A Similarity Based Approach for Chemical Category Classification

This report aims to describe the main outcomes of an IHCP Exploratory Research Project carried out during 2005 by the European Chemicals Bureau (Computational Toxicology Action). The original aim of this project was to develop a computational method to facilitate the classification of chemicals into similarity-based chemical categories, which would be both useful for building (Q)SAR models (research application) and for defining chemical category proposals (regulatory application).JRC.I-Institute for Health and Consumer Protection (Ispra

Predicting Skin Permeability by means of Computational Approaches : Reliability and Caveats in Pharmaceutical Studies

© 2019 American Chemical Society.The skin is the main barrier between the internal body environment and the external one. The characteristics of this barrier and its properties are able to modify and affect drug delivery and chemical toxicity parameters. Therefore, it is not surprising that permeability of many different compounds has been measured through several in vitro and in vivo techniques. Moreover, many different in silico approaches have been used to identify the correlation between the structure of the permeants and their permeability, to reproduce the skin behavior, and to predict the ability of specific chemicals to permeate this barrier. A significant number of issues, like interlaboratory variability, experimental conditions, data set building rationales, and skin site of origin and hydration, still prevent us from obtaining a definitive predictive skin permeability model. This review wants to show the main advances and the principal approaches in computational methods used to predict this property, to enlighten the main issues that have arisen, and to address the challenges to develop in future research.Peer reviewedFinal Accepted Versio

Quantum theory of QSAR

Es discuteix aquí la forma de desenvolupar un formalisme on les mesures de semblança quàntiques (QSM) es transformen en un producte natural, que sorgeix d'un marc de treball específic relacionat amb la teoria quàntica. Aquesta fita s'empra per establir una connexió fonamental entre la teoria quàntica i les QSAR, que s'estudien més endavant des del punt de vista de la química quàntica discreta. A fi d'assolir aquest objectiu es revisen en un primer pas diverses eines teòriques. D'aquesta manera la primera secció s'associa a la construcció del concepte de conjunt etiquetat. Més tard, la definició d'objecte quàntic (QO) s'aclareix emprant tant el rerefons de la teoria quàntica com els conceptes previs, que formen part del formalisme de conjunt etiquetat. Per definir un QO, es demostra que les funcions de densitat (DF) tenen un paper principal i es presenta una possible forma matemàtica simplificada amb propòsits computacionals. En el camí de preparar les eines per dilucidar el problema, els conjunts convexos resulten ser prominents, mentre que la noció de semiespai vectorial, apareix com a conseqüència. Les regles de transformació, un aparell dissenyat per connectar les funcions d'ona amb les DF, es defineixen en un proper pas. També es descriuen diversos aspectes d'aquest tipus de discussió preliminar, entre altres el concepte de distribucions d'energia cinètica, que apareixen dins la definició dels espais de Hilbert generals i els espais de Sobolev. Les QSM, com una font de la representació discreta de les estructures moleculars, es fan evidents dins d'aquest concepte. Un desenvolupament posterior de la teoria intenta estudiar els processos de discretització; això és: la transformació dels espais funcionals d'infinites dimensions en espais n-dimensionals. Aquest resultat afegeix noves perspectives a la representació discreta de QO, ja que: a) esdevé una font de nous descriptors, b) descriu el fonament de les QSAR, cosa que permet la construcció de models adequats comWays of developing the formalism where Quantum Similarity Measures (QSM) become a natural product issuing from a specific mathematical framework related to quantum theory are discussed. This fact is used to establish a fundamental connection between Quantum Theory and QSAR, which is analysed in turn within the realm of discrete quantum chemistry. In order to achieve such an objective several theoretical tools are revised in a previous step. The first section is devoted to constructing the concept of the Tagged Set. Next, the definition of Quantum Object (QO) is clarified by means of Quantum Theory background ideas and the previous Tagged Set formalism. In the definition of QO, Density Functions (DF) are shown to play a fundamental role and a possible simplified mathematical picture is presented for possible computational purposes. In the process of preparing the problem-solving tools, convex sets become prominent, and the notion of Vector Semispace appears as a consequence. The Transformation Rule, a device to connect Wavefunctions with DF, is defined in a new step. Various products of this preliminary discussion are described, among them the concept of Kinetic Energy distributions, issuing from the background concept of extended Hilbert and Sobolev spaces. QSM as a source of discrete representation of molecular structures is made evident in this context. Further theoretical development undertakes precise study of discretization, that is, the transformation of infinite-dimensional functional spaces into n-dimensional ones. This result adds new perspectives to the discrete representation of QO, because a) It provides a source of new QO descriptors, b) It describes the QSAR theoretical background enabling the construction of adequate models like tuned-QSAR, and c) It allows the construction of sound and general alternatives of Hammet?s ó or log P parameters. In this context, QSM appear to produce QSAR models constructed with unbiased descriptors, deducible from quant

Collection and Evaluation of (Q)SAR Models for Mutagenicity and Carcinogenicity

This evaluation of the non-commercial (Q)SARs for mutagenicity and carcinogenicity consisted of a preliminary survey (Phase I), and then of a more detailed analysis of short listed models (Phase II). In Phase I, the models were collected from the literature, and then assessed according to the OECD principles based on the information provided by the authors-. Phase I provided the support for short listing a number of promising models, that were analyzed more in depth in Phase II. In Phase II, the information provided by the authors was completed and complemented with a series of analyses aimed at generating an overall profile of each of the short listed models. The models can be divided into two families based on their target: a) congeneric; and b) non-congeneric sets of chemicals. The QSARs for congeneric chemicals include most of the chemical classes top ranking in the EU High Production Volume list, with the notable exception of the halogenated aliphatics. They almost exclusively aim at modeling Salmonella mutagenicity and rodent carcinogenicity, which are crucial toxicological endpoints in the regulatory context. The lack of models for in vivo genotoxicity should be remarked. Overall the short listed models can be interpreted mechanistically, and agree with, and/or support the available scientific knowledge, and most of the models have good statistics. Based on external prediction tests, the QSARs for the potency of congeneric chemicals are 30 to 70 % correct, whereas the models for discriminating between active and inactive chemicals have considerably higher accuracy (63 to 100 %), thus indicating that predicting intervals is more reliable than predicting individual data points. The internal validation procedures (e.g., cross-validation, etc...) did not seem to be a reliable measure of external predictivity. Among the non-local, or global approaches for non-congeneric data sets, four models based on the use of Structural Alerts (SA) were short listed and investigated in more depth. The four sets did not differ to a large extent in their performance. In the general databases of chemicals the SAs appear to agree around 65% with rodent carcinogenicity data, and 75% with Salmonella mutagenicity data. The SAs based models do not seem to work equally efficiently in the discrimination between active and inactive chemicals within individual chemical classes. Thus, their main role is that of preliminary, or large-scale screenings. A priority for future research on the SAs is their expansion to include alerts for nongenotoxic carcinogens. A general indication of this study, valid for both congeneric and noncongeneric models, is that there is uncertainty associated with (Q)SARs; the level of uncertainty has to be considered when using (Q)SAR in a regulatory context. However, (Q)SARs are not meant to be black-box machines for predictions, but have a much larger scope including organization and rationalization of data, contribution to highlight mechanisms of action, complementation of other data from different sources (e.g., experiments). Using only non-testing methods, the larger the evidence from QSARs (several different models, if available) and other approaches (e.g. chemical categories, read across) the higher the confidence in the prediction.JRC.I.3-Toxicology and chemical substance

Lipophilicity in drug design: an overview of lipophilicity descriptors in 3D-QSAR studies

The pharmacophore concept is a fundamental cornerstone in drug discovery, playing a critical role in determining the success of in silico techniques, such as virtual screening and 3D-QSAR studies. The reliability of these approaches is influenced by the quality of the physicochemical descriptors used to characterize the chemical entities. In this context, a pivotal role is exerted by lipophilicity, which is a major contribution to host-guest interaction and ligand binding affinity. Several approaches have been undertaken to account for the descriptive and predictive capabilities of lipophilicity in 3D-QSAR modeling. Recent efforts encode the use of quantum mechanical-based descriptors derived from continuum solvation models, which open novel avenues for gaining insight into structure-activity relationships studies

Review of Data Sources, QSARs and Integrated Testing Strategies for Skin Sensitisation

This review collects information on sources of skin sensitisation data and computational tools for the estimation of skin sensitisation potential, such as expert systems and (quantitative) structure-activity relationship (QSAR) models. The review also captures current thinking of what constitutes an integrated testing strategy (ITS) for this endpoint. The emphasis of the review is on the usefulness of the models for the regulatory assessment of chemicals, particularly for the purposes of the new European legislation for the Registration, Evaluation, Authorisation and Restriction of CHemicals (REACH), which entered into force on 1 June 2007. Since there are no specific databases for skin sensitisation currently available, a description of experimental data found in various literature sources is provided. General (global) models, models for specific chemical classes and mechanisms of action and expert systems are summarised. This review was prepared as a contribution to the EU funded Integrated Project, OSIRIS.JRC.I.3-Consumer products safety and qualit

Skin Sensitisation (Q)SARs/Expert Systems: from Past, Present to Future

This review describes the state of the art of available (Q)SARs/expert systems for skin sensitisation and evaluates their utility for potential regulatory use. There is a strong mechanistic understanding with respect to skin sensitisation which has facilitated the development of different models. Most existing models fall into one of two main categories either they are local in nature, usually specific to a chemical class or reaction chemical mechanism or else they are global in form, derived empirically using statistical methods. Some of the published global QSARs available have been recently characterised and evaluated elsewhere in accordance with the OECD principles. An overview of expert systems capable of predicting skin sensitisation is also provided. Recently, a new perspective regarding the development of mechanistic skin sensitisation QSARs so-called Quantitative Mechanistic Modelling (QMM) has been proposed, where reactivity and hydrophobicity, are used as the key parameters in mathematically modelling skin sensitisation. Whilst hydrophobicity can be conveniently modelled using log P, the octanol-water partition coefficient; reactivity is less readily determined from chemical structure. Initiatives are in progress to generate reactivity data for reactions relevant to skin sensitisation but more resources are required to realise a comprehensive set of reactivity data. This is a fundamental and necessary requirement for the future assessment of skin sensitisation.JRC.I.3-Toxicology and chemical substance