RETAIL DRUG QUALITY PROJECT ON HEROIN: A QUALITATIVE AND QUANTITATIVE APPROACH

thesis</p

THE ANALYSIS OF ILLICIT DRUGS COLLECTED DURING A SUMMER FESTIVAL AND EVALUATION OF THE NARCOREADER FOR ON-SITE ANALYSIS OF MDMA

Background: The most used on-site methods to identify illicit drugs consist of color tests or spectroscopic methods with as a downside the lack of accuracy and selectivity. The law still requires confirmation of these results with chromatographic methods. These conventional methods are precise but time-consuming and of high cost. There is a need for more rapid and still accurate analysis, with cost-effective and portable methods. Aim: As the first goal of this study, a selection of drug samples is analysed and the results are compared with statistics from Belgium and Europe for the years 2019, 2020 and 2021. This is to portray the possible influence of COVID-19 on drugs. Secondly, the suspected MDMA samples are analysed with four alternative techniques. The pros and cons are compared of these alternative methods for on-site use by non-experts. Methods: The samples are identified with GC-MS and quantified with UV-spectroscopy. The obtained results are compared with statistics from the statistical bulletin and the European Drug Report. The suspected MDMA samples are identified with mid-IR, near-IR, Raman spectroscopy and the Narcoreader. The results of these four alternative devices are compared with the conventional methods. Results: The MDMA dosage in tablets and the MDMA purity in crystals followed an increasing trend in Belgium. The purity of cocaine stays quite stable in Belgium. The purity of amphetamine showed fluctuations in Belgium, with an important increase in median purity in 2022. After weighing the pros and cons of the alternative methods, the mid-IR device had all over the best results and could be far more attractive for on-site use in its already existing portable format. The Narcoreader had also good results and characteristics. It is portable, user-friendly, fast and precise. Conclusion: The comparison of the statistics is not representative due to the lack of statistics and sampling in different sources. It is interesting to portray how the pandemic could influence the purities and dosages of illicit drugs, although the correlation cannot be assured. The search for an ideal portable device for on-site identification should include more recent and portable spectroscopic devices to make a fair match with the novel Narcoreader.</p

Factors Influencing Benzene Formation from the Decarboxylation of Benzoate in Liquid Model Systems

Benzene may occur in foods due to the oxidative decarboxylation of benzoate in the presence of hydroxyl radicals. This study investigated factors influencing benzene formation in liquid model systems. The type of buffer, other sources of hydroxyl radical formation in food (photo oxidation of riboflavin and lipid oxidation), transition metal ion concentrations, and the inhibitory effect of antioxidants were tested in benzoate containing model systems. Regarding the hydroxyl radical sources tested, the highest benzene formation was observed in light exposed model systems containing ascorbic acid, Cu2+, and riboflavin in Na-citrate buffer (1250 -¦ 131 ?g kg?1). In practice, it seems that the combination ascorbic acid/transition metal ion remains the biggest contributor to benzene formation in food. However, the concentration of Cu2+ influences significantly benzene formation in such a system with highest benzene yields observed for Cu2+ 50 ?M (1400 ?g kg?1). The presence of antioxidants with metal chelation or reduction properties could prevent completely benzene formationBenzene may occur in foods due to the oxidative decarboxylation of benzoate in the presence of hydroxyl radicals. This study investigated factors influencing benzene formation in liquid model systems. The type of buffer, other sources of hydroxyl radical formation in food (photo oxidation of riboflavin and lipid oxidation), transition metal ion concentrations, and the inhibitory effect of antioxidants were tested in benzoate containing model systems. Regarding the hydroxyl radical sources tested, the highest benzene formation was observed in light exposed model systems containing ascorbic acid, Cu2+, and riboflavin in Na-citrate buffer (1250 -¦ 131 ?g kg?1). In practice, it seems that the combination ascorbic acid/transition metal ion remains the biggest contributor to benzene formation in food. However, the concentration of Cu2+ influences significantly benzene formation in such a system with highest benzene yields observed for Cu2+ 50 ?M (1400 ?g kg?1). The presence of antioxidants with metal chelation or reduction properties could prevent completely benzene formation</p

Use of near infrared spectroscopy and chemometrics for the classification of different Cannabis spp. samples found in Belgium

Introduction Cannabis is known for a variety of applications e.g. . in the textile industry but more importantly for recreational and medical use. Cannabis and its derived products are available on the Belgium market via both illegal and legal ways. CBD flowers as well as medicinal cannabis can both be purchased in a legal way, but cannabis to be used as recreational drug is illegal. Indeed, cannabis is the most widely consumed illicit drug in Europe and products contain generally a concentration of Δ9-THC up to 15 %&nbsp; (m/m) [1,2]. Δ9-tetrahydrocannabinol is one of the main cannabinoids responsible for the psychotropic effects and the Belgian law authorizes a maximal concentration of 0.2 % (m/m) [2]. However, there are only limited cannabis seizures because it is difficult to discriminate between legal CBD flowers and cannabis flowers with a Δ9-THC concentration higher than 0.2% m/m, at least for police officers on site. There is a need to characterize these products with a rapid, ecological and cheap analytical method. The current methods of reference are GC-MS, GC-FID and HPLC-DAD [4]. These methods are very efficient but slow, expensive and require a thorough sample preparation. Furthermore, they require trained personnel, and aren’t ecological [4,5] and they are not suited for onsite analysis. Near infrared spectroscopy combined with chemometric tools has the potential for quantitative and qualitative prediction of plant natural products compounds [6]. In addition, near infrared spectroscopic tools are easy to employ, green, rapid and relatively cheap and can be used on-site (handheld&nbsp;devices). &nbsp; Material and&nbsp;methods For this study 189 samples, found in Belgium, were used. They were composed of (i) flowers seized on festivals and on the street and supplied by the Belgian authorities, (ii) agricultural hemp from Belgian farmers and (iii) flowers, used or sold as “other tobacco to smoke”, either seized by Belgian authorities, bought on the Belgium market or voluntarily donated by sellers in order to analyze their&nbsp;samples. Cannabis samples were analyzed by GC-FID for total-tetrahydrocannabinol detection and quantification with an officially validated&nbsp;method. The analysis of these products was performed by FT-NIR spectrometer (spectra were recorded in&nbsp; reflectance mode with the Near Infrared Reflectance Accessory (NIRA)) and dispersive NIR handheld devices combined with&nbsp;chemometrics.&nbsp; All pretreated spectra were analysed with chemometrics. Principal component analysis (PCA) is applied as exploratory data analysis. Soft independent modelling of class analogy (SIMCA) was used to build a binary classification model according to the Belgian&nbsp;legislation. All chemometric treatments were performed with Matlab R2018b (The Mathworks®). The algorithms were part of the ChemoAC toolbox (Freeware®, ChemoAC consortium, version&nbsp;4.1). Results and&nbsp;discussion &nbsp; Figures&nbsp;: Score plot of the PC1, PC2 and PC3 of spectra pretreated. Samples with &gt;0.2% THC in red and blue and sample with &lt; 0.2% THC in&nbsp;green. The SIMCA classification model has a correct classification rate of 92 % (for the benchtop) and 93 % (for the handheld device),&nbsp; meaning that 51 of the 56 samples and 52 of 56 samples in the external test set are correctly classified as recreational drug, industrial hemp or cannabis flowers containing less than 0.2% m/m of Δ9-THC. Conclusion Near infrared spectroscopy allows to discriminate various samples. The difference is caused by the totality of compounds of the plant and not only by the Δ9-THC content. Indeed, plant material is a complex matrix and the fingerprint is a combination of the totality of compounds. This preliminary study is a first step to prove that NIR spectroscopy could be used as a preliminary screening method for the authorities to make the decision to seize or&nbsp;not.</p

Headspace-gas chromatographic fingerprints to discriminate and classify counterfeit medicines

&lt;p&gt;Counterfeit medicines are a global threat to public health. These pharmaceuticals are not subjected to quality control and therefore their safety, quality and efficacy cannot be guaranteed. Today, the safety evaluation of counterfeit medicines is mainly based on the identification and quantification of the active substances present. However, the analysis of potential toxic secondary components, like residual solvents, becomes more important. Assessment of residual solvent content and chemometric analysis of fingerprints might be useful in the discrimination between genuine and counterfeit pharmaceuticals. Moreover, the fingerprint approach might also contribute in the evaluation of the health risks different types of counterfeit medicines pose. In this study a number of genuine and counterfeit Viagra(®) and Cialis(®) samples were analyzed for residual solvent content using headspace-GC-MS. The obtained chromatograms were used as fingerprints and analyzed using different chemometric techniques: Principal Component Analysis, Projection Pursuit, Classification and Regression Trees and Soft Independent Modelling of Class Analogy. It was tested whether these techniques can distinguish genuine pharmaceuticals from counterfeit ones and if distinct types of counterfeits could be differentiated based on health risks. This chemometric analysis showed that for both data sets PCA clearly discriminated between genuine and counterfeit drugs, and SIMCA generated the best predictive models. This technique not only resulted in a 100% correct classification rate for the discrimination between genuine and counterfeit medicines, the classification of the counterfeit samples was also superior compared to CART. This study shows that chemometric analysis of headspace-GC impurity fingerprints allows to distinguish between genuine and counterfeit medicines and to differentiate between groups of counterfeit products based on the public health risks they pose.&lt;/p&gt;</p

Discrepancies between validated GC‐FID and UHPLC‐DAD methods for the analysis of Δ‐9‐THC and CBD in dried hemp flowers

info:eu-repo/semantics/publishe

Analytical characterization of &quot;etonitazepyne,&quot; a new pyrrolidinyl-containing 2-benzylbenzimidazole opioid sold online.

This paper reports on the identification and full chemical characterization of the substance colloquially called “etonitazepyne” or “N-pyrrolidino etonitazene” (2-(4-ethoxybenzyl)-5-nitro-1-(2-(pyrrolidin-1-yl)ethyl)-1H-benzo[d]imidazole), a potent NPS opioid of the 5-nitrobenzimidazole class. Identification of etonitazepyne was performed, on a sample purchased during routine monitoring of the drug market, using GC-MS, HRAM LC-MS/MS, H NMR, and FTIR. The chromatographic data, together with the FTIR data, indicated the presence of a highly pure compound and already indicated a benzimidazole structure. The specific benzimidazole regio-isomer was confirmed using H NMR spectroscopy, resulting in the unambiguous identification of&nbsp;etonitazepyne.</p

Development of a “Freeze-Pour” Sample Preparation Method for the GC Analysis of Semivolatile Flavouring Chemicals Present in E-cigarette Refill Liquids

During the past decade, e-cigarettes have become increasingly popular. To guarantee their safe use and to comply with the notification requirements of the EU Tobacco Product Directive, the EU member state regulatory authorities need information about the exact composition of the e-liquids and their emissions. However, one of the challenges encountered during the analysis of e-liquids is the presence of the highly abundant e-liquid matrix components propylene glycol and glycerol. In this study, headspace gas chromatography (HS-GC) analysis is presented as an excellent method for the analysis of high volatile components in e-liquids. For the analysis of semivolatile ingredients, an additional sample preparation step is proposed based on a liquid–liquid extraction (LLE) followed by a freeze-out of the matrix components. The developed method was successfully validated in accordance with the validation requirements of ICH guidelines for the quantification of four flavourings with a potential health concern for e-cigarette&nbsp;users.</p

CBD oils on the Belgian market: A validated MRM GC-MS/MS method for routine quality control using QuEChERS sample clean up

Quality control of CBD oils on the Belgium market showed that the CBD content not always corresponds to the label claim. There is a pressing need to develop new analytical methods specifically developed to the assay of such oily samples. Analytical issues are, however, encountered for routine analyses due to the matrix complexity, high cost of cannabinoid standards and low Δ9-THC concentrations. An oily matrix could cause technical damages to analytical instruments and reduce the lifetime of the chromatographic columns. This paper proposes a procedure combining a sample cleanup by QuEChERS, removing the oily matrix, followed by a validated MRM GC-MS/MS method for the routine analysis of CBD oil samples. Eighteen CBD samples were selected on the Belgium market for analysis. This method allows the quantification of CBD, the legality check for the Δ9-THC content by a CBN standard and the screening of seven other cannabinoids namely CBN, CBDV, CBT, CBC, Δ8-THC, THCV and CBG. The method was validated at three concentration levels (0.5–1–2% (w/v)) for CBD and (0.05–0.1–0.2% (w/v)) for CBN. The detection limits for CBT, CBD, CBC, Δ8-THC, CBN and for the other cannabinoids of interest, were 10 and 14 ng/mL respectively. The accuracy profile values for CBD and CBN showed that the β-expectation tolerance intervals did not exceed the acceptance limits of 20%, meaning that 90% of future measurements will be included within this error&nbsp;range.</p