As new approach methodologies (NAMs) burst onto the scene amid the wider shift away from animal testing, biotech and pharma companies are increasingly harnessing these models to better guide drug development.
In a keynote session at the 2026 AngloNordic conference, held on 23 April in London, Merck KGaA’s chief science and technology officer, Laura Matz, shared the company’s ambitions to replace the majority of its animal testing protocols with newer preclinical models over the next decade.
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Matz’s comments come as global regulators look to move away from traditional animal testing models – instead encouraging developers to use newer, more “human-centric” approaches with better predictive power.
In a panel discussion at the conference, experts discussed the growing role of NAMs, including organs-on-a-chip, computational methods and organoids – which some say are emerging as the next frontier in preclinical safety and efficacy testing. While all the panellists expressed their belief in these approaches, they say that developers must carefully consider how to implement NAMs effectively.
Using human-relevant models
With technology evolving at a rapid pace, drug discovery is becoming more efficient than ever before.
When looking for the next big therapy, David Apelion, CEO of British biotech Theolytics, noted that the use of human-relevant tissue could be the key to greater success.
In the case of Theolytics, which is developing immunotherapies for ovarian cancer, the team uses fresh tumours with an intact microenvironment as a model, which closely represents a patient seeking treatment in the real world. “That kind of relevant component in preclinical selection will reduce the failure rate dramatically,” he said.
When discussing the potential of NAMs in this setting, Orr Inbar, co-founder and CEO of clinical trial simulation company QuantHealth, said that they are currently having the greatest impact when used to model out efficacy, specifically in fields like oncology and cardiovascular disease.
When employing these models, however, Chris Floyd, head of neuroscience at pharmaceutical consultancy tranScrip, cautioned that developers must look closely at why they are running a model in the first place, and what question it will answer. “If you’re not learning something from the model that’ll move the programme forward, or you’re not able to interpret the results, it’s not a very valuable use of your time or resources,” Floyd commented.
Merck KGaA Healthcare’s clinical AI lead, Harel Kotler, echoed Floyd’s sentiments, adding that experiments should be carefully designed around the key aspects you want to achieve, as the industry can increasingly produce large amounts of information from a single experiment.
When employing new technologies in drug discovery and development, Floyd stressed the importance of caution: “We must be careful not to swap an expensive model we understand for an expensive one we don’t.
Developers should understand how they will use a model and how it’ll impact decision making.”
