Computational Approaches for Pharmacovigilance Signal Detection: Toward Integrated and Semantically-Enriched Frameworks

Abstract

Computational signal detection constitutes a key element of postmarketing drug monitoring and surveillance. Diverse data sources are considered within the ‘search space’ of pharmacovigilance scientists, and respective data analysis methods are employed, all with their qualities and shortcomings, towards more timely and accurate signal detection. Recent systematic comparative studies highlighted not only event-based and data-source-based differential performance across methods but also their complementarity. These findings reinforce the arguments for exploiting all possible information sources for drug safety and the parallel use of multiple signal detection methods. Combinatorial signal detection has been pursued in few studies up to now, employing a rather limited number of methods and data sources but illustrating well-promising outcomes. However, the large-scale realization of this approach requires systematic frameworks to address the challenges of the concurrent analysis setting. In this paper, we argue that semantic technologies provide the means to address some of these challenges, and we particularly highlight their contribution in (a) annotating data sources and analysis methods with quality attributes to facilitate their selection given the analysis scope; (b) consistently defining study parameters such as health outcomes and drugs of interest, and providing guidance for study setup; (c) expressing analysis outcomes in a common format enabling data sharing and systematic comparisons; and (d) assessing/supporting the novelty of the aggregated outcomes through access to reference knowledge sources related to drug safety. A semantically-enriched framework can facilitate seamless access and use of different data sources and computational methods in an integrated fashion, bringing a new perspective for large-scale, knowledge-intensive signal detection.

An Investigation Into the Value of a Standardised Global Pharmacovigilance Reporting System

“…The likely impact of standardisation on the various stages of the PV reporting system will be discussed in the next section. The most widespread challenge facing PV is a high level of under-reporting ( [15], [16], [17], [18], [12]), typically attributed to a lack of knowledge, time, and incentive ( [19], [16], [20]), which alludes to the lack of standardised reporting protocols and methodologies. PV also faces socio-cultural challenges, such as the existence of a culture of fear surrounding the reporting of ADRs due to a fear of undue disciplinary action being taken against HCPs ( [21], [22], [19]).…”

MedDRA® automated term groupings using OntoADR: evaluation with upper gastrointestinal bleedings

“…Some authors have recently proposed to use formal semantics to improve traditional statistical-based methods for detecting signals in pharmacovigilance [32]. The new method we propose for grouping MedDRA terms may help to achieve this goal.…”

Translational Biomedical Informatics and Pharmacometrics Approaches in the Drug Interactions Research

“…For instance, the reviews by Harpaz et al . and Koutkias & Jaulent focused on computational models for SRS and EMR databases. Text and data mining techniques to detect ADE signals were reviewed by Karimi et al .…”

“…Pioneered by Tatonetti et al, 9 the FDA adverse event reporting system (FAERS) and electronic medical records (EMRs) were utilized to generate and validate drug-induced ADE and drug-drug-induced ADE associations. Duke et al 10 proposed a text mining strategy for DDI molecular pharmacology evidence discovery from the public literature, which discovered 13,197 potential DDIs. In the follow-up in vitro study, Han et al 11 validated the loratadine-simvastatin myotoxicity interaction, and its increased myopathy risk in both EMR and FAERS databases.…”