MUSCLE: automated multi-objective evolutionary optimization of targeted LC-MS/MS analysis.

Area of Science:

• Analytical Chemistry
• Biochemistry
• Pharmacology

Background:

• Developing liquid chromatography tandem mass spectrometry (LC-MS/MS) methods for (bio)chemical analysis is time-consuming due to extensive parameter optimization.
• This optimization bottleneck hinders the establishment of new bioanalytical methods in crucial research areas like pharmacology, metabolomics, and pesticide research.
• Existing methods lack robust, automated solutions for multi-objective optimization of LC-MS/MS parameters.

Purpose of the Study:

• To develop a user-friendly, multi-platform software tool for the automated optimization of targeted LC-MS/MS analysis.
• To address the bottleneck in establishing new bioanalytical methods by streamlining the LC-MS/MS optimization process.
• To demonstrate the tool's effectiveness in reducing analysis time and improving analytical sensitivity.

Main Methods:

• Development of MUSCLE (multi-platform unbiased optimization of spectrometry via closed-loop experimentation), a software tool for automated LC-MS/MS optimization.
• Implementation of a closed-loop experimental approach for robust, multi-objective optimization.
• Testing and validation of MUSCLE on LC-MS/MS instruments from different manufacturers.

Main Results:

• MUSCLE successfully achieved robust and fully automated multi-objective optimization of targeted LC-MS/MS analysis.
• The software significantly shortened analysis times for targeted metabolite analysis.
• Analytical sensitivities for targeted metabolite analysis were demonstrably increased using MUSCLE.

Conclusions:

• MUSCLE provides a powerful, user-friendly solution to the challenges of LC-MS/MS method development.
• The automated optimization capabilities of MUSCLE accelerate the establishment of new bioanalytical methods.
• The tool's multi-platform compatibility and demonstrated performance on different instruments highlight its broad applicability in scientific research.