Rapid In Situ Detection of THC and CBD in Cannabis sativa L. by 1064 nm Raman Spectroscopy

The need to find a rapid and worthwhile technique for the in situ detection of the content of delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD) in Cannabis sativa L. is an ever-increasing problem in the forensic field. Among all the techniques for the detection of cannabinoids, Raman spectroscopy can be identified as the most cost-effective, fast, noninvasive, and nondestructive. In this study, 42 different samples were analyzed using Raman spectroscopy with 1064 nm excitation wavelength. The use of an IR wavelength laser showed the possibility to clearly identify THC and CBD in fresh samples, without any further processing, knocking out the contribution of the fluorescence generated by visible and near-IR sources. The results allow assigning all the Raman features in THC- and CBD-rich natural samples. The multivariate analysis underlines the high reproducibility of the spectra and the possibility to distinguish immediately the Raman spectra of the two cannabinoid species. Furthermore, the ratio between the Raman bands at 1295/1440 and 1623/1663 cm^–1 is identified as an immediate test parameter to evaluate the THC content in the samples.

Filter Feature Selection for Unsupervised Clustering of Designer Drugs Using DFT Simulated IR Spectra Data

The rapid emergence of novel psychoactive substances (NPS) poses new challenges and requirements for forensic testing/analysis techniques. This paper aims to explore the application of unsupervised clustering of NPS compounds' infrared spectra. Two statistical measures, Pearson and Spearman, were used to quantify the spectral similarity and to generate similarity matrices for hierarchical clustering. The correspondence of spectral similarity clustering trees to the commonly used structural/pharmacological categorization was evaluated and compared to the clustering generated using 2D/3D molecular fingerprints. Hybrid model feature selections were applied using different filter-based feature ranking algorithms developed for unsupervised clustering tasks. Since Spearman tends to overestimate the spectral similarity based on the overall pattern of the full spectrum, the clustering result shows the highest degree of improvement from having the nondiscriminative features removed. The loading plots of the first two principal components of the optimal feature subsets confirmed that the most important vibrational bands contributing to the clustering of NPS compounds were selected using non-negative discriminative feature selection (NDFS) algorithms.

Piloting the UK's First Home-Office-Licensed Pharmacist-Led Drug Checking Service at a Community Substance Misuse Service

(1) Introduction: Drug-related deaths in the UK are at concerning high levels. The unknown content and purity of illicit substances can cause unpredictable adverse effects and thus a public health risk with no sign of abating. On-site drug checking is a public health strategy that has previously been implemented, predominantly in festival settings, but without Home Office licensing. (2) Aims: The aim of this study was to pilot the UK's first pharmacist-led, Home Office-licensed community drug checking service. (3) Methods: A bespoke protocol incorporating legally, professionally and ethically binding documents was implemented. This free, confidential service ran between February and March 2019, was available to anyone over 18 who were purposefully recruited, gave informed consent and agreed to relinquish their drug sample. Samples were checked on-site within an established Substance Misuse Service (SMS) using a handheld Raman spectrometer to determine likely drug content and adulterants. In parallel, participants completed a questionnaire about their substance use and the drug sample(s) being tested. A pharmacist-led multidisciplinary approach was adopted to discuss the analytical findings. Informed by the results of the analysis and the questionnaire, people who used the service received tailored harm reduction advice. (4) Results and Discussion: The pilot operated for a total of four days over four weeks. Eleven people visited and relinquished a total of thirteen samples. Half of the participants had previously overdosed and were known to the SMS. Seventy per cent were male, all were White British individuals, 30% were employed and two people disclosed visiting from another nearby town. Samples included what was thought to be heroin, synthetic cannabinoids, stimulants, benzodiazepines and LSD and none required activation of the "alerts cascade" process. Most participants drank alcohol regularly and the concomitant use of traditional illicit drugs and prescribed medication (including opioids, anxiolytics and antidepressants) with sedating profiles was common. Given some of the ethical decisions and interpretation of the results, specialist pharmacist involvement was deemed essential. (5) Conclusions: This pilot demonstrated the proof-of-concept that a pharmacist-led Home Office-licensed drug checking service can be successfully implemented in community SMSs.

Design of Novel Drug-like Molecules Using Informatics Rich Secondary Metabolites Analysis of Indian Medicinal and Aromatic Plants

BACKGROUND

Several medicinal plants are being used in Indian medicine systems from ancient times. However, in most cases, the specific molecules or the active ingredients responsible for the medicinal or therapeutic properties are not yet known.

OBJECTIVE

This study aimed to report a computational protocol as well as a tool for generating novel potential drug candidates from the bioactive molecules of Indian medicinal and aromatic plants through the chemoinformatics approach.

METHODS

We built a database of the Indian medicinal and aromatic plants coupled with associated information (plant families, plant parts used for the medicinal purpose, structural information, therapeutic properties, etc.) We also developed a Java-based chemoinformatics open-source tool called DoMINE (Database of Medicinally Important Natural products from plantaE) for the generation of virtual library and screening of novel molecules from known medicinal plant molecules. We employed chemoinformatics approaches to in-silico screened metabolites from 104 Indian medicinal and aromatic plants and designed novel drug-like bioactive molecules. For this purpose, 1665 ring containing molecules were identified by text mining of literature related to the medicinal plant species, which were later used to extract 209 molecular scaffolds. Different scaffolds were further used to build a focused virtual library. Virtual screening was performed with cluster analysis to predict drug-like and lead-like molecules from these plant molecules in the context of drug discovery. The predicted drug-like and lead-like molecules were evaluated using chemoinformatics approaches and statistical parameters, and only the most significant molecules were proposed as the candidate molecules to develop new drugs.

RESULTS AND CONCLUSION

The supra network of molecules and scaffolds identifies the relationship between the plant molecules and drugs. Cluster analysis of virtual library molecules showed that novel molecules had more pharmacophoric properties than toxicophoric and chemophoric properties. We also developed the DoMINE toolkit for the advancement of natural product-based drug discovery through chemoinformatics approaches. This study will be useful in developing new drug molecules from the known medicinal plant molecules. Hence, this work will encourage experimental organic chemists to synthesize these molecules based on the predicted values. These synthesized molecules need to be subjected to biological screening to identify potential molecules for drug discovery research.

Designer drugs: a medicinal chemistry perspective (II)

During 2012-2018, the clandestine manufacture of new psychoactive substances (NPS) designed to circumvent substance control regulations increased exponentially worldwide, with concomitant increase in fatalities. This review focuses on three compound classes identified as synthetic opioids, synthetic amphetamines, and synthetic cannabinoids and highlights the medicinal chemistry precedents utilized by clandestine laboratories to develop new NPS with increased brain penetration, longer duration of action, and greater potency. Chemical approaches to illicit drug abuse treatment options, particularly for opioid use disorder, are also discussed.

Exploration of the Use of New Psychoactive Substances by Individuals in Treatment for Substance Misuse in the UK

Substance misuse services need to meet the growing demand and needs of individuals using new psychoactive substances (NPS). A review of the literature identified a paucity of research regarding NPS use by these individuals and UK guidelines outline the need for locally tailored strategies. The purpose of this qualitative study was to identify and explore key themes in relation to the use of NPS by individuals receiving community treatment for their substance use. Electronic records identified demographics and semi-structured interviews were undertaken. A thematic analysis of transcripts identified a variety of substance use histories; 50% were prescribed opiate substitutes and 25% used NPS as a primary substance. All were males, age range 26–59 years (SD = 9), who predominantly smoked cannabinoids and snorted/injected stimulant NPS. The type of NPS used was determined by affordability, availability, side-effect profile and desired effects (physical and psychological: 25% reported weight loss as motivation for their use). Poly-pharmacy, supplementation and displacement of other drugs were prevalent. In conclusion, NPS use and associated experiences vary widely among people receiving substance use treatment. Development of effective recovery pathways should be tailored to individuals, and include harm reduction strategies, psychosocial interventions, and effective signposting. Services should be vigilant for NPS use, “on top” use and diversion of prescriptions.

Detection of newly emerging psychoactive substances using Raman spectroscopy and chemometrics

A novel approach for the identification of New Psychoactive Substances (NPS) by means of Raman spectroscopy coupled with Principal Components Analysis (PCA) employing the largest dataset of NPS reference materials to date is reported here. Fifty three NPS were selected as a structurally diverse subset from an original dataset of 478 NPS compounds. The Raman spectral profiles were experimentally acquired for all 53 substances, evaluated using a number of pre-processing techniques, and used to generate a PCA model. The optimum model system used a relatively narrow spectral range (1300–1750 cm⁻¹) and accounted for 37% of the variance in the dataset using the first three principal components, despite the large structural diversity inherent in the NPS subset. Nonetheless, structurally similar NPS (i.e., the synthetic cannabinoids FDU-PB-22 & NM-2201) grouped together in the PCA model based on their Raman spectral profiles, while NPS with different chemical scaffolds (i.e., the benzodiazepine flubromazolam and the cathinone α-PBT) were well delineated, occupying markedly different areas of the three-dimensional scores plot. Classification of NPS based on their Raman spectra (i.e., chemical scaffolds) using the PCA model was further investigated. NPS that were present in the initial dataset of 478 NPS but were not part of the selected 53 training set (validation set) were observed to be closely aligned to structurally similar NPS within the generated model system in all cases. Furthermore, NPS that were not present in the original dataset of 478 NPS (test set) were also shown to group as expected in the model (i.e., methamphetamine and N-ethylamphetamine). This indicates that, for the first time, a model system can be applied to potential ‘unknown’ psychoactive substances, which are new to the market and absent from existing chemical libraries, to identify key structural features to make a preliminary classification. Consequently, it is anticipated that this study will be of interest to the broad scientific audience working with large structurally diverse chemical datasets and particularly to law enforcement agencies and associated scientific analytical bodies worldwide investigating the development of novel identification methodologies for psychoactive substances.

A novel approach for the identification of New Psychoactive Substances (NPS) by means of Raman spectroscopy coupled with Principal Components Analysis (PCA) employing the largest dataset of NPS reference materials to date is reported here.