Humans, deep media and CEOs of OpenAI often view drug discovery as the region where AI will have the biggest positive impact in the next decade. The first generation of AI drug discovery startups have not been delivered yet, but citing the belief that human CEO Dario Amodei is that the next generation “powerful AI can make these discoveries at least 10 times faster, which gives us 5-10 years of biology advancement in the next 50-100 years.”
If this Cambrian explosion did occur, we are not ready yet. Determining the order of magnitude of more candidate drugs is only part of the battle. Silicon Valley tends to focus on discovery and elimination development, but until we have viable alternatives, clinical trials remain the only way to bring these findings to patients.
Clinical trials are the most expensive and time-consuming part of drug development, costing hundreds of millions of dollars and taking up to ten years. Unless we find a way to accelerate clinical trials, we will eventually add a hundredfold drug candidate, but no more drugs attract patients who need them.
The good news is that AI is not only a tool for discovery. It can also change development, but this will require a fundamentally different type of intelligence. Unlike Discovery, you can’t just put in raw horsepower while experimenting and 10x output. Among too many stakeholders, the ecosystem is too fragmented. You need to support patients, healthcare professionals, and AI agents of trial sponsors through a complex and high-announced process.
Sponsors, websites and patients have their own challenges and goals. Sponsors hope to bring new drugs to market faster. The website hopes to provide more trials for patients. Patients want to make the best decisions for their health. Now, generated AI can solve the big problem of every stakeholder that was out of reach a year ago. Sponsors can use AI to design better protocols. The site can use it to identify qualified patients in all active trials. Patients themselves increasingly use chatbots to support their decisions and control their health.
However, AI is not enough to improve the personal experience in the silo. This approach always collapses in practice. The sponsors use AI to generate a list of dreams, which are historical data for decades, but find that some sites are not even interested in experimenting. The site applied for a trial that exactly suited its patients’ needs, only after three months they found out that they were not selected by the sponsor. Patients expressed interest in the trial and then waited for three weeks to successfully share their medical records with the site and find out if they were eligible to participate.
This is a six-legged game. A stakeholder cannot sprint and expects success. Sponsors, on-site and patient experiences are too interconnected; they need to synchronize operations to drive real medical progress. When everyone adopts the technology that is about to happen, there is no real change in AI, but when these AISs can actually communicate and coordinate with each other.
In the near future, we expect all stakeholders to have dedicated AI agents that can collaborate with each other without standardized APIs or integrations. That’s what it looks like. The study site maps each patient’s need to all available trials. They will automatically provide the right trial to the right patients at the right time. They join them within a few days and get personalized support to get them involved throughout the trial.
Imagine the large scale of thousands of sites around the world. Sponsors immediately see where each drug meets the needs of the real patient. They prioritize programs that have the highest demands that are not met and work with websites of qualified, interested patients around the world. The trials filled for weeks, not years.
Development has been around for a long time, but the reality is that you cannot be alone. It’s time to build AI agents that can collaborate across the entire research ecosystem to quickly translate drug breakthroughs into real patient impacts.
Photo: Yuuji, Getty Images
Kourosh Davarpanah is the co-founder and CEO of Inato. Under his leadership, Inato is building a platform that combines early planning, field selection and enrollment to make clinical trials more efficient and inclusive.
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