SUPPORTING THE CHALLENGE OF NITROSAMINE DRUG SUBSTANCE RELATED IMPURITIES ANALYSIS
Brochures and specifications | 2023 | WatersInstrumentation
Long-term human exposure to N-nitrosamines, known mutagenic and probable carcinogens, poses significant health risks. Regulatory agencies worldwide have mandated stringent control of nitrosamine drug substance-related impurities (NDSRIs) since 2018 to ensure patient safety and prevent drug recalls.
Effective monitoring of NDSRIs requires highly sensitive analytical methodologies capable of detecting low-level impurities in complex pharmaceutical matrices.
This work presents a suite of fit-for-purpose LC-MS/MS assays developed to quantify various NDSRIs across multiple drug products, including diltiazem, labetalol, mycophenolate mofetil, propranolol, amitriptyline, argatroban, and cholestyramine formulations. The aim was to deliver robust and reproducible methods meeting regulatory thresholds and facilitating routine quality control.
Each assay employed ultra-high-performance liquid chromatography (UPLC) separations combined with tandem quadrupole mass spectrometry. Key methodological elements include sample extraction to enhance recovery, optimized column chemistries for selective retention, and targeted multiple reaction monitoring (MRM) transitions for specific NDSRIs. An advanced acquisition mode, RADAR scan, was integrated to simultaneously collect MRM and full-scan data, enabling real-time matrix interference assessment without loss of sensitivity.
All methods achieved low limits of quantification (0.01–0.3 ppm) and detection (0.002–0.015 ppm), with signal-to-noise ratios exceeding 70 at LOD levels. Linear calibration ranges covered regulatory relevant concentrations (down to low ppb levels). Spiked recovery rates consistently fell within 70–120%, demonstrating method accuracy across different drug matrices. The RADAR scan approach effectively identified and mitigated matrix interferences, improving method robustness.
These tailored assays provide pharmaceutical laboratories with reliable tools for monitoring NDSRIs to comply with regulatory guidelines. The high sensitivity and selectivity support early detection of impurity formation during manufacturing and storage, facilitating proactive quality assurance and safeguarding patient health.
The presented LC-MS/MS assays, leveraging advanced UPLC separations and RADAR-enhanced acquisition, offer robust, sensitive, and reproducible solutions for quantifying nitrosamine drug substance-related impurities. Implementing these methods strengthens quality control frameworks and supports regulatory compliance.
No external references were provided in the source document.
LC/MS, LC/MS/MS, LC/QQQ
IndustriesPharma & Biopharma
ManufacturerWaters
Summary
Importance of Topic
Long-term human exposure to N-nitrosamines, known mutagenic and probable carcinogens, poses significant health risks. Regulatory agencies worldwide have mandated stringent control of nitrosamine drug substance-related impurities (NDSRIs) since 2018 to ensure patient safety and prevent drug recalls.
Effective monitoring of NDSRIs requires highly sensitive analytical methodologies capable of detecting low-level impurities in complex pharmaceutical matrices.
Study Objectives and Overview
This work presents a suite of fit-for-purpose LC-MS/MS assays developed to quantify various NDSRIs across multiple drug products, including diltiazem, labetalol, mycophenolate mofetil, propranolol, amitriptyline, argatroban, and cholestyramine formulations. The aim was to deliver robust and reproducible methods meeting regulatory thresholds and facilitating routine quality control.
Methods and Instrumentation
Each assay employed ultra-high-performance liquid chromatography (UPLC) separations combined with tandem quadrupole mass spectrometry. Key methodological elements include sample extraction to enhance recovery, optimized column chemistries for selective retention, and targeted multiple reaction monitoring (MRM) transitions for specific NDSRIs. An advanced acquisition mode, RADAR scan, was integrated to simultaneously collect MRM and full-scan data, enabling real-time matrix interference assessment without loss of sensitivity.
Used Instrumentation
- Waters ACQUITY UPLC H-Class Plus System
- Waters Xevo TQ-S micro and Xevo TQ-S Cronos tandem quadrupole mass spectrometers
- Columns: Atlantis Premier BEH C18 AX, ACQUITY UPLC BEH C18, Symmetry C8, ACQUITY UPLC HSS T3
- RADAR scan acquisition mode for combined MRM and full-scan MS analysis
Main Results and Discussion
All methods achieved low limits of quantification (0.01–0.3 ppm) and detection (0.002–0.015 ppm), with signal-to-noise ratios exceeding 70 at LOD levels. Linear calibration ranges covered regulatory relevant concentrations (down to low ppb levels). Spiked recovery rates consistently fell within 70–120%, demonstrating method accuracy across different drug matrices. The RADAR scan approach effectively identified and mitigated matrix interferences, improving method robustness.
Benefits and Practical Applications
These tailored assays provide pharmaceutical laboratories with reliable tools for monitoring NDSRIs to comply with regulatory guidelines. The high sensitivity and selectivity support early detection of impurity formation during manufacturing and storage, facilitating proactive quality assurance and safeguarding patient health.
Future Trends and Potential Applications
- Expansion to additional nitrosamine impurities and emerging drug substances
- Integration of high-resolution mass spectrometry for non-targeted impurity screening
- Automation and high-throughput workflows to increase laboratory efficiency
- Machine learning-driven method optimization for rapid assay development
Conclusion
The presented LC-MS/MS assays, leveraging advanced UPLC separations and RADAR-enhanced acquisition, offer robust, sensitive, and reproducible solutions for quantifying nitrosamine drug substance-related impurities. Implementing these methods strengthens quality control frameworks and supports regulatory compliance.
Reference
No external references were provided in the source document.
Content was automatically generated from an orignal PDF document using AI and may contain inaccuracies.
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