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Development of Chemical Sensors for Rapid Identification of Amphetamine-Related New Psychoactive Substances

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Development of Chemical Sensors for Rapid Identification of Amphetamine-Related New Psychoactive Substances Development of Chemical Sensors for Rapid Identification of Amphetamine-Related New Psychoactive Substances Kathryn Emily Kellett Submitted to the University of Hertfordshire in partial fulfilment of the requirements of the degree of Doctor of Philosophy. Supervisors: Dr. Jacqueline L. Stair, Dr. Suzanne Fergus, Dr. Stewart B. Kirton and Prof. Andrew J. Hutt Department of Pharmacy, School of Life and Medical Sciences, University of Hertfordshire November 2016 Abstract A molecular receptor for mephedrone, an amphetamine-like NPS, was developed using host-guest chemistry and pharmacophoric design. The in-field detection of new psychoactive substances (NPS) is an area that has garnered considerable attention in the last few years. With the continuously expanding number of NPS on the market, traditional detection mechanisms lack the selectivity needed. In this project a new methodology has been developed for the design of host molecules for use in in-field detection, based on biomimetic design. To understand what a sensory molecular needs to be selective against, GC-MS and HPLC analysis were employed to identify and quantify thirteen aminoindane internet samples. It was found that the composition of internet samples varies greatly in terms of concentration of active ingredient, with a range of 17-95 % w/w of active ingredient identified. It was also found that caffeine was the most common cutting agent with a range of 27.7-30.2 % w/w identified. This highlights the need for both selectivity and sensitivity in detection mechanisms. Using the principles of biomimetic design, a methodology for the treatment of protein-ligand interactions was developed. Protein-ligand binding data collected from the Protein Databank was analysed for mephedrone related structures and common cutting agents, identified through aminoindane internet sample analysis and literature sources. From this work a three-point pharmacophoric model was developed, upon which two host molecules were considered, macrocyclic calixarenes and acyclic anthraquinones. Both contained the three binding interactions deduced from the pharmacophore design; two p-stacking interactions and one hydrogen bond acceptor. The final host molecule taken forward for testing was 1,8-dibenzylthiourea anthracene (Probe 1). The binding affinity of Probe 1 to mephedrone was tested using 1H-NMR. An estimated association constant of 104 M-1 was calculated, with a 1:1 binding stoichiometry. Along with ESI-MS and DFT calculations, it was found that mephedrone binds to Probe 1 in a concerted fashion with a three-point binding geometry, with two hydrogen bonds and one p-stacking interaction. A modest optical response using fluorescence spectroscopy was also observed between mephedrone and Probe 1 at high molar concentrations. A more pronounced response was observed upon addition of high molar concentrations of flephedrone. 1H-NMR showed that Probe 1 selectively bound mephedrone over methamphetamine as well as the four most common cutting agents identified from literature: lidocaine, caffeine, paracetamol and benzocaine, which have been shown to cause false positives in previous studies. Probe 1 showed significant selectivity for the β-ketoamine arrangement. This is supported by the systematic analysis of mephedrone, methamphetamine, mephedrone precursor and flephedrone. This is the first time this has been achieved using host-guest chemistry. A protocol was developed to successfully detect mephedrone via Probe 1 using NMR spectroscopy in a simulated street sample containing two of the most common cutting agents, benzocaine and caffeine. To further aid future design of small host molecules a methodology for the in silico analysis of small molecule host-guest binding using metadynamics was explored. Solvent interactions with the host and guest molecules were observed, highlighting the importance of solvent choice in binding studies. Metadynamics shows potential to be used in further work for improving the approach in which host molecules are designed in future. 3 Acknowledgements First and foremost I must thank my principal supervisor, Dr. Jacqueline Stair. It goes without saying that without her I could never have got through the last 5 years. She has gone above and beyond the role of a supervisor time and time again, and I will be forever grateful for her guidance and support. I would also like thank the rest of my supervisory team, Dr. Suzanne Fergus, Dr. Stewart Kirton and Prof. Andrew Hutt for their patience and help throughout my PhD. To Prof. Karl Wallace, who welcomed me into his group at USM, I am very grateful for your time and support. I learnt so much from you in such a short time, which has helped me find new areas of chemistry that I truly love. I would also like to thank the Winston Churchill Memorial Trust for giving me this opportunity to learn. Dr. Hugh Broome I am incredibly grateful for your friendship and support during and after my time at USM, and I look forward to the many adventures still to come. To all the chemistry lecturers at UH, thank you for always allowing me to call on your expertise. In particular Dr. Ute Gerhard for her guidance on everything NMR! Also Prof. Mire Zloh for all your help with the metadynamics and inspiring me every day, and for all the DFT calculations you performed for the sensor work. I would like to thank all my colleagues at UH who have made my experience one to remember; in particular my office mates Andy, Hassan, Amira and Dhruv. I would like to thank all the technical staff at UH. Especially Dr. Mark Scott, without whom none of the instruments would work, and I would have no data. You have continuously gone above and beyond your role and I am very grateful for all you have taught me. I would like to thank all my family; Mum, Dad, Jenny, Naomi and Mick for your unwavering support for all my crazy plans. To Dad, thank you for everything. To my extremely patient Fiancé Ruan, thank you for your patience, support, love and the countless Ryanair flights. 4 List of Abbreviations Abbreviation Definition ∆E Change in Energy 2-AI 2-Aminoindane 5-IAI 5-Iodo-2-aminoindane Å Angstroms ACMD Advisory Council for the Misuse of Drugs ACN Acetonitrile ADHD Attention deficit hyperactivity disorder AlCl3 Aluminium Chloride BZP Benzylpiperazine CAS Chemical Abstract Service CDCl3 Chloroform CI Chloride CNS Central nervous system CPU Central processing unit CV Collective Variable DCM Dichloromethane DFT Density functional theory DMSO Dimethyl sulfoxide DXM Dextromethorphan EI Electron ionisation EMCDDA European Monitoring Centre for Drugs and Drug Addiction eq. Equivalence ESI Electrospray ionisation EtOAc Ethyl acetate FT-IR Fourier transform infrared spectroscopy GC-MS Gas chromatography mass spectrometry HCl Hydrochloride HOMO Highest occupied molecular orbital HPLC High performance liquid chromatography Hz Hertz ICH International Council for Harmonisation ICP-AES Inductively coupled plasma atomic emission spectroscopy ICP-MS Inductively coupled plasma mass spectrometry ICT Intermolecular charge transfer IPA Isopropyl alcohol IR Infrared K Kelvin Ka Association constant Kd Dissociation constant LC-MS Liquid chromatography mass spectrometry LC-MS-MS Liquid chromatography tandem mass spectrometry LGC Laboratory of the Government Chemist LLOQ Lower limit of quantification LOD Limit of detection LOQ Limit of quantification LSD Lysergic acid diethylamide 6 LUMO Lowest unoccupied molecular orbital m/z Mass to charge ratio MCMM Monte Carlo molecular modelling MDAI 5,6-Methylenedioxy-2-aminoindane MDMA 3,4-Methylenedioxymethamphetamine MgSO4 Magnesium Sulphate MMAI 5-Methoxy-6-methyl-2-aminoidane m.p. Melting Point MPA Methiopropamine MS Mass spectrometry MXE Methoxetamine NIST National Institute of Standards and Technology NMR Nuclear magnetic resonance NPS New psychoactive substance OPLS Optimized Potentials for Liquid Simulations Pd/C Palladium on Carbon PDA Photodiode array PDB Protein DataBank pKa Acid dissociation constant ppm Parts per Million PRCG Polak-Ribière conjugate gradient PTFE Polytetrafluoroethylene RMS Root mean square RMSD Root mean square deviation 7 SA Simulated annealing SWGDRUG Scientific Working Group Drug TBACl Tetrabutylammonium chloride TLC Thin layer chromatography TMS Tetramethylsilane UNODC United Nations Office on Drugs and Crime UV/Vis Ultraviolet and visible spectroscopy �max Maximum wavelength �ex Excitation wavelength mg Milligrams µg Microgram 8 Table of Contents Abstract ............................................................................................................................ 2 Acknowledgements ........................................................................................................... 4 List of Abbreviations .......................................................................................................... 5 Chapter 1 General Introduction .................................................................................. 23 1.1 History of Psychoactive Substances ............................................................................... 23 1.1.1 Emergence of New Psychoactive Substances (NPS) ..................................................... 25 1.1.2 Cathinones .................................................................................................................... 28 1.1.3 Aminoindanes ............................................................................................................... 29 1.1.4 Constituents in ‘Marketed’ Products ...........................................................................
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