New Approach Methodologies Are Here. Is the Industry Ready?

There's a shift happening in how safety and efficacy are evaluated across drug development, chemicals, and beyond, and it's well past the theoretical stage. New Approach Methodologies (NAMs)—the broad family of in vitro systems and computational models—have matured into working tools. The question of whether these technologies can deliver value has largely been answered. What organizations are grappling with now is harder and more practical: which tools to use, when to use them, and how to generate data that holds up when it matters most.

The Case for New Approach Methodologies

NAMs are a growing family of tools designed to evaluate safety, efficacy and biological risk in ways that are more human-relevant than conventional preclinical approaches. The category spans two main types:

• Complex in vitro models such as organoids, three-dimensional tissues and organ-on-chip platforms built from human cells
• Computational and in silico approaches that use mathematical models and data-driven methods to predict how biological systems respond to drugs, chemicals or other agents.

Used together, they form an ecosystem that can be tailored to specific questions, endpoints and decision contexts. Over the past several years, the field has seen a dramatic expansion in available platforms, from commercial off-the-shelf systems to academic innovations, giving researchers and developers more options than ever before.

The momentum behind NAMs reflects growing pressure across industries to reduce reliance on in vivo models while accelerating early-stage research. Traditional preclinical models, while deeply embedded in regulatory practice, are often slow, expensive and limited in their ability to predict human outcomes. NAMs respond directly to those pressures, delivering three key benefits that are reshaping how organizations approach safety and risk assessment.

Faster. Traditional preclinical studies are time-intensive by design. Building a study-ready cohort can take months, and long study durations push critical safety and efficacy decisions deep into the development timeline, past the point when course corrections can be made easily or cost-effectively. NAMs have the potential to compress that timeline significantly. Organoids and chip-based models can be established in weeks, and computational approaches can generate predictive insight faster still. The result is earlier decision-making: organizations can screen candidates, identify liabilities and prioritize development paths before committing to the lengthy, resource-intensive studies that come later.

More efficient. The cost of late-stage failure in drug development is staggering. When safety signals or efficacy gaps surface early, programs can be redirected or stopped before significant downstream investment has been made. NAMs are increasingly used to front-load that insight, reducing late-stage attrition, minimizing costly program resets and focusing in vivo studies where they are most informative. For organizations managing complex pipelines across multiple candidates, that efficiency compounds quickly.

More human-relevant. This is perhaps the most consequential benefit. Because NAMs are built from human cells and tissues, they capture molecular and functional responses that conventional models often miss, particularly for mechanisms of toxicity, metabolic pathways and target-specific biological effects that are known to translate poorly across species. A gut-on-a-chip that mimics peristalsis and includes commensal bacteria has the potential to provide fundamentally different insight into drug absorption than a static cell culture or an in vivo study. And as personalized medicine advances, patient-derived models are opening new possibilities for evaluating therapies against specific genetic backgrounds, a capability that population-level studies simply cannot replicate.

Taken together, these benefits have moved NAMs from a promising research direction to an increasingly essential part of how safety and risk are evaluated across sectors.

Where NAMs are Making an Impact

The case for NAMs isn't theoretical; it's being made in practice, across a widening range of scientific and regulatory contexts.

• In pharmaceutical development, NAMs support candidate screening, early toxicology assessment and efficacy signals for both small molecules and biologics, helping organizations make earlier go/no-go decisions before significant downstream investment.
• In chemical safety and toxicology, they're being used to evaluate hazards, dose-response relationships and mechanisms of toxicity for industrial chemicals, agrochemicals, environmental exposures and consumer products.
• In vaccine and therapeutic development, NAMs are providing human-relevant insight into immune responses, target engagement and biological effects that are difficult to capture through other means.

Two examples illustrate how far the field has come. Computational NAMs are now being used to predict drug-induced liver injury, one of the most common causes of late-stage drug failure, with a level of specificity that was not previously achievable. And in oncology, patient-derived organoids are already being used to guide individualized treatment decisions, offering a glimpse of what personalized medicine could look like when NAMs are fully integrated into clinical workflows.

Regulatory recognition is accelerating alongside the science. The FDA has been actively encouraging the submission of NAM-based nonclinical data, and recent legislative and policy changes have expanded the types of evidence that can support drug development and approval decisions. For organizations operating across multiple sectors, these signals matter: NAMs are no longer a scientific experiment. They are becoming part of how evidence is built and how decisions get made.

Moving NAMs from Promise to Practice

NAMs have reached an inflection point. The tools exist, the evidence is building and regulatory momentum is real. But realizing their full potential requires more than access to the right platforms. It requires the judgment to select methods that are truly fit for purpose, the rigor to generate data that holds up under scrutiny, and the experience to translate complex outputs into conclusions that decision-makers can trust and act on.

That's where the work gets harder. With hundreds of academic and commercial platforms now available—each designed to answer different questions under different assumptions—the risk of poor model selection is real.

At Battelle, we don't advocate for any single platform or methodology. Instead, we work with organizations to determine which combination of tools is appropriate for a given question, how to apply them with rigor and reproducibility, and how to build the weight of evidence that regulatory and funding decisions require. In a field where the toolkit is expanding faster than the guidance for using it, having an objective partner that can navigate the landscape, select the right tools and build the evidence that matters may be as important as the technology itself.

The promise of NAMs has always been about more than faster or cheaper science. It's about building a more human-relevant foundation for the decisions that ultimately determine which therapies reach patients—and which don't. That's a goal worth getting right.