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A computational method for the identification of candidate drugs for non-small cell lung cancer

Author

Listed:
  • Lei Chen
  • Jing Lu
  • Tao Huang
  • Yu-Dong Cai

Abstract

Lung cancer causes a large number of deaths per year. Until now, a cure for this disease has not been found or developed. Finding an effective drug through traditional experimental methods invariably costs millions of dollars and takes several years. It is imperative that computational methods be developed to integrate several types of existing information to identify candidate drugs for further study, which could reduce the cost and time of development. In this study, we tried to advance this effort by proposing a computational method to identify candidate drugs for non-small cell lung cancer (NSCLC), a major type of lung cancer. The method used three steps: (1) preliminary screening, (2) screening compounds by an association test and a permutation test, (3) screening compounds using an EM clustering algorithm. In the first step, based on the chemical-chemical interaction information reported in STITCH, a well-known database that reports interactions between chemicals and proteins, and approved NSCLC drugs, compounds that can interact with at least one approved NSCLC drug were picked. In the second step, the association test selected compounds that can interact with at least one NSCLC-related chemical and at least one NSCLC-related gene, and subsequently, the permutation test was used to discard nonspecific compounds from the remaining compounds. In the final step, core compounds were selected using a powerful clustering algorithm, the EM algorithm. Six putative compounds, protoporphyrin IX, hematoporphyrin, canertinib, lapatinib, pelitinib, and dacomitinib, were identified by this method. Previously published data show that all of the selected compounds have been reported to possess anti-NSCLC activity, indicating high probabilities of these compounds being novel candidate drugs for NSCLC.

Suggested Citation

  • Lei Chen & Jing Lu & Tao Huang & Yu-Dong Cai, 2017. "A computational method for the identification of candidate drugs for non-small cell lung cancer," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-15, August.
  • Handle: RePEc:plo:pone00:0183411
    DOI: 10.1371/journal.pone.0183411
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    References listed on IDEAS

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    1. Lei Chen & Wei-Ming Zeng & Yu-Dong Cai & Kai-Yan Feng & Kuo-Chen Chou, 2012. "Predicting Anatomical Therapeutic Chemical (ATC) Classification of Drugs by Integrating Chemical-Chemical Interactions and Similarities," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-7, April.
    2. Lei Chen & Jing Lu & Tao Huang & Jun Yin & Lai Wei & Yu-Dong Cai, 2014. "Finding Candidate Drugs for Hepatitis C Based on Chemical-Chemical and Chemical-Protein Interactions," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-6, September.
    3. Lei Chen & Jing Lu & Jian Zhang & Kai-Rui Feng & Ming-Yue Zheng & Yu-Dong Cai, 2013. "Predicting Chemical Toxicity Effects Based on Chemical-Chemical Interactions," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-9, February.
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