Pharma R&D Today
Ideas and Insight supporting all stages of Drug Discovery & Development
Pharma’s Path to Adopting AI and Other Emerging Technologies
Posted on February 7th, 2018 by Christy J. Wilson in Pharma R&D
Right now in the pharmaceutical space, emerging technologies are dominating the conversation. As the cost of drug development soars, and the potential for success seems more and more elusive, the industry is pinning its hopes on promising technological breakthroughs. Artificial intelligence (AI), a concept that has long been familiar to sci-fi fans, is now being taken very seriously among pharmaceutical executives who are anxious to put it to work in the lab.
Following the recent J.P. Morgan Healthcare Conference in San Francisco, David Shaywitz of Forbes shared his impression that while pharma leaders are thrilled by this tech, it hasn’t yet had much impact on how R&D is done. “These emerging approaches and ways of thinking have generally not penetrated most biopharma organizations at the line/operations level, and have generally not yet impacted how these organizations actually approach their basic work of discovering and developing new therapeutics,” he writes.
“From what I can gather, it’s not a hostility to technology as much as a sense that it’s not immediately clear to most of those in the trenches how (or even whether) the emerging technologies will meaningfully impact the work they need to do, and many are concerned about, or at least wary of, the additional work it may create.” A McKinsey survey of AI-aware C-level executives at 3,000+ companies, representing multiple sectors and countries, seems to back this up with results that show only 20% of firms consider themselves “adopters” of one or more technologies—health care being among the sectors with the lowest adoption rates overall.
So what might pharmaceutical companies do to move from aspiration to adoption? In the article Creating an Artificial Intelligence Innovation Ecosystem to Drive Drug Discovery Research, Chris Willis, Ph.D., Manager of Life Sciences R&D Consulting at Accenture, says that implementing a successful AI strategy in the drug discovery process means including data architects, data engineers and data scientists on the team—which requires establishing external collaborations while also building in-house expertise for internal innovation. “Building the in-house AI expertise mainly involves a combination of re-skilling and talent acquisition. However, training and hiring can be riddled with roadblocks due to the scarcity of talent and cross-sector demand,” he admits, noting that this challenge is what’s driving an increase in partnerships between pharma firms and companies focused on harnessing AI tech.
Denise Myshko shares advice gleaned from a number of pharma industry insiders in a PharmaVOICE feature on innovation and disruptive technology: Michael Griffith of inVentiv Health suggests that changes in corporate structure will accelerate the adoption of new technology—for instance, putting people in the C-suite who can achieve operational objectives rather than simply promoting top sales execs. Nick Colucci of Publicis Healthcare Communications Group emphasizes that top-down commitment is necessary, and that includes a willingness to encourage experimentation and failure (since you can’t really have one without the other). Medidata Solutions president Glen de Vries says that executives and managers must figure out how to plan and run studies differently, rethinking traditional processes in order to bring about change.
Meanwhile, Richard Klinghoffer of Presage Biosciences believes the most important factor of all in enabling innovation is “courage”—people have to be willing to take bold, risky steps. Technologies like AI are called “disruptive” for a reason, and that is partly because organizations that embrace them often must commit to making big, daunting changes. There are no guarantees of success, but the possibilities are tantalizing.
The authors of a TM Capital Industry Spotlight titled The Next Generation of Medicine: Artificial Intelligence and Machine Learning highlight one exciting example, citing BERG Health’s success using their AI platform to select a drug candidate for rare brain cancers that went on to clinical trials. The report describes the platform’s process of “analyzing data from thousands of cancer patients to build an in silico (computer simulated) disease model and suggest possible drug treatments.” BERG Health’s President and co-founder likened it to a reversal of the scientific method—instead of starting with a hypothesis and doing experiments, they let the biological data lead them to their hypothesis. As more pharmaceutical companies gain the courage to fully adopt emerging technologies throughout the organization, we can hope to see many other stories like this in the near future.
Jabe Wilson predicts in the new article The Augmented Researcher that “We will see a greater interest in and acceptance of AI as a valid technique in R&D,” arguing that the building blocks are already in place. “I believe, in the next 12 months, we will begin to see the first fruits of these advances in the use of AI to augment research; helping us to make meaningful progress towards reversing the productivity crisis in science and R&D, and improving outcomes for humanity.”
All opinions shared in this post are the author’s own.
R&D Solutions for Pharma & Life SciencesWe're happy to discuss your needs and show you how Elsevier's Solution can help.
Christy J. Wilson
Sr. Director, Pharma and Biotech Segment
Connect on LinkedIn
Follow on Twitter
- When Looking to Accelerate the Pace of Research, Having the Right Multidisciplinary Tools Matters
- Big Talks on Big Data are Coming to Bio IT World
- Harnessing big data — it’s challenging, but it can be done
- Drug Development Insights from the 36th Annual J.P. Morgan Healthcare Conference
- Translational Medicine, Data Access and Pharmaceutical Innovation