Two-dimensional quantitative structure-activity relationship studies on bioactive ligands of peroxisome proliferator-activated receptor δ

Abstract

Os receptores PPAR formam uma subclasse da superfamília dos receptores nucleares e são considerados importantes alvos para o desenvolvimento de novos agentes terapêuticos para o tratamento de vários distúrbios metabólicos, como dislipidemia e diabetes mellitus tipo 2. Neste trabalho, estudos utilizando o método do holograma QSAR (HQSAR) foram realizados para uma série de potentes ligantes da isoforma PPARd. Resultados estatísticos significativos (r 2 = 0,947 e q 2 = 0,791) foram obtidos, indicando a confiabilidade do modelo 2D QSAR gerado. A seguir, o modelo 2D foi utilizado para predizer a atividade biológica de um conjunto de compostos-testes e os valores preditos a partir do modelo de HQSAR estão em boa concordância com os resultados experimentais. Desta forma, é possível dizer que o modelo de QSAR bidimensional obtido neste trabalho, juntamente com as informações extraídas de mapas 2D de contribuição atômica podem ser muito úteis para o desenvolvimento de novos ligantes do receptor PPARd para o tratamento de doenças metabólicas.Peroxisome proliferator-activated receptors (PPARs) form a subclass of the nuclear receptor superfamily and are attractive drug targets for the development of novel therapeutic agents to treat several metabolic disorders, such as dyslipidemia and type 2 diabetes mellitus. In this work, hologram quantitative structure-activity relationship (HQSAR) studies were carried out on a series of potent PPARd ligands. Significant correlation coefficients (r 2 = 0.947 and q 2 = 0.791) were obtained, indicating the reliability of the 2D QSAR model in predicting the biological activity of untested compounds. The 2D model was then used to predict the potency of an external test set, and the predicted values obtained from the HQSAR model were in good agreement with the experimental results. The final QSAR model, along with the information obtained from 2D contribution maps, should be useful for the design of novel PPARd ligands having improved potency.

Polymorphisms in the Egl nine homolog 3 (EGLN3) and Peroxisome proliferator activated receptor-alpha (PPARα) genes and their correlation with hypoxia adaptation in Tibetan chickens

“…There are three isotypes named PPARα , PPARβ , and PPARγ . PPARα is the main regulator of lipid metabolism [ 6 ]. Carbohydrate and lipid metabolism are two important components of energy metabolic pathways.…”

Machine Learning Techniques and Drug Design

“…* In the past decades, several statistical methods have broadened the arsenal of tools that can be applied to QSAR studies. A certain number of computational techniques have been found useful for the establishment of these relationships such as multiple linear regression (MLR) [11][12][13][14] and partial least squares (PLS) [15][16][17][18][19][20]. Recently, there is also a growing interest in the application of artificial neural networks (ANNs) and support vector machines (SVM) in the field of QSAR, as well as other molecular modeling approaches have been recognized as important tools in drug discovery [21][22][23][24][25][26][27][28][29].…”

Predicting the DPP-IV Inhibitory ActivitypIC50Based on Their Physicochemical Properties

“…The development of a structurally diverse collection of DPP-4 inhibitors is a hot research [5–8]. Computational and various mathematical approaches have been widely employed in the quantitative structure-activity relationship (QSAR) analysis [9–13]. Using statistical methods, QSAR analyses were carried out on a dataset of 47 pyrrolidine analogs acting as DPP-IV inhibitors by Paliwal et al [14].…”