A stepwise approach for defining the applicability domain of SAR and QSAR models

Using quantitative structure-activity relationships (QSAR) to predict toxic endpoints for polycyclic aromatic hydrocarbons (PAH)

Quantitative structure-activity relationships (QSAR) offer a reliable, cost-effective alternative to the time, money, and animal lives necessary to determine chemical toxicity by traditional methods. Additionally, humans are exposed to tens of thousands of chemicals in their lifetimes, necessitating the determination of chemical toxicity and screening for those posing the greatest risk to human health. This study developed models to predict toxic endpoints for three bioassays specific to several stages of carcinogenesis. The ethoxyresorufin O-deethylase assay (EROD), the Salmonella/microsome assay, and a gap junction intercellular communication (GJIC) assay were chosen for their ability to measure toxic endpoints specific to activation-, induction-, and promotion-related effects of polycyclic aromatic hydrocarbons (PAH). Shape-electronic, spatial, information content, and topological descriptors proved to be important descriptors in predicting the toxicity of PAH in these bioassays. Bioassay-based toxic equivalency factors (TEF(B)) were developed for several PAH using the quantitative structure-toxicity relationships (QSTR) developed. Predicting toxicity for a specific PAH compound, such as a bioassay-based potential potency (PP(B)) or a TEF(B), is possible by combining the predicted behavior from the QSTR models. These toxicity estimates may then be incorporated into a risk assessment for compounds that lack toxicity data. Accurate toxicity predictions are made by examining each type of endpoint important to the process of carcinogenicity, and a clearer understanding between composition and toxicity can be obtained.

Allergic contact dermatitis--formation, structural requirements, and reactivity of skin sensitizers

Contact allergy is caused by a wide range of chemicals after skin contact. Its clinical manifestation, allergic contact dermatitis (ACD), is developed upon repeated contact with the allergen. This perspective focuses on two areas that have yielded new useful information during the last 20 years: (i) structure-activity relationship (SAR) studies of contact allergy based on the concept of hapten-protein binding and (ii) mechanistic investigations regarding activation of nonsensitizing compounds to contact allergens by air oxidation or skin metabolism. The second area is more thoroughly reviewed since the full picture has previously not been published. Prediction of the sensitizing capacity of a chemical is important to avoid outbreaks of ACD in the population. Much research has been devoted to the development of in vitro and in silico predictive testing methods. Today, no method exists that is sensitive enough to detect weak allergens and that is robust enough to be used for routine screening. To cause sensitization, a chemical must bind to macromolecules (proteins) in the skin. Expert systems containing information about the relationship between the chemical structure and the ability of chemicals to haptenate proteins are available. However, few designed SAR studies based on mechanistic investigations of prohaptens have been published. Many compounds are not allergenic themselves but are activated in the skin (e.g., metabolically) or before skin contact (e.g., via air oxidation) to form skin sensitizers. Thus, more basic research is needed on the chemical reactions involved in the antigen formation and the immunological mechanisms. The clinical importance of air oxidation to activate nonallergenic compounds has been demonstrated. Oxidized fragrance terpenes, in contrast to the pure terpenes, gave positive patch test reactions in consecutive dermatitis patients as frequently as the most common standard allergens. This shows the importance of using compounds to which people are exposed when screening for ACD in dermatology clinics.

A kinetic model for predicting biodegradation

Biodegradation plays a key role in the environmental risk assessment of organic chemicals. The need to assess biodegradability of a chemical for regulatory purposes supports the development of a model for predicting the extent of biodegradation at different time frames, in particular the extent of ultimate biodegradation within a '10 day window' criterion as well as estimating biodegradation half-lives. Conceptually this implies expressing the rate of catabolic transformations as a function of time. An attempt to correlate the kinetics of biodegradation with molecular structure of chemicals is presented. A simplified biodegradation kinetic model was formulated by combining the probabilistic approach of the original formulation of the CATABOL model with the assumption of first order kinetics of catabolic transformations. Nonlinear regression analysis was used to fit the model parameters to OECD 301F biodegradation kinetic data for a set of 208 chemicals. The new model allows the prediction of biodegradation multi-pathways, primary and ultimate half-lives and simulation of related kinetic biodegradation parameters such as biological oxygen demand (BOD), carbon dioxide production, and the nature and amount of metabolites as a function of time. The model may also be used for evaluating the OECD ready biodegradability potential of a chemical within the '10-day window' criterion.

Quantitative structure-activity relationship model for prediction of genotoxic potential for quinolone antibacterials

Antibiotics are of concern because of their widespread usage, their potential role in the spread and maintenance of bacterial resistance, and because of the selection pressure on microbes. In this study, the genotoxic potential of 20 quinolone antibacterials, including 5 first-generation, 8 second-generation, and 7 third-generation quinolones, was determined. While all of the antibacterials studied showed genotoxic potential, the molar concentration for each antibacterial that produces 10% (EC10) of the maximum response of corresponding antibacterial ranged from 0.61 to 2917.0 nM, and was greatly dependent on chemical structures. A quantitative structure-activity relationship (QSAR) was established by applying a quantum chemical modeling method to determine the factors required for the genotoxic potential of quinolone antibacterials. The octanol-water coefficient (logP(ow)) adjusted bythe pH and energies of the highest occupied molecular orbital (epsilon(HOMO)) and lowest unoccupied molecular orbital (epsilon(LUMO)) were selected as hydrophobic and electronic chemical descriptors, respectively. The genotoxic potentials of quinolone antibacterials were found to be dependent on their logP(ow) and epsilon(HOMO), while the effects of epsilon(LUMO) on the genotoxic potentials could not be identified. The QSAR model was also used to predict the genotoxic potentials for 14 quinolone antibacterials, including 1 second-generation, 2 third-generation, and 11 fourth-generation quinolone antibacterials. A correlation between the genotoxic potentials and their minimal inhibition concentrations (MIC50) against Streptococcus pneumoniae from the literature for 18 quinolone antibacterials was observed, providing a potential method to estimate MIC50.

TIMES-SS—A promising tool for the assessment of skin sensitization hazard. A characterization with respect to the OECD validation principles for (Q)SARs and an external evaluation for predictivity

The TImes MEtabolism Simulator platform used for predicting Skin Sensitization (TIMES-SS) is a hybrid expert system that was developed at Bourgas University using funding and data from a Consortium comprising industry and regulators. The model was developed with the aim of minimizing animal testing and to be scientifically valid in accordance with the OECD principles for (Q)SAR validation. TIMES-SS encodes structure-toxicity and structure-skin metabolism relationships through a number of transformations, some of which are underpinned by mechanistic 3D QSARs. Here, we describe the extent to which the five OECD principles are met and in particular the results from an external evaluation exercise that was recently carried out. As part of this exercise, data were generated for 40 new chemicals in the murine local lymph node assay (LLNA) and then compared with predictions made by TIMES-SS. The results were promising with an overall good concordance (83%) between experimental and predicted values. Further evaluation of these results highlighted certain inconsistencies which were rationalized by a consideration of reaction chemistry principles for sensitization. Improvements for TIMES-SS were proposed where appropriate. TIMES-SS is a promising tool to aid in the evaluation of skin sensitization hazard under legislative programs such as REACH.

In silico pharmacology for drug discovery: applications to targets and beyond

Computational (in silico) methods have been developed and widely applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, similarity searching, pharmacophores, homology models and other molecular modeling, machine learning, data mining, network analysis tools and data analysis tools that use a computer. Such methods have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The first part of this review discussed the methods that have been used for virtual ligand and target-based screening and profiling to predict biological activity. The aim of this second part of the review is to illustrate some of the varied applications of in silico methods for pharmacology in terms of the targets addressed. We will also discuss some of the advantages and disadvantages of in silico methods with respect to in vitro and in vivo methods for pharmacology research. Our conclusion is that the in silico pharmacology paradigm is ongoing and presents a rich array of opportunities that will assist in expediating the discovery of new targets, and ultimately lead to compounds with predicted biological activity for these novel targets.

In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling

Pharmacology over the past 100 years has had a rich tradition of scientists with the ability to form qualitative or semi-quantitative relations between molecular structure and activity in cerebro. To test these hypotheses they have consistently used traditional pharmacology tools such as in vivo and in vitro models. Increasingly over the last decade however we have seen that computational (in silico) methods have been developed and applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, pharmacophores, homology models and other molecular modeling approaches, machine learning, data mining, network analysis tools and data analysis tools that use a computer. In silico methods are primarily used alongside the generation of in vitro data both to create the model and to test it. Such models have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The aim of this review is to illustrate some of the in silico methods for pharmacology that are used in drug discovery. Further applications of these methods to specific targets and their limitations will be discussed in the second accompanying part of this review.

QSAR and mechanistic interpretation of estrogen receptor binding

A multi-dimensional formulation of the COmmon REactivity PAttern (COREPA) modeling approach has been used to investigate chemical binding to the human estrogen receptor (hER). A training set of 645 chemicals included 497 steroid and environmental chemicals (database of the Chemical Evaluation and Research Institute, Japan - CERI) and 148 chemicals to further explore hER-structure interactions (selected J. Katzenellenbogen references). Upgrades of modeling approaches were introduced for multivariate COREPA analysis, optimal conformational generation and description of the local hydrophobicity of chemicals. Analysis of reactivity patterns based on the distance between nucleophilic sites resulted in identification of distinct interaction types: a steroid-like A-B type described by frontier orbital energies and distance between nucleophilic sites with specific charge requirements; an A-C type where local hydrophobic effects are combined with electronic interactions to modulate binding; and mixed A-B-C (AD) type. Chemicals were grouped by type, then COREPA models were developed for within specific relative binding affinity ranges of >10%, 10 > RBA > or = 0.1%, and 0.1 > RBA > 0.0%. The derived models for each interaction type and affinity range combined specific prefiltering requirements (interatomic distances) and a COREPA classification node using no more than 2 discriminating parameters. The interaction types are becoming less distinct in the lowest activity range for each chemicals of each type; here, the modeling was performed within chemical classes (phenols, phthalates, etc.). The ultimate model was organized as a battery of local models associated to interaction type and mechanism.

Identification of the structural requirements for mutagencitiy, by incorporating molecular flexibility and metabolic activation of chemicals. II. General Ames mutagenicity model

The tissue metabolic simulator (TIMES) modeling approach is a hybrid expert system that couples a metabolic simulator together with structure toxicity rules, underpinned by structural alerts, to predict interaction of chemicals or their metabolites with target macromolecules. Some of the structural alerts representing the reactivity pattern-causing effect could interact directly with the target whereas others necessitated a combination with two- or three-dimensional quantitative structure-activity relationship models describing the firing of the alerts from the rest of the molecules. Recently, TIMES has been used to model bacterial mutagenicity [Mekenyan, O., Dimitrov, S., Serafimova, R., Thompson, E., Kotov, S., Dimitrova, N., and Walker, J. (2004) Identification of the structural requirements for mutagenicity by incorporating molecular flexibility and metabolic activation of chemicals I: TA100 model. Chem. Res. Toxicol. 17 (6), 753-766]. The original model was derived for a single tester strain, Salmonella typhimurium (TA100), using the Ames test by the National Toxicology Program (NTP). The model correctly identified 82% of the primary acting mutagens, 94% of the nonmutagens, and 77% of the metabolically activated chemicals in a training set. The identified high correlation between activities across different strains changed the initial strategic direction to look at the other strains in the next modeling developments. In this respect, the focus of the present work was to build a general mutagenicity model predicting mutagenicity with respect to any of the Ames tester strains. The use of all reactivity alerts in the model was justified by their interaction mechanisms with DNA, found in the literature. The alerts identified for the current model were analyzed by comparison with other established alerts derived from human experts. In the new model, the original NTP training set with 1341 structures was expanded by 1626 proprietary chemicals provided by BASF AG. Eventually, the training set consisted of 435 chemicals, which are mutagenic as parents, 397 chemicals that are mutagenic after S9 metabolic activation, and 2012 nonmutagenic chemicals. The general mutagenicity model was found to have 82% sensitivity, 89% specificity, and 88% concordance for training set chemicals. The model applicability domain was introduced accounting for similarity (structural, mechanistic, etc.) between predicted chemicals and training set chemicals for which the model performs correctly.

Enhancement of chemical rules for predicting compound reactivity towards protein thiol groups

Non-specific chemical modification of protein thiol groups continues to be a significant source of false positive hits from high-throughput screening campaigns and can even plague certain protein targets and chemical series well into lead optimization. While experimental tools exist to assess the risk and promiscuity associated with the chemical reactivity of existing compounds, computational tools are desired that can reliably identify substructures that are associated with chemical reactivity to aid in triage of HTS hit lists, external compound purchases, and library design. Here we describe a Bayesian classification model derived from more than 8,800 compounds that have been experimentally assessed for their potential to covalently modify protein targets. The resulting model can be implemented in the large-scale assessment of compound libraries for purchase or design. In addition, the individual substructures identified as highly reactive in the model can be used as look-up tables to guide chemists during hit-to-lead and lead optimization campaigns.

Workgroup report: review of fish bioaccumulation databases used to identify persistent, bioaccumulative, toxic substances

Chemical management programs strive to protect human health and the environment by accurately identifying persistent, bioaccumulative, toxic substances and restricting their use in commerce. The advance of these programs is challenged by the reality that few empirical data are available for the tens of thousands of commercial substances that require evaluation. Therefore, most preliminary assessments rely on model predictions and data extrapolation. In November 2005, a workshop was held for experts from governments, industry, and academia to examine the availability and quality of in vivo fish bioconcentration and bioaccumulation data, and to propose steps to improve its prediction. The workshop focused on fish data because regulatory assessments predominantly focus on the bioconcentration of substances from water into fish, as measured using in vivo tests or predicted using computer models. In this article we review of the quantity, features, and public availability of bioconcentration, bioaccumulation, and biota-sediment accumulation data. The workshop revealed that there is significant overlap in the data contained within the various fish bioaccumulation data sources reviewed, and further, that no database contained all of the available fish bioaccumulation data. We believe that a majority of the available bioaccumulation data have been used in the development and testing of quantitative structure-activity relationships and computer models currently in use. Workshop recommendations included the publication of guidance on bioconcentration study quality, the combination of data from various sources to permit better access for modelers and assessors, and the review of chemical domains of existing models to identify areas for expansion.

Discriminating toxicant classes by mode of action: 3. Substructure indicators

Decision support for selecting suitable QSARs for predictive purposes is suggested by a stepwise procedure: The first tier pre-filters the compounds based on substructure indicators for baseline versus excess toxicity. This step, if sufficiently conservative, discriminates chemicals, whose toxicity can be reliably estimated from their log KOW from those, that require further classification by biological and chemical domain. A test set of 115 chemicals from 9 different MOA classes was used to compare the discriminatory power of several classification schemes based on substructure indicators. Performance, evaluated by contingency table statistics, is varied and no single scheme provides sufficient applicability and reliability for pre-filtering chemical inventories. Major improvements are feasible with combined use of three classification schemes: assignments of baseline toxicants are protective, recognition of excess toxicants is acceptable and applicability range increases favourably.

Prediction of the sorption of organic compounds into soil organic matter from molecular structure

A new model to estimate the soil-water partition coefficient of non-ionic organic compounds normalized to soil organic carbon, Koc, from the two-dimensional molecular structure is presented. Literature data of log Koc for 571 organic chemicals were fitted to 29 parameters with a squared correlation coefficient r2 of 0.852 and a standard error of 0.469 log units. The application domain includes the atom types C, H, N, O, P, S, F, Cl, and Br in various important compound classes. The multilinear model contains the variables molecular weight, bond connectivity, molecular E-state, an indicator for nonpolar and weakly polar compounds, and 24 fragment corrections representing polar groups. The prediction capability is evaluated through an initial two-step development using an 80%:20% split of the data into training and prediction, cross-validation, permutation, and application to three external data sets. The discussion includes separate analyses for subsets of H-bond donors and acceptors as well as for nonpolar and weakly polar compounds. Comparison with existing models including linear solvation energy relationships illustrates the superiority of the new model.

Chapter 9 Molecular Similarity: Advances in Methods, Applications and Validations in Virtual Screening and QSAR

This chapter discusses recent developments in some of the areas that exploit the molecular similarity principle, novel approaches to capture molecular properties by the use of novel descriptors, focuses on a crucial aspect of computational models-their validity, and discusses additional ways to examine data available, such as those from high-throughput screening (HTS) campaigns and to gain more knowledge from this data. The chapter also presents some of the recent applications of methods discussed focusing on the successes of virtual screening applications, database clustering and comparisons (such as drug- and in-house-likeness), and the recent large-scale validations of docking and scoring programs. While a great number of descriptors and modeling methods has been proposed until today, the recent trend toward proper model validation is very much appreciated. Although some of their limitations are surely because of underlying principles and limitations of fundamental concepts, others will certainly be eliminated in the future.

Can we estimate the accuracy of ADME–Tox predictions?

There have recently been developments in the methods used to access the accuracy of the prediction and applicability domain of absorption, distribution, metabolism, excretion and toxicity models, and also in the methods used to predict the physicochemical properties of compounds in the early stages of drug development. The methods are classified into two main groups: those based on the analysis of similarity of molecules, and those based on the analysis of calculated properties. An analysis of octanol-water distribution coefficients is used to exemplify the consistency of estimated and calculated accuracy of the ALOGPS program (http://www.vcclab.org) to predict in-house and publicly available datasets.

QSARs in ecotoxicological risk assessment

The need for more ecotoxicological data encourages the use of QSARs because of the reduction of (animal) testing, time and cost. QSARs may however only be used if they prove to be reliable and accurate. In this paper, four QSARs were attempted to predict toxicity for 170 compounds from a broad chemical class, using them as a black-box. Predictions were obtained for 122 compounds, indicating an important drawback of QSARs, i.e., for 28% of the compounds QSARs cannot be used at all. Ecosar, Topkat, and QSARs for non-polar and polar narcosis generated predictions for 120, 39, 24, and 11 compounds, respectively. Correlations between experimental and predicted effect concentrations were significant for Topkat and the QSAR for polar narcosis, but generally poor for Ecosar and the QSAR for non-polar narcosis. When predicted effect concentrations for fish were allowed to deviate from experimental values by a factor of 5, correct predictions were generated for 77%, 54%, 68%, and 91% of the compounds using Ecosar, Topkat, and the QSARs for non-polar and polar narcosis, respectively. It was impossible to indicate specific chemical classes for which a QSAR should be used or not. The results show that currently available QSARs cannot be used as a black-box.

Prediction of estrogenicity: validation of a classification model

(Q)SAR models can be used to reduce animal testing as well as to minimise the testing costs. In particular, classification models have been widely used for estimating endpoints with binary activity. The aim of the present study was to develop and validate a classification-based quantitative structure-activity relationship (QSAR) model for endocrine disruption, based on interpretable mechanistic descriptors related to estrogenic gene activation. The model predicts the presence or absence of estrogenic activity according to a pre-defined cut-off in activity as determined in a recombinant yeast assay. The experimental data was obtained from the literature. A two-descriptor classification model was developed that has the form of a decision tree. The predictivity of the model was evaluated by using an external test set and by taking into account the limitations associated with the applicability domain (AD) of the model. The AD was determined as coverage of the model descriptor space. After removing the compounds present in the training set and the compounds outside of the AD, the overall accuracy of classification of the test chemicals was used to assess the predictivity of the model. In addition, the model was shown to meet the OECD Principles for (Q)SAR Validation, making it potentially useful for regulatory purposes.

Metabolic activation of chemicals: in-silico simulation

The role of metabolism in prioritising chemicals according to their potential adverse health effects is extremely important given the fact that innocuous parents can be transformed into toxic metabolites. Our recent efforts in simulating metabolic activation of chemicals are reviewed in this work. The application of metabolic simulators to predict biodegradation (microbial degradation pathways), bioaccumulation (fish liver metabolism), skin sensitisation (skin metabolism), mutagenicity (rat liver S-9 metabolism) are discussed. The ability of OASIS approach to predict metabolism (toxicokinetics) and toxicity (toxicodynamics) of chemicals resulting from their metabolic activation in a single modelling platform is an important advantage of the method. It allows prioritisation of chemicals due to predicted toxicity of their metabolites.

Designing Novel Polymers with Targeted Properties Using the Signature Molecular Descriptor

A method for solving the inverse quantitative structure-property relationship (QSPR) problem is presented which facilitates the design of novel polymers with targeted properties. Here, we demonstrate the efficacy of the approach using the targeted design of polymers exhibiting a desired glass transition temperature, heat capacity, and density. We present novel QSPRs based on the signature molecular descriptor capable of predicting glass transition temperature, heat capacity, density, molar volume, and cohesive energies of linear homopolymers with cross-validation squared correlation coefficients ranging between 0.81 and 0.95. Using these QSPRs, we show how the inverse problem can be solved to design poly(N-methyl hexamethylene sebacamide) despite the fact that the polymer was used not used in the training of this model.

Base-line model for identifying the bioaccumulation potential of chemicals

The base-line modeling concept presented in this work is based on the assumption of a maximum bioconcentration factor (BCF) with mitigating factors that reduce the BCF. The maximum bioconcentration potential was described by the multi-compartment partitioning model for passive diffusion. The significance of different mitigating factors associated either with interactions with an organism or bioavailability were investigated. The most important mitigating factor was found to be metabolism. Accordingly, a simulator for fish liver was used in the model, which has been trained to reproduce fish metabolism based on related mammalian metabolic pathways. Other significant mitigating factors, depending on the chemical structure, e.g. molecular size and ionization were also taken into account in the model. The results (r(2)=0.84) obtained for a training set of 511 chemicals demonstrate the usefulness of the BCF base line concept. The predictability of the model was evaluated on the basis of 176 chemicals not used in the model building. The correctness of predictions (abs(logBSF(Obs)-logBCF(Calc))=0.75)) for 59 chemicals included within the model applicability domain was 80%.