Ultrafast Analysis of Metabolic Stability Assays Using Agilent RapidFire High-Resolution MS
Applications | 2019 | Agilent TechnologiesInstrumentation
The metabolic stability of drug candidates is a crucial determinant of pharmacokinetics, influencing oral bioavailability, plasma exposure, and ultimately therapeutic efficacy. As compound libraries grow and automation advances, traditional LC/MS/MS workflows become a throughput bottleneck for in vitro ADME screening. Developing faster, yet reliable, analytical platforms is essential for accelerating early drug discovery decisions.
This study compared conventional LC/MS/MS analysis with an Agilent RapidFire 360 high-throughput system coupled to a high-resolution Q-TOF mass spectrometer. The goal was to assess whether RapidFire-Q-TOF can deliver equivalent metabolic half-life measurements for a diverse set of compounds while significantly reducing analysis time and method development overhead.
Half-life values obtained by both platforms showed excellent agreement (R2 = 0.965 for t1/2 < 60 min) and consistent binning into fast (< 20 min), intermediate (20–60 min) and slow (> 60 min) categories. RapidFire-Q-TOF achieved over a 13-fold reduction in analysis time without compound-specific MRM optimization. Carryover testing on 30 representative compounds demonstrated negligible residual signal (< 0.4%), supporting robustness of the rapid SPE-MS approach.
Ongoing advances in automated sample handling and high-resolution mass spectrometry will further streamline in vitro ADME profiling. Integration with robotic platforms, expanded use of generic acquisition methods, and real-time data analytics promise to extend ultrafast workflows to clearance prediction, metabolite identification, and multiplexed assays in drug discovery.
The Agilent RapidFire 360-Q-TOF system delivers metabolic half-life data equivalent to conventional LC/MS/MS while achieving a dramatic increase in throughput and operational efficiency. By removing MRM development and leveraging high-resolution full-scan MS, this ultrafast platform addresses critical bottlenecks in early ADME screening and supports rapid decision-making in drug discovery.
Sample Preparation, LC/TOF, LC/HRMS, LC/MS, LC/MS/MS
IndustriesClinical Research
ManufacturerAgilent Technologies
Summary
Importance of the Topic
The metabolic stability of drug candidates is a crucial determinant of pharmacokinetics, influencing oral bioavailability, plasma exposure, and ultimately therapeutic efficacy. As compound libraries grow and automation advances, traditional LC/MS/MS workflows become a throughput bottleneck for in vitro ADME screening. Developing faster, yet reliable, analytical platforms is essential for accelerating early drug discovery decisions.
Objectives and Overview of the Study
This study compared conventional LC/MS/MS analysis with an Agilent RapidFire 360 high-throughput system coupled to a high-resolution Q-TOF mass spectrometer. The goal was to assess whether RapidFire-Q-TOF can deliver equivalent metabolic half-life measurements for a diverse set of compounds while significantly reducing analysis time and method development overhead.
Methodology and Data Analysis
- Compound Selection: 39 drug-like molecules spanning MW 218–734 and predicted XLogP3 0.4–7.1.
- Sample Preparation: Human liver microsomal incubations in 96-well plates with NADPH-driven metabolism, quenching by acetonitrile containing internal standard.
- LC/MS/MS Workflow: Agilent 1260 Infinity LC with ZORBAX SB-C18 column, binary gradient, Agilent 6460 Triple Quadrupole in MRM mode, software-optimized transitions, ~2.2 min cycle time per sample.
- RapidFire-Q-TOF Workflow: Agilent RapidFire 360 SPE sampling with generic reverse-phase C4 cartridge, Agilent 6530 Q-TOF in full-scan high-resolution mode, exact mass extraction, 9.5 s cycle time per sample.
- Data Processing: Percent substrate remaining calculated against t0, natural log plotted versus time, and half-life (t1/2) derived by linear regression (t1/2 = –0.693/slope).
Použitá instrumentace
- Agilent 1260 Infinity Binary LC System
- Agilent 6460 Triple Quadrupole MS
- Agilent RapidFire 360 High-Throughput System (G9214AA)
- Agilent 6530 Q-TOF MS
- Agilent MassHunter Workstation and Integrator Software
Main Results and Discussion
Half-life values obtained by both platforms showed excellent agreement (R2 = 0.965 for t1/2 < 60 min) and consistent binning into fast (< 20 min), intermediate (20–60 min) and slow (> 60 min) categories. RapidFire-Q-TOF achieved over a 13-fold reduction in analysis time without compound-specific MRM optimization. Carryover testing on 30 representative compounds demonstrated negligible residual signal (< 0.4%), supporting robustness of the rapid SPE-MS approach.
Benefits and Practical Applications
- Throughput Enhancement: Reduces per-sample cycle time from ~2.2 min to < 10 s, accelerating ADME screening capacity.
- Workflow Simplification: Eliminates labor-intensive MRM method development, lowering resource demands and analysis turnaround.
- Broad Applicability: Suitable for early-stage metabolic stability assays and adaptable to other in vitro ADME endpoints.
Future Trends and Potential Applications
Ongoing advances in automated sample handling and high-resolution mass spectrometry will further streamline in vitro ADME profiling. Integration with robotic platforms, expanded use of generic acquisition methods, and real-time data analytics promise to extend ultrafast workflows to clearance prediction, metabolite identification, and multiplexed assays in drug discovery.
Conclusion
The Agilent RapidFire 360-Q-TOF system delivers metabolic half-life data equivalent to conventional LC/MS/MS while achieving a dramatic increase in throughput and operational efficiency. By removing MRM development and leveraging high-resolution full-scan MS, this ultrafast platform addresses critical bottlenecks in early ADME screening and supports rapid decision-making in drug discovery.
Reference
- McNaney CA et al. Assay Drug Dev. Technol. 2008;6(1):121–129.
- Obach RS. Drug Metab Dispos. 1999;27(11):1350–1359.
- Cheng T et al. J Chem Inf Model. 2007;47:2140–2148.
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