Drug development has a data problem. Clinical trials are expensive, slow, and often fail because researchers cannot get enough of the right patients. Mantis Biotech thinks it has found a workaround: build virtual versions of human biology that can stand in for real people.
The company is developing what it calls “digital twins” — AI models that simulate how individual human bodies work, respond to drugs, and develop disease. These are not simple avatars or health dashboards. They are computational replicas trained on biological data, designed to predict how a specific patient type might react to a treatment.
Why Medicine Needs Synthetic Data
Real patient data is hard to come by. Privacy regulations limit access. Rare diseases affect too few people to build statistically meaningful datasets. Diverse populations are underrepresented in clinical research, which means drugs often reach the market without being tested on the people who will actually use them.
Mantis Biotech’s approach addresses this by generating synthetic patient data — information that behaves like real data but does not belong to any actual person. The company’s AI models learn patterns from existing biological datasets, then extrapolate to create realistic simulations of patients who do not exist.
This is not about replacing clinical trials entirely. It is about making the early stages of drug development faster and cheaper. If a pharmaceutical company can test a thousand virtual patients before recruiting a single real one, it can kill bad drug candidates earlier and focus resources on treatments more likely to work.
How Digital Twins Actually Work
A digital twin in this context is an AI model trained on multiple types of biological information — genomic sequences, protein interactions, metabolic pathways, clinical histories. The model learns how these systems connect and influence each other. When you feed it a hypothetical drug compound, it predicts the cascade of effects through the virtual body.
Mantis Biotech is not alone in this space. Companies like Unlearn.AI and Twin Health have been building similar technologies, though with different focuses. Unlearn targets clinical trial optimization, while Twin Health concentrates on metabolic conditions like diabetes. Mantis appears to be aiming for a broader platform that could apply across therapeutic areas.
The quality of these models depends entirely on the data used to train them. Garbage in, garbage out. This is where partnerships with hospitals, research institutions, and existing biobanks become critical. A digital twin is only as useful as its connection to real biological truth.
The Business Case for Healthcare Leaders
For CIOs and CTOs in healthcare and life sciences, digital twin technology creates several immediate considerations. First, it changes the economics of early-stage research. Simulation costs a fraction of physical experimentation, which could shift how R&D budgets are allocated.
Second, it raises questions about data infrastructure. Organizations that want to benefit from digital twin platforms will need clean, well-organized biological data to feed into them. If your patient records are scattered across incompatible systems, you are not ready for this technology.
Third, regulatory frameworks are still catching up. The FDA and other agencies are beginning to accept computational evidence in drug approval processes, but the rules are not settled. Companies that invest early in digital twin capabilities will also need to invest in regulatory strategy.
There are risks to watch. Models trained on biased or incomplete data will produce biased predictions. Synthetic data that looks realistic but misses critical biological nuances could lead researchers down expensive dead ends. The technology is promising, but it is not magic.
What This Means for You
If you lead technology or strategy at a healthcare organization, digital twins should be on your radar — not as a project to launch tomorrow, but as a capability to understand and prepare for. Start by auditing your biological data assets. Can they be structured, anonymized, and made available for computational use?
Watch how Mantis Biotech and its competitors progress through the next 18 months. Look for published validation studies that compare digital twin predictions against real clinical outcomes. That evidence will separate serious platforms from overhyped prototypes.
The companies that figure out how to blend synthetic and real-world data effectively will have a meaningful edge in drug development speed and personalized treatment design. The question is not whether this technology will matter, but how quickly it will mature — and whether your organization will be ready to use it.