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Chemometric Processing of DART-HRMS-derived Dark Matter for the Identification of New Psychoactive Substances

Award Information

Award #
Funding Category
Competitive Discretionary
Congressional District
Funding First Awarded
Total funding (to date)

Description of original award (Fiscal Year 2017, $558,282)

As submitted by the proposer:

The rapid and continued emergence of new psychoactive substances (NPSs), including synthetic cathinones, cannabinoids, opioids, and tryptamines, has imposed unique challenges on crime labs, primarily because law enforcement agencies have been unable to keep abreast of the influx of novel variants that appear on the market within days-to-weeks after earlier generations of new synthetic structures have been identified and banned.

One of the most significant of these challenges is the resource-intensive and time-consuming nature of the structure elucidation process. Conventional approaches to structure determination are often sub-optimal in this regard because the classes of molecules to which NPS belong often undergo fragmentation by EI-MS that is so extensive as to render the acquired data of little use. Further complicating matters is the challenge toxicologists face in determining the cause of death in overdose cases where the drug that caused death is unknown, and its metabolites, although detected, cannot be structurally characterized because of limited amounts of sample.

The proposed project exploits the unique capabilities of DART-HRMS in retaining parent molecule and fragment ion information (under in-source collision-induced dissociation conditions), for the purpose of elucidation of the structures of novel variants of NPSs.

The approach involves compilation of a training set of DART-HRMS-derived neutral loss or “dark matter” spectra of known psychoactive substances (specific aims I and II). The neutral loss spectra of each of the four classes of synthetic molecules will be subjected to multivariate statistical analysis to develop a tool to classify known compounds based on shared structural features (specific aim III). The further application of multivariate statistical analysis tools to this data is then used to develop a classification system that permits the sorting and classification of the data from unknown samples in terms of their similarity to the structural features of compounds in the training set (specific aim IV). In specific aim V, we will confirm that parent NPSs from which metabolites produced by human liver enzymes are derived, can be determined using the screening mechanism developed in specific aims I-IV.

Anticipated outcomes include the creation of an accessible neutral loss database against which NPS unknowns can be screened in order to classify them and rapidly identify their structures. Semi-annual and final progress and financial reports as well as scholarly products (e.g., peer reviewed articles) will be produced.

Note: This project contains a research and/or development component, as defined in applicable law, and complies with Part 200 Uniform Requirements - 2 CFR 200.210(a)(14).


Date Created: September 29, 2017