Next Generation Quality Control in Pharma Applications

Importance of the Topic

Pharmaceutical quality control is critical for ensuring patient safety and regulatory compliance. The 2018 discoveries of N-nitrosodimethylamine (NDMA) and N-nitrosodiethylamine (NDEA) in generic valsartan products highlighted vulnerabilities in existing analytical workflows. These carcinogenic impurities originated from process changes that were originally approved but later revealed as sources of genotoxic contaminants. Advanced screening methods that can rapidly detect trace-level impurities are essential to prevent costly recalls, protect public health, and maintain trust in drug manufacturers and regulators.

Objectives and Study Overview

This study demonstrates a General Unknown Comparative Screening (GUCS) workflow on the SCIEX X500R QTOF LC-MS/MS platform to identify and quantify NDMA and other impurities in valsartan. Key goals include:

• Establishing a targeted method for sensitive detection of NDMA and related nitrosamines.
• Developing a generic non-targeted screening approach for comprehensive impurity profiling.
• Comparing batch-to-batch variability across multiple valsartan products to uncover undocumented differences.

Used Instrumentation

• SCIEX X500R QTOF LC-MS/MS System with high-resolution accurate mass capabilities
• SCIEX ExionLC AD UHPLC system
• APCI source in positive ion mode for targeted nitrosamine analysis
• ESI source in positive/negative ion modes for generic impurity profiling
• Phenomenex Synergi Polar and Luna Omega Polar columns
• SWATH® Acquisition on the QTOF
• SCIEX OS Software 1.5 (Analytics and GUCS workflows)
• MarkerView™ Software for multivariate statistical analysis
• ChemSpider database and AutoFrag tool for formula prediction and in-situ fragmentation

Methodology

Sample Preparation:
Valsartan tablets were ground and extracted with methanol:water (1:1), followed by centrifugation and filtration into amber vials.

Chromatography:
Two LC methods were optimized: a rapid APCI-compatible gradient (Method 1) for NDMA and a generic ESI gradient (Method 2) for broad profiling. Method 1 used a Phenomenex Synergi Polar column at 900 µL/min. Method 2 employed a Luna Omega Polar column at 600 µL/min.

Mass Spectrometry:
1. APCI + IDA for targeted screening of NDMA and related nitrosamines (65–400 amu MS, 50–400 amu MS/MS, 0.9 s cycle).
2. ESI + SWATH for non-targeted profiling (70–750 amu MS, 50–750 amu MS/MS, 2 s cycle with 12 windows).

Data Processing:
SCIEX OS software executed the GUCS workflow to compare sample vs. reference, flag features with >7× area ratio, and perform library searches. Unidentified peaks were subjected to ChemSpider formula matching and AutoFrag in-situ fragmentation for structural confirmation. MarkerView conducted principal component analysis (PCA) on SWATH data for batch comparison.

Main Results and Discussion

Targeted Analysis:
NDMA was detected at over 1,000× higher intensity in a generic valsartan sample compared to the reference standard. Twenty-four impurities exceeded the 7× threshold, including dimethylvaleramide and valeramide, which were confirmed by standards.

Unknown Identification:
ChemSpider predicted formulas based on accurate mass and isotopic pattern. AutoFrag verified NDMA with 100% MS/MS coverage and distinguished isobaric interferences.

Generic Profiling and Batch Comparison:
SWATH-based GUCS increased the number of detected differentials ~24-fold. PCA of nine commercial valsartan lots revealed tight clustering by manufacturer and flagged one batch spiked with azilsartan and azilsartan-medoxomil, demonstrating sensitivity to undocumented formulation differences.

Benefits and Practical Applications

• Rapid identification of known and unknown impurities without extensive method development.
• Quantitative comparison between batches to monitor synthesis consistency and detect adulteration.
• High resolution and mass accuracy enable unambiguous structural assignments.
• Seamless integration into routine QC workflows with user-friendly software.
• Reduced risk of regulatory recalls by early screening of trace-level genotoxicants.

Future Trends and Opportunities

• Integration of machine learning for predictive impurity profiling and automation of structural elucidation.
• Expansion of GUCS workflows to other high-risk APIs and complex biologics.
• Real-time process monitoring by coupling in-line sampling with high-resolution MS.
• Development of standardized impurity libraries to streamline regulatory submissions.
• Enhanced statistical frameworks for multivariate QC across global manufacturing sites.

Conclusion

The GUCS workflow on the SCIEX X500R QTOF platform offers a robust solution for modern pharma QC labs. By combining targeted NDMA detection with broad non-targeted screening, it delivers comprehensive impurity profiling, batch consistency evaluation, and rapid identification of unknowns. This approach enhances drug safety, supports regulatory compliance, and mitigates the risk of costly recalls.

References

• German Federal Institute for Drugs and Medical Devices (BfArM), Press Release No. 5/18, July 4, 2018.
• U.S. Food and Drug Administration (FDA), “FDA Announces Voluntary Recall of Several Medicines Containing Valsartan Following Detection of an Impurity,” July 13, 2018.
• FDA ARB Recalls: UCM615703.pdf, November 27, 2018.