Prediction of the Binding Affinity between Fenoterol Derivatives and the β2-Adrenergic Receptor Using Atom-Based 3D-Chiral Linear Indices

The non-stochastic and stochastic atom-based 3D-chiral quadratic indices were applied to the study of the β2-adrenoceptor (β2-AR) agonist effect (binding affinities) between a set of 26 stereoisomers of fenoterol, reported with this activity. Linear multiple regression analysis was carried out to predict the β2-AR binding affinities of the stereoisomers. Two statistically significant QSAR models, able to describe more than the 92% of the variance of the experimental binding affinities, were obtained using non-stochastic (R2 = 0.924 and s = 0.21) and stochastic (R2 = 0.92 and s = 0.22) 3D-chiral linear indices, respectively. The predictability and stability (robustness) of the obtained models (assessed by the leave-one-out cross-validation experiment) yielded values of q2 = 0.893 (scv = 0.237) and q2 = 0.886 (scv = 0.245), respectively. The results obtained with our approach were slightly better than the results of a 3D-QSAR model, obtained with the CoMFA method (R2 = 0.920, q2 = 0.847 and scv = 0.309). The results of our work demonstrate the usefulness of our topological approach for drug discovery of new lead compounds, even in those studies in which the three-dimensional configuration of the chemicals play an important role in the biological activity.Los índices lineales 3D-quirales no-estocásticos y estocásticos basados en relaciones de átomos son aplicados al estudio del efecto agonista (afinidad de unión) sobre el receptor adrenérgico β2 (β2-AR) entre una serie de 26 estereoisómeros del fenoterol, a los cuales se les ha reportado esta actividad. Una regresión lineal múltiple es llevada a cabo para predecir la afinidad de unión β2-AR de los estereoisómeros. Se obtienen dos modelos QSAR estadísticamente significativos, capaces de describir más del 92 % de la varianza experimental de las afinidades de unión, empleando los índices lineales 3D-quirales no-estocásticos (R2 = 0.924 y s = 0.21) y estocásticos (R2 = 0.92 y s = 0.22) respectivamente. El poder predictivo y la robustez de los modelos obtenidos (comprobados mediante una validación cruzada dejando-uno-fuera) alcanzan valores de q2 = 0.893 (scv = 0.237) y q2 = 0.886 (scv = 0.245), correspondientemente. Los resultados obtenidos con nuestro enfoque fueron ligeramente superiores a aquellos resultados obtenidos previamente con un modelo 3D-QSAR, empleando el método CoMFA (R2 = 0.920, q2 = 0.847 y scv = 0.309). Los resultados de nuestro trabajo demuestran la utilidad de nuestro enfoque topológico para el descubrimiento de nuevos compuestos líderes candidatos a fármacos, incluso para estudios en los cuales las conformaciones tridimensionales de los compuestos juegan un rol fundamental en la actividad biológica.Ciencias Experimentale

Scaffold Perception, ComPharmacophore Model Development, And Quantitative Structure-Affinity Relationships Of Sigma Site Ligands

Sigma receptors are endogenous proteins with potential utility in treating psychological disorders, ischemia, the psychological and convulsive effects of drugs of abuse, and as an imaging agent for cancerous tissues, among others. Drug design efforts targeting these receptors have been hindered by a lack of structural information of the receptors themselves. Traditional ligand-based approaches have succeeded in generating many compounds with high affinity, and quite a few with selectivity for σ-1 receptors. There are few selective ligands for use as pharmacological probes for the σ-2 receptor. Much effort has gone into exploring the structure activity relationships of ligands targeting these receptors. A critical review of the existing literature covering pharmacophore development for σ receptors was undertaken with the intent to develop computational models to assist in ligand-based drug design efforts. Inspired by the lack of pharmacophore models with general utility, and confronted by the obstacles of data heterogeneity, a database of σ ligands and their binding affinity data was collected. Cohorts of data collected under similar experimental methodologies were assembled and clustered by measures of scaffold dissimilarity. Multiple-Instance Learning techniques were used to train classification models that differentiated molecules as active or inactive, and to assist in the identification of relevant conformations of σ ligands at their macromolecular targets. Conformations of high-affinity ligands were then used to develop general pharmacophore models as part of a virtual screening approach. Structure-activity relationship models based on virtual screening alignments of known sigma ligands were developed in the search for selective σ-1 and σ-2 receptor probes