This study compares, qualitatively and quantitatively, an existing workflow for seized drug analysis to an experimental workflow.
As the challenges faced by drug chemists persist, due to the presence of emerging drugs, laboratories continue to look for new solutions, ranging from existing methods to implementation of entirely new technology. A common barrier for making workflow changes is a lack of pre-existing data demonstrating the potential impact of these changes. In the current project, four chemists were asked to analyze a total of 50 mock case samples across the two workflows. The existing workflow employed color tests for screening alongside general purpose GC-FID and GC-MS analyses for confirmation. The experimental workflow combined DART-MS screening with class-specific (targeted) GC-MS analysis for confirmation. Comparison of the workflows showed that screening by DART-MS required the same amount of time as color tests but yielded more accurate and specific information. Confirmation using the existing workflow required more than twice the amount of instrument time and data interpretation time while also presenting other analytical challenges that prevented compound confirmation in select samples. Targeted GC-MS methods simplified data interpretation, reduced consumption of reference materials, and addressed almost all limitations of general-purpose methods. While the experimental workflow requires modifications and answering of additional research questions, this study shows how rethinking analytical workflows for seized drug analysis could reduce turnaround times, backlogs, and standards consumption. It also demonstrates the potential impact of being able to investigate workflow changes prior to implementation. (Publisher Abstract)
Downloads
Similar Publications
- Improving and Evaluating Computed Tomography and Magnetic Resonance Imaging in the Investigation of Fatalities Involving Suspected Head Trauma
- Large-scale Selection of Highly Informative Microhaplotypes for Ancestry Inference and Population Specific Informativeness
- IS2aR, a Computational Tool to Transform Voxelized Reference Phantoms into Patient-specific Whole-body Virtual CTs for Peripheral Dose Estimation