Researchers Develop New, Automated, Powerful Diagnostic Tool for Drug Detection – Zoo House News

In recent years, a mass spectrometry technique that measures the amounts of drugs in a biological sample, such as blood, has evolved into a powerful diagnostic tool to help medical professionals identify and monitor levels of therapeutic drugs in patients that can cause unwanted or dangerous side effects.

What holds this technique – which is called liquid chromatography-tandem mass spectrometry, or LC-MS/MS for short – is that it often requires relatively large biological samples and a series of complicated steps that must be performed by hand to prepare samples for the prepare analysis.

At Brown University, a team of biomedical engineers has been working to simplify and much more automate this time-consuming process, a key part of the technique widely used by clinicians. The researchers shared their findings in Scientific Reports on Monday, February 6th.

In the study, they present a robust new method to accurately measure and identify eight antidepressant drugs most commonly prescribed to women: bupropion, citalopram, desipramine, imipramine, milnacipran, olanzapine, sertraline and vilazodone.

The method does exactly what the researchers had hoped. It is able to identify and monitor these drugs using small biological samples – 20 microliters each, which is roughly the equivalent of blood drawn from a random sample. The procedure can also be performed almost entirely by liquid-handling robots found in most clinical mass spectrometry laboratories.

“We developed our method and put together kits so that once the samples are collected, we can plug them into a computer program for a liquid handling robot and essentially all the user has to do is take the caps off, push a few buttons, and it’s done from start to finish going,” said lead author Ramisa Fariha, a Brown Ph.D. student working in a microfluidic diagnostics and biomedical engineering laboratory led by Brown professor Anubhav Tripathi.

Once the samples are ready, the user runs them through the mass spectrometer, which breaks the sample down into tiny fragments that contain telltale signs of the drugs they’re looking for. The accuracy of the method is comparable to other LC-MS/MS-based techniques, but has the advantage of a much smaller sample size and can be largely automated with the Liquid Handlers.

These innovations open up the system’s immediate potential for widespread roll-out into clinical settings to help monitor the effects of medications prescribed to patients diagnosed with depression, including women with postpartum depression.

“We’ve taken a very big step,” said Tripathi, a Brown engineering professor, the lab’s principal investigator and the study’s author. “When fitting in the clinical lab, you want to reduce human error. The more you automate, the more robust you become and the more doctors trust.”

Depression is a growing global crisis, and women are diagnosed more often than men. The percentage of patients prescribed antidepressants has tripled in the past two decades, and clinicians are at a crossroads between finding the right drug for a patient and monitoring the amount of it in the body, the researchers write in the Study.

There are currently no commercial products in the US that allow clinicians to directly monitor how much these drugs are in patients, the researchers noted. Clinicians often rely on more qualitative methods such as surveys because mass spectrometry methods are intrusive to patients in terms of sample size and the time-consuming preparation of samples for the instrument.

Tripathi and colleagues in his lab began work on this potential solution in 2021 after being asked to evaluate a commercial European kit using LC-MS/MS to detect drugs in humans. The work was largely the result of collaboration between Brown grads and students working in the lab.

The researchers, led by Fariha, decided to take a stab at designing their own kit that could be just as accurate but much simpler. They started by identifying some of the most commonly used tranquilizers and worked from there to refine the way the LC-MS/MS technique identifies the drugs, including the amount of sample required, and established a control that detects them compared to actual samples.

After performing a barrage of quality controls, tweaking and testing different methods of measuring the samples under different conditions, the researchers took their entire sample preparation process and disassembled it so it could be programmed into a machine that could handle the preparation of the liquids .

The Brown researchers used a JANUS G3 robotic liquid handler in their work, but say clinicians can use simpler or more advanced machines. The team explained how they programmed their machine so that others could easily mimic it using their own equipment.

“Every time our lab and team publish work, we go into detail so our results can be easily replicated by others,” said Fariha.

The team also created prototype kits that can be sent to clinicians so they can implement the method in their labs. The kits contain the chemicals and solvents needed, as well as detailed instructions for use, showing what clinicians should look out for based on their own experience and the many tweaks they made during the quality control process.

The team — known within the lab as the clinical diagnostics and automation team — plans to next work on automation projects in oncology, such as developing a kit that could detect ovarian cancer.

The automation team has a number of students participating – an example of how Brown students work together with each other and with faculty to tackle real-world problems. Emma Rothkopf, a biomedical engineering senior and the article’s author, said the experience was critical in helping her transfer concepts she learned in the academic setting directly to the lab.

“I would look at dates or take certain steps and I would think, ‘Oh my God, I learned that in class,'” Rothkopf said.

In addition to Fariha, Tripathi, and Rothkopf, other study authors include Prutha S. Deshpande, Mohannad Jabrah, Adam Spooner, and Oluwanifemi David Okoh. The work was supported by PerkinElmer.