MSGID: 1:317/3 63e719f9   
   PID: hpt/lnx 1.9.0-cur 2019-01-08   
   TID: hpt/lnx 1.9.0-cur 2019-01-08   
    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.   
      
      
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   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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