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There has been a tremendous amount of growth in the pharmaceutical industry in recent years and the outlook continues to be positive for 2020. In fact, the industry is expected to exceed a $1 trillion by the following year. This is partly due to thousands of compounds that are currently in the latter stages of clinical development, coupled with hundreds of new products with approvals anticipated in 2020 and beyond. This happens to be a level of pharmaceutical production that is not customary and has not occurred for about a decade. Let’s take a close look at 10 trends that will transform the pharmaceutical industry in 2020.
Read more: Trends That Will Transform The Pharmaceutical Industry Outlook in 2020
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Last December, a conference of biologists gathered in Cancun, Mexico, to review a shocking finding. DeepMind, Alphabet’s artificial intelligence lab and sister company to Google, had beat a roomful of biologists in a contest to predict the shape of a protein based on its genetic code.
That might not sound monumental, but understanding the way proteins fold into three-dimensional shapes is crucial to helping create drugs, which often fight disease by latching onto proteins and altering the way they work in the body. DeepMind was able to predict these proteins’ shapes with significantly more accuracy than the many esteemed academics and professionals at the conference.
“It dawned on me that this is a field that people have been working in for decades,” Mohammed AlQuraishi, a biologist and researcher at Harvard who participated in the contest, told Vox. “The fact that a new group could come in and do so well, so quickly—I felt bad because it demonstrated the structural inefficiency of academia.”
It was startling moment for the drug discovery business: Could an outsider with little experience in biology really barge in and do science better than the experts?
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Daphne Koller is best known as the cofounder of Coursera, the open database for online learning that launched in 2012. But before her work on Coursera, she was doing something much different. In 2000, Koller started working on applying machine learning to biomedical data sets to understand gene activity across cancer types. She put that work on hold to nurture Coursera, which took many more years than she initially thought it would. She didn’t return to biology until 2016 when she joined Alphabet’s life science research and development arm Calico.
Two years later, Koller started Insitro, a drug discovery and development company that combines biology with machine learning. “I’m actually coming back to this space,” she says.
There’s a lot of hope that artificial intelligence could help speed up the time it takes to make a drug and also increase the rate of success. Several startups have emerged to capitalize on this opportunity. But Insitro is a bit different from some of these other companies, which rely more heavily on machine learning than biology
By contrast, Insitro has taken the time to build a cutting-edge laboratory, an expensive and time-consuming project. Still, having equal competency in lab-based science and computer science may prove to be the winning ticket. Though only two years old, Insitro has already caught the attention of old-guard pharmaceutical companies. Last year, the company struck a deal with pharmaceutical giant Gilead to develop tools and hopefully new drug targets to help stop the progression of non-alcoholic fatty liver disease (NASH). The partnership netted Insitro $15 million with the potential to earn up to $200 million for each drug target.
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“It all started in the 1950s with the famous mathematician Alan Turing, who asked the question – can machines think?” – Pieter Peeters, Janssen Research & Development.
In a recent interview with Technology Networks, Pieter Peeters, Leader of High Dimensional Biology and Discovery Data Sciences Group at Janssen Research & Development, discusses the evolution of artificial intelligence (AI), and how it can be used to discover, develop and test new drugs.
“Artificial intelligence is a discipline in computer science that deals with building smart computer algorithms that mimic the things we typically associate with the human brain,” explains Peeters.
“Personally, I don’t like the term artificial intelligence, because I think we still have a way to go before machines can be said to have real intelligence, but we are heading in that direction.”
So, what has changed since the 1950s, and how is AI now integrated into many industries, including drug discovery? Peeters explains that AI’s ability to influence these industries, is due to developments in three main areas – data volume and accessibility, hardware, and the algorithms themselves.