Translate NAMs into clinical success
The FDA’s roadmap to reduce, refine, and replace animal testing signals a transformative shift in drug development. New Approach Methodologies (NAMs) are now central to delivering safe and effective medicines ethically, while improving the predictive value of preclinical testing.
NAMs are no longer a future promise but are the standard the industry is moving toward. Evotec makes NAMs work for you by uniting advanced in vitro models, high-throughput automation and in silico prediction to deliver insights that regulators trust and innovators depend on. With over 30 years of leadership in drug discovery and development services, Evotec leverages Cyprotex and its global network of scientific excellence to help partners reduce animal testing, accelerate development timelines, and bring safer, more effective medicines to patients worldwide.
What are NAMs?
NAMs or New Approach Methodologies represent a modern paradigm in drug discovery and safety assessment. The US FDA, OECD, and other regulatory bodies recognize NAMs as innovative tools designed to reduce or replace traditional animal testing, while delivering insights that are more predictive of human biology. NAMs encompass a range of technologies designed to model human biology more faithfully:
- In vitro systems that probe the complexities of hepatotoxicity, nephrotoxicity, and cardiotoxicity with high mechanistic detail.
- In silico models and AI-driven simulations that forecast pharmacokinetics, off-target activity, and toxicity before the first patient is exposed.
- High-throughput, high-content platforms that merge automation with multiplexed analysis.
- Advanced physiological models, such as 3D cultures and organ-on-chip platforms, that replicate the dynamic environment of human tissues.
Together, these methodologies provide what traditional animal testing often cannot: insights that are not only faster and scalable, but intrinsically human-relevant.
Why do NAMs matter now?
The case for NAMs has never been stronger. Regulatory bodies such as the FDA and OECD are actively guiding industry toward non-animal, human-centric methods. The FDA’s Modernization Act 2.0 underscores this momentum, signaling confidence in alternatives that improve predictivity and safety. The emergence of NAMs reflects both a scientific awakening and a regulatory shift. The life sciences community is reimagining how we evaluate medicines and chemicals. At the same time, the industry faces mounting pressure to reduce late-stage clinical failures, to cut timelines and costs and to respond to growing societal expectations for animal-free testing. NAMs answer all these needs.
Yet implementing NAMs is not trivial. They require not only technical expertise but also rigorous validation, regulatory alignment, and seamless integration into existing pipelines. This is where Evotec leads.
What are the benefits of adopting NAMs into the drug development strategy?
Implementing NAMs requires technical expertise, rigorous validation, regulatory alignment, and seamless integration into existing pipelines. This is where Evotec leads. Our approach supports:
- Reduced Regulatory Risk
Our longstanding experience with NAMs and in silico approaches means that safety packages developed through our platforms are already aligned with the FDA's evolving expectations, reducing the risk of regulatory delays or requests for additional studies. - More Predictive Human Outcomes
By application of our well-established human-relevant systems rather than relying solely on animal models, our approach provides deeper insights into how compounds will behave in human patients—the ultimate goal of any safety assessment. - Accelerated Development Timelines
Implementation of NAMs may allow for more rapid safety evaluations and identify potential issues earlier in the development process when they can be addressed more efficiently and cost-effectively.
Evotec NAMs Resource
Blogs
- FDA's New Roadmap to Reducing Animal Testing Validates Evotec's Approach to Safety Assessment >
- Toxicogenomics and AI: A Breakthrough in DILI Prediction >
- Cardiotoxicity Risk Assessment using AI/ML and In Vitro Assays >
- Advances in Cardiotoxicity Prediction Using Transcriptomics and Machine Learning >
- Improving Drug Safety With Transcriptomics >
- Drugging the Gut Microbiome >
- MRP Transporters and Statin-Induced Myopathy: an additional consideration to inhibition of BCRP, OATP1B1 & OAT3 for statin DDIs >
- Important Considerations for Choosing the in vitro Cell Test System for Correct Identification of BCRP Substrates >
- CYP Induction: The Journey from Drug Discovery to IND >
- New Early Stage Genotoxicity Screening Approach for Food Additives >
Webinars
- The Future of Toxicology Prediction by Paul Walker >
- New Approaches in Toxicology by Chris Strock >
- Transcriptomics: The Future of Toxicology Prediction by Paul Walker >
- Drugging the Gut Microflora To Reduce Drug Toxicity by Anna Kerins >
- Integrating Mitochondrial Toxicity Screening Into Early Drug Discovery by Julie Eakins >
- PK Prediction for Early Drug Discovery by Simon Thomas >
- Transcriptomics in 3D Cellular Models by Alicia Rosell-Hidalgo >
- New Approaches in Toxicology by Ruth Roberts >
- New Approaches in Toxicology by Takafumi Takai >
- New Approaches in Toxicology by Monday Ogese >
- Fast and Easy In Vitro Tox in PanHunter >
- hiPSC High-Throughput Screening Platform for Teratogenicity Hazard Identification by Henrik Renner & Bastian Zimmer >
Publications
- Real-World Applications of AI/ML in Drug Discovery >
- Making Safety Decisions for a Sunscreen Active Ingredient Using Next-Generation Risk Assessment >
- Harnessing Conformational Drivers in Drug Design >
- Improved Predictive Power in Cardiac Risk Assessment >
- Prediction of Functional and Structural Cardiotoxicants Using hiPSC Cardiomyocytes >
- Filling a Nick in NIK >
- Transcriptomics Brings New Era of Toxicology Prediction >
- The Future of MEA in Early Toxicology Assessment >
- DDUp: 3D Cell Models >
- Machine Learning Using Omics Data >
- https://www.evotec.com/sciencepool/mechanistic-understanding-of-telithromycin-and-simvastatin-acid-ddi >
- Ensuring Bioanalytical Quality in a Highly Automated HT-ADME Laboratory >
- Improving the Prediction of Mitochondrial Toxicity >
- Strategies to Minimize the Risk of DILI >
- Importance of Metabolism, PK and Toxicity in Drug Design >
- DDUp: Toxicology Prediction via Panomics >
- Shining a Spotlight on the Mighty Mitochondria >
Presentations
- Using in vitro 3D Cell-Based Models to Detect Tissue Specific Toxicity >
- The Importance of 3D Neuronal Microtissues for Safety Testing >
- Methods to Investigate In Vitro Skin Metabolism >
- Transporter DDI: An Evaluation of Approaches and Methodology >
- Are TripleTOFs the Future for HT-ADME/Toxicity Screening? >
- Deciphering the Clinical DDI Between Atazanavir and Rosuvastatin >
- Understanding the Mechanism Behind Pilocarpine Neurotoxicity >
- Mitobiogenesis: A Key Mechanism in Drug-Induced Toxicity >
- Detecting Cardiotoxicity of Chronically Exposed Drugs Using MEA >
Posters
- Toxicogenomics and AI: A Breakthrough in DILI Prediction >
- Phenotypic Screening – Application of Cell Painting >
- Cell Painting: Application in Safety/Tox Screening >
- Choosing the Optimal Model for in vitro Neurotoxicity Assessment >
- Measuring the Local Extracellular Action Potential (LEAP) with MEA to Enhance Prediction of Cardiotoxicity >
- Genotoxicity Risk Assessment During Lead Optimization Phase in Pharmaceutical Drug Development >
- Predicting Cardiac Hypertrophy using 3D Microtissues >
- High-Content Screening and High-Throughput RNA Sequencing Using hiPSC-CMs for the Assessment of Functional and Structural Cardiotoxicity >
- High-Content Imaging for the Detection of Compound Reactive Metabolite Formation and Cytotoxicity >
- Ultrafast LC-MS for HT-ADME Analysis in Drug Discovery >
- Validation of a Serum-Free Approach to Facilitate the Development of High-Throughput Immunogenicity Screening Assays in vitro >
- Optimization of a Rat DRG Neurite Outgrowth Assay for Peripheral Neuropathy Prediction >
- A Comprehensive Approach Using in vitro Assays to Detect and Identify Mechanism of Mitochondrial Toxicity >
- The in vitro Kidney: Developments in Nephrotoxicity >
- In vitro 3D Heart: Microtissue Model for Understanding Cardiotoxicity >
- Strategies in In vitro Mitochondrial Toxicity Assessment >
- Comparing Basic Static Models for Predicting Clinical CYP3A4 Induction Risk >
- Screening for Drug-Induced Effects on Cholesterol Metabolism >
- New Insights Into Cardiotoxicity Prediction >
- Addressing the Challenges of Non-Specific Binding in a HT-ADME Environment >
- Use of Quantitative LC-MS/MS Methods to Compare Conventional Blood Collection and Microsampling in Non-Human Primate >
- The Benefits of Stable Labelled Glutathione for Reactive Metabolite Screening >
- Determining Chromatographic Hydrophobicity Index Using LC-TOF >
- Dual Detection of Functional and Structural Cardiotoxicity >
- Echo MS in High Throughput ADME >
- Understanding the Hepatotoxicity of Fasiglifam (TAK-875) >
- Evaluating Pharmacology and Neurotoxicity Using MEA >
- Detecting Vanoxerine Arrhythmia in Human iPSC-Derived Cardiomyocytes >
- Unravelling the Mechanism of TAK875 DILI >
- Ultra-Fast LC-MS/MS in DMPK Screening >
- Application of a Low Intrinsic Clearance Assay in Drug Discovery >
- Neural Activity of the Muscarinic Receptor Agonist Pilocarpine >
- Multiparametric Human Liver Models for Predicting DILI >
- The Role of Mitochondrial Toxicity in DILI Prediction >
- Mass Defect Filtering for Metabolite Soft Spot ID >
- Comparison of MEA and Neurite Outgrowth for Determining CNS Liability >
- Pilocarpine Neurotoxicity Using MEA >
- Inhibitor Preincubation Time on Human OATP1B1, P-gp and BCRP >
- Mechanisms of Mitochondrial Toxicity - An In-Depth Analysis Using in vitro Assay >
- HµREL Co-Culture Model for Low Clearance >
- HCS Approach for Detection of Genotoxic Aneugens and Clastogens >
- Investigating Rosuvastatin DDI Prediction >
- Comparing in vitro Mitochondrial Toxicity Assays >
- DILI Prediction Using Human 3D Models, HCS and Concentration Normalization >
- New Approaches in Cardiotoxicity Prediction >
- Detecting BMS-986094 Cardiotoxicity Using Chronic Multiplatform Assay >
- Understanding BMS-986094 Cardiotoxicity Using Human iPSC-Derived Cardiomyocytes >
Servicesheets
- AI Drug Discovery >
- Diverse Modalities >
- Cell Painting in Toxicology >
- Mitochondrial Toxicity (Seahorse) >
- PK Prediction >
- Immunotoxicity >
- Bioactivation Driven Toxicity Assay >
- Drug-Induced Liver Injury (DILI) >
- Nuclear Receptor Activation >
- Safety Prediction and Transcriptomics >
- 3D Hepatotoxicity >
- HCS of 3D Microtissues >
- Skin sensitization >
- Diverse Modalities >
- eCiphr®Neuro-Human >
- eCiphr®Neuro >
- eCiphr®Cardio >
- Metabolite Profiling and Identification >
- Drug Transporter Inhibition >
- Microsomal Stability >
- Drug Transporter Substrate Identification >
- CellCiphr® Premier >
- Cell Stress Panel >
- High Throughput ADME >
- Mitochondrial Oxidative Stress >
- Drug Drug Interaction (DDI) >
- Discovery Bioanalysis >