Analytical sample preparation techniques are essential for assessing chemicals in various biological matrices. The development of extraction techniques is a modern trend in the bioanalytical sciences. We fabricated customized filaments using hot-melt extrusion techniques followed by fused filament fabrication-mediated 3D printing technology to rapidly prototype sorbents that extract non-steroidal anti-inflammatory drugs from rat plasma for determining pharmacokinetic profiles. The filament was prototyped as a 3D-printed sorbent for extracting small molecules using AffinisolTM, polyvinyl alcohol, and triethyl citrate. The optimized extraction procedure and parameters influencing the sorbent extraction were systematically investigated by the validated LC-MS/MS method. Furthermore, a bioanalytical method was successfully implemented after oral administration to determine the pharmacokinetic profiles of indomethacin and acetaminophen in rat plasma. The Cmax was found to be 0.33 ± 0.04 µg/mL and 27.27 ± 9.9 µg/mL for indomethacin and acetaminophen, respectively, at the maximum time (Tmax) (h) of 0.5–1 h. The mean area under the curve (AUC0–t) for indomethacin was 0.93 ± 0.17 µg h/mL, and for acetaminophen was 32.33± 10.8 µg h/mL. Owing to their newly customizable size and shape, 3D-printed sorbents have opened new opportunities for extracting small molecules from biological matrices in preclinical studies.
Gutka,
a form of smokeless tobacco, is widely used in the Indian
subcontinent and in other regions of South Asia. Smokeless tobacco
exposure is most likely to increase the incidence of oral cancer in
the Indian population, and metabolic changes are a hallmark of cancer.
The development of biomarkers for early detection and better prevention
measures for smokeless tobacco users at risk of oral cancer can be
aided by studying urinary metabolomics and offering insight into altered
metabolic profiles. This study aimed to investigate urine metabolic
alterations among smokeless tobacco users using targeted LC-ESI-MS/MS
metabolomics approaches to better understand the effects of smokeless
tobacco on human metabolism. Smokeless tobacco users’ specific
urinary metabolomics signatures were extracted using univariate, multivariate
analysis and machine learning methods. Statistical analysis identified
30 urine metabolites significantly associated with metabolomic alterations
in humans who chew smokeless tobacco. Receiver operator characteristic
(ROC) curve analysis evidenced the 5 most discriminatory metabolites
from each approach that could differentiate between smokeless tobacco
users and controls with higher sensitivity and specificity. An analysis
of multiple-metabolite machine learning models and single-metabolite
ROC curves revealed discriminatory metabolites capable of distinguishing
smokeless tobacco users from nonusers more effectively with higher
sensitivity and specificity. Furthermore, metabolic pathway analysis
depicted several dysregulated pathways in smokeless tobacco users,
including arginine biosynthesis, beta-alanine metabolism, TCA cycle,
etc. This study devised a novel strategy to identify exposure biomarkers
among smokeless tobacco users by combining metabolomics and machine
learning algorithms.
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