Data processing for high-throughput mass spectrometry in drug discovery

ABSTRACT

Introduction

High-throughput mass spectrometry that could deliver > 10 times faster sample readout speed than traditional LC-based platforms has emerged as a powerful analytical technique, enabling the rapid analysis of complex biological samples. This increased speed of MS data acquisition has brought a critical demand for automatic data processing capabilities that should match or surpass the speed of data acquisition. Those data processing capabilities should serve the different requirements of drug discovery workflows.

Areas covered

This paper introduced the key steps of the automatic data processing workflows for high-throughput MS technologies. Specific examples and requirements are detailed for different drug discovery applications.

Expert opinion

The demand for automatic data processing in high-throughput mass spectrometry is driven by the need to keep pace with the accelerated speed of data acquisition. The seamless integration of processing capabilities with LIMS, efficient data review mechanisms, and the exploration of future features such as real-time feedback, automatic method optimization, and AI model training is crucial for advancing the drug discovery field. As technology continues to evolve, the synergy between high-throughput mass spectrometry and intelligent data processing will undoubtedly play a pivotal role in shaping the future of high-throughput drug discovery applications.

KEYWORDS:

• High-throughput analysis
• mass spectrometry
• data processing
• drug discovery
• data review and visualization

Acknowledgements

We thank Dr. Lucein Ghislain from Beckman Coulter Life Sciences and Dr. Jonathan Wingfield from AstraZenaca for the fruitful discussion during the preparation of this review.

Article highlights

• The demand for high-throughput mass spectrometry continues to grow, necessitating advancements in automatic data processing to keep pace with accelerated data acquisition.

• There is a critical demand for automatic data processing capabilities that match or exceed the pace of data acquisition.

• The key steps of automatic data processing include the correlation between MS signal and sample information, data integration, assay-specific post-processing, and data review/visualization.

• Background processing is a critical feature to be considered to process the high-throughput mass spectrometry data.

• The post-integration processing is assay/workflow dependent and customized according to the users’ requirements.

• The integration of seamless, automatic processing with features such as real-time feedback, method optimization, and AI model training represents the future of data processing for high-throughput mass spectrometry.

Declaration of interest

C. Liu is an employee of SCIEX, who are commercial provider of the Acoustic Ejection Mass Spectrometry (AEMS) technology system called the Echo MS system while H. Zhang is an employee of Iambic Therapeutics. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

This paper was not funded.