Nitrosamines in Medicines: From Risk to Control

A practical guide for pharma teams to assess, test, and control nitrosamine risks—clear workflow, analytical tactics, limits, and lifecycle governance.

Nitrosamines in Medicines: From Risk to Control
Written by TechnoLynx Published on 29 Sep 2025

Why nitrosamines remain a critical issue

The presence of nitrosamines in medicines has been a major concern since the first recalls in 2018. These compounds are classified as probable human carcinogens, and even trace amounts can raise questions about patient safety. Regulators across the world, including the European Medicines Agency and the Food and Drug Administration, have issued strict guidance to control the presence of nitrosamine impurities in both active substances and finished products (European Medicines Agency, 2025; U.S. Food and Drug Administration, 2023).

The issue is not limited to one therapeutic class. It spans small molecules, complex generics, and even some biological products. The challenge lies in the many possible routes for nitrosamine formation during synthesis, formulation, packaging, and storage. These risks demand a structured, science-based approach that covers the entire product lifecycle.

Regulatory expectations and acceptable intake limits

Authorities have set acceptable intake limits for individual nitrosamines based on lifetime cancer risk models. These limits are extremely low, often in the nanogram per day range. For example, the AI for N-nitrosodimethylamine (NDMA) is 96 ng/day for chronic exposure. When nitrosamine levels exceed these thresholds, companies must act quickly to protect patients and maintain compliance (European Medicines Agency, 2025).

The EMA and the FDA both require marketing authorisation holders to perform risk assessments, confirmatory testing, and implement corrective actions. These steps are not one-off exercises. They form part of a continuous monitoring process because new information, new suppliers, or changes in manufacturing can alter the risk profile.

Read more: Barcodes in Pharma: From DSCSA to FMD in Practice

How nitrosamines enter the picture

Understanding the chemistry is key. Nitrosamine formation often involves secondary or tertiary amines reacting with nitrosating agents such as nitrite under acidic conditions. This can happen in the API synthesis route, during recovery of solvents, or through contaminated raw materials.

Excipients can also contribute. Some contain trace nitrite, which can react with amines in the formulation. Packaging is another source. Certain lidding foils, inks, and adhesives may release nitrite or nitrogen oxides into the headspace of a sealed pack.

Even storage conditions matter. Heat and humidity can accelerate reactions that create nitrosamine impurities over time.

A structured approach to risk assessment

The first step is a thorough risk evaluation. Map every plausible pathway for nitrosamine formation across the manufacturing process and supply chain.

Consider starting materials, reagents, catalysts, and recycled solvents. Review excipient specifications for nitrite content. Audit packaging components for potential migration.

Once the map is complete, prioritise scenarios by likelihood and patient exposure. High-risk cases move to confirmatory testing. This testing must use sensitive and selective methods. For volatile nitrosamines like NDMA, GC–MS with headspace sampling is common.

For non-volatile species, LC–HRMS or LC–MS/MS is preferred. Detection limits should be well below the AI to provide confidence in results.

Read more: Pharma’s EU AI Act Playbook: GxP‑Ready Steps

Controlling nitrosamine levels in practice

When testing confirms the presence of nitrosamines, companies must act. The most effective strategy is to remove the root cause.

This could mean switching to low-nitrite excipients, tightening pH control, or replacing a reagent. In some cases, adding scavengers or antioxidants can help. Packaging changes may also be necessary, such as moving to foils with proven low migration.

Process adjustments should be documented and justified with data. Each change must show a measurable reduction in nitrosamine levels. End-product testing alone is not enough. Regulators expect a control strategy that prevents formation rather than relying on detection after the fact.

Risk does not end after implementation. Companies must trend results over time to confirm that controls remain effective. This includes routine testing of high-risk products, periodic checks on excipient lots, and monitoring of packaging suppliers. Trending helps detect early signals of drift and supports decisions on shelf-life or storage conditions.

The EMA’s Q&A guidance stresses that marketing authorisation holders remain responsible for ongoing vigilance. The FDA echoes this in its updates, reminding firms that presence of nitrosamine impurities can occur even after years on the market if processes or materials change (European Medicines Agency, 2025; U.S. Food and Drug Administration, 2023).

Read more: Cell Painting: Fixing Batch Effects for Reliable HCS

Documentation that stands up to inspection

Regulators expect clear, concise evidence. Keep three core files ready:

  • A live risk assessment with dates and rationales.

  • Analytical reports with method details, validation data, and raw results.

  • A control strategy that links each mitigation to its effect on nitrosamine levels.

When acceptable intake limits cannot be met immediately, companies may apply for temporary measures under less-than-lifetime exposure principles. These cases require strong justification and a clear timeline for corrective action.

Common pitfalls to avoid

One frequent error is assuming that absence of nitrite equals zero risk. Trace amounts can vary between lots and still drive nitrosamine formation under the right conditions. Another mistake is overlooking packaging.

Migration from foils or inks has caused several confirmed cases. Finally, some firms treat the initial assessment as a one-time task. In reality, this is a lifecycle obligation that demands periodic review.

Read more: Explainable Digital Pathology: QC that Scales

Why this matters for global compliance

Both EMA and FDA align on the fundamentals: science-based risk assessment, sensitive testing, and proactive control. Other agencies, supported by the World Health Organization, follow similar principles to protect patients worldwide. For companies, this means a harmonised approach can satisfy multiple markets and reduce duplication. For patients, it means safer medicines and fewer recalls.

How TechnoLynx can help

TechnoLynx works with pharmaceutical companies to design and implement robust nitrosamine control programmes. We start with a detailed risk map tailored to your processes and materials. Our team develops and validates advanced analytical methods for trace detection, including LC–HRMS and GC–MS workflows.

We also build trending dashboards that link results to suppliers, batches, and packaging lots. Every solution comes with audit-ready documentation and a lifecycle monitoring plan. This ensures compliance with EMA, FDA, and other global requirements while keeping your products safe for patients.

References

  • European Medicines Agency (2025) Nitrosamine impurities: guidance for marketing authorisation holders. Available at: https://www.ema.europa.eu/en/human-regulatory-overview/post-authorisation/pharmacovigilance-post-authorisation/referral-procedures-human-medicines/nitrosamine-impurities/nitrosamine-impurities-guidance-marketing-authorisation-holders (Accessed: 26 September 2025).

  • European Medicines Agency (2025) Q&A on the CHMP Article 5(3) opinion on nitrosamine impurities. Available at: https://www.ema.europa.eu (Accessed: 26 September 2025).

  • U.S. Food and Drug Administration (2023) Control of nitrosamine impurities in human drugs. Available at: https://www.fda.gov (Accessed: 26 September 2025).

  • World Health Organization (2023) Medication safety and nitrosamine risk management. Available at: https://www.who.int (Accessed: 26 September 2025).

  • Image credits: Freepik

CUDA vs ROCm: Choosing for Modern AI

CUDA vs ROCm: Choosing for Modern AI

20/01/2026

A practical comparison of CUDA vs ROCm for GPU compute in modern AI, covering performance, developer experience, software stack maturity, cost savings, and data‑centre deployment.

Best Practices for Training Deep Learning Models

Best Practices for Training Deep Learning Models

19/01/2026

A clear and practical guide to the best practices for training deep learning models, covering data preparation, architecture choices, optimisation, and strategies to prevent overfitting.

Measuring GPU Benchmarks for AI

Measuring GPU Benchmarks for AI

15/01/2026

A practical guide to GPU benchmarks for AI; what to measure, how to run fair tests, and how to turn results into decisions for real‑world projects.

GPU‑Accelerated Computing for Modern Data Science

GPU‑Accelerated Computing for Modern Data Science

14/01/2026

Learn how GPU‑accelerated computing boosts data science workflows, improves training speed, and supports real‑time AI applications with high‑performance parallel processing.

CUDA vs OpenCL: Picking the Right GPU Path

CUDA vs OpenCL: Picking the Right GPU Path

13/01/2026

A clear, practical guide to cuda vs opencl for GPU programming, covering portability, performance, tooling, ecosystem fit, and how to choose for your team and workload.

Performance Engineering for Scalable Deep Learning Systems

Performance Engineering for Scalable Deep Learning Systems

12/01/2026

Learn how performance engineering optimises deep learning frameworks for large-scale distributed AI workloads using advanced compute architectures and state-of-the-art techniques.

Choosing TPUs or GPUs for Modern AI Workloads

Choosing TPUs or GPUs for Modern AI Workloads

10/01/2026

A clear, practical guide to TPU vs GPU for training and inference, covering architecture, energy efficiency, cost, and deployment at large scale across on‑prem and Google Cloud.

GPU vs TPU vs CPU: Performance and Efficiency Explained

GPU vs TPU vs CPU: Performance and Efficiency Explained

10/01/2026

Understand GPU vs TPU vs CPU for accelerating machine learning workloads—covering architecture, energy efficiency, and performance for large-scale neural networks.

Energy-Efficient GPU for Machine Learning

Energy-Efficient GPU for Machine Learning

9/01/2026

Learn how energy-efficient GPUs optimise AI workloads, reduce power consumption, and deliver cost-effective performance for training and inference in deep learning models.

Accelerating Genomic Analysis with GPU Technology

Accelerating Genomic Analysis with GPU Technology

8/01/2026

Learn how GPU technology accelerates genomic analysis, enabling real-time DNA sequencing, high-throughput workflows, and advanced processing for large-scale genetic studies.

GPU Computing for Faster Drug Discovery

GPU Computing for Faster Drug Discovery

7/01/2026

Learn how GPU computing accelerates drug discovery by boosting computation power, enabling high-throughput analysis, and supporting deep learning for better predictions.

The Role of GPU in Healthcare Applications

The Role of GPU in Healthcare Applications

6/01/2026

GPUs boost parallel processing in healthcare, speeding medical data and medical images analysis for high performance AI in healthcare and better treatment plans.

Data Visualisation in Clinical Research in 2026

5/01/2026

Learn how data visualisation in clinical research turns complex clinical data into actionable insights for informed decision-making and efficient trial processes.

Computer Vision Advancing Modern Clinical Trials

19/12/2025

Computer vision improves clinical trials by automating imaging workflows, speeding document capture with OCR, and guiding teams with real-time insights from images and videos.

Modern Biotech Labs: Automation, AI and Data

18/12/2025

Learn how automation, AI, and data collection are shaping the modern biotech lab, reducing human error and improving efficiency in real time.

AI Computer Vision in Biomedical Applications

17/12/2025

Learn how biomedical AI computer vision applications improve medical imaging, patient care, and surgical precision through advanced image processing and real-time analysis.

AI Transforming the Future of Biotech Research

16/12/2025

Learn how AI is changing biotech research through real world applications, better data use, improved decision-making, and new products and services.

AI and Data Analytics in Pharma Innovation

15/12/2025

AI and data analytics are transforming the pharmaceutical industry. Learn how AI-powered tools improve drug discovery, clinical trial design, and treatment outcomes.

AI in Rare Disease Diagnosis and Treatment

12/12/2025

Artificial intelligence is transforming rare disease diagnosis and treatment. Learn how AI, deep learning, and natural language processing improve decision support and patient care.

Large Language Models in Biotech and Life Sciences

11/12/2025

Learn how large language models and transformer architectures are transforming biotech and life sciences through generative AI, deep learning, and advanced language generation.

Top 10 AI Applications in Biotechnology Today

10/12/2025

Discover the top AI applications in biotechnology that are accelerating drug discovery, improving personalised medicine, and significantly enhancing research efficiency.

Generative AI in Pharma: Advanced Drug Development

9/12/2025

Learn how generative AI is transforming the pharmaceutical industry by accelerating drug discovery, improving clinical trials, and delivering cost savings.

Digital Transformation in Life Sciences: Driving Change

8/12/2025

Learn how digital transformation in life sciences is reshaping research, clinical trials, and patient outcomes through AI, machine learning, and digital health.

AI in Life Sciences Driving Progress

5/12/2025

Learn how AI transforms drug discovery, clinical trials, patient care, and supply chain in the life sciences industry, helping companies innovate faster.

AI Adoption Trends in Biotech and Pharma

4/12/2025

Understand how AI adoption is shaping biotech and the pharmaceutical industry, driving innovation in research, drug development, and modern biotechnology.

AI and R&D in Life Sciences: Smarter Drug Development

3/12/2025

Learn how research and development in life sciences shapes drug discovery, clinical trials, and global health, with strategies to accelerate innovation.

Interactive Visual Aids in Pharma: Driving Engagement

2/12/2025

Learn how interactive visual aids are transforming pharma communication in 2025, improving engagement and clarity for healthcare professionals and patients.

Automated Visual Inspection Systems in Pharma

1/12/2025

Discover how automated visual inspection systems improve quality control, speed, and accuracy in pharmaceutical manufacturing while reducing human error.

Pharma 4.0: Driving Manufacturing Intelligence Forward

28/11/2025

Learn how Pharma 4.0 and manufacturing intelligence improve production, enable real-time visibility, and enhance product quality through smart data-driven processes.

Pharmaceutical Inspections and Compliance Essentials

27/11/2025

Understand how pharmaceutical inspections ensure compliance, protect patient safety, and maintain product quality through robust processes and regulatory standards.

Machine Vision Applications in Pharmaceutical Manufacturing

26/11/2025

Learn how machine vision in pharmaceutical technology improves quality control, ensures regulatory compliance, and reduces errors across production lines.

Cutting-Edge Fill-Finish Solutions for Pharma Manufacturing

25/11/2025

Learn how advanced fill-finish technologies improve aseptic processing, ensure sterility, and optimise pharmaceutical manufacturing for high-quality drug products.

Vision Technology in Medical Manufacturing

24/11/2025

Learn how vision technology in medical manufacturing ensures the highest standards of quality, reduces human error, and improves production line efficiency.

Predictive Analytics Shaping Pharma’s Next Decade

21/11/2025

See how predictive analytics, machine learning, and advanced models help pharma predict future outcomes, cut risk, and improve decisions across business processes.

AI in Pharma Quality Control and Manufacturing

20/11/2025

Learn how AI in pharma quality control labs improves production processes, ensures compliance, and reduces costs for pharmaceutical companies.

Generative AI for Drug Discovery and Pharma Innovation

18/11/2025

Learn how generative AI models transform the pharmaceutical industry through advanced content creation, image generation, and drug discovery powered by machine learning.

Scalable Image Analysis for Biotech and Pharma

18/11/2025

Learn how scalable image analysis supports biotech and pharmaceutical industry research, enabling high-throughput cell imaging and real-time drug discoveries.

Real-Time Vision Systems for High-Performance Computing

17/11/2025

Learn how real-time vision innovations in computer processing improve speed, accuracy, and quality control across industries using advanced vision systems and edge computing.

AI-Driven Drug Discovery: The Future of Biotech

14/11/2025

Learn how AI-driven drug discovery transforms pharmaceutical development with generative AI, machine learning models, and large language models for faster, high-quality results.

AI Vision for Smarter Pharma Manufacturing

13/11/2025

Learn how AI vision and machine learning improve pharmaceutical manufacturing by ensuring product quality, monitoring processes in real time, and optimising drug production.

The Impact of Computer Vision on The Medical Field

12/11/2025

See how computer vision systems strengthen patient care, from medical imaging and image classification to early detection, ICU monitoring, and cancer detection workflows.

High-Throughput Image Analysis in Biotechnology

11/11/2025

Learn how image analysis and machine learning transform biotechnology with high-throughput image data, segmentation, and advanced image processing techniques.

Mimicking Human Vision: Rethinking Computer Vision Systems

10/11/2025

See how computer vision technologies model human vision, from image processing and feature extraction to CNNs, OCR, and object detection in real‑world use.

Pattern Recognition and Bioinformatics at Scale

9/11/2025

See how pattern recognition and bioinformatics use AI, machine learning, and computational algorithms to interpret genomic data from high‑throughput DNA sequencing.

Visual analytic intelligence of neural networks

7/11/2025

Understand visual analytic intelligence in neural networks with real time, interactive visuals that make data analysis clear and data driven across modern AI systems.

Visual Computing in Life Sciences: Real-Time Insights

6/11/2025

Learn how visual computing transforms life sciences with real-time analysis, improving research, diagnostics, and decision-making for faster, accurate outcomes.

AI-Driven Aseptic Operations: Eliminating Contamination

21/10/2025

Learn how AI-driven aseptic operations help pharmaceutical manufacturers reduce contamination, improve risk assessment, and meet FDA standards for safe, sterile products.

AI Visual Quality Control: Assuring Safe Pharma Packaging

20/10/2025

See how AI-powered visual quality control ensures safe, compliant, and high-quality pharmaceutical packaging across a wide range of products.

Back See Blogs
arrow icon