Drug Safety最新文献

A focus group organised by the Drug Safety Research Unit (DSRU) International Working Group (IWG) on New Developments in Pharmacovigilance discussed current challenges and opportunities in pharmacovigilance (PV), emphasising the need for a multimodal approach in data analysis and accessibility of diverse data sources for drug safety surveillance. Nine participants, selected purposefully for their multisectoral expertise in PV, discussed the value of various data types, including data from clinical trials and real-world data (RWD), each offering distinct strengths and limitations. Key challenges identified included data standardisation, quality variability, technological barriers and ethical concerns, particularly with data derived from social media. Emerging tools such as knowledge graphs were highlighted for their potential to enhance data integration and signal detection, however further research is required. The group also addressed disparities in data access, with particular attention to regulatory restrictions, limited infrastructure in low-resource settings and restricted access to industry-held data. Proposed solutions included fostering greater data transparency, establishing secure data-sharing platforms and forming collaborative consortia to facilitate responsible and ethical data use. Overall, the discussion underscored the need for improved integration, access and methodological rigour to strengthen PV practices and enhance global drug safety monitoring.

Background and hypothesis: The anticholinergic burden is the cumulative effect of drugs with anticholinergic properties and is typically measured using one of several anticholinergic scales. We hypothesised that these scales may not fully capture all the relevant adverse drug reactions (ADRs). By accessing the French national pharmacovigilance database (FPVD) and focusing on drug classes known to induce anticholinergic ADRs, the objectives of the present study were to describe the reported ADRs, characterise the drugs involved, and examine the drugs' classification within anticholinergic scales.

Methods: Cases were extracted from the FPVD (1985-2024) when the suspected drug (i) had a high anticholinergic score, according to one or more of 22 anticholinergic burden scales, or (ii) belonged to the same class as the drug identified in (i). The anticholinergic ADRs investigated were confusion, glaucoma, tachycardia, urinary retention, constipation, intestinal obstruction and mydriasis.

Results: Of the 101,365 cases reported in the FPVD, regarding the selected drugs, 9629 (9.5%) involved at least one anticholinergic ADR investigated. Patients who experienced at least one anticholinergic ADR had a median age of 61 years (interquartile range: 38-79), and the majority were women (58%). Confusion was the most frequently reported anticholinergic ADR (4603 cases, of which 81% were classified as serious), followed by tachycardia (n = 1541 cases, 70% serious), and urinary retention (1061 cases, 75% serious). It is noteworthy that 98% of the 561 reported cases of intestinal obstruction were classified as serious. The drug classes with the highest number of reports were (by far) anxiolytics, antidepressants, and antipsychotics. Some drugs linked to anticholinergic ADRs in the FPVD were not present in (or were assigned a low score by) commonly used anticholinergic scales, such as the Anticholinergic Cognitive Burden.

Conclusions: Anticholinergic ADRs affect both older and younger adults. The existing scoring systems might not fully capture the range of medications involved in real-world anticholinergic-related events.

Background: The COVID-19 mass vaccination led to a substantial increase in spontaneous reports submitted to pharmacovigilance (PV) databases, potentially introducing masking effects that could conceal safety signals.

Objectives: To examine the masking effect of COVID-19 vaccines on disproportionality analyses and to compare two unmasking interventions in the Dutch (Lareb database) and Spanish (Farmacovigilancia Española, Datos de Reacciones Adversas, FEDRA) national PV databases: removal of all drug-event combinations (DEC) involving a COVID-19 vaccine versus excluding influential outliers DECs only.

Methods: The masking effect was explored retrospectively on the basis of the number of signals of disproportionate reporting (SDR). DECs involving a COVID-19 vaccine were excluded using crude and outlier techniques, and reporting odds ratios were recalculated. Subsets of important medical events (IME) were analysed in both databases.

Results: Both crude and influential outlier removal methods led to reductions in the number of reports, DECs and SDRs. Both in the Lareb database and FEDRA, crude removal excluded 2.1% of DECs, while the outlier method excluded 0.1%. Crude removal had a greater impact on SDRs, reducing them by 9.8% in the Lareb database and 3.9% in FEDRA, compared with 5.7% and 1.1% with the outlier method. In the Lareb database, 1301 SDRs (20 IME-related) were unmasked using crude removal, and 1942 (95 IME-related) with the outlier method. FEDRA showed 1453 and 1226 SDRs unmasked, including 41 and 70 IME-related.

Conclusions: COVID-19 vaccines caused substantial masking in both databases. Both strategies effectively revealed new SDRs, though their impact varied. The choice of approach should be tailored to the database context.

Background: Potential drug-drug interactions (pDDIs) are frequent in clinical care, particularly among older patients. Accurate identification and management of pDDIs are essential for patient safety. Prescribers often rely on interaction checkers (ICs) to screen for pDDIs. However, these tools may provide inconsistent recommendations, potentially leading to suboptimal clinical decisions.

Objective: This study aimed to develop a standardized approach for classifying and prioritizing pDDIs based on the clinical relevance of their management recommendations and to compare how these pDDIs are categorized across ICs.

Methods: A scale was developed through a structured iterative process to classify pDDIs into four management categories (high priority, intermediate priority, low priority, data unavailable), based on the management recommendations extracted from six ICs. This scale was applied to 218 real-world pDDIs identified from 1923 patients, and the agreement was primarily assessed using Gwet's AC1.

Main results: Overall agreement among the ICs was moderate (Gwet's AC1 = 0.44; 95% CI 0.39-0.50), with values ranging from 0.58 (0.51, 0.65) to 0.38 (0.31, 0.44) in leave-one-out analyses. The agreement was higher in binary analyses dichotomizing the scale into high- and intermediate-priority versus low-priority pDDIs (AC1 = 0.72; 95% CI 0.65-0.79), and in the classification of high-priority versus all other pDDIs (AC1 = 0.62; 95% CI 0.54-0.69).

Conclusion: This study developed and tested a structured approach to systematically compare pDDI management across ICs and prioritize clinically relevant interactions. Its application revealed a generally limited agreement between ICs, pointing to the need for harmonized approaches and further studies to support more consistent, evidence-aligned pDDI management.

Cell and gene therapies, including CAR T-cells, CRISPR-based genome editing, and next-generation viral and non-viral delivery platforms, are transforming treatment paradigms across cancer, rare genetic disorders, immune dysregulation, and neurodegenerative disease. These therapies offer curative potential but also present safety challenges owing to prolonged biological activity, systemic immune engagement, and lasting genomic alterations. This review examines the range of related toxicities, including immune complications, genotoxicity, and organ-specific effects, with attention to atypical presentations, gaps in clinical trial safety capture, and disparities in global long-term follow-up infrastructure. Central to our analysis is a risk-adaptive, digitally enabled pharmacovigilance model that incorporates real-world data, artificial intelligence-based signal detection, and seamless pediatric-to-adult follow-up to proactively protect patients while supporting innovation. Integrated safety dashboards, pediatric transition roadmaps, and predictive monitoring tools are proposed as practical solutions to improve coordination among sponsors, regulators, and clinical sites. We also outline best practices for aligning risk evaluation and mitigation strategies with risk management plans and examine how wearable biosensors, electronic patient-reported outcomes, and multi-omics biomarkers contribute to near real-time safety surveillance. Ethical priorities such as informed consent, data privacy, and equitable access are addressed throughout. By positioning pharmacovigilance as a proactive and predictive foundation within the therapeutic landscape, this review offers a forward-looking blueprint to advance innovation while ensuring long-term patient safety.

Introduction: A case-crossover study is a self-controlled design most appropriate for evaluating transient medication exposures. However, it has increasingly been used in studies of chronic medications and can cause bias in effect estimates that vary based on the pattern of medication use. The goal of this study was to evaluate the magnitude of this bias across different medication-use patterns.

Objective: To quantify the magnitude of the bias introduced by different medication patterns and evaluate different case-crossover approaches to mitigate the bias.

Methods: We conducted a simulation study evaluating the bias introduced by (1) seven common medication patterns separately, and (2) cohort with 15 different patterns combined. We evaluated each scenario under risk ratios of 0.50, 0.75, 1.00, 1.50, and 2.00. Each approach was analyzed using conditional logistic regression comparing the probability of exposure on the outcome day to 30 days prior. A case-time-control design was used in each of the scenarios. Sensitivity analysis was performed to evaluate the impact on the estimates when changing the length of the risk and control windows. We conducted a real-world example focusing on sodium-glucose co-transporter-2 inhibitor users as real-world examples.

Results: The case-crossover design resulted in unbiased estimates when patterns were consistent with transient exposures but were biased upward with prolonged exposure patterns. The magnitude of the bias varies by patterns or pattern combinations. When evaluating prolonged exposures individually or combined as a cohort with mixture patterns, case-time-control with extended risk and control window (30 days) produced unbiased results (mean bias ≤ 0.03).

Conclusion: Researchers who use the case-crossover design to evaluate non-transient exposures should implement recommended methods to account for biases.

Enzalutamide is an oral androgen receptor signaling inhibitor used in the treatment of prostate cancer. Elderly patients with prostate cancer commonly have age-related comorbidities that require concomitant, active treatment. As a moderate inducer of the cytochrome P450 (CYP) enzymes CYP2C9 and CYP2C19, and a strong inducer of CYP3A4, there is potential for drug-drug interactions (DDIs) when enzalutamide is coadministered with other drugs that are CYP3A4 substrates-resulting in loss of efficacy or increased risk of unintended drug-related adverse events. In this podcast, we describe enzalutamide including its dosing, pharmacokinetics, and potential for interaction with coadministered drugs using several hypothetical patient cases with real-world clinical implications. Discussion of each patient case will highlight management strategies and illustrate that nearly all enzalutamide drug-drug interactions can be effectively managed with appropriate knowledge of which drugs pose interaction risks, when dose adjustments are indicated, and when alternative drugs can be substituted. Supplementary file1 (MP4 192541 KB).