Human Aromatase

Chen, Shiuan; Besman, Marc J.; Shively, John E.; Yanagibashi, Kazutoshi; Hall, Peter F.

doi:10.3109/03602538909103557

Cited by 10 publications

(10 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Increasing of HDL-cholesterol in this study may be associated with induction of cytochrome p-450. Because the cytochrome p-450 was induced by the exposure of chemicals and it was involved in the metabolism of cholesterol (Chen et al ., 1989) .…”

Section: Discussionmentioning

confidence: 99%

The Effects of Hazardous Chemical Exposure on Cardiovascular Disease in Chemical Products Manufacturing Workers

Kim¹,

Won²,

Ko³

et al. 2012

Toxicological Research

View full text Add to dashboard Cite

The purpose of this study was to understand the mechanism of cardiovascular disease (CVD) caused by exposure to hazardous chemicals. We investigated changes in the symptoms of metabolic syndrome, which is strongly related to CVD, and in levels of other CVD risk factors, with a special emphasis on the roles of catecholamines and oxidative stress. The results revealed that neither body mass index (BMI) nor waist and hip circumferences were associated with exposure to hazardous chemicals. Among metabolic syndrome criteria, only HDL-cholesterol level increased on exposure to hazardous chemicals. Levels of epinephrine (EP) and norepinephrine (NEP) were not influenced by exposure to hazardous chemicals; however, the total antioxidative capacity (TAC) reduced because of increased oxidative stress. Both hazardous chemical exposure level and metabolite excretion were related to EP, NEP, and the oxidative stress index (OSI). Logistic regression analysis with these factors as independent variables and metabolic syndrome criteria as dependent variables revealed that EP was associated with blood pressure, and NEP with metabolic syndrome in the chemicalexposed group. In conclusion, the results suggest that reactive oxygen species generated and oxidative stress due to exposure to hazardous chemicals act as mediators and cause changes in the physiological levels of EP and NEP to increase blood pressure. This ultimately leads to the development of CVD through increase in cholesterol, triglyceride, and blood glucose levels by lipid peroxidation.

show abstract

Section: Discussionmentioning

confidence: 99%

The Effects of Hazardous Chemical Exposure on Cardiovascular Disease in Chemical Products Manufacturing Workers

Kim¹,

Won²,

Ko³

et al. 2012

Toxicological Research

View full text Add to dashboard Cite

show abstract

“…The aromatase enzyme catalyzes the conversion of testosterone to estradiol by the aromatization of the A-ring in steroids. , Estrogen production from aromatase enzyme activity is important in the evolution and development of estrogen-dependent tumors. , Inhibition of the aromatase enzyme, a cytochrome P450 complex that converts androgens to estrogens, is therapeutically significant because it may control breast cancer …”

Section: Introductionmentioning

confidence: 99%

¹³C NMR Quantitative Spectrometric Data-Activity Relationship (QSDAR) Models of Steroids Binding the Aromatase Enzyme

Beger

Buzatu

Wilkes

et al. 2001

J. Chem. Inf. Comput. Sci.

View full text Add to dashboard Cite

Five quantitative spectroscopic data-activity relationships (QSDAR) models for 50 steroidal inhibitors binding to aromatase enzyme have been developed based on simulated (13)C nuclear magnetic resonance (NMR) data. Three of the models were based on comparative spectral analysis (CoSA), and the two other models were based on comparative structurally assigned spectral analysis (CoSASA). A CoSA QSDAR model based on five principal components had an explained variance (r(2)) of 0.78 and a leave-one-out (LOO) cross-validated variance (q(2)) of 0.71. A CoSASA model that used the assigned (13)C NMR chemical shifts from a steroidal backbone at five selected positions gave an r(2) of 0.75 and a q(2) of 0.66. The (13)C NMR chemical shifts from atoms in the steroid template position 9, 6, 3, and 7 each had correlations greater than 0.6 with the relative binding activity to the aromatase enzyme. All five QSDAR models had explained and cross-validated variances that were better than the explained and cross-validated variances from a five structural parameter quantitative structure-activity relationship (QSAR) model of the same compounds. QSAR modeling suffers from errors introduced by the assumptions and approximations used in partial charges, dielectric constants, and the molecular alignment process of one structural conformation. One postulated reason that the variances of QSDAR models are better than the QSAR models is that (13)C NMR spectral data, based on quantum mechanical principles, are more reflective of binding than the QSAR model's calculated electrostatic potentials and molecular alignment process. The QSDAR models provide a rapid, simple way to model the steroid inhibitor activity in relation to the aromatase enzyme.

show abstract

“…Cytochrome P450 aromatase (CYP19A1; EC 1.14.14.1) is a member of the Cytochrome P450 (CYP) superfamily of mono-oxygenases. This enzyme catalyzes a key step in estrogen biosynthesis, i.e., aromatization of androgens such as androstenedione and testosterone to estrone and 17β-estradiol, respectively. − Common to most CYPs, CYP19A1 can bind a variety of low molecular weight compounds, but it has a high catalytic specificity toward steroid substrates due to the distribution of polar and nonpolar residues within the binding site. , …”

Section: Introductionmentioning

confidence: 99%

Comprehensive and Automated Linear Interaction Energy Based Binding-Affinity Prediction for Multifarious Cytochrome P450 Aromatase Inhibitors

Dijk

Laak

Wichard

et al. 2017

J. Chem. Inf. Model.

View full text Add to dashboard Cite

Cytochrome P450 aromatase (CYP19A1) plays a key role in the development of estrogen dependent breast cancer, and aromatase inhibitors have been at the front line of treatment for the past three decades. The development of potent, selective and safer inhibitors is ongoing with in silico screening methods playing a more prominent role in the search for promising lead compounds in bioactivity-relevant chemical space. Here we present a set of comprehensive binding affinity prediction models for CYP19A1 using our automated Linear Interaction Energy (LIE) based workflow on a set of 132 putative and structurally diverse aromatase inhibitors obtained from a typical industrial screening study. We extended the workflow with machine learning methods to automatically cluster training and test compounds in order to maximize the number of explained compounds in one or more predictive LIE models. The method uses protein–ligand interaction profiles obtained from Molecular Dynamics (MD) trajectories to help model search and define the applicability domain of the resolved models. Our method was successful in accounting for 86% of the data set in 3 robust models that show high correlation between calculated and observed values for ligand-binding free energies (RMSE < 2.5 kJ mol–1), with good cross-validation statistics.

show abstract

Human Aromatase

Cited by 10 publications

References 6 publications

The Effects of Hazardous Chemical Exposure on Cardiovascular Disease in Chemical Products Manufacturing Workers

The Effects of Hazardous Chemical Exposure on Cardiovascular Disease in Chemical Products Manufacturing Workers

¹³C NMR Quantitative Spectrometric Data-Activity Relationship (QSDAR) Models of Steroids Binding the Aromatase Enzyme

Comprehensive and Automated Linear Interaction Energy Based Binding-Affinity Prediction for Multifarious Cytochrome P450 Aromatase Inhibitors

Contact Info

Product

Resources

About

Human Aromatase

Cited by 10 publications

References 6 publications

The Effects of Hazardous Chemical Exposure on Cardiovascular Disease in Chemical Products Manufacturing Workers

The Effects of Hazardous Chemical Exposure on Cardiovascular Disease in Chemical Products Manufacturing Workers

13C NMR Quantitative Spectrometric Data-Activity Relationship (QSDAR) Models of Steroids Binding the Aromatase Enzyme

Comprehensive and Automated Linear Interaction Energy Based Binding-Affinity Prediction for Multifarious Cytochrome P450 Aromatase Inhibitors

Contact Info

Product

Resources

About

¹³C NMR Quantitative Spectrometric Data-Activity Relationship (QSDAR) Models of Steroids Binding the Aromatase Enzyme