2D QSAR of PPARγ agonist binding and transactivation

Developing QSAR Models with Defined Applicability Domains on PPARγ Binding Affinity Using Large Data Sets and Machine Learning Algorithms

Chemicals may cause adverse effects on human health through binding to peroxisome proliferator-activated receptor γ (PPARγ). Hence, binding affinity is useful for evaluating chemicals with potential endocrine-disrupting effects. Quantitative structure-activity relationship (QSAR) regression models with defined applicability domains (ADs) are important to enable efficient screening of chemicals with PPARγ binding activity. However, lack of large data sets hindered the development of QSAR models. In this study, based on PPARγ binding affinity data sets curated from various sources, 30 QSAR models were developed using molecular fingerprints, two-dimensional descriptors, and five machine learning algorithms. Structure-activity landscapes (SALs) of the training compounds were described by network-like similarity graphs (NSGs). Based on the NSGs, local discontinuity scores were calculated and found to be positively correlated with the cross-validation absolute prediction errors of the models using the different training sets, descriptors, and algorithms. Moreover, innovative ADs were defined based on pairwise similarities between compounds and were found to outperform some conventional ADs. The curated data sets and developed regression models could be useful for evaluating PPARγ-involved adverse effects of chemicals. The SAL analysis and the innovative ADs could facilitate understanding of prediction results from QSAR models.

Novel QSAR Models for Molecular Initiating Event Modeling in Two Intersecting Adverse Outcome Pathways Based Pulmonary Fibrosis Prediction for Biocidal Mixtures

The adverse outcome pathway (AOP) was introduced as an alternative method to avoid unnecessary animal tests. Under the AOP framework, an in silico methods, molecular initiating event (MIE) modeling is used based on the ligand-receptor interaction. Recently, the intersecting AOPs (AOP 347), including two MIEs, namely peroxisome proliferator-activated receptor-gamma (PPAR-γ) and toll-like receptor 4 (TLR4), associated with pulmonary fibrosis was proposed. Based on the AOP 347, this study developed two novel quantitative structure-activity relationship (QSAR) models for the two MIEs. The prediction performances of different MIE modeling methods (e.g., molecular dynamics, pharmacophore model, and QSAR) were compared and validated with in vitro test data. Results showed that the QSAR method had high accuracy compared with other modeling methods, and the QSAR method is suitable for the MIE modeling in the AOP 347. Therefore, the two QSAR models based on the AOP 347 can be powerful models to screen biocidal mixture related to pulmonary fibrosis.

Identification of novel peroxisome proliferator-activated receptor-gamma (PPARγ) agonists using molecular modeling method

Peroxisome proliferator-activated receptor-gamma (PPARγ) plays a critical role in lipid and glucose homeostasis. It is the target of many drug discovery studies, because of its role in various disease states including diabetes and cancer. Thiazolidinediones, a synthetic class of agents that work by activation of PPARγ, have been used extensively as insulin-sensitizers for the management of type 2 diabetes. In this study, a combination of QSAR and docking methods were utilised to perform virtual screening of more than 25 million compounds in the ZINC library. The QSAR model was developed using 1,517 compounds and it identified 42,378 potential PPARγ agonists from the ZINC library, and 10,000 of these were selected for docking with PPARγ based on their diversity. Several steps were used to refine the docking results, and finally 30 potentially highly active ligands were identified. Four compounds were subsequently tested for their in vitro activity, and one compound was found to have a K i values of <5 μM.

Important pharmacophoric features of pan PPAR agonists: common chemical feature analysis and virtual screening

HipHop program was used to generate a common chemical feature hypothesis for pan Peroxisome Proliferator-Activated Receptor (PPAR) agonists. The top scoring hypothesis (hypo-1) was found to differentiate the pan agonists (actives) from subtype-specific and dual PPAR agonists (inactives). The importance of individual features in hypo-1 was assessed by deleting a particular feature to generate a new hypothesis and observing its discriminating ability between 'actives' and 'inactives'. Deletion of aromatic features AR-1 (hypo-1b), AR-2 (hypo-1e) and a Hydrophobic feature HYD-1 (hypo-1c) individually did not affect the discriminating power of the hypo-1 significantly. However, deletion of a Hydrogen Bond Acceptor (HBA) feature (hypo-1f) in the hydrophobic tail group was found to be highly detrimental for the specificity of hypo-1 leading to high hit rate of 'inactives'. Since hypo-1 did not produce any useful hits from the database search, hypo-1b, hypo-1c and hypo-1e were used for virtual screening leading to the identification of new potential pan PPAR ligands. The docking studies were used to predict the binding pose of the proposed molecules in PPARgamma active site.

y-Randomization and Its Variants in QSPR/QSAR

y-Randomization is a tool used in validation of QSPR/QSAR models, whereby the performance of the original model in data description (r2) is compared to that of models built for permuted (randomly shuffled) response, based on the original descriptor pool and the original model building procedure. We compared y-randomization and several variants thereof, using original response, permuted response, or random number pseudoresponse and original descriptors or random number pseudodescriptors, in the typical setting of multilinear regression (MLR) with descriptor selection. For each combination of number of observations (compounds), number of descriptors in the final model, and number of descriptors in the pool to select from, computer experiments using the same descriptor selection method result in two different mean highest random r2 values. A lower one is produced by y-randomization or a variant likewise based on the original descriptors, while a higher one is obtained from variants that use random number pseudodescriptors. The difference is due to the intercorrelation of real descriptors in the pool. We propose to compare an original model's r2 to both of these whenever possible. The meaning of the three possible outcomes of such a double test is discussed. Often y-randomization is not available to a potential user of a model, due to the values of all descriptors in the pool for all compounds not being published. In such cases random number experiments as proposed here are still possible. The test was applied to several recently published MLR QSAR equations, and cases of failure were identified. Some progress also is reported toward the aim of obtaining the mean highest r2 of random pseudomodels by calculation rather than by tedious multiple simulations on random number variables.

A consideration of PPAR-gamma ligands with respect to lipophilicity: current trends and perspectives

The fact that PPAR-gamma is expressed dramatically higher in fat, regulating gene transcription in response to small lipophilic ligands, supports an essential role of increased lipophilicity for those ligands. On the other hand, the skepticism concerning high lipophilicity as a characteristic associated with undesirable effects and formulation problems raises the question of how much lipophilicity should be incorporated in the ligand molecules so that they comply with generally accepted guidelines. A survey on the lipophilic behavior of thiazolidinediones and tyrosine-based derivatives with well-established PPAR-gamma affinity and functional activity suggests that excessive lipophilicity may not be favorable for their action.

Sulfonylureas and glinides exhibit peroxisome proliferator-activated receptor gamma activity: a combined virtual screening and biological assay approach

Most drugs currently employed in the treatment of type 2 diabetes either target the sulfonylurea receptor stimulating insulin release (sulfonylureas, glinides), or target the peroxisome proliferator-activated receptor (PPARgamma) improving insulin resistance (thiazolidinediones). Our work shows that sulfonylureas and glinides additionally bind to PPARgamma and exhibit PPARgamma agonistic activity. This activity was predicted in silico by virtual screening and confirmed in vitro in a binding assay, a transactivation assay, and by measuring the expression of PPARgamma target genes. Among the measured compounds, gliquidone and glipizide (two sulfonylureas), as well as nateglinide (a glinide), exhibit PPARgamma agonistic activity at concentrations comparable with those reached under pharmacological treatment. The most active of these compounds, gliquidone, is shown to be as potent as pioglitazone at inducing PPARgamma target gene expression. This dual mode of action of sulfonylureas and glinides may open new perspectives for the molecular pharmacology of antidiabetic drugs, because it provides evidence that drugs can be designed that target both the sulfonylurea receptor and PPARgamma. Targeting both receptors could increase pancreatic insulin secretion and improve insulin resistance. Glinides, sulfonylureas, and other acidified sulfonamides may be promising leads in the development of new PPARgamma agonists. In addition, we provide a unified concept of the PPARgamma binding ability of seemingly disparate compound classes.