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 value question has largely been settled. 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 NAMs
NAMs are a growing family of tools designed to evaluate safety, efficacy and biological risk in more human-relevant ways than conventional preclinical approaches. The category spans two main types:
- complex in vitro models such as organoids, three‑dimensional tissues and organ‑on‑chip systems built from human cells; and
- computational and in silico approaches that use mathematical and data‑driven methods to predict biological responses.
Together, these approaches can be combined to address specific scientific questions and decision contexts.
The momentum reflects growing pressure across industries to reduce reliance on traditional models while accelerating early-stage research. While deeply embedded in regulatory practice, these models are often slow, expensive and limited in their ability to predict human outcomes. NAMs respond to those pressures by reshaping how organizations approach safety and risk assessment.
Faster
Traditional preclinical studies are time-intensive by design. Building a study can take months, pushing critical safety and efficacy decisions deep into the development timeline, often beyond the point where course correction is manageable. NAMs can significantly compress that timeline. Organoids and chip‑based models can be established in weeks, while computational tools can generate predictive insight faster still. The result is earlier screening, earlier identification of liabilities, and more informed prioritization before committing to resource‑intensive studies.
More efficient
The cost of late-stage failure in drug development is staggering. When safety signals or efficacy issues surface early, programs can adapt before major downstream investment. NAMs are increasingly used to front‑load insight, reducing attrition, minimizing resets and focusing in vivo studies where they are most informative. For organizations managing complex pipelines, these efficiencies compound 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 toxicity mechanisms and biological effects that translate poorly across species.
A gut‑on‑a‑chip that incorporates human tissue dynamics can provide insight into drug absorption that static cultures cannot. As personalized medicine advances, patient‑derived models are opening new possibilities for evaluating therapies against specific genetic backgrounds—capabilities population‑level studies 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.
Making an Impact
The case for NAMs isn’t theoretical; it’s being made in practice, across a widening range of contexts.
- In pharmaceutical and therapeutic development, NAMs support candidate screening, early toxicology and efficacy assessment, enabling earlier go/no‑go decisions.
- In chemical safety and toxicology, they are used to evaluate hazards, dose‑response relationships and mechanisms of toxicity across industrial, environmental and consumer exposures.
- 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.
Computational NAMs are now being used to predict drug‑induced liver injury—one of the leading causes of late‑stage failure—with levels of specificity not previously achievable. In oncology, patient‑derived organoids are already informing individualized treatment decisions, offering a glimpse of how NAMs could integrate into future clinical workflows.
Regulatory recognition is accelerating alongside the science. The FDA has encouraged the submission of NAM‑based nonclinical data, and recent legislative and policy changes have expanded the types of evidence that can support drug development decisions. NAMs are no longer experimental—they are becoming part of how evidence is built and evaluated.
From Promise to Practice
NAMs have reached an inflection point. The tools exist, the evidence is building and regulatory momentum is real. But impact depends on how—and when—they are used.
With hundreds of academic and commercial platforms available, poor model selection remains a real risk. Realizing the potential of NAMs requires fit-for-purpose methods, rigorous study design, and experienced interpretation that translates complex outputs into actionable conclusions.
At Battelle, we work with organizations to determine which combination of NAMs is appropriate for a given question, how to apply them with rigor and reproducibility, and how to build weight‑of‑evidence aligned with regulatory expectations. In a rapidly expanding and unevenly guided landscape, objective expertise can be as critical 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 decisions that determine which therapies reach patients—and which don’t. That’s a goal worth getting right.
The editorial staff had no role in this post’s creation.
