Predictability and prediction of lowest observed adverse effect levels in a structurally heterogeneous set of chemicals

In Silico Models for Repeated-Dose Toxicity (RDT): Prediction of the No Observed Adverse Effect Level (NOAEL) and Lowest Observed Adverse Effect Level (LOAEL) for Drugs

Many regulatory contexts require the evaluation of repeated-dose toxicity (RDT) studies conducted in laboratory animals. The main outcome of RDT studies is the identification of the no observed adverse effect level (NOAEL) and the lowest observed adverse effect level (LOAEL) that are normally used as point of departure for the establishment of health-based guidance values. Since in vivo RDT studies are expensive and time-consuming, in silico approaches could offer a valuable alternative. However, NOAEL and LOAEL modeling suffer some limitations since they do not refer to a single end point but to several different effects, and the doses used in experimental studies strongly influence the results. Few attempts to model NOAEL and LOAEL have been reported. The available database and models for the prediction of NOAEL and LOAEL are reviewed here.

Integrated In Silico Models for the Prediction of No-Observed-(Adverse)-Effect Levels and Lowest-Observed-(Adverse)-Effect Levels in Rats for Sub-chronic Repeated-Dose Toxicity

Repeated-dose toxicity (RDT) is a critical endpoint for hazard characterization of chemicals and is assessed to derive safe levels of exposure for human health. Here we present the first attempt to model simultaneously no-observed-(adverse)-effect level (NO(A)EL) and lowest-observed-(adverse)-effect level (LO(A)EL). Classification and regression models were derived based on rat sub-chronic repeated dose toxicity data for 327 compounds from the Fraunhofer RepDose database. Multi-category classification models were built for both NO(A)EL and LO(A)EL though a consensus of statistics- and fragment-based algorithms, while regression models were based on quantitative relationships between the endpoints and SMILES-based attributes. NO(A)EL and LO(A)EL models were integrated, and predictions were compared to exclude inconsistent values. This strategy improved the performance of single models, leading to R² greater than 0.70, root-mean-square error (RMSE) lower than 0.60 (for regression models), and accuracy of 0.61-0.73 (for classification models) on the validation set, based on the endpoint and the threshold applied for selecting predictions. This study confirms the effectiveness of the modeling strategy presented here for assessing RDT of chemicals using in silico models.

The study of the index of ideality of correlation as a new criterion of predictive potential of QSPR/QSAR-models

Acetylcholinesterase (AChE) inhibitors, dihydrofolate reductase inhibitors (DHFR), Toxicity in Tetrahymena pyriformis (TP), Acute Toxicity in fathead minnow (TFat), Water solubility (WS), and Acute Aquatic Toxicity in Daphnia magna (DM) are examined as endpoints to establish quantitative structure - property/activity relationships (QSPRs/QSARs). The Index of Ideality of Correlation (IIC) is a measure of predictive potential. The IIC has been studied in a few recent works. The comparison of models for the six endpoints above confirms that the index can be a useful tool for building up and validation of QSPR/QSAR models. All examined endpoints are important from an ecologic point of view. The diversity of examined endpoints confirms that the IIC is real criterion of the predictive potential of a model.

In silico prediction of chronic toxicity with chemical category approaches

Chemical chronic toxicity, referring to the toxic effect of a chemical following long-term or repeated sub lethal exposures, is an important toxicological end point in drug design and environmental risk assessment.

In Silico Models for Repeated-Dose Toxicity (RDT): Prediction of the No Observed Adverse Effect Level (NOAEL) and Lowest Observed Adverse Effect Level (LOAEL) for Drugs

The preclinical stage in drug development requires the determination of repeated-dose toxicity (RDT) in animal models. The main outcome of RDT studies is the determination of the no observed adverse effect level (NOAEL) and the lowest observed adverse effect level (LOAEL). NOAEL is important since it serves to calculate the maximum recommended starting dose (MRSD) which is the safe starting dose for clinical studies in human beings. Since in vivo RDT studies are expensive and time-consuming, in silico approaches could offer a valuable alternative. However, NOAEL and LOAEL modeling suffer some limitations since they do not refer to a single end point but to several different effects and the doses used in experimental studies strongly influence the final results. Few attempts to model NOAEL and LOAEL have been reported. The available database and models for the prediction of NOAEL and LOAEL are reviewed here.

Identification of structural alerts for liver and kidney toxicity using repeated dose toxicity data

BACKGROUND

The potential for a compound to cause hepatotoxicity and nephrotoxicity is a matter of extreme interest for human health risk assessment. To assess liver and kidney toxicity, repeated-dose toxicity (RDT) studies are conducted mainly on rodents. However, these tests are expensive, time-consuming and require large numbers of animals. For early toxicity screening, in silico models can be applied, reducing the costs, time and animals used. Among in silico approaches, structure-activity relationship (SAR) methods, based on the identification of chemical substructures (structural alerts, SAs) related to a particular activity (toxicity), are widely employed.

RESULTS

We identified and evaluated some SAs related to liver and kidney toxicity, using RDT data on rats taken from the hazard evaluation support system (HESS) database. We considered only SAs that gave the best percentages of true positives (TP).

CONCLUSIONS

It was not possible to assign an unambiguous mode of action for all the SAs, but a mechanistic explanation is provided for some of them. Such achievements may help in the early identification of liver and renal toxicity of substances.

Acute Toxicity-Supported Chronic Toxicity Prediction: A k-Nearest Neighbor Coupled Read-Across Strategy

A k-nearest neighbor (k-NN) classification model was constructed for 118 RDT NEDO (Repeated Dose Toxicity New Energy and industrial technology Development Organization; currently known as the Hazard Evaluation Support System (HESS)) database chemicals, employing two acute toxicity (LD₅₀)-based classes as a response and using a series of eight PaDEL software-derived fingerprints as predictor variables. A model developed using Estate type fingerprints correctly predicted the LD₅₀ classes for 70 of 94 training set chemicals and 19 of 24 test set chemicals. An individual category was formed for each of the chemicals by extracting its corresponding k-analogs that were identified by k-NN classification. These categories were used to perform the read-across study for prediction of the chronic toxicity, i.e., Lowest Observed Effect Levels (LOEL). We have successfully predicted the LOELs of 54 of 70 training set chemicals (77%) and 14 of 19 test set chemicals (74%) to within an order of magnitude from their experimental LOEL values. Given the success thus far, we conclude that if the k-NN model predicts LD₅₀ classes correctly for a certain chemical, then the k-analogs of such a chemical can be successfully used for data gap filling for the LOEL. This model should support the in silico prediction of repeated dose toxicity.

An Integrated Decision-tree Testing Strategy for Repeat Dose Toxicity with Respect to the Requirements of the EU REACH Legislation

This paper presents some results of a joint research project conducted by FRAME and Liverpool John Moores University, and 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 end-points associated with REACH. This paper focuses on the use of alternative (non-animal) methods (both in vitro and in silico) for repeat dose (sub-acute, sub-chronic and chronic) toxicity testing. It reviews the limited number of in silico and in vitro tests available for this endpoint, and outlines new technologies which could be used in the future, e.g. the use of biomarkers and the ‘omics’ technologies. An integrated testing strategy is proposed, which makes use of as much non-animal data as possible, before any essential in vivo studies are performed. Although none of the non-animal tests are currently undergoing validation, their results could help to reduce the number of animals required for testing for repeat dose toxicity.

An Integrated Decision-tree Testing Strategy for Repeat Dose Toxicity with Respect to the Requirements of the EU REACH Legislation

This paper presents some results of a joint research project conducted by FRAME and Liverpool John Moores University, and 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 end-points associated with REACH. This paper focuses on the use of alternative (non-animal) methods (both in vitro and in silico) for repeat dose (sub-acute, sub-chronic and chronic) toxicity testing. It reviews the limited number of in silico and in vitro tests available for this endpoint, and outlines new technologies which could be used in the future, e.g. the use of biomarkers and the ‘omics’ technologies. An integrated testing strategy is proposed, which makes use of as much non-animal data as possible, before any essential in vivo studies are performed. Although none of the non-animal tests are currently undergoing validation, their results could help to reduce the number of animals required for testing for repeat dose toxicity.

Prediction of acute toxicity of organophosphorus pesticides using topological indices

Topological indices were used in the prediction of the acute toxicity (intraperitoneal and oral LD(50)) of organophosphorus pesticides on rats. Models with six variables for the prediction of LD(50)-i.p. (r = 0.849, Q(2) = 0.613) and eight variables for LD(50)-oral (r = 0.906, Q(2) = 0.701) were selected. External group and cross-validation by use of leave-n-out tests were also performed in order to assess the stability and the prediction performance of the selected topological models.

Implementing Molecular Connectivity Theory, a Basic Tool in Modeling Drugs

The concepts of chain graph, general graph, and complete graph have been used to implement the graph framework of molecular connectivity (MC) theory. Some concepts of this theory have been addressed using "external" theoretical concepts belonging mostly to quantum or structural chemistry, with no direct counterpart in graph theory. Thus, while the concept of chain graph can be used to tackle the cis-trans isomerism problem, the concept of pseudograph, or general graph can be used to tackle the description of the sigma-, pi-, and nonbonding n-electrons. The concept of complete graph can instead be used to tackle the electron core problem of the atoms of a molecule. Graph concepts can also be used to tackle the problem of the hydrogen contribution in hydrogen depleted graphs, which are encoded by the aid of a perturbation parameter, which differentiates between compounds with similar hydrogen-suppressed chemical graphs, like the graphs of CH(3)F and BH(2)F. These concepts have allowed redesign of a central parameter of MC theory, the valence delta, giving MC indices with improved model quality as exemplified here with different properties for each treated topic.

True prediction of lowest observed adverse effect levels

A database of structurally heterogeneous chemical structures with their experimental values of Lowest Observed Adverse Effect Levels (LOAELs) was modeled using graph theoretical descriptors. Variable selection for multiple linear regression (MLR) and linear discriminant analysis (LDA) was accomplished by the Internal Test Set (ITS) method in order to achieve true predicted LOAEL values. The results obtained can be considered good if we take in count the structural diversity of the training set.