Hybrid Classification/Regression Approach to QSAR Modeling of Stoichiometric Antiradical Capacity Assays' Endpoints

Quantitative structure-activity relationships (QSAR) are a widely used methodology allowing not only a better understanding of the mechanisms of chemical reactions, including radical scavenging, but also to predict the relevant properties of chemical compounds without their synthesis, isolation and experimental testing. Unlike the QSAR modeling of the kinetic antioxidant assays, modeling of the assays with stoichiometric endpoints depends strongly on the number of hydroxyl groups in the antioxidant molecule, as well as on some integral molecular descriptors characterizing the proportion of OH-groups able to enter and complete the radical scavenging reaction. In this work, we tested the feasibility of a "hybrid" classification/regression approach, consisting of explicit classification of individual OH-groups as involved in radical scavenging reactions, and using further the number of these OH-groups as a descriptor in simple-regression QSAR models of antiradical capacity assays with stoichiometric endpoints. A simple threshold classification based on the sum of trolox-equivalent antiradical capacity values was used, selecting OH-groups with specific radical stability- and reactivity-related electronic parameters or their combination as "active" or "inactive". We showed that this classification/regression modeling approach provides a substantial improvement of the simple-regression QSAR models over those built on the number of total phenolic OH-groups only, and yields a statistical performance similar to that of the best reported multiple-regression QSARs for antiradical capacity assays with stoichiometric endpoints.Bulgarian Ministry of Education and Science under the National Research Programme “Healthy Foods for a Strong Bio-Economy and Quality of Life”(DCM #577/17.08.2018)

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

In Silico Identification of Multi-Target Ligands as Promising Hit Compounds for Neurodegenerative Diseases Drug Development

The conventional treatment of neurodegenerative diseases (NDDs) is based on the “one molecule—one target” paradigm. To combat the multifactorial nature of NDDs, the focus is now shifted toward the development of small-molecule-based compounds that can modulate more than one protein target, known as “multi-target-directed ligands” (MTDLs), while having low affinity for proteins that are irrelevant for the therapy. The in silico approaches have demonstrated a potential to be a suitable tool for the identification of MTDLs as promising drug candidates with reduction in cost and time for research and development. In this study more than 650,000 compounds were screened by a series of in silico approaches to identify drug-like compounds with predicted activity simultaneously towards three important proteins in the NDDs symptomatic treatment: acetylcholinesterase (AChE), histone deacetylase 2 (HDAC2), and monoamine oxidase B (MAO-B). The compounds with affinities below 5.0 µM for all studied targets were additionally filtered to remove known non-specifically binding or unstable compounds. The selected four hits underwent subsequent refinement through in silico blood-brain barrier penetration estimation, safety evaluation, and molecular dynamics simulations resulting in two hit compounds that constitute a rational basis for further development of multi-target active compounds against NDDs

Hybrid Classification/Regression Approach to QSAR Modeling of Stoichiometric Antiradical Capacity Assays’ Endpoints

Quantitative structure–activity relationships (QSAR) are a widely used methodology allowing not only a better understanding of the mechanisms of chemical reactions, including radical scavenging, but also to predict the relevant properties of chemical compounds without their synthesis, isolation and experimental testing. Unlike the QSAR modeling of the kinetic antioxidant assays, modeling of the assays with stoichiometric endpoints depends strongly on the number of hydroxyl groups in the antioxidant molecule, as well as on some integral molecular descriptors characterizing the proportion of OH-groups able to enter and complete the radical scavenging reaction. In this work, we tested the feasibility of a “hybrid” classification/regression approach, consisting of explicit classification of individual OH-groups as involved in radical scavenging reactions, and using further the number of these OH-groups as a descriptor in simple-regression QSAR models of antiradical capacity assays with stoichiometric endpoints. A simple threshold classification based on the sum of trolox-equivalent antiradical capacity values was used, selecting OH-groups with specific radical stability- and reactivity-related electronic parameters or their combination as “active” or “inactive”. We showed that this classification/regression modeling approach provides a substantial improvement of the simple-regression QSAR models over those built on the number of total phenolic OH-groups only, and yields a statistical performance similar to that of the best reported multiple-regression QSARs for antiradical capacity assays with stoichiometric endpoints

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

In Silico Identification of Multi-Target Ligands as Promising Hit Compounds for Neurodegenerative Diseases Drug Development

The conventional treatment of neurodegenerative diseases (NDDs) is based on the “one molecule—one target” paradigm. To combat the multifactorial nature of NDDs, the focus is now shifted toward the development of small-molecule-based compounds that can modulate more than one protein target, known as “multi-target-directed ligands” (MTDLs), while having low affinity for proteins that are irrelevant for the therapy. The in silico approaches have demonstrated a potential to be a suitable tool for the identification of MTDLs as promising drug candidates with reduction in cost and time for research and development. In this study more than 650,000 compounds were screened by a series of in silico approaches to identify drug-like compounds with predicted activity simultaneously towards three important proteins in the NDDs symptomatic treatment: acetylcholinesterase (AChE), histone deacetylase 2 (HDAC2), and monoamine oxidase B (MAO-B). The compounds with affinities below 5.0 µM for all studied targets were additionally filtered to remove known non-specifically binding or unstable compounds. The selected four hits underwent subsequent refinement through in silico blood-brain barrier penetration estimation, safety evaluation, and molecular dynamics simulations resulting in two hit compounds that constitute a rational basis for further development of multi-target active compounds against NDDs

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