Evidence map›Paper›PMID 36747000›Full record

ArticleScientific reports2023

An in-depth analysis of four classes of antidepressants quantification from human serum using LC-MS/MS.

Ramisa Fariha, Prutha S Deshpande, Emma Rothkopf, Mohannad Jabrah, Adam Spooner, Oluwanifemi David Okoh, Anubhav Tripathi

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.7field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Ramisa FarihaBrown University Center for Biomedical Engineering, Providence, RI, USA.
Prutha S DeshpandeBrown University Center for Biomedical Engineering, Providence, RI, USA.
Emma RothkopfBrown University Center for Biomedical Engineering, Providence, RI, USA.
Mohannad JabrahBrown University Center for Biomedical Engineering, Providence, RI, USA.
Adam SpoonerBrown University Center for Biomedical Engineering, Providence, RI, USA.
Oluwanifemi David OkohBrown University Center for Biomedical Engineering, Providence, RI, USA.
Anubhav TripathiBrown University Center for Biomedical Engineering, Providence, RI, USA. anubhav_tripathi@brown.edu.
Brown University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Depression is a growing global crisis, with females at a higher rate of diagnosis than males. While the percentage of patients on prescribed antidepressants have tripled over the last two decades, we are still at a crossroad where the discrepancy lies between finding a drug to suit a patient and monitoring the abundance of it in the body to prevent unwanted side effects. Liquid Chromatography tandem mass spectrometry (LC-MS/MS) has garnered the attention of clinicians as a technique to accurately monitor therapeutic drugs in human serum with high specificity and accuracy. This may be a potential solution, but the challenge persists in the realm of sample preparation, where a method is automatable. We have developed and validated an LC-MS/MS-based assay for simultaneous quantification of 4 different classes of commonly prescribed antidepressants in women that is automated using a JANUS G3 Robotic Liquid Handler. Our method utilizes a simple sample preparation technique, utilizing only 20 μL of a serum sample, to accurately measure Bupropion, Citalopram, Desipramine, Imipramine, Olanzapine, Sertraline and Vilazodone across a range of 1.0 to 230 ng/mL. Our method exhibits a linearity of R2 ≥ 0.99 when detected in MRM mode and % CV of ≤ 20% for all analytes across the board. In addition, we have designed a prototype that can be utilized at a clinical mass spectrometry lab and assessed the long-term use of this prototype using an accelerated stability study. 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.

Indexed as

Antidepressive AgentsTandem Mass SpectrometryChromatography, LiquidFemaleHumansImipramineMaleReproducibility of ResultsSertralineAntidepressive AgentsImipramineSertraline

Identifiers

PMID36747000
PMCPMC9902619
OpenAlexW4319315009

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.