3D-QSAR study of 20 (S)-camptothecin analogs

Authors

  • Ai-jun LU,

    1. Jiangsu Simcere Pharmaceutical Research Company Ltd, Nanjing 210042, China
    2. Drug Discovery and Design Center, State Key Lab of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
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  • Zhen-shan ZHANG,

    1. Drug Discovery and Design Center, State Key Lab of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
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  • Ming-yue ZHENG,

    1. Drug Discovery and Design Center, State Key Lab of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
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  • Han-jun ZOU,

    1. Drug Discovery and Design Center, State Key Lab of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
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  • Xiao-min LUO,

    1. Drug Discovery and Design Center, State Key Lab of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
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  • Hua-liang JIANG

    Corresponding author
    1. Drug Discovery and Design Center, State Key Lab of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
    2. School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
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4Correspondence to Prof Hua-liang JIANG. Phn/Fax 86-21-5080-7188. E-mail hljiang@mail.shcnc.ac.cn

Abstract

Aim: To build up a quantitative structure-activity relationship (QSAR) model of 20 (S)-camptothecin (CPT) analogs for the prediction of the activity of new CPT analogs for drug design. Methods: A training set of 43 structurally diverse CPT analogs which were inhibitors of topoisomerase I were used to construct a quantitative structure–activity relationship model with a comparative molecular field analysis (CoMFA). The QSAR model was optimized using partial least squares (PLS) analysis. A test set of 10 compounds was evaluated using the model. Results: The CoMFA model was constructed successfully, and a good cross-validated correlation was obtained in which q2was 0.495. Then, the analysis of the non-cross-validated PLS model in which r2was 0.935 was built and permitted demonstrations of high predictability for the activities of the 10 CPT analogs in the test set selected in random. Conclusion: The CoMFA model indicated that bulky negative-charged group at position 9, 10 and 11 of CPT would increase activity, but excessively increasing bulky group at position 10 is adverse to inhibitory activity; substituents that occupy position 7 with the bulky positive group will enhance the inhibitive activity. The model can be used to design new CPT analogs and understand the mechanism of action.

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