Ontology-based literature mining and class effect analysis of adverse drug reactions associated with neuropathy-inducing drugs

Abstract

BackgroundAdverse drug reactions (ADRs), also called as drug adverse events (AEs), are reported in the FDA drug labels; however, it is a big challenge to properly retrieve and analyze the ADRs and their potential relationships from textual data. Previously, we identified and ontologically modeled over 240 drugs that can induce peripheral neuropathy through mining public drug-related databases and drug labels. However, the ADR mechanisms of these drugs are still unclear. In this study, we aimed to develop an ontology-based literature mining system to identify ADRs from drug labels and to elucidate potential mechanisms of the neuropathy-inducing drugs (NIDs).ResultsWe developed and applied an ontology-based SciMiner literature mining strategy to mine ADRs from the drug labels provided in the Text Analysis Conference (TAC) 2017, which included drug labels for 53 neuropathy-inducing drugs (NIDs). We identified an average of 243 ADRs per NID and constructed an ADR-ADR network, which consists of 29 ADR nodes and 149 edges, including only those ADR-ADR pairs found in at least 50% of NIDs. Comparison to the ADR-ADR network of non-NIDs revealed that the ADRs such as pruritus, pyrexia, thrombocytopenia, nervousness, asthenia, acute lymphocytic leukaemia were highly enriched in the NID network. Our ChEBI-based ontology analysis identified three benzimidazole NIDs (i.e., lansoprazole, omeprazole, and pantoprazole), which were associated with 43 ADRs. Based on ontology-based drug class effect definition, the benzimidazole drug group has a drug class effect on all of these 43 ADRs. Many of these 43 ADRs also exist in the enriched NID ADR network. Our Ontology of Adverse Events (OAE) classification further found that these 43 benzimidazole-related ADRs were distributed in many systems, primarily in behavioral and neurological, digestive, skin, and immune systems.ConclusionsOur study demonstrates that ontology-based literature mining and network analysis can efficiently identify and study specific group of drugs and their associated ADRs. Furthermore, our analysis of drug class effects identified 3 benzimidazole drugs sharing 43 ADRs, leading to new hypothesis generation and possible mechanism understanding of drug-induced peripheral neuropathy.

Machine learning-based identification and rule-based normalization of adverse drug reactions in drug labels

“…We recently developed and applied an expansion of SciMiner focusing on ADR study, named as ADR-SciMiner, to a study of ontology-based literature mining and drug class effect analysis of ADRs associated with drug-induced neuropathy [35]. Manual review of these terms was also performed to identify such terms that are unlikely to be ADRs such as various cancers.…”

“…The TAC dataset included 200 manually curated labels (101 in the Training and 99 in the Unannotated sets) and the details have been recently published [35, 36]. These XML files contained raw texts with sections, mentions, relations and normalizations for reactions.…”

A 2018 workshop: vaccine and drug ontology studies (VDOS 2018)

“…The ML approach employed a deep learning architecture, integrating bi-directional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Network (CNN), and Conditional Random Fields (CRF) for entity recognition. Rule-and dictionary-based approach was implemented on their in-house text mining system, Sci-Miner [35,52], which was also used for normalizing the identified ADR mentions to MedDRA terms. The MLbased approach outperformed the rule-based approach, achieving a 77.0% F1 score on the task of ADR mention recognition and 82.6% micro-averaged F1 score on the task of ADR normalization.…”

OntoPlot: A Novel Visualisation for Non-hierarchical Associations in Large Ontologies

“…Therefore, identifying and predicting ADEs are major focuses in pharmacovigilance. Many ontologies have been developed to capture and analyse ADEs [25,29,33,34,41,42,57,67]. In the ontological context, ADEs are often investigated on the class level, where a given ADE may be common to all drugs in the corresponding class, or conversely, an ADE may be associated with some class members but not with all of them.…”