A data-driven methodology to discover similarities between cocaine samples

Abstract

Machine learning has been used for distinct purposes in the science field but no applications on illegal drug have been done before. This study proposes a new web-based system for cocaine classification, profiling relations and comparison, that is capable of producing meaningful output based on a large amount of chemical profiling’s data. In particular, the Profiling Relations In Drug trafficking in Europe (PRIDE) system, offers several advantages to intelligence actions across Europe. Thus, it provides a standardized, broad methodology which uses machine learning algorithms to classify and compare drug profiles, highlight how similar drug samples are, and how probable it is that they share a common origin, batch, or preparation process. We evaluated the proposed algorithms using precision and recall metrics and analyzed the quality of predictions performed by the algorithms, with respect to our gold standard. In our experiments, we reached a value of 88% for F0.5-measure, 91% for precision, and 78% for recall, confirming our main hypothesis: machine learning can learn and be applied to have an automatic classification of cocaine profiles.

Cocaine profiling method retrospectively developed with nontargeted discovery of markers using liquid chromatography with time‐of‐flight mass spectrometry data

“…In this study, machine learning concepts such as unsupervised clustering algorithms were utilized; however, model development was mainly inspired by strategies adopted by others. Recent research has successfully produced a model for automatic cocaine classification using data‐driven approaches based on machine learning classification tools 39 . This demonstrates the possibility for further innovation within impurity profiling research by incorporating machine learning tools in the model development and comparison processes.…”

“…Recent research has successfully produced a model for automatic cocaine classification using data‐driven approaches based on machine learning classification tools. 39 This demonstrates the possibility for further innovation within impurity profiling research by incorporating machine learning tools in the model development and comparison processes. Further research should therefore aim to explore the wider possibilities of these tools and aim to incorporate them within the research.…”

Approaches to priority identification in digital health in ten countries of the Global Digital Health Partnership

“…The U.S. Food and Drug Administration (FDA) has already promoted the international harmonization of approaches for expediting the global adoption of emerging technologies in the drug monitoring system in order to avoid drug shortages ( 46 ). Different examples of AI applications in the illicit traffic and usage of substances can be found in literature ( 47 ).…”