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Tag: drug discovery

How to make digital microfluidics suit biological need

Droplet-based microfluidics is a technology that allows for the manipulation of small liquid droplets with a volume ranging from microliters to picolitres. The technology that handles discrete droplets is called digital microfluidics. The small volume offers various advantages such as quick mixing, simple handling, high throughput with a large number of droplets. The small volume also poses a serious challenge to engineering droplet-based microfluidic platforms: evaporation.

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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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Integrating AI and HCI for drug discovery

Bringing new therapeutics to patients is easier said than done due to the enormous intrinsic complexity behind being efficacious and safe.

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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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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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Pricing Pressure, Payer Perspectives and Innovative Diagnostic Platforms

We spoke to Alex Bastian about how he got started in precision medicine, where he sees it going and how he intends to use real world evidence to unlock the potential around personalised approaches to drug development:

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