Modes-of-Action Related to Repeated Dose Toxicity: Tissue-Specific Biological Roles of PPAR γ

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

A Comprehensive Evaluation of Sdox, a Promising H2S-Releasing Doxorubicin for the Treatment of Chemoresistant Tumors

Sdox is a hydrogen sulfide (H2S)-releasing doxorubicin effective in P-glycoprotein-overexpressing/doxorubicin-resistant tumor models and not cytotoxic, as the parental drug, in H9c2 cardiomyocytes. The aim of this study was the assessment of Sdox drug-like features and its absorption, distribution, metabolism, and excretion (ADME)/toxicity properties, by a multi- and transdisciplinary in silico, in vitro, and in vivo approach. Doxorubicin was used as the reference compound. The in silico profiling suggested that Sdox possesses higher lipophilicity and lower solubility compared to doxorubicin, and the off-targets prediction revealed relevant differences between Dox and Sdox towards several cancer targets, suggesting different toxicological profiles. In vitro data showed that Sdox is a substrate with lower affinity for P-glycoprotein, less hepatotoxic, and causes less oxidative damage than doxorubicin. Both anthracyclines inhibited CYP3A4, but not hERG currents. Unlike doxorubicin, the percentage of zebrafish live embryos at 72 hpf was not affected by Sdox treatment. In conclusion, these findings demonstrate that Sdox displays a more favorable drug-like ADME/toxicity profile than doxorubicin, different selectivity towards cancer targets, along with a greater preclinical efficacy in resistant tumors. Therefore, Sdox represents a prototype of innovative anthracyclines, worthy of further investigations in clinical settings

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: q(2)cv=0.610, Nopt=7, SEPcv=0.505, r(2)pr=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

Structural and Dynamical Insight into PPARγ Antagonism: In Silico Study of the Ligand-Receptor Interactions of Non-Covalent Antagonists

The structural and dynamical properties of the peroxisome proliferator-activated receptor γ (PPARγ) nuclear receptor have been broadly studied in its agonist state but little is known about the key features required for the receptor antagonistic activity. Here we report a series of molecular dynamics (MD) simulations in combination with free energy estimation of the recently discovered class of non-covalent PPARγ antagonists. Their binding modes and dynamical behavior are described in details. Two key interactions have been detected within the cavity between helices H3, H11 and the activation helix H12, as well as with H12. The strength of the ligand-amino acid residues interactions has been analyzed in relation to the specificity of the ligand dynamical and antagonistic features. According to our results, the PPARγ activation helix does not undergo dramatic conformational changes, as seen in other nuclear receptors, but rather perturbations that occur through a significant ligand-induced reshaping of the ligand-receptor and the receptor-coactivator binding pockets. The H12 residue Tyr473 and the charge clamp residue Glu471 play a central role for the receptor transformations. Our results also demonstrate that MD can be a helpful tool for the compound phenotype characterization (full agonists, partial agonists or antagonists) when insufficient experimental data are available

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

New Potential Pharmacological Targets of Plant-Derived Hydroxyanthraquinones from Rubia spp.

The increased use of polyphenols nowadays poses the need for identification of their new pharmacological targets. Recently, structure similarity-based virtual screening of DrugBank outlined pseudopurpurin, a hydroxyanthraquinone from Rubia cordifolia spp., as similar to gatifloxacin, a synthetic antibacterial agent. This suggested the bacterial DNA gyrase and DNA topoisomerase IV as potential pharmacological targets of pseudopurpurin. In this study, estimation of structural similarity to referent antibacterial agents and molecular docking in the DNA gyrase and DNA topoisomerase IV complexes were performed for a homologous series of four hydroxyanthraquinones. Estimation of shape- and chemical feature-based similarity with (S)-gatifloxacin, a DNA gyrase inhibitor, and (S)-levofloxacin, a DNA topoisomerase IV inhibitor, outlined pseudopurpurin and munjistin as the most similar structures. The docking simulations supported the hypothesis for a plausible antibacterial activity of hydroxyanthraquinones. The predicted docking poses were grouped into 13 binding modes based on spatial similarities in the active site. The simultaneous presence of 1-OH and 3-COOH substituents in the anthraquinone scaffold were emphasized as relevant features for the binding modes’ variability and ability of the compounds to strongly bind in the DNA-enzyme complexes. The results reveal new potential pharmacological targets of the studied polyphenols and help in their prioritization as drug candidates and dietary supplements

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

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 Lipinski's “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

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