Pub Date : 2002-11-01DOI: 10.1002/1521-3838(200211)21:5<486::AID-QSAR486>3.0.CO;2-Y
S. Ren
Phenols are widely used in agriculture and various industries. Many quantitative structure-activity relationships (QSARs) have been developed for phenols. The most toxicologically meaningful QSARs have been established byseparating compounds by their mechanisms of action (MOAs). However, correctly determining the MOA of a compound is not easy. Discriminant analysis was employed in this study to separate selected phenols by three MOAs (polar narcosis, weak acid respiratory uncoupling, and soft electrophilicity) using molecular descriptors as discriminating variables. Results showed that quadratic terms of several molecular descriptors were needed in addition to their linear terms as discriminating variables. Cross-validation of the linear discriminant functions showed that a small total error rate for mechanism classification was achieved.
{"title":"Use of Molecular Descriptors in Separating Phenols by Three Mechanisms of Toxic Action","authors":"S. Ren","doi":"10.1002/1521-3838(200211)21:5<486::AID-QSAR486>3.0.CO;2-Y","DOIUrl":"https://doi.org/10.1002/1521-3838(200211)21:5<486::AID-QSAR486>3.0.CO;2-Y","url":null,"abstract":"Phenols are widely used in agriculture and various industries. Many quantitative structure-activity relationships (QSARs) have been developed for phenols. The most toxicologically meaningful QSARs have been established byseparating compounds by their mechanisms of action (MOAs). However, correctly determining the MOA of a compound is not easy. Discriminant analysis was employed in this study to separate selected phenols by three MOAs (polar narcosis, weak acid respiratory uncoupling, and soft electrophilicity) using molecular descriptors as discriminating variables. Results showed that quadratic terms of several molecular descriptors were needed in addition to their linear terms as discriminating variables. Cross-validation of the linear discriminant functions showed that a small total error rate for mechanism classification was achieved.","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74521076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-10-01DOI: 10.1002/1521-3838(200210)21:4<348::AID-QSAR348>3.0.CO;2-D
H. Kubinyi
{"title":"From Narcosis to Hyperspace: The History of QSAR","authors":"H. Kubinyi","doi":"10.1002/1521-3838(200210)21:4<348::AID-QSAR348>3.0.CO;2-D","DOIUrl":"https://doi.org/10.1002/1521-3838(200210)21:4<348::AID-QSAR348>3.0.CO;2-D","url":null,"abstract":"","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75634470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-10-01DOI: 10.1002/1521-3838(200210)21:4<357::AID-QSAR357>3.0.CO;2-D
R. Snyder, R. Sangar, Jibo Wang, S. Ekins
Without a crystallized structure for a human cytochrome P450, the use of computational molecular modeling is one approach to examine the active site requirements of substrate and inhibitor specificity. CYP2D6 is undoubtedly the most studied polymorphic human CYP and it is therefore desirable for drug companies to limit its role in the metabolism of drug-candidate molecules due to the need for therapeutic drug monitoring. The purpose of this study was to use a three-dimensional quantitative structure activity relationship (3D-QSAR) approach for understanding the CYP2D6 substrate requirements. Using literature Km values (n=24) derived solely from recombinant sources we were able to build and test one such pharmacophore. This was able to significantly rank-order using the (Spearman's rho coefficient 0.55, p=0.0022) predicted against observed literature Km values (n=28) also derived from recombinant sources. The pharmacophore generated in this study was then fitted into the homology model of the human CYP2D6 based on an alignment of bacterial CYPs and the mammalian CYP2C5 to further validate these modeling approaches. Such models as these represent important tools for quantitative prediction of the level of interaction between a molecule and CYP2D6.
{"title":"Three‐Dimensional Quantitative Structure Activity Relationship for Cyp2d6 Substrates","authors":"R. Snyder, R. Sangar, Jibo Wang, S. Ekins","doi":"10.1002/1521-3838(200210)21:4<357::AID-QSAR357>3.0.CO;2-D","DOIUrl":"https://doi.org/10.1002/1521-3838(200210)21:4<357::AID-QSAR357>3.0.CO;2-D","url":null,"abstract":"Without a crystallized structure for a human cytochrome P450, the use of computational molecular modeling is one approach to examine the active site requirements of substrate and inhibitor specificity. CYP2D6 is undoubtedly the most studied polymorphic human CYP and it is therefore desirable for drug companies to limit its role in the metabolism of drug-candidate molecules due to the need for therapeutic drug monitoring. The purpose of this study was to use a three-dimensional quantitative structure activity relationship (3D-QSAR) approach for understanding the CYP2D6 substrate requirements. Using literature Km values (n=24) derived solely from recombinant sources we were able to build and test one such pharmacophore. This was able to significantly rank-order using the (Spearman's rho coefficient 0.55, p=0.0022) predicted against observed literature Km values (n=28) also derived from recombinant sources. The pharmacophore generated in this study was then fitted into the homology model of the human CYP2D6 based on an alignment of bacterial CYPs and the mammalian CYP2C5 to further validate these modeling approaches. Such models as these represent important tools for quantitative prediction of the level of interaction between a molecule and CYP2D6.","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77802943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-10-01DOI: 10.1002/1521-3838(200210)21:4<369::AID-QSAR369>3.0.CO;2-1
O. Santos-Filho, A. Hopfinger
D-QSAR analysis incorporates pharmacophore, confor- mational and alignment freedom into the development of 3D-QSAR models for training sets of structure-activity data by performing ensemble averaging, the fourth ™di- mension∫. The data required to perform 4D-QSAR analysis includes a training set of compounds, usually analogs, and their measured biological activities in a common screen/assay. The 4D-QSAR approach can be applied to both receptor-dependent (RD) and receptor- independent (RI) problems. In the first scheme, the geometry of the receptor (molecular target, usually an enzyme) is available. In contrast, in the second scheme the geometry of the receptor is not part of the data available to perform the analysis. The descriptors in 4D-QSAR analysis are lattice grid cell (spatial) occupancy measures of atoms composing each molecule in the training set realized from the sampling of conformational and align- ment spaces. These grid cell occupancy descriptors (GCODs) are generated for a number of different atom types, the interaction pharmacophoric elements (IPEs). Non-GCOD descriptors can also be included with the set of GCODs in building the trial descriptor pool for model development. The idea underlying 4D-QSAR analysis is that the differences in activity among a set of ligands are related to differences in their Boltzmann average spatial distribution of molecular shape with respect to the IPEs. The 3D-QSAR models are generated and evaluated by a scheme that combines a genetic algorithm (GA) optimi- zation with partial least-squares (PLS) regression. A single ™active∫ conformation is postulated for each compound in the training set, which, when combined with the optimal alignment, can be used in additional molecular design applications, including other 3D-QSAR methods. The 4D- QSAR models can also be used as virtual screens in the processing of real and/or virtual ligand libraries. In this paper the 4D-QSAR paradigm is given in detail. More- over, we report the application of the (RI) 4D-QSAR formalism to a set of novel nonpeptidic HIV protease inhibitors. The 4D-QSAR models generated are robust and provide insight regarding the probable mechanism of action of the analogs, as well as hints concerning new synthetic routes. Furthermore, these models can be used as a starting point for future receptor-dependent anti-HIV drug design.
{"title":"The 4D-QSAR Paradigm: Application to a Novel Set of Non-peptidic HIV Protease Inhibitors","authors":"O. Santos-Filho, A. Hopfinger","doi":"10.1002/1521-3838(200210)21:4<369::AID-QSAR369>3.0.CO;2-1","DOIUrl":"https://doi.org/10.1002/1521-3838(200210)21:4<369::AID-QSAR369>3.0.CO;2-1","url":null,"abstract":"D-QSAR analysis incorporates pharmacophore, confor- mational and alignment freedom into the development of 3D-QSAR models for training sets of structure-activity data by performing ensemble averaging, the fourth ™di- mension∫. The data required to perform 4D-QSAR analysis includes a training set of compounds, usually analogs, and their measured biological activities in a common screen/assay. The 4D-QSAR approach can be applied to both receptor-dependent (RD) and receptor- independent (RI) problems. In the first scheme, the geometry of the receptor (molecular target, usually an enzyme) is available. In contrast, in the second scheme the geometry of the receptor is not part of the data available to perform the analysis. The descriptors in 4D-QSAR analysis are lattice grid cell (spatial) occupancy measures of atoms composing each molecule in the training set realized from the sampling of conformational and align- ment spaces. These grid cell occupancy descriptors (GCODs) are generated for a number of different atom types, the interaction pharmacophoric elements (IPEs). Non-GCOD descriptors can also be included with the set of GCODs in building the trial descriptor pool for model development. The idea underlying 4D-QSAR analysis is that the differences in activity among a set of ligands are related to differences in their Boltzmann average spatial distribution of molecular shape with respect to the IPEs. The 3D-QSAR models are generated and evaluated by a scheme that combines a genetic algorithm (GA) optimi- zation with partial least-squares (PLS) regression. A single ™active∫ conformation is postulated for each compound in the training set, which, when combined with the optimal alignment, can be used in additional molecular design applications, including other 3D-QSAR methods. The 4D- QSAR models can also be used as virtual screens in the processing of real and/or virtual ligand libraries. In this paper the 4D-QSAR paradigm is given in detail. More- over, we report the application of the (RI) 4D-QSAR formalism to a set of novel nonpeptidic HIV protease inhibitors. The 4D-QSAR models generated are robust and provide insight regarding the probable mechanism of action of the analogs, as well as hints concerning new synthetic routes. Furthermore, these models can be used as a starting point for future receptor-dependent anti-HIV drug design.","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85046293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-10-01DOI: 10.1002/1521-3838(200210)21:4<391::AID-QSAR391>3.0.CO;2-L
G. Müller
{"title":"nD QSAR: a Medicinal Chemist's point of view","authors":"G. Müller","doi":"10.1002/1521-3838(200210)21:4<391::AID-QSAR391>3.0.CO;2-L","DOIUrl":"https://doi.org/10.1002/1521-3838(200210)21:4<391::AID-QSAR391>3.0.CO;2-L","url":null,"abstract":"","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73000516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-10-01DOI: 10.1002/1521-3838(200210)21:4<382::AID-QSAR382>3.0.CO;2-L
A. Vedani, M. Dobler
Quantitative structure-activity relationships (QSAR) is an area of computational research which correlates structural features and quantities such as the binding affinity, toxic potential, or pharmacokinetic properties of an existing or a hypothetical molecule. This correlation may be obtained by analyzing topology and fields of the molecules of interest or by simulating their spatial interactions with biological receptors or models thereof. While 3D-QSAR simulations allow for a specific interaction scheme with the virtual receptor, they presume the knowledge of the bioactive conformation of the ligand molecules and require a sophisticated guess about manifestation and magnitude of the associated induced fit – the adaptation of the receptor binding pocket to the individual ligand topology.
{"title":"Multidimensional QSAR: Moving from three‐ to five‐dimensional concepts","authors":"A. Vedani, M. Dobler","doi":"10.1002/1521-3838(200210)21:4<382::AID-QSAR382>3.0.CO;2-L","DOIUrl":"https://doi.org/10.1002/1521-3838(200210)21:4<382::AID-QSAR382>3.0.CO;2-L","url":null,"abstract":"Quantitative structure-activity relationships (QSAR) is an area of computational research which correlates structural features and quantities such as the binding affinity, toxic potential, or pharmacokinetic properties of an existing or a hypothetical molecule. This correlation may be obtained by analyzing topology and fields of the molecules of interest or by simulating their spatial interactions with biological receptors or models thereof. While 3D-QSAR simulations allow for a specific interaction scheme with the virtual receptor, they presume the knowledge of the bioactive conformation of the ligand molecules and require a sophisticated guess about manifestation and magnitude of the associated induced fit – the adaptation of the receptor binding pocket to the individual ligand topology.","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82326480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-08-01DOI: 10.1002/1521-3838(200208)21:3<249::AID-QSAR249>3.0.CO;2-S
J. Zuegge, U. Fechner, O. Roche, N. Parrott, O. Engkvist, G. Schneider
Current virtual screening applications focus not only on biological activity, but also on additional relevant properties of drug candidates, like absorption, distribution, metabolism, and excretion (ADME). In first-pass virtual screening, these prediction systems must be very fast because typically several millions of compounds must be processed. We have developed a linear PLS-based prediction system for binary classification of drug-drug interaction liability caused by cytochrome P450 3A4 inhibition. The system was trained using IC 5 0 values of 311 carefully selected molecules out of a raw data set containing 1152 compounds. It correctly predicts 95% of the training data and 90% of a semi-independent validation data set. The PLS model was calculated from 333 descriptors encoding a molecule. It outperforms an approach utilizing a three layered feed-forward artificial neural network architecture. The average calculation time required for a prediction is less than 0.3 seconds per molecule on a single microprocessor.
{"title":"A fast virtual screening filter for cytochrome P450 3A4 inhibition liability of compound libraries","authors":"J. Zuegge, U. Fechner, O. Roche, N. Parrott, O. Engkvist, G. Schneider","doi":"10.1002/1521-3838(200208)21:3<249::AID-QSAR249>3.0.CO;2-S","DOIUrl":"https://doi.org/10.1002/1521-3838(200208)21:3<249::AID-QSAR249>3.0.CO;2-S","url":null,"abstract":"Current virtual screening applications focus not only on biological activity, but also on additional relevant properties of drug candidates, like absorption, distribution, metabolism, and excretion (ADME). In first-pass virtual screening, these prediction systems must be very fast because typically several millions of compounds must be processed. We have developed a linear PLS-based prediction system for binary classification of drug-drug interaction liability caused by cytochrome P450 3A4 inhibition. The system was trained using IC 5 0 values of 311 carefully selected molecules out of a raw data set containing 1152 compounds. It correctly predicts 95% of the training data and 90% of a semi-independent validation data set. The PLS model was calculated from 333 descriptors encoding a molecule. It outperforms an approach utilizing a three layered feed-forward artificial neural network architecture. The average calculation time required for a prediction is less than 0.3 seconds per molecule on a single microprocessor.","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81088078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-08-01DOI: 10.1002/1521-3838(200208)21:3<257::AID-QSAR257>3.0.CO;2-W
E. Heimstad, P. Andersson
A quantitative structure-activity relationship (QSAR) of hydroxylated polychlorinated biphenyls (OH-PCBs) potency to inhibit human estrogen sulfotransferase (hEST), was modeled using multivariate methods and calculated semi-empirical descriptors. The QSAR model included 10 selected OH-PCBs, recently reported experimental inhibition potencies of hEST and 17 physico- chemical parameters. The cross-validated explained variance of 0.71 indicates a high predictive capacity of the model. The most important parameters were sub-molecular electronic parameters, such as partial atomic charges and nucleophilic electron densities. The natural substrate estradiol and OH-PCBs have been suggested to inhibit hEST by non-competitive binding to a not yet identified allosteric site. Potential allosteric binding sites in the crystal structure of mouse estrogen sulfotransferase (mEST) and in a homology model of hEST were investigated and discussed using the programs CAST and GRAMM. In general, the most abundant docking sites using GRAMM were in agreement with pockets defined by CAST. These preliminary results should be verified and more detailed studies undertaken when the active dimer of hEST is available. The results indicate that other similar pollutants may well interfere with the sulfonation of estradiol.
{"title":"Docking and QSAR studies of an indirect estrogenic effect of hydroxylated PCBs","authors":"E. Heimstad, P. Andersson","doi":"10.1002/1521-3838(200208)21:3<257::AID-QSAR257>3.0.CO;2-W","DOIUrl":"https://doi.org/10.1002/1521-3838(200208)21:3<257::AID-QSAR257>3.0.CO;2-W","url":null,"abstract":"A quantitative structure-activity relationship (QSAR) of hydroxylated polychlorinated biphenyls (OH-PCBs) potency to inhibit human estrogen sulfotransferase (hEST), was modeled using multivariate methods and calculated semi-empirical descriptors. The QSAR model included 10 selected OH-PCBs, recently reported experimental inhibition potencies of hEST and 17 physico- chemical parameters. The cross-validated explained variance of 0.71 indicates a high predictive capacity of the model. The most important parameters were sub-molecular electronic parameters, such as partial atomic charges and nucleophilic electron densities. The natural substrate estradiol and OH-PCBs have been suggested to inhibit hEST by non-competitive binding to a not yet identified allosteric site. Potential allosteric binding sites in the crystal structure of mouse estrogen sulfotransferase (mEST) and in a homology model of hEST were investigated and discussed using the programs CAST and GRAMM. In general, the most abundant docking sites using GRAMM were in agreement with pockets defined by CAST. These preliminary results should be verified and more detailed studies undertaken when the active dimer of hEST is available. The results indicate that other similar pollutants may well interfere with the sulfonation of estradiol.","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74834231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-08-01DOI: 10.1002/1521-3838(200208)21:3<276::AID-QSAR276>3.0.CO;2-S
T. Schmidt, J. Heilmann
In continuation of a previous QSAR study on the cytotoxicity of 20 sesquiterpene lactones (STLs) of the helenanolide type towards a mouse tumor cell line where a very strong correlation of activity with only two indicator variables encoding the nature of the present alpha ,beta -unsatd. carbonyl structure elements (cyclopentenone and alpha -methylene-gamma -lactone structure) was found, it was the major goal of this study to establish a QSAR model for a set of STLs with wider structural variability. Cytotoxicity towards the human KB cervix carcinoma cell line was exptl. detd. for a set of 40 STLs representing five different structural groups 2 germacranolides, 6 guaianolides, 23 pseudoguaianolides, 8 eudesmanolides and 1 carabranolide (cyclopropane type xanthanolide) and the resulting IC50 values were submitted to a QSAR study using the mol. modeling program MOE. As major result it could be shown that variance in STL cytotoxicity data can be explained to a high degree by electronic and surface properties. QSAR models of considerable internal and external predictivity could be obtained by PCR and PLS anal. of a descriptor set representing fractional accessible mol. surface areas (Q_frASAs). This set of descriptors is calcd. by partitioning the mol. surface accessible to a spheric probe of radius 1.4 .ANG. into fractions attributable to atoms within 14 charge intervals from -0.3 to 0.3 e. The applicability of such Q_frASA descriptors is validated by anal. of several sets of literature data, yielding QSAR models of good statistical quality. It is therefore assumed that Q_frASA descriptors may be of wider applicability in QSAR and QSPR.
{"title":"Quantitative Structure-Cytotoxicity Relationships of Sesquiterpene Lactones derived from partial charge (Q)-based fractional Accessible Surface Area Descriptors (Q_frASAs)","authors":"T. Schmidt, J. Heilmann","doi":"10.1002/1521-3838(200208)21:3<276::AID-QSAR276>3.0.CO;2-S","DOIUrl":"https://doi.org/10.1002/1521-3838(200208)21:3<276::AID-QSAR276>3.0.CO;2-S","url":null,"abstract":"In continuation of a previous QSAR study on the cytotoxicity of 20 sesquiterpene lactones (STLs) of the helenanolide type towards a mouse tumor cell line where a very strong correlation of activity with only two indicator variables encoding the nature of the present alpha ,beta -unsatd. carbonyl structure elements (cyclopentenone and alpha -methylene-gamma -lactone structure) was found, it was the major goal of this study to establish a QSAR model for a set of STLs with wider structural variability. Cytotoxicity towards the human KB cervix carcinoma cell line was exptl. detd. for a set of 40 STLs representing five different structural groups 2 germacranolides, 6 guaianolides, 23 pseudoguaianolides, 8 eudesmanolides and 1 carabranolide (cyclopropane type xanthanolide) and the resulting IC50 values were submitted to a QSAR study using the mol. modeling program MOE. As major result it could be shown that variance in STL cytotoxicity data can be explained to a high degree by electronic and surface properties. QSAR models of considerable internal and external predictivity could be obtained by PCR and PLS anal. of a descriptor set representing fractional accessible mol. surface areas (Q_frASAs). This set of descriptors is calcd. by partitioning the mol. surface accessible to a spheric probe of radius 1.4 .ANG. into fractions attributable to atoms within 14 charge intervals from -0.3 to 0.3 e. The applicability of such Q_frASA descriptors is validated by anal. of several sets of literature data, yielding QSAR models of good statistical quality. It is therefore assumed that Q_frASA descriptors may be of wider applicability in QSAR and QSPR.","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83782306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-08-01DOI: 10.1002/1521-3838(200208)21:3<267::AID-QSAR267>3.0.CO;2-S
P. Fossa, F. Giordanetto, G. Menozzi, L. Mosti
Cyclic nucleotide phosphodiesterases (PDEs) catalyse the hydrolysis of the second messengers adenosine-3',5'-cyclic phosphate cAMP and cGMP. At least 11 different PDE types have been described: each of these groups a number of subtypes and splice variants. The PDE types differ in their amino acid sequence, substrate specificity, inhibitor sensitivity and in their organ, tissue and subcellular distribution. The recently solved X-ray structure of PDE4B as well as the results of site-directed mutagenesis experiments on PDE3A, prompted us to further investigate into the molecular mechanism that leads to effective PDE3 inhibition, as a prosecution of our previous studies on characterisation of the catalytic site of PDE family enzymes. On the basis of the experimental data available, a theoretical model of the catalytic site of PDE3A employing homology-modelling techniques was built. On this model thorough docking studies with potent and selective PDE3 inhibitors were performed. The derived inhibition model individuated structural requirements for potent PDE3 inhibition and can now be exploited for rational drug design purposes.
{"title":"Structural basis for selective PDE 3 inhibition: a docking study","authors":"P. Fossa, F. Giordanetto, G. Menozzi, L. Mosti","doi":"10.1002/1521-3838(200208)21:3<267::AID-QSAR267>3.0.CO;2-S","DOIUrl":"https://doi.org/10.1002/1521-3838(200208)21:3<267::AID-QSAR267>3.0.CO;2-S","url":null,"abstract":"Cyclic nucleotide phosphodiesterases (PDEs) catalyse the hydrolysis of the second messengers adenosine-3',5'-cyclic phosphate cAMP and cGMP. At least 11 different PDE types have been described: each of these groups a number of subtypes and splice variants. The PDE types differ in their amino acid sequence, substrate specificity, inhibitor sensitivity and in their organ, tissue and subcellular distribution. The recently solved X-ray structure of PDE4B as well as the results of site-directed mutagenesis experiments on PDE3A, prompted us to further investigate into the molecular mechanism that leads to effective PDE3 inhibition, as a prosecution of our previous studies on characterisation of the catalytic site of PDE family enzymes. On the basis of the experimental data available, a theoretical model of the catalytic site of PDE3A employing homology-modelling techniques was built. On this model thorough docking studies with potent and selective PDE3 inhibitors were performed. The derived inhibition model individuated structural requirements for potent PDE3 inhibition and can now be exploited for rational drug design purposes.","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87318869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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