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Design, synthesis and experimental validation of novel potential chemopreventive agents using random forest and support vector machine binary classifiers

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Abstract

Compared to the current knowledge on cancer chemotherapeutic agents, only limited information is available on the ability of organic compounds, such as drugs and/or natural products, to prevent or delay the onset of cancer. In order to evaluate chemical chemopreventive potentials and design novel chemopreventive agents with low to no toxicity, we developed predictive computational models for chemopreventive agents in this study. First, we curated a database containing over 400 organic compounds with known chemoprevention activities. Based on this database, various random forest and support vector machine binary classifiers were developed. All of the resulting models were validated by cross validation procedures. Then, the validated models were applied to virtually screen a chemical library containing around 23,000 natural products and derivatives. We selected a list of 148 novel chemopreventive compounds based on the consensus prediction of all validated models. We further analyzed the predicted active compounds by their ease of organic synthesis. Finally, 18 compounds were synthesized and experimentally validated for their chemopreventive activity. The experimental validation results paralleled the cross validation results, demonstrating the utility of the developed models. The predictive models developed in this study can be applied to virtually screen other chemical libraries to identify novel lead compounds for the chemoprevention of cancers.

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Abbreviations

CCR:

Correct classification rate

Combi-QSAR:

Combinatorial quantitative structure–activity relationship

CPT:

Consensus prediction thresholds

EBV-EA:

Epstein–Barr virus early activation

MLR:

Multiple linear regression

MOE:

Molecular operating environment

PKC:

Protein Kinase C

PLS:

Partial least square

PTLC:

Preparative thin layer chromatography

QSAR:

Quantitative structure–activity relationship

RF:

Random forest

SVM:

Support vector machines

TLC:

Thin layer chromatography

TMS:

Tetramethylsilane

TPA:

12-O-tetradecanoylphorbol-13-acetate

ZND:

ZINC natural derivative

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Acknowledgments

We thank Kimberlee Moran, the Director of the Center for Forensic Science Research & Education, for her help with the manuscript preparation for the entire project. We also appreciate the help of Dr. Susan Morris-Natschke, Natural Product Research Laboratory, UNC Eshelman School of Pharmacy with proofreading and editing the manuscript. This investigation was also supported in part by NIH Grant CA 177584-01 from National Cancer Institute awarded to K.H. Lee. This study was also supported in part by the Taiwan Department of Health, China Medical University Hospital Cancer Research Center of Excellence (DOH100-TD-C-111-005).

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Authors and Affiliations

  1. Department of Chemistry, Rutgers University, 315 Penn St., Camden, NJ, 08102, USA

    Brienne Sprague, Marlene T. Kim & Hao Zhu

  2. Natural Products Research Laboratories, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, 310 Beard Hall, CB# 7568, Chapel Hill, NC, 27599-7568, USA

    Qian Shi & Kuo-Hsiung Lee

  3. The Rutgers Center for Computational and Integrative Biology, Camden, NJ, 08102, USA

    Marlene T. Kim & Hao Zhu

  4. Pfizer Worldwide Research and Development, Groton, CT, 06340, USA

    Liying Zhang

  5. Multicase Inc, 23811 Chagrin Blvd, Suite 305, Beachwood, OH, 44122, USA

    Alexander Sedykh

  6. Department of Internal Medicine, International University of Health and Welfare Hospital, Tochigi, 329-2763, Japan

    Eiichiro Ichiishi

  7. Department of Complementary and Alternative Medicine Clinical R&D, Kanazawa University of Graduate School of Medical Science, Kanazawa, 920-8640, Japan

    Harukuni Tokuda

  8. Chinese Medicine Research and Development Center, China Medical University and Hospital, Taichung, Taiwan

    Kuo-Hsiung Lee

Authors
  1. Brienne Sprague
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  2. Qian Shi
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  3. Marlene T. Kim
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  4. Liying Zhang
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  5. Alexander Sedykh
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  6. Eiichiro Ichiishi
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  7. Harukuni Tokuda
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  8. Kuo-Hsiung Lee
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  9. Hao Zhu
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Correspondence to Qian Shi or Hao Zhu.

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Sprague, B., Shi, Q., Kim, M.T. et al. Design, synthesis and experimental validation of novel potential chemopreventive agents using random forest and support vector machine binary classifiers. J Comput Aided Mol Des 28, 631–646 (2014). https://doi.org/10.1007/s10822-014-9748-9

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