Biomedical subjects

Alessandra Roncaglioni

Publications and source records attributed to Alessandra Roncaglioni.

4 recordsLinked to original sources

PubMed2005

Identification of the structural requirements of the receptor-binding affinity of diphenolic azoles to estrogen receptors alpha and beta by three-dimensional quantitative structure-activity relationship and structure-activity relationship analysis.

Three-dimensional (3D) quantitative structure-activity relationship (QSAR) and structure-activity relationship (SAR) analyses were applied concurrently to a data set of highly selective estrogen receptor beta (ERbeta) agonists. The data set consisted of diphenolic azoles characterized by similar structural skeletons but with different binding modes to the estrogen receptor site. Models were developed separately with respect to the relative binding affinities (RBAs) to ERalpha and ERbeta. Steric and electrostatic fields were calculated for a training set of 72 compounds using comparative molecular field analysis (CoMFA). The model developed for ERalpha RBA yielded R2 of 0.91 and q(cv)2 of 0.60. The model developed for ERbeta RBA yielded R2 of 0.95 and q(cv)2 of 0.40. Both models were validated successfully using an external test set of 32 compounds. A new concept of test set evaluation based on the variability of the biological response due to the variability of the living organism has been introduced. The CoMFA analysis was supported by a SAR study. In addition to the most favorable steric and electrostatic regions identified by CoMFA, a number of structural descriptors were identified as being important for binding. These are the number of substituents attached to the main skeleton of each compound, the largest distance between the oxygen atoms of each molecule, and the angle defined by the planes that split the phenyl or the naphthyl and the benzisoxazole or the benzoxazole moiety in a morphometrically longitudinal way.

Azoles↗

PubMed2004

A protocol to select high quality datasets of ecotoxicity values for pesticides.

The key to any QSAR model is the underlying dataset. In order to construct a reliable dataset to develop a QSAR model for pesticide toxicity, we have derived a protocol to critically evaluate the quality of the underlying data. In developing an appropriate protocol that would enable data to be selected in constructing a QSAR, we concentrated on one toxicity end point, the 96 h LC50 from the acute rainbow trout study. This end point is key in pesticide regulation carried out under 91/414/EEC. The dataset used for this exercise was from the US EPA-OPP database.

Animals↗

PubMedDate not supplied

Classification of potential endocrine disrupters on the basis of molecular structure using a nonlinear modeling method.

A methodology for the classification of endocrine disruption chemicals is proposed. It is based on a data set of 106 substances extracted from the list of 553 chemicals that were inspected by the European Union Commission for the scientific evidence of their endocrine disruption activity. The substances belong to different categories defined in the EU Commission report: (i) literature evidence for certainly active as endocrine disrupters, (ii) for potentially active, (iii) for less probable active--lacking evidence, and (iv) for certainty nonactive. 3D molecular coordinates were calculated using the AM1or the PM3 optimization method. From 3D coordinates an extensive set of molecular descriptors was calculated. The classification model based on the counterpropagation neural network was constructed and evaluated. This is the first time that the counterpropagation neural network is applied for the classification of compounds regarding their literature evidence for the endocrine disruption activity. The developed classification model is proposed as a tool for a preliminary assessment of potential endocrine disrupters, which would help the assessors to make the priority list for a large amount of chemicals that have to be tested with more expensive in vitro and in vivo methods.

Journal Article↗

PubMedDate not supplied

Variable selection and interpretation in structure-affinity correlation modeling of estrogen receptor binders.

A computational approach for the identification and investigation of correlations between a chemical structure and a selected biological property is described. It is based on a set of 132 compounds of known chemical structures, which were tested for their binding affinities to the estrogen receptor. Different multivariate modeling methods, i.e., partial least-squares regression, counterpropagation neural network, and error-back-propagation neural network, were applied, and the prediction ability of each model was tested in order to compare the results of the obtained models. To reduce the extensive set of calculated structural descriptors, two types of variable selection methods were applied, depending on the modeling approach used. In particular, the final partial least-squares regression model was built using the "variable importance in projection" variable selection method, while genetic algorithms were applied in neural network modeling to select the optimal set of descriptors. A thorough statistical study of the variables selected by genetic algorithms is shown. The results were assessed with the aim to get insight to the mechanisms involved in the binding of estrogenic compounds to the receptor. The variable selection on the basis of genetic algorithm was controlled with the test set of compounds, extracted from the data set available. To compare the predictive ability of all the optimized models, a leave-one-out cross-validation procedure was applied, the best model being the nonlinear neural network model based on error back-propagation algorithm, which resulted in R2= 92.2% and Q2= 70.8%.

Algorithms↗