Automated Extraction of Information on Chemical–P-glycoprotein
Interactions from the Literature
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Abstract
Knowledge
of the interactions between drugs and transporters is
important for drug discovery and development as well as for the evaluation
of their clinical safety. We recently developed a text-mining system
for the automatic extraction of information on chemical–CYP3A4
interactions from the literature. This system is based on natural
language processing and can extract chemical names and their interaction
patterns according to sentence context. The present study aimed to
extend this system to the extraction of information regarding chemical–transporter
interactions. For this purpose, the key verb list designed for cytochrome
P450 enzymes was replaced with that for known drug transporters. The
performance of the system was then tested by examining the accuracy
of information on chemical–P-glycoprotein (P-gp) interactions
extracted from randomly selected PubMed abstracts. The system achieved
89.8% recall and 84.2% precision for the identification of chemical
names and 71.7% recall and 78.6% precision for the extraction of chemical–P-gp
interactions