Brown University researchers developing serum-based tool for antidepressant monitoring

Mental Health Depression Social

Brown University researchers have developed a technique to monitor therapeutic antidepressant drugs in human serum samples.

The research, published recently in Scientific Reports, addresses a biomedical need by offering the potential to simplify and automate the time-consuming process of drug monitoring and facilitate its adoption by medical professionals.

Depression is a growing global crisis, with women facing higher rates of diagnosis than men. The number of patients prescribed antidepressants has tripled over the past two decades. Doctors are called upon not only to prescribe the right therapeutic drugs to help their patients but also to monitor the levels of these drugs, as their overabundance in the body can cause unwanted or dangerous side effects.

Currently, there are no commercial products in the U.S. to help clinicians easily monitor patient drug levels, the authors noted.

In recent years, liquid chromatography tandem mass spectrometry, or LC-MS/MS, has become the diagnostic tool of choice for detecting the drug levels in a biological sample, such as blood. However, because LC-MS/MS requires relatively large biological sample sizes and time-consuming preparation of these samples for analysis, clinicians often fall back upon more qualitative methods, such as surveys.

The researchers developed a method for measuring and identifying eight antidepressants most commonly prescribed to women: bupropion, citalopram, desipramine, imipramine, milnacipran, olanzapine, sertraline, and vilazodone. Their method can identify and monitor these drugs using small biological samples of 20 microliters -- about the amount of blood in a pinprick.

Prepared samples are put through a mass spectrometer, which breaks the sample down into tiny fragments that contain signs of the drugs being monitored.

The method’s accuracy is comparable to other LC-MS/MS-based techniques, but has the advantages of requiring a much smaller sample size and the ability to be largely automated, the authors said.

The researchers were initially asked in 2021 to evaluate a commercial European kit that uses LC-MS/MS to detect drugs in humans. They then sought to design an equally accurate but simpler kit by refining the LC-MS/MS drug identification technique and the sample amount needed. They broke down their sample preparation process so that it could be programmed into a machine.

Although the researchers used a Janus G3 Robotic Liquid Handler, they also provided details about how they programmed their machine so that others could replicate the process using their own equipment.

The team also created prototype kits which include the chemicals and solvents needed and detailed instructions, to enable clinicians to implement the method in their labs.

The researchers said they hope these innovations will facilitate the system’s widespread adoption in clinical settings, helping to monitor the impacts of antidepressant drugs on patients, including women experiencing postpartum depression.

"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," the researchers wrote.