Systemizing Read-Across: Building Confidence in Regulatory Science

After more than 25 years at the forefront of regulatory toxicology, Grace Patlewicz , Ph.D., is applying her expertise to new frontiers at ToxStrategies, a division of BlueRidge Life Sciences. Her career has spanned consumer product safety at Unilever, shaping the technical implementation of chemical legislation at the European Commission, devising regulatory strategy at DuPont, and leading research at the U.S. EPA. Today, she brings this broad experience to bear on a familiar challenge: making chemical safety assessment more systematic, transparent, and efficient.

Building the Framework for Read-Across

Grace is widely regarded as a leader in read-across—a method of predicting toxicity using data from ‘similar’ chemicals. She has written the technical guidance documents relied upon by regulatory agencies internationally, developed the EPA Generalised Read-Across (GenRA) software tool, and trained scientists around the world in how to apply the method responsibly.

But for Grace, the work isn’t just about tools, it’s also about improving trust and consistency. “Read-across traditionally relies on expert judgment,” she says. “I have wanted to codify the thinking behind it, to make it more objective and reproducible.” That includes identifying the key decision points in a read-across argument and integrating different data streams, such as high-throughput screening data and toxicokinetic information, to strengthen scientific justification.

Translating Complexity into Practical Insight

Throughout her career, Grace has bridged the gap between scientific innovation and real-world application. At Unilever, she built predictive models to reduce the use of animal testing in product development. At the European Commission, she helped write the technical guidance that underpins REACH. At DuPont, she faced the operational challenges of regulatory submissions firsthand—experience that shaped her pragmatic, solutions-oriented approach.

“One thing I’ve learned is that even good guidance can be difficult to implement,” she says. “It’s not just about knowing the science, it’s also about understanding what’s workable in practice.”

Advancing the Field, Without Reinventing the Wheel

Grace remains driven by a deep curiosity and a commitment to progress. She’s particularly interested in how AI and data integration tools can help scientists work more efficiently, automating the tedious parts of information gathering so they can focus on interpretation and decision-making. But she also cautions against over-reliance on automation. “We need better data literacy across the field. AI is a tool, not a solution. You have to understand what it’s doing and why.”

For organizations looking to implement NAMs, her advice is clear: start now and be strategic. “Define what you're trying to achieve. Understand the regulatory context. Choose tools that align with your goals. And don’t wait, because regulators are already moving.”

At BlueRidge Life Sciences, we help teams move from complexity to clarity, drawing on decades of experience to support fit-for-purpose, science-driven strategies.

Let’s talk about how to put NAMs to work in your organization, before you’re asked why you haven’t.