Investigations Into Illicit Synthesis of Fentanyl

Fentanyl is a powerful synthetic opioid that is an effective and widely used analgesic in medicine. Over the last decade however, it has also had a devastating impact in some countries as an illicit drug. Fentanyl is approximately 80 to 100 times more potent as an analgesic than morphine, and due to its widespread availability and low cost, it has fuelled the opioid crisis in North America, leading to record numbers of overdoses and fatalities, with an estimated 76,226 deaths in the US for 2022 (National Center for Health Statistics 2024). Efforts to combat its impact have included law enforcement measures and public health campaigns to raise awareness. Despite these efforts, the crisis continues to strain healthcare and emergency services and cause misery for millions, with an estimated impact of $1.5 trillion on the US economy in 2020 alone (US Congress Joint Economic Committee 2022). This highlights the need for more comprehensive strategies, including greater research into how illicit fentanyl is manufactured. Over the last decade, five alternative synthesis methods have been published in scientific journals and on drug enthusiast websites detailing optimised processes for producing fentanyl. As governments have sought to stem the flood of the drug into the market, key precursors were placed under tight regulations and controls. Sadly, this only resulted in adaptation from organised crime groups, who now employ these alternative methods to maintain production. This thesis largely focuses on four of these principal synthetic approaches. These are the Siegfried, Valdez, One-Pot and Dieckmann methods, with some supplemental analysis of the original Janssen and emerging Gupta-patent method. Chemical profiling methods are based on determining and quantifying a particular compound of interest while also identifying the impurities present. The characterisation of the impurities can be used as an impurity profile, chemical fingerprint, or Chemical Attribution Signature (CAS). In the case of illicit synthetic drugs, certain impurities are often route-specific. This might be due to the available starting material, poor chemical handling during synthesis, side reactions of the intermediates formed, inadequate purification procedures, or contamination either in the reagents, the adulterants or diluents. Identifying and understanding the impurity profiles would allow drug samples to be exploited for chemical forensic information, such as determining their method of synthesis. The need to detect fentanyl and its analogues in the field has increasingly become important to prevent unintentional exposure to first responders or the public. Because of this, the use of portable spectrometers has been increasing, but these can lack sensitivity. To address this, machine-learning algorithms can be used to improve the information gained from handheld devices to help protect law enforcement and first responders and inform the next steps for more detailed laboratory analyses. In support of both drug profiling and improving portable spectrometers, the integration of chemometric data analysis techniques such as Multivariate Analysis (MVA) can assist in the analysis and interpretation of complex impurity profile patterns derived from spectroscopy data. They can also predict if/how samples can be classified by their synthetic methods due to unique impurity profiles. To address these challenges, this thesis seeks to contribute to existing knowledge of impurity profiles and investigate the utility of portable instruments coupled with chemometric techniques. The first study in this thesis was an in-depth review and evaluation of the known synthesis methods, to determine each method's optimum conditions and key features. This was followed by the analysis of low-field Nuclear Magnetic Resonance (NMR) spectroscopy data by MVA techniques, which effectively facilitated the classification of the fentanyl precursors, N-phenethyl-4-piperidone (NPP) and 4-anilino-N-phenethylpiperidine (ANPP). This study revealed that 1H low-field NMR spectra contain sufficient information for successful MVA, and subsequent classification based on the distinctive impurity profiles associated with each synthetic method. It has underscored the utility of low-field benchtop NMR in the forensic attribution of clandestine fentanyl, particularly in situations where high-field NMR may not be accessible. In a third study investigating the utility of field-portable instruments, data obtained from portable Fourier-transform infrared (FTIR) and Raman instruments for NPP and ANPP samples were analysed to determine their specific synthesis methods using chemometric approaches. The study revealed that both FTIR and Raman spectra contain sufficient information for effective MVA and subsequent classification for ANPP, with models exhibiting excellent fitting and predictive abilities. However, when it came to discriminating between the Siegfried, Valdez, or Dieckmann classes for NPP, neither FTIR nor Raman alone proved sufficient. To address this limitation, both low-level and mid-level data fusion were explored, where mid-level data fusion yielded the best results, surpassing the individual instruments by significantly reducing variables, demonstrating good predictive power, and correctly classifying all test samples. In the final two studies, NPP, ANPP and fentanyl were investigated by Liquid Chromatography – High-Resolution Mass Spectrometry (LC-HRMS) and MVA. The fourth study identified twenty-two impurities specific to the Janssen and twenty-one to the Siegfried method. Additional profiling of NPP and ANPP identified a further twenty-three impurities present in both ANPP and the final fentanyl. Of these, eleven were specific to the Janssen method and five to the Siegfried method. A group of carbamate impurities identified during this study was of particular interest, with three being indicative of the Valdez or Siegfried methods and one specific for just the Valdez method. The fifth study saw the NPP and ANPP LC-HRMS data coupled with MVA to build models for method attribution. This study again identified the same group of carbamate impurities and provided a proof of concept for identifying ANPP samples and subsequently classifying them by a synthetic method. This research is significant because it adds to the existing knowledge of impurity profiles of fentanyl and its precursors NPP and ANPP, which could attribute an unknown sample to a specific method. This could assist forensic chemists in any future studies or investigations and support community awareness as well as other harm reduction strategies. Due to the significant threat posed by the high toxicity of fentanyl, any additional forensic information that can supplement the current knowledge pool for law enforcement and federal security agencies will be of significant benefit.<p></p>