Rationale
The low speed and low flexibility of most liquid chromatography/tandem mass spectrometry (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 with 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.
Rationale: Low speed and flexibility of most LC-MS/MS approaches in early drug discovery
delays sample analysis from routine in vivo studies within the same day of measurements. A highthroughput 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 require 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 keeping 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 while speed,
flexibility and overall automation was significantly improved.
Conclusions: The developed platform provided an analysis time of only 10 min (batch-mode) and
50 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
decision-making regarding the evaluation strategy of the AST
Rationale: Low speed and flexibility of most LC-MS/MS approaches in early drug discovery
delays sample analysis from routine in vivo studies within the same day of measurements. A highthroughput 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 require 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 keeping 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 while speed,
flexibility and overall automation was significantly improved.
Conclusions: The developed platform provided an analysis time of only 10 min (batch-mode) and
50 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
decision-making regarding the evaluation strategy of the AST
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