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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.
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When it comes to effectiveness of machine learning, more data almost always yields better results—and the healthcare sector is sitting on a data goldmine. McKinsey estimates that big data and machine learning in pharma and medicine could generate a value of up to $100B annually, based on better decision-making, optimized innovation, improved efficiency of research/clinical trials, and new tool creation for physicians, consumers, insurers, and regulators.
Where does all this data come from? If we could look at labeled data streams, we might see research and development (R&D); physicians and clinics; patients; caregivers; etc. The array of (at present) disparate origins is part of the issue in synchronizing this information and using it to improve healthcare infrastructure and treatments. Hence, the present-day core issue at the intersection of machine learning and healthcare: finding ways to effectively collect and use lots of different types of data for better analysis, prevention, and treatment of individuals.
Burgeoning applications of ML in pharma and medicine are glimmers of a potential future in which synchronicity of data, analysis, and innovation are an everyday reality.
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Gauging a patient’s recovery status is tricky if you don’t know what they should be expected to recover to. Researchers are using data collected from patient-worn sensors, such as Apple Watch or Fitbit, to build a "digital twin" of baseline patient health information.
A digital twin is essentially like creating a backup of a patient’s physical state before a procedure, so providers know what to look for a patient to work towards in recovery, said Dr. Mohamed Rehman, a professor and clinician at Johns Hopkins All Children’s Hospital, who will explain the concept in a session at HIMSS20 on March 11.
Currently, data points such as steps, heart rate, and hours of sleep are used to monitor patients in a variety of settings, but Rehman said there are greater capabilities on the horizon.