A QSAR Study on Indole-Based PPAR-γ Agonists in Respect to Receptor Binding and Gene Transactivation Data

Machine Learning-Based Toxicological Modeling for Screening Environmental Obesogens

The emerging presence of environmental obesogens, chemicals that disrupt energy balance and contribute to adipogenesis and obesity, has become a major public health challenge. Molecular initiating events (MIEs) describe biological outcomes resulting from chemical interactions with biomolecules. Machine learning models based on MIEs can predict complex toxic end points due to chemical exposure and improve the interpretability of models. In this study, a system was constructed that integrated six MIEs associated with adipogenesis and obesity. This system showed high accuracy in external validation, with an area under the receiver operating characteristic curve of 0.78. Molecular hydrophobicity (SlogP_VSA) and direct electrostatic interactions (PEOE_VSA) were identified as the two most critical molecular descriptors representing the obesogenic potential of chemicals. This system was further used to predict the obesogenic effects of chemicals on the candidate list of substances of very high concern (SVHCs). Results from 3T3-L1 adipogenesis assays verified that the system correctly predicted obesogenic or nonobesogenic effects of 10 of the 12 SVHCs tested, and identified four novel potential obesogens, including 2-benzotriazol-2-yl-4,6-ditert-butylphenol (UV-320), 4-(1,1,5-trimethylhexyl)phenol (p262-NP), 2-[4-(1,1,3,3-tetramethylbutyl)phenoxy]ethanol (OP1EO) and endosulfan. These validation data suggest that the screening system has good performance in adipogenic prediction.

In silico identification of potential drug compound against Peroxisome proliferator-activated receptor-gamma by virtual screening and toxicity studies for the treatment of Diabetic Nephropathy

Diabetic Nephropathy is a serious complication of diabetes mellitus. Current therapeutic strategies of Diabetic Nephropathy are based on control of modifiable risks like hypertension, glucose levels, and dyslipidemia. Peroxisome proliferator activated receptor-gamma (PPAR-γ) is implicated in several metabolic syndromes including Diabetic Nephropathy, besides obesity, insulin insensitivity, dislipidemia, inflammation, and hypertension. In the present study, virtual screening of 617 compounds from two different public databases was done against PPAR-γ with an objective to find a possible lead compound. Two softwares, PyRx and iGEMDOCK, were used to achieve the docking accuracy in order to avoid loss of candidate compounds. Rosiglitazone (used to treat Diabetic Nephropathy) was taken as the standard compound. A total of 30 compounds with good binding affinity with PPAR-γ were selected for further filtering, on the basis of absorption, distribution, metabolism, excretion, and toxicity (ADMET). The interaction profiling of these 30 compounds, showed a minimum of one and maximum of three interactions with reference to rosiglitazone (SER-289, HIS-449, HIS-323, TYR-473). The fulfilling of ADMET analysis criteria of 30 compounds led to the selection of four compounds (ZINC ID 00181552, 00276456, 00298314, 00448009). Molecular dynamics simulation of these lead compounds in complex with PPAR-γ revealed that three of the four compounds formed a stable complex in the ligand-binding pocket of PPAR-γ during 20-ns simulation. Hence, these three (ZINC ID 00181552, 00276456, 00298314) of the four compounds are potential candidates for experimental validation of biological activity against PPAR-γ in future drug discovery studies.

Peroxisome Proliferator-Activated Receptor γ is a Sensitive Target for Oil Sands Process-Affected Water: Effects on Adipogenesis and Identification of Ligands

Identification of toxic components of complex mixtures is a challenge. Here, oil sands process-affected water (OSPW) was used as a case study to identify those toxic components with a known protein target. Organic chemicals in OSPW exhibited dose-dependent activation of peroxisome proliferator-activated receptor γ (PPARγ) at concentrations less than those currently in the environment (0.025× equivalent of full-strength OSPW), by use of a luciferase reporter gene assay. Activation of PPARγ-mediated adipogenesis by OSPW was confirmed in 3T3L1 preadipocytes, as evidenced by accumulation of lipids and up-regulation of AP2, LPL, and PPARγ gene expression after exposure to polar fractions of OSPW. Unexpectedly, the nonpolar fractions of OSPW inhibited differentiation of preadipocytes via activation of the Wnt signaling pathway. Organic chemicals in OSPW that were ligands of PPARγ were identified by use of a pull-down system combined with untargeted chemical analysis (PUCA), with a recombinant PPARγ protein. Thirty ligands of PPARγ were identified by use of the PUCA assay. High resolution MS(1) and MS(2) spectra were combined to predict the formulas or structures of a subset of ligands, and polyoxygenated or heteroatomic chemicals, especially hydroxylated carboxylic/sulfonic acids, were the major ligands of PPARγ.

Structure-based virtual screening and discovery of New PPARδ/γ dual agonist and PPARδ and γ agonists

Peroxisome proliferator-activated receptors (PPARs) are involved in the control of carbohydrate and lipid metabolism and are considered important targets to treat diabetes mellitus and metabolic syndrome. The available PPAR ligands have several side effects leading to health risks justifying the search for new bioactive ligands to activate the PPAR subtypes, in special PPARδ, the less studied PPAR isoform. Here, we used a structure-based virtual screening protocol in order to find out new PPAR ligands. From a lead-like subset of purchasable compounds, we identified 5 compounds with potential PPAR affinity and, from preliminary in vitro assays, 4 of them showed promising biological activity. Therefore, from our in silico and in vitro protocols, new PPAR ligands are potential candidates to treat metabolic diseases.

The impact of physicochemical and molecular properties in drug design: navigation in the "drug-like" chemical space

Physicochemical and molecular properties influence both pharmacokinetic and pharmacodynamic process, as well as drug safety, often in a conflicting way. In this aspect the current trend in drug discovery is to consider ADME (T) properties in parallel with target affinity. The concept of "drug-likeness" defines acceptable boundaries of fundamental properties formulated as simple rules of thumb, in order to aid the medicinal chemist to prioritize drug candidates. Special attention is given to lipophilicity and molecular weight, since there is a tendency for those parameters to increase in regard to complex compounds generated by new technologies, with potential consequences in bioavailability, while high lipophilicity is also associated with undesired effects. Such rules have the advantage to be very simple and are easy to interpret; however their drawback is that they do not take into consideration uncertainties in measurements and calculations as well as the receptor requirements. The case of PPARs, a nuclear receptor family, is discussed in detail in regard to the chemical space covered by the ligands, focusing on the high demands of the ligand binding domain in both lipophilicity and molecular size. Such paradigms indicate that it would be more appropriate to adapt drug-like properties according to specific drug discovery projects.