ChemInform Abstract: ESSESA: An Expert System for Structure Elucidation from Spectra. Part 6. Substructure Constraints from Analysis of 13C NMR Spectra.

Harvey

Palmese

et al. 2016

Bisphenol A (BPA) is a ubiquitous compound used in polymer manufacturing for a wide array of applications; however, increasing evidence has shown that BPA causes significant endocrine disruption and this has raised public concerns over safety and exposure limits. The use of renewable materials as polymer feedstocks provides an opportunity to develop replacement compounds for BPA that are sustainable and exhibit unique properties due to their diverse structures. As new bio-based materials are developed and tested, it is important to consider the impacts of both monomers and polymers on human health. Molecular docking simulations using the Estrogenic Activity Database in conjunction with the decision forest were performed as part of a two-tier in silico model to predict the activity of 29 bio-based platform chemicals in the estrogen receptor-α (ERα). Fifteen of the candidates were predicted as ER binders and fifteen as non-binders. Gaining insight into the estrogenic activity of the bio-based BPA replacements aids in the sustainable development of new polymeric materials.

Section: Methodsmentioning

confidence: 99%

Experimental Data Extraction and in Silico Prediction of the Estrogenic Activity of Renewable Replacements for Bisphenol A

Harvey

Palmese

et al. 2016

“…Mold 2 is a free software which calculates molecular descriptors from 2D chemical structures. This software is very fast because it adopts the extremely rapid algorithm for cyclic structure recognition [ 56 ] and uses the efficient chemical structure representation system [ 57 , 58 ] that has shown high efficiency in the system for chemicals structure elucidation based on infrared [ 59 ] and nuclear magnetic resonance (NMR) spectra [ 60 , 61 , 62 ]. Mold 2 has been demonstrated to be reliable for developing QSAR models [ 63 , 64 ].…”

Section: Methodsmentioning

confidence: 99%

A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals

Shen

et al. 2016

Endocrine disruptors such as polychlorinated biphenyls (PCBs), diethylstilbestrol (DES) and dichlorodiphenyltrichloroethane (DDT) are agents that interfere with the endocrine system and cause adverse health effects. Huge public health concern about endocrine disruptors has arisen. One of the mechanisms of endocrine disruption is through binding of endocrine disruptors with the hormone receptors in the target cells. Entrance of endocrine disruptors into target cells is the precondition of endocrine disruption. The binding capability of a chemical with proteins in the blood affects its entrance into the target cells and, thus, is very informative for the assessment of potential endocrine disruption of chemicals. α-fetoprotein is one of the major serum proteins that binds to a variety of chemicals such as estrogens. To better facilitate assessment of endocrine disruption of environmental chemicals, we developed a model for α-fetoprotein binding activity prediction using the novel pattern recognition method (Decision Forest) and the molecular descriptors calculated from two-dimensional structures by Mold2 software. The predictive capability of the model has been evaluated through internal validation using 125 training chemicals (average balanced accuracy of 69%) and external validations using 22 chemicals (balanced accuracy of 71%). Prediction confidence analysis revealed the model performed much better at high prediction confidence. Our results indicate that the model is useful (when predictions are in high confidence) in endocrine disruption risk assessment of environmental chemicals though improvement by increasing number of training chemicals is needed.

“…Mold2 is a free software package that calculates molecular descriptors based on two-dimensional structures of chemicals. Mold2 is utmost fast in computation because it utilizes the extremely fast ring structure recognition algorithm [11] and adopted the efficient system of representation of chemical structures [12,13] that were initially developed in a structure elucidation expert system based on infrared [14] and nuclear magnetic resonance (NMR) spectra [15,16,17]. Mold2 molecular descriptors have been used in the development of various successful QSAR models [18,19,20,21,22].…”

Section: Methodsmentioning

confidence: 99%

Consensus Modeling for Prediction of Estrogenic Activity of Ingredients Commonly Used in Sunscreen Products

Rua

Sakkiah

et al. 2016

Sunscreen products are predominantly regulated as over-the-counter (OTC) drugs by the US FDA. The “active” ingredients function as ultraviolet filters. Once a sunscreen product is generally recognized as safe and effective (GRASE) via an OTC drug review process, new formulations using these ingredients do not require FDA review and approval, however, the majority of ingredients have never been tested to uncover any potential endocrine activity and their ability to interact with the estrogen receptor (ER) is unknown, despite the fact that this is a very extensively studied target related to endocrine activity. Consequently, we have developed an in silico model to prioritize single ingredient estrogen receptor activity for use when actual animal data are inadequate, equivocal, or absent. It relies on consensus modeling to qualitatively and quantitatively predict ER binding activity. As proof of concept, the model was applied to ingredients commonly used in sunscreen products worldwide and a few reference chemicals. Of the 32 chemicals with unknown ER binding activity that were evaluated, seven were predicted to be active estrogenic compounds. Five of the seven were confirmed by the published data. Further experimental data is needed to confirm the other two predictions.