Biomedical subjects

Elena Lo Piparo

Publications and source records attributed to Elena Lo Piparo.

4 recordsLinked to original sources

PubMed2006

Virtual screening for aryl hydrocarbon receptor binding prediction.

The overall goal of this study has been to validate computational models for predicting aryl hydrocarbon receptor (AhR) binding. Due to the unavailability of the AhR X-ray crystal structure we have decided to use QSARs models for the binding prediction virtual screening. We have built up CoMFA, Volsurf, and HQSAR models using as a training set 84 AhR ligands. Additionally, we have built a hybrid model combining two of the final selected models in order to give a single operational system. The results show that CoMFA, VolSurf, HQSAR, and the hybrid models gives good results (R(2) equal to 0.91, 0.79, 0.85, and 0.82 and q(2) 0.62, 0.58, 0.62, and 0.70, respectively). Since the techniques analyzed show a good correlation and good prediction also for an external test set, particularly the HQSAR and the hybrid model, we can conclude that these models can be used for predicting AhR binding in virtual screening.

Dioxins↗

PubMed2006

Preliminary analysis of toxicity of benzoxazinones and their metabolites for folsomia Candida.

The overall objective of this study was to explore the toxicity of benzoxazinone allelochemicals and their metabolites to Folsomia candida (Collembola: Isotomidae) (Willem, 1902). Experimental tests showed transformation products to have more pronounced toxicity than parent compounds. The underlying relationship between the chemical structure and toxicity was then studied using three-dimensional QSAR approaches, and results highlighted the role of the steric contribution.

Animals↗

PubMed2006

QSAR models for Daphnia magna toxicity prediction of benzoxazinone allelochemicals and their transformation products.

The overall objective of this study is the ecotoxicological characterization of the benzoxazinone 2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3-one (DIMBOA), the benzoxazolinones benzoxazolin-2-one (BOA) and 6-methoxybenzoxazolin-2-one (MBOA), and their transformation products: phenoxazinones 2-acetylamino-7-methoxy-3H-phenoxazin-3-one (AAMPO), 2-acetylamino-3H-phenoxazin-3-one (AAPO), 2-amino-7-methoxy-3H-phenoxazin-3-one (AMPO), and 2-amino-3H-phenoxazin-3-one (APO); aminophenol 2-aminophenol AP); acetamide N-(2-hydroxyphenyl)acetamide (HPAA); and malonamic acid amide N-(2-hydroxyphenyl)malonamic acid (HPMA). A comparison between empirical results and theoretical ones using rules-based prediction of toxicity was done, and it can be concluded that only the degradation metabolites exhibited significant ecotoxic effect. Using synthetic pesticides knowledge, several QSAR models were trained with various approaches and descriptors. The models generated exhibited good internal predictive ability (R(cv)2 > 0.6) and were used to predict the toxicity of the natural compounds studied.

Animals↗

PubMedDate not supplied

QSAR model for predicting pesticide aquatic toxicity.

A hierarchical QSAR approach was applied for the prediction of acute aquatic toxicity. Chemical structures were encoded into molecular descriptors by an automated, seamless procedure available within the OpenMolGRID system. Finally, various linear and nonlinear regression techniques were used to obtain stable and thoroughly validated QSARs. The final model was developed by a counterpropagation neural network coupled with genetic algorithms for variable selection. The proposed QSAR is consistent with McFarland's principle for biological activity and makes use of seven molecular descriptors, namely HACA-2, HOMO-LUMO energy gap, Kier and Hall index, HA dependent HDSA-1, BETA polarizability, FHBCA fractional HBSA, and LogP. The model was extensively tested by the test set (R2= 0.79), the y-scrambling test, and sensitivity/stability tests.

Algorithms↗