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Mass Spectrometric Characterization of Emerging Synthetic Drugs and an Algorithm for Confident Identifications
Monday, March 08, 2021: 3:05 PM - 3:25 PM
Speaker(s)
Description
The primary focus of this work is to develop a better understanding of the fragmentation behavior of emerging synthetic drugs in different types of mass spectrometers. The goals are to assist practitioners with their understanding of current casework, and to help them more-readily identify new drugs as they emerge. A secondary focus of this work is the development of a novel algorithm for the comparison of mass spectra. The goals of this sub-project are to provide higher levels of confidence in drug identifications and to provide more reliable estimates for the uncertainty of drug identifications. The first project uses isotope labeling, multi-stage mass spectrometry (MSn), ion spectroscopy, and accurate mass measurements with high resolution mass spectrometry to demonstrate some important—but previously unidentified—rearrangement mechanisms in the spectra of cathinones and fentanyl analogs. We also employ gas-phase ion spectroscopy and DFT calculations have helped to identify two important intermediates in the fragmentation spectra of alpha-PVP, both of which are based on a phthalane core. These intermediates are important in understanding the mass spectra of a wide variety of cathinones. The novel algorithm for the comparison of mass spectra takes advantage of the fact that the variance in ion abundances of replicate spectra are not independently variable, as has long been assumed. The algorithm uses a general linear regression model (GLM) to predict the ion abundances of each of the 15 most abundant ions in a mass spectrum of a questioned sample. A binary classifier then uses the correctness of the predicted ion abundances to decide whether or not to identity the questioned sample as a particular drug. Using external validation spectra of hundreds of replicate spectra, the algorithm predicts abundances with a precision that is typically five times better than models that assume a fixed exemplar.
Additional Info
Keywords: Please select up to 4 keywords ONLY:
Clinical/Toxicology,Data Analysis and Manipulation
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