The opioid offensive: Fighting fentanyl

Any drug lord worth his salt knows he needs to stay one step ahead of the law. In today’s world, that means the chemists in his clandestine laboratories play a mean game of molecular hide-and-seek. For example, constantly tweaking the chemical structure of the potent opioid fentanyl to create novel analogs that evade forensic detection. Now, researchers have developed a new detection method using machine learning and computer-generated libraries to assist in the identification of fentanyl variants. Rachel Berkowitz looks through the proverbial microscope for SN.

🧪 Chemical catalogs to the rescue

The core challenge in identifying fentanyl variants is that while they all share a common molecular profile, the peripheral chemical groups can be endlessly modified, leading to billions of possible formulas. By digitally breaking up known fentanyl analogs into fragments and shuffling them into new combinations, scientists created a database of over 1 billion hypothetical fentanyl analogs. Then they applied machine learning to predict their real-world signatures in techniques including mass spectrometry. While the results have not yet been peer-reviewed, the accuracy with which the research team was able to identify fentanyl analogs in samples was promising for forensic labs that monitor the rapidly evolving synthetic drug landscape.