Erlinda V. Ortiz

Prediction of drug intestinal absorption by new linear and non-linear QSPR

In order to minimize the high attrition rate that usually characterizes drug research and development projects, current medicinal chemists aim to characterize both pharmacological and ADME profiles at the beginning of drug R&D initiatives. Thus, the development of ADME High-Throughput Screening in vitro and in silico ADME models has become an important growing research area. Here we present new linear and non-linear predictive QSPR models to predict the human intestinal absorption rate, which are derived from a medium sized, balanced and diverse training set of organic compounds. The structure-property relationships so obtained involve only 4 molecular descriptors, and display an excellent ratio of number of cases to number of descriptors. Their adjustment of the training set data together with the performance achieved during the internal and external validation procedures are comparable to previously reported modeling efforts.

Quantitative Structure–Activity Relationships of Mosquito Larvicidal Chalcone Derivatives

The mosquito larvicidal activities of a series of chalcones and some derivatives were subjected to a quantitative structure-activity relationship (QSAR) study, using more than a thousand constitutional, topological, geometrical, and electronic molecular descriptors calculated with Dragon software. The larvicidal activity values for 28 active compounds of the series were predicted, showing in general a good approximation to the experimental values found in the literature. Chalcones having one or both electron-rich rings showed high toxicity. However, the activity of chalcones was reduced by electron-withdrawing groups, and this was roughly diminished by derivatization of the carbonyl group. A set of six chalcones being structurally similar to some of the active ones, with a still unknown larvicidal activity, were prepared. Their activity values were predicted by applying the developed QSAR models, showing that two chalcones of such set, both 32 and 34, were expected to be highly active.

Sustainable synthesis of flavonoid derivatives, QSAR study and insecticidal activity against the fall armyworm, Spodoptera frugiperda (Lep.: Noctuidae).

A simple, clean, solvent-free preparation of flavones by the use of a silica-supported Preyssler heteropolyacid as reusable catalyst is described. High selectivity and very good yields (87-94%) were obtained in short reaction times (7-13 min). Bioassays for insecticidal activity against Spodoptera frugiperda were carried out with a set of flavones. Bioassays showed that some of the flavones had moderate insecticidal activity. Quantitative structure-activity relationships were established on the available data with the purpose of predicting the insecticidal activity of a number of structurally related flavones. A relationship between the molecular structure and biological activity is proposed.

QSAR analysis on Spodoptera litura antifeedant activities for flavone derivatives.

We establish useful models that relate experimentally measured biological activities of compounds to their molecular structure. The pED(50) feeding inhibition on Spodoptera litura species exhibited by aurones, chromones, 3-coumarones and flavones is analyzed in this work through the hypothesis encompassed in the Quantitative Structure-Activity Relationships (QSAR) Theory. This constitutes a first necessary computationally based step during the design of more bio-friendly repellents that could lead to insights for improving the insecticidal activities of the investigated compounds. After optimizing the molecular structure of each furane and pyrane benzoderivative with the semiempirical molecular orbitals method PM3, more than a thousand of constitutional, topological, geometrical and electronic descriptors are calculated and multiparametric linear regression models are established on the antifeedant potencies. The feature selection method employed in this study is the Replacement Method, which has proven to be successful in previous analyzes. We establish the QSAR both for the complete molecular set of compounds and also for each chemical class, so that acceptably describing the variation of the inhibitory activities from the knowledge of their structure and thus achieving useful predictive results. The main interest of developing trustful QSAR models is that these enable the prediction of compounds having no experimentally measured activities for any reason. Therefore, the structure-activity relationships are further employed for investigating the antifeedant activity on previously synthesized 2-,7-substituted benzopyranes, which do not pose any measured values on the biological expression. One of them, 2-(alpha-naphtyl)-4H-1-benzopyran-4-one, results in a promising structure to be experimentally analyzed as it has predicted pED(50)=1.162.

Exploring 3‐arylquinoxaline‐2‐carbonitrile 1,4‐di‐N‐oxides Activities Against Neglected Diseases with QSAR

This work reveals our efforts to continue identifying new active compounds against neglected diseases, such as malaria and tuberculosis. We took several 3‐arylquinoxaline‐2‐carbonitrile 1,4‐di‐N‐oxide derivative activity results from the reference literature and established useful quantitative structure–activity relationships. We hoped that the development of in silico models would broaden our knowledge regarding the overwhelming problem of drug resistance to both illnesses. The optimized molecular structures of 60 compounds were represented by 1497 DRAGON‐type descriptors and subjected to linear regression analyses; the quantitative structure–activity relationships resulted predictive when searching for new active compounds. We obtained a rational guide for the proposal of new candidate structures whose activities still remain unknown.

The conformation-independent QSPR approach for predicting the oxidation rate constant of water micropollutants

In advanced water treatment processes, the degradation efficiency of contaminants depends on the reactivity of the hydroxyl radical toward a target micropollutant. The present study predicts the hydroxyl radical rate constant in water (kOH) for 118 emerging micropollutants, by means of quantitative structure-property relationships (QSPR). The conformation-independent QSPR approach is employed, together with a large number of 15,251 molecular descriptors derived with the PaDEL, Epi Suite, and Mold2 freewares. The best multivariable linear regression (MLR) models are found with the replacement method variable subset selection technique. The proposed five-descriptor model has the following statistics for the training set: Rtrain2=0.88

QSAR on aryl-piperazine derivatives with activity on malaria

{R}_{\mathrm{train}}^2=0.88

Linear and non-linear relationships mapping the Henry’s law parameters of organic pesticides

, RMStrain = 0.21, while for the test set is Rtest2=0.87