Tirzepatide was introduced several years ago for the management of obesity. Its side-effect profile includes gastrointestinal symptoms and hormonal shifts similar to other glucagon-like peptide-1 receptor agonists (GLP-1RAs). This class of medications is currently being investigated for its potential utility in treating depressive and anxiety disorders. While safety data generally indicate a low risk for adverse psychiatric events - such as suicidal ideation or depressive episodes - and some evidence suggests a beneficial impact on mood, anecdotal reports have linked these agents to panic attacks. However, scientific evidence for this remains limited. We present the cases of two female patients with histories of depressive disorder who experienced panic attacks following tirzepatide injections for weight loss. Both required hospitalization for the exacerbation of moderate-to-severe recurrent depressive disorder. The panic attacks occurred in close temporal proximity to the injections and resolved upon discontinuation. No clear medical aetiology was identified. Changes in pharmacokinetics by delayed gastric emptying, GABAergic deficits, an activation of the nucleus of the solitary tract (NTS) or activation of the hypothalamus-pituitary-adrenal axis are considered as potential mechanisms for panic attacks.
Li Ma, Johnna Berryhill, John Ihrie, Ru Chen, Beverly Lyn-Cook, Paul Rogers, Weigong Ge, Ningning Wu, Huixiao Hong, Weida Tong, Wen Zou
Prescription opioids are essential for pain management but have been increasingly linked to cardiovascular adverse events. Understanding these risks is critical for informed prescribing opioid drugs. Approximately 18 million adverse event reports from 2004 Quarter 1 to 2024 Quarter 3 in the U.S. FDA Adverse Event Reporting System (FAERS) were downloaded and processed. RxNorm was used to normalize prescription opioid drug names. Empirical Bayes Geometric Mean analysis was then performed to detect signals of disproportionate reporting. Cardiovascular-related Preferred Terms from MedDRA were applied to identify drug-adverse event pairs. Unsupervised machine learning approaches, such as network analysis, hierarchical clustering, and Pearson correlation analysis were employed to study cardiovascular risk relationships among the prescription opioids. R code for the correlation analysis was generated with the assistance of a large language model (Claude, Anthropic) and verified by the authors. A total of 79,620 prescription opioid-cardiovascular adverse event pairs were identified, comprising 15 groups of FDA-approved prescription opioids and 291 unique cardiovascular adverse events. Fentanyl and oxycodone were the top two contributing drugs, while hypertension, hypotension, cardiac arrest, cardio-respiratory arrest, and tachycardia were the top five cardiovascular adverse events. The top 10 cardiovascular adverse events for each of the 15 groups of opioids were characterized. Network analysis and hierarchical clustering revealed close associations among fentanyl-remifentanil-sufentanil-morphine, hydrocodone-tramadol, and buprenorphine-oxymorphone-pentazocine based on shared cardiovascular adverse event profiles. The AI-assisted Pearson correlation heatmap further quantified pairwise profile similarities, identifying exceptionally strong correlations between pentazocine and buprenorphine (0.96), oxymorphone and methadone (r = 0.92), and between remifentanil and sufentanil (r = 0.91), and confirming the fentanyl-remifentanil-sufentanil-morphine cluster as the most internally coherent cardiovascular risk profiling group. This study provides the first large-scale, cardiovascular adverse events-focused pharmacovigilance analysis across FDA-approved prescription opioids using over two decades of FAERS data. The findings highlight the need for targeted cardiovascular risk assessment and monitoring, and support more refined prescribing practices and opioid safety labeling. The results showed that the integration of traditional Empirical Bayes signal detection with unsupervised machine learning may enable scalable pharmacovigilance analyses and uncover latent cardiovascular risk patterns that may be masked in broader adverse event assessments.
Spontaneous reporting systems (SRSs) gather millions of reports of adverse events occurring to patients worldwide. For its volume and breadth of data, they are often regarded as the cornerstone of pharmacovigilance. However, their individual and intra-database characteristics remain incompletely described. Four SRSs were considered, namely, the WHO global database VigiBase, the FDA Adverse Event Reporting System (FAERS), the Vaccine Adverse Event Reporting System (VAERS), and the Japanese Adverse Drug Event Report database (JADER). As of 31 December 2025, these databases contain approximately 42.4 million, 26.3 million, 2.7 million and 0.9 million reports, respectively. Following deduplication, approximately 11.1 million reports were uniquely shared between VigiBase and FAERS and close to 1.3 million between VigiBase and VAERS. We described and compared report features such as drug and adverse event reporting patterns, reporter types, geographic distribution, demographics, and medical outcomes. Most reports have been received in recent years across the four databases with up to 70% of the reports accumulated in the last 7–14 years, depending on the SRS. Notable patterns appeared in reporter types (consumer-dominated in FAERS versus physician-dominated in JADER), sex (female predominance in VigiBase, FAERS and VAERS, versus near-parity in JADER), and the influence of COVID-19 vaccination campaigns, which disproportionately affected VigiBase and VAERS. Time-series trends were analysed by comparing reports pre and post 1 January 2015, excluding COVID-19 vaccine reports. Changes in reporting patterns of therapeutic subgroups and adverse events since 2015 were consistent across SRSs with some region-specific variations. By highlighting differences between SRSs, this manuscript serves as a reference for researchers and regulators.
Chiara G. M. Gennari, Sara Azari, Antonella Casiraghi, Umberto M. Musazzi
The gastrointestinal absorption of macromolecules represents a major challenge in both drug development and regulatory evaluation, as the intestinal barrier is highly effective in limiting the systemic uptake of large molecules. Nevertheless, understanding the mechanisms that govern macromolecule permeability is essential for assessing the safety, efficacy profile, and regulatory qualification of substance-based healthcare products. Polyethylene glycols (PEGs) represent a useful reference system for understanding the physiological limits of macromolecule absorption and for supporting evidence-based regulatory decision-making. Indeed, PEGs are widely used as excipients and active ingredients in numerous healthcare products due to their chemical stability, low immunogenicity, and good tolerability; however, their therapeutic use is strongly influenced by their gastrointestinal absorption. Low-molecular-weight (lMW) PEGs (200–600 g/mol) can be absorbed through the gastrointestinal mucosa and therefore are effective probes for testing gastrointestinal functionality. On the contrary, high-MW (hMW) PEGs (e.g., polyethylene glycol 3350 and PEG 4000), are negligible absorbed, acting as osmotic laxatives. Under Regulation (EU) 2017/745, hMW PEGs are classified as Class IIb substance-based medical devices. This review critically analyzes the current evidence on the gastrointestinal fate of PEGs with different molecular weights and discusses their relevance for the interpretation of macromolecule absorption. The overall evidence is particularly relevant for the regulatory qualification of PEG-containing healthcare products.
Konstantinos Kassandros, Maria Samakouri, Maria Panagopoulou, Theodoros Constantinidis, Christos Kontogiorgis
The study quantified national trends in psychotropic medication dispensing among children and adolescents (0–17 years) and young adults (18–29 years) in Greece during 2020–2024 and characterized patterns by drug class, sex, diagnosis, and region. Repeated cross-sectional analyses used nationwide administrative e-prescription dispensing data (used as a proxy for medication exposure) aggregated by year, age group, and sex. Outcomes were total dispensed packs, dispensing prevalence per 1,000 inhabitants, and dispensing intensity (packs per patient). Pharmacologic classes followed Anatomical Therapeutic Chemical (ATC) groupings, while International Classification of Diseases, 10th Revision (ICD-10) macro-groups were constructed to link dispensing with psychiatric and selected G-chapter and related diagnostic categories. Overall psychotropic dispensing prevalence increased from 64.5 to 71.8 per 1,000 inhabitants between 2020 and 2024. SSRI dispensing prevalence increased from 6.65 to 9.09 per 1,000, whereas hypnotic benzodiazepine prevalence declined from 1.01 to 0.94 per 1,000 and Z-drugs prevalence decreased from 0.20 to 0.15 per 1,000. Females had higher odds of antidepressant dispensing. Regional antidepressant dispensing prevalence was highest in the Ionian Islands (13.46 per 1,000), followed by Attica (11.93 per 1,000) and Western Greece (11.86 per 1,000). Psychotropic medication dispensing among Greek adolescents and young adults increased during 2020–2024, largely due to SSRIs, with stable dispensing intensity and marked heterogeneity by age, sex, diagnosis, and region. These findings support age-appropriate prescribing stewardship and targeted regional monitoring.
