Drug Safety最新文献

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).

Introduction: The use of drugs carries risks, as adverse drug reactions (ADRs) can occur. In the Netherlands, a voluntary pharmacovigilance system is in place, allowing healthcare professionals and patients to report (suspected) ADRs. Previous research has highlighted underreporting as a significant problem; however, barriers for ADR reporting are not clear.

Objectives: The aim was to assess perceptions of ADR reporting among pediatricians (in training), to identify barriers hindering reporting, and to study differences between our hospital and other Dutch hospitals.

Methods: A cross-sectional survey was conducted among pediatricians (in training) in the Netherlands. The study questionnaire was based on a validated questionnaire and adjusted for the Dutch context, addressing aspects related to ADR reporting, attitudes toward ADRs in work environment, personal vision, reasons for nonreporting, and future perspectives.

Results: A dataset of 127 respondents was included. Of these, 93% reported knowing how to report an ADR. Overall, 95% believed that reporting ADRs has the potential to enhance knowledge and improve drug safety, and 93% acknowledged the overall importance of ADR reporting. However, 19% of respondents indicated that they had never reported an ADR. The most commonly cited reason (61%) for not reporting was prior knowledge of the ADR. Other barriers included uncertainty about whether a symptom constituted an ADR, the perception that the ADR was not severe, and time constraints.

Conclusions: This study highlights the importance of addressing barriers to ADR reporting in pediatric healthcare. While healthcare professionals recognize the significance of ADR reporting, several impediments hinder their reporting efforts.

While the acute impact of the coronavirus disease 2019 (COVID-19) pandemic has waned, implications for clinical trials remain. In particular, guidance for evaluation of elevated liver tests due to COVID-19, its treatments, and COVID-19 vaccination is lacking. The IQ DILI Initiative, composed of experts from academia, regulatory agencies, and industry herein propose recommendations to address this gap. Extensive literature review was conducted and structured discussions were held between IQ DILI industry members, regulators, and academic experts in hepatology and DILI. Liver-related manifestations in nonhospitalized patients with COVID-19 are highly varied. Evidence of liver injury may occur after COVID-19 symptoms resolve and testing is negative. Treatments for COVID-19 may cause liver injury or alter pharmacokinetics. COVID-19 vaccination has been associated with rare but clear hepatotoxicity, typically consistent with drug-induced autoimmune-like hepatitis, although other presentations, severity, latency, and time to resolution have been reported. Liver injury occurred with mRNA and viral vector vaccines, and in individuals with and without underlying autoimmune or liver diseases. Drug developers and investigators should be aware of the potential liver-related manifestations related to COVID-19, its treatments, and COVID-19 vaccination, as this may impact study eligibility and causality assessment during a trial. COVID-19 testing should be considered part of DILI causality assessment, as a positive test may prevent premature termination of the investigational drug. Since clinical trial participants may not consider vaccinations in their medical history, specific inquiry about their receipt is important when liver tests are abnormal during screening and as part of DILI causality assessment.