Toward creation of a cancer drug toxicity knowledge base: automatically extracting cancer drug-side effect relationships from the literature

Abstract

Objective: A comprehensive and machine-understandable cancer drug-side effect (drug-SE) relationship knowledge base is important for in silico cancer drug target discovery, drug repurposing, and toxicity predication, and for personalized risk-benefit decisions by cancer patients. While US Food and Drug Administration (FDA) drug labels capture well-known cancer drug SE information, much cancer drug SE knowledge remains buried the published biomedical literature. We present a relationship extraction approach to extract cancer drug-SE pairs from the literature.

Data and methods: We used 21,354,075 MEDLINE records as the text corpus. We extracted drug-SE co-occurrence pairs using a cancer drug lexicon and a clean SE lexicon that we created. We then developed two filtering approaches to remove drug-disease treatment pairs and subsequently a ranking scheme to further prioritize filtered pairs. Finally, we analyzed relationships among SEs, gene targets, and indications.

Results: We extracted 56,602 cancer drug-SE pairs. The filtering algorithms improved the precision of extracted pairs from 0.252 at baseline to 0.426, representing a 69% improvement in precision with no decrease in recall. The ranking algorithm further prioritized filtered pairs and achieved a precision of 0.778 for top-ranked pairs. We showed that cancer drugs that share SEs tend to have overlapping gene targets and overlapping indications.

Conclusions: The relationship extraction approach is effective in extracting many cancer drug-SE pairs from the literature. This unique knowledge base, when combined with existing cancer drug SE knowledge, can facilitate drug target discovery, drug repurposing, and toxicity prediction.

Keywords: Cancer Drug Toxicity; Drug Repurposing; Drug Target Discovery; Information Extraction; Natural Language Processing; Text Mining.

Figure 1

Flow chart depicting the process of cancer drug–SE relationship extraction, filtering, ranking, and analysis. SE, side effect.

Figure 2

Filtered drug–SE pairs extracted from sentences (‘Filtered_Sentence’) and abstracts (‘Filtered_Abstract’) and ranked by MEDLINE frequency. SE, side effect.

Figure 3

Unfiltered drug–SE pairs ranked by MEDLINE frequency and evaluated using dataset ‘Irinotecan–SE’ pairs (‘Unfiltered_Sentence_Irinotecan_SE’) and drug–disease treatment pairs from ClinicalTrials.gov (‘Unfiltered_Sentence_Drug_Disease_ClinicalTrials’). SE, side effect.

Figure 4

The positive association between drug target genes and drug–SE pairs: cancer specific drug–SE pairs extracted from MEDLINE (‘Cancer_Drug_SE’) and all pairs from the SIDER database (‘SIDER_Drug_SE’). SE, side effect.

Figure 5

The positive association between drug disease indications and drug–SE pairs: cancer drug–SE pairs extracted from MEDLINE (‘Cancer_Drug_SE’) and all pairs from the SIDER database (‘SIDER_Drug_SE’). SE, side effect.