MedDRA Labeling Groupings to Improve Safety Communication in Product Labels.

The granularity and structure of the International Council for Harmonisation's (ICH) Medical Dictionary for Regulatory Activities (MedDRA) are useful for precise coding of adverse events (AEs) for data analysis. In product labeling for healthcare practitioners, however, the granularity of MedDRA Preferred Terms (PTs) can obscure the communication of adverse reactions (ARs). Driven by a focus on patient safety, business needs, and regulatory guidance, many sponsors and regulators have begun to develop institution-specific approaches to clustering similar AR terms in medical product prescribing information on a product-by-product basis. However, there are no agreed upon conventions that describe which AR terms may be appropriate to group together. In order to improve safety communication to patients and healthcare providers, there is an urgent need for a harmonized international approach to the creation and use of groups of MedDRA PTs which we refer to as "MedDRA Labeling Groupings (MLGs)" in medical product prescribing information. Given its long-standing contributions towards the design of Standardised MedDRA Queries (SMQs), the Council for International Organizations of Medical Sciences (CIOMS) convened an Expert Working Group (EWG) with involvement of multiple major stakeholders to produce a consensus document on principles and points to consider in the development of MLGs. The CIOMS MLG EWG identified variations in grouping of MedDRA PTs in product labels, and in the current document, proposes a strategy for improving the communication of drug safety labeling. It is envisaged that the use of these consensus recommendations would be voluntary and applied to product labels in a manner that is consistent with existing regulatory frameworks.

Development of an automated assessment tool for MedWatch reports in the FDA adverse event reporting system.

OBJECTIVE: As the US Food and Drug Administration (FDA) receives over a million adverse event reports associated with medication use every year, a system is needed to aid FDA safety evaluators in identifying reports most likely to demonstrate causal relationships to the suspect medications. We combined text mining with machine learning to construct and evaluate such a system to identify medication-related adverse event reports. METHODS: FDA safety evaluators assessed 326 reports for medication-related causality. We engineered features from these reports and constructed random forest, L1 regularized logistic regression, and support vector machine models. We evaluated model accuracy and further assessed utility by generating report rankings that represented a prioritized report review process. RESULTS: Our random forest model showed the best performance in report ranking and accuracy, with an area under the receiver operating characteristic curve of 0.66. The generated report ordering assigns reports with a higher probability of medication-related causality a higher rank and is significantly correlated to a perfect report ordering, with a Kendall's tau of 0.24 ( P = .002). CONCLUSION: Our models produced prioritized report orderings that enable FDA safety evaluators to focus on reports that are more likely to contain valuable medication-related adverse event information. Applying our models to all FDA adverse event reports has the potential to streamline the manual review process and greatly reduce reviewer workload.

Evaluation of Facebook and Twitter Monitoring to Detect Safety Signals for Medical Products: An Analysis of Recent FDA Safety Alerts.

INTRODUCTION: The rapid expansion of the Internet and computing power in recent years has opened up the possibility of using social media for pharmacovigilance. While this general concept has been proposed by many, central questions remain as to whether social media can provide earlier warnings for rare and serious events than traditional signal detection from spontaneous report data. OBJECTIVE: Our objective was to examine whether specific product-adverse event pairs were reported via social media before being reported to the US FDA Adverse Event Reporting System (FAERS). METHODS: A retrospective analysis of public Facebook and Twitter data was conducted for 10 recent FDA postmarketing safety signals at the drug-event pair level with six negative controls. Social media data corresponding to two years prior to signal detection of each product-event pair were compiled. Automated classifiers were used to identify each 'post with resemblance to an adverse event' (Proto-AE), among English language posts. A custom dictionary was used to translate Internet vernacular into Medical Dictionary for Regulatory Activities (MedDRA®) Preferred Terms. Drug safety physicians conducted a manual review to determine causality using World Health Organization-Uppsala Monitoring Centre (WHO-UMC) assessment criteria. Cases were also compared with those reported in FAERS. FINDINGS: A total of 935,246 posts were harvested from Facebook and Twitter, from March 2009 through October 2014. The automated classifier identified 98,252 Proto-AEs. Of these, 13 posts were selected for causality assessment of product-event pairs. Clinical assessment revealed that posts had sufficient information to warrant further investigation for two possible product-event associations: dronedarone-vasculitis and Banana Boat Sunscreen--skin burns. No product-event associations were found among the negative controls. In one of the positive cases, the first report occurred in social media prior to signal detection from FAERS, whereas the other case occurred first in FAERS. CONCLUSIONS: An efficient semi-automated approach to social media monitoring may provide earlier insights into certain adverse events. More work is needed to elaborate additional uses for social media data in pharmacovigilance and to determine how they can be applied by regulatory agencies.

Ensuring participant safety and trial integrity with clinical trials oversight.

Clinical trial oversight is a critical element that ensures the protection of research participants and integrity of the data collected. The trial sponsor, a local Institutional Review Board, and independent monitoring committees all contribute with complementary but overlapping responsibilities. Consistency among these groups is essential for the smooth conduct of a clinical trial but may be challenging in resource-limited settings (RLS). Capacity building and training for RLS may improve clinical trials oversight and ultimately medical management. In this article, we review the components necessary for optimal clinical trial oversight and the issues that arise in the RLS, with some suggested strategies for improvement.

Clinical and analytical considerations in the study of health status in device trials for heart failure.

BACKGROUND: Measures of health status (including symptoms, functional status, or quality of life) assess patients' experiences of their disease, and may therefore be used to quantify the benefits and risks of treatment. The aim of this article is to provide recommendations to regulatory agencies and research sponsors regarding the use of health status measures in medical device trials. METHODS AND RESULTS: A workshop jointly planned by the Heart Failure Society of America and the US Food and Drug Administration was convened in October 2003 in Washington, DC. A Working Group to address health status measures initiated its collaboration at the workshop and continued its efforts throughout the next year. The Working Group recommended assessment of health status in all studies of heart failure therapy. Standardized instruments known to be valid, reliable, responsive to changes, and available in the languages of target populations should be used. Minimizing bias may be accomplished by using blinded, independent evaluators; collecting multiple health status measures; using valid statistical methods; and creating a health status resource bank. CONCLUSION: Assessment of health status should be part of any device trial and should occur regardless of whether the device is intended as destination or bridging therapy. Health status endpoints should be chosen, collected, and analyzed with the same level of scientific rigor as traditional clinical endpoints. Regulatory agencies should require use of analytic methods that handle the complexity of health status data in addition to usual protocol protections.