PROVIDENCE, R.I. [Brown University] — 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.”