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American Chemical Society, Journal of Chemical Information and Modeling, 10(49), p. 2312-2322, 2009

DOI: 10.1021/ci9002427

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3D-Pharmacophore Mapping Using 4D-QSAR Analysis for the Cytotoxicity of Lamellarins Against Human Hormone-Dependent T47D Breast Cancer Cells

Journal article published in 2009 by Poonsiri Thipnate, Jianzhong Liu, Supa Hannongbua ORCID, A. J. Hopfinger
This paper is available in a repository.
This paper is available in a repository.

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Abstract

4D-QSAR and 3D-pharmacophore models were built and investigated for the cytotoxicity using a training set of 25 lamellarins against human hormone dependent T47D breast cancer cells. Receptor-independent (RI) 4D-QSAR models were first constructed from the exploration of eight possible receptor binding alignments for the entire training set. Since the training set is small (25 compounds), the generality of the 4D-QSAR paradigm was then exploited to devise a strategy to maximize the extraction of binding information from the training set, and to also permit virtual screening of diverse lamellarin chemistry. 4D-QSAR models were sought for only six of the most potent lamellarins of the training set as well as another subset composed of lamellarins with constrained ranges in molecular weight and lipophilicty. This overall modeling strategy has permitted maximizing 3D-pharmacophore information from this small set of structurally complex lamellarins that can be used to drive future analog synthesis and the selection of alternate scaffolds. Overall, it was found that formation of an intermolecular hydrogen bond and hydrophobic interactions for substituents on the E ring most modulate the cytotoxicity against T47D breast cancer cells. Hydrophobic substitutions on the F-ring can also enhance cytotoxic potency. A complementary high throughput virtual screen to the 3D-pharmacophore models, a 4D-fingerprint QSAR model, was constructed using absolute molecular similarity. This 4D-fingerprint virtual high throughput screen permits a larger range of chemistry diversity to be assayed than the 4D-QSAR models. The optimized 4D-QSAR 3D-pharmacophore model has a LOO cross-correlation value of xv-r2 = 0.947, while the optimized 4D-fingerprint virtual screening model has a value of xv-r2 = 0.719. This work reveals that it is possible to develop significant QSAR, 3D-pharmacophore and virtual screening models for a small set of lamellarins showing cytotoxic behavior in breast cancer screens that can guide future drug development based upon lamellarin chemistry.