Abstract Introduction:
Depression is a growing global crisis, with females at a higher rate of diagnosis than males. While the percentage of patients on prescribed antidepressants have tripled over the last two decades, we are still at a crossroad where the discrepancy lies between finding a drug to suit a patient and monitoring the abundance of it in the body to prevent unwanted side-effects. Liquid Chromatography tandem mass spectrometry (LC-MS/MS) has garnered the attention of clinicians as a technique to accurately monitor therapeutic drugs in human serum with high specificity and accuracy. This may be a potential solution, but the challenge persists in the realm of sample preparation, where a method is automatable.
Objectives:
We have developed and validated an LC-MS/MS-based assay for simultaneous quantification of 5 different classes of commonly prescribed antidepressants in women that is automated using a JANUS® G3 Robotic Liquid Handler.
Methods:
Our method utilizes a simple sample preparation technique, utilizing only 20 uL of serum sample, to accurately measure Bupropion, Citalopram, Desipramine, Imipramine, Olanzapine, Sertraline and Vilazodone across a range of 1.0 to 230 ng/mL. Standards were created using DDC gold serum and analytes from Cerillient. The sample preparation method included temperature-controlled mixing and centrifugation for separation. The number of samples tested was optimized using DoE principles, and all statistical analysis was performed using JMP Pro 16.
Results:
Our method exhibits a linearity of R2 ≥0.99 when detected in MRM mode and % CV of ≤20% for all analytes across the board. In addition, we have designed a prototype that can be utilized at a clinical mass spectrometry lab, and we have assessed the long-term use of this prototype using an accelerated stability study. Our prototype resulted in a 32 minute and 15 seconds full plating time on the liquid handler versus a commercial kit (Eureka, Italy) that takes about 50 minutes for a full 96 well plate.
Conclusion:
Overall, our developed method has the potential to be translated to clinical settings to monitor postpartum depression for a large number of patient samples using automation.
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