Petko Alov

Computational Studies of Free Radical-Scavenging Properties of Phenolic Compounds

For more than half a century free radical-induced alterations at cellular and organ levels have been investigated as a probable underlying mechanism of a number of adverse health conditions. Consequently, significant research efforts have been spent for discovering more effective and potent antioxidants / free radical scavengers for treatment of these adverse conditions. Being by far the most used antioxidants among natural and synthetic compounds, mono- and polyphenols have been the focus of both experimental and computational research on mechanisms of free radical scavenging. Quantum chemical studies have provided a significant amount of data on mechanisms of reactions between phenolic compounds and free radicals outlining a number of properties with a key role for the radical scavenging activity and capacity of phenolics. The obtained quantum chemical parameters together with other molecular descriptors have been used in quantitative structure-activity relationship (QSAR) analyses for the design of new more effective phenolic antioxidants and for identification of the most useful natural antioxidant phenolics. This review aims at presenting the state of the art in quantum chemical and QSAR studies of phenolic antioxidants and at analysing the trends observed in the field in the last decade.

Molecular Modelling Study of the PPARγ Receptor in Relation to the Mode of Action/Adverse Outcome Pathway Framework for Liver Steatosis

The comprehensive understanding of the precise mode of action and/or adverse outcome pathway (MoA/AOP) of chemicals has become a key step toward the development of a new generation of predictive toxicology tools. One of the challenges of this process is to test the feasibility of the molecular modelling approaches to explore key molecular initiating events (MIE) within the integrated strategy of MoA/AOP characterisation. The description of MoAs leading to toxicity and liver damage has been the focus of much interest. Growing evidence underlines liver PPARγ ligand-dependent activation as a key MIE in the elicitation of liver steatosis. Synthetic PPARγ full agonists are of special concern, since they may trigger a number of adverse effects not observed with partial agonists. In this study, molecular modelling was performed based on the PPARγ complexes with full agonists extracted from the Protein Data Bank. The receptor binding pocket was analysed, and the specific ligand-receptor interactions were identified for the most active ligands. A pharmacophore model was derived, and the most important pharmacophore features were outlined and characterised in relation to their specific role for PPARγ activation. The results are useful for the characterisation of the chemical space of PPARγ full agonists and could facilitate the development of preliminary filtering rules for the effective virtual ligand screening of compounds with PPARγ full agonistic activity.

Modes-of-Action Related to Repeated Dose Toxicity: Tissue-Specific Biological Roles of PPAR γ Ligand-Dependent Dysregulation in Nonalcoholic Fatty Liver Disease.

Comprehensive understanding of the precise mode of action/adverse outcome pathway (MoA/AOP) of chemicals becomes a key step towards superseding the current repeated dose toxicity testing methodology with new generation predictive toxicology tools. The description and characterization of the toxicological MoA leading to non-alcoholic fatty liver disease (NAFLD) are of specific interest, due to its increasing incidence in the modern society. Growing evidence stresses on the PPARγ ligand-dependent dysregulation as a key molecular initiating event (MIE) for this adverse effect. The aim of this work was to analyze and systematize the numerous scientific data about the steatogenic role of PPARγ. Over 300 papers were ranked according to preliminary defined criteria and used as reliable and significant sources of data about the PPARγ-dependent prosteatotic MoA. A detailed analysis was performed regarding proteins which PPARγ-mediated expression changes had been confirmed to be prosteatotic by most experimental evidence. Two probable toxicological MoAs from PPARγ ligand binding to NAFLD were described according to the Organisation for Economic Cooperation and Development (OECD) concepts: (i) PPARγ activation in hepatocytes and (ii) PPARγ inhibition in adipocytes. The possible events at different levels of biological organization starting from the MIE to the organ response and the connections between them were described in details.

Recent advances in the molecular modeling of estrogen receptor-mediated toxicity.

In the past two decades, there has been increasing concern about the potentially adverse effects of exogenous endocrine active substances (EAS) that alter the function of the endocrine system by interfering with hormone regulation. The mechanistic pathways by which EAS may elicit adverse effects, such as developmental and reproductive toxicity, often involve direct binding to nuclear hormone receptors. Certainly, the best studied nuclear receptor is the estrogen receptor (ER). Large-scale in vitro and in vivo methods have been developed to assess the estrogenic toxicity of chemicals. However, there are financial and animal welfare concerns related to their application. Quantitative structure-activity relationship (QSAR) approaches have proven their utility as a priority setting tool in the risk assessment of EAS. In addition, the models help to clarify the binding mode of the interacting substances. As estrogen-mediated effects are usually related to ligand-receptor interactions, and as there have been comprehensive structural studies on the ER, molecular modeling together with other in silico approaches provide a suitable means of studying these estrogenic effects. This chapter provides an overview of the molecular modeling approaches applied to ligand-ER interactions. The progress in the field is outlined, and some critical issues are analyzed based on recently published models where these approaches are applied.

The application of molecular modelling in the safety assessment of chemicals: A case study on ligand-dependent PPARγ dysregulation

The aim of this paper was to provide a proof of concept demonstrating that molecular modelling methodologies can be employed as a part of an integrated strategy to support toxicity prediction consistent with the mode of action/adverse outcome pathway (MoA/AOP) framework. To illustrate the role of molecular modelling in predictive toxicology, a case study was undertaken in which molecular modelling methodologies were employed to predict the activation of the peroxisome proliferator-activated nuclear receptor γ (PPARγ) as a potential molecular initiating event (MIE) for liver steatosis. A stepwise procedure combining different in silico approaches (virtual screening based on docking and pharmacophore filtering, and molecular field analysis) was developed to screen for PPARγ full agonists and to predict their transactivation activity (EC50). The performance metrics of the classification model to predict PPARγ full agonists were balanced accuracy=81%, sensitivity=85% and specificity=76%. The 3D QSAR model developed to predict EC50 of PPARγ full agonists had the following statistical parameters: q2cv=0.610, Nopt=7, SEPcv=0.505, r2pr=0.552. To support the linkage of PPARγ agonism predictions to prosteatotic potential, molecular modelling was combined with independently performed mechanistic mining of available in vivo toxicity data followed by ToxPrint chemotypes analysis. The approaches investigated demonstrated a potential to predict the MIE, to facilitate the process of MoA/AOP elaboration, to increase the scientific confidence in AOP, and to become a basis for 3D chemotype development.

Molecular dynamics simulation of the human estrogen receptor alpha: contribution to the pharmacophore of the agonists

Human estrogen receptor alpha (ERα) is one of the most studied targets for in silico screening of bioactive compounds. The estrogenic activity of a vast number of chemicals has been studied for their potentially adverse effects on the hormone regulation of the endocrine system. The commonly accepted presentation of the ERα agonist pharmacophore includes terminal phenolic groups and a hydrophobic rigid backbone. In this study we report on molecular dynamics (MD) simulations of ERα to get a deeper structural insight into the agonist–receptor interactions and the pharmacophore pattern of compounds with agonistic activity. We rely on a crystallographic structure of a complex of ERα (PDB ID 2P15) with an agonist of picomolar affinity. As the X-ray structure has a mutation next to a key structural element for ERα agonistic activity (helix H12, Y537S), a series of MD simulations have been performed on the mutated and on the wild type receptor to prove the stability of the agonist–receptor interactions. No significant difference in the ligand–protein interactions has been detected between the studied proteins implying that the Y537S mutant structure can be used for refinement of the pharmacophore model of the ERα agonists. The results suggest that the pharmacophore of compounds with ERα agonistic activity can be extended by a feature that occupies a free hydrophobic region of the binding pocket. The extended pharmacophore model has been evaluated by a pharmacophore-based virtual screening of databases of ERα binders and decoys. The results also imply that MD simulations are a powerful in silico tool for both protein dynamics and structure investigation, especially when mutations are available that can potentially disturb the protein structure and functions.

Natural modulators of nonalcoholic fatty liver disease: Mode of action analysis and in silico ADME-Tox prediction

&NA; Nonalcoholic fatty liver disease (NAFLD) is considered to be the most common chronic liver disease. The discovery of natural product‐based NAFLD modulators requires a more comprehensive study of their modes of action (MoAs). In this study we analysed available in the literature data for 26 naturally‐derived compounds associated with experimental evidence for NAFLD alleviation and outlined potential biomolecular targets and a network of pharmacological MoAs for 12 compounds with the highest number of experimentally supported MoA key events, modulated by them. Despite the general perception that the therapeutic agents of natural origin are safe, an evaluation of ADME‐Tox properties of these compounds has also been performed in order to estimate their suitability as drug candidates. We evaluated how the investigated structures fit to Lipinskis “Rule of five” and predicted their potential Phase I biotransformation pathways and toxicological effects using the ACD/Percepta platform, and the Meteor Nexus and Derek Nexus knowledge‐based systems. Our results revealed the potential of the studied compounds as lead structures and outlined those of them that needed further optimisation of their pharmacokinetic profiles. The presented combined MoA/in silico approach could be extrapolated to naturally‐derived and pathology‐relevant lead structures with other biological activities. It could direct their optimisation by a mechanistically justified in silico evaluation. Graphical abstract Figure. No caption available. HighlightsData about 26 natural NAFLD modulators and 141 relevant biomarkers are collected.Modes of action network and potential protein targets of 12 compounds are outlined.Metabolic transformations of the compounds considered in the network are predicted.In silico toxicity evaluation is performed for the studied compounds and metabolites.

Quantitative structure-skin permeability relationships

This paper reviews in silico models currently available for the prediction of skin permeability. A comprehensive discussion on the developed methods is presented, focusing on quantitative structure-permeability relationships. In addition, the mechanistic models and comparative studies that analyse different models are discussed. Limitations and strengths of the different approaches are highlighted together with the emergent issues and perspectives.

In vitro and in silico studies of the membrane permeability of natural flavonoids from Silybum marianum (L.) Gaertn. and their derivatives

BACKGROUND In recent years the number of natural products used as pharmaceuticals, components of dietary supplements and cosmetics has increased tremendously requiring more extensive evaluation of their pharmacokinetic properties. PURPOSE This study aims at combining in vitro and in silico methods to evaluate the gastrointestinal absorption (GIA) of natural flavonolignans from milk thistle (Silybum marianum (L.) Gaertn.) and their derivatives. METHODS A parallel artificial membrane permeability assay (PAMPA) was used to evaluate the transcellular permeability of the plant main components. A dataset of 269 compounds with measured PAMPA values and specialized software tools for calculating molecular descriptors were utilized to develop a quantitative structure-activity relationship (QSAR) model to predict PAMPA permeability. RESULTS The PAMPA permeabilities of 7 compounds constituting the main components of the milk thistle were measured and their GIA was evaluated. A freely-available and easy to use QSAR model predicting PAMPA permeability from calculated physico-chemical molecular descriptors was derived and validated on an external dataset of 783 compounds with known GIA. The predicted permeability values correlated well with obtained in vitro results. The QSAR model was further applied to predict the GIA of 31 experimentally untested flavonolignans. CONCLUSIONS According to both in vitro and in silico results most flavonolignans are highly permeable in the gastrointestinal tract, which is a prerequisite for sufficient bioavailability and use as lead structures in drug development. The combined in vitro/in silico approach can be used for the preliminary evaluation of GIA and to guide further laboratory experiments on pharmacokinetic characterization of bioactive compounds, including natural products.

Optimized Structure-based Methodology for Studying PPARγ Partial Agonists

The peroxisome proliferator-activated receptor (PPAR) γ is a master regulator of the lipid and glucose metabolism, and thus is a valuable drug target. Since its full activation is accompanied by a number of adverse effects, researchers focus on discovery of novel compounds with ligand-receptor interaction patterns of PPARγ partial agonists. Molecular modelling is an appropriate way to achieve this goal. In this study we aimed at optimization of the docking algorithm for structure-based investigation of PPARγ partial agonists. A dataset with structures and activities of PPARγ partial agonists was constructed. A comparative study of different scoring functions’ performance was conducted by redocking the partial agonists’ structures selected from experimentally resolved 3D structures of PPARγ protein-ligand complexes. The docking protocols’ performance regarding pose scoring, reproducibility and interpretability in the context of the collected activity data was estimated. An optimized docking protocol was developed to successfully correlate the docking scores of the studied compounds with their experimentally derived activity values and to provide the best matching degree with their experimental binding modes. Overall, these results could be useful for further molecular modelling studies of novel PPARγ partial agonists by selection of reliable docking poses to predict their binding mode and for ranking them in respect to their agonistic activity using the calculated docking scores.