Calithera is running registered clinical trials on its products to study their safety, whether they're effective in patients with specific gene mutations, and how well they work in combination with other therapies. The company must collect detailed data on hundreds of patients. While some of its trials are in early stages and involve only a small number of patients, others span more than 100 research centers across the globe. "In the life-sciences world, one of the biggest challenges we have is the enormous amount of data we generate, more than any other business," says Behrooz Najafi, Calithera's lead information technology strategist. Calithera must store and manage the data while making sure it's readily available when needed, even years from now.

Calithera is running registered clinical trials on its products to study their safety, whether they're effective in patients with specific gene mutations, and how well they work in combination with other therapies. The company must collect detailed data on hundreds of patients. While some of its trials are in early stages and involve only a small number of patients, others span more than 100 research centers across the globe. "In the life-sciences world, one of the biggest challenges we have is the enormous amount of data we generate, more than any other business," says Behrooz Najafi, Calithera's lead information technology strategist. Calithera must store and manage the data while making sure it's readily available when needed, even years from now.

The fountain of eternal youth could come in technological form thanks to $100 million investment in a life sciences company. Juvenescence is working with drug developers and AI experts to create treatments and technologies to treat age-related diseases and to increase human longevity. The firm, set up by London City of London entrepreneur Michael Spencer, announced a total investment of $10 million from its founders. A further $10 million each will come from four cornerstone investors, including Grok Ventures, Mike Cannon-Brookes and Mr Spencer's private investment company. This brings the total to $165 Million that Juvenescence has raised in 18 months.

At long last, we seem to be on the threshold of departing the earliest phases of AI, defined by the always tedious "will AI replace doctors/drug developers/occupation X?" discussion, and are poised to enter the more considered conversation of "Where will AI be useful?" As I've watched this evolution in both drug discovery and medicine, I've come to appreciate that in addition to the many technical barriers often considered, there's a critical conceptual barrier as well – the threat some AI-based approaches can pose to our "explanatory models" (a construct developed by physician-anthropologist Arthur Kleinman, and nicely explained by Dr. Namratha Kandula here), our need to ground so much of our thinking in models that mechanistically connect tangible observation and outcome. In contrast, AI relates often imperceptible observations to outcome in a fashion that's unapologetically oblivious to mechanism, which challenges physicians and drug developers by explicitly severing utility from foundational scientific understanding. A physician examines her patient and tries to integrate her observations – what she sees, feels, hears, and is told – and what she learns from laboratory and radiological tests – sodium level, CT scans – to formulate an understanding of what's wrong with her patient, and to fashion a treatment approach. The idea is that this process of understanding of what's wrong and developing a therapeutic plan is fundamentally rooted in science.

A poster is seen at the Next forum, which was held by New York-listed Medidata earlier this month in New York. Artificial intelligence is playing a key role in new drug development and smart medical treatment by capturing, analyzing and activating data. It's a category of AI often underestimated by the public, even though it influences the health of millions of people. China, as well as the Asia-Pacific region, is regarded as fertile ground for technology firms working with drug developers, providing artificial intelligence and cloud services. For example, US-based Medidata, the biggest software firm in the life sciences industry, has 357 clients in the Asia Pacific, or about one-third of total client numbers.

IMAGE: The first drug-candidate compounds targeting age-related diseases discovered using AI are licensed for development. Jan.6, 2018, Baltimore, Maryland- Insilico Medicine, Inc. ("Insilico"), a Baltimore-based next-generation artificial intelligence (AI) company specializing in the application of deep learning for drug discovery, announces that Juvenescence.AI, its joint venture with Juvenescence Limited ("Juvenescence"), has licensed its first compound family for clinical development. This is one of five compound families that Juvenescence.AI is able to license each year under its license agreement with Insilico. "The selection of our first compound family is a landmark event for Juvenescence, and a broader comment on the potential of AI to transform the drug discovery and development industry" commented Jim Mellon, Chairman of Juvenescence. This deal is a result of a deep collaboration between the senior drug developers at Juvenescence and AI experts at Insilico and signifies a new era in drug discovery where highly sophisticated AI finds viable drug candidates.

On a recent Friday in Boston, Randell Sanders gave a nurse two samples of his blood, plus a sample of urine and saliva. Clinicians would test some of the samples to see how he is responding to treatment for pancreatic cancer. But samples also were sent to a lab where computers using artificial intelligence are changing the way pharmaceutical companies develop drugs. The idea is that machines, which are adept at pattern recognition, can sift through vast amounts of new and existing genetic, metabolic and clinical information to unravel the complex biological networks that underpin diseases. That, in turn, can help identify medications likely to work in specific patient populations, while simultaneously steering companies away from drugs that are likely to fail.