QSAR in Toxicology. 3. Prediction of Chronic Toxicities

SAR and QSAR modeling of a large collection of LD50 rat acute oral toxicity data

The median lethal dose for rodent oral acute toxicity (LD50) is a standard piece of information required to categorize chemicals in terms of the potential hazard posed to human health after acute exposure. The exclusive use of in vivo testing is limited by the time and costs required for performing experiments and by the need to sacrifice a number of animals. (Quantitative) structure-activity relationships [(Q)SAR] proved a valid alternative to reduce and assist in vivo assays for assessing acute toxicological hazard. In the framework of a new international collaborative project, the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods and the U.S. Environmental Protection Agency's National Center for Computational Toxicology compiled a large database of rat acute oral LD50 data, with the aim of supporting the development of new computational models for predicting five regulatory relevant acute toxicity endpoints. In this article, a series of regression and classification computational models were developed by employing different statistical and knowledge-based methodologies. External validation was performed to demonstrate the real-life predictability of models. Integrated modeling was then applied to improve performance of single models. Statistical results confirmed the relevance of developed models in regulatory frameworks, and confirmed the effectiveness of integrated modeling. The best integrated strategies reached RMSEs lower than 0.50 and the best classification models reached balanced accuracies over 0.70 for multi-class and over 0.80 for binary endpoints. Computed predictions will be hosted on the EPA's Chemistry Dashboard and made freely available to the scientific community.

A Mechanism-based QSTR Model for Acute to Chronic Toxicity Extrapolation: A Case Study of Antibiotics on Luminous Bacteria

The determination of the chronic toxicity is time-consumed and costly, so it's of great interest to predict the chronic toxicity based on acute data. Current methods include the acute to chronic ratios (ACRs) and the QSTR models, both of which have some usage limitations. In this paper, the acute and chronic mixture toxicity of three types of antibiotics, namely sulfonamides, sulfonamide potentiators and tetracyclines, were determined by a bioluminescence inhibition test. A novel QSTR model was developed for predicting the chronic mixture toxicity using the acute data and docking-based descriptors. This model revealed a complex relationship between the acute and chronic toxicity, i.e. a linear correlation between the acute and chronic lg(-lgEC50)s, rather than the simple EC50s or -lgEC50s. In particular, the interaction energies (Ebind) of the chemicals with luciferase and LitR in the bacterial quorum sensing systems were introduced to represent their acute and chronic actions, respectively, regardless of their defined toxic mechanisms. Therefore, the present QSTR model can apply to the chemicals with distinct toxic mechanisms, as well as those with undefined mechanism. This study provides a novel idea for the acute to chronic toxicity extrapolation, which may benefit the environmental risk assessment on the pollutants.

QSAR modeling for predicting mutagenic toxicity of diverse chemicals for regulatory purposes

The safety assessment process of chemicals requires information on their mutagenic potential. The experimental determination of mutagenicity of a large number of chemicals is tedious and time and cost intensive, thus compelling for alternative methods. We have established local and global QSAR models for discriminating low and high mutagenic compounds and predicting their mutagenic activity in a quantitative manner in Salmonella typhimurium (TA) bacterial strains (TA98 and TA100). The decision treeboost (DTB)-based classification QSAR models discriminated among two categories with accuracies of >96% and the regression QSAR models precisely predicted the mutagenic activity of diverse chemicals yielding high correlations (R ²) between the experimental and model-predicted values in the respective training (>0.96) and test (>0.94) sets. The test set root mean squared error (RMSE) and mean absolute error (MAE) values emphasized the usefulness of the developed models for predicting new compounds. Relevant structural features of diverse chemicals that were responsible and influence the mutagenic activity were identified. The applicability domains of the developed models were defined. The developed models can be used as tools for screening new chemicals for their mutagenicity assessment for regulatory purpose.

Chemical structure informing statistical hypothesis testing in metabolomics

MOTIVATION

Metabolomics has been shown as an effective tool to study various biological and biomedical phenotypes, whereas interrogating the inherently noisy metabolite concentration data with limited sample size remains a major challenge. Accumulating evidence suggests that metabolites' structures are relevant to their bioactivities.

RESULTS

We present a new strategy to boost the statistical power of hypothesis testing in metabolomics by incorporating quantitative molecular descriptors for each metabolite. The strategy selects potentially informative summary molecular descriptors and outputs chemical structure-informed false discovery rates. The effectiveness of the proposed strategy is demonstrated by both simulation studies and a real application. In a metabolomic study on Alzheimer's disease, the posterior inclusion probability for summary molecular descriptors reaches 0.97. By incorporating the structure data, our approach uniquely identifies multiple Alzheimer's disease signatures, which are consistent with existing evidence. These results evidently suggest the value of the proposed approach for metabolomic hypothesis-testing problems.

AVAILABILITY AND IMPLEMENTATION

A code package implementing the strategy is freely available at https://github.com/HongjieZhu/CIMA.git.

A docking-based receptor library of antibiotics and its novel application in predicting chronic mixture toxicity for environmental risk assessment

As organisms are typically exposed to chemical mixtures over long periods of time, chronic mixture toxicity is the best way to perform an environmental risk assessment (ERA). However, it is difficult to obtain the chronic mixture toxicity data due to the high expense and the complexity of the data acquisition method. Therefore, an approach was proposed in this study to predict chronic mixture toxicity. The acute (15 min exposure) and chronic (24 h exposure) toxicity of eight antibiotics and trimethoprim to Vibrio fischeri were determined in both single and binary mixtures. The results indicated that the risk quotients (RQs) of antibiotics should be based on the chronic mixture toxicity. To predict the chronic mixture toxicity, a docking-based receptor library of antibiotics and the receptor-library-based quantitative structure-activity relationship (QSAR) model were developed. Application of the developed QSAR model to the ERA of antibiotic mixtures demonstrated that there was a close affinity between RQs based on the observed chronic toxicity and the corresponding RQs based on the predicted data. The average coefficients of variations were 46.26 and 34.93 % and the determination coefficients (R (2)) were 0.999 and 0.998 for the low concentration group and the high concentration group, respectively. This result convinced us that the receptor library would be a promising tool for predicting the chronic mixture toxicity of antibiotics and that it can be further applied in ERA.

Quantitative structure-Activity relationship (QSAR) analysis of the acute sublethal neurotoxicity of solvents

Acute, sublethal neurotoxicity data for the neurotropic effects of some common solvents were subjected to quantitative structure-activity relationship (QSAR) analysis. Hydrophobicity was found to be important in modelling neurotoxicity; however, highly significant QSARs from regression analysis were not obtained, which may imply that metabolic activity and differing mechanisms of action play an important role in neurotoxicity. It is proposed that hydrophobicity could be used to predict a 'minimal' neurotoxic effect level, above which there will be a neurotoxic effect. Principal component analysis was applied to separate chemicals with high neurotoxicity (EC(30) less than 50 micromoles/litre) from those with low neurotoxicity. This was successful when parameters describing hydrophobicity, molecular volume and size, melting and boiling point were included and provides a potential method to predict whether a compound is highly neurotoxic. This suggests that in addition to partitioning through a membrane, aqueous solubility and volatility are also important factors governing neurotoxicity.

Chemometrics in Medicine and Pharmacy

This minireview summarizes the basic ways of application of chemometrics in medicine and pharmacy. It brings a collection of applications of chemometric used for the solution of diverse practical problems, e.g. exploitation of biologically active species, effective use of biomarkers, advancement of clinical diagnosis, monitoring of the patient's state and prediction of its perspectives, drug design or classification of toxic chemical substances. The aim of this contribution is a brief presentation of versatile potentialities of contemporary chemometrical techniques and relevant software. They are exemplified by typical cases from literature as well as by own research results of the Chemometrics group at Department of Chemistry, the University of Ss. Cyril & Methodius in Trnava.

Flexible use of QSAR models in predictive toxicology: a case study on aromatic amines

Over the last years, predictive toxicology approaches based on Structure-Activity Relationships have emerged as fundamental tools in the regulatory assessments of chemicals, especially in those programs where regulatory constraints and assessment schemes limit the amount of data available from experimental test methods. Both the qualitative (e.g., Structural Alerts) and the quantitative (Quantitative Structure-Activity Relationships, QSAR) approach can play important roles. However, the two approaches are not familiar to the same extent to the regulators that most often use only the qualitative approach, so that the potentiality of the more sophisticated QSAR approach is neglected. In fact, QSAR is a very flexible tool that allows the user to modulate its response according to different goals and requirements. Here, we present a non-naïve approach to the use of a QSAR relative to a dichotomous biological activity (such as mutagen/nonmutagen), and we show how the user can maximize alternatively the reliability of the prediction of negative compounds (i.e., safe chemicals) or that of positive chemicals (i.e., chemicals that pose high hazard). Because of the environmental and industrial importance of the class of aromatic amines, we apply the approach to a previously published QSAR on the Salmonella typhimurium mutagenicity of these chemicals.

Integrated decision-tree testing strategies for mutagenicity and carcinogenicity with respect to the requirements of the EU REACH legislation

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.

Integrated Decision-tree Testing Strategies for Developmental and Reproductive Toxicity with Respect to the Requirements of the EU REACH Legislation

Liverpool John Moores University and FRAME 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 the use of alternative methods (both in vitro and in silico) in developmental and reproductive toxicity testing. It considers many tests based on primary cells and cell lines, and the available expert systems and QSARs for developmental and reproductive toxicity, and also covers tests for endocrine disruption. Ways in which reduction and refinement measures can be used are also discussed, particularly the use of an enhanced one-generation reproductive study, which could potentially replace the two-generation study, and therefore considerably reduce the number of animals required in reproductive toxicity. Decision-tree style integrated testing strategies are also proposed for developmental and reproductive toxicity and for endocrine disruption, followed by a number of recommendations for the future facilitation of developmental and reproductive toxicity testing, with respect to human risk assessment.

Integrated Decision-tree Testing Strategies for Developmental and Reproductive Toxicity with Respect to the Requirements of the EU REACH Legislation

Liverpool John Moores University and FRAME 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 the use of alternative methods (both in vitro and in silico) in developmental and reproductive toxicity testing. It considers many tests based on primary cells and cell lines, and the available expert systems and QSARs for developmental and reproductive toxicity, and also covers tests for endocrine disruption. Ways in which reduction and refinement measures can be used are also discussed, particularly the use of an enhanced one-generation reproductive study, which could potentially replace the two-generation study, and therefore considerably reduce the number of animals required in reproductive toxicity. Decision-tree style integrated testing strategies are also proposed for developmental and reproductive toxicity and for endocrine disruption, followed by a number of recommendations for the future facilitation of developmental and reproductive toxicity testing, with respect to human risk assessment.

Proposed integrated decision-tree testing strategies for mutagenicity and carcinogenicity in relation to the EU REACH legislation

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.

Biopartitioning micellar chromatography to predict mutagenicity of aromatic amines

Mutagenicity is a toxicity endpoint associated with the chronic exposure to chemicals. Aromatic amines have considerable industrial and environmental importance due to their widespread use in industry and their mutagenic capacity. Biopartitioning micellar chromatography (BMC), a mode of micellar liquid chromatography that uses micellar mobile phases of Brij35 in adequate experimental conditions, has demonstrated to be useful in mimicking the drug partitioning process into biological systems. In this paper, the usefulness of BMC for predicting mutagenicity of aromatic amines is demonstrated. A multiple linear regression (MLR) model based on BMC retention data is proposed and compared with other ones reported in bibliography. The proposed model present better or similar descriptive and predictive capability.

Predicting multiple drugs side effects with a general drug-target interaction thermodynamic Markov model

Most of present molecular descriptors just consider the molecular structure. In the present article we pretend extending the use of Markov chain models to define novel molecular descriptors, which consider in addition to molecular structure other parameters like target site or toxic effect. Specifically, this molecular descriptor takes into consideration not only the molecular structure but the specific system the drug affects too. Herein, it is developed a general Markov model that describes 39 different drugs side effects grouped in 11 affected systems for 301 drugs, being 686 cases finally. The data was processed by linear discriminant analysis (LDA) classifying drugs according to their specific side effects, forward stepwise was fixed as strategy for variables selection. The average percentage of good classification and number of compounds used in the training/predicting sets were 100/100% for systemic phenomena (47 out of 47)/(12 out of 12) and metabolic (18 out of 18)/(5 out of 5), muscular-skeletal (23 out of 23)/(6 out of 6) and neurological manifestations (33 out of 33)/(8 out of 8); 97.6/96.7% for cardiovascular manifestation (122 out of 125)/(30 out of 31); 97.1/97.5% for breathing manifestations (34 out of 35)/(8 out of 9); 97/99.4% for gastrointestinal manifestations (159 out of 164)/(40 out of 41); 97/95% for endocrine manifestations (32 out of 33)/(7 out of 8); 96.4/94.6% for psychiatric manifestations (53 out of 55)/(13 out of 14); 95.1/99.1% for hematological manifestations (98 out of 103)/(25 out of 26) and 88/92.3% for dermal manifestations (44 out of 50)/(12 out of 13). In addition, we report preliminary experimental reversible decrease of lymphocytes differential count after administration of the antibacterial drug G-1 in mice, which coincide with a posterior probability (P%=74.91) predicted by the model. This article develops a model that encompasses a large number of side effects grouped in specific organ systems in a single stochastic framework for the first time.

Quantitative structure-property relationships for physiologically based pharmacokinetic modeling of volatile organic chemicals in rats

The objective of present study was to develop quantitative structure-property relationships (QSPRs) for the chemical-specific input parameters of rat physiologically based pharmacokinetic (PBPK) models (i.e., blood:air partition coefficient (P(b)), liver:air partition coefficient (P(l)), muscle:air partition coefficient (P(m)), fat:air partition coefficient (P(f)), and hepatic clearance (CL(h))), for simulating the inhalation pharmacokinetics of volatile organic chemicals (VOCs). The literature data on P(b), P(l), P(f), and P(m) for 46 low-molecular-weight VOCs as well as CL(h) for 25 such VOCs primarily metabolized by CYP2E1 (alkanes, haloalkanes, haloethylenes, and aromatic hydrocarbons) were analysed to develop QSPRs. The QSPRs developed in this study were essentially multilinear additive models, which imply that each fragment in the molecular structure has an additive and constant contribution to partition coefficients and hepatic clearance. Most of the values in the calibration set could be reproduced adequately with the QSPR approach, which involved the calculation of the sum of the frequency of occurrence of fragments (CH(3), CH(2), CH, C, C=C, H, Cl, Br, F, benzene ring, and H in benzene ring structure) times the fragment-specific contributions determined in this study. The QSPRs for P(b), P(l), P(m), P(f), and CL(h) were then included within a PBPK model, which only required the specification of the frequency of occurrence of fragments in a molecule along with exposure concentration and duration as input for conducting pharmacokinetic simulations. This QSPR-PBPK model framework facilitated the prediction of the inhalation pharmacokinetics of four VOCs present in the calibration dataset (toluene, dichloromethane, trichloroethylene, and 1,1,1-trichloroethane) and four VOCs that were not part of the calibration set (1,2,4-trimethyl benzene, ethyl benzene, 1,3-dichloropropene, and 2,2-dichloro-1,1,1-trifluoroethane) but that could be described using the molecular fragments investigated in the present study. The QSPRs developed in this study should be potentially useful for providing a first-cut evaluation of the inhalation pharmacokinetics of VOCs prior to experimentation, as long as the number and nature of the fragments do not exceed the ones in the calibration dataset used in this study.

AI and SAR approaches for predicting chemical carcinogenicity: survey and status report

A wide variety of artificial intelligence (AI) and structure-activity relationship (SAR) approaches have been applied to tackling the general problem of predicting rodent chemical carcinogenicity. Given the diversity of chemical structures and mechanisms relative to this endpoint, the shared challenge of these approaches is to accurately delineate classes of active chemicals representing distinct biological and chemical mechanism domains, and within those classes determine the structural features and properties responsible for modulating activity. In the following discussion, we present a survey of AI and SAR approaches that have been applied to the prediction of rodent carcinogenicity, and discuss these in general terms and in the context of the results of two organized prediction exercises (PTE-1 and PTE-2) sponsored by the US National Cancer Institute/National Toxicology Program. Most models participating in these exercises were successful in identifying major structural-alerting classes of active carcinogens, but failed in modeling the more subtle modifiers to activity within those classes. In addition, methods that incorporated mechanism-based reasoning or biological data along with structural information outperformed models limited to structural information exclusively. Finally, a few recent carcinogenicity-modeling efforts are presented illustrating progress in tackling some aspects of the carcinogenicity prediction problem. The first example, a QSAR model for predicting carcinogenic potency of aromatic amines, illustrates that success is possible within well-represented classes of carcinogens. From the second example, a newly developed FDA/OTR MultiCASE model for predicting the carcinogenicity of pharmaceuticals, we conclude that the definitions of biological activity and nature of chemicals in the training set are important determinants of the predictive success and specificity/sensitivity characteristics of a derived model.

Quantitative structure-toxicity relationships using TOPS-MODE. 2. Neurotoxicity of a non-congeneric series of solvents

Neurotoxicities of a series of solvents in rats and mice have been modeled by means of the TOPS-MODE approach. Two quantitative structure-toxicity relationship (QSTR) models were obtained explaining more than 80% of the variance in the experimental values of neurotoxicity of 45 solvents. Only one compound was detected as statistical outlier for these models. In contrast, previous models explained less than 60% of the variance in this property for 44 solvents. Finally, the contributions to neurotoxicity in rats and mice for a series of structural fragments were found. Structural characteristics of chlorinated fragments responsible for their different neurotoxicities were analyzed. The differences in neurotoxic behavior of some fragments in rats and mice were also analyzed, which could give insights on the toxicological mechanism of action of solvents studied.

Quantitative structure--toxicity relationships using TOPS-MODE. 1. Nitrobenzene toxicity to Tetrahymena pyriformis

The TOPological Sub-Structural MOlecular DEsign (TOPS-MODE) approach (Estrada, E. SAR QSAR Environ. Res. 2000, 11, 55-73) has been introduced to the study of toxicological properties. The toxicity of 42 nitrobenzenes was studied with this approach obtaining a good quantitative structure--toxicity model. For the first time we compare the use of eight different weights in the diagonal entries of the bond matrix for selecting the best TOPS-MODE model. TOPS-MODE was used to derive the contribution of different fragments to the toxicity of studied compounds. These contributions were applied to calculate toxicity substituent constants for the groups present in the nitrobenzenes studied.

Quantitative structure-property relationships in pharmaceutical research - Part 2

Part one of this two-part review described the advantages and limitations of quantitative structure-property relationships (QSPR), and offered an overview of the components involved in the development of correlations1. Part two provides a discussion of a few notable examples of relationships with organoleptic, physicochemical and pharmaceutical properties.

Quantitative structure-based modeling applied to characterization and prediction of chemical toxicity

Quantitative modeling methods, relating aspects of chemical structure to biological activity, have long been applied to the prediction and characterization of chemical toxicity. The early linear free-energy approaches of Hansch and Free Wilson provided a fundamental scientific framework for the quantitative correlation of chemical structure with biological activity and spurred many developments in the field of quantitative structure-activity relationships (QSARs). In addition to modeling of chemical toxicity, these methods have been extensively applied to modeling of medicinal properties of chemicals. However, there are important differences in the nature and objectives of these two applications, which have led to the evolution of different modeling approaches (namely, the need for treating sets of noncongeneric toxic compounds). In this paper are discussed those approaches to chemical toxicity that have taken a more "personalized" configuration and have undergone implementation into software programs able to perform the various steps of the assessment of the hazard posed by the chemicals. These models focus both on a variety of toxicological endpoints and on key elements of toxicity mechanisms, such as metabolism.

The first US National Toxicology Program exercise on the prediction of rodent carcinogenicity: definitive results

A few years ago, the US National Toxicology Program sponsored an exercise aimed at comparing different prediction approaches for carcinogenicity by challenging them on a common set of chemicals. The exercise was considered to be sufficiently completed when 40 (out of 44) chemicals were actually experimentally tested, and the experimental and estimated carcinogenicity were compared. More recently, the rodent results for the remaining 4 chemicals have been disclosed, making it possible to draw definitive conclusions on the comparative exercise. Having analyzed the first subset of results with multivariate statistical methods, we present here the analysis of the complete set of results. The present analysis also considers aspects (e.g., the complementarity of the different systems in identifying the carcinogens), which had not been investigated previously. The conclusion of this study were: (a) the expansion of the data base from 40 to 44 chemicals did not significantly change the results of the exercise; (b) the structure-activity approaches generated prediction profiles different from those generated by the prediction systems mainly relying on the use of experimental data (in vitro and in vivo); (c) the performance of the predictive systems was generally rather limited; (d) the prediction systems were affected by over sensitivity; they were generally capable of identifying the molecules containing the potentially alerting substructures, but were not so refined as to be able to discriminate between potential and actual carcinogenicity; (e) the combination of the systems into batteries did not permit a significant increase in the performance of the individual methods. The need for, and possible approaches to finely tuning the systems are discussed.