Collaborate with us
We work with leading partners across academia, industry, the scientific community and regulatory bodies to advance predictive safety science.
Pioneering novel predictive science
New Approach Methodologies
Home / R&D / Science and Technologies / NAMs
What are New Approach Methodologies?
New Approach Methodologies (NAMs) are innovative technologies, including advanced cell models, multiomics and AI, that model how a drug candidate interacts with human biology before it reaches the patient.
NAMs help predict safety and efficacy profiles so we can select drug candidates with the highest probability of clinical success for further development.
NAMs complement traditional approaches and offer human-relevant predictive accuracy to an extent that was not previously possible. As key tools in translational science, NAMs are transforming drug development by bridging the gap between laboratory research and clinical trials.
Why are New Approach Methodologies important?
As we develop ever more innovative medicines to target disease in new ways, their complexity continues to grow. We need predictive methods, including computational biology, cell-based assays and AI-powered drug discovery tools, that keep pace with this innovation.
NAMs offer a more human relevant way to predict safety and efficacy, with the potential to help us design better molecules and increase the probability that the right drug candidates progress to patients faster.
Predicting drug safety and efficacy at even earlier stages of drug discovery has the potential to help us bring new medicines for patients faster. NAMs connect novel, humanrelevant science with AI across our pipeline, helping us progress drug candidates with the most promising profiles. This is not a future ambition; it is how we make decisions today.
Stefan Platz, Senior Vice President, Clinical Pharmacology and Safety Science, R&D, AstraZeneca
New Approach Methodologies at AstraZeneca
Our NAMs capability is an integrated set of innovative technologies – including advanced cell models, multiomics and AI – that inform scientific decisions across our R&D pipeline. Spanning multiple organ systems and therapeutic areas, it is their advanced integration across drug discovery and development, not any single technology, that sets our approach apart. We use NAMs to inform molecule design and selection, optimise dosing before clinical trials, and evaluate drug candidates across multiple organ systems.
At AstraZeneca, NAMs are already reducing reliance on animal models in specific contexts, with this expected to grow as NAMs mature and gain broader regulatory acceptance.
Advanced cell models
Advanced cell models are sophisticated models of human biology used in laboratory research to test drug effects.
In 2D: cell layers
We use specialised human cells grown in two-dimensional cell layers, which enable us to rapidly screen large numbers of drug candidates. This provides early indication of how a potential medicine may interact with specific cell types.
In 3D: cell structures
Miniature three-dimensional structures, also known as organoids or spheroids, mimic the architecture of human tissue. For example, liver spheroids and intestinal organoids enable more accurate predictions of how these organs will respond to treatment.
Beyond 3D: mechanical forces
The most advanced models, often referred to as microphysiological systems or organ-on-a-chip, combine cells with fluidics flow and mechanical forces that mimic human biology, such as breathing motion in a lung chip or blood flow through bone marrow.
Multiomics and AI
Multiomics and AI capture, connect and draw insights from past and present research, enabling faster decisions on which drug candidates to progress toward clinical trials.
Multiomics
Multiomics captures how genes, proteins, and other molecular signals interact, providing a deep and multilayered view on the molecular level of how a drug candidate interacts with human biology.
Data
Safety and efficacy data from across our research, including advanced cell models, multiomics, animal studies, and decades of historical assessments, interconnected to compound predictive value.
Artificial intelligence
Finds meaningful patterns from previously siloed data sources and turns them into actionable predictions that inform which drug candidates to advance in development.
How we apply NAMs across R&D stages
Accelerating R&D with NAMs
NAMs are integrated from the earliest R&D stages, so the drug candidate with most potential can reach patients faster.
Right target
Before designing a medicine, we first identify what it should act on in the body.
NAMs help us confirm we have the right drug target by engaging it and evaluating the effect – so it is more likely to work for patients.
Right candidate
Once we know the target we enter drug design where NAMs help us test and optimise molecules – so we select candidates with the best possible properties.
Right safety profile
NAMs model human biology across multiple organs, so we can better predict safety and efficacy before a drug candidate reaches patients.
Clinical trials
Finally, NAMs combine human-relevant data with AI and predictive modelling, enhancing our ability to design clinical trials with the right dose, schedule and patients from the start.
The future of New Approach Methodologies at AstraZeneca
We already see NAMs outperform traditional methods in some areas. As NAMs mature further and gain regulatory acceptance, they will play an increasingly central role in how we generate evidence for the safety and efficacy profiles of potential future medicines. This positions us to design better molecules, faster, and develop future medicines more precisely, tailored to patients who need them most.
Jorrit Hornberg, Vice President, Global Head of Safety Sciences, R&D, AstraZeneca
AstraZeneca's latest updates in New Approach Methodologies
Collaborations
We are accelerating the adoption of human-relevant predictive science through collaborations across academia, technology developers, industry and regulators. These partnerships help build the evidence, standards and confidence needed to qualify and scale new approach methodologies (NAMs) across therapeutic areas.
Collaborations
- Innovation and Quality Consortium Microphysiological Systems Affiliate:
We contribute to this collaboration to advance the evaluation and application of microphysiological systems (MPS) through shared learnings, case studies and pre-competitive evidence generation alongside the US and global regulators. - European Federation of Pharmaceutical Industries and Associations AWA3RE Group:
Through this collaboration, we support the replacement, reduction and refinement of animal use (3Rs) and help advance the development and acceptance of NAMs through cross-industry partnership. - European Research Actions (ERA) on New Approach Methodologies (NAMs):
In this European Commission-led initiative we serve on the Steering Committee of Working Group 2, aimed at defining the criteria and validation pathways needed for regulatory acceptance of NAMs in the assessment of medicinal products. - International Organization for Standardization Microphysiological Systems Standards subcommittee on MPS and organ-on-chips:
This working group advances international standards for microphysiological systems, helping improve consistency, reproducibility and confidence in their use.
New Approach Methodologies (NAMs)
What are New Approach Methodologies (NAMs)?
New Approach Methodologies (NAMs) are innovative technologies that model how a drug candidate interacts with human biology. At AstraZeneca, they include advanced cell models, mathematical modelling, multiomics and AI. They are used in combination to build human-relevant evidence in drug safety and efficacy profiles, to advance development of targeted treatments and accelerate bringing novel medicines to patients. NAMs are an emerging part of translational science and an example of pharmaceutical innovation, sitting at the intersection of computational biology, precision medicine and AI-powered drug discovery.
How does AstraZeneca use NAMs?
At AstraZeneca NAMs are embedded across the R&D pipeline. Advanced cell models, multiomics and AI work together to help predict how drug candidates will interact with human biology early in drug discovery and development, informing drug candidate selection, optimising dosing before clinical trials, and building evidence in safety and efficacy profiles across multiple organ systems. The advanced integration of these innovative technologies supports clinical trial readiness across therapeutic areas.
How can NAMs help improve drug safety prediction?
NAMs generate human relevant mechanistic evidence continuously through drug discovery and development. This helps us understand drug candidate safety and efficacy profiles earlier, more precisely inform dose selection, and select better candidates to advance to clinical trials. This means patients have the potential to benefit from medicines, sooner.
How do advanced cell models help improve drug discovery?
Advanced cell models have the potential to improve drug discovery by providing human-relevant data on how drug candidates interact with specific tissues and organ systems earlier in development. This enables earlier scientific decisions on which drug candidates to advance in development, and informs more precise dose selection before clinical trials begin.
What is organ-on-chip technology?
Organ-on-chip technology, also known as a microphysiological system, models the environment of a human organ by incorporating features such as fluid flow, mechanical forces, and multiple cell types working together. These human biology models allow scientists to observe how medicines interact with cells that more closely mimic those in the human body, supporting predictive toxicology and reducing reliance on animal models in drug development.
How does AI help improve drug safety prediction?
Artificial intelligence in drug discovery finds meaningful patterns by integrating historical and newly generated safety and efficacy data, including from advanced cell models and multiomics. It turns the complex data into actionable insights which help scientists identify which drug candidates are most likely to succeed in the clinic. At AstraZeneca, we are building agentic AI that autonomously integrates data across sources to deliver unified, multi-organ safety profiles.
How is multiomics used in pharmaceutical research?
Multiomics captures how genes, proteins and other molecular signals interact, providing a multilayered view of how a drug candidate interacts with human biology. In pharmaceutical research, multiomics is used to build mechanistic evidence of drug safety and efficacy profiles, confirm that advanced cell models reflect real human biology, and strengthen the predictive power of our NAMs platform. It is a key tool in translational science and precision medicine.
Are you a clinical trial participant?
If you are looking for trial information, please visit our clinical trials website.
Innovate with us
Join us as we pioneer human-relevant science that predicts how medicines will behave in patients. Be part of the next generation of predictive drug development.
Veeva ID: Z4-86265
Date of preparation: October 2026