In Silico Infrared Characterization of Synthetic Cannabinoids by Quantum Chemistry and Chemometrics

The concept of forensic sciences as mere trace analysis has been modified by the idea of forensic intelligence, which entails applying data to make decisions within the investigative process. Many countries are engaged in combating drug trafficking and drug use because they are related to public health and safety issues. Prohibiting the consumption of traditional drugs has led new psychoactive substances (NPSs) to emerge. NPSs consist of compounds that resemble the initially banned substance and which aim to mimic the traditional drug recreational effects while circumventing drug legislation. For example, synthetic cannabinoids are sprayed on herbal products to reproduce the cannabis recreational effects. According to the United Nations Office on Drugs and Crime (UNODC), the toxic effects of synthetic cannabis types are unknown, and harm and fatalities associated with the use of these drugs have been reported. Information on the characterization related to these species is lacking. The rate at which NPSs appear poses a significant challenge because employing conventional methods to understand the characteristics of these compounds may require time and be costly. This work uses in silico practices as an alternative to understand how NPSs related to cannabis behave. We apply quantum chemistry methods to evaluate several synthetic cannabinoids recognized in forensic samples. More specifically, we generate infrared spectra that can be employed as a benchmark for NPSs. We apply a multivariate classification to evaluate the results. We conclude that in silico methods are an alternative that provide information about the spectra of undetected substances. This information can help to identify new drugs, to increase knowledge about them, and to feed information procedures.