Drug Safety最新文献

Introduction

Current drug–drug interaction (DDI) detection methods often miss the aspect of temporal plausibility, leading to false-positive disproportionality signals in spontaneous reporting system (SRS) databases.

Objective

This study aims to develop a method for detecting and prioritizing temporally plausible disproportionality signals of DDIs in SRS databases by incorporating co-exposure time in disproportionality analysis.

Methods

The method was tested in the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). The CRESCENDDI dataset of positive controls served as the primary source of true-positive DDIs. Disproportionality analysis was performed considering the time of co-exposure. Temporal plausibility was assessed using the flex point of cumulative reporting of disproportionality signals. Potential confounders were identified using a machine learning method (i.e. Lasso regression).

Results

Disproportionality analysis was conducted on 122 triplets with more than three cases, resulting in the prioritization of 61 disproportionality signals (50.0%) involving 13 adverse events, with 61.5% of these included in the European Medicine Agency’s (EMA’s) Important Medical Event (IME) list. A total of 27 signals (44.3%) had at least ten cases reporting the triplet of interest, and most of them (n = 19; 70.4%) were temporally plausible. The retrieved confounders were mainly other concomitant drugs.

Conclusions

Our method was able to prioritize disproportionality signals with temporal plausibility. This finding suggests a potential for our method in pinpointing signals that are more likely to be furtherly validated.

For any therapeutic intervention in an individual, there is a balance between the potential benefits and the possible harms. The extent to which the benefits are desirable in a given condition depends on the efficacy of the intervention, the chance of obtaining it and the seriousness and intensity of the condition. The extent to which the harms are undesirable depends on the nature of the hazard that can lead to harm, the chance that the harm will occur and its seriousness and intensity. Rational therapeutic decisions require clinicians to consider competing courses of action, with potential benefits of different desirability and potential harms of different undesirability. They also have a duty to explain to the patient, for the contemplated interventions, both the possible benefits and the potential harms that the patient may consider significant. In an individual patient, it is necessary to consider (a) the probabilities of benefit from both intervention and non-intervention and (b) the probabilities of harm from both intervention and non-intervention. However, there are several potential problems. Here, we consider how failure to distinguish maximum benefits from probable benefits, or hazards (potential harms) from probable harms, and failure to consider all the competing probabilities may lead to imperfect therapeutic decisions. We also briefly discuss methods to assess the benefit to harm balance.

Introduction

Ixekizumab, a monoclonal antibody against interleukin-17A, is efficacious and well tolerated for the treatment of moderate-to-severe plaque psoriasis. However, there are limited data on the real-world safety of ixekizumab in Chinese patient populations. We performed an observational study of ixekizumab for the treatment of moderate-to-severe plaque psoriasis in routine clinical practice in China. Here we present a further safety analysis of this study.

Methods

In this prospective, observational, single-arm, multicenter, post-marketing safety study, adults (≥18 years) with moderate-to-severe plaque psoriasis receiving ixekizumab were enroled at dermatology departments in hospitals across China and prospectively followed for 12 weeks or until their last dose of ixekizumab. In this analysis, we evaluated adverse events (AEs) of special interest (AESIs) identified using MedDRA^® search strategies. We also analyzed AEs and AESIs occurring in greater than ten patients in subgroups by age (< 65/≥ 65 years), sex, body weight (< 60/60 kg to < 80/≥ 80 kg), renal impairment, hepatic impairment, history of tuberculosis, history of HBV infection, recent or active infection, history of allergic reaction/hypersensitivity, and number (0–1/2–4/5–7) of ixekizumab 80 mg injections after baseline until day 105.

Results

This analysis included 663/666 patients enrolled in the primary study. At least one AESI was reported in 224 (33.8%) patients and considered related to ixekizumab in 181 (27.3%); the most common were injection site reactions (n = 131, 19.8%), infections (n = 80, 12.1%), and allergic reactions/hypersensitivity events (n = 59, 8.9%). The proportion of patients with ≥ 1 AE was higher for females versus males (99/186, 53.2% versus 184/477, 38.6%, p = 0.0006). The proportion of patients with ≥ 1 AE increased with the number of ixekizumab injections after baseline [61/188 (32.4%) for zero to one injection, 151/338 (44.7%) for two to four injections, and 61/106 (57.5%) for five to seven injections; p = 0.0001].

Conclusions

In this real-world study, ixekizumab was well tolerated in Chinese patients with moderate-to-severe plaque psoriasis, with no difference in safety across most patient subgroups.

Introduction

On-treatment excursions of liver laboratory test values in clinical trials involving subjects with underlying liver disease are relevant for the efficacy and safety assessment of drug products and biologics. Existing visualization and analysis tools do not efficiently provide an integrated view of these excursions when baseline liver tests are abnormal.

Objective

The aim of this study was to develop a composite plot that enables visualization of on-treatment changes in liver test results both as multiples of the upper limit of normal defined by each laboratory’s reference population (×ULN) and multiples of the subjects’ baseline (×BLN) values.

Methods

The composite plot approach combines biochemical evaluation for drug-induced severe hepatotoxicity (eDISH) plots sequentially applied to subjects’ baseline and peak on-treatment liver test results normalized by ULN and integrates them into a four-panel shift plot of peak on-treatment values normalized by BLN.

Results

The composite plot enabled efficient assessment of improvement in liver test values during treatment compared with pretreatment in subjects treated with the investigational drug (or the natural history of placebo-treated subjects) and identified outlier subjects for potential drug-induced liver injury.

Conclusion

For studies in subjects with abnormal baseline values, the composite plot has potential application in the assessment of beneficial and concerning on-treatment modifications in liver test values in reference to the individual subject’s baseline and population threshold values.

Enzalutamide is an oral androgen receptor signaling inhibitor utilized in the treatment of men with prostate cancer. It is a moderate inducer of the cytochrome P450 (CYP) enzymes CYP2C9 and CYP2C19, and a strong inducer of CYP3A4. It was also shown to be a mild inhibitor of the efflux transporter P-glycoprotein in patients with prostate cancer. Enzalutamide is primarily metabolized by CYP3A4 and CYP2C8. The risk of enzalutamide drug interactions arises primarily when it is coadministered with other drugs that interact with these CYPs, including CYP3A4. In this review, we begin by providing an overview of enzalutamide including its dosing, use in special populations, pharmacokinetics, changes to its prescribing information, and potential for interaction with coadministered drugs. Enzalutamide interactions with drugs from a wide range of medication classes commonly prescribed to patients with prostate cancer are described, including oral androgen deprivation therapy, agents used to treat a range of cardiovascular diseases, antidiabetic drugs, antidepressants, anti-seizure medications, common urology medications, analgesics, proton pump inhibitors, immunosuppressants, and antigout drugs. Enzalutamide interactions with common vitamins and supplements are also briefly discussed. This review provides a resource for healthcare practitioners and patients that will help provide a basis for the understanding and management of enzalutamide drug–drug interactions to inform decision making, improve patient safety, and optimize drug efficacy.

Background and Objective

During the COVID-19 vaccination campaign, over 34,000 reports of heavy menstrual bleeding following the administration of COVID-19 vaccines originating in the Economic European Area were submitted to EudraVigilance, the European Union database of suspected adverse drug reactions. More than 90% of these reports were sent by consumers while the remaining by healthcare professionals. Public concerns regarding menstruation disorders in COVID-19 vaccinees were also covered by the media. We investigated the impact of media attention on the reporting trends of heavy menstrual bleeding to EudraVigilance.

Methods

We used media outlets published in the Economic European Area on menstrual disorders and COVID-19 vaccines from the beginning of the vaccination campaign in the Economic European Area (1 January, 2021) until December 2022 (i.e., after the regulatory request to add the adverse event to the product information) and spontaneous reports from EudraVigilance.

Results

We found that the publication of safety updates from regulatory authorities and subsequent coverage in media outlets preceded increased reporting to EudraVigilance. Furthermore, the heavy menstrual bleeding reported in the cases occurred several weeks or months earlier and were not submitted to the respective date. The analysis suggests that the spikes in reporting of heavy menstrual bleeding were to some extent influenced by media coverage in some countries.

Conclusions

Consumer reporting to the European Union spontaneous data collection system, EudraVigilance, was of high value for regulatory safety reviews, albeit the reporting behaviours were not free of the influence of the media. These sources of information can be investigated to understand the context of safety concerns of public health interest.

During the COVID-19 vaccination campaign, observed-to-expected analysis was used by the European Medicines Agency to contextualise data from spontaneous reports to generate real-time evidence on emerging safety concerns that may impact the benefit-risk profile of COVID-19 vaccines. Observed-to-expected analysis compares the number of cases spontaneously reported for an event of interest after vaccination (‘observed’) to the ‘expected’ number of cases anticipated to occur in the same number of individuals had they not been vaccinated. Observed-to-expected analysis is a robust methodology that relies on several assumptions that have been described in regulatory guidelines and scientific literature. The use of observed-to-expected analysis to support the safety monitoring of COVID-19 vaccines has provided valuable insights and lessons on its design and interpretability, which could prove to be beneficial in future analyses. When undertaking an observed-to-expected analysis within the context of safety monitoring, several aspects need attention. In particular, we emphasise the importance of stratified and harmonised data collection both for vaccine exposure and spontaneous reporting data, the need for alignment between coding dictionaries and the crucial role of accurate background incidence rates for adverse events of special interest. While these considerations and recommendations were determined in the context of the COVID-19 mass vaccination setting, they are generalisable in principle.

Additional risk minimization strategies may be required to assure a positive benefit–risk balance for some therapeutic products associated with serious adverse drug reactions/risks of use, without which these products may be otherwise unavailable to patients. The goals of risk minimization strategies are often fundamentally to influence the behavior of healthcare professionals (HCPs) and/or patients and can include appropriate patient selection, provision of education and counselling, appropriate medication use, adverse drug reaction monitoring, and adoption of other elements to assure safe use, such as pregnancy prevention. Current approaches to additional risk minimization strategy development rely heavily on information provision, without full consideration of the contextual factors and multi-level influences on patient and HCP behaviors that impact adoption and long-term adherence to these interventions. Application of evidence-based behavioral science methods are urgently needed to improve the quality and effectiveness of these strategies. Evidence from the fields of adherence, health promotion, and drug utilization research underscores the value and necessity for using established behavioral science frameworks and methods if we are to achieve clinical safety goals for patients. The current paper aims to enhance additional risk minimization strategy development and effectiveness by considering how a behavioral science approach can be applied, drawing from evidence in understanding of engagement with pharmaceutical medicines as well as wider public health interventions for patients and HCPs.

Introduction: The Food and Drug Administration Adverse Event Reporting System (FAERS) is a vital source of new drug safety information, but whether adverse event (AE) information collected from these systems adequately captures experiences of the overall United States (US) population is unknown.

Objective: To examine determinants of consumer AE reporting in the USA.

Methods: Five-year AE reporting rate per 100,000 residents per US county were calculated, mapped, and quartiled for AE reports received directly from consumers between 2011 and 2015. Associations between county-level sociodemographic factors obtained from County Health Rankings and AE reporting rates were evaluated using negative binomial regression.

Results: Reporting rates were variable across US counties with > 17.6 reports versus ≤ 5.5 reports/100,000 residents in the highest and lowest reporting quartile, respectively. Controlling for drug utilization, counties with higher reporting rates had higher proportions of individuals age ≥ 65 years (e.g., 2.4% reporting increase per 1% increase in individuals age > 65, incidence rate ratio (IRR): 1.024, 95% confidence interval (CI): 1.017-1.030), higher proportions of females (IRR: 1.027, 95% CI 1.012-1.043), uninsured (IRR: 1.009, 95% CI 1.005-1.013), higher median log household incomes (IRR: 1.897, 95% CI 1.644-2.189) and more mental health providers per 100,000 residents (IRR: 1.003, 95% CI 1.001-1.004). Lower reporting was observed in counties with higher proportions of individuals age ≤ 18 years (IRR: 0.966, 95% CI 0.959-0.974), American Indian or Alaska Native individuals (IRR: 0.991, 95% CI 0.986-0.996), individuals not proficient in English (IRR: 0.978, 95% CI 0.965-0.991), and individuals residing in rural areas within a county (IRR: 0.998, 95% CI 0.997-0.998).

Conclusions: Observed variations in consumer AE reporting may be related to sociodemographic factors and healthcare access. Because these factors may also correspond to AE susceptibility, voluntary AE reporting systems may be suboptimal for capturing emerging drug safety concerns among more vulnerable populations.