A cognitive digital twin is an AI-driven representation of a real-world system. The objects being twinned can be mechanical such as vehicles, ships, and airplanes, or biological, such as organs and biological processes. It is the latter that is radically altering pharmaceutical research and may very well change the nature of clinical drug trials forever.
Traditional drug discovery is a long and complex process that can take years and millions and millions of dollars. The long and intensive process of bringing a new drug through all phases of clinical trials and to market starts with recruiting the right candidates and then proceeds through many steps and phases of testing the drugs vs. placebos in the candidates that have been recruited for the trial.
Finding those patients is one of the most time-consuming aspects of the process. But that is all changing thanks to AI and, specifically, cognitive digital twin (CDT) technologies.
Cognitive digital twins behave virtually the same way, statistically, as their physical counterparts, which makes them ideal for the powerful ability of AI to assimilate massive amounts of data and make remarkably accurate predictions.
Digital twins have been used quite effectively for monitoring health and providing preventive maintenance for some very highly complex systems, such as high-performance sports cars to military aircraft.
Now, they are changing the very landscape of drug discovery by modeling perhaps the most complex system of all organs and even complete human beings. For example, digital twins of patients are now being used to find ideal candidates in that all-important recruitment phase of a drug trial. The twin is created using AI algorithms and machine learning to create a “virtual patient” by leveraging data from previous clinical trials and from individual patient records. The model predicts how the patient’s health would progress during the course of the trial.
This kind of CDT technology is also being used to create “virtual patients” who are “stand-ins” for the control group – the ones getting a placebo – in the typical double-blind drug trial protocol. The digital twin patient predicts how that individual patient would react if they were given a placebo, essentially creating a simulated control group for a particular patient. Think of it as splitting yourself into two distinct exact copies of yourself, one given the actual drug and the other given the placebo as a control. This makes for an even more accurate control group than just splitting all those in the trial into two groups as in typical trials, because the control group is now exactly the same as the group getting the drug. The digital twin virtually eliminates any variance between the drug group and the placebo group that could be based on genetic, physical, and lifestyle differences between the two groups.
Furthermore, replacing or augmenting control groups with digital twins could help patient volunteers as well as researchers. Most people who join a trial do so, hoping to get a new drug that might help them when already-approved drugs have failed. But there’s a 50/50 chance they’ll be put into the control group and won’t get the experimental treatment. Replacing control groups with digital twins could mean more people have access to experimental drugs.
In Silico Research
And finally, another area where CDT technology is making a tremendous difference in drug discovery is in the emerging area of “in silico” research, where digital twinning is used to create so-called “organs on a chip.” Digital twins of the human heart, lungs, and other organs are already being used to hyper-accelerate drug discovery.
One of the promises of CDT is to make complete in silico drug trials from start to finish a reality. Early successes occurring now are paving the way to a time in the not-so-distant future where no humans, nor animals, not even a single living cell will be required for drug discovery — and yet the impact of any given therapeutic or treatment option on a targeted organ, system or even an individual cell can be perfectly charted.
Citadel and AI for Drug Discovery
AI and machine learning are having a tremendous impact on healthcare in America, from streamlining hospital operations, to improved diagnostics and more intuitive telemedicine applications. However, AI’s greatest impact will likely be in the way digital twins and other AI solutions are revolutionizing pharmaceutical research.
To that end, Citadel Discovery was launched in 2021 with the purpose of giving a kind of “open access” to the data and technology that will drive the future of pharma research streamlining and lowering the costs of drug discovery and biological research.
The costs of drug discovery continue to rise, with current estimates exceeding $2 Billion. Not to mention that bringing a drug successfully through all clinical trial phases takes, on average, 10-12 years in research and development. Artificial intelligence and machine learning in drug discovery hold the key to reducing these costs and timelines.
Rohit Mahajan is the President and Co-Founder of Citadel Discovery. He has a particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.
Citadel Discovery is dedicated to leveraging AI and MI for the purpose of democratizing access to the data and technology that will drive the future of biological exploration, drug discovery, and health technologies. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.