Integrated approach to assess the domain of applicability of some commercial (Q)SAR models1

An integrated framework of data analysis has been proposed to systematically address the determination of the domain of applicability (DA) of some commercial Quantitative Structure Activity Relationship ((Q)SAR) models based on the structure of test chemicals. This framework forms one of the important steps in dealing with the growing concerns on reliability of model-based predictions on toxicity of chemicals specifically in the regulatory context. The present study uses some of the well-known mutagenicity and carcinogenicity models that are available within the Casetox (MultiCASE Inc.) and TOPKAT (Accelrys Software Inc.) programs. The approach enumerated in this paper employs chemoinformatics tools that facilitate comparisons of key structural features as well as application of cluster analysis techniques. The approach has been illustrated using a set of eleven chemical structures selected from the Canadian Domestic Substances List (DSL) that are not present in the model training sets, and the efficacy of the approach has also been assessed using seven chemicals with known toxicities. The methodologies presented here could help address the issue of DA of complex (Q)SAR models and at the same time, serve as useful tools for regulators to make a preliminary assessment of (Q)SAR based systems thereby helping the process of hazard-based regulatory assessments of chemicals.     

Integrated approach to assess the domain of applicability of some commercial (Q)SAR models

An integrated framework of data analysis has been proposed to systematically address the determination of the domain of applicability (DA) of some commercial Quantitative Structure Activity Relationship ((Q)SAR) models based on the structure of test chemicals. This framework forms one of the important steps in dealing with the growing concerns on reliability of model-based predictions on toxicity of chemicals specifically in the regulatory context. The present study uses some of the well-known mutagenicity and carcinogenicity models that are available within the Casetox (MultiCASE Inc.) and TOPKAT (Accelrys Software Inc.) programs. The approach enumerated in this paper employs chemoinformatics tools that facilitate comparisons of key structural features as well as application of cluster analysis techniques. The approach has been illustrated using a set of eleven chemical structures selected from the Canadian Domestic Substances List (DSL) that are not present in the model training sets, and the efficacy of the approach has also been assessed using seven chemicals with known toxicities. The methodologies presented here could help address the issue of DA of complex (Q)SAR models and at the same time, serve as useful tools for regulators to make a preliminary assessment of (Q)SAR based systems thereby helping the process of hazard-based regulatory assessments of chemicals.     

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

A stepwise approach for determining the model applicability domain is proposed. Four stages are applied to account for the diversity and complexity of the current SAR/QSAR models, reflecting their mechanistic rationality (including metabolic activation of chemicals) and transparency. General parametric requirements are imposed in the first stage, specifying in the domain only those chemicals that fall in the range of variation of the physicochemical properties of the chemicals in the training set. The second stage defines the structural similarity between chemicals that are correctly predicted by the model. The structural neighborhood of atom-centered fragments is used to determine this similarity. The third stage in defining the domain is based on a mechanistic understanding of the modeled phenomenon. Here, the model domain combines the reliability of specific reactive groups hypothesized to cause the effect and the domain of explanatory variables determining the parametric requirements in order for functional groups to elicit their reactivity. Finally, the reliability of simulated metabolism (metabolites, pathways, and maps) is taken into account in assessing the reliability of predictions, if metabolic activation of chemicals is a part of the (Q)SAR model. Some of the stages of the proposed approach for defining the model domain can be eliminated depending on the availability and quality of the experimental data used to derive the model, the specificity of (Q)SARs, and the goals of their ultimate application. The performance of the proposed definition of the model domain is tested using several examples of (Q)SARs that have been externally validated, including models for predicting acute toxicity, skin sensitization, and biodegradation. The results clearly showed that credibility in predictions of QSAR models for chemicals belonging to their domain is much higher than for chemicals outside this domain.     

CATALOGIC 301C model – validation and improvement

In Europe, REACH legislation encourages the use of alternative in silico methods such as (Q)SAR models. According to the recent progress of Chemical Substances Control Law (CSCL) in Japan, (Q)SAR predictions are also utilized as supporting evidence for the assessment of bioaccumulation potential of chemicals along with read across. Currently, the effective use of read across and QSARs is examined for other hazards, including biodegradability. This paper describes the results of external validation and improvement of CATALOGIC 301C model based on more than 1000 tested new chemical substances of the publication schedule under CSCL. CATALOGIC 301C model meets all REACH requirements to be used for biodegradability assessment. The model formalism built on scientific understanding for the microbial degradation of chemicals has a well-defined and transparent applicability domain. The model predictions are adequate for the evaluation of the ready degradability of chemicals.     

Identification of mechanisms of toxic action for skin sensitisation using a SMARTS pattern based approach

Skin sensitisation is a key endpoint under REACH as it is costly and its assessment currently has a high dependency on animal testing. In order to reduce both the cost and the numbers of animals tested, it is likely that (quantitative) structure–activity relationships ((Q)SAR) and read-across methods will be utilised as part of intelligent testing strategies. The majority of skin sensitisers elicit their effect via covalent bond formation with skin proteins. These reactions have been understood in terms of well defined nucleophilic–electrophilic reaction chemistry. Thus, a first step in (Q)SAR analysis is the assignment of a chemical's potential mechanism of action enabling it to be placed in an appropriate reactivity domain. The aim of this study was to design a series of SMARTS patterns capable of defining these reactivity domains. This was carried out using a large database of local lymph node assay (LLNA) results that had had potential mechanisms of action assigned to them using expert knowledge. A simple algorithm was written enabling the SMARTS patterns to be used to screen a database of SMILES strings. The SMARTS patterns were then evaluated using a second, smaller, test set of LLNA results which had also had potential mechanisms of action assigned by experts. The results showed that the SMARTS patterns provided an excellent method of identifying potential electrophilic mechanisms. The findings are supported, in part, by molecular orbital calculations which confirm assignment of reactive mechanism of action. The ability to define a chemical's potential reaction mechanism is likely to be of significant benefit to regulators and risk assessors as it enables category formation and subsequent read-across to be performed.     

Progress and perspectives of quantitative structure-activity relationships used for ecological risk assessment of toxic organic compounds

Structure-activity relationship (SAR) and quantitative structure-activity relationship (QSAR), collec- tively referred to as (Q)SARs, play an important role in ecological risk assessment (ERA) of organic chemicals. (Q)SARs can fill the data gap for physical-chemical, environmental behavioral and ecotoxicological parameters of organic compounds; they can decrease experimental expenses and reduce the extent of experimental testing (especially animal testing); they can also be used to assess the uncertainty of the experimental data. With the development for several decades, (Q)SARs in envi- ronmental sciences show three features: application orientation, multidisciplinary integration, and in- telligence. Progress of (Q)SAR technology for ERA of toxic organic compounds, including endpoint selection and mathematic methods for establishing simple, transparent, easily interpretable and portable (Q)SAR models, is reviewed. The recent development on defining application domains and diagnosing outliers is summarized. Model characterization with respect to goodness-of-fit, stability and predictive power is specially presented. The purpose of the review is to promote the development of (Q)SARs orientated to ERA of organic chemicals.     

Applicability domain: towards a more formal definition$

Abstract

In recent years the applicability domain (AD) of a prediction system has become an important concern in (Q)SAR modelling, especially in the context of human safety assessment. Today AD is an active research topic, and many methods have been designed to estimate the adequacy of a model and the confidence in its outcome for a given prediction task. Unfortunately, the wide spectrum of techniques developed for this purpose is based on various definitions of the concept of AD, often taking into account different types of information. This variety of methodologies confuses the end users and makes the comparison of the AD for different models almost impossible. In this article, we demonstrate that AD is not a monolithic concept and can be broken down into three well-defined sub-domains assessing confidence at the model, prediction and decision levels, respectively. By leveraging this separation of concerns we have an opportunity to clarify, formalize and extend the definition of AD. We propose a framework that captures this new vision with the aim to initiate a global effort to converge towards a common AD definition within the (Q)SAR community.     

Comparative PBT screening using (Q)SAR tools within REACH legislation

Small to medium sized enterprises (SMEs) in the EU are facing challenges due to the introduction of new legislation designed to protect consumers and the environment, REACH (Registration, Evaluation, Authorisation and Restriction of CHemicals). There can be high costs associated with implementing REACH because data on mammalian toxicity, environmental toxicity and environmental fate properties is required and if this data is obtained experimentally the cost is significant. These costs can be reduced if reliable quantitative structure–activity relationships ((Q)SAR) models are instead used to obtain the required information. In this paper we investigate how easily freely available (Q)SAR models can be applied for persistent, bioaccumulative and toxic (PBT) screening of 17 chemicals of interest to SMEs. In this study the PBT predictions obtained from the more user-friendly PBT Profiler and the Danish(Q)SAR database for the chemicals were compared with the results taken directly from the EPI Suite software. It was found that these widely used (Q)SAR databases might have some errors and examples are provided. It was concluded that extra care must be taken when considering the use of these databases for PBT screening. In addition, to increase the likelihood of a correct prediction, data estimates from various (Q)SAR models relevant to the PBT endpoints must be compared.     

Improving confidence in (Q)SAR predictions under Canada’s Chemicals Management Plan – a chemical space approach$

Abstract

One of the key challenges of Canada’s Chemicals Management Plan (CMP) is assessing chemicals with limited/no empirical hazard data for their risk to human health. In some instances, these chemicals have not been tested broadly for their toxicological potency; as such, limited information exists on their potential to induce human health effects following exposure. Although (quantitative) structure activity relationship ((Q)SAR) models are able to generate predictions to address data gaps for certain toxicological endpoints, the confidence in predictions also needs to be addressed. One way to address this issue is to apply a chemical space approach. This approach uses international toxicological databases, for example, those available in the Organisation for Economic Co-operation and Development (OECD) QSAR Toolbox. The approach,assesses a model’s ability to predict the potential hazards of chemicals that have limited hazard data that require assessment under the CMP when compared to a larger, data-rich chemical space that is structurally similar to chemicals of interest. This evaluation of a model’s predictive ability makes (Q)SAR analysis more transparent and increases confidence in the application of these predictions in a risk-assessment context. Using this approach, predictions for such chemicals obtained from four (Q)SAR models were successfully classified into high, medium and low confidence levels to better inform their use in decision-making.     

Comparison of Benzodiazepine-Like Compounds Using Topological Analysis and Genetic Algorithms

Chemical category is a regulatory concept facilitating filling safety data gaps. Practically, all chemical management programs like the OECD HPV Program, EU REACH, or the Canadian DSL Categorization are planning to use or are already using categorization approaches to reduce resources including animal testing. The aim of the study was to discuss the feasibility to apply computational structural similarity methods to augment formation of a category. The article discusses also how this understanding can be translated into computer readable format, an ultimate need for practical, broad scope applications. We conclude that for the skin sensitization endpoint, used as a working example, mechanistic understanding expressed as chemical reactivity can be exploited by computational structural similarity methods to augment category formation process. We propose a novel method, atom environments ranking (AER), to assess similarity to a reference training set representing a common mechanism of action, as a potential method for grouping chemicals into reactivity domains.     

How can structural similarity analysis help in category formation?

Chemical category is a regulatory concept facilitating filling safety data gaps. Practically, all chemical management programs like the OECD HPV Program, EU REACH, or the Canadian DSL Categorization are planning to use or are already using categorization approaches to reduce resources including animal testing. The aim of the study was to discuss the feasibility to apply computational structural similarity methods to augment formation of a category. The article discusses also how this understanding can be translated into computer readable format, an ultimate need for practical, broad scope applications. We conclude that for the skin sensitization endpoint, used as a working example, mechanistic understanding expressed as chemical reactivity can be exploited by computational structural similarity methods to augment category formation process. We propose a novel method, atom environments ranking (AER), to assess similarity to a reference training set representing a common mechanism of action, as a potential method for grouping chemicals into reactivity domains.     

The role of the European centre for the validation of alternative methods (ECVAM) in the validation of (Q)SARs

Under the current chemicals legislation, the regulatory use of structure-activity relationships (SARs) and quantitative structure-activity relationships (QSARs), collectively referred to as (Q)SARs, for the assessment of chemicals is limited, partly due to concerns about the extent to which (Q)SAR estimates can be relied upon. On 29 October 2003, the European Commission adopted a legislative proposal that foresees the introduction of a new regulatory system for chemicals called REACH (Registration, Evaluation, and Authorisation of Chemicals), which will impose equivalent information requirements on both new and existing chemicals. For reasons of practicality, cost-effectiveness and animal welfare, it is envisaged that (Q)SARs will play an important role in the assessment of some 30,000 existing chemicals for which further information may be required under the REACH system. It will therefore be essential that the (Q)SAR models used will produce reliable estimates. To overcome the barriers in the acceptance of (Q)SARs for regulatory purposes, it is widely acknowledged that there needs to be international agreement on the principles of (Q)SAR validation, and that the process of (Q)SAR validation should be managed by independent organisations, with a view to providing independent advice to the regulators who make decisions on the acceptability of (Q)SARs. The European Centre for the Validation of Alternative Methods (ECVAM), which is part of the European Commission's Joint Research Centre (JRC), has a well-established role in providing independent scientific and technical advice to European policy makers. This paper describes progress made at an international level regarding the principles of validation, and explains the role of ECVAM regarding the practical validation of (Q)SARs.     

Theoretical studies to estimate the skin sensitization potential of chemicals of the Schiff base domain

Skin sensitization occurs when an exogenous chemical substance forms a covalent adduct with a dermal protein electrophile or nucleophile. This instigates an immune response which leads to inflammation. The local lymph node assay is an in vivo model used in the assessment of relative skin sensitizing potency of chemicals. The method is time consuming and expensive, as well as poses ethical questions given that a number of mice must be sacrificed for each compound assessed. In this work, we investigate the use of an inexpensive, rapid, and ethical method to predict the skin sensitization potential of Schiff base chemicals. We employ quantum chemical methods to rationalize the sensitization potential of 22 compounds with a diverse range of activities. To this end, we have evaluated the mechanistic profile associated with this type of reaction using gas-phase models. We subsequently use the predicted rate determining barriers and key physico-chemical parameters (such as logP) to establish stucture activity relationship (SAR) guidelines to predict the skin sensitization potential for new chemicals. We find that the predicted rate determining barriers for aldehydes, ketone, and 1,2 and 1,3 diones generally decrease in the given order, which concurs with the overall trends in sensitization. We find that lipophilicity also plays a role, with those chemicals displaying both low barriers to reaction, and lower lipophilicity (ie, diones), being more likely to display undesirable skin sensitization effects. These findings are in line with experiment-based observations in the literature and point to the value 3D quantum chemical calculations could have if combined with other orthogonal approaches to estimate skin sensitization potential of chemicals.     

Global (Q)SARs for skin sensitisation–assessment against OECD principles‖

As part of a European Chemicals Bureau contract relating to the evaluation of (Q)SARs for toxicological endpoints of regulatory importance, we have reviewed and analysed (Q)SARs for skin sensitisation. Here we consider some recently published global (Q)SAR approaches against the OECD principles and present re-analysis of the data. Our analyses indicate that “statistical” (Q)SARs which aim to be global in their applicability tend to be insufficiently robust mechanistically, leading to an unacceptably high failure rate. Our conclusions are that, for skin sensitisation, the mechanistic chemistry is very important and consequently the best non-animal approach currently applicable to predict skin sensitisation potential is with the help of an expert system. This would assign compounds into mechanistic applicability domains and apply mechanism-based (Q)SARs specific for those domains and, very importantly, recognise when a compound is outside its range of competence. In such situations, it would call for human expert input supported by experimental chemistry studies as necessary.     

Global (Q)SARs for skin sensitisation: assessment against OECD principles

As part of a European Chemicals Bureau contract relating to the evaluation of (Q)SARs for toxicological endpoints of regulatory importance, we have reviewed and analysed (Q)SARs for skin sensitisation. Here we consider some recently published global (Q)SAR approaches against the OECD principles and present re-analysis of the data. Our analyses indicate that "statistical" (Q)SARs which aim to be global in their applicability tend to be insufficiently robust mechanistically, leading to an unacceptably high failure rate. Our conclusions are that, for skin sensitisation, the mechanistic chemistry is very important and consequently the best non-animal approach currently applicable to predict skin sensitisation potential is with the help of an expert system. This would assign compounds into mechanistic applicability domains and apply mechanism-based (Q)SARs specific for those domains and, very importantly, recognise when a compound is outside its range of competence. In such situations, it would call for human expert input supported by experimental chemistry studies as necessary.     

Galeon: A Biologically Active Molecule with In Silico Metabolite Prediction,In Vitro Metabolic Profiling in Rat Liver Microsomes,and In Silico Binding Mechanisms with CYP450 Isoforms

Galeon, a natural cyclic-diarylheptanoid (CDH), which was first isolated from Myrica gale L., is known to have potent cytotoxicity against A549 cell lines, anti-tubercular activity against Mycobacterium tuberculosis H37Rv, chemo-preventive potential, and moderate topoisomerase inhibitory activity. Here, in silico metabolism and toxicity prediction of galeon by CYP450, in vitro metabolic profiling study in rat liver microsomes (RLMs), and molecular interactions of galeon-CYP450 isoforms were performed. An in silico metabolic prediction study showed demethyl and mono-hydroxy galeon were the metabolites with the highest predictability. Among the predicted metabolites, mono-hydroxy galeon was found to have plausible toxicities such as skin sensitization, thyroid toxicity, chromosome damage, and carcinogenicity. An in vitro metabolism study of galeon, incubated in RLMs, revealed eighteen Phase-I metabolites, nine methoxylamine, and three glutathione conjugates. Identification of possible metabolites and confirmation of their structures were carried out using ion-trap tandem mass spectrometry. In silico docking analysis of galeon demonstrated significant interactions with active site residues of almost all CYP450 isoforms.