We hear so much about how technology is transforming life as we know it — from autonomous vehicles to the likely prospect of space travel to Mars — so why then does the treatment of disease still represent one of the greatest unmet human needs?
Many of us have lived through the agonising experience of someone we love suffering from a disease that has no cure, and the realisation that if that disease is not a candidate for a blockbuster drug, there won’t be a medicine developed for it anytime soon.
In a world where so much has been reimagined by technology, you would be right to ask why there are approximately 9000 diseases that have no treatment, and why there are an estimated 300 million people suffering from rare diseases for which no treatment will ever be developed as long as the current economic model persists.
Couple that with the fact that we live in a time where our lives will likely extend well into our nineties, far beyond what we imagined when we were young. But while we may well live longer, we can also expect to spend our later years unwell. Eight out of ten elderly are affected by debilitating diseases such as cancer, Alzheimer’s and diabetes. This is already placing an unprecedented burden on families and society and the cost of developing new medicines continues to rise unabated.
I have witnessed my share of tech revolutions since arriving in Silicon Valley in the late 1980s, and I can tell you that this one, perhaps more than any before it, has the potential to bring about the change that we so desperately need in healthcare.
Artificial intelligence is augmenting human intelligence in ways we never thought possible, giving us superpowers to move beyond the limits of our intelligence to discover breakthrough treatments for diseases we have been unable to cure.
Artificial intelligence is already part of our everyday lives. We have it on our phones through Siri, we have it in our homes with Alexa and Google Home, and of course, in Spotify, Netflix and Amazon, as they make recommendations for the music we listen to, the films we watch and the purchases we make.
So why has technology not yet made a big impact in medicine? The simple explanation is that understanding the underlying cause of a disease, let alone finding a cure for it, is incredibly difficult. The human body is one of the largest known data systems, with over 37 trillion cells. It is the end result of millions of years of evolution and an infinite number of factors and permutations. The deeper we go in our understanding of the human body, such as sequencing the human genome, the more we realise we have so much left to uncover and understand.
And the problem is not just the complexity of biology, it’s that we, as humans, are limited by the amount of information we are able to absorb and process. To give you a sense of just how much information is out there, consider that in the biomedical domain alone, 10,000 new scientific papers are published every day. Add this to the millions of patents, chemical databases, and patient trials, and the countless data sources in the public domain, and you quickly realise that it simply isn’t possible for mere mortals to process all that information.
Last year, national health spending in the U.S. was $3.5 trillion. What drives this staggering figure is the fact that it now costs an average of $2.5B to take a new drug from discovery to FDA approval, a process that can take ten to fifteen years with a 97% probability of failure in clinic.
We haven't yet hit the steep arc of innovation and exponential advancement in biology and life sciences that we experienced in computer science and engineering because we've been limited by the progress of human understanding. Until now.
The rise of computational medicine is already giving us better tools to detect cancer, read radiology scans and diagnose medical conditions. Even Apple got into the game recently with its new Watch sporting your own personal electrocardiogram! But we're just at the beginning.
Artificial intelligence can help us uncover relationships between diseases and symptoms, drugs and their effects, patient endotypes (responders and non-responders) and much more. Relationships that would previously not have been uncovered due to the overwhelming volume of biomedical information and the inherent complexity of the terrain.
This is exactly what we’re trying to do at BenevolentAI. We have built the world’s only end-to-end computational and experimental platform for drug discovery that spans everything from data ingestion to clinical development. We are combining the power of computational medicine and advanced artificial intelligence with the principles of open systems and cloud computing to transform the way drugs are designed, developed, tested and brought to market.
Here’s how it works. The Benevolent platform is continuously ingesting and analysing unstructured and structured biomedical information, from academic papers to compound databases to clinical trials to scientific patents. This information is combined with deep learning to create the world’s leading bioscience knowledge graph, coupled with an automated platform for hypothesis generation and validation followed by the design, refinement and synthesis of molecules. This powerful engine is used by our scientists to find new ways to treat and target diseases and to personalise drugs for the patients that benefit most from them.
This isn’t just about running code on powerful computers. You can’t develop and test drugs in the cloud. That is why earlier this year we acquired a drug development facility in Cambridge, making us the only AI company that is able to take our own drugs from discovery to clinic.
But why do we need companies like BenevolentAI to usher in this new revolution? After all, why can’t today's major players in the pharmaceutical industry simply use AI to speed up drug development? I don’t believe that this revolution will be driven by the traditional drug discovery and development industry. If the pharmaceutical industry were to lead the AI revolution for medicinal discovery, it would be the first incumbent in history to disrupt from within.
Given the evidence, this is unlikely - Amazon, which became a trillion-dollar company recently, wasn’t invented by a retailer. Uber wasn’t invented by a limousine company. Netflix wasn’t invented by a TV network. LinkedIn wasn’t invented by a recruiting firm. We simply can’t rely on traditional incumbents to lead the revolution.
We have a unique way of working, that brings engineering and drug discovery together, to create something that’s radically more valuable than the sum of its parts. Experts across data science, machine learning, informatics, and engineering literally work side by side with drug discoverers, crossing traditional boundaries and breaking down knowledge silos.
And we are working on some of the most complex and challenging diseases, including ALS. ALS, or motor neuron disease, is a devastating disease that destroys the nervous system and currently has no cure. It is poorly understood and enormously complicated. There are 30 genes associated with this disease, but 85% of patients do not have any of those genes, making it incredibly difficult to find treatments that work across such diverse patient populations. Using our platform, we have recently reached an important milestone in identifying novel targets, and we are now collaborating with SITraN, a world-leading ALS treatment centre in Sheffield, to develop a compound we expect to take to the clinic within the next year.
Every day, we push the boundaries of artificial intelligence and machine learning to unlock the power of decades of research to understand the underlying cause of disease and develop new treatments for patients. But we know we can’t do this alone.
We aim to foster greater collaboration with scientists and to connect data from typically siloed, disease-specific entities to bring about the the large-scale innovation and scientific discoveries required to truly transform the industry. And what gets me really excited is that we are creating a new model for drug discovery and development that will dramatically increase the number of treatments brought to market and expand the knowledge base and insights available to scientists and researchers. The Benevolent platform unites biology, chemistry, data science and engineering, and operates with purpose.
In an age of unprecedented technological advancement, I believe it’s our obligation to find new ways to treat even the most challenging diseases, and I couldn’t be more proud to be part of the Benevolent team that is working towards that goal.
Originally posted on benevolent.ai and The Huffington Post.
