The Use of Computational Methods in the Grouping and Assessment of Chemicals - Preliminary Investigations

This document presents a perspective of how computational approaches could potentially be used in the grouping and assessment of chemicals, and especially in the application of read-across and the development of chemical categories. The perspective is based on experience gained by the authors during 2006 and 2007, when the Joint Research Centre's European Chemicals Bureau was directly involved in the drafting of technical guidance on the applicability of computational methods under REACH. Some of the experience gained and ideas developed resulted from a number of research-based case studies conducted in-house during 2006 and the first half of 2007. The case studies were performed to explore the possible applications of computational methods in the assessment of chemicals and to contribute to the development of technical guidance. Not all of the methods explored and ideas developed are explicitly included in the final guidance documentation for REACH. Many of the methods are novel, and are still being refined and assessed by the scientific community. At present, many of the methods have not been tried and tested in the regulatory context. The authors therefore hope that the perspective and case studies compiled in this document, whilst not intended to serve as guidance, will nevertheless provide an input to further research efforts aimed at developing computational methods, and at exploring their potential applicability in regulatory assessment of chemicals.JRC.I.3-Toxicology and chemical substance

Quantitative Structure - Skin permeability Relationships

This paper reviews in silico models currently available for the prediction of skin permeability with the main focus on the quantitative structure-permeability relationship (QSPR) models. A comprehensive analysis of the main achievements in the field in the last decade is provided. In addition, the mechanistic models are discussed and comparative studies that analyse different models are discussed

Chemical Similarity and Threshold of Toxicological Concern (TTC) Approaches: Report of an ECB Workshop held in Ispra, November 2005

There are many national, regional and international programmes – either regulatory or voluntary – to assess the hazards or risks of chemical substances to humans and the environment. The first step in making a hazard assessment of a chemical is to ensure that there is adequate information on each of the endpoints. If adequate information is not available then additional data is needed to complete the dataset for this substance. For reasons of resources and animal welfare, it is important to limit the number of tests that have to be conducted, where this is scientifically justifiable. One approach is to consider closely related chemicals as a group, or chemical category, rather than as individual chemicals. In a category approach, data for chemicals and endpoints that have been already tested are used to estimate the hazard for untested chemicals and endpoints. Categories of chemicals are selected on the basis of similarities in biological activity which is associated with a common underlying mechanism of action. A homologous series of chemicals exhibiting a coherent trend in biological activity can be rationalised on the basis of a constant change in structure. This type of grouping is relatively straightforward. The challenge lies in identifying the relevant chemical structural and physicochemical characteristics that enable more sophisticated groupings to be made on the basis of similarity in biological activity and hence purported mechanism of action. Linking two chemicals together and rationalising their similarity with reference to one or more endpoints has been very much carried out on an ad hoc basis. Even with larger groups, the process and approach is ad hoc and based on expert judgement. There still appears to be very little guidance about the tools and approaches for grouping chemicals systematically. In November 2005, the ECB Workshop on Chemical Similarity and Thresholds of Toxicological Concern (TTC) Approaches was convened to identify the available approaches that currently exist to encode similarity and how these can be used to facilitate the grouping of chemicals. This report aims to capture the main themes that were discussed. In particular, it outlines a number of different approaches that can facilitate the formation of chemical groupings in terms of the context under consideration and the likely information that would be required. Grouping methods were divided into one of four classes – knowledge-based, analogue-based, unsupervised, and supervised. A flowchart was constructed to attempt to capture a possible work flow to highlight where and how these approaches might be best applied.JRC.I.3-Toxicology and chemical substance

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

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)

Kinase signaling adaptation supports dysfunctional mitochondria in disease

Mitochondria form a critical control nexus which are essential for maintaining correct tissue homeostasis. An increasing number of studies have identified dysregulation of mitochondria as a driver in cancer. However, which pathways support and promote this adapted mitochondrial function? A key hallmark of cancer is perturbation of kinase signalling pathways. These pathways include mitogen activated protein kinases (MAPK), lipid secondary messenger networks, cyclic-AMP-activated (cAMP)/ AMP-activated kinases (AMPK), and Ca2+/calmodulin-dependent protein kinase (CaMK) networks. These signalling pathways have multiple substrates which support initiation and persistence of cancer. Many of these are involved in the regulation of mitochondrial morphology, mitochondrial apoptosis, mitochondrial calcium homeostasis, mitochondrial associated membranes (MAMs), and retrograde ROS signalling. This review will aim to both explore how kinase signalling integrates with these critical mitochondrial pathways and highlight how these systems can be usurped to support the development of disease. In addition, we will identify areas which require further investigation to fully understand the complexities of these regulatory interactions. Overall, this review will emphasize how studying the interaction between kinase signalling and mitochondria improves our understanding of mitochondrial homeostasis and can yield novel therapeutic targets to treat disease

Fluphenazine dihydro­chloride dimethanol solvate

In the title compound {systematic name: 1-(2-hy­droxy­eth­yl)-4-[3-(2-trifluoro­methyl-10H-phenothia­zin-10-yl)prop­yl]piperazine-1,4-diium dichloride dimethanol disolvate}, C22H28F3N3OS2+·2Cl−·2CH3OH, the dihedral angle between the planes of the two outer benzene rings of the tricyclic phenothia­zine system is 46.91 (13)°. The piperazine ring adopts a chair conformation. The crystal structure is stabilized by O—H⋯Cl, N—H⋯Cl, C—H⋯O, C—H⋯Cl and C—H⋯F hydrogen bonds and contacts

catena-Poly[[[triaqua­cobalt(II)]-μ-10-methyl­phenothia­zine-3,7-dicarboxyl­ato] monohydrate]

The polymeric title compound, {[Co(C15H9NO4S)(H2O)3]·H2O}n, consists of chains along [001] made up from Co2+ ions bridged by 10-methyl­phenothia­zine-3,7-dicarboxyl­ate anions. The Co2+ ion, coordinated by three O atoms from two different carboxyl­ate groups and three water mol­ecules, displays a distorted octa­hedral environment. In the crystal, π–π inter­chain inter­actions, with centroid–centroid distances of 3.656 (2) and 3.669 (2) Å between the benzene rings of the ligands, assemble the chains into sheets parallel to (100). O—H⋯O hydrogen-bonding inter­actions between the coordinating water mol­ecules and carboxyl­ate O atoms link the sheets into a three-dimensional network

The Characterisation of (Quantitative) Structure-Activity Relationships - Preliminary Guidance

In November 2004, the OECD Member Countries and the European Commission adopted five principles for the validation of (quantitative) structure-activity relationships ([Q]SARs) intended for use in the regulatory assessment of chemicals. International agreement on a set of valdation principles was important, not only to provide regulatory bodies with a scientific basis for making decisions on the acceptability of data generated by (Q)SARs, but also to promote the mutual acceptance of (Q)SAR models by improving the transparency and consistency of (Q)SAR reporting. According to the OECD Principles for (Q)SAR validation, a (Q)SAR model that is proposed for regulatory use should be associated with five types of information: 1) a defined endpoint; 2) an unambiguous algorithm; 3) a defined domain of applicability; 4) appropriate measures of goodness-of-fit, robustness and predictivity; and 5) a mechanistic interpretation, if possible. Taken together, these five principles form the basis of a conceptual framework for characterising (Q)SAR models, and of reporting formats for describing the model characteristics in a transparent manner. Under the proposed REACH legislation in the EU, there are provisions for the use of estimated data generated by (Q)SARs, both as a substitute for experimental data, and as a supplement to experimental data in weight-of-evidence approaches. It is foreseen that (Q)SARs will be used for the three main regulatory goals of hazard assessment, risk assessment and PBT/vPvB assessment. In the Registration process under REACH, the registrant will be able to use (Q)SAR data in the registration dossier provided that adequate documentation is provided to argue for the validity of the model(s) used. This report provides preliminary guidance on how to characterise (Q)SARs according to the OECD validation principles. It is emphasised that the understanding of how to characterise (Q)SAR models is evolving, and that the content of the current report reflects the understanding and perspectives of the authors at the time of writing (November 2005). It is therefore likely that an update will be produced in the future for the benefit of those who need to submit (Industry) or evaluate (Authorities) chemical information based (partly) on (Q)SARs. It is also noted that this document does not provide guidance on the use of (Q)SAR reporting formats, or on criteria for the acceptance of (Q)SAR estimates, since EU guidance on these topics stills need to be developed.JRC.I.3 - Toxicology and chemical substance

Improvement of conventional anti-cancer drugs as new tools against multidrug resistant tumors

Multidrug resistance (MDR) is the dominant cause of the failure of cancer chemotherapy. The design of antitumor drugs that are able to evade MDR is rapidly evolving, showing that this area of biomedical research attracts great interest in the scientific community. The current review explores promising recent approaches that have been developed with the aim of circumventing or overcoming MDR. Encouraging results have been obtained in the investigation of the MDR-modulating properties of various classes of natural compounds and their analogues. Inhibition of P-gp or downregulation of its expression have proven to be the main mechanisms by which MDR can be surmounted. The use of hybrid molecules that are able to simultaneously interact with two or more cancer cell targets is currently being explored as a means to circumvent drug resistance. This strategy is based on the design of hybrid compounds that are obtained either by merging the structural features of separate drugs, or by conjugating two drugs or pharmacophores via cleavable/non-cleavable linkers. The approach is highly promising due to the pharmacokinetic and pharmacodynamic advantages that can be achieved over the independent administration of the two individual components. However, it should be stressed that the task of obtaining successful multivalent drugs is a very challenging one. The conjugation of anticancer agents with nitric oxide (NO) donors has recently been developed, creating a particular class of hybrid that can combat tumor drug resistance. Appropriate NO donors have been shown to reverse drug resistance via nitration of ABC transporters and by interfering with a number of metabolic enzymes and signaling pathways. In fact, hybrid compounds that are produced by covalently attaching NO-donors and antitumor drugs have been shown to elicit a synergistic cytotoxic effect in a variety of drug resistant cancer cell lines. Another strategy to circumvent MDR is based on nanocarrier-mediated transport and the controlled release of chemotherapeutic drugs and P-gp inhibitors. Their pharmacokinetics are governed by the nanoparticle or polymer carrier and make use of the enhanced permeation and retention (EPR) effect, which can increase selective delivery to cancer cells. These systems are usually internalized by cancer cells via endocytosis and accumulate in endosomes and lysosomes, thus preventing rapid efflux. Other modalities to combat MDR are described in this review, including the pharmaco-modulation of acridine, which is a well-known scaffold in the development of bioactive compounds, the use of natural compounds as means to reverse MDR, and the conjugation of anticancer drugs with carriers that target specific tumor-cell components. Finally, the outstanding potential of in silico structure-based methods as a means to evaluate the ability of antitumor drugs to interact with drug transporters is also highlighted in this review. Structure-based design methods, which utilize 3D structural data of proteins and their complexes with ligands, are the most effective of the in silico methods available, as they provide a prediction regarding the interaction between transport proteins and their substrates and inhibitors. The recently resolved X-ray structure of human P-gp can help predict the interaction sites of designed compounds, providing insight into their binding mode and directing possible rational modifications to prevent them from becoming P-gp drug substrates. In summary, although major efforts were invested in the search for new tools to combat drug resistant tumors, they all require further implementation and methodological development. Further investigation and progress in the abovementioned strategies will provide significant advances in the rational combat against cancer MDR