Computational modelling of the antischistosomal activity for neolignan derivatives based on the MIA-SAR approach

Theoretical models for exploring the antischistosomal activity of a dataset of 18 synthetic neolignans are built using the multivariate image analysis applied to structure–activity relationships (MIA-SAR) approach. The obtained models were validated using the accuracy (Acc) in leave-one-out cross-validation, external validation and Y-randomization procedures, yielding correct classification superior to 80%, 70% and 60%, respectively. Additionally, a comparison was made of the models obtained from binary (black and white) and coloured images; the colours (pixel values) were selected to correspond to chemical properties. It was observed that the models obtained from coloured images with pixel values corresponding to electronegativity (known as the aug-MIA-SAR_colour approach) generally yielded superior statistical parameters compared with those obtained from binary images (MIA-SAR) and randomly coloured images (atoms are coloured according to their type) with atomic sizes corresponding to Van der Waals radius (aug-MIA-SAR), respectively. Mechanistic interpretation of the influence of different substituents on the antischistosomal activity revealed that methoxy substituents in the R1 (or R2) and R5 positions of the neolignan scaffold are indispensable for the antischistosomal activity. The obtained results provide knowledge of the possible structural modifications to yield novel neolignan compounds with antischistosomal activity.     

Elucidating the aryl hydrocarbon receptor antagonism from a chemical-structural perspective

ABSTRACT

The aryl hydrocarbon receptor (AhR) plays an important role in several biological processes such as reproduction, immunity and homoeostasis. However, little is known on the chemical-structural and physicochemical features that influence the activity of AhR antagonistic modulators. In the present report, in vitro AhR antagonistic activity evaluations, based on a chemical-activated luciferase gene expression (AhR-CALUX) bioassay, and an extensive literature review were performed with the aim of constructing a structurally diverse database of contaminants and potentially toxic chemicals. Subsequently, QSAR models based on Linear Discriminant Analysis and Logistic Regression, as well as two toxicophoric hypotheses were proposed to model the AhR antagonistic activity of the built dataset. The QSAR models were rigorously validated yielding satisfactory performance for all classification parameters. Likewise, the toxicophoric hypotheses were validated using a diverse set of 350 decoys, demonstrating adequate robustness and predictive power. Chemical interpretations of both the QSAR and toxicophoric models suggested that hydrophobic constraints, the presence of aromatic rings and electron-acceptor moieties are critical for the AhR antagonism. Therefore, it is hoped that the deductions obtained in the present study will contribute to elucidate further on the structural and physicochemical factors influencing the AhR antagonistic activity of chemical compounds.