Literature DB >> 25817969

Large-scale automatic extraction of side effects associated with targeted anticancer drugs from full-text oncological articles.

Rong Xu1, QuanQiu Wang2.   

Abstract

Targeted anticancer drugs such as imatinib, trastuzumab and erlotinib dramatically improved treatment outcomes in cancer patients, however, these innovative agents are often associated with unexpected side effects. The pathophysiological mechanisms underlying these side effects are not well understood. The availability of a comprehensive knowledge base of side effects associated with targeted anticancer drugs has the potential to illuminate complex pathways underlying toxicities induced by these innovative drugs. While side effect association knowledge for targeted drugs exists in multiple heterogeneous data sources, published full-text oncological articles represent an important source of pivotal, investigational, and even failed trials in a variety of patient populations. In this study, we present an automatic process to extract targeted anticancer drug-associated side effects (drug-SE pairs) from a large number of high profile full-text oncological articles. We downloaded 13,855 full-text articles from the Journal of Oncology (JCO) published between 1983 and 2013. We developed text classification, relationship extraction, signaling filtering, and signal prioritization algorithms to extract drug-SE pairs from downloaded articles. We extracted a total of 26,264 drug-SE pairs with an average precision of 0.405, a recall of 0.899, and an F1 score of 0.465. We show that side effect knowledge from JCO articles is largely complementary to that from the US Food and Drug Administration (FDA) drug labels. Through integrative correlation analysis, we show that targeted drug-associated side effects positively correlate with their gene targets and disease indications. In conclusion, this unique database that we built from a large number of high-profile oncological articles could facilitate the development of computational models to understand toxic effects associated with targeted anticancer drugs.
Copyright © 2015 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Drug discovery; Drug repositioning; Drug side effects; Drug toxicity prediction; Information extraction; Targeted anticancer drugs; Text mining

Mesh:

Substances:

Year:  2015        PMID: 25817969      PMCID: PMC4582661          DOI: 10.1016/j.jbi.2015.03.009

Source DB:  PubMed          Journal:  J Biomed Inform        ISSN: 1532-0464            Impact factor:   6.317


  34 in total

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2.  Towards building a disease-phenotype knowledge base: extracting disease-manifestation relationship from literature.

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3.  Data-driven prediction of drug effects and interactions.

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Journal:  Sci Transl Med       Date:  2012-03-14       Impact factor: 17.956

Review 4.  Computational drug repositioning: from data to therapeutics.

Authors:  M R Hurle; L Yang; Q Xie; D K Rajpal; P Sanseau; P Agarwal
Journal:  Clin Pharmacol Ther       Date:  2013-01-15       Impact factor: 6.875

Review 5.  Tumor control versus adverse events with targeted anticancer therapies.

Authors:  Dorothy M K Keefe; Emma H Bateman
Journal:  Nat Rev Clin Oncol       Date:  2011-12-20       Impact factor: 66.675

6.  Combining automatic table classification and relationship extraction in extracting anticancer drug-side effect pairs from full-text articles.

Authors:  Rong Xu; QuanQiu Wang
Journal:  J Biomed Inform       Date:  2014-10-13       Impact factor: 6.317

7.  Phase 1 trial of everolimus plus sunitinib in patients with metastatic renal cell carcinoma.

Authors:  Ana M Molina; Darren R Feldman; Martin H Voss; Michelle S Ginsberg; Michael S Baum; Dion R Brocks; Patricia M Fischer; Michael J Trinos; Sujata Patil; Robert J Motzer
Journal:  Cancer       Date:  2011-09-06       Impact factor: 6.860

8.  How to optimise treatment compliance in metastatic renal cell carcinoma with targeted agents.

Authors:  A Ravaud
Journal:  Ann Oncol       Date:  2009-05       Impact factor: 32.976

9.  Role of erlotinib in first-line and maintenance treatment of advanced non-small-cell lung cancer.

Authors:  Noemí Reguart; Andrés Felipe Cardona; Rafael Rosell
Journal:  Cancer Manag Res       Date:  2010-06-01       Impact factor: 3.989

10.  Extraction of potential adverse drug events from medical case reports.

Authors:  Harsha Gurulingappa; Abdul Mateen-Rajput; Luca Toldo
Journal:  J Biomed Semantics       Date:  2012-12-20
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  7 in total

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3.  tcTKB: an integrated cardiovascular toxicity knowledge base for targeted cancer drugs.

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Journal:  BMC Complement Altern Med       Date:  2017-12-29       Impact factor: 3.659

5.  Immunotherapy-related adverse events (irAEs): extraction from FDA drug labels and comparative analysis.

Authors:  QuanQiu Wang; Rong Xu
Journal:  JAMIA Open       Date:  2018-10-15

6.  Nanoformulation improves antitumor efficacy of MAOI immune checkpoint blockade therapy without causing aggression-related side effects.

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Review 7.  State of the art of overcoming efflux transporter mediated multidrug resistance of breast cancer.

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Journal:  Transl Cancer Res       Date:  2019-02       Impact factor: 1.241

  7 in total

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