Decision tree SAR models for developmental toxicity based on an FDA/TERIS database

Humans are exposed to thousands of environmental chemicals for which no developmental toxicity information is available. Structure-activity relationships (SARs) are models that could be used to efficiently predict the biological activity of potential developmental toxicants. However, at this time, no adequate SAR models of developmental toxicity are available for risk assessment. In the present study, a new developmental database was compiled by combining toxicity information from the Teratogen Information System (TERIS) and the Food and Drug Administration (FDA) guidelines. We implemented a decision tree modeling procedure, using Classification and Regression Tree software and a model ensemble approach termed bagging. We then assessed the empirical distributions of the prediction accuracy measures of the single and ensemble-based models, achieved by repeating our modeling experiment many times by repeated random partitioning of the working database. The decision tree developmental SAR models exhibited modest prediction accuracy. Bagging tended to enhance the accuracy of prediction. Also, the model ensemble approach reduced the variability of prediction measures compared to the single model approach. Further research with data derived from animal species- and endpoint-specific components of an extended and refined FDA/TERIS database has the potential to derive SAR models that would be useful in the developmental risk assessment of the thousands of untested chemicals.     

Development of an information-intensive structure–activity relationship model and its application to human respiratory chemical sensitizers

Structure–activity relationship (SAR) models are recognized as powerful tools to predict the toxicologic potential of new or untested chemicals and also provide insight into possible mechanisms of toxicity. Models have been based on physicochemical attributes and structural features of chemicals. We describe herein the development of a new SAR modeling algorithm called cat-SAR that is capable of analyzing and predicting chemical activity from divergent biological response data. The cat-SAR program develops chemical fragment-based SAR models from categorical biological response data (e.g. toxicologically active and inactive compounds). The database selected for model development was a published set of chemicals documented to cause respiratory hypersensitivity in humans. Two models were generated that differed only in that one model included explicate hydrogen containing fragments. The predictive abilities of the models were tested using leave-one-out cross-validation tests. One model had a sensitivity of 0.94 and specificity of 0.87 yielding an overall correct prediction of 91%. The second model had a sensitivity of 0.89, specificity of 0.95 and overall correct prediction of 92%. The demonstrated predictive capabilities of the cat-SAR approach, together with its modeling flexibility and design transparency, suggest the potential for its widespread applicability to toxicity prediction and for deriving mechanistic insight into toxicologic effects.     

Development of an information-intensive structure-activity relationship model and its application to human respiratory chemical sensitizers

Structure-activity relationship (SAR) models are recognized as powerful tools to predict the toxicologic potential of new or untested chemicals and also provide insight into possible mechanisms of toxicity. Models have been based on physicochemical attributes and structural features of chemicals. We describe herein the development of a new SAR modeling algorithm called cat-SAR that is capable of analyzing and predicting chemical activity from divergent biological response data. The cat-SAR program develops chemical fragment-based SAR models from categorical biological response data (e.g. toxicologically active and inactive compounds). The database selected for model development was a published set of chemicals documented to cause respiratory hypersensitivity in humans. Two models were generated that differed only in that one model included explicate hydrogen containing fragments. The predictive abilities of the models were tested using leave-one-out cross-validation tests. One model had a sensitivity of 0.94 and specificity of 0.87 yielding an overall correct prediction of 91%. The second model had a sensitivity of 0.89, specificity of 0.95 and overall correct prediction of 92%. The demonstrated predictive capabilities of the cat-SAR approach, together with its modeling flexibility and design transparency, suggest the potential for its widespread applicability to toxicity prediction and for deriving mechanistic insight into toxicologic effects.     

QSAR Modeling of Large Heterogeneous Sets of Molecules

Abstract

In aquatic toxicology, QSAR models are generally designed for chemicals presenting the same mode of toxic action. Their proper use provides good simulation results. Problems arise when the mechanism of toxicity of a chemical is not clearly identified. Indeed, in that case, the inappropriate application of a specific QSAR model can lead to a dramatic error in the toxicity estimation. With the advent of powerful computers and easy access to them, and the introduction of soft modeling and artificial intelligence in SAR and QSAR, radically different models, designed from large non-congeneric sets of chemicals have been proposed. Some of these new QSAR models are reviewed and their originality, advantages, and limitations are stressed.     

An in silico ensemble method for lead discovery: decision forest

Recent progress in combinatorial chemistry and parallel synthesis has radically changed the approach to drug discovery in the pharmaceutical industry. At present, thousands of compounds can be made in a short period, creating a need for fast and effective in silico methods to select the most promising lead candidates. Decision forest is a novel pattern recognition method, which combines the results of multiple distinct but comparable decision tree models to reach a consensus prediction. In this article, a decision forest model was developed using a structurally diverse training data set containing 232 compounds whose estrogen receptor binding activity was tested at the U.S. Food and Drug Administration (FDA)'s National Center for Toxicological Research (NCTR). The model was subsequently validated using a test data set of 463 compounds selected from the literature, and then applied to a large data set with 57,145 compounds as a screening example. The results show that the decision forest method is a fast, reliable and effective in silico approach, which could be useful in drug discovery.     

SAR—The U.S. Regulatory Perspective

Abstract

Structure-Activity Relationships (SAR) have been used for over a decade by the U.S. EPA's Office of Pollution Prevention and Toxics (OPPT) in their new chemicals program. The development and use of SAR resulted from the need to make rapid risk-based decisions on thousands of new chemicals per year while seldom receiving data on chemical properties, potential exposures, or hazards to humans or organisms in the environment. Qualitative SAR and quantitative SAR methods (QSAR) have been used to fill some of these data gaps by estimating the potential properties and hazards of such chemicals. SAR has been used to assess chemical hazards, identify testing needs, and set priorities. Validation of these SAR assessment tools is an ongoing process.     

An in silico ensemble method for lead discovery: decision forest

Recent progress in combinatorial chemistry and parallel synthesis has radically changed the approach to drug discovery in the pharmaceutical industry. At present, thousands of compounds can be made in a short period, creating a need for fast and effective in silico methods to select the most promising lead candidates. Decision forest is a novel pattern recognition method, which combines the results of multiple distinct but comparable decision tree models to reach a consensus prediction. In this article, a decision forest model was developed using a structurally diverse training data set containing 232 compounds whose estrogen receptor binding activity was tested at the U.S. Food and Drug Administration (FDA)'s National Center for Toxicological Research (NCTR). The model was subsequently validated using a test data set of 463 compounds selected from the literature, and then applied to a large data set with 57,145 compounds as a screening example. The results show that the decision forest method is a fast, reliable and effective in silico approach, which could be useful in drug discovery.     

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

Abstract

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

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

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

Comparison of criteria used to access carcinogenicity in CPANN QSAR models versus the knowledge-based expert system Toxtree

The primary goal of this study was to describe and compare the criteria used to assess carcinogenic activity. The statistically-based predictive quantitative structure–activity relationship (QSAR) models based on the counter propagation artificial neural network (CPANN) algorithm, and knowledge-based expert systems based on a decision tree structural alert (SA) approach (Toxtree application), were considered. The integration of the QSAR (CPANN models) and SAR (Toxtree SA application) approach contributed to the mechanistic understanding of the QSAR model considered. The mapping technique inherent to CPANN Kohonen enables us to relate the similarities or dissimilarities within a congeneric set of chemicals with particular SAs for carcinogenicity. The focus of our investigations was the similarities and dissimilarities of the features used in the QSAR and SAR methods. Due to the complexity of the carcinogenic endpoint, the integration of different approaches allows the models to be improved and provides a valuable technique for evaluating the safety of chemicals.     

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

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

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.     

Genetic programming for the induction of decision trees to model ecotoxicity data

Automatic induction of decision trees and production rules from data to develop structure-activity models for toxicity prediction has recently received much attention, and the majority of methodologies reported in the literature are based upon recursive partitioning employing greedy searches to choose the best splitting attribute and value at each node. These approaches can be successful; however, the greedy search will necessarily miss regions of the search space. Recent literature has demonstrated the applicability of genetic programming to decision tree induction to overcome this problem. This paper presents a variant of this novel approach, using fewer mutation options and a simpler fitness function, demonstrating its utility in inducing decision trees for ecotoxicity data, via a case study of two data sets giving improved accuracy and generalization ability over a popular decision tree inducer.