For decades, women have been excluded from clinical research, but AI-powered digital twinning is helping make trials safer, more inclusive, and more representative of real patients.

For Karen Yeo, innovation is not just about technology, but how it is used to change things for the better.

Yeo (pictured top left) is senior vice president of client and regulatory strategy at Certara, a biosimulation company focused on “transforming drug development for good”, which since 2014, has supported 90 per cent of novel FDA drug approvals.

Yeo and her team are using AI to make scientific research more inclusive by creating digital twins, matching the characteristics of a real patient with their digital counterpart to predict optimal dosing.

Women, particularly pregnant women, have typically been excluded from clinical trials for decades.

In 1977, following the thalidomide crisis of the 1950s and 1960s, the US FDA introduced guidelines preventing women of childbearing age from participating in many studies to avoid possible risks to pregnancies.

This was eventually lifted by the FDA in 1993, and the same year, the National Institutes of Health issued its Revitalisation Act, which required the inclusion of women and minorities in federally funded clinical research.

But while these changes opened the door to more representative trials, pregnant women continued to be excluded due to safety and ethical concerns.

“[Researchers] fear that exposing expectant mothers and their babies to experimental medicines could cause harm, so the default has been to leave them out,” Yeo tells Femtech World.

While “well-intentioned”, policies which prevented them from participating in clinical trials have contributed to “major gaps” in our understanding of how treatments work in women, and slowed down access to potentially life-saving treatments, leaving them facing “more risk, not less”, says Yeo.

“The result is a healthcare system where women are underserved,” she adds. “And both mothers and infants miss out on advances that could have been better supported through careful, inclusive trial design.”

The evidence gap

Excluding women from trials has delayed access to lifesaving therapies, and without the right evidence, clinicians often prescribe medications off-label during pregnancy with little understanding of how those drugs behave in women’s bodies.

For years, drug dosing was determined largely from small studies in men, even though women may metabolise and respond to drugs differently, Yeo explains.

“This gap has contributed to higher rates of adverse drug reactions in women and less clarity about the effectiveness of medicines in conditions unique to them, such as pregnancy and postpartum care,” she says.

Yeo points to the example of the antimalarial drug primaquine.

Pregnant and lactating women have typically been excluded from primaquine studies, leaving physicians without the right guidance.

Using biosimulation, Yeo and her team took the findings from a small clinical study conducted in breastfeeding women and were able to predict infant and newborn drug exposures through breast milk.

“The results provided evidence of safe dosing of primaquine in mothers, showing how model-informed methods can begin to close those gaps,” she adds.

Digital twins

By creating virtual representations of women’s physiology, including during pregnancy, biosimulation can predict how a drug will behave in these populations without exposing patients to any unnecessary risk.

This allows regulators and clinicians to access insights early and encourages trial sponsors to include women more confidently.

“Models can estimate how a drug transfers through breast milk, giving trial designers a foundation for guidance before patients are enrolled,” Yeo explains.

“This reduces ethical concerns by minimising direct risk while still advancing inclusive research.

“Over time, it shifts the approach from protecting women from research to protecting them through research.”

These digital twins start as virtual populations, representing typical physiological scenarios. But as more personalised clinical data become available in pregnant women, these can be replaced by digital twins.

These are digital replicas of individual patients reflecting real-time individual physiology.

Yeo says these AI-powered digital twins can provide a safe way to generate evidence in individuals that are difficult to study directly, such as pregnant women with preeclampsia.

While no model has yet perfectly captured human biology, Yeo says biosimulation has advanced to a point where it can reflect many of the complexities of women’s health.

“Virtual populations can account for physiological changes during pregnancy, such as altered metabolism, which can affect the exposure of the drug across each of the trimesters,” she says.

“Rather than oversimplifying, they provide a framework that can help inform decision-making for clinicians.

“As real-world and clinical trial results with personalised data become increasingly available, virtual populations can evolve into more complex digital twins.”

“Safeguards start with data”

But as with any AI-driven technology, poorly designed models or those that rely on narrow datasets risk reinforcing existing biases, rather than correcting them.

Yeo says “safeguards start with data”.

Certara emphasises reproducibility and clear documentation, allowing every assumption to be reviewed, and works closely with regulators, clinicians, and patient advocates.

“Trust grows through representation and openness,” she continues.

“Patients need to see that the data behind digital twins includes people like them, and that models continue to be refined with clinical and real-world evidence.

“Clear communication about how the models work and why they matter helps show that these tools are designed with patients in mind.

“By involving women directly, researchers can ensure the models address concerns that matter most, from pregnancy safety to postpartum care.

“When women see that their priorities influence science, they are more likely to trust and benefit from it.”

Accelerating innovation

Having more inclusive datasets will also accelerate innovation in femtech, ensuring developers design new diagnostic tools and therapies for women’s health based on representative science, while data and insights collected through biosimulation can potentially shorten the timeline to regulatory approval.

“Femtech innovation depends on evidence that reflects women’s realities,” says Yeo.

“Digital twins make this possible by generating early data where traditional studies fall short.”

According to Yeo, AI is already having an impact on diversity in clinical trials, with regulators encouraging trial designs that use biosimulation to fill data gaps.

She believes digital twins could become a standard part of trial planning to ensure underrepresented groups are included within the next five years.

“Inclusivity will take time, but the momentum is here. Each step toward more representative datasets and more confident dosing guidance brings us closer to equitable clinical research.”

So what does success look like? That depends on the stakeholder, says Yeo.

For Yeo and her team, it’s being able to generate virtual subjects that reflect the characteristics of the patients receiving the medicine, giving a more realistic view of how therapies perform in practice.

For regulators, it’s the ability to approve drugs with confidence that dosing and safety information work across diverse groups.

And for patients, it is the assurance that the evidence was generated with their needs in mind.

“Success is not about technology alone,” adds Yeo.

“But about the trust and outcomes it creates for people who were once excluded.”