{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T17:37:38Z","timestamp":1774460258817,"version":"3.50.1"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>During drug development, it is essential to gather information about the change of clinical exposure of a drug (object) due to the pharmacokinetic (PK) drug-drug interactions (DDIs) with another drug (precipitant). While many natural language processing (NLP) methods for DDI have been published, most were designed to evaluate if (and what kind of) DDI relationships exist in the text, without identifying the direction of DDI (object vs. precipitant drug). Here we present a method for the automatic identification of the directionality of a PK DDI from literature or drug labels.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>We reannotated the Text Analysis Conference (TAC) DDI track 2019 corpus for identifying the direction of a PK DDI and evaluated the performance of a fine-tuned BioBERT model on this task by following the training and validation steps prespecified by TAC.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>This initial attempt showed the model achieved an F-score of 0.82 in identifying sentences as containing PK DDI and an F-score of 0.97 in identifying object versus precipitant drugs in those sentences.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Discussion and conclusion<\/jats:title>\n                <jats:p>Despite a growing list of NLP methods for DDI extraction, most of them use a common set of corpora to perform general purpose tasks (e.g., classifying a sentence into one of several fixed DDI categories). There is a lack of coordination between the drug development and biomedical informatics method development community to develop corpora and methods to perform specific tasks (e.g., extract clinical exposure changes due to PK DDI). We hope that our effort can encourage such a coordination so that more \u201cfit for purpose\u201d NLP methods could be developed and used to facilitate the drug development process.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-023-05520-9","type":"journal-article","created":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T07:01:56Z","timestamp":1698822116000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Deep learning-enabled natural language processing to identify directional pharmacokinetic drug\u2013drug interactions"],"prefix":"10.1186","volume":"24","author":[{"given":"Joel","family":"Zirkle","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaomei","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rebecca","family":"Racz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammadreza","family":"Samieegohar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anik","family":"Chaturbedi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John","family":"Mann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shilpa","family":"Chakravartula","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihua","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,1]]},"reference":[{"issue":"5","key":"5520_CR1","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1016\/j.jbi.2013.07.011","volume":"46","author":"M Herrero-Zazo","year":"2013","unstructured":"Herrero-Zazo M, et al. The DDI corpus: an annotated corpus with pharmacological substances and drug\u2013drug interactions. J Biomed Inform. 2013;46(5):914\u201320.","journal-title":"J Biomed Inform"},{"key":"5520_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103451","volume":"106","author":"Y Zhu","year":"2020","unstructured":"Zhu Y, et al. Extracting drug-drug interactions from texts with BioBERT and multiple entity-aware attentions. J Biomed Inform. 2020;106: 103451.","journal-title":"J Biomed Inform"},{"issue":"8","key":"5520_CR3","doi-asserted-by":"publisher","first-page":"1185","DOI":"10.1007\/s00702-020-02214-x","volume":"127","author":"G Hefner","year":"2020","unstructured":"Hefner G, et al. Prevalence and sort of pharmacokinetic drug\u2013drug interactions in hospitalized psychiatric patients. J Neural Transm (Vienna). 2020;127(8):1185\u201398.","journal-title":"J Neural Transm (Vienna)"},{"key":"5520_CR4","unstructured":"Harmonisation, I.C.f. ICH E14\/S7B Clinical and Nonclinical Evaluation of QT\/QTc Interval Prolongation and Proarrhythmic Potential Questions and Answers 2022 3\/1\/2023]; Available from: https:\/\/www.fda.gov\/regulatory-information\/search-fda-guidance-documents\/e14-and-s7b-clinical-and-nonclinical-evaluation-qtqtc-interval-prolongation-and-proarrhythmic."},{"key":"5520_CR5","unstructured":"Segura-Bedmar I, Martinez P, Sanchez-Cisneros D. The 1st DDIExtraction-2011 challenge task: extraction of drug\u2013drug interactions from biomedical texts. 2011;2011:1\u20139."},{"key":"5520_CR6","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.jbi.2014.05.007","volume":"51","author":"I Segura-Bedmar","year":"2014","unstructured":"Segura-Bedmar I, Martinez P, Herrero-Zazo M. Lessons learnt from the DDIExtraction-2013 shared task. J Biomed Inform. 2014;51:152\u201364.","journal-title":"J Biomed Inform"},{"key":"5520_CR7","unstructured":"Demner-Fushman D, Fung KW, Do P, Boyce RD, Goodwin TR. Overview of the TAC 2018 drug\u2013drug interaction extraction from drug labels track. In: Text analysis conference 2018. 2018."},{"key":"5520_CR8","unstructured":"Goodwin TR, Demner-Fushman D, Fung KW, Do P. Overview of the TAC 2019 Track on drug\u2013drug interaction extraction from drug labels. In: Text analysis conference 2019. 2019."},{"key":"5520_CR9","unstructured":"FDA, U. Drug Development and Drug Interactions | Table of Substrates, Inhibitors and Inducers. 3\/1\/2023]; Available from: https:\/\/www.fda.gov\/drugs\/drug-interactions-labeling\/drug-development-and-drug-interactions-table-substrates-inhibitors-and-inducers."},{"issue":"5","key":"5520_CR10","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1046\/j.1365-2125.1999.00923.x","volume":"47","author":"TS Tracy","year":"1999","unstructured":"Tracy TS, et al. Cytochrome P450 isoforms involved in metabolism of the enantiomers of verapamil and norverapamil. Br J Clin Pharmacol. 1999;47(5):545\u201352.","journal-title":"Br J Clin Pharmacol"},{"key":"5520_CR11","unstructured":"Devlin J, et al. Bert: pre-training of deep bidirectional transformers for language understanding. 2018. arXiv preprint arXiv:1810.04805"},{"issue":"4","key":"5520_CR12","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2019","unstructured":"Lee J, et al. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics. 2019;36(4):1234\u201340.","journal-title":"Bioinformatics"},{"key":"5520_CR13","unstructured":"Soares LB, et al. Matching the blanks: distributional similarity for relation learning. In: 57th Annual meeting of the association for computational linguistics (Acl 2019). 2019;2895\u20132905."},{"key":"5520_CR14","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1093\/bioinformatics\/btaa430","volume":"36","author":"L Weber","year":"2020","unstructured":"Weber L, et al. PEDL: extracting protein-protein associations using deep language models and distant supervision. Bioinformatics. 2020;36:490\u20138.","journal-title":"Bioinformatics"},{"issue":"6","key":"5520_CR15","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1002\/psp4.12423","volume":"8","author":"Z Li","year":"2019","unstructured":"Li Z, Garnett C, Strauss DG. Quantitative systems pharmacology models for a new international cardiac safety regulatory paradigm: an overview of the comprehensive in vitro proarrhythmia assay in silico modeling approach. CPT Pharmacomet Syst Pharmacol. 2019;8(6):371\u20139.","journal-title":"CPT Pharmacomet Syst Pharmacol"},{"key":"5520_CR16","unstructured":"Boyce R, Gardener G, Harkema H. Using natural language processing to extract drug\u2013drug interaction information from package inserts. In: BioNLP: proceedings of the 2012 workshop on biomedical natural language processing. Montr\u00e9al, Canada; 2012."},{"key":"5520_CR17","unstructured":"Maldonado R, Weinzierl M, Harabagiu S. The University of Texas at Dallas HLTRI at TAC 2019. In: The text analysis conference (TAC) drug\u2013drug interaction track. 2019."},{"issue":"10","key":"5520_CR18","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1056\/NEJM197109022851005","volume":"285","author":"JK Weser","year":"1971","unstructured":"Weser JK, Sellers E. Drug interactions with coumarin anticoagulants. 2. N Engl J Med. 1971;285(10):547\u201358.","journal-title":"N Engl J Med"},{"issue":"2","key":"5520_CR19","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1002\/psp4.12267","volume":"7","author":"PY Zhang","year":"2018","unstructured":"Zhang PY, et al. Translational biomedical informatics and pharmacometrics approaches in the drug interactions research. CPT Pharmacomet Syst Pharmacol. 2018;7(2):90\u2013102.","journal-title":"CPT Pharmacomet Syst Pharmacol"},{"issue":"1","key":"5520_CR20","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/s10032-019-00317-0","volume":"22","author":"N Milosevic","year":"2019","unstructured":"Milosevic N, et al. A framework for information extraction from tables in biomedical literature. Int J Doc Anal Recogn. 2019;22(1):55\u201378.","journal-title":"Int J Doc Anal Recogn"},{"issue":"4","key":"5520_CR21","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1136\/amiajnl-2011-000116","volume":"18","author":"SJ Nelson","year":"2011","unstructured":"Nelson SJ, et al. Normalized names for clinical drugs: RxNorm at 6 years. J Am Med Inform Assoc. 2011;18(4):441\u20138.","journal-title":"J Am Med Inform Assoc"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-023-05520-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-023-05520-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-023-05520-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T07:02:04Z","timestamp":1698822124000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-023-05520-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,1]]},"references-count":21,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["5520"],"URL":"https:\/\/doi.org\/10.1186\/s12859-023-05520-9","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,1]]},"assertion":[{"value":"20 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 November 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"413"}}