AI-generated digital organs and humans are transforming clinical trials, driving innovation in medical research and treatment development.
What Are Digital Twins?
A digital twin heart beats like a human heart but lacks blood or a physical body. This heart model tests cardiovascular devices like stents and valves. Adsilico, the company behind this technology, creates multiple heart models using AI and large datasets.
These AI-generated hearts represent differences in age, weight, gender, blood pressure, and ethnicity. Clinical trials often overlook these differences, but digital twins address this gap. Device manufacturers can now conduct more inclusive and representative trials. “This approach captures patient diversity, making devices safer and more inclusive,” says Adsilico CEO Sheena Macpherson.
A 2018 investigation revealed that 83,000 deaths and 1.7 million injuries were linked to medical devices. Macpherson hopes AI-powered digital twins can reduce these figures. “Thorough testing requires more trials, but clinical trials are costly,” she says. Virtual testing reduces costs and increases testing scope. “A single virtual heart can be tested under varying blood pressures or disease progressions,” she adds.
Digital twins allow manufacturers to test underrepresented subgroups, like women and marginalized communities. Adsilico’s AI models use cardiovascular data and MRI and CT scans from consenting patients. The data helps create accurate digital models for precise device testing on diverse anatomies.
Testing involves inserting a virtual device into a digital twin heart within an AI simulation. Thousands of simulations can run quickly, unlike human and animal trials, which use hundreds of participants. This approach accelerates testing and broadens analysis.
Cutting Costs and Boosting Success
Drug manufacturers also embrace digital twins. Sanofi, a pharmaceutical company, aims to reduce testing time by 20% and increase drug success rates. Sanofi uses biological data from real people to create AI-simulated patients. These virtual patients increase trial diversity, improving trial relevance.
Sanofi’s AI models simulate drug properties and predict how drugs interact with human biology. The technology mimics real trials, offering early insights into drug performance. “With a 90% drug failure rate, a 10% success increase could save $100 million,” says Matt Truppo, Sanofi’s research head. Truppo notes that AI-powered digital twins could tackle complex diseases.
However, digital twins have limitations. Charlie Paterson from PA Consulting warns that AI models depend on their training data. Many datasets are outdated or lack diversity, introducing biases into simulations. To address this, Sanofi supplements internal data with external sources like health records and biobanks.
Despite these challenges, Adsilico’s Macpherson hopes AI digital twins will eliminate animal testing in clinical trials. “A virtual heart is closer to a human heart than those of dogs, cows, or pigs,” she says. This shift could lead to safer, more ethical medical research. AI-powered digital twins are making drug discovery faster, safer, and more inclusive.