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What’s ahead for the Life Sciences industry?

Posted on December 30th, 2019 by in Pharma R&D

As the year comes to a close, we’re thinking about trends in the landscape and where the industry is headed. What do some of the members of Elsevier’s Life Sciences team think?

Tom Pianko VP of Global Key Accounts, counts machine learning, artificial intelligence, data normalization and advance analytics tools among the trends that point to where the Life Sciences industry is going—and many of his colleagues agree.

Data, data, data

“The need for clean and normalized data will continue,” assures Lead Product Manager Iveta Petrova, in regard to what has been an ongoing issue in the industry.

“Data sharing is becoming more common and leading to tremendous breakthroughs due to the collaborative nature of this practice, resulting in increased advancements in understanding diseases and treatments,” says Tom Williams, Life Sciences Professional Services Project Manager. 

“FAIR Data Principles adoption shows no signs of slowing down, and so will continue to present a compelling way forward for us,” asserts Ted Slater, Senior Director of Product Management PaaS. “This is especially true as data ‘cleansing’ and integration continue to be among the top complaints we hear from our customers across Life Sciences. Next year we’ll see more FAIR Data than ever, together with more tools and services to make adoption of the Principles easier and further commitment to data stewardship across the board. We will see an uptick in demand for interoperation between FAIR repositories.”

Google’s announcement of ‘Quantum Supremacy’ means we’re nearing an age where the ability to compute massive amounts of data in a previously unthinkably short amount of time becomes a reality. This will only accelerate the industry’s investments in data science and need for FAIR datasets if they wish to fully-realize the potential,” says Christy Wilson, Senior Director of Pharma and Biotech Segment.

Deeper learning

“This year, successes with machine learning and with deep learning in particular started to appear more often in applications beyond image analysis,” observes Slater. “However, this has served to emphasize that deep learning training typically needs very large amounts of high-quality data. We will see pharma reaching out to content providers even more, with an increasing emphasis on quality, as some efforts over large data sets assembled from a variety of sources just to gain quantity fail because of various kinds of heterogeneity or other issues in the source data.”

“New technologies (e.g., AI, deep learning, etc.) at the core of innovative solutions will bring more breakthroughs, improving efficiencies in various key areas with costs reduction and better outcomes,” predicts David Cruz, Senior Global Key Account Manager for Pharmaceuticals. “There will be more evidence they deliver ROI, thus the industry will continue to invest in these solutions. Budgets will shift towards projects involving those expected outcomes.”

Tackling disease research

“Personalized medicine is becoming more widely available and used to treat complex diseases that are having higher success rates in a range of diseases, particularly in the cancer field,” says Williams.

“More pharma are sharing risk. More companies are looking at data to drive innovations around rare disease research and drug repurposing,” reports Timothy Hoctor, VP of Life Science Solutions Services, also noting that “predictive analytics are playing bigger and more impactful roles in company discovery strategies.”

As Xuanyan Xu, Senior Marketing Manager of Life Sciences Audience, explains, “Real-world evidence is gaining prominence in the drug approval process for promising and breakthrough treatments—this can be important and extremely useful for rare diseases where clinical trials suffer due to a low amount of available patient population.”

Big changes in food and agriculture

Cruz also looks to agribusiness for some intriguing developments in the future: “’Green’ food and agriculture production will increase due not only to feed the worldwide population, but to produce healthier and environmentally friendly products—thus the whole R&D value chain dedicated to that industry has to change with major transformations (Monsanto’s acquisition by Bayer that is considered as a financial disaster will actually lead those changes because they have to),” he suggests.

“So, producers (companies, as well farmers) will change the way they interact and operate. Developed countries will drive high-value food and healthcare product demand. Emerging economies/countries will follow too at different pace, but digital technology will boost their speed to innovate.”

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