Automated development of an LC-MS/MS method for measuring multiple vitamin D metabolites using MUSCLE software
Research output: Contribution to journal › Article › peer-review
- Department of Orthopaedic Surgery, University of California, Los Angeles.
Manual development of liquid chromatography tandem-mass spectrometry (LC-MS/MS) methods is a rate limiting step in analytical laboratories, particularly if several compounds have the same multiple reaction monitoring (MRM) transitions. This study describes the application of Multi-platform Unbiased optimisation of Spectrometry via Closed-Loop Experimentation (MUSCLE) software to automate the development of an LC-MS/MS method to measure multiple metabolites of vitamin D. Comparison with a manually developed method for the same compounds was used to evaluate the effectiveness of MUSCLE in improving method parameters. LC and MS parameter ranges were set up in MUSCLE, which optimised the method during a fully-automated 200 sample sequence. Visual scripts altered method parameters after each sample run while a closed-loop multi-objective optimisation approach identified optimum instrument parameters throughout the sequence to improve sensitivity and run time. The optimised sample run developed using MUSCLE shortened analysis time for 10 metabolites from 8.2 minutes to 6.2 minutes. This was achieved by increased initial methanol concentration in the mobile phase and an altered gradient that increased the in-run organic mobile phase. However, MS parameters could not be optimised further to improve analyte sensitivity over manual optimisation, although in most cases MUSCLE confirmed the manually optimised conditions. Comparison between each of the developed methods showed no significant analyte bias between methods. MUSCLE has been shown here to automate and improve the throughput of a multiple analyte vitamin D LC-MS/MS method. Utilisation of this software could be applied to industries requiring fast automated method development such as clinical and pharmaceutical laboratories.
|Early online date||20 Apr 2017|
|Publication status||E-pub ahead of print - 20 Apr 2017|