DEMONSTRATING AN EASE-OF-USE BENCHTOP TIME OF FLIGHT DETECTOR AS AN EASE-OF-USE SOLUTION FOR ACCURATE MASS MEASUREMENTS IN FORCED DEGRADATION STUDIES
Posters | 2021 | WatersInstrumentation
Forced degradation experiments are essential for developing stability-indicating methods in pharmaceutical research and regulatory submissions. Accurate mass measurement aids in identifying and characterizing drug degradants, but high-resolution mass spectrometry typically demands specialized expertise. The benchtop ACQUITY RDa time-of-flight detector offers a user-friendly solution to perform accurate mass analysis without the need for HRMS proficiency.
This study evaluates the performance of the ACQUITY RDa detector coupled to UPLC for forced degradation analysis of the antidiabetic API glipizide. Outcomes include the identification and structural confirmation of degradation products generated under acidic, basic, and oxidative stress.
Acidic and oxidative stress generated impurities II (m/z 320.0943) and III (m/z 378.0998), with impurity III forming rapidly. Basic conditions produced chiefly impurity V (m/z 325.1460) at lower levels. Mass errors for identified compounds ranged from 0.8 to 4.1 ppm. UNIFI’s binary comparison enabled direct visual confirmation of peak identities against reference spectra, while full-scan fragmentation furnished structural insights without manual interpretation.
The adoption of benchtop ToF detectors is expected to grow in QA/QC and routine stability labs. Integration with artificial intelligence–driven data processing will further reduce user input, while expanded compound libraries and cloud connectivity will facilitate collaborative research and remote method deployment.
The Waters ACQUITY RDa detector, when coupled with UPLC and UNIFI software, offers a straightforward, reliable platform for accurate mass analysis in forced degradation studies. It simplifies workflow, delivers structural data, and supports regulatory requirements without the need for high-resolution mass spectrometry expertise.
1. Patolia VN. An Introduction to Forced Degradation Studies for Drug Substance & Drug Product. Pharmaceutical Online, Jan 9, 2020.
2. FDA Guidance for Industry, INDs for Phase II and III Studies—Chemistry, Manufacturing, and Controls Information, U.S. Food and Drug Administration.
HPLC, LC/TOF, LC/HRMS, LC/MS
IndustriesPharma & Biopharma
ManufacturerWaters, ENTECH
Summary
Significance of the topic
Forced degradation experiments are essential for developing stability-indicating methods in pharmaceutical research and regulatory submissions. Accurate mass measurement aids in identifying and characterizing drug degradants, but high-resolution mass spectrometry typically demands specialized expertise. The benchtop ACQUITY RDa time-of-flight detector offers a user-friendly solution to perform accurate mass analysis without the need for HRMS proficiency.
Objectives and study overview
This study evaluates the performance of the ACQUITY RDa detector coupled to UPLC for forced degradation analysis of the antidiabetic API glipizide. Outcomes include the identification and structural confirmation of degradation products generated under acidic, basic, and oxidative stress.
Methodology and instrumentation
- Sample Preparation: A glipizide standard was stressed with formic acid, sodium hydroxide, and hydrogen peroxide at 80 °C. Aliquots were taken over time, quenched, diluted, and analyzed.
- Chromatography: Waters ACQUITY UPLC I-Class PLUS with BEH C18 column (2.1 × 100 mm, 1.7 µm), 45 °C column temperature, 0.4 mL/min flow, gradient from 5 % to 100 % acetonitrile (0.1 % formic acid) over 8 min, injection volume 1 µL, detection at 254 nm.
- Mass Spectrometry: ACQUITY RDa detector in positive ESI mode, acquisition range 100–2000 Da, capillary voltage 1.5 kV, cone voltage 30 V with fragmentation ramp 60–150 V, scan rate 10 Hz, desolvation at 550 °C. Full-scan with simultaneous high- and low-energy data collection.
- Data Processing: UNIFI software imported MOL files for glipizide and five known impurities, applied a stepwise workflow including binary compare and impurity profiling to assign molecular features automatically.
Key results and discussion
Acidic and oxidative stress generated impurities II (m/z 320.0943) and III (m/z 378.0998), with impurity III forming rapidly. Basic conditions produced chiefly impurity V (m/z 325.1460) at lower levels. Mass errors for identified compounds ranged from 0.8 to 4.1 ppm. UNIFI’s binary comparison enabled direct visual confirmation of peak identities against reference spectra, while full-scan fragmentation furnished structural insights without manual interpretation.
Benefits and practical applications
- Ease of use: No specialized HRMS training required.
- Automated data interpretation: Libraries and workflows in UNIFI streamline compound identification.
- Regulatory compliance: Integration with waters_connect ensures 21 CFR Part 11–compliant reporting.
- Structural confidence: Simultaneous high- and low-energy acquisition enhances identification certainty.
Future trends and opportunities
The adoption of benchtop ToF detectors is expected to grow in QA/QC and routine stability labs. Integration with artificial intelligence–driven data processing will further reduce user input, while expanded compound libraries and cloud connectivity will facilitate collaborative research and remote method deployment.
Conclusion
The Waters ACQUITY RDa detector, when coupled with UPLC and UNIFI software, offers a straightforward, reliable platform for accurate mass analysis in forced degradation studies. It simplifies workflow, delivers structural data, and supports regulatory requirements without the need for high-resolution mass spectrometry expertise.
References
1. Patolia VN. An Introduction to Forced Degradation Studies for Drug Substance & Drug Product. Pharmaceutical Online, Jan 9, 2020.
2. FDA Guidance for Industry, INDs for Phase II and III Studies—Chemistry, Manufacturing, and Controls Information, U.S. Food and Drug Administration.
Content was automatically generated from an orignal PDF document using AI and may contain inaccuracies.
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