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What were the most important lessons in Life Sciences in 2019?
Posted on December 20th, 2019 by Neal Katz in Pharma R&D
As 2019 comes to a close, we asked members of Elsevier’s Life Sciences team:
What were the biggest lessons or most noteworthy developments in the Life Sciences industry this year?
New technologies make an impact
“One of the exciting advancements this year has been the application of CRISPR-Cas9 for gene editing in neurodegenerative diseases,” contends Tom Williams, Life Sciences Professional Services Project Manager. “Studies in murine models and human cell culture have shown potential application of the technique to lower the expression of Alzheimer’s related genes, providing new insights into potential mechanisms of how the disease occurs and also potential novel treatments.” (See Williams’ recent post on Alzheimer’s research.)
Timothy Hoctor, VP of Life Science Solutions Services believes that “collaboration amongst life science and the willingness to invest in and embrace emerging technologies are leading to leaner organizations and entirely new discovery models.” Hoctor adds that “some of the biggest traditional data players servicing life science are following Elsevier’s lead to be more service driven, including CAS and Clarivate.”
“The industry has talked a lot about patient centricity and digitalization,” notes Christy Wilson, Senior Director of Pharma and Biotech Segment. “I saw an example of one major pharma digitally enabling clinical trials in a way that makes them more patient-friendly, using all sorts of technology to ‘bring the site to the patient’.”
Xuanyan Xu, Senior Marketing Manager of Life Sciences Audience, has noticed how technological advancement depends on the business’s culture. “Pharma and the healthcare industry are undergoing the digital transformation, and it has been buzzing around for quite some time. What l have learned is that this is not just about data, tech and people (or talent)—it’s also about culture, and retaining the spirit of the team and company.”
Iveta Petrova, Lead Product Manager, says that “Pharma companies are actively looking at AI and machine learning solutions across their departments, including virtual reality solutions for drug targets.”
David Cruz, Senior Global Key Account Manager for Pharma concurs. “Breakthrough technologies like AI are now part of various solutions to solve specific business problems in the drug discovery value chain. Also, there are many new players out there, some who we might not have expected (e.g., Google partnering with Sanofi to develop a new Healthcare Innovation Lab aiming at shortening the drug discovery cycle).”
Olivier Barberan, Director of Translational Medicine Solutions, also highlights the importance of AI, with the addendum of “but first, data.” This is a kind of mantra that emphasizes how AI is essentially only as good as the data that fuels it.
Wilson echoes this sentiment, saying that “Many companies are experimenting with AI and machine learning, and this experimentation has brought to light the struggles the industry has with data quality and amassing a sufficient quantity of quality datasets to train algorithms and build robust, ‘trustworthy’ predictive models.”
Data that is big and FAIR
“While we don’t talk much about ‘Big Data’ anymore, it is still delivering on the promise of large amounts of data being injected into research programs,” opines Ted Slater, Senior Director of Product Management PaaS. “Somewhat as predicted, this has served to emphasize the need for clean, high-quality data that are FAIR (findable, accessible, interoperable, and reusable). This is causing emphasis to shift even more towards data and data stewardship.”
Tom Pianko, VP of Global Key Accounts, says “Finding meaningful relationships from exponentially growing multiple sources of big data remain one of the largest challenges facing the life sciences industry. Solving our most difficult human diseases depends on successfully harnessing the tremendous wealth of information. Elsevier plays a major role in supporting these efforts.”
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