High Throughput Non-Targeted Screening Using a Rapid Gradient Microbore UPLC Method and a Library of FDA-Approved Small Molecule Drugs
Applications | 2021 | WatersInstrumentation
Modern analytical workflows increasingly demand rapid, high-throughput methods for screening small molecule compounds in complex matrices. UltraPerformance Liquid Chromatography coupled with Ion Mobility Mass Spectrometry (UPLC-IM-MS) offers multidimensional separation—based on hydrophobicity, gas-phase mobility, and mass-to-charge ratio—allowing robust non-targeted screening with high specificity. Incorporating collision cross section (CCS) metrics alongside retention time, accurate mass, and product ion data enhances confidence in compound identification, critical for pharmaceutical profiling, clinical toxicology, and metabolomics studies.
This study aimed to develop and demonstrate a rapid gradient microbore UPLC-IM-MS method (RGM-UPLC-IM-MS) and build a comprehensive reference library for FDA-approved small molecule drugs. Key objectives included:
RGM-UPLC-IM-MS was performed on a Waters ACQUITY UPLC I-Class system with a 1.0×50 mm, 1.8 µm HSS T3 C18 microbore column at 0.4 mL/min. A rapid gradient (2.5-min cycle) used water and acetonitrile (both with 0.1% formic acid). The SYNAPT G2-Si instrument provided HDMSE acquisition, combining travelling-wave ion mobility separation with ToF detection. Key parameters:
The resulting library comprised 1,206 positive-mode entries ([M+H]+, [M+Na]+) and 756 negative-mode entries ([M–H]–, [M–H+HCOO]–) covering 1,285 drugs. Although rapid gradients reduce chromatographic peak capacity (~50 vs. 150), the ion mobility dimension restores separation power 2–10×, enabling clear resolution of coeluting analytes.
Application to a urine sample from a volunteer dosed with carbamazepine and acetaminophen yielded accurate identifications of parent drugs, metabolites (e.g., carbamazepine-10,11-epoxide, theophylline), and caffeine. CCS values matched within 1%, mass error <5 ppm, retention time error <0.1 min, and product ions ≥1. Sequential filtering steps (retention time, mass accuracy, CCS tolerance, product ion count) reduced false positives dramatically—from hundreds of candidates to fewer than 10 confirmed identifications.
The RGM-UPLC-IM-MS approach offers:
Advances in Ion Mobility resolution and machine-learning based CCS prediction will further enhance screening specificity. Expansion of libraries to include multi-class contaminants, natural toxins, and environmental pollutants will broaden applicability. Integration with automated data processing platforms will streamline post-acquisition filtering, enabling real-time decision support in clinical, forensic, environmental, and QC laboratories.
This work demonstrates that rapid gradient microbore UPLC coupled with ion mobility MS and a comprehensive CCS-enhanced library facilitates efficient, high-confidence non-targeted screening of small molecule drugs in complex biological samples. The method balances speed with specificity, supporting diverse analytical needs while reducing costs and resource demands.
1. Rainville PD et al. Anal. Chim. Acta 982:1–8 (2017).
2. Goshawk J, Barknowitz G, McCullagh M. Waters Application Note 720006783EN (2020).
3. Gray N et al. Anal. Chem. 88:5742–5750 (2016).
4. Sundström M et al. Drug Test. Anal. 7:420–427 (2015).
5. Mollerup CB et al. Drug Test. Anal. (2016).
6. López M et al. J. Chromatogr. A 1373:40–50 (2014).
7. Dzumana Z et al. Anal. Chim. Acta 863:29–40 (2015).
8. Dwivedi P et al. J. Mass Spectrom. 45:1383–1393 (2010).
9. Letertre M et al. Chromatographia 83:853–861 (2020).
10. McCullagh M et al. Anal. Chem. 90:4585–4595 (2018).
Ion Mobility, LC/TOF, LC/HRMS, LC/MS, LC/MS/MS
IndustriesClinical Research
ManufacturerWaters
Summary
Importance of the Topic
Modern analytical workflows increasingly demand rapid, high-throughput methods for screening small molecule compounds in complex matrices. UltraPerformance Liquid Chromatography coupled with Ion Mobility Mass Spectrometry (UPLC-IM-MS) offers multidimensional separation—based on hydrophobicity, gas-phase mobility, and mass-to-charge ratio—allowing robust non-targeted screening with high specificity. Incorporating collision cross section (CCS) metrics alongside retention time, accurate mass, and product ion data enhances confidence in compound identification, critical for pharmaceutical profiling, clinical toxicology, and metabolomics studies.
Objectives and Study Overview
This study aimed to develop and demonstrate a rapid gradient microbore UPLC-IM-MS method (RGM-UPLC-IM-MS) and build a comprehensive reference library for FDA-approved small molecule drugs. Key objectives included:
- Generating a dual-mode (positive and negative ion) library with retention times, precursor and product ions, and CCS values for over 1,200 compounds.
- Comparing screening efficiency and detection rates between conventional UPLC-IM-MS (12-min cycle) and rapid microbore methods (2.5-min cycle).
- Applying the library to non-targeted screening of human urine to identify exogenous drugs and distinguish them from endogenous metabolites.
Methodology and Instrumentation
RGM-UPLC-IM-MS was performed on a Waters ACQUITY UPLC I-Class system with a 1.0×50 mm, 1.8 µm HSS T3 C18 microbore column at 0.4 mL/min. A rapid gradient (2.5-min cycle) used water and acetonitrile (both with 0.1% formic acid). The SYNAPT G2-Si instrument provided HDMSE acquisition, combining travelling-wave ion mobility separation with ToF detection. Key parameters:
- ESI in positive and negative modes, m/z 50–1,200, 10 Hz acquisition.
- Collision energy ramp (15–25 eV) for fragmentation.
- Ion mobility: T-wave velocity ramp (1,000 → 300 m/s), pulse height 40 V, nitrogen/helium gas flows yielding ~3.2 mbar.
- Calibration: Leucine enkephalin for mass lock, CCS calibrated via standard kit.
Results and Discussion
The resulting library comprised 1,206 positive-mode entries ([M+H]+, [M+Na]+) and 756 negative-mode entries ([M–H]–, [M–H+HCOO]–) covering 1,285 drugs. Although rapid gradients reduce chromatographic peak capacity (~50 vs. 150), the ion mobility dimension restores separation power 2–10×, enabling clear resolution of coeluting analytes.
Application to a urine sample from a volunteer dosed with carbamazepine and acetaminophen yielded accurate identifications of parent drugs, metabolites (e.g., carbamazepine-10,11-epoxide, theophylline), and caffeine. CCS values matched within 1%, mass error <5 ppm, retention time error <0.1 min, and product ions ≥1. Sequential filtering steps (retention time, mass accuracy, CCS tolerance, product ion count) reduced false positives dramatically—from hundreds of candidates to fewer than 10 confirmed identifications.
Benefits and Practical Applications
The RGM-UPLC-IM-MS approach offers:
- High throughput: 5-fold faster than conventional UPLC methods, supporting large-scale screening initiatives.
- Improved specificity via multifactor authentication (retention time, accurate mass, CCS, fragmentation).
- Versatility: library is applicable to tandem ToF, HDMSE, or IM-enabled analyses.
- Cost and time savings: reduced cycle times, solvent usage, and instrument runtime.
Future Trends and Potential Applications
Advances in Ion Mobility resolution and machine-learning based CCS prediction will further enhance screening specificity. Expansion of libraries to include multi-class contaminants, natural toxins, and environmental pollutants will broaden applicability. Integration with automated data processing platforms will streamline post-acquisition filtering, enabling real-time decision support in clinical, forensic, environmental, and QC laboratories.
Conclusion
This work demonstrates that rapid gradient microbore UPLC coupled with ion mobility MS and a comprehensive CCS-enhanced library facilitates efficient, high-confidence non-targeted screening of small molecule drugs in complex biological samples. The method balances speed with specificity, supporting diverse analytical needs while reducing costs and resource demands.
Reference
1. Rainville PD et al. Anal. Chim. Acta 982:1–8 (2017).
2. Goshawk J, Barknowitz G, McCullagh M. Waters Application Note 720006783EN (2020).
3. Gray N et al. Anal. Chem. 88:5742–5750 (2016).
4. Sundström M et al. Drug Test. Anal. 7:420–427 (2015).
5. Mollerup CB et al. Drug Test. Anal. (2016).
6. López M et al. J. Chromatogr. A 1373:40–50 (2014).
7. Dzumana Z et al. Anal. Chim. Acta 863:29–40 (2015).
8. Dwivedi P et al. J. Mass Spectrom. 45:1383–1393 (2010).
9. Letertre M et al. Chromatographia 83:853–861 (2020).
10. McCullagh M et al. Anal. Chem. 90:4585–4595 (2018).
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