Molecular modelling study of the PPARγ receptor in relation to the mode of action/adverse outcome pathway framework for liver steatosis

Addressing chemically-induced obesogenic metabolic disruption: selection of chemicals for in vitro human PPARα, PPARγ transactivation, and adipogenesis test methods

Whilst western diet and sedentary lifestyles heavily contribute to the global obesity epidemic, it is likely that chemical exposure may also contribute. A substantial body of literature implicates a variety of suspected environmental chemicals in metabolic disruption and obesogenic mechanisms. Chemically induced obesogenic metabolic disruption is not yet considered in regulatory testing paradigms or regulations, but this is an internationally recognised human health regulatory development need. An early step in the development of relevant regulatory test methods is to derive appropriate minimum chemical selection lists for the target endpoint and its key mechanisms, such that the test method can be suitably optimised and validated. Independently collated and reviewed reference and proficiency chemicals relevant for the regulatory chemical universe that they are intended to serve, assist regulatory test method development and validation, particularly in relation to the OECD Test Guidelines Programme. To address obesogenic mechanisms and modes of action for chemical hazard assessment, key initiating mechanisms include molecular-level Peroxisome Proliferator-Activated Receptor (PPAR) α and γ agonism and the tissue/organ-level key event of perturbation of the adipogenesis process that may lead to excess white adipose tissue. Here we present a critical literature review, analysis and evaluation of chemicals suitable for the development, optimisation and validation of human PPARα and PPARγ agonism and human white adipose tissue adipogenesis test methods. The chemical lists have been derived with consideration of essential criteria needed for understanding the strengths and limitations of the test methods. With a weight of evidence approach, this has been combined with practical and applied aspects required for the integration and combination of relevant candidate test methods into test batteries, as part of an Integrated Approach to Testing and Assessment for metabolic disruption. The proposed proficiency and reference chemical list includes a long list of negatives and positives (20 chemicals for PPARα, 21 for PPARγ, and 11 for adipogenesis) from which a (pre-)validation proficiency chemicals list has been derived.

The therapeutic effect of wine-processed Corni Fructus on chronic renal failure in rats through the interference with the LPS/IL-1-mediated inhibition of RXR function

ETHNOPHARMACOLOGICAL RELEVANCE

Corni Fructus, derived from the fruit of Cornus officinalis Sieb. et Zucc, is a widely utilized traditional Chinese medicine (TCM) with established efficacy in the treatment of diverse chronic kidney diseases. Crude Corni Fructus (CCF) and wine-processed Corni Fructus (WCF) are the main processed forms of Corni Fructus. Generally, TCM is often used after processing (paozhi). Despite the extensive use of processed TCM, the underlying mechanisms of processing for most TCMs have been unclear so far.

AIM OF THE STUDY

In this study, an integrated strategy combined renal metabolomics with proteomics was established and investigated the potential processing mechanisms of CCF or WCF on chronic renal failure (CRF) models.

MATERIALS AND METHODS

Firstly, the differences in biochemical parameters and pathological histology were compared to evaluate the effects of CCF and WCF on CRF model rats. Then, the tissue differential metabolites and proteins between CCF and WCF on CRF model rats were screened based on metabolomics and proteomics technology. Concurrently, a combined approach of metabolomics and proteomics was employed to investigate the underlying mechanisms associated with these marker metabolic products and proteins.

RESULTS

Compared to the MG group, there were 27 distinct metabolites and 143 different proteins observed in the CCF-treatment group, while the WCF-treatment group exhibited 24 distinct metabolites and 379 different proteins. Further, the integration interactions analysis of the protein and lipid metabolite revealed that both WCF and CCF improved tryptophan degradation and LPS/IL-1-mediated inhibition of RXR function. WCF inhibited RXR function more than CCF via the modulation of LPS/IL-1 in the CRF model. Experimental results were validated by qRT-PCR and western blotting. Notably, the gene expression amount and protein levels of FMO3 and CYP2E1 among 8 genes influenced by WCF were higher compared to CCF.

CONCLUSION

The results of this study provide a theoretical basis for further study of Corni Fructus with different processing techniques in CRF. The findings also offer guidance for investigating the mechanism of action of herbal medicines in diseases employing diverse processing techniques.

Comparative subchronic toxicity of copper and a tertiary copper mixture to early life stage rainbow trout (Oncorhynchus mykiss): impacts on growth, development, and histopathology

This research aimed to characterize and compare the subchronic impacts of Cu to a Cu, Cd, and Zn mixture in early life stages of rainbow trout (Oncorhynchus mykiss) by examining uptake, survival, growth, development, and histopathology parameters. To accomplish this, rainbow trout were exposed for 31 days from eyed embryos to the swim-up fry life stage to waterborne Cu (31, 47, 70, and 104 μg/L) individually or as mixture containing Cd (4.1, 6.2, 9.3, and 14 μg/L) and Zn (385, 578, 867, and 1300 μg/L). Exposures elicited pronounced effects on survival when Cu was administered as a mixture (LC25 = 32.9 μg/L Cu) versus individually (LC25 = 46.3 μg/L Cu). Mixtures of Cu, Cd, and Zn also elicited more pronounced sublethal toxicity relative to equivalent Cu treatments with respect to reduced yolk sac resorption and increased incidence and/or severity of gill, liver, and kidney lesions. Our findings of reduced body weight (EC10, Cu = 55.0 μg/L Cu; EC10, Cu+Cd+Zn = 58.9 μg/L Cu), yolk sac resorption (LOECCu = 70 μg/L Cu; LOECCu+Cd+Zn = 70 μg/L Cu), coelomic fat (LOECCu = 47 μg/L Cu; LOECCu+Cd+Zn = 70 μg/L Cu), and increased hepatocellular cytoplasmic vacuolation (LOECCu = 70 μg/L Cu; LOECCu+Cd+Zn = 47 μg/L Cu) collectively indicate a complicated metabolic interference by metals in exposed fish. These lethal and sublethal effects observed in the laboratory could translate to reduced survival and fitness of wild salmonid populations inhabiting waterbodies receiving wastewater or runoff containing multiple metals at elevated concentrations.

The interaction of DNMT1 and DNMT3A epigenetic enzymes with phthalates and perfluoroalkyl substances: an in silico approach

The occurrence of long-lasting adverse effects of the environmental contaminants on human health is a current emerging issue. In particular, phthalates, poly- and perfluoroalkyl substances are proposed to trigger toxic effects as well as persistent changes on human development and metabolism by different mechanisms, including epigenetic modifications, although the specific underlying pathways are still unknown. This study contributes to identify the potential molecular initiating events of epigenetic-mediated adverse effects by an in silico approach, which combines molecular docking and molecular dynamics simulation. The approach probes the potential molecular interaction between several different phthalates and persistent organic pollutants and a specific class of epigenetic modulators, namely the DNA methyltransferases (DNMTs). The dynamics of interaction and the binding free energies of the ligand-DNMTs complexes demonstrated that pollutants can be classified into two main groups, according to the ligand-target complex stability: (1) a larger class of phthalates (DBP, DEHP, MBP and MEHP) acting as inhibitors of the enzymatic activity of the epigenetic targets and (2) a smaller class of phthalates (DMP and MMP) and perfluoroalkyl substances (PFOA and PFOS) which do not interact stably with the human DNMTs. These findings provide the first valuable in silico insights on the ability of these specific environmental pollutants to directly bind and inhibit a key class of epigenetic regulators. Communicated by Ramaswamy H. Sarma.

Identification of proteome markers for drug-induced liver injury in zebrafish embryos

The zebrafish embryo (ZFE) is a promising alternative non-rodent model in toxicology, and initial studies suggested its applicability in detecting hepatic responses related to drug-induced liver injury (DILI). Here, we hypothesize that detailed analysis of underlying mechanisms of hepatotoxicity in ZFE contributes to the improved identification of hepatotoxic properties of compounds and to the reduction of rodents used for hepatotoxicity assessment. ZFEs were exposed to nine reference hepatotoxicants, targeted at induction of steatosis, cholestasis, and necrosis, and effects compared with negative controls. Protein profiles of the individual compounds were generated using LC-MS/MS. We identified differentially expressed proteins and pathways, but as these showed considerable overlap, phenotype-specific responses could not be distinguished. This led us to identify a set of common hepatotoxicity marker proteins. At the pathway level, these were mainly associated with cellular adaptive stress-responses, whereas single proteins could be linked to common hepatotoxicity-associated processes. Applying several stringency criteria to our proteomics data as well as information from other data sources resulted in a set of potential robust protein markers, notably Igf2bp1, Cox5ba, Ahnak, Itih3b.2, Psma6b, Srsf3a, Ces2b, Ces2a, Tdo2b, and Anxa1c, for the detection of adverse responses.

In Silico Models for Hepatotoxicity

In this chapter, we review the state of the art of predicting human hepatotoxicity using in silico techniques. There has been significant progress in this area over the past 20 years but there are still some challenges ahead. Principally, these challenges are our partial understanding of a very complex biochemical system and our ability to emulate that in a predictive capacity. Here, we provide an overview of the published modeling approaches in this area to date and discuss their design, strengths and weaknesses. It is interesting to note the diversity in modeling approaches, whether they be statistical algorithms or evidenced-based approaches including structural alerts and pharmacophore models. Irrespective of modeling approach, it appears a common theme of access to appropriate, relevant, and high-quality data is a limitation to all and is likely to continue to be the focus of future research.

A Review of In Silico Tools as Alternatives to Animal Testing: Principles, Resources and Applications

Across the spectrum of industrial sectors, including pharmaceuticals, chemicals, personal care products, food additives and their associated regulatory agencies, there is a need to develop robust and reliable methods to reduce or replace animal testing. It is generally recognised that no single alternative method will be able to provide a one-to-one replacement for assays based on more complex toxicological endpoints. Hence, information from a combination of techniques is required. A greater understanding of the time and concentration-dependent mechanisms, underlying the interactions between chemicals and biological systems, and the sequence of events that can lead to apical effects, will help to move forward the science of reducing and replacing animal experiments. In silico modelling, in vitro assays, high-throughput screening, organ-on-a-chip technology, omics and mathematical biology, can provide complementary information to develop a complete picture of the potential response of an organism to a chemical stressor. Adverse outcome pathways (AOPs) and systems biology frameworks enable relevant information from diverse sources to be logically integrated. While individual researchers do not need to be experts across all disciplines, it is useful to have a fundamental understanding of what other areas of science have to offer, and how knowledge can be integrated with other disciplines. The purpose of this review is to provide those who are unfamiliar with predictive in silico tools, with a fundamental understanding of the underlying theory. Current applications, software, barriers to acceptance, new developments and the use of integrated approaches are all discussed, with additional resources being signposted for each of the topics.

Ecotoxico-lipidomics: An emerging concept to understand chemical-metabolic relationships in comparative fish models

Lipids play an essential role in development, homeostatic functions, immune signaling, reproduction, and growth. Although it is evident that changes in lipid biosynthesis and metabolism can affect organismal physiology, few studies have determined how environmental stressors affect lipid pathways, let alone alter global lipid profiles in fish. This is a significant research gap, as a number of environmental contaminants interact with lipid signaling and metabolic pathways. In this review, we highlight the utility of lipidomics as a tool in environmental toxicology, discussing the current state of knowledge regarding chemical-lipidomic perturbations. As with most oviparous animals, the processing and storage of lipids during oocyte development is also particularly important for embryogenesis in fish. Using largemouth bass (Micropterus salmoides) as an example, transcriptomics data suggest that various chemicals alter lipid metabolism and regulation, highlighting the need for more sophisticated investigations into how toxicants impact lipid responses. We also point out the challenges ahead; these include a lack of understanding about lipid processing and signaling in fish, tissue and species-specific lipid composition, and extraneous factors (e.g., nutrition, temperature) that confound interpretation. For example, toxicant exposure can lead to oxidative stress and lipid peroxidation, resulting in complex lipid byproducts that are challenging to measure. With the emergence of lipidomics in systems toxicology, multi-omics approaches are expected to more clearly define effects on physiology, creating stronger linkages between multiple molecular entities (gene-protein-lipid/metabolite). The development and implementation of novel technologies such as ion mobility-mass spectrometry and ozone-induced dissociation support the complete structural elucidation of lipid molecules. This has implications in the adverse outcome pathway framework, which will enhance the application of lipidomics in toxicology by linking these molecular changes to effects at higher levels of biological organization.

Critical Insight into the Design of PPAR-γ Agonists by Virtual Screening Techniques

BACKGROUND

Design of novel PPAR-γ modulators with better binding efficiency and fewer side effects to treat type 2 diabetes is still a challenge for medicinal chemists. Cost and time efficient computational methods have presently become an integral part of research in nuclear receptors and their ligands, enabling hit to lead identification and lead optimization. This review will focus on cutting-edge technologies used in most recent studies on the design of PPAR- γ agonists and will discuss the chemistry of few molecules which emerged successful.

METHODS

Literature review was carried out in google scholar using customized search from 2011- 2017. Computer-aided design methods presented in this article were used as search terms to retrieve corresponding literature.

RESULTS

Virtual screening of natural product libraries is an effective strategy to harness nature as the source of ligands for PPARs. Rigid and induced fit docking and core hopping approach in docking are rapid screening methods to predict the PPAR- γ and PPAR-α/ γ dual agonistic activity. Onedimensional drug profile matching is one of the recent virtual screening methods by which an antiprotozoal drug, Nitazoxanide was identified as a PPAR- γ agonist.

CONCLUSION

It is concluded that to achieve a convincing and reliable design of PPAR-γ agonist by virtual screening techniques, customized workflow comprising of appropriate models is essential in which methods may be applied either sequentially or simultaneously.

In Silico Guidance for In Vitro Androgen and Glucocorticoid Receptor ToxCast Assays

Molecular initiating events (MIEs) are key events in adverse outcome pathways that link molecular chemistry to target biology. As they are based on chemistry, these interactions are excellent targets for computational chemistry approaches to in silico modeling. In this work, we aim to link ligand chemical structures to MIEs for androgen receptor (AR) and glucocorticoid receptor (GR) binding using ToxCast data. This has been done using an automated computational algorithm to perform maximal common substructure searches on chemical binders for each target from the ToxCast dataset. The models developed show a high level of accuracy, correctly assigning 87.20% of AR binders and 96.81% of GR binders in a 25% test set using holdout cross-validation. The 2D structural alerts developed can be used as in silico models to predict these MIEs and as guidance for in vitro ToxCast assays to confirm hits. These models can target such experimental work, reducing the number of assays to be performed to gain required toxicological insight. Development of these models has also allowed some structural alerts to be identified as predictors for agonist or antagonist behavior at the receptor target. This work represents a first step in using computational methods to guide and target experimental approaches.

Machine-Learning Prediction of Oral Drug-Induced Liver Injury (DILI) via Multiple Features and Endpoints

Drug discovery is a costly process which usually takes more than 10 years and billions of dollars for one successful drug to enter the market. Despite all the safety tests, drugs may still cause adverse reactions and be restricted in use or even withdrawn from the market. Drug-induced liver injury (DILI) is one of the major adverse drug reactions, and computational models may be used to predict and reduce it. To assess the computational prediction performance of DILI, we curated DILI endpoints from three databases and prepared drug features including chemical descriptors, therapeutic classifications, gene expressions, and binding proteins. We trained machine-learning models to predict the various DILI endpoints using different drug features. Using the optimal feature sets, the top-performing models obtained areas under the receiver operating characteristic curve (AUC) around 0.8 for some DILI endpoints. We found that some features, including therapeutic classifications and proteins, have good prediction performance towards DILI. We also discovered that the severity of DILI endpoints as well as the selection of negative samples may significantly affect the prediction results. Overall, our study provided a comprehensive collection, curation, and prediction of DILI endpoints using various drug features, which may help the drug researchers to better understand and prevent DILI during the drug discovery process.

Molecular modelling, synthesis, and biological evaluations of a 3,5-disubstituted isoxazole fatty acid analogue as a PPARα-selective agonist

The peroxisome proliferator activated receptors (PPARs) are important drug targets in treatment of metabolic and inflammatory disorders. Fibrates, acting as PPARα agonists, have been widely used lipid-lowering agents for decades. However, the currently available PPARα targeting agents show low subtype-specificity and consequently a search for more potent agonists have emerged. In this study, previously isolated oxohexadecenoic acids from the marine algae Chaetoceros karianus were used to design a PPARα-specific analogue. Herein we report the design, synthesis, molecular modelling studies and biological evaluations of the novel 3,5-disubstituted isoxazole analogue 6-(5-heptyl-1,2-oxazol-3-yl)hexanoic acid (1), named ADAM. ADAM shows a clear receptor preference and significant dose-dependent activation of PPARα (EC₅₀ = 47 µM) through its ligand-binding domain (LBD). Moreover, ADAM induces expression of important PPARα target genes, such as CPT1A, in the Huh7 cell line and primary mouse hepatocytes. In addition, ADAM exhibits a moderate ability to regulate PPARγ target genes and drive adipogenesis. Molecular modelling studies indicated that ADAM docks its carboxyl group into opposite ends of the PPARα and -γ LBD. ADAM interacts with the receptor-activating polar network of amino acids (Tyr501, His447 and Ser317) in PPARα, but not in PPARγ LBD. This may explain the lack of PPARγ agonism, and argues for a PPARα-dependent adipogenic function. Such compounds are of interest towards developing new lipid-lowering remedies.

Agonism activities of lyso-phosphatidylcholines (LPC) Ligands binding to peroxisome proliferator-activated receptor gamma (PPARγ)

PPARγ is an isoform of peroxisome proliferator-activated receptor (PPAR) belonging to a super family of nuclear receptors and is a primary target of the effective drug to treat the type II diabetes. The experiments found that Lyso-phosphatidylcholines (LPC) could bind to PPARγ, but the binding modes remain unknown. We used the Molecular Docking and Molecular Dynamic (MD) simulations to study the binding of four LPC ligands (LPC16:0, LPC18:0, LPC18:1-1 and LPC18:1-2) to PPARγ. The two-step MD simulations were employed to determine the final binding modes. The 20 ns MD simulations for four final LPC-PPARγ complexes were performed to analyze their structures, the binding key residues, and agonism activities. The results reveal that three LPC ligands (LPC16:0, LPC18:0 and LPC18:1-1) bind to Arm II and III regions of the Ligand Binding Domain (LBD) pocket, whereas they do not interact with Tyr473 of Helix 12 (H12). In contrast, LPC18:1-2 can form the hydrogen bonds with Tyr473 and bind into Arm I and II regions. Comparing with the paradigm systems of the full agonist (Rosiglitazone-PPARγ) and the partial agonist (MRL24-PPARγ), our results indicate that LPC16:0, LPC18:0 and LPC18:1-1 could be the potential partial agonists and LPC18:1-2 could be a full agonist. The in-depth analysis of the residue fluctuations and structure alignment confirm the present prediction of the LPC agonism activities.Communicated by Ramaswamy H. Sarma.

Identification of a Novel PPAR-γ Agonist through a Scaffold Tuning Approach

Peroxisome proliferator-activated receptors (PPARs) are important targets in metabolic diseases including obesity, metabolic syndrome, diabetes, and non-alcoholic fatty liver disease. Recently, they have been highlighted as attractive targets for the treatment of cardiovascular diseases and chronic myeloid leukemia. The PPAR agonist structure is consists of a polar head, a hydrophobic tail, and a linker. Each part interacts with PPARs through hydrogen bonds or hydrophobic interactions to stabilize target protein conformation, thus increasing its activity. Acidic head is essential for PPAR agonist activity. The aromatic linker plays an important role in making hydrophobic interactions with PPAR as well as adjusting the head-to-tail distance and conformation of the whole molecule. By tuning the scaffold of compound, the whole molecule could fit into the ligand-binding domain to achieve proper binding mode. We modified indol-3-ylacetic acid scaffold to (indol-1-ylmethyl)benzoic acid, whereas 2,4-dichloroanilide was fixed as the hydrophobic tail. We designed, synthesized, and assayed the in vitro activity of novel indole compounds with (indol-1-ylmethyl)benzoic acid scaffold. Compound 12 was a more potent PPAR-γ agonist than pioglitazone and our previous hit compound. Molecular docking studies may suggest the binding between compound 12 and PPAR-γ, rationalizing its high activity.

Molecular determinants of PPARγ partial agonism and related in silico/in vivo studies of natural saponins as potential type 2 diabetes modulators

The metabolic syndrome, which includes hypertension, type 2 diabetes (T2D) and obesity, has reached an epidemic-like scale. Saponins and sapogenins are considered as valuable natural products for ameliorating this pathology, possibly through the nuclear receptor PPARγ activation. The aims of this study were: to look for in vivo antidiabetic effects of a purified saponins' mixture (PSM) from Astragalus corniculatus Bieb; to reveal by in silico methods the molecular determinants of PPARγ partial agonism, and to investigate the potential PPARγ participation in the PSM effects. In the in vivo experiments spontaneously hypertensive rats (SHRs) with induced T2D were treated with PSM or pioglitazone as a referent PPARγ full agonist, and pathology-relevant biochemical markers were analysed. The results provided details on the PSM modulation of the glucose homeostasis and its potential mechanism. The in silico studies focused on analysis of the protein-ligand interactions in crystal structures of human PPARγ-partial agonist complexes, pharmacophore modelling and molecular docking. They outlined key pharmacophoric features, typical for the PPARγ partial agonists, which were used for pharmacophore-based docking of the main PSM sapogenin. The in silico studies, strongly suggest possible involvement of PPARγ-mediated mechanisms in the in vivo antidiabetic and antioxidant effects of PSM from A. corniculatus.

Ab initio chemical safety assessment: A workflow based on exposure considerations and non-animal methods

We describe and illustrate a workflow for chemical safety assessment that completely avoids animal testing. The workflow, which was developed within the SEURAT-1 initiative, is designed to be applicable to cosmetic ingredients as well as to other types of chemicals, e.g. active ingredients in plant protection products, biocides or pharmaceuticals. The aim of this work was to develop a workflow to assess chemical safety without relying on any animal testing, but instead constructing a hypothesis based on existing data, in silico modelling, biokinetic considerations and then by targeted non-animal testing. For illustrative purposes, we consider a hypothetical new ingredient x as a new component in a body lotion formulation. The workflow is divided into tiers in which points of departure are established through in vitro testing and in silico prediction, as the basis for estimating a safe external dose in a repeated use scenario. The workflow includes a series of possible exit (decision) points, with increasing levels of confidence, based on the sequential application of the Threshold of Toxicological (TTC) approach, read-across, followed by an "ab initio" assessment, in which chemical safety is determined entirely by new in vitro testing and in vitro to in vivo extrapolation by means of mathematical modelling. We believe that this workflow could be applied as a tool to inform targeted and toxicologically relevant in vitro testing, where necessary, and to gain confidence in safety decision making without the need for animal testing.

In Silico Prediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects

In silico methods to predict toxicity include the use of (Quantitative) Structure-Activity Relationships ((Q)SARs) as well as grouping (category formation) allowing for read-across. A challenging area for in silico modelling is the prediction of chronic toxicity and the No Observed (Adverse) Effect Level (NO(A)EL) in particular. A proposed solution to the prediction of chronic toxicity is to consider organ level effects, as opposed to modelling the NO(A)EL itself. This review has focussed on the use of structural alerts to identify potential liver toxicants. In silico profilers, or groups of structural alerts, have been developed based on mechanisms of action and informed by current knowledge of Adverse Outcome Pathways. These profilers are robust and can be coded computationally to allow for prediction. However, they do not cover all mechanisms or modes of liver toxicity and recommendations for the improvement of these approaches are given.

A History of the Molecular Initiating Event

The adverse outcome pathway (AOP) framework provides an alternative to traditional in vivo experiments for the risk assessment of chemicals. AOPs consist of a number of key events (KEs) linked by key event relationships across a range of biological organization backed by scientific evidence. The first KE in the pathway is the molecular initiating event (MIE)-the initial chemical trigger that starts an AOP. Over the past 3 years the AOP conceptual framework has gained a large amount of momentum in toxicology as an alternative to animal methods, and so the MIE has come into the spotlight. What is an MIE? How can MIEs be measured or predicted? What research is currently contributing to our understanding of MIEs? In this Perspective we outline answers to these key questions.

Toward Good Read-Across Practice (GRAP) guidance

Grouping of substances and utilizing read-across of data within those groups represents an important data gap filling technique for chemical safety assessments. Categories/analogue groups are typically developed based on structural similarity and, increasingly often, also on mechanistic (biological) similarity. While read-across can play a key role in complying with legislation such as the European REACH regulation, the lack of consensus regarding the extent and type of evidence necessary to support it often hampers its successful application and acceptance by regulatory authorities. Despite a potentially broad user community, expertise is still concentrated across a handful of organizations and individuals. In order to facilitate the effective use of read-across, this document presents the state of the art, summarizes insights learned from reviewing ECHA published decisions regarding the relative successes/pitfalls surrounding read-across under REACH, and compiles the relevant activities and guidance documents. Special emphasis is given to the available existing tools and approaches, an analysis of ECHA's published final decisions associated with all levels of compliance checks and testing proposals, the consideration and expression of uncertainty, the use of biological support data, and the impact of the ECHA Read-Across Assessment Framework (RAAF) published in 2015.

In Silico Models for Hepatotoxicity

In this chapter we review the challenges of predicting human hepatotoxicity. Principally, this is our partial understanding of a very complex biochemical system and our ability to emulate that in a predictive capacity. We give an overview of the published modeling approaches in this area to date and discuss their design, strengths, and weaknesses. It is interesting to note the shift during the period of this review in the direction of evidenced-based approaches including structural alerts and pharmacophore models. Proposals on how best to utilize the data emerging from modeling studies are also discussed.

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.