Project Updates

Congratulations to Dr. Kathleen Hayes!

Author: Marya Lieberman

June 22, 2023--After defending her dissertation, "Methods for Pharmaceutical and Illicit Drug Analysis," Kathleen Hayes is now a PhD and will move on to Abbvie as a medical writer.  Good luck Kat, we will miss you!  

 

2023 Kathleen Kat Hayes Copy
2023 Kathleen Kat Hayes Copy

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Congratulations to Naxos Neighbors and Marya Lieberman, winners of ND Lucy Institute's $51,000 Next Changemaker Prize

Author: Marya Lieberman

Joanne Cogdell, CEO of harm reduction organization Naxos Neighbors, and her collaborator Marya Lieberman, University of Notre Dame, were awarded the 2023 Next Changemaker Award from the Lucy Institute for Data and Society. The award of $51,000 will support our efforts to find emerging hazards in street drugs such as fentanyl and fentanyl analogs, xylazine, benzodiazepines, and nitazines.  These superpotent opioids are associated with over 100 fatal overdose per year in this county and cause many other types of harm to people who use drugs, their families and friends, and the entire community.  …

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Ola presents NIR project at NOBCChe

Author: Marya Lieberman

Olatunde Awosiji Awotunde presented a poster on "Combining orthogonal analytical techniques to identify substandard or falsified formulations of pharmaceuticals " at the 2023 NOBCChE meeting.  Congratulations to Ola on representing Notre Dame at this national event!  

 

2023 Olatunde At Nobcche Meeting

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Heather Whitehead and Kathleen Hayes published in Forensic Chemistry, part 2

Author: Marya Lieberman

Congratulations to Heather and Kat--their manuscript, "Development and validation of a liquid chromatography tandem mass spectrometry method for the analysis of 53 benzodiazepines in illicit drug samples," was just accepted by Forensic Chemistry! This manuscript follows the earlier method for analysis of common drugs and drug metabolites published in 2022.  …

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How does the Scott Test for cocaine work?

Author: Marya Lieberman

The Scott test has been used for over 50 years as a presumptive test for cocaine.   It involves a series of reagent additions and color changes...and if it's not done and interpreted correctly, it can send innocent people to jail.  This crystal structure now published in Acta Cryst E gives insight into what's going on in the test.  

Bis(N,N-diethyl-4-methyl-4-piperazine-1-carboxamide) tetra­kis­(iso­thio­cyanato-κN)­cobalt(II), a model compound for the blue color developed in the Scott test. Oliver, A. G., Lockwood, T.-L. E., Zinna, J. & Lieberman, M. (2023). Acta Cryst. E79, 163-166

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Turkey Run 2022

Author: Marya Lieberman

2022 Group At Turkey Run Analytical Conference Heather And Kat Organizers
The Lieberman group takes a moment to relax at Turkey Run

Turkey Run Analytical Conference 2022 was held at Turkey Run State Park in Indiana, and attracted researchers from Indiana, Ohio, and Illinois. In addition to Heather, Kat, Alyssa, Rachel, and Ornella, undergraduates Devena and Sarah presented posters, and Yukun from the Peaslee group also gave a poster. Congratulations to Heather Whitehead and Kathleen Hayes for organizing a great scientific meeting!  …

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Congratulations to Olatunde Awosiji Awotunde, Nicholas Roseboom, and Jin Cai!

Author: Marya Lieberman

Discrimination of Substandard and Falsified Formulations from Genuine Pharmaceuticals Using NIR Spectra and Machine Learning

Near-infrared (NIR) spectroscopy is a low-cost analytical tool for rapid characterization of pharmaceuticals. Usually, it's necessary to build up a "library" of NIR spectra from authentic products in order to identify real and fake formulations.  In this paper, we developed a simple training method that uses binary mixtures of a pharmaceutical (acetaminophen) with two different cutting agents, and used several different data analysis models to extrapolate the training samples to real and fake samples of popular brands of acetaminophen tablets.   While none of the models was able to correctly classify all the samples, a simple voting algorithm was able to compensate for the weaknesses of the models and classify 93% of the samples correctly.  …

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