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Anticancer Activity of Selected Phenolic Compounds: QSAR Studies Using Ridge Regression and Neural Networks

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Title Anticancer Activity of Selected Phenolic Compounds: QSAR Studies Using Ridge Regression and Neural Networks
 
Creator Nandi, Sisir
Vracko, Marjan
Bagchi, Manish C
 
Subject Structural Biology & Bioinformatics
 
Description Phenol and its congeners are known to induce caspase-
mediated apoptosis activity and cytotoxicity
on various cancer cell lines. Apoptosis, scavenging
of radicals, antioxidant, and pro-oxidant characteristics
are primarily responsible for the
antitumor activities of phenolic compounds. Quantitative
structure–activity relationship studies on
the cellular apoptosis and cytotoxicity of phenolic
compounds have been investigated recently by
Selassie and colleagues (J Med Chem;48:7234,
2005) wherein models were developed for various
carcinogenic cell lines. These quantitative structure
–activity relationship models are based on few
experimentally obtained physicochemical parameters
such as Verloop’s sterimol descriptor, hydrophobicity,
Hammett electronic parameter, and
octanol . water partition coefficient. The paper
deals with structure–activity relationships of phenols
and its derivatives for the development of
predictive models from the standpoint of theoretical
structural parameters and ridge regression
methodology. The quantitative structure–activity
relationship studies developed here for the caspase-
mediated apoptosis activity and cytotoxicity
on murine leukemia cell line (L1210), human
promylolytic cell line (HL-60), human breast cancer
cell line (MCF-7), parenteral human acute lymphoblastic
cells (CCRF-CEM), and multidrug-resistant
subline of CCRF-resistant to vinblastine (CEM. VLB)
cells utilize physicochemical molecular descriptors
calculated solely from the structure of phenolic
compounds under investigation along with the
descriptors used by Selassie and group. It is seen
that such quantitative structure–activity relationships
can provide a better quality predictive
model for the phenolic compounds. The biological
activities of the nine sets of phenolic compounds
have been calculated based on ridge regression
analysis that clearly gives a better significant correlation
compared to the activities predicted by
Selassie and co-workers. Counter-propagation arti-
ficial neural network studies have been introduced
in the present investigation for a better understanding
of multidimensional rational patterns in
more complex data sets. The counter-propagation
artificial neural network studies were performed
on the same data set and with the same descriptors
as have been carried out in developing ridge
regression models and the result of counter-propagation
neural network models produces very interesting
findings in terms of leave-one-out test.
Finally, an attempt has been made for a comparative
study of the relative effectiveness of linear
statistical methods versus nonlinear techniques,
such as counter-propagation neural networks in
modeling structure–activity studies of the phenolic
compounds.
 
Date 2007
 
Type Article
PeerReviewed
 
Format application/pdf
 
Identifier http://www.eprints.iicb.res.in/261/1/CHEMICAL_BIOLOGY_%26_DRUG_DESIGN%2C_70(5)%2C_424%2D436%2C2007_[19].pdf
Nandi, Sisir and Vracko, Marjan and Bagchi, Manish C (2007) Anticancer Activity of Selected Phenolic Compounds: QSAR Studies Using Ridge Regression and Neural Networks. Chem Biol Drug Des, 70 (5). pp. 424-436.
 
Relation http://dx.doi.org/10.1111/j.1747-0285.2007.00575.x
http://www.eprints.iicb.res.in/261/