Comparative Structural Connectivity Spectra Analysis (CoSCoSA) Models of Steroid Binding to the Corticosteroid Binding Globulin

Beger, Richard D.; Buzatu, Dan A.; Wilkes, Jon G.; Lay, Jackson O.

doi:10.1021/ci025511e

Cited by 18 publications

(183 citation statements)

References 38 publications

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“…Finally, it should be emphasized that the 13 C NMR data employed here were unassigned, i.e., no supplementary structural information was used. It has been shown previously that it is possible to achieve improved performance by adding assigned structural information to the one-dimensional CoSA-type QSAR model …”

Section: Discussionmentioning

confidence: 99%

“…Spectroscopic QSAR Methods. A set of QSAR methods have recently been put forward in which vibration frequencies (EVA, eigenvalue, i.e., vibrational normal mode in this context), − MO energies (EEVA, electronic eigenvalue), − and NMR chemical shifts (CoSA, comparative spectra analysis and its modifications − ) are used to derive QSAR descriptors. It has been shown that spectroscopic methods can provide robust, predictive QSAR models for a large number of biological data sets.…”

Section: Introductionmentioning

confidence: 99%

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Spectroscopic QSAR Methods and Self-Organizing Molecular Field Analysis for Relating Molecular Structure and Estrogenic Activity

Asikainen

Ruuskanen

Tuppurainen

2003

J. Chem. Inf. Comput. Sci.

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The performance of three "spectroscopic" quantitative structure-activity relationship (QSAR) methods (eigenvalue (EVA), electronic eigenvalue (EEVA), and comparative spectra analysis (CoSA)) for relating molecular structure and estrogenic activity are critically evaluated. The methods were tested with respect to the relative binding affinities (RBA) of a diverse set of 36 estrogens previously examined in detail by the comparative molecular field analysis method. The CoSA method with (13)C chemical shifts appears to provide a predictive QSAR model for this data set. EEVA (i.e., molecular orbital energy in this context) is a borderline case, whereas the performances of EVA (i.e., vibrational normal mode) and CoSA with (1)H shifts are substandard and only semiquantitative. The CoSA method with (13)C chemical shifts provides an alternative and supplement to conventional 3D QSAR methods for rationalizing and predicting the estrogenic activity of molecules. If CoSA is to be applied to large data sets, however, it is desirable that the chemical shifts are available from common databases or, alternatively, that they can be estimated with sufficient accuracy using fast prediction schemes. Calculations of NMR chemical shifts by quantum mechanical methods, as in this case study, seem to be too time-consuming at this moment, but the situation is changing rapidly. An inherent shortcoming common to all spectroscopic QSAR methods is that they cannot take the chirality of molecules into account, at least as formulated at present. Moreover, the symmetry of molecules may cause additional problems. There are three pairs of enantiomers and nine symmetric (C(2) or C(2)(v)) molecules present in the data set, so that the predictive ability of full 3D QSAR methods is expected to be better than that of spectroscopic methods. This is demonstrated with SOMFA (self-organizing molecular field analysis). In general, the use of external test sets with randomized data is encouraged as a validation tool in QSAR studies.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spectroscopic QSAR Methods and Self-Organizing Molecular Field Analysis for Relating Molecular Structure and Estrogenic Activity

Asikainen

Ruuskanen

Tuppurainen

2003

J. Chem. Inf. Comput. Sci.

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“…The indirect approach confers advantages in simplicity of implementation and objectivity of model building. In most but not all cases, it has also shown improved accuracy and estimation performance compared with SAR and QSAR [21,22,23,24].…”

Section: Methodsmentioning

confidence: 99%

“…Previously, the SDAR methodology has been employed only in the development of models based upon the 13 C-NMR chemical shifts [20]. That implementation works well for carbon-rich organic chemicals [22,23,24], although it may be insufficient for adequate description of nitrogenated compounds. Lipophilic bases with a basic nitrogen atom are the major substrates of CYP2D6; the substrate nitrogen atom binds to aspartic acid on CYP2D, and substrate oxidation takes place at a distance of 5–7 Å from the nitrogen atom [25].…”

Section: Introductionmentioning

confidence: 99%

Modeling Chemical Interaction Profiles: I. Spectral Data-Activity Relationship and Structure-Activity Relationship Models for Inhibitors and Non-inhibitors of Cytochrome P450 CYP3A4 and CYP2D6 Isozymes

et al. 2012

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An interagency collaboration was established to model chemical interactions that may cause adverse health effects when an exposure to a mixture of chemicals occurs. Many of these chemicals-drugs, pesticides, and environmental pollutants-interact at the level of metabolic biotransformations mediated by cytochrome P450 (CYP) enzymes. OPEN ACCESSMolecules 2012, 17 3384In the present work, spectral data-activity relationship (SDAR) and structure-activity relationship (SAR) approaches were used to develop machine-learning classifiers of inhibitors and non-inhibitors of the CYP3A4 and CYP2D6 isozymes. The models were built upon 602 reference pharmaceutical compounds whose interactions have been deduced from clinical data, and 100 additional chemicals that were used to evaluate model performance in an external validation (EV) test. SDAR is an innovative modeling approach that relies on discriminant analysis applied to binned nuclear magnetic resonance (NMR) spectral descriptors. In the present work, both 1D 13 C and 1D 15 N-NMR spectra were used together in a novel implementation of the SDAR technique. It was found that increasing the binning size of 1D 13 C-NMR and 15 N-NMR spectra caused an increase in the tenfold cross-validation (CV) performance in terms of both the rate of correct classification and sensitivity. The results of SDAR modeling were verified using SAR. For SAR modeling, a decision forest approach involving from 6 to 17 Mold 2 descriptors in a tree was used. Average rates of correct classification of SDAR and SAR models in a hundred CV tests were 60% and 61% for CYP3A4, and 62% and 70% for CYP2D6, respectively. The rates of correct classification of SDAR and SAR models in the EV test were 73% and 86% for CYP3A4, and 76% and 90% for CYP2D6, respectively. Thus, both SDAR and SAR methods demonstrated a comparable performance in modeling a large set of structurally diverse data. Based on unique NMR structural descriptors, the new SDAR modeling method complements the existing SAR techniques, providing an independent estimator that can increase confidence in a structure-activity assessment. When modeling was applied to hazardous environmental chemicals, it was found that up to 20% of them may be substrates and up to 10% of them may be inhibitors of the CYP3A4 and CYP2D6 isoforms. The developed models provide a rare opportunity for the environmental health branch of the public health service to extrapolate to hazardous chemicals directly from human clinical data. Therefore, the pharmacological and environmental health branches are both expected to benefit from these reported models.

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“…Beger and co-workers demonstrated that 13 C-NMR data can be used to produce reliable quantitative spectrometric data-activity relationship (QSDAR) models of binding to the corticosterone binding globulin, aromatase enzyme, and aryl hydrocarbon receptor [14][15][16][17]. Comparative structural connectivity spectra analysis (CoSCoSA) models of steroids binding to the corticosteroid were also presented [18]. The success of spectroscopic methods in SAR can be explained recalling that all spectroscopic quantities reflect certain intrinsic physicochemical properties of molecules that are related to their 3D structure.…”

Section: Introductionmentioning

confidence: 99%

NMR QSAR Model for the Analysis of 4‐(5‐Arylamino‐1,3,4‐thiadiazol‐2‐yl)benzene‐1,3‐diols

Matysiak

Niewiadomy

Paw

et al. 2011

Archiv der Pharmazie

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We have developed a NMR data quantitative structure-activity relationship NMR-QSAR model based on (1)H- and (13)C-NMR experimental spectral data of 4-(5-arylamino-1,3,4-thiadiazol-2-yl)benzene-1,3-diols. Compounds show in-vitro antiproliferative activity against some human cancer cell lines. Two-parameter equations obtained by the multiple linear regression procedure showed that chemical shifts of the protons of hydroxyl groups and carbon atoms of the 1,3,4-thiadiazole ring are the decisive descriptors of inhibition interactions of the compounds. The models gave leave-one-out (LOO) cross-validation ranges from 78% to 93%. The obtained NMR-QSAR equations provide a rapid, reliable, and simple way for predicting the antiproliferative activity of N-substituted 4-(5-amino-1,3,4-thiadiazol-2-yl)benzene-1,3-diols.

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Comparative Structural Connectivity Spectra Analysis (CoSCoSA) Models of Steroid Binding to the Corticosteroid Binding Globulin

Cited by 18 publications

References 38 publications

Spectroscopic QSAR Methods and Self-Organizing Molecular Field Analysis for Relating Molecular Structure and Estrogenic Activity

Spectroscopic QSAR Methods and Self-Organizing Molecular Field Analysis for Relating Molecular Structure and Estrogenic Activity

Modeling Chemical Interaction Profiles: I. Spectral Data-Activity Relationship and Structure-Activity Relationship Models for Inhibitors and Non-inhibitors of Cytochrome P450 CYP3A4 and CYP2D6 Isozymes

NMR QSAR Model for the Analysis of 4‐(5‐Arylamino‐1,3,4‐thiadiazol‐2‐yl)benzene‐1,3‐diols

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