Researchers develop new, automated, powerful diagnostic tool for drug detection

Date:
February 10, 2023
Source:
Brown University
Summary:
Biomedical engineers present a robust new method for accurately
measuring and identifying eight antidepressants most commonly
prescribed to women.


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FULL STORY ==========================================================================
In recent years, a mass spectrometry process that can detect the amounts
of drugs in a biological sample, such as blood, has become a powerful diagnostic tool for helping medical professionals identify and monitor
levels of therapeutic drugs in patients, which can cause unwanted or
dangerous side effects.


========================================================================== Holding back 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 number of complicated
steps that must be done by hand to prepare samples for analysis.

At Brown University, a team of biomedical engineers has been working
to make this time-consuming process simpler and much more automated, a
key ingredient to the technique being widely adopted by clinicians. The researchers shared their results in Scientific Reports on Monday, Feb. 6.

In the study, they present a robust new method for accurately measuring
and identifying eight antidepressants most commonly prescribed to women: bupropion, citalopram, desipramine, imipramine, milnacipran, olanzapine, sertraline and vilazodone.

The method does just what the researchers hoped. It is able to identify
and monitor these drugs from small biological samples -- 20 microliters
each, which is about the equivalent of blood taken from a prick. The
method is also able to be done almost entirely by liquid-handling robots
found in most clinical mass spectrometry labs.

"We designed our method and put together kits so that once the samples
have been collected, they can be put in a computer program for a robotic
liquid handler, and all the user essentially has to do is take off the
caps, press some buttons, and it will go start to finish," 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 puts them through the mass
spectrometer, which breaks the sample down into tiny fragments that
contain tell-tale signs of the drugs they are looking for. The method's accuracy is comparable to other LC-MS/MS-based techniques but has
the advantage of a much smaller sample size and is able to be largely
automated using the liquid handlers.

These innovations set up the system's immediate potential to be widely translated to clinical settings to help monitor the impacts of drugs
prescribed for patients diagnosed with depression, including women
experiencing postpartum depression.

"We have made a very big step," said Tripathi, a Brown engineering
professor, the lab's principal investigator and an author on the
study. "For clinical lab adaptation, you want to reduce the error by
humans. The more you automate, the more robustness you get and the more
trust there is from doctors." Depression is a growing global crisis,
and women face higher rates of diagnosis than men. The percentage of
patients prescribed antidepressants has tripled over the past two decades,
and clinicians find themselves at a crossroad between finding the right
drug to suit a patient and monitoring the abundance of it in the body,
the researchers wrote in the study.

Currently, there are no commercial products in the U.S. to help clinicians directly monitor how much these drugs are present in patients, the
researchers noted. Clinicians often end up relying on more qualitative
methods, like surveys, because of how obtrusive mass spectrometry methods
are to patients in terms of sample size and the time-consuming nature
of preparing the samples for the machine.

Tripathi and colleagues in his lab started working on this potential
solution in 2021 after they were asked to evaluate a commercial
European kit that uses LC-MS/MS to detect drugs in humans. The work has
largely been the result of a collaboration between Brown graduate and undergraduate students who work in the lab.

The researchers, led by Fariha, decided to take a crack at designing their
own kit that could be just as accurate but much simpler. They started by identifying some of the most commonly used depressants and from there
worked to refine the how the LC-MS/MS technique identifies the drugs,
including how much of a sample it needs and establishing a control they
could run against actual samples.

After running a barrage of quality control checks, tweaking and testing different methods of measuring the samples at different conditions,
the researchers took their entire process for preparing the sample and
broke it down so that 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 said that clinicians can use simpler or more advanced machines. The
team detailed how they programmed their machine in a way that others
can easily replicate with their own equipment.

"Every time our lab and our team publishes a paper, we go into the nitty
gritty so our results can be easily replicated by others," Fariha said.

The team also created prototype kits that can be sent to clinicians
so they can implement the method in their labs. The kits include the
chemicals and solvents needed along with a detailed instruction booklet
that calls out what clinicians should be on the lookout for based on
their own experiences and the numerous tweaks they made during quality
control process.

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

The automation team has a number of undergraduates who participate -- an example of how Brown students collaborate with each other and with faculty
to address real-world problems. Emma Rothkopf, a senior concentrating in biomedical engineering and an author on the paper, said the experience
was critical in helping her directly bridge concepts she learned in the academic setting to the lab.

"I'd find myself looking at data or doing certain steps and think,
'Oh, my gosh, I learned this in class,'" Rothkopf said.

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

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========================================================================== Story Source: Materials provided by Brown_University. Note: Content may
be edited for style and length.


========================================================================== Journal Reference:
1. Ramisa Fariha, Prutha S. Deshpande, Emma Rothkopf, Mohannad
Jabrah, Adam
Spooner, Oluwanifemi David Okoh, Anubhav Tripathi. An in-depth
analysis of four classes of antidepressants quantification from
human serum using LC-MS/MS. Scientific Reports, 2023; 13 (1) DOI:
10.1038/s41598-023-29229- 0 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2023/02/230210185142.htm

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