Simultaneous detection of salivary cortisol and cortisone using an automated high-throughput sample preparation method for LC-MS/MS

High-Performance Liquid Chromatography-Tandem Mass Spectrometry has emerged triumphant over the years as a reliable and high throughput clinical instrument to assess different metabolic irregularities. One of the latest applications of LC-MS/MS has been in the field of quantification of glucocorticoids, such as cortisol and cortisone from saliva, that can be an indicator of abnormalities such as Congenital Adrenal Hyperplasia (CAH), Cushing's Syndrome, and Addison's disease. We have developed and validated an LC-MS/MS-based assay for simultaneous detection of cortisol and cortisone in human saliva, which requires only 20 µL of sample, to measure cortisol across a 0.5 – 70 ng/mL range, and cortisone across a 1.2– 100 ng/mL range, respectively. The developed method exhibits linearity of R2>0.99, for both analytes, inclusive of both MRMs, and a percent coefficient of variation that is less than or equal to 20%. Using dilute-and-shoot for sample preparation, we have exhibited sample accuracy of 100±20 for the assay calibrators, and integrated Needle Wash Solvent Chemistry for minimal sample carryover, making it adaptable for crucial for potential diagnostic use. We have exhibited a method that is simplistic, specific, and highly automatable on liquid handling platforms, such as the JANUS® G3 Workstation. With our innovation, we have introduced a potential to test 81-unknown samples in singlicate within an on-deck plating time of fewer than four minutes. We performed additional verification studies, including an accelerated stability study and a freeze-thaw study showcasing the potential long-term usability of our proposed prototype kit. Overall, this work presents an optimized LC-MS/MS method with automated sample preparation that is ready to be utilized for cortisol-cortisone detection for clinical diagnostic contexts related to Cushing's Disease, among other adrenal and endocrine disorders.