Human Ingenuity and Artificial Intelligence

"I thought we should live in a better world. One in which we can accurately detect and treat genetic diseases."
"Drug development has traditionally been a serendipitous activity, like throwing a stick into a tree and seeing if an apple falls. This worked in the early days, but the low-hanging fruit is gone and this traditional approach is leading to more failures, greater delays and rising costs."
"Only eight percent of discovered drugs are deemed by regulatory agencies to be worthy of approval. To turn these numbers around, we need to face the wonderful fact that for the first time in human history, our ability to collect data on our biology has outpaced our ability to interpret and act on it."
"Researchers can sketch out roughly what the solution should look like, then use AI to fill in the missing pieces, using large amounts of data and computer systems that can learn from examples."
"Want  to discover a new drug target for a new patient mutation? We have a tool for that. Want to design a drug that will address the problem? We have an AI tool for that, too."
Brendan Frey, professor of engineering and medicine, University of Toronto

AI and R&D start-ups raised more than $156 million in the first quarter of 2018 alone. Getty Images

"What is now a niche is poised to grow into perhaps the leading subsector in BioPharma in the next two to three years, one that will have the greatest transformational impact on the industry."
Deep Learning Analytics, Virginia

"Traditionally, drugs were designed for one target, reflecting a lock and key model where they're developed to bind to a single protein."
"However, a growing body of research has shown that drugs often have hundreds of off-target interactions [i.e. they don't just bind to a single protein], leading to unanticipated and unwanted side effects. Our goal is to examine all possible proteins in the body that a drug can bind to."
"Our goal is to decrease drug discovery time down to two years. It's ambitious, but we believe by collaborating with pharma and augmenting the capabilities of scientists with breakthrough technologies, we can achieve it."
"Diseases that currently have no treatment, such as Alzheimer's, and those where treatment is arduous and unreliable, like cancer or diabetes, may move into the realm of treatable or even curable much sooner than they would under the old model."
"AI is most suitable for systems that have an abundance of data and it's insufficient at creating novel hypotheses that add to that data -- AI can't 'think outside the box' on its own."
Naheed Kurji, president and CEO, Cyclica, Toronto

"The larger goal is to get a fuller understanding of how patients are doing with respect to their symptoms and response to therapies as they go through their daily lives."
"Eventually, using AI in drug development will improve outcomes for patients, as better therapies reach patients sooner -- which is good news in a world where 'patients are waiting'."
"Using AI, we have an opportunity to diagnose early, predict the course of a disease and thus treat the disease early, potentially before symptoms manifest."
Allan Miranda, head, Johnson & Johnson Innovation JLABS, Toronto

Artificial Intelligence holds out promise to accelerate and improve so many aspects of life and communications, and it appears that the promise inherent in its potentially powerful attributes will also advance the discovery of vital new pharmaceutical combinations of drugs meant to drive the future of medicine's treatment of chronic medical conditions, disease and other health complications to enable people to live longer and more comfortable lives.

Researchers are now committed to the use of Artificial Intelligence  in the field of drug discovery and innovation. The company Deep Genetics, at the forefront of this emerging data-driven approach to medical breakthroughs in Canada hopes to be able to find itself in possession of formulae and a way forward in its quest to make life better for people afflicted with disease and illnesses. At the present time the development of a new drug represents a prolonged, costly process.

An average of ten to twelve years is how long it can take in new medicine development, with a cost of approximately $2 billion, with no absolute guarantees after the time and funding has been deployed, that the drug will go beyond clinical trials to success in treatment, with efficacy proven to match expectations. AI holds out promise to be realized as the most promising technology now available, to automatically analyze and apply vast data collections, far beyond what the human mind can grapple with.

Skepticism abounded in the hoped-for practical value of AI being used for the purpose in mind. Until Deep Learning Analytics concluded that AI and Research and Development start-ups were able to raise over $156 million in the first quarter of 2018. In addition to reducing the cost of developing drugs, AI "might be useful in helping to treat or even cure patients, and to reduce pain and suffering", according to Russ Greiner, a fellow at the Alberta Machine Intelligence Institute and professor at the University of Alberta who has studied AI for over 38 years.

Toronto-based Cyclica was named one of the top 20 AI drug development companies in the world last year. It is now partnering with pharmaceutical companies including Merck, Bayer, Eurofarma and WuXi, using AI along with biophysics, statistics and big data to enable learning how drugs might act on multiple targets and disease pathways. According to Dr. Kurji its CEO, his company's research could also aid scientists to a fuller understanding of why it is that some drugs work differently for various people, in anticipation of development new, more precise drugs.

Deep Genomics is absorbed at the present time in developing therapies for rare metabolic, ophthalmologic and neurodegenerative disorders. Clinical trials are expected to begin in 2020. Janssen Research & Development is in the process of exploring the practicality of wearable technologies to gather continuous, real-world data, identifying factors to predict disease progression or relapse.

All these researchers are aware that AI is not a prospective 'magic bullet'. But the goal of its use in accelerating research remains undiminished with the realization that human ingenuity must still play a vital part in the process of its use.

"Instead of an eight percent success rate, a 13-year development time and a billion-dollar price tag, we're aiming for a 50 percent success rate, a four-year development time and a 50-million-dollar price tag. That would be a game changer", said Brandan Frey.

AI is the best technology available for automatically analyzing and applying a mounting treasure trove of data.   Mike Faille