Beyond the Hype: The Realities and Roadblocks of New Approach Methodologies (NAMs)

Feb 16

New Approach Methodologies (NAMs) are often promoted as the future of toxicology and drug safety. But for experts at ToxStrategies, a BlueRidge Life Sciences Company, like Nigel Greene, Ph.D., and Grace Patlewicz, Ph.D., who’ve spent decades advancing these tools, the term “NAMs” itself is part of the problem. It’s not just misleading, it masks the deeper cultural and organizational challenges that continue to limit adoption.

The “New” in NAMs: A Misleading Label

Despite the buzz, many NAMs are far from new. They include long-established tools like:

QSAR (Quantitative Structure–Activity Relationship) models – dating back to the 1950s
Threshold of Toxicological Concern (TTC) – developed in the late 1990s
Read-across methods – first formally applied in the early 2000s

“These are mature technologies,” says Dr. Greene, who has worked in this field for over 30 years. “The key difference today is how they’re being applied and for what purpose.” Dr. Patlewicz agrees, questioning when, exactly, these methods will no longer be referred to as “new.”

The real issue? NAMs are a catch-all for a wide range of tools and approaches. This umbrella term flattens important distinctions and can undermine credibility with scientists, regulators, and decision-makers.

It’s Not the Tech. It’s the Culture.

Both Dr. Greene and Dr. Patlewicz agree: the biggest obstacles to broader NAMs adoption aren’t technical, they’re organizational.

Dr. Greene points to persistent skepticism within pharmaceutical project teams. “Even when senior leadership supports these tools, teams on the ground are often hesitant,” he explains. Many are reluctant to move away from traditional animal-based assays without a clear precedent.

Dr. Patlewicz adds that internal data management is another major barrier. At companies, she saw firsthand how fragmented, outdated data systems made it difficult to develop a strategic approach to NAMs. “When your historical data is on microfiche or in basic index files, it’s nearly impossible to design a rational implementation plan.”

Strategy Starts with Purpose

For organizations beginning their NAMs journey, the first step is clarity of purpose.

“What's the question you're trying to answer?” Dr. Greene asks. “Everything starts there.” Dr. Patlewicz underscores that the context of use determines the appropriate tools. A chemical registration in the EU requires a different approach than a U.S. EPA Pre-Manufacture Notification.

Both emphasize moving quickly but strategically. “If you're not thinking about this,” Dr. Patlewicz warns, “regulators will be ahead of you.”

Regulation Drives Direction, But Differs by Region

NAMs adoption is heavily shaped by regional regulatory frameworks:

In the EU, animal testing bans in cosmetics accelerated the adoption of non-animal methods. “That had a huge impact,” Dr. Greene notes.
In the U.S., the EPA has championed high-throughput screening through programs like ToxCast. The agency’s mandate to prove harm leads to a more proactive stance on new tools, compared to the FDA’s more conservative, safety-first approach.

Understanding these nuances is critical for global companies navigating compliance and innovation across markets.

The Future: AI, Data Literacy, and Responsible Use

With technologies like high-resolution imaging and multi-omics generating billions of data points, the field is evolving fast. Dr. Greene describes the pace as “incredibly fast,” but cautions that the volume of data is outpacing our ability to make sense of it.

Dr. Patlewicz sees a major opportunity—and risk—in the rise of AI. “There's going to be an explosion in AI applications,” she says, “but we need to be smart about how we build workflows.”

Both experts urge caution:

AI isn’t infallible – Dr. Greene stresses that algorithms lack ethics and LLMs (large language models) can “hallucinate.” Human oversight is essential.
Data literacy is non-negotiable – Dr. Patlewicz emphasizes that this does not mean everyone needs to become a programmer, but future toxicologists should be comfortable enough with data concepts to ask the right questions, understand analytical approaches, and critically evaluate computational outputs – whether they are doing the work themselves or collaborating with data specialists.

NAMs are no longer standalone tools. They are part of a larger, integrated system of predictive models and computational methods. To succeed, organizations need not only the right technology but the right people, structures, and mindset.

Moving Forward with Clarity and Confidence

Successfully integrating NAMs requires more than technical capability. It demands a shift in mindset, strategic alignment across teams, and a deep understanding of both regulatory expectations and data complexity. At BlueRidge Life Sciences, our cross-functional experts help clients navigate these challenges with scientific rigor and practical insight. If your organization is exploring how to implement or expand NAMs, we’re here to support you. Let’s talk about what success looks like for your team.

Frequently Asked Questions (FAQs)

Q. What qualifies as a NAM?

A. NAMs include any non-animal methods used to assess safety, such as computational models (e.g., QSAR), high-throughput screening, in vitro assays, and read-across approaches. Many of these have been in use for decades, even if they are now applied in new ways.

Q. Why haven’t NAMs been widely adopted if the science is sound?

A. The biggest barriers are cultural and organizational, not technical. Many teams are hesitant to move away from animal studies without regulatory certainty or internal precedent.

Q. Are NAMs accepted by regulatory agencies?

A. Yes, but acceptance varies by region and application. The EU has been more aggressive in mandating NAMs, particularly in cosmetics, while U.S. agencies like the EPA are building strong capabilities. Understanding the context of use is key to aligning with regulatory expectations.

Q. How can my organization get started with NAMs?

A. Start by clearly defining your goals and the specific question you want to answer. From there, build a strategy that integrates appropriate tools, data infrastructure, and cross-functional collaboration.