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An ultrafast and flexible LC-MS/MS system paves the way for machine learning driven in vivo sample processing and data evaluation in early drug discovery


Rapid Commun Mass Spectrom. 2021 Apr 9:e9096. doi: 10.1002/rcm.9096. Online ahead of print.

ABSTRACT

RATIONALE: Low speed and low flexibility of most LC-MS/MS approaches in early drug discovery delay sample analysis from routine in vivo studies within the same day. A high-throughput platform for the rapid quantification of drug compounds in various in vivo assays was developed and established in routine bioanalysis.

METHODS: Automated selection of an efficient and adequate LC method was realized by autonomous sample qualification for ultrafast batch gradients (9 s/sample) or for fast linear gradients (45 s/sample) if samples required chromatography. The hardware and software components of our Rapid and Integrated Analysis System (RIAS) were streamlined for increased analytical throughput via state-of-the-art automation while maintaining high analytical quality.

RESULTS: Online decision-making was based on a quick assay suitability test (AST), based on a small and dedicated sample set evaluated by two different strategies. 84% of the acquired data points were within ±30% accuracy and 93% of the deviations between the lower limit of quantitation (LLOQ) values were ≤2-fold compared to standard LC-MS/MS systems. Speed, flexibility and overall automation significantly improved.

CONCLUSIONS: The developed platform provided an analysis time of only 10 min (batch-mode) and 47 min (gradient-mode) per standard pharmacokinetic (PK) study (62 injections). Automation, data evaluation and results handling were optimized to pave the way for machine learning based on decision-making regarding the evaluation strategy of the AST.

PMID:33837598 | DOI:10.1002/rcm.9096

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