Drug Safety最新文献

Background: The natural health products (NHPs) industry is a key stakeholder in pharmacovigilance for NHPs. However, the specific contributions that the NHPs industry makes to pharmacovigilance for NHPs are not well-understood.

Objective: This scoping review aimed to identify and map published literature describing the contributions of the NHPs industry to pharmacovigilance activities for NHPs. Assessment of benefit-harm balance for individual NHPs/natural ingredients is outside the scope of this review.

Methods: Using predetermined keywords and Medical Subject Headings, seven international electronic biomedical journal databases were searched to identify articles describing the contributions of the NHPs industry to pharmacovigilance for NHPs in relation to product surveillance and stakeholders' views on the NHPs industry and its pharmacovigilance activities.

Results: Of the 2285 records identified, 40 articles (representing 40 studies) met the inclusion criteria for this review. Among these, 33 described post-marketing surveillance activities and seven explored stakeholders' views. Of the articles describing post-marketing surveillance studies, 22 were authored and/or sponsored by the industry; the remaining 11 involved contributions from the NHPs industry in the form of safety data submitted as spontaneous reports to a national or state pharmacovigilance database. Contributions of the NHPs industry were primarily through passive surveillance via spontaneous reporting. In total, 13 active surveillance studies were undertaken by the NHPs industry, mainly in clinical care settings such as medical centres, hospitals, and private practices, and were focused on single products. There were limited findings relating to stakeholders' views on pharmacovigilance and the NHPs industry's involvement.

Conclusions: The NHPs industry contributes to pharmacovigilance for NHPs primarily through passive surveillance measures. Active surveillance involving the NHPs industry was typically undertaken in non-community settings. Additional research exploring stakeholders' views on and preparedness for participating in active surveillance involving the industry, focusing particularly on models based on the consumer-industry reporting pathway, could identify new strategies for strengthening post-marketing safety monitoring for NHPs.

In the pharmaceutical industry, due diligence (DD) is a critical, cross-functional process used to evaluate the scientific and regulatory viability of products, particularly during in-licensing or acquisition. Among the various dimensions assessed, safety evaluation plays a pivotal role in determining whether a product is likely to achieve regulatory approval, maintain a favourable benefit-risk balance, and support sustainable market access. This article focuses on the unique role of pharmacovigilance (PV) and safety experts in the DD process. It outlines the key safety-related questions that must be addressed, highlights potential red flags-such as organ-specific toxicities, labelling liabilities, or risk management burdens-and provides practical considerations for evaluating the robustness of the safety data package. Drawing on extensive industry experience and regulatory precedents, this manuscript offers structured guidance for an area where no formal framework exists. It emphasizes the importance of forecasting the likelihood of achieving a positive benefit-risk profile and identifying foreseeable safety-related barriers-ranging from boxed warnings to withdrawal risk-that could delay approval or diminish product value. By proposing a systematic approach to safety DD, including a playbook and an inclusive checklist with colour-coded categorization framework, this paper aims to support more informed, proactive, and risk-calibrated decision making in pharmaceutical transactions.

Background and objective: Several systematic reviews and meta-analyses have been published with conflicting results on the risk of herpes zoster after coronavirus disease 2019 (COVID-19) vaccination. We aimed to study the risk of herpes zoster after COVID-19 vaccination using electronic health record data of general practices, from a large cohort in the Netherlands.

Methods: Persons aged ≥ 12 years who received at least one COVID-19 vaccination and were registered in the general practice databases of PHARMO and Nivel Primary Care Database were included. This study used a self-controlled design comparing the risk of herpes zoster in the risk period (28 days after COVID-19 vaccination) with the control period. Poisson regression was used to calculate incidence rate ratios, adjusting for severe acute respiratory syndrome coronavirus 2 infection.

Results: There were 2,098,683 COVID-19 vaccinated persons aged ≥ 12 years included, of whom 1,058,646 (50.4%) were female. An increased risk for herpes zoster was found after all the doses grouped together and the third dose of all COVID-19 vaccination (adjusted incidence rate ratio: all doses 1.07, 95% confidence interval [CI] 1.02-1.13 and third dose 1.21, 95% CI 1.05-1.38). After stratification on vaccine type, all doses and the third dose of messenger RNA vaccination (adjusted incidence rate ratio: all doses 1.06, 95% CI 1.00-1.12 and third dose 1.21, 95% CI 1.05-1.40) showed an increased risk.

Conclusions: Our study showed a slight increased risk of herpes zoster when taking into account all doses and all types of vaccines. After stratification on vaccine type, no increased risk of herpes zoster after the primary vaccination series and a slightly elevated risk after the third/booster vaccination with a messenger RNA vaccine were found.

Introduction: Disproportionality analysis, finding associations in the co-reporting of drugs and events, is widely used in pharmacovigilance to detect potential safety signals of adverse drug reactions. However, inherent biases and unique data features often cause disproportionality to diverge from causation, and a comprehensive framework to address these issues is lacking.

Objective: We showcase how directed acyclic graphs (DAGs) can enhance disproportionality analysis-related inferences, better qualifying its limitations and catalysing its inclusion in the broader evidence landscape.

Methods: We introduce a DAG-based causal framework to systematically document and address biases in disproportionality analyses (e.g., confounding, colliders, measurement and reporting biases). We illustrate its application to case studies from the Food & Drug Administration (FDA) Adverse Event Reporting System-using the Information Component as a disproportionality metric and restriction as conditioning.

Results: Directed acyclic graphs facilitate the formalisation of existing knowledge and causal assumptions, optimise the design of disproportionality analysis to mitigate biases-thereby enhancing sensitivity and specificity-improve transparency, better enable the formulation of critiques, highlight limitations of disproportionality and guide follow-up studies to address residual confounding and broader evidence synthesis.

Conclusion: Using DAGs to map and mitigate biases requires caution and does not allow to obtain definitive answers to causal questions. Still, it results in more reliable and knowledge-based safety signals, reducing and mapping the gap between what we find (association) and what we look for (causation). Additional research should further tailor DAGs to pharmacovigilance challenges, map the generative mechanisms of pharmacovigilance data, and better integrate disproportionality analysis results into evidence-synthesis workflows.

Background: Databases for safety monitoring of medicinal products contain records of a huge number of pairings of drugs and adverse events (AEs). Existing disproportionality methods for safety monitoring in such databases estimate background rates of AE occurrence in ways that may be susceptible to masking effects that can hinder signal detection, particularly in the context of large overall counts of AE or drug occurrence in the data.

Objectives: To develop a new statistical model for determining the background rate against which individual drug-AE pairs are to be evaluated, which is robust against masking effects, and to incorporate this into an algorithm which simultaneously estimates background rates and detects drug-AE pair counts that deviate significantly from these rates.

Methods: We constructed a hierarchical Bayesian model for background rates, and background rate samples were drawn from the model parameters using an iterative Markov Chain Monte Carlo (MCMC) method. At each iteration, any counts whose probability is low given current background rate estimation were removed from the computation that sampled the next set of background rates. The algorithm, called Markov Chain Signal Generation (MCSG), was implemented using a combination of Python and the probabilistic programming language Stan.

Results: The MCSG algorithm outperformed routinely used quantitative approaches for signal detection on both synthetic data designed to include a drug-AE pair with very strong masking effects and a reference set featuring 69 unique active substances and 792 unique AEs. On a synthetic dataset where selected pairs occurred at rates deviating from a constant background, MCSG accurately identified these pairs in the presence of strong masking signals. On a subset of some real data from the FDA Adverse Event Report System (FAERS), it effectively identified a reference set of positive and negative controls and was able to identify drug-AE pairs suggested in the literature.

Conclusion: We have demonstrated a new approach to signal generation, which avoids the confounding effect of masking more effectively than currently used methods. The algorithm is best used in a setting of multiple drug-AE pairs, the majority of which are expected to have counts at background rate, although with substantial datasets the algorithm can take minutes or hours to run. It is therefore particularly suitable for infrequent, large-scale analysis (for example, quarterly analysis of the entirety of a pharmacovigilance database).

Background: Many adverse drug reactions (ADRs) to dermatological drugs may be underrecognized due to limitations in traditional surveillance. Systematic, hypothesis-free screening for such ADRs using real-world data is an underutilized approach in dermatology.

Objective: The aim was to systematically screen commonly used dermatological drugs for potential unknown ADR signals using nationwide Danish health register data and to evaluate sequence symmetry analysis (SSA) as an epidemiological screening method in pharmacovigilance. This evaluation focuses on identifying key signals for dedicated follow-up studies, not on an exhaustive analysis of all potential associations.

Methods: A nationwide, register-based, hypothesis-free screening study using Danish national health registers was conducted. The study cohort comprised 5,877,711 Danish residents prescribed dermatological drugs or relevant immunosuppressants linked to a dermatological indication between 1996 and 2022. Data were analyzed from 1995 to 2024. Exposures were the first dispensation of included dermatological drugs. The primary outcome measure was the trend-adjusted sequence ratio (SR), an estimate of the incidence rate ratio for rare events, with 95% confidence intervals (CIs), for incident drug-drug and drug-diagnosis pairs within ±12-month windows of exposure initiation. To assess directionality, supplementary case-crossover (CCO) analyses were performed on top-ranked signals, estimating odds ratios (ORs) with 95% CIs.

Results: The screening of 22.5 million incident exposure prescriptions yielded 4010 drug-drug and 22,234 drug-diagnosis pairs. After filtering and review, several potential unknown ADR signals were identified and prioritized. Signals were manually reviewed and categorized by clinical experts to identify plausible novel ADRs. Notable associations (adjusted SR [95% CI]; CCO OR [95% CI]) included isotretinoin with systemic corticosteroids (1.44 [1.35-1.53]; 1.78 [1.61-1.98]) and hemorrhoid treatments (1.58 [1.49-1.68]; 1.83 [1.68-1.98]); azathioprine with bone-modifying drugs (1.42 [1.33-1.53]; 1.57 [1.36-1.81]); terbinafine with lipid-lowering therapy (1.04 [1.01-1.08]; 1.14 [1.07-1.20]) and vitamin B12/folate use (1.20 [1.14-1.26]; 1.12 [1.02-1.22]); and clobetasol with atrial fibrillation/flutter (1.11 [1.06-1.17]; 1.07 [1.00-1.14]).

Conclusions: Systematic hypothesis-free screening using SSA on nationwide real-world data can effectively identify potential unknown ADRs for dermatological drugs. The specific signals found in this study potentially have clinical implications and warrant further targeted investigations to support causality. This approach supports the implementation of routine real-world data screening to supplement traditional pharmacovigilance.

Chimeric antigen receptor T-cell (CAR-T) therapy is a rapidly expanding key therapeutic category, originally pioneered for haematological malignancies, now being developed into treatments for solid tumours and non-malignant immune-mediated conditions. Chimeric antigen receptor T-cell therapies have some relatively unique toxicities which can affect the liver, in addition to potential drug-induced liver injury and hepatitis B virus reactivation. This manuscript was developed by the IQ Consortium (International Consortium for Innovation and Quality in Pharmaceutical Development) Drug-induced Liver Injury (DILI) Initiative that consists of members from 17 pharmaceutical companies, in collaboration with academic and regulatory DILI experts. The aim was to produce a comprehensive guide to summarise the hepatic effects of CAR-T, and to propose an approach to the investigation of liver test changes. The clinical characteristics of liver test changes in association with cytokine release syndrome and immune-effector cell haemophagocytic lymphohistiocytosis are described, to enable these anticipated hepatic effects to be distinguished from other causes of abnormal liver tests. The frequency and timing of many primary and secondary liver conditions that may present after CAR-T therapy are described. This review provides the first detailed description of both anticipated and unpredictable hepatic effects of CAR-T cell therapies and is intended to assist in the future characterisation of hepatic effects of CAR-T therapies as programmes move into areas with a different benefit/risk profile, such as autoimmune or other non-oncology indications.

Observed versus expected (O/E) analyses have been used in an unprecedented scale for the safety monitoring of the COVID-19 mass vaccination. The extent of their usage changed its nature, which consisted of a mixture of medical expertise and epidemiology, into something more algorithmic and automated. By doing so, the observed versus expected analysis became closer to disproportionality analysis (DPA), which is also a type of observed versus expected analysis that differs in the way the expected is calculated. A qualitative assessment of the strengths and limitations of both methods concludes that the algorithmic O/E is more likely to underestimate under-reporting, is more likely to be sensitive to asymmetrical differences in the definition of the condition of interest, and is more dependent on a greater variety of data sources or medical knowledge that might not be accurate for emerging safety issues (exposure, background incidence rate, and risk window). Provided some adjustment (stratification and/or subgrouping) of the routine disproportionality into a targeted disproportionality occurs, which would account for the epidemiological specifics of the vaccine and event-of-interest, the targeted DPA has the potential to be promoted from a signal detection method into a signal evaluation method that could advantageously replace the algorithmic O/E analysis. Research on the setup of a sensitivity analysis framework integrating several standardized choices of disproportionality settings, along with measures (qualitative or quantitative) of the biases for each choice, could be more beneficial for the pharmacovigilance field than studies designed to estimate the background incidence rates of adverse events of special interest for the sole purpose of being used in O/E analyses.