A Focus on Abuse/Misuse and Withdrawal Issues with Selective Serotonin Reuptake Inhibitors (SSRIs): Analysis of Both the European EMA and the US FAERS Pharmacovigilance Databases

Despite increasing reports, antidepressant (AD) misuse and dependence remain underestimated issues, possibly due to limited epidemiological and pharmacovigilance evidence. Thus, here we aimed to determine available pharmacovigilance misuse/abuse/dependence/withdrawal signals relating to the Selective Serotonin Reuptake Inhibitors (SSRI) citalopram, escitalopram, paroxetine, fluoxetine, and sertraline. Both EudraVigilance (EV) and Food and Drug Administration-FDA Adverse Events Reporting System (FAERS) datasets were analysed to identify AD misuse/abuse/dependence/withdrawal issues. A descriptive analysis was performed; moreover, pharmacovigilance measures, including the reporting odds ratio (ROR), the proportional reporting ratio (PRR), the information component (IC), and the empirical Bayesian geometric mean (EBGM) were calculated. Both datasets showed increasing trends of yearly reporting and similar signals regarding abuse and dependence. From the EV, a total of 5335 individual ADR reports were analysed, of which 30% corresponded to paroxetine (n = 1,592), 27% citalopram (n = 1,419), 22% sertraline (n = 1,149), 14% fluoxetine (n = 771), and 8% escitalopram (n = 404). From FAERS, a total of 144,395 individual ADR reports were analysed, of which 27% were related to paroxetine, 27% sertraline, 18% citalopram, 16% fluoxetine, and 13% escitalopram. Comparing SSRIs, the EV misuse/abuse-related ADRs were mostly recorded for citalopram, fluoxetine, and sertraline; conversely, dependence was mostly associated with paroxetine, and withdrawal to escitalopram. Similarly, in the FAERS dataset, dependence/withdrawal-related signals were more frequently reported for paroxetine. Although SSRIs are considered non-addictive pharmacological agents, a range of proper withdrawal symptoms can occur well after discontinuation, especially with paroxetine. Prescribers should be aware of the potential for dependence and withdrawal associated with SSRIs

NPS detection in prison: A systematic literature review of use, drug form, and analytical approaches

This paper presents a systematic literature review on the detection of new psychoactive substances (NPS) in prison settings. It includes the most frequently reported NPS classes, the routes and forms used for smuggling, and the methods employed to analyse biological and non-biological samples. The search was carried out using MEDLINE (EBSCO), Scopus (ELSEVIER), PubMed (NCBI), and Web of Science (Clarivate) databases, along with reports from the grey literature in line with the PRISMA-S guidelines. A total of 2708 records were identified, of which 50 met the inclusion criteria. Findings showed the most prevalent NPS class reported in prison was synthetic cannabinoids (SCs). The most frequently reported SCs in non-biological samples were 4F-MDMB-BINACA, MDMB-4en-PINACA, and 5F-ADB. These were smuggled mainly through the postal services deposited on paper or herbal matrices. Concentrations of SCs detected on seized paper ranged between 0.05 and 1.17 mg/cm2. The SCs most frequently reported in biological specimens (i.e., urine, blood, saliva, and wastewater) were 5F-MDMB-PICA, 4F-MDMB-BINACA, and MDMB-4en-PINACA. Concentrations of SCs reported in femoral blood and serum were 0.12–0.48 ng/ml and 34–17 ng/ml, respectively. Hyphenated techniques were predominantly employed and generally successful for the detection of NPS in biological (i.e., LC-HRMS/MS) and non-biological samples (i.e., LC-HRMS/MS and GC–MS). The onsite technique IMS showed promise for detecting SCs in various forms; however, immunoassays were not recommended. Future work should focus on accurate in-field detection of SCs deposited on paper and in urine and saliva to improve real-time decision-making, as well as wastewater and air monitoring for overall drug use trends

Presence of metals in herbal extracts

This is the pre-peer-reviewed version of the following article: Amira Guirguis, et al, 'Presence of metals in herbal extracts', The Pharmaceutical Journal, Vol. 289, p. 536, November 2012, URI: 11110858, which has been published in final form at : http://www.pharmaceutical-journal.com/news-and-analysis/news/presence-of-metals-in-herbal-extracts/11110858.article.Do metals from raw herbs transport into herbal preparations during manufacture? Amira Guirguis and colleagues take a look at the issue using St John’s wort as an example.Non peer reviewe

Assessing the 2004-2018 fentanyl misusing issues reported to an international range of adverse reporting systems

© 2019 Schifano, Chiappini, Corkery and Guirguis. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Submitted 2 October 2018, Accepted 14 January 2019, published 1 February 2019.Objective: A recent, global, increase in the use of opioids including the prescribing, highly potent, fentanyl has been recorded. Due its current popularity and the potential lethal consequences of its intake, we aimed here at analyzing the fentanyl misuse, abuse, dependence and withdrawal-related adverse drug reactions (ADRs) identified within the European Medicines Agency (EMA), the United Kingdom Yellow Card Scheme (YCS), and the United States Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) databases. Methods: Descriptive analysis of both ADRs and related cases. Results: The analysis of fentanyl-related misuse, abuse, dependence and withdrawal cases reported during years 2004-2018 to the EMA, the YCS, and the FAERS showed increasing levels overtime, specifically, EMA-related data presented two peaks (e.g., in 2008 and 2015), whilst the FAERS dataset was characterized by a dramatic increase of the ADRs collected over the last 18 months, and particularly from 2016. Some 127,313 ADRs (referring to n = 6,161 patients/single cases) related to fentanyl's misuse/abuse/dependence/withdrawal issues were reported to EMA, with 14,287 being judged by the reporter as "suspect." The most represented ADRs were: "drug dependence "(76.87%), "intentional product misuse" (13.06%), and "drug abuse" (7.45%). Most cases involved adult males and the concomitant use of other prescribing/illicit drugs. A range of idiosyncratic (i.e., ingestion/injection of transdermal patches' fentanyl) and very high-dosage intake cases were here identified. Significant numbers of cases required either a prolonged hospitalization (192/559 = 34.35%) or resulted in death (185/559 = 33.09%). Within the same time frame, YCS collected some 3,566 misuse/abuse/dependence/withdrawal ADRs, corresponding to 1,165 single patients/cases, with those most frequently reported being "withdrawal," "intentional product misuse," and "overdose" ADRs. Finally, FAERS identified a total of 19,145 misuse/abuse/dependence/withdrawal-related cases, being "overdose," withdrawal, and "drug use disorder/drug abuse/drug diversion" the most represented ADRs (respectively, 43.11, 20.80, and 20.29%). Conclusion: Fentanyl abuse may be considered a public health issue with significant implications for clinical practice. Spontaneous pharmacovigilance reporting systems should be considered for mapping new trends of drug abuse.Peer reviewe

Identification and Classification of New Psychoactive Substances Using Raman Spectroscopy and Chemometrics

The sheer number, continuous emergence, heterogeneity and wide chemical and structural diversity of New Psychoactive Substance (NPS) products are factors being exploited by illicit drug designers to obscure detection of these compounds. Despite the advances in analytical techniques currently used by forensic and toxicological scientists in order to enable the identification of NPS, the lack of a priori knowledge of sample content makes it very challenging to detect an ‘unknown’ substance. The work presented in this thesis serves as a proof-of-concept by combining similarity studies, Raman spectroscopy and chemometrics, underpinned by robust pre-processing methods for the identification of existing or newly emerging NPS. It demonstrates that the use of Raman spectroscopy, in conjunction with a ‘representative’ NPS Raman database and chemometric techniques, has the potential for rapidly and non-destructively classifying NPS according to their chemical scaffolds. The work also demonstrates the potential of indicating the purity in formulations typical of those purchased by end users of the product i.e. ‘street-like’ mixtures. Five models were developed, and three of these provided an insight into the identification and classification of NPS depending on their purity. These are: the ‘NPS and non-NPS/benchtop’ model, the ‘NPS reference standards/handheld’ model and the ‘NPS and non-NPS/handheld’ model. In the ‘NPS and non-NPS/benchtop’ model (laser λex = 785 nm), NPS internet samples were projected onto a PCA model derived from a Raman database comprising ‘representative’ NPSs and cutting agent/ adulterant reference standards. This proved the most successful in suggesting the likely chemical scaffolds for NPS present in samples bought from the internet. In the ‘NPS reference standards/handheld’ model (laser λex = 1064 nm), NPS reference standards were projected onto a PCA model derived from a Raman database comprising ‘representative’ NPSs. This was the most successful of the three models with respect to the accurate identification of pure NPS. This model suggested chemical scaffolds in 89% of samples compared to 76% obtained with the benchtop instrument, which generally had higher fluorescent backgrounds. In the ‘NPS and non-NPS/handheld’ model (laser λex = 1064 nm), NPS internet samples were projected onto a PCA model derived from a Raman database comprising ‘representative’ NPSs and cutting agent/ adulterant reference standards. This was the most successful in differentiating between NPS internet samples dependent on their purity. In all models, the main challenges for identification of NPS were spectra displaying high fluorescent backgrounds and low purity profiles. The ‘first pass’ matching identification of NPS internet samples on a handheld platform was improved to ~50% using a laser source of 1064 nm because of a reduction in fluorescence at this wavelength. We outline limitations in using a handheld platform that may have added to problems with appropriate identification of NPS in complex mixtures. However, the developed models enabled the appropriate selection of Raman signals crucial for identification of NPS via data reduction, and the extraction of important patterns from noisy and/or corrupt data. The models constitute a significant contribution in this field with respect to suggesting the likely chemical scaffold of an ‘unknown’ molecule. This insight may accelerate the screening of newly emerging NPS in complex matrices by assigning them to: a structurally similar known molecule (supercluster/ cluster); or a substance from the same EMCDDA/EDND class of known compounds. Critical challenges in instrumentation, chemometrics, and the complexity of samples have been identified and described. As a result, future work should focus on: optimising the pre-processing of Raman data collected with a handheld platform and a 1064 nm laser λex; and optimising the ‘representative’ database by including other properties and descriptors of existing NPS