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Tag: big data

Lab Data Automation, Optimizing R&D Workflows and Lead Discovery from Biologically Complex Bioassays

With an increasing amount of data being generated today, pharma and biotech companies need to develop new technologies and strategies in order to structure, analyse and integrate this big data. If you weren’t able to attend our Global Pharma R&D Informatics & AI Congress, we have made these slides from Ralph Haffner, Dana Caulder and Pierre Farmer available.

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What is a Bioprocess Digital Twin?

Bioprocess digital twins are an exciting new technology that changes the way we do bioprocess development, process validation, and cGMP operations.

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How to cope with an evolving Immuno-Oncology field and changes in pathology

2018 will be remembered as a decisive year for immuno-oncology. In particular, Nobel Prize winners James Allison of MD Anderson Cancer Center in Houston, Texas, and Tasuku Honjo of Kyoto University in Japan lifted the field of immunotherapy to international recognition for those outside the scientific and medical communities. For the patient, these new alternatives to standard oncology therapies offer new hope for life-extending treatments using immunotherapy.

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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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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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Big Data Analytics and Artificial Intelligence: 7 Design Principles to Empower Life Science Researchers

Life sciences research is increasingly relying on big data analytics to drive scientific discovery. One of the contributing factors to this trend is the fact that researchers are confronted with a rapidly growing body of scientific literature and databases required to interpret experimental outcomes.

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