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Pharma Informatics

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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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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Staying Ahead of Life Sciences Research and Implementation Trends in 2017 and Beyond

The traditional product pipeline, which information technology will compress and alter significantly in the life sciences in the coming years. Source: Wikimedia user MattBishop5 (CC BY-SA 3.0).

This article was originally published by Dassault Systèmes BIOVIA in October 2017, and is published here with permission.

Where can life science companies go operationally to maintain or create a competitive advantage? This question has been a constant in the industry since its inception, but recent advances in older technologies like Laboratory Information Management Systems (LIMS) and the appearance of promising newer technologies like the Internet of Things (IoT) have changed the nature of the answer. Which new technologies that are theoretically worthwhile have matured enough to be gracefully implemented operationally?

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Big Data, Informatics and Pharma: An Open Letter From Vaibhav Narayan

Dear colleagues,

Therapeutic interventions have traditionally been developed for patients with established pathologies and overt clinical symptoms. Many diseases however, are not tractable in later stages and therefore therapies are needed that intervene earlier in the disease continuum.

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Big data, knowing and AI: do they influence drug discovery?

The quest for magic bullets started with Paul Ehrlich. He wanted to target the syphilis bacteria without harming the patients, which led to Salvarsan. Ehrlich was a foundational thinker, and we owe him modern concepts such as receptor and pharmacophore. His impact on the field – seeking the ideal molecules – pervades drug discovery.

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Not Another IT System: Communities of Practice in Pharma

Ever thought about how you avoid introducing “just another IT system”? How to get to the point where people work with the system joyfully and don’t mutter about the small faults or that no-one improves the system?

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What’s Driving Change in Drug Discovery and Development?

The past decade has witnessed huge advances in both scientific research and technological innovations within the drug discovery market. Here Dr James Willans, Chief Technology Officer of the not-for-profit organisation and educational charity, Lhasa Limited, discusses two significant areas that have the potential to further transform the industry over the coming years.

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