for patients”. ATOM stemmed from the Cancer Moonshot, an initiative that aims to improve the availability of cancer therapies, enhance detection of cancer early on, and improve our ability to prevent cancer.

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From data to discovery – the journey to digital innovation

Digital innovation in healthcare promises much. But potential and realisation are two very different things.

Researchers at Stanford in the US, for example, have developed an AI algorithm designed to diagnose skin cancer[1]. AI diagnosis was compared with that of 21 dermatologists and it matched the efficacy of its human counterparts. This has significant implications for patients, particularly those who find it difficult to access healthcare professionals. But the implications don’t stop there.

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Cutting through the hype: Brandon Allgood talks AI and machine learning

When we last spoke, Brandon Allgood told us about The Future of AI. This week, we caught up with Brandon and spoke about drug discovery and machine learning algorithms.

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Analytics and Big Data in Pharma

Drug discovery and development is a complex process that requires integration of multiple data points, experiments and calculated risk/benefit assumptions. It is therefore only natural that virtualization and big data analytics are a natural fit for implementations, expected to demonstrate significant cost-effective gains for the betterment of society, providing access to effective and safe medications.

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Failing Blockbuster Medicines, Eroding Operator Margins & Increased Pricing Pressures: What’s Next for Precision Medicine?

The Precision Medicine and Biomarkers Leaders Summit: Europe takes place this week and in anticipation, we spoke to Unmesh Lal of Frost and Sullivan about his views on what the future holds for precision medicine and how the pharmaceutical industry has changed in recent times.

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Without challenge, there is no change – the move to precision medicine

I don’t need to tell you that pharma has changed, that it is still changing at speed, or that change is the new normal. I don’t need to tell you because you − more than anyone else − already know. The passing of the blockbuster era has caused uncertainty. Without the superstar drugs of old, where does the industry go next? It’s a serious challenge, but without challenge, there is no change. The answer, of course, is the scalar shift from blockbuster generic to precision medicine specific.

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The Future of AI: A Q&A with Brandon Allgood

Brandon Allgood is the Chief Technology Officer at Numerate, as well as a co-founder. In addition to being responsible for the development of Numerate’s cloud-based machine learning platforms, Brandon runs the software & data science teams, and is the technical lead on all of the companies internal drug programs, as well as external collaborations.

Here, Brandon talks about what he thinks the future holds for AI and machine learning:

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