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Pharma startup Quris aims to use a 'patient on a chip' to target drug delivery

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Nobel Laureate, Aaron Ciechanover, is one of several notable names behind pharma startup Quris. The company aims to bring together artificial intelligence, the industry's vast knowledge of the human genome, and the concept of the "patient on a chip" to improve the effectiveness of drug delivery. Last month, the startup announced the launch of its AI platform and a $9 million seed round, led by Moshe Yanai (the mind behind EMC Symmetrix) and Dr. Judith Richter, and Dr. Jacob Richter (founders of Medinol, which has sold more than 2 million cardiovascular stents). Ciechanover, as well as Moderna cofounder, Robert Langer, are among Quris' noteworthy advisers. For decades, medical research has successfully cured cancer and treated rare diseases in innumerable quantities of mice – but has not done so as frequently in humans.


AI can be a shortcut to faster, cheaper drug development

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Exscientia's "Centaur Chemist" AI platform computationally sorts through and compares millions of potential small molecules, looking for a handful to synthesize, test and optimize in the lab before selecting a candidate for clinical trials -- all of which enabled the company to help get a cancer drug into trials in just eight months, compared to a more standard four to five years. Quris is working to speed the trial process by testing drugs on miniaturized organs and tissues on a chip that "represent the full genomic diversity of the potential patient population," notes Bentwich, which in turn generates data that can help train its AI platform to predict the clinical safety and efficacy of novel drugs. Lantern Pharma is partnering with digital health care company Deep Lens to use AI to match the right kind of novel molecule with the right patient profile for clinical trials for accelerated clinical trials. That AI-driven approach "can rescue hundreds of millions of dollars in prior drug development costs by ensuring it's being tested on a very specific patient platform," says Panna Sharma, Lantern Pharma's CEO. Exscientia's "Centaur Chemist" AI platform computationally sorts through and compares millions of potential small molecules, looking for a handful to synthesize, test and optimize in the lab before selecting a candidate for clinical trials -- all of which enabled the company to help get a cancer drug into trials in just eight months, compared to a more standard four to five years.


Pharmaceutical Artificial Intelligence in 2020: The Sector is Heating Up For Investments

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Artificial Intelligence (AI) has been a top trend in many industries lately, attracting massive media attention and investments. Over the last decade, this complex area of research has rapidly progressed from being a "resurrected cool technology from the past" to a full-blown driver of nothing less than a new industrial revolution -- a digital one. As of today, AI is widely commercialized in such applications as manufacturing robots, smart assistants (e.g. Siri), automated financial investing systems, virtual travel booking agents, social media monitoring tools, conversational bots, surveillance systems, online security systems, language translators, self-driving cars, and much more. In some industries, AI (including its many technologies and sub-disciplines, such as deep learning, recommender systems, and natural language processing), is becoming a standardized component rather than a cutting-edge innovation it once was. This rapid progress in AI adoption is also seen in the pharmaceutical industry -- not without caveats, however. Unlike "mainstream" use cases, like image recognition or spam email filtering, drug discovery research appears to be a much harder case for several reasons.


AI Pharma Deals: Bayer and AI Startups

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So far, the pharmaceutical industry has contributed more to the well-being of humanity than any other industry. But lately its business model has been under significant pressure since the return on R&D investment has dropped to its lowest level in decades (lack of innovation amid digital disruption, rapid technological advances and other issues such as lack of data reproducibility) and its public reputation in US and around the world (anti vaccine movement in Europe) is worse than ever. This worrisome mix of little growth potential and low reputation is the main reason why investors are increasingly worried, not to mention that the current drug development process needs a big dose of digital innovation to deal with its messy data. As a matter of fact, Stefan Oelrich member of the Board Management of Bayer AG, President Pharmaceuticals, wrote in an article -- that the title perfectly summarises the AI pharma situation "Artificial Intelligence - When we Suddenly Know What we Don't Know" -- the following: "As we open the first doors in this unknown land we start to discover how much more is out there for our entire pharmaceutical value chain spanning from research to product supply. I expect AI to help us know what we have not known so far. Artificial Intelligence will become instrumental in our search for new medicines to better serve patients around the world as we leverage Science For A Better Life".


AION Labs' Challenge To AI Drug Development Innovators

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Mati Gill's exposure to the power of pharma collaboration can't be overstated. During his 11 years as COO of the Global Legal Group, and later, head of government affairs, corporate & international markets at Teva Pharmaceuticals, he was exposed to virtually every aspect of the business. When Teva made a concerted effort to build its corporate innovation strategy to strengthen its development platforms and pipeline, he was an undisputed pick to lead the exercise in support of Teva R&D for the Israeli pharma titan. As he helped make inroads with Israeli's academic and emerging life sciences ecosystems, a promising opportunity began to reveal itself and became the focus of his work: The roles of computational biology and artificial intelligence (AI) in drug discovery and development. Gill found the concept nascent among the next generation of innovators but hampered by the pharmaceutical industry at large.