Loading...
Please wait, while we are loading the content...
Similar Documents
Anticancer Activity of Selected Phenolic Compounds: QSAR Studies Using Ridge Regression and Neural Networks
| Content Provider | Scilit |
|---|---|
| Author | Nandi, Sisir Vračko, Marjan Bagchi, Manish C. |
| Copyright Year | 2007 |
| Description | Journal: Chemical Biology & Drug Design |
| Abstract | Phenol and its congeners are known to induce caspase-ud mediated apoptosis activity and cytotoxicityud on various cancer cell lines. Apoptosis, scavengingud of radicals, antioxidant, and pro-oxidant characteristicsud are primarily responsible for theud antitumor activities of phenolic compounds. Quantitativeud structure–activity relationship studies onud the cellular apoptosis and cytotoxicity of phenolicud compounds have been investigated recently byud Selassie and colleagues (J Med Chem;48:7234,ud 2005) wherein models were developed for variousud carcinogenic cell lines. These quantitative structureud –activity relationship models are based on fewud experimentally obtained physicochemical parametersud such as Verloop's sterimol descriptor, hydrophobicity,ud Hammett electronic parameter, andud octanol . water partition coefficient. The paperud deals with structure–activity relationships of phenolsud and its derivatives for the development ofud predictive models from the standpoint of theoreticalud structural parameters and ridge regressionud methodology. The quantitative structure–activityud relationship studies developed here for the caspase-ud mediated apoptosis activity and cytotoxicityud on murine leukemia cell line (L1210), humanud promylolytic cell line (HL-60), human breast cancerud cell line (MCF-7), parenteral human acute lymphoblasticud cells (CCRF-CEM), and multidrug-resistantud subline of CCRF-resistant to vinblastine (CEM. VLB)ud cells utilize physicochemical molecular descriptorsud calculated solely from the structure of phenolicud compounds under investigation along with theud descriptors used by Selassie and group. It is seenud that such quantitative structure–activity relationshipsud can provide a better quality predictiveud model for the phenolic compounds. The biologicalud activities of the nine sets of phenolic compoundsud have been calculated based on ridge regressionud analysis that clearly gives a better significant correlationud compared to the activities predicted byud Selassie and co-workers. Counter-propagation arti-ud ficial neural network studies have been introducedud in the present investigation for a better understandingud of multidimensional rational patterns inud more complex data sets. The counter-propagationud artificial neural network studies were performedud on the same data set and with the same descriptorsud as have been carried out in developing ridgeud regression models and the result of counter-propagationud neural network models produces very interestingud findings in terms of leave-one-out test.ud Finally, an attempt has been made for a comparativeud study of the relative effectiveness of linearud statistical methods versus nonlinear techniques,ud such as counter-propagation neural networks inud modeling structure–activity studies of the phenolicud compounds.u |
| Related Links | http://onlinelibrary.wiley.com/doi/10.1111/j.1747-0285.2007.00575.x/pdf |
| Ending Page | 436 |
| Page Count | 13 |
| Starting Page | 424 |
| e-ISSN | 17470285 |
| DOI | 10.1111/j.1747-0285.2007.00575.x |
| Journal | Chemical Biology & Drug Design |
| Issue Number | 5 |
| Volume Number | 70 |
| Language | English |
| Publisher | Wiley-Blackwell |
| Publisher Date | 2007-11-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Chemical Biology & Drug Design Medicinal Chemistry Artificial Neural Networks Cancer Cell Lines Quantitative Structure–activity Relationship Ridge Regression Theoretical Structural Descriptors |
| Content Type | Text |
| Resource Type | Article |
