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Abstract Introduction:
Therapeutic drug monitoring (TDM) for immunosuppressants helps providers optimize drug dosing to prevent toxicity and rejection. Therapeutic strategies rely on accurate and precise assays, particularly at low drug concentrations. Liquid chromatography–tandem mass spectrometry (LC-MS/MS) methods are extensively employed due to their sensitivity, specificity and ability to detect multiple analytes simultaneously. However, these assays are manually intensive and require skilled personnel, who are becoming more difficult to hire due to national staffing shortages, which appear to be getting worse each year. Automation of the sample preparation needed for LC-MS/MS analysis would greatly increase efficiency and would likely lead to improved throughput, robustness, and cost savings.
Objectives:
We aimed to develop and validate an automated sample-preparation method on a liquid-handling platform for the multiplexed quantification of sirolimus, tacrolimus, and cyclosporine in whole blood. We also validated the transfer of a LC-MS/MS method to newer systems for improved performance.
Methods:
The concentrations of sirolimus, tacrolimus, and cyclosporine A were determined in whole blood samples by Xevo TQ-MS LC-MS/MS systems with either manual or automated sample preparation. The Hamilton Microlab Starlet automated liquid handler performed barcode reading, transferring of whole blood samples, addition of internal standard solution, mixing, and liquid re-suspension in 96-well plates without manual involvement. The LC-MS/MS method utilized a ballistic LC gradient of 1.5 min for a cycle time (injection-to-injection) of 3.5 min.
Results:
The assay was linear throughout the therapeutic range for each analyte and imprecision was <7%. Method comparison between manual and automated sample preparation was excellent (r2>0.99) for sirolimus (N=58), tacrolimus (N=85), and cyclosporine A (N=56).
Conclusion
Our automated sample-preparation method with a ballistic LC gradient is suitable for routine clinical analysis. It reduces hands-on time, which also reduces errors, increases staff availability for other functions, and improves analytical throughput.
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