Photonic platform coupled with machine learning algorithms to detect pyrolysis products of crack cocaine in saliva: A proof-of-concept animal study

Abstract

The non-invasive detection of crack/cocaine and other bioactive compounds from its pyrolysis in saliva can provide an alternative for drug analysis in forensic toxicology. Therefore, a highly sensitive, fast, reagent-free, and sustainable approach with a non-invasive specimen is relevant in public health. In this animal model study, we evaluated the effects of exposure to smoke crack cocaine on salivary flow, salivary gland weight, and salivary composition using Attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy. The exposure to crack cocaine was performed in an acrylic box apparatus with a burned activation of crack/cocaine 400 mg for 10 min for 14 consecutive days. Crack/cocaine exposure increased the salivary secretion without changes in parotid and submandibular weights. Hierarchical Clustering Analysis (HCA) was applied to depict subgrouping patterns in infrared spectra, and Principal components analysis (PCA) explained 83.2 % of the cumulative variance using 3 PCs. ATR-FTIR platforms were coupled to AdaBoost, Artificial Neural Networks, Naïve Bayes, Random Forest, and Support Vector Machine (SVM) algorithms tool to identify changes in the infrared salivary spectra of rats exposed to crack cocaine. The best classification of crack cocaine exposure using the salivary spectra was performed by Naïve Bayes, presenting a sensitivity of 100 %, specificity of 80 %, and accuracy of 90 % between crack cocaine and control rats. The SHAP features of salivary infrared spectra mostly indicate the vibrational modes at 1331 cm^-1 and 2806 cm^-1, representing CH₂ wagging commonly linked in lipids and C-H stretch often attributed to the CH₂ or CH₃ groups in lipid molecules, respectively, as the main responsible vibrational modes for crack cocaine exposure discrimination. In summary, the present pre-clinical findings indicate the potential of the ATR-FTIR platform coupled with machine learning to effectively detect changes in salivary infrared spectra promoted by exposure to crack cocaine.

Keywords: ATR-FTIR; Artificial intelligence algorithms; Crack cocaine; Flow salivary; Screening tool.