LC/MS, LC/MS/MS, LC/QQQ
IndustriesPharma & Biopharma
ManufacturerWaters
Significance of the Topic
The presence of nitrosamine impurities in pharmaceutical products has emerged as a critical safety concern due to their probable carcinogenicity and stringent regulatory limits at sub-nanogram levels. Metformin, a widely prescribed anti-diabetic drug, has been recalled after exceeding accepted nitrosamine thresholds, underscoring the need for highly sensitive analytical methods for routine quality control and compliance.
Objectives and Study Overview
This application note describes the development and validation of an ultra-sensitive UPLC-MS/MS method for the quantification of nine nitrosamine impurities (NDMA, NDEA, NEIPA, NMOR, NDIPA, NDPA, NMPA, NMBA, NDBA) in metformin drug substance. Key performance metrics include limits of detection and quantitation, linearity, reproducibility, and accuracy in a high-matrix API environment.
Methodology
Standard nitrosamine mixtures were prepared in methanol and diluted to define LOD, LOQ, and calibration ranges. Metformin API samples (20 mg/mL) were filtered and analyzed directly. Chromatographic separation used an Atlantis Premier BEH C18 AX column (2.1 × 100 mm, 1.7 µm) at 40 °C with a 0.4 mL/min gradient of 5 mM ammonium formate with 0.1% formic acid in water and methanol. Detection was performed in APCI positive ion mode using MRM transitions optimized for each nitrosamine.
Instrumentation
- ACQUITY Premier UPLC System
- Xevo TQ Absolute Tandem Quadrupole Mass Spectrometer
- Atlantis Premier BEH C18 AX Column
- MassLynx v4.2 and TargetLynx Software
Main Results and Discussion
Column screening confirmed optimal separation of nitrosamines from the high-concentration metformin peak on the BEH C18 AX phase, enabling diversion of the API to waste and minimizing ion suppression. LOQs ranged from 0.01 to 0.1 ng/mL in solvent and 0.025 to 0.1 ng/mL in the API matrix, with signal-to-noise ratios ≥10 and RSD <10% at LOQ. All compounds exhibited linear detector response (R² ≥0.996 in solvent, ≥0.999 in matrix). Recovery studies spiked at 0.025–1 ng/mL in metformin showed 85–110% accuracy with RSD ≤6.5%.
Benefits and Practical Applications
- Trace-level detection of nine nitrosamines in high-matrix samples
- Robust and reproducible separation of target analytes from metformin API
- High sensitivity and specificity using APCI-MRM on a tandem quadrupole platform
- Accurate quantification suitable for regulatory compliance and QC workflows
Future Trends and Applications
Expanding this methodology to other pharmaceutical APIs and complex matrices can enhance routine impurity screening. Integration with high-resolution MS, automated sample preparation, and data processing workflows will streamline analysis. Ongoing regulatory refinements and emerging guidelines will drive further method optimization and broader adoption in drug safety laboratories.
Conclusion
The described UPLC-MS/MS method offers a rapid, sensitive, and accurate approach for monitoring critical nitrosamine impurities in metformin drug substance. Achieving sub-nanogram quantitation with strong linearity, precision, and recovery, it supports stringent quality control and regulatory requirements to ensure patient safety.
Reference
- ICH M7(R1) Assessment and Control of NDMA Reactive (Mutagenic) Impurities in Pharmaceuticals, ICH.
- Brambilla G., Martelli A., Genotoxic and Carcinogenic Risk to Humans of Drug–Nitrite Interaction Products, Mutat. Res. 635 (2007) 17–52.
- FDA Alert on Nitrosamine Impurity Findings in Metformin, FDA, 2020.
- FDA Guidance for Industry: Control of Nitrosamine Impurities in Human Drugs, CDER, 2021.
- EMA Nitrosamine Impurities in Human Medicinal Products, EMA, 2020.
- Parr M.K., Joseph J.F., NDMA Impurity Analysis in Pharmaceuticals, JPBA 164 (2019) 536–549.
- Gushargi A.J., Halden R.U., Critical Review of Major Sources of Human Exposure to N-nitrosamines, Chemosphere 210 (2018) 1124–1136.
- Lame M.E., Lindsay H., High Sensitivity Quantification of Nitrosamines in Ranitidine, Waters App Note 720006899 (2020).
Content was automatically generated from an orignal PDF document using AI and may contain inaccuracies.