Towards compound identification of synthetic opioids in nontargeted screening using machine learning techniques

Area of Science:

• Forensic Chemistry
• Analytical Chemistry
• Computational Chemistry

Background:

• The evolving illicit drug market presents challenges in identifying novel psychoactive substances.
• Reliance on certified reference materials is often costly and impractical for a wide range of emerging drug analogues.

Purpose of the Study:

• To develop and evaluate machine learning models for the rapid, putative identification of synthetic opioids.
• To aid forensic analysts in prioritizing analytical efforts and reducing the need for extensive reference material procurement.

Main Methods:

• Development of a Naïve Bayes classifier for opioid class prediction (fentanyl analogues, AH series, U series).
• Implementation of Gaussian process regression for predicting analyte retention times based on molecular features.
• Training and validation of models using experimental chromatographic and structural data of synthetic opioids.

Main Results:

• The class prediction model achieved 89.5% accuracy in classifying opioids, performing best on fentanyl analogues.
• The retention time prediction model accurately predicted retention times within ±0.1 minutes for 79.7% of synthetic opioid samples.
• These models demonstrated the potential to guide analysts in structure elucidation and reduce reliance on physical standards.

Conclusions:

• Machine learning offers a powerful, cost-effective approach to supplement traditional methods for identifying unknown synthetic opioids.
• Integrating predictive models into workflows can enhance confidence in putative identifications and streamline the analysis process.
• This strategy can significantly reduce the financial burden associated with acquiring numerous certified reference materials.