Screening and Identification of Potential Genotoxic Degradation Impurities using Q-TOF LC/MS with Advanced Software Solutions

Significance of the Topic

Monitoring active pharmaceutical ingredients for genotoxic degradants is critical to ensure drug safety and regulatory compliance. Epoxide and hydroperoxide impurities can form under oxidative stress and pose mutagenic risks. This study focuses on forced oxidative degradation of atorvastatin calcium to reveal trace genotoxic impurities using combined chromatographic and mass spectrometric approaches.

Objectives and Study Overview

The primary aim is to establish a robust workflow for screening, structural identification, and preparative isolation of potential genotoxic impurities generated during the oxidative stress degradation of atorvastatin. The study integrates HPLC-UV monitoring, accurate-mass Q-TOF LC/MS analysis, advanced data processing, and mass-triggered fraction collection for subsequent characterization.

Methodology

Atorvastatin samples were oxidized with hydrogen peroxide at elevated temperature. Degraded and control samples were analyzed by:

• HPLC-UV using a C8 column and USP-based gradient to quantify degradation extent.
• Q-TOF LC/MS in positive and negative ion modes to obtain MS and MS/MS spectra of impurities.
• Software-assisted data processing with MassHunter Profinder, Mass Profiler Professional, and Molecular Structure Correlator to extract, filter, and propose structures for unknown degradants.
• Mass-based fraction collection via an Agilent 1260 Infinity fraction collector and single quadrupole LC/MS to isolate alerting genotoxic impurities.

Used Instrumentation

• Agilent 1290 Infinity Binary LC System with diode array detector.
• Agilent 6530 Accurate-Mass Q-TOF LC/MS with Jet Stream source.
• Agilent MassHunter software suite: Qualitative Analysis, Profinder, Mass Profiler Professional, Molecular Structure Correlator.
• Agilent 1260 Infinity Fraction Collector and Agilent 6150 Single Quadrupole LC/MS for preparative isolation.

Main Results and Discussion

HPLC-UV monitoring indicated approximately 4 percent degradation with new impurity peaks emerging in degraded samples. Q-TOF LC/MS analysis combined with recursive feature extraction and fold-change filtering identified 15 significant impurities, of which five matched known degradation products. Molecular Structure Correlator proposed structures for unknown features, revealing three alerting genotoxic impurities bearing epoxide or hydroperoxide moieties. MS/MS fragmentation patterns confirmed structural assignments by comparison with known reference compounds. Mass-triggered fraction collection successfully isolated these impurities with high purity for further structural confirmation.

Contributions and Practical Applications

• Fast and comprehensive screening of trace genotoxic degradants in drug substances.
• Seamless integration of chromatography, high-resolution mass spectrometry, and advanced software tools.
• Reliable structure elucidation through MS/MS pattern matching and database searches.
• Scalable preparative isolation workflow to support downstream NMR or other structural analyses.

Future Trends and Potential Applications

Advances in automated data processing, machine learning-driven structure prediction, and coupling with orthogonal techniques such as NMR or ion mobility spectrometry are expected to enhance the sensitivity and speed of genotoxic impurity profiling. Extension of this workflow to other pharmaceutical compounds will improve overall impurity risk assessment.

Conclusion

This work demonstrates a targeted analytical strategy to detect, identify, and isolate genotoxic impurities formed under oxidative stress in atorvastatin. The described Agilent platforms and software ecosystem provide a powerful framework for ensuring drug safety and supporting regulatory requirements.

References

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