No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Identification of the Structural Requirements for Mutagenicity by Incorporating Molecular Flexibility and Metabolic Activation of Chemicals I: TA100 Model
Abstract
Traditional attempts to model genotoxicity data have been limited to congeneric data sets, primarily because the mechanism of action was ignored, and frequently, the chemicals required metabolism to the active species. In this exercise, the COmmon REactivity PAtterns (COREPA) approach was used to delineate the structural requirements for eliciting mutagenicity in terms of ranges of descriptors associated with three-dimensional molecular structures. The database used to build the mutagenicity model includes 1196 structurally diverse chemicals tested in the Ames assay by the National Toxicology Program. This manuscript describes the development of the TA100 model that predicts the results of mutagenicity testing using only the Ames TA100 strain. The TA100 model was developed using 148 chemicals that tested positive in TA100 strain without rat liver enzymes (S-9) and 188 chemicals that tested positive in TA100 strain with rat liver enzymes. A decision tree was developed by first comparing the reactivity profile of chemicals that were positive in TA100 without rat liver enzymes to the reactivity profile of the remaining 1048 chemicals. This approach correctly identified 82% of the primary acting mutagens and 94% of the nonmutagens in the training set. The 188 chemicals in the training set that are positive only in the presence of metabolic activation would pass through the decision tree as negative. The next step was to identify the chemicals that are positive only in the presence of metabolic activation. To accomplish this, a series of hierarchically ordered metabolic transformations were used to develop an S-9 metabolism simulator that was applied to each of the 1048 chemicals. The potential metabolites were then screened through the decision tree to identify reactive mutagens. This model correctly identified 77% of the metabolically activated chemicals in a training set. A computer system that applies the COREPA models and predicts mutagenicity of chemicals, including their metabolic activation, was developed. Each prediction is accompanied by a probabilistic estimate of the chemical being in the structural domain covered by the training set.
... One of the traditional in silico methods used to assess the theoretical molecular toxicity of an active pharmaceutical ingredient (API) is the quantitative structure-activity relationship (QSAR). Its principal applicability is associated with reproducing and predicting xenobiotics' metabolic activation reactions and pathways, which may induce various in vivo genotoxic effects [2][3][4][5][6]. ...
... • DNA and protein binding by OASIS-scrutinizes the presence of alerts within target molecules that may interact with DNA and/or proteins; • Protein binding for skin-investigates the presence of alerts within the target molecules responsible for interaction with proteins, especially skin proteins; • Skin irritation/corrosion inclusion rules-contains structural alerts that can be used for positive classification of chemicals causing skin irritation and/or corrosion; • Carcinogenicity and in vitro and in vivo mutagenicity-both profilers work as a decision tree for estimating carcinogenicity/mutagenicity based on a list of structural alerts [2][3][4][5]. ...
This study aimed to investigate the toxicological profile of hyperforin (HP) in silico and to assess it in vivo after topical application of an HP-rich St. John’s wort (SJW) extract. The former analysis predicted low toxicity because of HP’s inability to bind DNA or proteins, but structural alerts for skin irritation/corrosion, carcinogenicity, and mutagenicity were found. Animal studies involved the treatment of excision wounds in Wistar rats with poloxamer 407/borage oil formulations (bigels; Bs) containing HP-rich SJW extract previously developed by us. The effects of semisolids comprising ‘free’ extract (B/SJW) or extract loaded in nanostructured lipid carriers (B/NLC-SJW) were compared to positive (commercial herbal product) and negative (untreated) controls after 2-, 7-, 14-, and 21-day applications. Malondialdehyde (MDA) and ABTS assays evaluated the degree of oxidative stress—treatment with bigels did not affect MDA favorably but led to an increased radical-cation scavenging capacity (compared to controls). Gamma-glutamyl transferase (GGT), aspartate aminotransferase (ASAT), alanine aminotransferase (ALAT), and lactate dehydrogenase (LDH) enzyme levels were measured as indicators for liver/tissue damage. Treatment with both B/SJW and B/NLC-SJW for 21 days resulted in lower GGT and ASAT levels than those in controls. Two-day application of the biphasic semisolids contributed to normalized ALAT levels (lower than in both negative and positive controls), and the same trends were observed in LDH levels after a 7-day treatment. The promising results obtained after the B/NLC-SJW application suggest that this drug delivery system may not only preserve HP in SJW extract effectively but also ‘expose’ its cyto-/hepatoprotective potential.
... In this case, the domain of the functional groups could be determined by the interpolation domain of the model descriptors. In a recently developed probabilistic approach, 19 the population density of chemicals within the training set is assessed to determine a probability density (or density function) f(X): ...
... The local correctness of predictions and local population density depend on the sphere radius, which should be determined on the basis of the general rules for forming frequency distributions. 20 As a measure of the local performance of the model, one can use the product between the probability that the prediction is correct, P Sph(Y,r) Corr (i.e., local correctness of predictions as defined by eq 19), and the probability that descriptors of training chemicals have values close to those of the query chemical P[X ∈ Sph(Y,r)] (i.e., local population density as defined by eq 20): ...
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.
... In terms of the use of category formation in the EFSA genotoxicity workflow noted above, defining similarity is relatively straightforward for potentially genotoxic chemicals. This is due to the key molecular initiating event for DNAreactive genotoxicity being the formation of a covalent bond between nucleophilic centres in DNA and a chemical capable of behaving as an electrophile (either directly or after metabolic activation) (7, 8,9,10,11,12,13). The associated chemistry can be encoded easily as structural alert-based in silico profilers that enable chemicals to be assigned to a category based on the presence of a common alert. ...
In dietary risk assessment of plant protection products, residues of active ingredients and their metabolites need to be evaluated for their genotoxic potential. The European Food Safety Authority recommend a tiered approach focussing assessment and testing on classes of similar chemicals. To characterise similarity, in terms of metabolism, a metabolic similarity profiling scheme has been developed from an analysis of 46 chemicals of strobilurin fungicides and their metabolites for which either Ames, chromosomal aberration or micronucleus test results are publicly available. This profiling scheme consists of a set of ten sub-structures, each linked to a key metabolic transformation present in the strobilurin metabolic space. This metabolic similarity profiling scheme was combined with covalent chemistry profiling and physico-chemistry properties to develop chemical categories suitable for chemical prioritisation via read-across. The method is a robust and reproducible approach to such read-across predictions, with the potential to reduce unnecessary testing. The key challenge in the approach was identified as being the need for metabolism data and individual groups of plant protection products as the basis for the development of such profiling schemes.
... Over the past decades, a variety of QSAR models have been developed to predict the outcome of the Ames assay (Greene et al., 1999;Hanser et al., 2014;Kasamatsu et al., 2021;Kazius et al., 2005;Klopman, 1984Klopman, , 1992Kumar et al., 2021;Lahl and Gundert-Remy, 2008;Mekenyan et al., 2004;Pavan and Worth, 2008;Roberts et al., 2000;Sanderson and Earnshaw, 1991;Schwab et al., 2016;Serafimova et al., 2007;Vian et al., 2019;Xu et al., 2012). For example, Xu et al. curated a comprehensive database of 6,786 diverse compounds from four published papers to develop predictive models (Xu et al., 2012). ...
The Ames assay is required by the regulatory agencies worldwide to assess the mutagenic potential risk of consumer products. As well as this in vitro assay, in silico approaches have been widely used to predict Ames test results as outlined in the International Council for Harmonization (ICH) guidelines. Building on this in silico approach, here we describe DeepAmes, a high performance and robust model developed with a novel deep learning (DL) approach for potential utility in regulatory science. DeepAmes was developed with a large and consistent Ames dataset (>10,000 compounds) and was compared with other five standard Machine Learning (ML) methods. Using a test set of 1,543 compounds, DeepAmes was the best performer in predicting the outcome of Ames assay. In addition, DeepAmes yielded the best and most stable performance up to when compounds were >30% outside of the applicability domain (AD). Regarding the potential for regulatory application, a revised version of DeepAmes with a much-improved sensitivity of 0.87 from 0.47. In conclusion, DeepAmes provides a DL-powered Ames test predictive model for predicting the results of Ames tests; with its defined AD and clear context of use, DeepAmes has potential for utility in regulatory application.
... The profilers 'DNA binding by OASIS, ' 'Protein binding by OASIS, ' 'Toxic hazard classification by Cramer, ' 'Carcinogenicity, ' 'In vitro mutagenicity, ' and 'In vivo mutagenicity' were used to elicit the toxicological profile of Hyp and its metabolites. The scope of 'DNA and Protein binding by OASIS' is to investigate the presence of alerts within target molecules that may interact with DNA and/ or proteins (Mekenyan et al. 2004;Serafimova et al. 2007; https://qsartoolbox.org/). Categorization rules of chemicals into different levels (Class I (Low), II (Intermediate) and III (High)) of toxicological concern (when administered orally) are organized in a tree-like scheme in the 'Toxic hazard classification by Cramer' (https://qsartoolbox.org/). ...
St. John’s wort is a medicinal herb well-known for its antidepressant, anti-inflammatory, antimycotic, and wound-healing properties. Hyperforin, the major phloroglucinol derivative, has been implicated as one of the main contributors to these therapeutic effects. Because of its high reactivity, this phytochemical can cause various adverse effects, such as allergic reactions, dizziness, dry mouth, and fatigue. To predict critical parameters of hyperforin’s possible behavior after oral administration, in silico methods were applied. The pharmacokinetic profile, bioactivity, and toxicity of the phytochemical were analyzed by applying Molinspiration cheminformatics, SwissADME, PreADME/Tox, and OECD QSAR Toolbox software. The results showed adequate absorption, a high affinity for plasma proteins, and a prolonged renal excretion of the acylphloroglucinol. The high metabolic activity, a reason for potential cyto- and genotoxicity, and the predicted carcinogenicity and mutagenicity of hyperforin, necessitate further in vitro and in vivo tests.
... In terms of the use of category formation in the EFSA genotoxicity workflow noted above, defining similarity is relatively straightforward for potentially genotoxic chemicals. This is due to the key molecular initiating event for DNA-reactive genotoxicity being the formation of a covalent bond between nucleophilic centres in DNA and a chemical capable of behaving as an electrophile (either directly or after metabolic activation) Cronin, 2010, 2012;Benigni and Bossa, 2008;Benigni et al., 2009;Mekenyan et al., 2004Mekenyan et al., , 2007Serafimova et al., 2007). The associated chemistry can be encoded easily as structural alert-based in silico profilers that enable chemicals to be assigned to a category based on the presence of a common alert. ...
In dietary risk assessment, residues of pesticidal ingredients or their metabolites need to be evaluated for their genotoxic potential. The European Food Safety Authority recommend a tiered approach focussing assessment and testing on classes of similar chemicals. To characterise similarity and to identify structural alerts associated with genotoxic concern, a set of chemical sub-structures was derived for an example dataset of 74 sulphonyl urea agrochemicals for which either Ames, chromosomal aberration or micronucleus test results are publicly available. This analysis resulted in a set of seven structural alerts that define the chemical space, in terms of the common parent and metabolic scaffolds, associated with the sulphonyl urea chemical class. An analysis of the available profiling schemes for DNA and protein reactivity shows the importance of investigating the predictivity of such schemes within a well-defined area of structural space. Structural space alerts, covalent chemistry profiling and physico-chemistry properties were combined to develop chemical categories suitable for chemical prioritisation. The method is a robust and reproducible approach to such read-across predictions, with the potential to reduce unnecessary testing. The key challenge in the approach was identified as being the need for pesticide-class specific metabolism data as the basis for structural space alert development.
... The scope of this profile is to examine the occurrence of structural alerts within the target chemical compound liable for interaction with DNA related to a genetic mutation. The profiler consists of 85 structural alerts disjointed into eight mechanistic domains, and each domain alienated into mechanistic alerts [45,46]. ...
Dichlorvos (DDVP) has been abused in Nigeria for suicide attempts, topical applications to treat an ectoparasitic infestation, and indiscriminate use on farm produce. Exposure to this compound in subacute concentration can cause toxicity in different tissues by alteration of the cellular antioxidative defence mechanism. This analysis is aimed at the systematic profiling of DDVP to assess its cytotoxic and mutagenic potential for human vulnerability using an in silico classification model. DDVP was grouped into categories of analogue chemical compounds generated from inventories based on structural alerts that measure the biological effects on cell lines and animal models using the quantitative structure-activity relationship (QSAR) model. The cytotoxic and mutagenic potential of DDVP was assessed by analyzing target endpoints like skin sensitization, oral/inhalation toxicity, neurotoxicity and mutagenicity. DDVP shows moderate sensitization potential that can induce skin irritation during prolonged exposure because of the presence of dichlorovenyl side-chain that interacts with cellular proteins and causes degradation. 50% lethal dose (LD50) of DDVP per body weight was determined to be 26.2 mg/kg in a rat model at 95% confidence range for acute oral toxicity, and 14.4 mmol/L was estimated as 50% lethal concentration (LC50) in the atmosphere due to acute inhalation toxicity. DDVP can also inhibit acetylcholinesterase in the nervous system to produce nicotinic and muscarinic symptoms like nausea, vomiting, lacrimation, salivation, bradycardia, and respiratory failure may cause death. The widely used pesticide causes weak DNA methylation which can repress gene transcription on promoter sites. DDVP is volatile so it can cause oral and inhalation toxicity coupled with neurotoxicity during prolonged exposure. Serum cholinesterase blood tests should be encouraged in federal and state hospitals to investigate related health challenges as DDVP is still used in Nigeria.
... OASIS/TIMES is equipped with a liver metabolism simulator based on metabolic pathways (Tissue Metabolite Simulator; TIMES). Chemical substances in the training set used in this model can be classified into those that have mutagenicity without metabolic activation, those that have mutagenicity after metabolic activation, and those that are not mutagenic regardless of metabolic activation [15,16]. When the Ames mutagenicity under the presence of rat S9 is predicted for queried compounds, the metabolite predicted to have mutagenicity is displayed, along with a metabolic map of the compound. ...
Abstract Currently, there are more than 100,000 industrial chemicals substances produced and present in our living environments. Some of them may have adverse effects on human health. Given the rapid expansion in the number of industrial chemicals, international organizations and regulatory authorities have expressed the need for effective screening tools to promptly and accurately identify chemical substances with potential adverse effects without conducting actual toxicological studies. (Quantitative) Structure–Activity Relationship ((Q)SAR) is a promising approach to predict the potential adverse effects of a chemical on the basis of its chemical structure. Significant effort has been devoted to the development of (Q) SAR models for predicting Ames mutagenicity, among other toxicological endpoints, owing to the significant amount of the necessary Ames test data that have already been accumulated. The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) M7 guideline for the assessment and control of mutagenic impurities in pharmaceuticals was established in 2014. It is the first international guideline that addresses the use of (Q) SAR instead of actual toxicological studies for human health assessment. Therefore, (Q) SAR for Ames mutagenicity now require higher predictive power for identifying mutagenic chemicals. This review introduces the advantages and features of (Q)SAR. Several (Q) SAR tools for predicting Ames mutagenicity and approaches to improve (Q) SAR models are also reviewed. Finally, I mention the future of (Q) SAR and other advanced in silico technology in genetic toxicology.
... From the OECD QSAR toolbox platform (version 4.2) [25] we used the profiler for DNA alerts for AMES by OASIS. It counts 85 SAs responsible for the interaction of chemicals with DNA extracted from the Ames Mutagenicity model, part of the OASIS TIMES system [26,27]. ...
Plant extracts are widely used as cosmetic ingredients and have to be investigated to guarantee consumer safety. However, these natural products are often complex mixtures of chemicals. No animal tests can be done, in compliance with cosmetic regulations, therefore non-testing methods (NTM) could be useful for preliminary screening to address the safety of finished cosmetic products.
We developed an integrated strategy (IS) to assess the genotoxic potential of ~18,000 molecules present in natural cosmetics ingredients by combining several quantitative structure-activity relationship (QSAR) models. This IS consists of a sequence of steps to formalize the expert reasoning. We also developed a new classification model based on a large dataset of compounds to clarify the outcomes that remain equivocal after the application of this strategy.
... For example, the TIMES program uses a quantitative structure− activity relationahip (QSAR) model for estimating the mutagenicity of genotoxic chemicals. 16,19 Such preliminary predictions help to assess the biotransformation net for intermediates with adverse drug effects. ...
Xenobiotics biotransformation in humans is a process of the chemical modifications, which may lead to the formation of toxic metabolites. The prediction of such metabolites is very important for drug development and ecotoxicology studies. We created the web-application MetaTox ( http://way2drug.com/mg ) for the generation of xenobiotics metabolic pathways in the human organism. For each generated metabolite, the estimations of the acute toxicity (based on GUSAR software prediction), organ-specific carcinogenicity and adverse effects (based on PASS software prediction) are performed. Generation of metabolites by MetaTox is based on the fragments datasets, which describe transformations of substrates structures to a metabolites structure. We added three new classes of biotransformation reactions: Dehydrogenation, Glutathionation, and Hydrolysis, and now metabolite generation for 15 most frequent classes of xenobiotic's biotransformation reactions are available. MetaTox calculates the probability of formation of generated metabolite - it is the integrated assessment of the biotransformation reactions probabilities and their sites using the algorithm of PASS ( http://way2drug.com/passonline ). The prediction accuracy estimated by the leave-one-out cross-validation (LOO-CV) procedure calculated separately for the probabilities of biotransformation reactions and their sites is about 0.9 on the average for all reactions.
... The quantitative evaluation of transformation by their probabilities (plausibility estimates) allowed prioritisation of metabolites by their stability, reactivity, solubility, etc. The simulators have been combined with toxicity prediction tools to facilitate the prediction of metabolic activation of chemicals in integrated systems; thus, the TIMES system has been used to predict mutagenicity and skin sensitisation whilst also accounting for the metabolism of chemicals [188,189]. ...
... These are based on the general idea that within the structural space of a single structural alert (considered to represent a single interaction mechanism), statistically derived models can quantitatively predict the variation in the reactivity of the alert conditioned by the rest of the molecular structure. Examples of the hydrid approach include models implemented in the OASIS TIMES (Mekenyan et al. 2004Serafimova et al. 2007), in CAESAR (Ferrari and Gini 2010) as well as some literature-based models not implemented in software (Purdy 1996). ...
... The tool is described in the works by Mekenyan and colleagues [27,28], and it is implemented within the QSAR Toolbox. Such an implementation does not provide any explicit indication on the AD of the model. ...
We evaluated the performance of seven freely available quantitative structure-activity relationship models predicting Ames genotoxicity thanks to a dataset of chemicals that were registered under the EU Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) regulation. The performance of the models was estimated according to Cooper's statistics and Matthew's Correlation Coefficients (MCC). The Benigni/Bossa rule base originally implemented in Toxtree and re-implemented within the Virtual models for property Evaluation of chemicals within a Global Architecture (VEGA) platform displayed the best performance (accuracy = 92%, sensitivity = 83%, specificity = 93%, MCC = 0.68) indicating that this rule base provides a reliable tool for the identification of genotoxic chemicals. Finally, we elaborated a consensus model that outperformed the accuracy of the individual models.
... Many of these alerts exist in software platforms to enable routine use, e.g. TIMES ( Mekenyan et al., 2004), Toxtree ( Benigni et al., 2008) as well as the OECD Toolbox. The last of these includes DNA binding profilers . ...
Read-across has generated much attention since it may be used as an alternative approach for addressing the information requirements under regulatory programmes, notably the EU's REACH regulation. Read-across approaches are conceptually accepted by ECHA and Member State Authorities (MS) but difficulties remain in applying them consistently in practice. Technical guidance is available and there are a plethora of models and tools that can assist in the development of categories and read-across, but guidance on how to practically apply categorisation approaches is still missing. This paper was prepared following an ECETOC (European Centre for Ecotoxicology and Toxicology) Task Force that had the objective of summarising guidance and tools available, reviewing their practical utility and considering what technical recommendations and learnings could be shared more widely to refine and inform on the current use of read-across. The full insights are recorded in ECETOC Technical Report TR No. 116. The focus of this present paper is to describe some of the technical and practical considerations when applying read-across under REACH. Since many of the deliberations helped identify the issues for discussion at a recent ECHA/Cefic LRI Workshop on "read-across", summary outcomes from this Workshop are captured where appropriate for completeness.
... It contained 382 Phase I and 48 Phase II metabolic transformations. The tissue metabolism simulator (TIMES) machinery was used as described in [154], [155]. ...
... Based on different assumptions, the predicted k M values are practically a function of log K OW which makes metabolism strongly dependent on the chemical's lipophilicity. In the BCF base-line model the tissue metabolism simulator (TIMES) machinery was used as described elsewhere [39,40] to simulate the effect of metabolism. The metabolic simulator utilizes a heuristic algorithm to generate plausible metabolic maps using the set of principal transformations. ...
The new development of the bioconcentration factor (BCF) base-line model of Dimitrov et al. [SAR QSAR Environ. Res. 6 (2005), pp. 531-554] is presented. The model applicability domain was expanded by enlarging the training set of the model up to 705 chemicals. The list of chemical-dependent mitigating factors was expanded by including water solubility of chemicals. The original empirical term for estimating ionization of chemicals was mechanistically analysed using two different approaches. In the first one, the ionization potential of chemicals was estimated based on the acid dissociation constant (pK(a) ). This term was found to be less adequate for inclusion in the ultimate BCF model, due to overestimating ionization of chemicals. The second approach, estimating the ionization as a ratio between distribution and partition coefficients (log P and log D), was found to be more successful. The new ionization term allows modelling of chemicals with both acidic and basic functionalities and chemicals undergoing different degrees of ionization. The significance of the different mitigating factors which can reduce the maximum bioconcentration potential of the chemicals was re-formulated and model parameters re-evaluated.
... An additional complicating factor lies in the wide range of metabolic conversions that can result in non-electrophile compounds being converted into electrophiles. Such mechanisms are out of the scope of this Review (several literature sources cover these mechanisms [49][50][51][52] ), which will focus on direct, non-metabolically activated mechanisms. ...
The chemical reactivity of xenobiotic electrophiles toward nucleophilic reference compounds is a model for their biochemical behavior toward bioactive sites. To obtain a holistic view about a particular compound, the results of different reactivity assays should be compared to each other, more than one chemical reaction mechanism might be involved in reactivity, and reactivity toward different nucleophilic sites is diverse. The comparison includes the use of different reference nucleophiles or different experimental conditions. A database of experimentally determined values for reactivity has been compiled, which contains a list of electrophilic compounds and their chemical structure, reactivity data, and kinetic rate constants of various in chemico assays, related to peptide binding or DNA binding, and qualitative information about adducts formed. The compiled database provides a tool to rank electrophilic compounds by examining the variation in reactivity to different nucleophilic sites and by comparison of related compounds.
... In previous work [15], we reported on an analysis of chemical mechanisms underpinning sensitization and mutagenicity by using structural alerts as encoded in the TIMES (Tissue Metabolism Simulator) expert system and substantiating these with experimental data taken from the datasets underpinning the TIMES system. TIMES houses a set of modelling algorithms for the prediction of Ames mutagenicity [16,17], in vitro chromosomal aberrations (CA) [18] and SS [19,20] (http://oasis-lmc.org/ ?section=software). ...
Our previous work has investigated the utility of mutagenicity data in the development and application of Integrated Testing Strategies (ITS) for skin sensitization by focusing on the chemical mechanisms at play and substantiating these with experimental data where available. The hybrid expert system TIMES (Tissue Metabolism Simulator) was applied in the identification of the chemical mechanisms since it encodes a comprehensive set of established structure-activity relationships for both skin sensitization and mutagenicity. Based on the evaluation, the experimental determination of mutagenicity was thought to be potentially helpful in the evaluation of skin sensitization potential. This study has evaluated the dataset reported by Wolfreys and Basketter (Cutan. Ocul. Toxicol. 23 (2004), pp. 197-205). Upon an update of the experimental data, the original reported concordance of 68% was found to increase to 88%. There were several compounds that were 'outliers' in the two experimental evaluations which are discussed from a mechanistic basis. The discrepancies were found to be mainly associated with the differences between skin and liver metabolism. Mutagenicity information can play a significant role in evaluating sensitization potential as part of an ITS though careful attention needs to be made to ensure that any information is interpreted in the appropriate context.
... There are several chemical classes (alerts) for each of the two broad toxicological categories. The relevance of the chemical categories (alerts) for risk assessment still needs to be investigated further (however, they are already used for genotoxicity assessment; [30][31][32][33][34]. The applicability domains of alerts are required; this is normally performed on a context-dependent basis, and is usually identified when a category has been populated with chemicals. ...
This report on The Potential of Mode of Action (MoA) Information Derived from Non-testing and Screening Methodologies to Support Informed Hazard Assessment, resulted from a workshop organised within OSIRIS (Optimised Strategies for Risk Assessment of Industrial Chemicals through Integration of Non-test and Test Information), a project partly funded by the EU Commission within the Sixth Framework Programme. The workshop was held in Liverpool, UK, on 30 October 2008, with 35 attendees. The goal of the OSIRIS project is to develop integrated testing strategies (ITS) fit for use in the REACH system, that would enable a significant increase in the use of non-testing information for regulatory decision making, and thus minimise the need for animal testing. One way to improve the evaluation of chemicals may be through categorisation by way of mechanisms or modes of toxic action. Defining such groups can enhance read-across possibilities and priority settings for certain toxic modes or chemical structures responsible for these toxic modes. Overall, this may result in a reduction of in vivo testing on organisms, through combining available data on mode of action and a focus on the potentially most-toxic groups. In this report, the possibilities of a mechanistic approach to assist in and guide ITS are explored, and the differences between human health and environmental areas are summarised.
Growing restrictions and bans on animal testing for chemical safety assessment under different regulations have led to an increasing use of alternative methods. Read-across is one of the major approaches used for this purpose, which relies on the identification of toxicological hazards of a data-poor or untested (target) chemical from data on other already-tested (source) similar chemicals. This requires the target substance to be first assigned to a group or category of ‘similar’ chemicals. The ‘similarity’ may be in terms of structural features alone, or in combination with certain rules that are based on mechanistic and/or toxicological aspects. In this regard, the OECD QSAR Toolbox - a major free-access in silico platform - is widely used to derive toxicity predictions for a range of (eco) toxicological endpoints. The Toolbox allows the user to identify a set of similar chemicals (analogues) by computational ‘profilers’ that incorporate different structural alerts, or a combination of structural alerts and physicochemical and/or toxicokinetic rules relevant to a specific toxicological endpoint.
The overall aim of this study was to assess the performance of the in silico profilers provided in the OECD QSAR Toolbox for reliability for identifying chemical analogues for category formation in a number of high-quality databases on mutagenicity, carcinogenicity, and skin sensitisation. The study also aimed to identify the reasons for any limitations in the performance of the profilers, and propose ways to improve their overall accuracy. The results showed that whilst some structural alerts are fit-for-purpose as such within the acceptable limits, others need refinement or a consideration for their possible exclusion from the profiler. Such refinements are imperative for a reliable use of the profilers in read-across and grouping/categorisation for classification, labelling and risk assessment of chemicals.
Background
Lately scientific and societal concern has emerged about persistent (P), mobile (M) and toxic (T) chemicals. Such chemicals, like some polyfluoroalkyl acids (PFAAs), are of concern due to their high mobility and persistence in aquatic compartments which relates to long-term biotic exposure and difficult removal from drinking water. In this study, a screening approach for identification of PMT chemicals was developed and applied to 6158 diverse chemicals.
Results
Chemicals are given a continuous score for P, M and T potential based on the modelled indicators (low to moderate potential is a score of 0–0.33, high potential is a score of 0.33–0.5 and very high potential a score of 0.5–1). The P score was based on the estimated aquatic environmental half life and the M score on the chemical’s organic carbon/water partition coefficient (Koc) using respectively the BIOWIN3 and KocWIN QSAR models of EPISuite™. The T score was based on the indicators for five human health endpoints: carcinogenicity (c), mutaganicity (m), reprotoxicity (r), endocrine disruption (ED) and general repeated dose systemic toxicity. Structural alerts for these endpoints taken from the OECD QSAR Toolbox™ and Toxtree™ were used as indicators of potential (human) toxicity. Chemical similarity values to Substances of Very High Concern (SVHC) with c, m and/or r properties were also included. Value functions were developed to translate the presence of alerts and similarity to the existing SVHCs to values between 0 and 1. Subsequently, all values were also aggregated to an overall PMT score, again ranging from 0 to 1. Applying the approach to chemicals from the Inventory of Existing Commercial chemical Substances, which are also REACH registered, resulted in 15% of the chemicals receiving high scores (≥ 0.33) for all three (P-, M- and T-) indicators and 4% getting very high scores (≥ 0.5) for both the P- and M-indicators.
Conclusions
The approach confirmed the properties of chemicals classified as SVHC due to PMT properties (e.g. 1,4-dioxane), illustrating the ability of the approach to identify PMT chemicals of concern. Water regulators, drinking water suppliers and others can use this approach to identify potential PMT/vPvM chemicals that need further investigation.
Background
Lately scientific and societal concern has emerged about persistent (P), mobile (M) and toxic (T) chemicals. Such chemicals, like some polyfluoroalkyl acids (PFAAs), are of concern due to their high mobility and persistence in aquatic compartments which relates to long-term biotic exposure and difficult removal from drinking water. In this study a screening approach for identification of PMT chemicals was developed and applied to 6,158 diverse chemicals.
Results
Chemicals are given a continuous score for P, M and T potential based on modelled indicators (low potential is a score of 0 to 0.33, high potential is a score of 0.33 to 0.5 and very high potential a score of 0.5 to 1). The P-score was based on the estimated aquatic environmental half-life and the M-score on the chemical’s organic carbon/water partition coefficient (Koc) using respectively the BIOWIN3 and KocWIN QSAR models of EPISuite™. The T-score was based on indicators for five human health endpoints: carcinogenicity (c), mutaganicity (m), reprotoxicity (r), endocrine disruption (ED) and general repeated dose systemic toxicity. Structural alerts for these endpoints taken from the OECD QSAR Toolbox™ and Toxtree™ were used as indicators of potential (human) toxicity. Chemical similarity values to Substances of Very High Concern (SVHC) with c, m and/or r properties were also included. Value functions were developed to translate the presence of alerts and similarity to existing SVHCs to values between 0 to 1. Subsequently, all values were also aggregated to an overall PMT-score, again ranging from 0 to 1. Applying the approach to chemicals from the Inventory of Existing Commercial chemical Substances, which are also REACH registered, resulted in 15% of the chemicals receiving high scores (≥0.33) for all three (P-, M- and T-) indicators, and 4% getting very high scores (≥0.5) for both the P- and M-indicators.
Conclusions
The approach confirmed the properties of chemicals classified as SVHC due to PMT properties (e.g. 1,4-dioxane), illustrating the ability of the approach to identify PMT chemicals of concern. Water regulators, drinking water suppliers and others can use this approach to identify potential PMT/vPvM chemicals that need further investigation.
Xenobiotic metabolism has evolved as a key protective system of organisms against potentially harmful chemicals or compounds typically not present in a particular organism. The system's primary purpose is to chemically transform xenobiotics into metabolites that can be excreted via renal or biliary routes. However, in a minority of cases, the metabolites formed are toxic, sometimes even more toxic than the parent compound. Therefore, the consideration of xenobiotic metabolism clearly is of importance to the understanding of the toxicity of a compound. Nevertheless, most of the existing computational approaches for toxicity prediction do not explicitly take metabolism into account and it is currently not known to what extent the consideration of (predicted) metabolites could lead to an improvement of toxicity prediction. In order to study how predictive metabolism could help to enhance toxicity prediction, we explored a number of different strategies to integrate predictions from a state-of-the-art metabolite structure predictor and from modern machine learning approaches for toxicity prediction. We tested the integrated models on five toxicological endpoints and assays, including in vitro and in vivo genotoxicity assays (AMES and MNT), two organ toxicity endpoints (DILI and DICC) and a skin sensitization assay (LLNA). Overall, the improvements in model performance achieved by including metabolism data were minor (up to +0.04 in the F1 scores and up to +0.06 in MCCs). In general, the best performance was obtained by averaging the probability of toxicity predicted for the parent compound and the maximum probability of toxicity predicted for any metabolite. Moreover, including metabolite structures as further input molecules for model training slightly improved the toxicity predictions obtained by this averaging approach. However, the high complexity of the metabolic system and associated uncertainty about the likely metabolites apparently limits the benefit of considering predicted metabolites in toxicity prediction.
The percentage of failures in late pharmaceutical development due to toxicity has increased dramatically over the last decade or so, resulting in increased demand for new methods to rapidly and reliably predict the toxicity of compounds. Today, computational toxicology can be used in every phase of drug discovery and development, from profiling large libraries early on, to predicting off-target effects in the mid-discovery phase, and to assess potential mutagenic impurities in development and degradants as part of life-cycle management. In this study, for the first time, in silico approaches were used to analyze the possible dark toxicity of photosensitive systems based on chlorin e6 and assessed possible toxicity of these compositions. By applying quantitative structure-activity relationship models (QSARs) and modeling adverse outcome pathways (AOPs), a potential toxic effect of water-soluble (chlorin e6 and chlorin e6 aminoamid) and hydrophobic (tetraphenylporphyrin) photosensitizers (PS) was predicted. Particularly, PSs’ protein binding ability, reactivity to form peptide adducts, glutathione conjugation, activity in dendritic cells, and gene expression activity in keratinocytes were explored. Using a metabolism simulator, possible PS metabolites were predicted and their potential toxicity was assessed as well. It was shown that all tested porphyrin PS and their predicted metabolites possess low activity in the mentioned processes and therefore are unable to cause significant adverse toxic effects under dark conditions.
Highlights
• New in silico models for genotoxicity assessment of organic chemicals are proposed.
• The fragment-based model is the best performing model.
• Proposed fragments include structural alerts for genotoxicity and non-genotoxicity.
• 24 active structural alerts are completely new.
• Best model will be freely available on VEGA hub platform.
Abstract
The evaluation of genotoxicity is a fundamental part of the safety assessment of chemicals due to the relevance of the potential health effects of genotoxicants. Among the testing methods available, the in vitro micronucleus assay with mammalian cells is one of the most used and required by regulations targeting several industrial sectors such as the cosmetic industry and food-related sectors. As an alternative to the testing methods, in recent years, lots in silico methods were developed to predict the genotoxicity of chemicals, including models for the Ames mutagenicity test, the in vitro chromosomal aberrations and the in vivo micronucleus assay. We developed several in silico models for the prediction of genotoxicity as reflected by the in vitro micronucleus assay. The resulting models include both statistical and knowledge-based models. The most promising model is the one based on fragments extracted with the SARpy platform. More than 100 structural alerts were extracted, including also fragments associated with the non-genotoxic activity. The model is characterized by high accuracy and the lowest false negative rate, making this tool suitable for chemical screening according to the regulators' needs. The SARpy model will be implemented on the VEGA platform (https://www.vegahub.eu) and will be freely available.
The threshold of toxicological concern (TTC) approach is a resource-effective de minimis method for the safety assessment of chemicals, based on distributional analysis of the results of a large number of toxicological studies. It is being increasingly used to screen and prioritize substances with low exposure for which there is little or no toxicological information. The first step in the approach is the identification of substances that may be DNA-reactive mutagens, to which the lowest TTC value is applied. This TTC value was based on the analysis of the cancer potency database and involved a number of assumptions that no longer reflect the state-of-the-science and some of which were not as transparent as they could have been. Hence, review and updating of the database is proposed, using inclusion and exclusion criteria reflecting current knowledge. A strategy for the selection of appropriate substances for TTC determination, based on consideration of weight of evidence for genotoxicity and carcinogenicity is outlined. Identification of substances that are carcinogenic by a DNA-reactive mutagenic mode of action and those that clearly act by a non-genotoxic mode of action will enable the protectiveness to be determined of both the TTC for DNA-reactive mutagenicity and that applied by default to substances that may be carcinogenic but are unlikely to be DNA-reactive mutagens (i.e. for Cramer class I-III compounds). Critical to the application of the TTC approach to substances that are likely to be DNA-reactive mutagens is the reliability of the software tools used to identify such compounds. Current methods for this task are reviewed and recommendations made for their application.
A new freely available web-application MetaTox (http://www.way2drug.com/mg) for prediction of xenobiotic's metabolism and calculation toxicity of metabolites based on the structural formula of chemicals has been developed. MetaTox predicts metabolites, which are formed by nine classes of reactions (aliphatic and aromatic hydroxylation, N- and O-glucuronidation, N-, S- and C-oxidation, and N- and O-dealkylation). The calculation of probability for generated metabolites is based on analyses of "structure-biotransformation reactions" and "structure-modified atoms" relationships using a Bayesian approach. Prediction of LD50 values is performed by GUSAR software for the parent compound and each of the generated metabolites using QSAR models created for acute rat toxicity with the intravenous type of administration.
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.
As part of its Environment Health and Safety Programme, the Organisation for Economic Co-operation and Development (OECD) has been active on the subject of the use of (quantitative) structure–activity relationships [(Q)SARs] for regulatory purposes since 1991. Early activities aimed to improve under...
The VirtualToxLab is an in silico technology for estimating the toxic potential - endocrine and metabolic disruption, some aspects of carcinogenicity and cardiotoxicity - of drugs, chemicals and natural products. The technology is based on an automated protocol that simulates and quantifies the binding of small molecules towards a series of currently 16 proteins, known or suspected to trigger adverse effects: 10 nuclear receptors (androgen, estrogen α, estrogen β, glucocorticoid, liver X, mineralocorticoid, peroxisome proliferator-activated receptor γ, progesterone, thyroid α, thyroid β), four members of the cytochrome P450 enzyme family (1A2, 2C9, 2D6, 3A4), a cytosolic transcription factor (aryl hydrocarbon receptor) and a potassium ion channel (hERG). The toxic potential of a compound - its ability to trigger adverse effects - is derived from its computed binding affinities toward these very proteins: The computationally demanding simulations are executed in client-server mode on a Linux cluster of the University of Basel. The graphical-user interface supports all computer platforms, allows building and uploading molecular structures, inspecting and downloading the results and, most important, rationalizing any prediction at the atomic level by interactively analyzing the binding mode of a compound with its target protein(s) in real-time 3D. Access to the VirtualToxLab is available free of charge for universities, governmental agencies, regulatory bodies and non-profit organizations.
Regulatory agencies worldwide are committed to the objectives of the Strategic Approach to International Chemicals Management to ensure that by 2020 chemicals are used and produced in ways that lead to the minimization of significant adverse effects on human health and the environment. Under the Government of Canada's Chemicals Management Plan, the commitment to address a large number of substances, many with limited data, has highlighted the importance of pursuing alternative hazard assessment methodologies that are able to accommodate chemicals with varying toxicological information. One such method is (Quantitative) Structure Activity Relationships ((Q)SAR) models. The current investigation into the predictivity of 20 (Q)SAR tools designed to model bacterial reverse mutation in Salmonella typhimurium is one of the first of this magnitude to be carried out using an external validation set comprised mainly of industrial chemicals which represent a diverse group of aromatic and benzidine-based azo dyes and pigments. Overall, this study highlights the value in challenging the predictivity of existing models using a small but representative subset of data-rich chemicals. Furthermore, external validation revealed that only a handful of models satisfactorily predicted for the test chemical space. The exercise also provides insight into using the Organisation for Economic Co-operation and Development (Q)SAR Toolbox as a read across tool.
Assessment of genotoxicity of chemicals is one of the utmost priorities of a regulatory agency since it is indicative of its
potential carcinogenic properties. A major challenge to the regulatory agencies today is how to assess the genotoxicity of
the large proportion of existing and new substances that have otherwise very little or no information on their genotoxicity
potential given the high costs and large time-scales associated with experimental testing. (Quantitative) structure–activity
relationships ((Q)SAR)-based methodologies have the potential to serve as rapid and reliable genotoxicity screening tools.
Such tools are very useful for regulatory agencies to assess the safety of chemicals, whereas for drug or new chemical manufacturers
these aid in providing an insight into the potential genotoxic/mutagenic properties of their novel molecules. To assess genotoxicity
of diverse groups of chemicals, various methods ranging from traditional linear modeling techniques to modern machine learning
algorithms have been applied by researchers to develop a large variety of (Q)SAR models. This chapter provides an overview
of some of the existing (Q)SAR models that have the potential to be integrated in a regulatory framework for nonempirical
genotoxicity assessment.
The bacterial reverse mutation assay (Ames test) is a biological assay used to assess the mutagenic potential of chemical compounds. In this paper approaches for the development of an in silico mutagenicity screening tool are described. Three individual in silico models, which cover both structure activity relationship methods (SARs) and quantitative structure activity relationship methods (QSARs), were built using three different modelling techniques: (1) an in-house alert model: which uses SAR approach where alerts are generated based on experts judgements; (2) a kNN approach (k-Nearest Neighbours), which is a QSAR model where a prediction is given based on outcomes of its k chemical neighbours; (3) a naive Bayesian model (NB), which is another QSAR model, where a prediction is derived using a Bayesian formula through preselected identified informative chemical features (e.g., physico-chemical, structural descriptors). These in silico models, were compared against two well-known alert models (DEREK and ToxTree) and also against three different consensus approaches (Categorical Bayesian Integration Approach (CBI), Partial Least Squares Discriminate Analysis (PLS-DA) and simple majority vote approach). By applying these integration methods on the validation sets it was shown that both integration models (PLS-DA and CBI) achieved better performance than any of the individual models or consensus obtained by simple majority rule. In conclusion, the recommendation of this paper is that when obtaining consensus predictions for Ames mutagenicity, approaches like PLS-DA or CBI should be the first choice for the integration as compared to a simple majority vote approach.
Introduction Birth of Computational Toxicology Linear Free Energy Related (LFER) Approaches Expert Systems Machine-Learning Approaches Web-Based Toxicity Predictors Conclusion References
The percentage of failures in late pharmaceutical development due to toxicity has increased dramatically over the last decade or so, resulting in increased demand for new methods to rapidly and reliably predict the toxicity of compounds. In this review we discuss the challenges involved in both the building of in silico models on toxicology endpoints and their practical use in decision making. In particular, we will reflect upon the predictive strength of a number of different in silico models for a range of different endpoints, different approaches used to generate the models or rules, and limitations of the methods and the data used in model generation. Given that there exists no unique definition of a 'good' model, we will furthermore highlight the need to balance model complexity/interpretability with predictability, particularly in light of OECD/REACH guidelines. Special emphasis is put on the data and methods used to generate the in silico toxicology models, and their strengths and weaknesses are discussed. Switching to the applied side, we next review a number of toxicity endpoints, discussing the methods available to predict them and their general level of predictability (which very much depends on the endpoint considered). We conclude that, while in silico toxicology is a valuable tool to drug discovery scientists, much still needs to be done to, firstly, understand more completely the biological mechanisms for toxicity and, secondly, to generate more rapid in vitro models to screen compounds. With this biological understanding, and additional data available, our ability to generate more predictive in silico models should significantly improve in the future.
The need to assess the ability of a chemical to act as a mutagen is one of the primary requirements in regulatory toxicology. Several pieces of legislation have led to an increased interest in the use of in silico methods, specifically the formation of chemical categories and read-across for the assessment of toxicological endpoints. One of the key steps in the development of chemical categories for mutagenicity is defining the mechanistic organic chemistry associated with the formation of a covalent bond between DNA and an exogenous chemical. To this end this study has analysed, by use of a large set of mutagenicity data (Ames test), the mechanistic coverage of a recently published set of in silico structural alerts developed for category formation. The results show that the majority of chemicals with a positive result in the Ames test were assigned at least one covalent binding mechanism related to the formation of a DNA adduct. The remaining chemicals with positive data in the Ames assay were subjected to a detailed mechanistic analysis from which 26 new structural alerts relating to covalent binding mechanisms were developed. In addition, structural alerts for radical and non-covalent intercalation mechanisms were also defined. The structural alerts outlined in this study are not intended to predict mutagenicity but rather to identify mechanisms associated with covalent and non-covalent DNA binding. This mechanistic profiling information can then be used to form chemical categories suitable for filling data gaps via read-across. A strategy for chemical category formation for mutagenicity is also presented.
Strategic testing as part of an integrated testing strategy (ITS) to maximize information and avoid the use of animals where possible is fast becoming the norm with the advent of new legislation such as REACH. Genotoxicity is an area where regulatory testing is clearly defined as part of ITS schemes. Under REACH, the specific information requirements depend on the tonnage manufactured or imported. Two types of test systems exist to meet these information requirements, in vivo genotoxicity assays, which take into account the whole animal, and in vitro assays, which are conducted outside the living mammalian organism using microbial or mammalian cells under appropriate culturing conditions. Clearly, with these different broad experimental categories, results for a given chemical can often differ, which presents challenges in the interpretation as well as in attempting to model the results in silico. This study attempted to compare the differences between in vitro and in vivo genotoxicity results, to rationalize these differences with plausible hypothesis in concert with available data. Two proof of concept (Q)SAR models were developed, one for in vivo genotoxicity effects in liver and a second for in vivo micronucleus formation in bone marrow. These "mechanistic models" will be of practical value in testing strategies, and both have been implemented into the TIMES software platform ( http://oasis-lmc.org ) to help predict the genotoxicity outcome of new untested chemicals.
Liverpool John Moores University and FRAME recently conducted a research project sponsored by Defra, on the status of alternatives to animal testing with regard to the European Union REACH (Registration, Evaluation and Authorisation of Chemicals) system for the safety testing and risk assessment of chemicals. The project covered all the main toxicity endpoints associated with the REACH system. This paper focuses on the prospects for using alternative methods (both in vitro and in silico) for mutagenicity (genotoxicity) and carcinogenicity testing--two toxicity endpoints, which, together with reproductive toxicity, are of pivotal importance for the REACH system. The manuscript critically discusses well-established testing approaches, and in particular, the requirement for short-term in vivo tests for confirming positive mutagenicity, and the need for the rodent bioassay for detecting non-genotoxic carcinogens. Recently-proposed testing strategies focusing on non-animal approaches are also considered, and our own testing scheme is presented and supported with background information. This scheme makes maximum use of pre-existing data, computer (in silico) and in vitro methods, with weight-of-evidence assessments at each major stage. The need for the improvement of in vitro methods, to reduce the generation of false-positive results, is also discussed. Lastly, ways in which reduction and refinement measures can be used are also considered, and some recommendations are made for future research to facilitate the implementation of the proposed testing scheme.
The 7th amendment to the EU Cosmetics Directive prohibits to put animal-tested cosmetics on the market in Europe after 2013. In that context, the European Commission invited stakeholder bodies (industry, non-governmental organisations, EU Member States, and the Commission's Scientific Committee on Consumer Safety) to identify scientific experts in five toxicological areas, i.e. toxicokinetics, repeated dose toxicity, carcinogenicity, skin sensitisation, and reproductive toxicity for which the Directive foresees that the 2013 deadline could be further extended in case alternative and validated methods would not be available in time. The selected experts were asked to analyse the status and prospects of alternative methods and to provide a scientifically sound estimate of the time necessary to achieve full replacement of animal testing. In summary, the experts confirmed that it will take at least another 7-9 years for the replacement of the current in vivo animal tests used for the safety assessment of cosmetic ingredients for skin sensitisation. However, the experts were also of the opinion that alternative methods may be able to give hazard information, i.e. to differentiate between sensitisers and non-sensitisers, ahead of 2017. This would, however, not provide the complete picture of what is a safe exposure because the relative potency of a sensitiser would not be known. For toxicokinetics, the timeframe was 5-7 years to develop the models still lacking to predict lung absorption and renal/biliary excretion, and even longer to integrate the methods to fully replace the animal toxicokinetic models. For the systemic toxicological endpoints of repeated dose toxicity, carcinogenicity and reproductive toxicity, the time horizon for full replacement could not be estimated.
The need to assess the ability of a chemical to act as a mutagen or a genotoxic carcinogen (collectively termed genotoxicity) is one of the primary requirements in regulatory toxicology. Several pieces of legislation have led to an increased interest in the use of in silico methods, specifically the formation of chemical categories for the assessment of toxicological endpoints. A key step in the development of chemical categories for genotoxicity is defining the organic chemistry associated with the formation of a covalent bond between DNA and an exogenous chemical. This organic chemistry is typically defined as structural alerts. To this end, this article has reviewed the literature defining the structural alerts associated with covalent DNA binding. Importantly, this review article also details the mechanistic organic chemistry associated with each of the structural alerts. This information is extremely important in terms of meeting regulatory requirements for the acceptance of the chemical category approach. The structural alerts and associated mechanistic chemistry have been incorporated into the Organisation for Economic Co-operation and Development (OECD) (Q)SAR Application Toolbox.
A methodology to characterize the chemical domain of qualitative and quantitative structure-activity relationship (QSAR) models based on the atom-centered fragment (ACF) approach is introduced. ACFs decompose the molecule into structural pieces, with each non-hydrogen atom of the molecule acting as an ACF center. ACFs vary with respect to their size in terms of the path length covered in each bonding direction starting from a given central atom and how comprehensively the neighbor atoms (including hydrogen) are described in terms of element type and bonding environment. In addition to these different levels of ACF definitions, the ACF match mode as degree of strictness of the ACF comparison between a test compound and a given ACF pool (such as from a training set) has to be specified. Analyses of the prediction statistics of three QSAR models with their training sets as well as with external test sets and associated subsets demonstrate a clear relationship between the prediction performance and the levels of ACF definition and match mode. The findings suggest that second-order ACFs combined with a borderline match mode may serve as a generic and at the same time a mechanistically sound tool to define and evaluate the chemical domain of QSAR models. Moreover, four standard categories of the ACF-based membership to a given chemical domain (outside, borderline outside, borderline inside, inside) are introduced that provide more specific information about the expected QSAR prediction performance. As such, the ACF-based characterization of the chemical domain appears to be particularly useful for QSAR applications in the context of REACH and other regulatory schemes addressing the safety evaluation of chemical compounds.
Legislation and prospective legislative proposals in for instance the USA, Europe, and Japan require, or may require that chemicals are tested for their ability to disrupt the hormonal systems of mammals. Chemicals found to test positive are considered to be endocrine active substances (EAS) and may be putative endocrine disruptors (EDs). To date, there is still little or no experience with incorporating metabolic and toxicokinetic aspects into in vitro tests for EAS. This is a situation in sharp contrast to genotoxicity testing, where in vitro tests are routinely conducted with and without metabolic capacity. Originally prepared for the Organisation of Economic Cooperation and Development (OECD), this detailed review paper reviews why in vitro assays for EAS should incorporate mammalian systems of metabolism and metabolic enzyme systems, and indicates how this could be done. The background to ED testing, the available test methods, and the role of mammalian metabolism in the activation and the inactivation of both endogenous and exogenous steroids are described. The available types of systems are compared, and the potential problems in incorporating systems in in vitro tests for EAS, and how these might be overcome, are discussed. Lastly, some recommendations for future activities are made.
Quantitative structure-activity relationships (QSARs) for predicting mutagenicity and carcinogenicity were reviewed. The QSARs for predicting mutagenicity and carcinogenicity have been mostly limited to specific classes of chemicals (e.g., aromatic amines and heteroaromatic nitro chemicals). The motivation to develop QSARs for predicting mutagenicity and carcinogenicity to screen inventories of chemicals has produced four major commercially available computerized systems that are able to predict these endpoints: Deductive estimation of risk from existing knowledge (DEREK) toxicity prediction by komputer assisted technology (TOPKAT), computer automated structure evaluation (CASE), and multiple computer automated structure evaluation (Multicase). A brief overview of these and some other expert systems for predicting mutagenicity and carcinogenicity is provided. The other expert systems for predicting mutagenicity and carcinogenicity include automatic data analysis using pattern recognition techniques (ADAPT), QSAR Expert System (QSAR-ES), OncoLogic computer optimized molecular parametric analysis of chemical toxicity system (COMPACT), and common reactivity pattern (COREPA).
Major scientific hurdles in the acceptance of quantitative structure-activity relationships (QSAR) for regulatory purposes have been identified. First, when quantifying important features of chemical structure complexities of molecular structure have often been ignored. More mechanistic modelling of chemical structure should proceed on two fronts: by developing a more in-depth understanding and representation of the multiple states possible for a single chemical by achieving greater rigor in understanding of conformational flexibility of chemicals; and, by considering families of activated metabolites that are derived in biological systems from an initial chemical substrate. Second, QSAR research is severely limited by the lack of systematic databases for important risk assessment endpoints, and despite many decades of research, the ability to cluster reactive chemicals by common toxicity pathways is in its infancy. Finally, computational tools are lacking for defining where a specific QSAR is applicable within the domain (universe) of chemical structures that are to be regulated. This paper describes some of the approaches being taken to address these needs. Applications of some of these new approaches are demonstrated for the prediction of chemical mutagenicity, where considerations of both molecular flexibility and metabolic activation improved the QSAR predictability and interpretations. Lastly, the applicability domain for a specific QSAR predicting estrogen receptor binding is presented in the context of a mechanistically-defined chemical structure space for large heterogeneous chemical datasets of regulatory concern. A strategic approach is discussed to selecting chemicals for model improvement and validation until regulatory acceptance criteria for risk assessment applications are met.
This paper presents the framework of a QSAR-based decision support system which provides a rapid screening of potential hazards, classification of chemicals with respect to risk management thresholds, and estimation of missing data for the early stages of risk assessment. At the simplest level, the framework is designed to rank hundreds of chemicals according to their profile of persistence, bioaccumulation potential and toxicity often called the persistent organic pollutant (POP) profile or the PBT (persistent bioaccumulative toxicant) profile. The only input data are the chemical structure. The POPs framework enables decision makers to introduce the risk management thresholds used in the classification of chemicals under various authorities. Finally, the POPs framework advances hazard identification by integrating a metabolic simulator that generates metabolic map for each parent chemical. Both the parent chemicals and plausible metabolites are systematically evaluated for metabolic activation and POPs profile.
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%.
The Ames Salmonella/microsome assay was employed to test the mutagenicity of benzidine and its analogs using strains TA98 and TA100 in the presence and absence of Aroclor 1254-induced rat S9 mix. 3,3*-Dichlorobenzidine-2HCl and 4,4*-dinitro-2-biphe- nylamine were directly mutagenic to TA98, while 4,4*-dinitro-2- biphenylamine was directly mutagenic to both TA98 and TA100 in the absence of S9 mix. 2-Aminobiphenyl, 3-aminobiphenyl, and 3,3*-5,5*-tetramethylbenzidine were not mutagenic in either strains in the presence or absence of S9. In the presence of S9 mix, 4-aminobiphenyl, benzidine, 3,3*-dichlorobenzidine-2HCl, 3,3*-di- methoxybenzidine, 3,3*-4,4*-tetraaminobiphenyl, o-tolidine, N, N-N* ,N *-tetramethylbenzidine, and 4,4*-dinitro-2-biphenylamine were mutagenic to TA98; 4-aminobiphenyl, 3,3*-dichlorobenzi- dine-2HCl, 3,3*-dimethoxybenzidine, and 4,4*-dinitro-2-biphe- nylamine were mutagenic to TA100. Physicochemical parameters of these compounds including oxidation potentials, the energy difference between the lowest unoccupied molecular orbital and the highest occupied molecular orbital, ionization potentials, di- pole moment, relative partition coefficient, and basicity did not
A large variety of chemicals of either natural or synthetic origin possess oestrogen-like activity and are thus called xeno-oestrogens. Some of these chemicals such as the pesticide methoxychlor require metabolic activation for their oestrogenic activity, whereas other compounds may themselves be oestrogenic and may be deactivated by their metabolism. In this chapter, the metabolism of representative examples of environmental oestrogen-like chemicals has been discussed to illustrate common trends in the large structural variety of xeno-oestrogens. The compounds included are zearalenone, methoxychlor, bisphenol A, DDT, β-sitosterol, and genistein and have been selected based on availability of information, the potential of exposure of humans and wildlife to the compounds, their industrial or agricultural importance, and the importance of metabolism for their activation or deactivation. The oestrogenic activity of phenolic xeno-oestrogens, a large class of compounds of natural or synthetic origin or their metabolites, likely is based on the weak oestrogen receptor binding of phenol. These compounds are mainly metabolized by analogy to steroidal oestrogenic hormones, i.e., by aromatic ring hydroxylation (catechol formation), subsequent methylation of the catechol and further phase II metabolism by glucuronide and/or sulfate formation. In contrast, the chlorinated hydrocarbon pesticides, which are weakly oestrogenic, are mainly metabolized by dehalogenation at relatively low metabolic rates. Thus, these compounds may persist in the body, accumulate in fatty tissues and provide a chronic reservoir of oestrogenic chemicals.
Styrene, oxidized by liver microsomal enzymes, can determine a liver enzyme induction. Urinary excretion of D-glucaric acid (DGA), which is believed to estimate this effect, was measured in 27 workers exposed to styrene in a fiberglass plant and in 27 control subjects in order to make a comparison with environmental and biological exposure indices. In exposed workers, airborne concentrations (8-h TWA) of styrene varied between 9 and 415 mg/m 3, with styrene metabolites [sum of mandelic acid (MA) + phenylglyoxylic acid (PGA)] ranging from 93 to 2130 mg/g Cr in endshift urine samples collected on a Thursday, and from 45 to 792 mg/g Cr in samples taken before work the following morning. The correlation coefficient (r) between 8- h TWAs and sum of urinary metabolites MA + PGA was 0.92 (y=4.06x - 36.05; p<0.001) for Thursday endshift (ES) samples, and 0.84 (y=1.46x + 46.82; p<0.001) for Friday morning samples (beginning of shift: BS). Urinary excretion of MA correlated better with exposure than that of PGA (MA: ES r=0.91; BS r=0.86. PGA: ES r=0.80; BS r=0.76). ES and BS levels of DGA in exposed subjects (equal to 4.41 ± 1.57 and 4.01 ± 1.18 mmol/mol Cr respectively) were both significantly higher than the 2.93 ± 0.88 observed in 27 control subjects (Mann-Whitney test; p<0.001). No significant correlation was found between individual exposure to styrene evaluated by the environmental concentration or by urinary metabolites and urinary excretion of DGA. Furthermore, both ES and BS urinary DGA levels increased across three classes of styrene exposure (<100, 101-200, and >200 mg/m 3, respectively, including 10, 7 and 10 workers). The DGA difference between the most exposed and the not exposed subjects was significant (ES-DGA: z=4.03 and p<0.05; BS- DGA: z=3.16 and p<0.05; Kruskall-Wallis test), but individual pairwise comparisons among all other groups were not. In spite of the above results, no significant correlation was found between individual exposure to styrene and urinary excretion of DGA, so that this biomarker cannot be used to monitor the exposure effects on an individual basis.
1 The development of DEREK, a computer-based expert system (derived from the LHASA chemical synthesis design program) for the qualitative prediction of possible toxic action of compounds on the basis of their chemical structure is described.
2 The system is able to perceive chemical sub-structures within molecules and relate these to a rulebase linking the sub-structures with likely types of toxicity.
3 Structures can be drawn in directly at a computer graphics terminal or retrieved automatically from a suitable in-house database.
4 The system is intended to aid the selection of compounds based on toxicological considerations, or separately to indicate specific toxicological properties to be tested for early in the evaluation of a compound, so saving time, money and some laboratory animals and resources.
The objective of this study was to evaluate the capability of an expert system described in the previous paper (S. Bradbury et al., Toxicol. Sci. 58, 253-269) to identify the potential for chemicals to act as ligands of mammalian estrogen receptors (ERs). The basis of the expert system was a structure activity relationship (SAR) model, based on relative binding affinity (RBA) values for steroidal and nonsteroidal chemicals derived from human ERalpha (hERalpha) competitive binding assays. The expert system enables categorization of chemicals into (RBA ranges of < 0.1, 0.1 to 1, 1 to 10, 10 to 100, and >150% relative to 17ss-estradiol. In the current analysis, the algorithm was evaluated with respect to predicting RBAs of chemicals assayed with ERs from MCF7 cells, and mouse and rat uterine preparations. The best correspondence between predicted and observed RBA ranges was obtained with MCF7 cells. The agreement between predictions from the expert system and data from binding assays with mouse and rat ER(s) were less reliable, especially for chemicals with RBAs less than 10%. Prediction errors often were false positives, i.e., predictions of greater than observed RBA values. While discrepancies were likely due, in part, to species-specific variations in ER structure and ligand binding affinity, a systematic bias in structural characteristics of chemicals in the hERalpha training set, compared to the rodent evaluation data sets, also contributed to prediction errors. False-positive predictions were typically associated with ligands that had shielded electronegative sites. Ligands with these structural characteristics were not well represented in the training set used to derive the expert system. Inclusion of a shielding criterion into the original expert system significantly increased the accuracy of RBA predictions. With this additional structural requirement, 38 of 46 compounds with measured RBA values greater than 10% in hERalpha, MCF7, and rodent uterine preparations were correctly categorized. Of the remaining 129 compounds in the combined data sets, RBA values for 65 compounds were correctly predicted, with 47 of the incorrect predictions being false positives. Based upon this exploratory analysis, the modeling approach, combined with a high-quality training set of RBA values derived from a diverse set of chemical structures, could provide a credible tool for prioritizing chemicals with moderate to high ER binding affinity for subsequent in vitro or in vivo assessments.
The common reactivity pattern (COREPA) approach is a 3-dimensional, quantitative structure activity relationship (3-D QSAR) technique that permits identification and quantification of specific global and local stereoelectronic characteristics associated with a chemical's biological activity. It goes beyond conventional 3-D QSAR approaches by incorporating dynamic chemical conformational flexibility in ligand-receptor interactions. The approach provides flexibility in screening chemical data sets in that it helps establish criteria for identifying false positives and false negatives, and is not dependent upon a predetermined and specified toxicophore or an alignment of conformers to a lead compound. The algorithm was recently used to screen chemical data sets for rat androgen receptor binding affinity. To further explore the potential application of the algorithm in establishing reactivity patterns for human estrogen receptor alpha (hERalpha) binding affinity, the stereoelectronic requirements associated with the binding affinity of 45 steroidal and nonsteroidal ligands to the receptor were defined. Reactivity patterns for relative hERalpha binding affinity (RBA; 17ss-estradiol = 100%) were established based on global nucleophilicity, interatomic distances between electronegative heteroatoms, and electron donor capability of heteroatoms. These reactivity patterns were used to establish descriptor profiles for identifying and ranking compounds with RBA of > 150%, 100-10%, 10-1%, and 1-0.1%. Increasing specificity of reactivity patterns was detected for ligand data sets with RBAs above 10%. Using the results of this analysis, an exploratory expert system was developed for use in ranking relative ER binding affinity potential for large chemical data sets.
To advance techniques for screening large data sets of diverse structures for toxicologically active compounds, an algorithm was developed that is not dependent upon a predetermined and specified toxicophore or an alignment of conformers to a lead compound. Instead, the approach provides the means to identify and quantify specific global and local stereoelectronic characteristics associated with active compounds through a comparison of energetically-reasonable conformer distributions for specific descriptors. To illustrate the algorithm, the stereoelectronic requirements associated with the binding affinity of 28 steroidal and non-steroidal ligands to the androgen receptor were defined. Common ranges of interatomic distances, atomic charges, and atom polarizabilities of oxygen atoms for conformers of the ligands with the highest affinity for the androgen receptor (most active) did not overlap with those identified for conformers with the lowest binding affinity (least active). Using a set of stereoelectronic parameters that provided a maximal measure of pair-wise similarity among the conformers of the most active ligands, a model was developed to screen compounds for binding affinity. The model was capable of discriminating inactive ligands, as defined by a specified binding affinity threshold. This modeling technique could be a useful initial component in an integrated approach of employing computational and toxicological techniques in hazard identifications for large databases.
For a set of 10 androgen steroids and estradiol (E2), the kinetic feasibility of conformation flexibility of the cyclic moieties was studied under the constraint of maintaining the B/C trans and C/D trans ring fusion of the natural and biologically active enantiomer. To this end, the conformational energy surface was quantified using the semiempirical quantum chemical AM1 model. The computational analysis included the location of Conformational transition states with associated barriers, and intrinsic reaction coordinate (IRC) calculations to characterize the trajectories of the rotations and the relationships of the transition states to neighbouring chair and twist conformations. Conformational transformations were observed only for the A and B rings except for E2, which yielded corresponding transformations for the B and C ring, respectively. Interestingly, the rotation barriers starting from the lowest-energy conformations differed substantially, ranging from below 10 kJ/mol for four compounds to 18–20 kJ/mol for another five compounds. Moreover, chair and twist conformations were found only for steroids with higher saturated rings, while semichairs and semitwists occurred for steroids with aromatic or partly unsaturated rings, and B-ring transformations lead to kinetically unstable conformations with very flat energy minima. Although the rotation barriers for most of the transitions are clearly above the thermal energy (kT) at room temperature when evaluated relative to the lowest-energy conformations, the associated energy demands are well below the gain in energy from ligand-receptor binding. The results suggest that conformer interconversion are feasible from both a thermodynamic and kinetic perspective, and support previous investigations in which conformer distributions rather than lowest energy confor-mations were considered when assessing hormone receptor topography and the biological activity of ligands.
Recently we described the Common REactivity PAttern (COREPA) technique to screen data sets of diverse structures for their ability to serve as ligands for steroid hormone receptors [1]. The approach identifies and quantifies similar global and local stereoelectronic characteristics associated with active ligands through a comparison of energetically-reasonable conformer distributions for selected descriptors. For each stereoelectronic descriptor selected, discrete conformer distributions from a training set of ligands are evaluated and parameter ranges common for conformers from all the chemicals in the training set are identified. The use of discrete partitions of parameter ranges to define common reactivity patterns can, however, influence the outcome of the algorithm. To address this limitation, the original method has been extended by approximating continuous conformer distributions as probability distributions. The COREPA-Continuous (COREPA-C) algorithm assesses the common reactivity pattern of biologically-similar molecules in terms of a product of probability distributions, rather than a collection of common population ranges determined by examination of discrete partitions of a distribution. To illustrate the algorithm, common reactivity patterns based on interatomic distance and charge on heteroatoms were developed and evaluated using a set of 28 androgen receptor ligands. Notable attributes of the COREPA-C algorithm include flexibility in establishing stereoelectronic descriptor criteria for identifying active and nonactive compounds and the ability to quantify three-dimensional chemical similarity without the need to predetermine a toxicophore or align compounds(s) to a lead ligand.
The bioaccumulation potential of chemicals is used to indicate when chemicals are likely to contaminate fish, birds and other wildlife, and humans. Together with knowledge of the persistence of chemicals, the bioaccumulation potential is useful in setting priorities for hazard identification as well as environmental monitoring. Because the measurement of the bioaccumulation potential is costly, developing reliable estimates of this important indicator directly from chemical structure has long been a goal of Quantitative Structure Activity Relationship (QSAR) practitioners. Many previous models for predicting bioconcentration factors (BCF) for organic chemicals have been based on linear and bilinear relationships between log(BCF) and octanol-water partition coefficient (log(Kow)) , some of which also included other structural parameters such as structural correction factors or molecular connectivity indices, Fujita's characters, etc. Most of these BCF models have been derived for predicting passive diffusion of chemicals with log. octanol-water partition coefficients log(Kow) <7. Most previous models showed large discrepancy for large number of chemicals (predominantly highly lipophilic) found in humans and fish. The effect of steric molecular attributes on predicting BCF was studied using 694 chemicals with available experimental BCF and Kow values. It was found that maximum cross sectional diameters and conformational flexibility of chemicals affect significantly bioconcentration and could be used to explain identification of certain highly hydrophobic chemicals in humans and fish.
Recent studies indicate that the potency and agonist or antagonist activity of steroid hormone ligands are dependent, in part, on ligand–receptor binding affinity as well as the conformation of the ligand–receptor complex. The binding of ligands to hormone receptors is thought to involve interactions by which shapes of both the receptor and ligand are modified in the formation of the ligand–receptor complex. As a consequence, it is essential to explore the significance of ligand flexibility in the development of screening-level structure–activity relationships. In this review, examples are provided of techniques used to generate and screen ligand conformers in the development of quantitative structure–activity relationships and active analogue search algorithms. The biological endpoint modeled was binding affinity of natural ligands and xenobiotics to the aryl hydrocarbon, estrogen, and androgen receptors. These approaches may be useful in future studies to evaluate relationships between ligand structure, receptor binding affinity, and, ultimately, transactivational events associated with receptor interactions with DNA response elements and associated proteins. An improved understanding of ligand–receptor interactions in the context of well-defined effector systems will enhance the development of credible predictive models that can be used to screen large sets of chemicals for potential agonist or antagonistic activity.
A quantitative structure-activity relationship (QSAR) has been derived for the mutagenic activity of 117 aromatic and heteroaromatic nitro compounds acting on Salmonella typhimurium TA100. Relative mutagenic activity is bilinearly dependent on hydrophobicity, with an optimal log P of 5.44, and is linearly dependent on the energy of the lowest unoccupied molecular orbital of the nitro compound. The dependence of mutagenic activity on hydrophobicity and electronic effects is very similar for TA98 and TA100. Mutagenic activity in TA100 does not depend on the size of the aromatic ring system, as its does in TA98. The effect of the choice of assay organism, TA98 versus TA100, on nitroarene QSAR is seen to be similar to the effect previously found for aminoarenes. Lateral verification of QSARs is presented as a tool for establishing the significance of a new QSAR.
Quantitative structure-activity relationships (QSAR) have been derived for the mutagenic activity of 88 aromatic and heteroaromatic amines acting on Salmonella typhimurium TA98 + S9 and 67 amines acting on TA100 + S9. Mutagenic activity is linearly dependent on hydrophobicity, the energy of the highest occupied molecular orbital, and the energy of the lowest unoccupied molecular orbital of the amine. The dependence of mutagenic activity on hydrophobicity and electronic effects is nearly identical for TA98 and TA100. Mutagenic activity in TA98 is also found to depend on the size of the aromatic ring system. Different QSARs are derived for the mutagenic activity of hydrophilic amines (log P < 1) acting on either TA98 or TA100. The mechanism of amine activation and reaction with DNA is considered in light of these findings.
A new algorithm is presented to analyze the structural features relevant to the biological activity of a set of molecules. The program called MULTICASE can, as its predecessor CASE (Computed Automated Structure Evaluator), automatically identify molecular sub-structures that have a high probability of being relevant or responsible for the observed biological activity of a learning set comprised of a mix of active and inactive molecules of diverse composition. New, untested molecules can then be submitted to the program, and an expert prediction of the potential activity of the new molecule is obtained. MULTICASE differs from CASE in a great many ways, but the major algorithmic difference is the use of Hierarchy in the selection of descriptors, leading to the concept of Biophores and Modulators.
Atom/fragment contribution values, used to estimate the log octanol–water partition coefficient (log P) of organic compounds, have been determined for 130 simple chemical substructures by a multiple linear regression of 1120 compounds with measured log P values. An additional 1231 compounds were used to determine 235 “correction factors” for various substructure orientations. The log P of a compound is estimated by simply summing all atom/fragment contribution values and correction factors occurring in a chemical structure. For the 2351 compound training set the correlation coefficient (r2) for the estimated vs measured log P values is 0.98 with a standard deviation (SD) of 0.22 and an absolute mean error (ME) of 0.16 log units. This atom/fragment contribution (AFC) method was then tested on a separate validation set of 6055 measured tog P values that were not used to derive the methodology and yielded an r2 of 0.943, an SD of 0.408, and an ME of 0.31. The method is able to predict tog P within ±0.8 log units for over 96% of the experimental dataset of 8406 compounds. Because of the simple atom/fragment methodology, “missing fragments” (a problem encountered in other methods) do not occur in the AFC method. Statistically, it is superior to other comprehensive estimation methods.
Recently, the COmmon REactivity PAttern (COREPA) approach was developed as a probabilistic classification method which was formalized specifically to advance mechanistic QSAR development by addressing the impact of molecular flexibility on stereoelectronic properties of chemicals. In the initial version of COREPA, the probability distributions for only one stereoelectronic parameter at a time were analyzed for the series of chemicals under analysis. To go beyond considering probability distributions of one parameter at a time requires the capability of analyzing a suite of parameters simultaneously for each chemical. This work creates that capability for a multi-dimensional formulation of the COREPA which is expected to enhance the reliability of the method to discriminate complex patterns. Using probability distance measures such as Kullback-Leibler divergence and Hellinger distance, the set of parameters are defined that best discriminate activity. The COREPA-M system automatically identifies the parameters that best discriminates chemicals in groups defined by comparable reactivity endpoints. A detailed Bayesian decision tree is then used for classifying untested chemicals with measures of “goodness of fit” criteria. COREPA-M is illustrated using the example of modelling binding affinity of chemicals at the aryl hydrocarbon receptor.
A survey has been conducted of 222 chemicals evaluated for carcinogenicity in mice and rats by the United States NCI/NTP. The structure of each chemical has been assessed for potential electrophilic (DNA-reactive) sites, its mutagenicity to Salmonella recorded, and the level of its carcinogenicity to rodents tabulated. Correlations among these 3 parameters were then sought.A strong association exists among chemical structure (S/A), mutagenicity to Salmonella (Salm.) and the extent and sites of rodent tumorigenicity among the 222 compounds. Thus, a ∼ 90% correlation exists between S/A and Salm. across the 115 carcinogens, the 24 equivocal carcinogens and the 83 non-carcinogens. This indicates the Salmonella assay to be a sensitive method of detecting intrinsic genotoxicity in a chemical. Concordance between S/A and Salm. have therefore been employed as an index of genotoxicity, and use of this index reveals two groups of carcinogens within the database, genotoxic and putatively non-genotoxic. These two broad groups are characterized by different overall carcinogenicity profiles. Thus, 16 tissues were subject to carcinogenesis only by genotoxins, chief among which were the stomach, Zymbal's glands, lung, subcutaneous tissue and circulatory system. Conclusions of carcinogenicity in these 16 tissues comprised 31% of the individual chemical/tissue reports of carcinogenicity. In contrast, both genotoxins and non-genotoxins were active in the remaining 13 tissues, chief among which was the mouse liver which accounted for 24% of all chemical/tissue reports of carcinogenicity. Further, the group of 70 carcinogens reported to be active in both species and/or in 2 or more tissues contained a higher proportion of Salmonella mutagens (70%) than observed for the group of 45 single-species/single-tissue carcinogens (39%).30% of the 83 non-carcinogens were mutagenic to Salmonella. This confirms earlier observations that a significant proportion of in vitro genotoxins are non-carcinogenic, probably due to their non-absorption or preferential detoxification in vivo. Also, only 30% of the mouse liver-specific carcinogens were mutagenic to Salmonella. This is consistent with tumors being induced in this tissue (and to a lesser extent in other tissues of the mouse and rat) by mechanisms not dependent upon direct interaction of the test chemical with DNA.Detection of 103 of the 115 carcinogens could be achieved by use of only male rats and female mice. 11 of the 12 carcinogens that would be missed using this binary carcinogenicity bioassay protocol were carcinogenic in only a single tissue of a single sex of a single species, 4 being specific to the mouse liver. In contrast, use of only rats would fail to detect 34 mouse-specific carcinogens, 17 of which were active at sites other than the liver, 7 at multiple sites.It is concluded that screening chemicals for genotoxicity using structural analysis and a minimum number of genotoxicity assays, and use of a reduced cancer bioassay protocol, would enable the detection of trans-species/multiple-site rodent carcinogens. The detection of tissue/sex/species-specific carcinogens can only be achieved by conducting life-time carcinogenicity bioassays according to the present NTP protocol. The transition over the past decade from selecting candidate chemicals for bioassay based on consideration of chemical structure, to selection based on relative environmental importance, is sufficient to explain the apparent decreasing sensitivity of the Salmonella assay to rodent carcinogens.
An analysis is presented in which are evaluated correlations among chemical structure, mutagenicity to Salmonella, and carcinogenicity to rats and mice among 301 chemicals tested by the U.S. NTP. Overall, there was a high correlation between structural alerts to DNA reactivity and mutagenicity, but the correlation of either property with carcinogenicity was low. If rodent carcinogenicity is regarded as a singular property of chemicals, then neither structural alerts nor mutagenicity to Salmonella are effective in its prediction. Given this, the database was fragmented and new correlations sought between the derived sub-groups. First, the 301 chemicals were segregated into six broad chemical groupings. Second, the rodent cancer data were partially segregated by target tissue.
In both industry and government, structure-activity relationships (SAR) are capable of playing an important decision-support role in estimating the potential mutagenicity or carcinogenicity of chemicals for which bioassay test results are unavailable. Traditional SAR modeling approaches, however, are usually restricted to the consideration of structurally similar chemical congeners. The highly structurally diverse nature of current carcinogenicity and mutagenicity data bases has motivated development of more general SAR approaches, potentially applicable to the treatment of diverse, non-congeneric mutagenicity and carcinogenicity data bases. Three specific approaches are considered in some detail — Ashby's structural alert moedl, classified as a “rule-based” SAR approach, and the computerized CASE fragment-base method and TOPKAT linear discriminant equation method, both classified as “correlative” SAR approaches. Relative strengths and limitations, and a number of common features and important distinctions betwee these 3 methods are discussed. Rule-base methods are highly flexible and able to incorporate many different types of relevant information, yet are biased towards current knowledge, viewpoints, and mechanistic assumptions, that may or may not hold true. Correlative SAR methods are less biased and offer the promise of “discorvering” potentially new SAR associations that could lend fresh insight into the basis for a structure-activity association. However, problems associated with their application to non-congeneric data bases relate to: modeling multiple or overlapping mechanisms of action with a single relationship; defining the range of applicability of models in complex multi-dimensional structure-activity space; assigning confidence levels to predictions in the absence of knowledge concerning mechanisms of activity; and determining the potential mechanistic significance of diervse model parameters. It is argued that many of these concerns can be partially alleviated by careful application of statistical procedures, scrutiny of model results, and estabilishment of reasoned limits to the range of model applicability. The most significant confidence-building measure, however, will be a rationalization of the correlative SAR model and model parameters in terms of principles of chemical reactivity and postulated molecular mechanism(s) for the biological activity. Hence, it is recommended that models and model descriptors be designed to facilitate mechanistic interpretation and hypothesis generation. Finally, problems in comparing the relative predictive capabilities of different SAR approaches are discussed, and strategies for SAR investigation involving integration of existing techniques are suggested.
As an introduction, a brief survey on QSARs (quantitative structure-activity relationships) related to molecular mutagenicity is given. The importance of hydrophobicity and frontier orbital parameters as physical determinants is emphasized. As a detailed example, QSAR models for the Ames Salmonella typhimurium TA 100 mutagenicity of halogenated hydroxyfuranones including MX, one of the strongest bacterial mutagens ever tested, are discussed. The results indicate that the electron-accepting ability of MX compounds, together with steric properties, predict their mutagenic activity almost completely. Furthermore, it appears that the mutagenicity of MX compounds does not depend on hydrophobicity, contrasting sharply with most other bacterial mutagens. Finally, some possible connections between the frontier electron theory and general electrophilic theory of genotoxic activity are discussed.
Since the inception of Section 5 (Premanufacturing/Premarketing Notification, PMN) of the Toxic Substances Control Act (TSCA), structure-activity relationship (SAR) analysis has been effectively used by U.S. Environmental Protection Agency's (EPA) Structure Activity Team (SAT) in the assessment of potential carcinogenic hazard of new chemicals for which test data are not available. To capture, systematize and codify the Agency's predictive expertise in order to make it more widely available to assessors outside the TSCA program, a cooperative project was initiated to develop a knowledge rule-based expert system to mimic the thinking and reasoning of the SAT. In this communication, we describe the overall structure of this expert system, discuss the scientific bases and principles of SAR analysis of chemical carcinogens used in the development of SAR knowledge rules, and delineate the major factors/rules useful for assessing the carcinogenic potential of fibers, polymers, metals/metalloids and several major classes of organic chemicals. An integrative approach using available short-term predictive tests and non-cancer toxicological data to supplement SAR analysis has also been described.
The current status of the use of Quantitative Structure–Activity Relationships (QSARs) in toxicology, both environmental (i.e. ecotoxicology) and human health effects, are described with a particular emphasis on the science since 1995. Discussions of ecotoxicity QSARs focus on recent information that relates to separation of effects based on modes of toxic action. Particular attention is given to the response-surface approach to modeling toxic potency of baseline and non-specific soft electrophiles (i.e. the majority of industrial organic chemicals) and the development of rules-based expert systems to aid in the selection of the most appropriate QSAR. In addition the more recent application self-organizing dynamical algorithms such as artificial neural networks to ecotoxicity data is described. Recent QSAR modeling of estrogenicity, an example of receptor-mediated effects, are described with particular emphasis on 2D structural alerts as screening tools and QSARs developed with data for the recombinant yeast assay. In addition the current status of modeling human health effects include mutagenesis and carcinogenesis, developmental toxicity, skin sensitization, and skin and eye irritation is described.
A new approach for coverage of the conformational space by a limited number of conformers is proposed. Instead of using a systematic search whose time complexity increases exponentially with degrees of freedom, a genetic algorithm (GA) is employed to minimize 3D similarity among the conformers generated. This makes the problem computationally feasible even for large, flexible molecules. The 3D similarity of a pair of conformers is assumed to be reciprocal to the root-mean-square (rms) distance between identical atomic sites in an alignment providing its minimum. Thus, in contrast to traditional GA, the fitness of a conformer is not quantified individually but only in conjunction with the population it belongs to. The approach handles the following stereochemical and conformational degrees of freedom: rotation around acyclic single and double bonds, inversion of stereocenters, flip of free corners in saturated rings, and reflection of pyramids on the junction of two or three saturated rings. The latter two were particularly introduced to encompass the structural diversity of polycyclic structures. However, they generally affect valence angles and can be restricted up to a certain level of severity of such changes. Stereochemical modifications are totally/selectively disabled when the stereochemistry is exactly/partially specified on input. Three quality criteria, namely robustness, reproducibility, and coverage of the conformational space, are used to assess the performance of various GA experimental settings employed on four molecules with different numbers of conformational degrees of freedom. It was found that with the increase of the ratio between the number of parents and children, the reproducibility of GA runs increases whereas their robustness and coverage decrease. Force field optimization of conformers for each generation was found to improve significantly the reproducibility of results, at the cost of worse conformational coverage.
1,3-Butadiene (BD) is used in the manufacture of styrene-BD and polybutadiene rubber. Differences seen in chronic toxicity studies in the susceptibility of B6C3F1 mice and Sprague-Dawley rats to BD raise the question of how to use the rodent toxicology data to predict the health risk of BD in humans. The purpose of this study was to determine if there are species differences in the metabolism of BD to urinary metabolites that might help to explain the differences in the toxicity of BD. The major urinary metabolites of BD in F344/N rats, Sprague-Dawley rats, B6C3F1 mice, Syrian hamsters, and cynomolgus monkeys were identified as 1,2-dihydroxy-4-(N-acetylcysteinyl)-butane (I) and the N-acetylcysteine conjugate of BD monoxide [1-hydroxy-2-(N-acetylcysteinyl)-3-butene] (II). These mercapturic acids are formed by addition of glutathione at either the double bond (I) or the epoxide (II) respectively. When exposed to approximately 8000 p.p.m. of BD for 2 h, the mice excreted 3-4 times as much metabolite II as I, the hamster and the rats produced approximately 1.5 times as much metabolite II as I, while the monkeys produced primarily metabolite I. The ratio of formation of metabolite I to the total formation of the two mercapturic acids correlated well with the known hepatic epoxide hydrolase activity in the different species. These data suggest that (i) the availability of the monoepoxide for conjugation with glutathione is highest in the mouse, followed by the hamster and the rat, and is lowest in the monkey; and (ii) the epoxide availability is inversely related to the hepatic activity of epoxide hydrolase, the enzyme that removes the epoxide by hydrolysis. The ratio of the two mercapturic acids in human urine following BD exposure may indicate the pathways of BD metabolism in humans and may aid in the determination of the most appropriate animal model for BD toxicity.
1. Beagle dogs dosed orally with 14C-tazadolene succinate excreted much of the dose in the urine (mean 63.1% in 5 days with most excreted in the first 24 h). A lesser proportion of the dose was excreted in the faeces (mean 20.7%) and again most of this was voided in the first 24 h. 2. Four metabolites were identified and quantified in the urine, namely 3-hydroxy-(M1), 4-hydroxy- (M2a), and 3-methoxy-4-hydroxy-tazadolene (M2b) and N-[2-(phenylmethylene)cyclohexyl]-beta-alanine (M3). 3. In the 24 h urine, M2a and b glucuronides accounted for 17.7% dose, unconjugated M2a and b for 11.3%, and M3 for 18.3%. Insufficient M1 was present to be quantified. The same metabolites were seen in the 24 h faeces, but at lower concn. Thus M2a and b glucuronides, M2a and b, and M3 were 3.2%, 4.9% and 3.5% dose respectively. 4. All three phenols were present in plasma as their glucuronides as well as the beta-alanine derivative. They all had the same tmax of 2 h and t1/2 lambda 1 of the order of 1 h.
An analysis is presented in which are evaluated correlations among chemical structure, mutagenicity to Salmonella, and carcinogenicity to rats and mice among 301 chemicals tested by the U.S. NTP. Overall, there was a high correlation between structural alerts to DNA reactivity and mutagenicity, but the correlation of either property with carcinogenicity was low. If rodent carcinogenicity is regarded as a singular property of chemicals, then neither structural alerts nor mutagenicity to Salmonella are effective in its prediction. Given this, the database was fragmented and new correlations sought between the derived sub-groups. First, the 301 chemicals were segregated into six broad chemical groupings. Second, the rodent cancer data were partially segregated by target tissue. Using the previously assigned structural alerts to DNA reactivity (electrophilicity), the chemicals were split into 154 alerting chemicals and 147 non-alerting chemicals. The alerting chemicals were split into three chemical groups; aromatic amino/nitro-types, alkylating agents and miscellaneous structurally-alerting groups. The non-alerting chemicals were subjectively split into three broad categories; non-alerting, non-alerting containing a non-reactive halogen group, and non-alerting chemical with minor concerns about a possible structural alert. The tumor data for all 301 chemicals are re-presented according to these six chemical groupings. The most significant findings to emerge from comparisons among these six groups of chemicals were as follows: (a) Most of the rodent carcinogens, including most of the 2-species and/or multiple site carcinogens, were among the structurally alerting chemicals. (b) Most of the structurally alerting chemicals were mutagenic; 84% of the carcinogens and 66% of the non-carcinogens. 100% of the 33 aromatic amino/nitro-type 2-species carcinogens were mutagenic. Thus, for structurally alerting chemicals, the Salmonella assay showed high sensitivity and low specificity (0.84 and 0.33, respectively). (c) Among the 147 non-alerting chemicals less than 5% were mutagenic, whether they were carcinogens or non-carcinogens (sensitivity 0.04).
Quantitative structure-activity relationships have been derived for the mutagenic activity of 47 nitroaromatic compounds acting on Salmonella typhimurium (TA100) and 66 acting on TA98. The mutagenicity is linearly dependent on the energy of the lowest occupied molecular orbital and bilinearly dependent on the hydrophobicity (octanol/water log P) of the mutagens. The mechanism of action is considered in the light of these findings.
The mutagenicities of five heterocyclic 3,3-dimethyltriazenes have been evaluated in the Ames test. The octanol-water partition coefficients (P) for these triazenes have been measured, and their electron distributions and molecular orbital energies were calculated using the MNDO semiempirical molecular orbital method. Molecular structures of three triazenes have been determined using X-ray crystallography. The mutagenicities of these five triazenes, which range from nearly inactive to very highly mutagenic, are well predicted by quantitative structure-activity relationships that had been derived previously for the mutagenicity of aryltriazenes. The form of these equations indicates that more hydrophobic and more electron-rich triazenes are more active in the Ames test. This supports the hypothesis that the ease of initial triazene activation by cytochrome P-450 governs the mutagenicity of these compounds.
Previous studies have demonstrated the formation of three glutathione conjugates during the hepatic and pulmonary microsomal metabolism of naphthalene in the presence of reduced glutathione and cytosolic enzymes containing the glutathione transferases. These glutathione conjugates now have been identified by negative ion fast atom bombardment mass spectrometry, by proton NMR spectroscopy, and by chemical synthesis from the (1S,2R)- and (1R, 2S)-naphthalene 1,2-oxide enantiomers as isomeric hydroxyglutathionyldihydronaphthalene derivatives. All three glutathione adducts yielded prominent mass spectral ions at m/z 450 (M-H)-, 432 (dehydration product), and 306 (glutathionyl moiety) which were consistent with the monoglutathionyl derivatives of hydroxydihydronaphthalene. Signals in the proton NMR spectra at 3.60 and 4.95 ppm (adduct 1) and 3.60 and 4.95 ppm (adduct 2) indicated that these conjugates were diastereomers of 1-hydroxy-2-glutathionyl-1,2-dihydronaphthalene. Corresponding signals for H1 and H2 at 4.22 and 4.45 ppm for adduct 3 showed that this isomer was generated from attack of glutathione at the 1 position of the naphthalene 1,2-oxide. Incubation of synthetic (1S, 2R)-naphthalene 1,2-oxide with glutathione in the presence of glutathione transferases resulted in the formation of adducts 1 and 3 in approximately equal proportions; under identical conditions, glutathione conjugate 2 was formed from (1R, 2S)-naphthalene 1,2-oxide. Incubation of naphthalene, glutathione, and glutathione transferases with pulmonary, hepatic, or renal microsomal preparations from mouse, rat, and hamster resulted in the formation of all three glutathione conjugates. Substantial differences in the rates of formation of the individual conjugates were observed.(ABSTRACT TRUNCATED AT 250 WORDS)
All three dinitrobenzene (DNB) isomers cause methemoglobinemia, but only 1,3-DNB produces testicular toxicity in rats. In order to determine whether major differences exist in the routes of DNB metabolism, male Fischer-344 rats were given an oral dose (0.15 mmol/kg) of 14C-labeled 1,2-, 1,3-, or 1,4-DNB, and excreta were collected over 48 hr. Elimination of radiolabel was rapid; 85%, 60%, and 75% of the 1,2-, 1,3-, and 1,4-DNB dose was recovered in 24 hr, respectively. Urine was the primary route of excretion, accounting for 82% of the total dose of 1,2-DNB and 75% of the dose of 1,4-DNB after 48 hr. Radiolabel from 1,3-DNB was excreted to a slightly lesser extent in the urine (63% of the dose). A greater portion of radiolabel was excreted in the feces than with the other isomers (18% of total dose, compared to 8% and 9% with 1,2-DNB and 1,4-DNB, respectively). The major urinary metabolites of 1,2-DNB were S-(2-nitrophenyl)-N-acetylcysteine (42% of the dose), 2-nitroaniline-N-glucuronide (4%), 4-amino-3-nitrophenylsulfate (17%), 2-amino-3-nitrophenylsulfate (1.5%), and 2-(N-hydroxylamino)nitrobenzene (1-2%). The major urinary metabolites of 1,3-DNB were 3-aminoacetanilide (22%), 4-acetamidophenylsulfate (6%), 1,3-diacetamidobenzene (7%), and 3-nitroaniline-N-glucuronide (4%). The major metabolites of 1,4-DNB were 2-amino-5-nitrophenylsulfate (35%), S-(4-nitrophenyl)-N-acetylcysteine (13%), and 1,4-diacetamidobenzene (7%). These results suggest that the DNB isomers are primarily metabolized by nitro group reduction and conjugation with glutathione. The testicular toxicant 1,3-DNB was apparently metabolized exclusively by reduction.
The use of structure-activity relationships (SAR) has proven practical for the development of equations which can be used to estimate the above-listed endpoints for a large variety of chemicals. The SAR models predict these endpoints correctly in 85 to 97% of the cases and often surpass in their predictive ability the results obtainable from the equivalent biological assays.
These SAR models are being used at several levels: drug, or more generally, chemical discovery; prioritization for testing; regulatory affairs; investigation of detoxification mechanisms; and risk estimation.
In the new compound (discovery) use, potential toxic effects of a set of related compounds are investigated before synthesis to select those chemicals with the lesser probabilities of producing toxic effects for further investigation, at considerable savings in research expenditure since fewer compounds need to be synthesized, and the avoidance of blind alleys. Prioritization for testing is used in numerous instances, such as selecting those chemicals in an environment which are most likely to have toxic effects for priority attention. SAR models are used by regulatory agencies to determine the possible toxic effects of chemicals for which data insufficient to render decisions have been submitted, and to gain insight into possible toxicity problems. SAR models are also used to investigate possible metabolites, and toxicity mechanisms due to the ability of making computer-based structural modifications and observing the effects on the modelled toxic endpoints. Risk analysis is a natural outgrowth of several of the above applications, and is particularly useful for SAR models of carcinogenicity.
SAR models as alternatives to animal bioassays should be used in the context of other information for the chemicals of concern. Just as bioassays and in vitro methods have their limitations, so do SAR models. These include the sometimes limited data base on which to base an SAR model, the temptation to extrapolate beyond the confines of the model, and the noise inherent in the bioassays on which the models are based. Within these constraints SAR models have a considerable potential in reducing the number of animals used in toxicity testing.
A survey has been conducted of 222 chemicals evaluated for carcinogenicity in mice and rats by the United States NCI/NTP. The structure of each chemical has been assessed for potential electrophilic (DNA-reactive) sites, its mutagenicity to Salmonella recorded, and the level of its carcinogenicity to rodents tabulated. Correlations among these 3 parameters were then sought. A strong association exists among chemical structure (S/A), mutagenicity to Salmonella (Salm.) and the extent and sites of rodent tumorigenicity among the 222 compounds. Thus, a approximately 90% correlation exists between S/A and Salm. across the 115 carcinogens, the 24 equivocal carcinogens and the 83 non-carcinogens. This indicates the Salmonella assay to be a sensitive method of detecting intrinsic genotoxicity in a chemical. Concordance between S/A and Salm. have therefore been employed as an index of genotoxicity, and use of this index reveals two groups of carcinogens within the database, genotoxic and putatively non-genotoxic. These two broad groups are characterized by different overall carcinogenicity profiles. Thus, 16 tissues were subject to carcinogenesis only by genotoxins, chief among which were the stomach, Zymbal's glands, lung, subcutaneous tissue and circulatory system. Conclusions of carcinogenicity in these 16 tissues comprised 31% of the individual chemical/tissue reports of carcinogenicity. In contrast, both genotoxins and non-genotoxins were active in the remaining 13 tissues, chief among which was the mouse liver which accounted for 24% of all chemical/tissue reports of carcinogenicity. Further, the group of 70 carcinogens reported to be active in both species and/or in 2 or more tissues contained a higher proportion of Salmonella mutagens (70%) than observed for the group of 45 single-species/single-tissue carcinogens (39%). 30% of the 83 non-carcinogens were mutagenic to Salmonella. This confirms earlier observations that a significant proportion of in vitro genotoxins are non-carcinogenic, probably due to their non-absorption or preferential detoxification in vivo. Also, only 30% of the mouse liver-specific carcinogens were mutagenic to Salmonella. This is consistent with tumors being induced in this tissue (and to a lesser extent in other tissues of the mouse and rat) by mechanisms not dependent upon direct interaction of the test chemical with DNA. Detection of 103 of the 115 carcinogens could be achieved by use of only male rats and female mice.(ABSTRACT TRUNCATED AT 400 WORDS)
The methods for detecting carcinogens and mutagens with the Salmonella mutagenicity test were described previously (Ames et al., 1975b). The present paper is a revision of the methods. Two new tester strains, a frameshift strain (TA97) and a strain carrying an ochre mutation on a multicopy plasmid (TA102), are added to the standard tester set. TA97 replaces TA1537. TA1535 and TA1538 are removed from the recommended set but can be retained at the option of the investigator. TA98 and TA100 are retained. We discuss other special purpose strains and present some minor changes in procedure, principally in the growth, storage, and preservation of the tester strains. Two substitutions are made in diagnostic mutagens to eliminate MNNG and 9-aminoacridine. Some test modifications are discussed.
The molecular dimensions and electronic structures of 100 chemicals of structural diversity have been determined from molecular orbital calculations and molecular mechanics. From these parameters of molecular structure, those chemicals that are likely substrates of cytochromes P4501 and P4502E have been identified by the computer-optimized molecular parametric analysis of chemical toxicity (COMPACT) programme, and their potential toxicity, mutagenicity and carcinogenicity evaluated. The degree of correlation between COMPACT prediction of toxicity and rodent two species life-span carcinogenicity data is estimated to be 92%, and between COMPACT and Salmonella mutagenicity (Ames test) data is 64%. Anomalous rodent carcinogens are rationalized on the basis of biochemical mechanisms of metabolism, genotoxicity and carcinogenicity. Correlation of the Ames test data with rodent carcinogenicity data was 64%, but correlation of COMPACT plus Ames data versus rodent carcinogenicity data provided the highest correlation of 94%.
Gold and her colleagues have tabulated the results of rodent bioassays on 522 chemicals and have analysed the data. The present study complements those analyses by providing a perspective from the viewpoint of the chemical structure of the carcinogens. The chemical structure of each of the carcinogens is displayed and the Gold database is represented with the test agents as the primary variable. The carcinogens are gathered into six chemical classes and each chemical is assessed for structural alerts to DNA reactivity. The database is then analysed using an integration of the following parameters: bioassay in rat, mouse or both; structural alert status; chemical class; sites and multiplicity of carcinogenesis, and trans-species carcinogenicity. A series of Figures is presented that enables rapid acquaintance with what represents the core database of rodent carcinogenicity. The several analyses presented combine in endorsing the reality of two broad classes of rodent carcinogen--presumed DNA-reactive and others (putative genotoxic and non-genotoxic carcinogens, but semantics have been largely avoided). Vainio and his colleagues have tabulated 55 situations in which humans have succumbed to chemically induced cancer, and have listed the tissues affected. This database of human carcinogens has been analysed in the present study as done for the rodent carcinogen database, and comparisons made between the two. The predominance of putative genotoxic carcinogens in the human database was confirmed, as was the reality of putative non-genotoxic carcinogenicity in humans. It is concluded that putative genotoxic rodent carcinogenesis can be correlated both with chemical structure and the extent and nature of the induced effect, and that it is of clear relevance to humans. In contrast, it is concluded that putative non-genotoxic rodent carcinogenesis is more closely related to the test species than to the test chemical, and that it is essentially unpredictable in the absence of mechanistic models. In the absence of such models nongenotoxic carcinogenic effects should be extrapolated to humans with caution. Progress in the accurate prediction and extrapolation of rodent carcinogenicity will be helped by a common, if only temporary, enabling acceptance that not all carcinogens are intrinsically genotoxic.
The urinary metabolite profile of hexachlorobenzene (HCB) and pentachlorobenzene (PCBz) in the rat is compared after dietary exposure for 13 weeks. Both HCB and PCBz are oxidized to pentachlorophenol (PCP) and tetrachlorohydroquinone (TCHQ), which were the only two mutual metabolites formed. Additional urinary metabolites of HCB are N-acetyl-S(pentachlorophenyl)cysteine (PCTP-NAC), which appeared to be quantitatively the most important product, and mercaptotetrachlorothioanisole (MTCTA), which was excreted as a glucuronide. PCBz is more extensively metabolized to the major metabolites 2,3,4,5-tetrachlorophenol (TCP), mercaptotetrachlorophenol (MTCP) and the glucuronide of pentachlorothiophenol (PCTP), and the minor metabolites methylthiotetrachlorophenol (MeTTCP), hydroxytetrachlorophenyl sulphoxide (HTCPS), and bis(methylthio)-trichlorophenol (bis-MeTTriCP). The biotransformation of HCB and PCBz was modulated by selective inhibition of cytochrome P450IIIA in rats which received combined treatment of HCB or PCBz with triacetyloleandomycin (TAO). Rats receiving this diet had a strongly diminished excretion of both PCP and TCHQ, as compared to rats fed HCB or PCBz alone, indicating the involvement of P450IIIA in the oxidation of both compounds. However, the excretion of 2,3,4,5-TCP was not diminished by co-treatment of rats with PCBz and TAO, indicating that: (i) the oxidation of PCBz to PCP and 2,3,4,5-TCP does not proceed via a common intermediate; and (ii) oxidation of PCBz to 2,3,4,5-TCP is not mediated by P450IIIA. Co-treatment of rats with PCBz and TAO had a differential effect on the excretion of sulphur-containing metabolites, resulting in a decrease in the excretion of PCTP glucuronide, whereas no change was observed in the excretion of MTCP, as compared to rats receiving PCBz alone. The observed differences in HCB and PCBz metabolites clearly deserve further in vitro studies to elucidate their origin.
Benzene metabolism was investigated using two purified rat hepatic MFO systems containing either cytochrome P450 2B1 or cytochrome P450 2E1. Studies performed over a wide substrate concentration range indicate that cytochrome P450 2B1 represents a relatively low-affinity form of cytochrome P450 with respect to benzene metabolism while cytochrome P450 2E1 is substantially more efficient at low benzene concentrations (apparent Km value 0.17 mM). Cytochrome b5 stimulated benzene metabolism by both cytochromes P450 2B1 and P450 2E1. With cytochrome P450 2E1 the stimulation of benzene metabolism by cytochrome b5 was very pronounced (up to 6-fold) at low concentrations of benzene and was most effective (up to 15-fold) with respect to formation of hydroquinone. The metabolites observed in these studies were phenol and hydroquinone. Cytochrome P450 2E1 metabolized phenol with an affinity and capacity comparable to those of benzene. Hydroquinone was the major product formed at all substrate concentrations, while some catechol was formed at all substrate concentrations, while some catechol was formed at higher concentrations of phenol. Phenol metabolism was also stimulated by cytochrome b5. The metabolism of benzene by cytochrome P450 2E1 in the presence of the major microsomal epoxide hydrolase, mEHb, yielded phenol, hydroquinone, and benzene dihydrodiol. Interestingly, the addition of mEHb did not lead to a decrease of the toxicologically important metabolite hydroquinone as might be expected from sequestration of the intermediate benzene oxide to the vicinal dihydrodiol pathway but rather led to a marked (more than 4-fold) increase in the formation of hydroquinone, suggesting catalysis by mEHb of a predominant attack at the homoallylic position rather than at a carbon atom which forms the epoxide ring of benzene oxide. The addition of glutathione transferases plus glutathione did not yield GSH conjugates during benzene metabolism. However, metabolism of phenol by cytochrome P450 2E1 in the presence of glutathione yielded a nonenzymatically formed glutathione conjugate derived from hydroquinone or from an oxidative product of hydroquinone.
Previous studies have established that TCDF is rapidly metabolized and excreted in rats and that pretreatment of rats with TCDD increases the rate of hepatic metabolism of this compound. The extrahepatic metabolism of TCDF was investigated to assess which enzyme was involved in the metabolism of this compound. Very little metabolism of TCDF was detected in control microsomes (0.3-3.0 pmol/mg/hr), while TCDF metabolism was increased 40- to 200-fold in TCDD-induced rat liver, kidney, and lung microsomes. Since TCDD induces cytochrome P4501A1 and P4501A2 (CYP1A1 and CYP1A2) in the rat liver but only CYP1A1 in kidney and lung, these results suggest that CYP1A1 metabolizes TCDF. To test this hypothesis, TCDF metabolism was investigated in the presence and absence of selective chemical inhibitors and antibodies to CYP1A1 and 1A2. 1-Ethynylpyrene, a suicide inhibitor of CYP1A1 and antibody to rat CYP1A1, produced a dose-dependent inhibition of TCDF metabolism in TCDD-induced rat liver microsomes. Conversely, 2-ethynylnaphthalene, a suicide inhibitor of CYP1A2 and antibody to rat CYP1A2, had no inhibitory effect on the hepatic microsomal metabolism of TCDF. Together, the results strongly indicate that rat CYP1A1 is the primary enzyme responsible for the metabolism of TCDF. 4-Hydroxy-2,3,7,8-TCDF was also identified as the major TCDF metabolite formed by rat CYP1A1. TCDF was also metabolized by human liver microsomes and recombinant yeast microsomes expressing human CYP1A1 and reductase but not by yeast microsomes expressing human CYP1A2 with or without reductase. A similar HPLC profile of TCDF metabolites was observed with microsomes from human liver and yeast expressing human CYP1A1. However, based on ethoxyresorufin-O-deethylase activity, a marker of CYP1A1, the relative rate of TCDF metabolism is about 100-fold greater in TCDD-induced rat liver microsomes than in yeast microsomes expressing human CYP1A1 and reductase. Thus, although TCDF is metabolized by rat and human CYP1A1, the results indicate that there are marked quantitative differences in metabolism which suggest that TCDF will be more persistent in humans.
The dose dependence of the urinary excretion of acrylonitrile (ACN) metabolites was studied after oral administration of [2,3-14C]ACN to male F-344 rats (0.09 to 28.8 mg/kg) and male B6C3F1 mice (0.09 to 10.0 mg/kg). Urine was the major route of excretion of ACN metabolites (77 to 104% of the dose), with less than 8% of the dose excreted in the feces. Reverse-phase HPLC analysis of urine from treated animals indicated five major components (1 through 5 in order of elution) that accounted for 75 to 100% of the total urinary radioactivity. Component 4 was observed in the urine of ACN-treated mice but was only present in trace amounts in the urine of ACN-treated rats. Components 1, 2, and 3 were present in the urine of animals administered [2,3-14C]cyanoethylene oxide (CEO), indicating that these components were derived from the epoxide metabolite of ACN. The ACN urinary metabolites were isolated by HPLC and identified by chromatographic and mass spectral analysis. Component 5 was N-acetyl-S-(2-cyanoethyl)cysteine and component 4 was S-(2-cyanoethyl)thioacetic acid, both derived from the glutathione (GSH) conjugate of ACN. Component 3 contained N-acetyl-S-(2-hydroxyethyl)cysteine, N-acetyl-S-(carboxymethyl)cysteine, and N-acetyl-S-(1-cyano-2-hydroxyethyl)cysteine. Component 2 was thiodiglycolic acid. These urinary metabolites are derived from catabolism of the GSH conjugates of CEO. The polar component 1 was not identified. These results demonstrate that GSH conjugation is the major disposition pathway of ACN. The excretion of metabolites derived from CEO was an approximately linear function of dose in both species, whereas the excretion of N-acetyl-S-(2-cyanoethyl)cysteine increased nonlinearly with dose. This nonlinearity indicates the presence of a saturable pathway competing with glutathione for ACN, most likely the cytochrome P450-dependent oxidation of ACN. Thiodiglycolic acid was formed 10-fold more in mice than in rats, but this species difference in the oxidative processing of GSH conjugates is probably not of toxicological significance. The ratio of ACN epoxidation to GSH conjugation was 0.50 in rats and 0.67 in mice. This species difference in ACN oxidation could have important toxicological implications, since CEO is believed to mediate the carcinogenic effects of ACN.
ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 100 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.
A hierarchical model consisting of quantitative structure-activity relationships based mainly on chemical reactivity was developed to predict the carcinogenicity of organic chemicals to rodents. The model is comprised of quantitative structure-activity relationships, QSARs based on hypothesized mechanisms of action, metabolism, and partitioning. Predictors included octanol/water partition coefficient, molecular size, atomic partial charge, bond angle strain, atomic acceptor delocalizibility, atomic radical superdelocalizibility, the lowest unoccupied molecular orbital (LUMO) energy of hypothesized intermediate nitrenium ion of primary aromatic amines, difference in charge of ionized and unionized carbon-chlorine bonds, substituent size and pattern on polynuclear aromatic hydrocarbons, the distance between lone electron pairs over a rigid structure, and the presence of functionalities such as nitroso and hydrazine. The model correctly classified 96% of the carcinogens in the training set of 306 chemicals, and 90% of the carcinogens in the test set of 301 chemicals. The test set by chance contained 84% of the positive thio-containing chemicals. A QSAR for these chemicals was developed. This posttest set modified model correctly predicted 94% of the carcinogens in the test set. This model was used to predict the carcinogenicity of the 25 organic chemicals the U.S. National Toxicology Program was testing at the writing of this article.
The machine learning program Progol was applied to the problem of forming the structure-activity relationship (SAR) for a set of compounds tested for carcinogenicity in rodent bioassays by the U.S. National Toxicology Program (NTP). Progol is the first inductive logic programming (ILP) algorithm to use a fully relational method for describing chemical structure in SARs, based on using atoms and their bond connectivities. Progol is well suited to forming SARs for carcinogenicity as it is designed to produce easily understandable rules (structural alerts) for sets of noncongeneric compounds. The Progol SAR method was tested by prediction of a set of compounds that have been widely predicted by other SAR methods (the compounds used in the NTP's first round of carcinogenesis predictions). For these compounds no method (human or machine) was significantly more accurate than Progol. Progol was the most accurate method that did not use data from biological tests on rodents (however, the difference in accuracy is not significant). The Progol predictions were based solely on chemical structure and the results of tests for Salmonella mutagenicity. Using the full NTP database, the prediction accuracy of Progol was estimated to be 63% (+/- 3%) using 5-fold cross validation. A set of structural alerts for carcinogenesis was automatically generated and the chemical rationale for them investigated- these structural alerts are statistically independent of the Salmonella mutagenicity. Carcinogenicity is predicted for the compounds used in the NTP's second round of carcinogenesis predictions. The results for prediction of carcinogenesis, taken together with the previous successful applications of predicting mutagenicity in nitroaromatic compounds, and inhibition of angiogenesis by suramin analogues, show that Progol has a role to play in understanding the SARs of cancer-related compounds.
A series of 15 quinoline congeners were assayed for mutagenicity and cytotoxicity in the Ames test using strain TA100 bacteria. Statistical analysis of the data allowed simultaneous determination of the mutagenicity and cytotoxicity of each quinoline. These data were used to develop three quantitative structure-activity relationships (QSAR). In all three QSAR, the strength of the relationship between hydrophobicity (as measured by log P) and biological activity was similar as h was near 1 in all three cases. For the mutagenicity of these quinolines, both hydrophobic and steric interactions appear to be important. In contrast, the cytotoxicity is mainly affected by increasing hydrophobicity and by the addition of electron withdrawing substituents to the quinoline ring. Comparison to other QSAR from our laboratory and others lends support to these findings. Both simultaneous consideration of different biological activities and the comparison of newly developed QSAR with previous data for the purpose of lateral validation should be encouraged in future QSAR studies.
This article reviews current knowledge of the metabolism of drugs that contain fluorine. The strategic value of fluorine substitution in drug design is discussed in terms of chemical structure and basic concepts in drug metabolism and drug toxicity.