QSAR models for reproductive toxicity and endocrine disruption in regulatory use--a preliminary investigation

Multi-Strategy Assessment of Different Uses of QSAR under REACH Analysis of Alternatives to Advance Information Transparency

Under the Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH) analysis of alternatives (AoA) process, quantitative structure–activity relationship (QSAR) models play an important role in expanding information gathering and organizing frameworks. Increasingly recognized as an alternative to testing under registration. QSARs have become a relevant tool in bridging data gaps and supporting weight of evidence (WoE) when assessing alternative substances. Additionally, QSARs are growing in importance in integrated testing strategies (ITS). For example, the REACH ITS framework for specific endpoints directs registrants to consider non-testing results, including QSAR predictions, when deciding if further animal testing is needed. Despite the raised profile of QSARs in these frameworks, a gap exists in the evaluation of QSAR use and QSAR documentation under authorization. An assessment of the different uses (e.g., WoE and ITS) in which QSAR predictions play a role in evidence gathering and organizing remains unaddressed for AoA. This study approached the disparity in information for QSAR predictions by conducting a substantive review of 24 AoA through May 2017, which contained higher-tier endpoints under REACH. Understanding the manner in which applicants manage QSAR prediction information in AoA and assessing their potential within ITS will be valuable in promoting regulatory use of QSARs and building out future platforms in the face of rapidly evolving technology while advancing information transparency.

Endocrine disruption: the noise in available data adversely impacts the models' performance

This paper is devoted to the analysis of available experimental data and preparation of predictive models for binding affinity of molecules with respect to two nuclear receptors involved in endocrine disruption (ED): the oestrogen (ER) and the androgen (AR) receptors. The ED-relevant data were retrieved from multiple sources, including the CERAPP, CoMPARA, and the Tox21 projects as well as ChEMBL and PubChem databases. Data analysis performed with the help of generative topographic mapping revealed the problem of low agreement between experimental values from different sources. Collected data were used to train both classification models for ER and AR binding activities and regression models for relative binding affinity (RBA) and median inhibition concentration (IC50). These models displayed relatively poor performance in classification (sensitivities ER = 0.34, AR = 0.49) and in regression (determination coefficient r ² for the RBA and IC50 models in external validation varied from 0.44 to 0.76). Our analysis demonstrates that low models' performance resulted from misinterpreted experimental endpoints or wrongly reported values, thus confirming the observations reported in CERAPP and CoMPARA studies. Developed models and collected data sets included of 6215 (ER) and 3789 (AR) unique compounds, which are freely available.

Predicting the reproductive toxicity of chemicals using ensemble learning methods and molecular fingerprints

Reproductive toxicity endpoints are a significant safety concern in the assessment of the adverse effects of chemicals in drug discovery. Computational models that can accurately predict a chemical's toxic potential are increasingly pursued to replace traditional animal experiments. Thus, ensemble learning models were built to predict the reproductive toxicity of compounds. Our ensemble models were developed using support vector machine, random forest, and extreme gradient boosting methods and 9 molecular fingerprints calculated for a dataset containing 1823 chemicals. The best prediction performance was achieved by the Ensemble-Top12 model, with an accuracy (ACC) of 86.33 %, a sensitivity (SEN) of 82.02 %, a specificity (SPE) of 90.19 %, and an area under the receiver operating characteristic curve (AUC) of 0.937 in 5-fold cross-validation and ACC, SEN, SPE, and AUC values of 84.38 %, 86.90 %, 90.67 %, and 0.920, respectively, in external validation. We also defined the applicability domain (AD) of the ensemble model by calculating the Tanimoto distance of the training set. Compared with models in existing literature, our ensemble model achieves relatively high ACC, SPE and AUC values. We also identified several fingerprint features related to chemical reproductive toxicity. Considering the performance of model, we recommend using the Ensemble-Top12 model to predict reproductive toxicity in early drug development.

KnowTox: pipeline and case study for confident prediction of potential toxic effects of compounds in early phases of development

Risk assessment of newly synthesised chemicals is a prerequisite for regulatory approval. In this context, in silico methods have great potential to reduce time, cost, and ultimately animal testing as they make use of the ever-growing amount of available toxicity data. Here, KnowTox is presented, a novel pipeline that combines three different in silico toxicology approaches to allow for confident prediction of potentially toxic effects of query compounds, i.e. machine learning models for 88 endpoints, alerts for 919 toxic substructures, and computational support for read-across. It is mainly based on the ToxCast dataset, containing after preprocessing a sparse matrix of 7912 compounds tested against 985 endpoints. When applying machine learning models, applicability and reliability of predictions for new chemicals are of utmost importance. Therefore, first, the conformal prediction technique was deployed, comprising an additional calibration step and per definition creating internally valid predictors at a given significance level. Second, to further improve validity and information efficiency, two adaptations are suggested, exemplified at the androgen receptor antagonism endpoint. An absolute increase in validity of 23% on the in-house dataset of 534 compounds could be achieved by introducing KNNRegressor normalisation. This increase in validity comes at the cost of efficiency, which could again be improved by 20% for the initial ToxCast model by balancing the dataset during model training. Finally, the value of the developed pipeline for risk assessment is discussed using two in-house triazole molecules. Compared to a single toxicity prediction method, complementing the outputs of different approaches can have a higher impact on guiding toxicity testing and de-selecting most likely harmful development-candidate compounds early in the development process.

Developing novel in silico prediction models for assessing chemical reproductive toxicity using the naïve Bayes classifier method

Assessment of reproductive toxicity is one of the important safety considerations in drug development. Thus, in the present research, the naïve Bayes (NB)-classifier method was applied to develop binary classification models. Six important molecular descriptors for reproductive toxicity were selected by the genetic algorithm. Then, 110 classification models were developed using six molecular descriptors and10 types of fingerprints with 11 different maximum diameters. Among these established models, the model based on six molecular descriptors and the SciTegic extended-connectivity fingerprints with 20 maximum diameters (LCFC_20) displayed the best prediction performance for reproductive toxicity (NB-1), which gave a 0.884 receiver operating characteristic (ROC) score and 91.8% overall prediction accuracy for the Training Set, and produced a 0.888 ROC score and 83.0% overall accuracy for the external Test Set I. In addition, for the external rat multi-generation reproductive toxicity dataset (Test Set II), the NB-1 model generated a 0.806 ROC score and 85.1% concordance. The generated prediction results indicated that the NB-1 model could give robust and reliable predictions for a reproductive toxicity potential of chemicals. Thus, the established model could be applied to filter early-stage molecules for potential reproductive adverse effects. In addition, six important molecular descriptors and new structural alerts for reproductive toxicity were identified, which could help medicinal chemists rationally guide the optimization of lead compounds and select chemicals with the best prospects of being safe and effective.

Marine Bacterial Compounds Evaluated by In Silico Studies as Antipsychotic Drugs Against Schizophrenia

Schizophrenia (SCZ) is one of the brain disorders which affects the thinking and behavioral skills of patients. This disorder comes along with an overproduction of kynurenic acid in the cerebrospinal fluid and the prefrontal cortex of SCZ patients. In this study, marine bacterial compounds were screened for their suitability as antagonists against human kynurenine aminotransferase (hKAT-1) which causes the synthesis of kynurenic acid downstream which ultimately causes the SCZ disorder according to the kynurenic hypothesis of SCZ. The marine actinobacterial compound bonactin shows more promising results than other tested marine compounds such as the histamine H2 blocker famotidine and indole-3-acetic acid (IAC) from docking and in silico toxicological studies carried out here. The obtained results of the Grid-based Ligand Docking with Energetics (Glide) scores of extra-precision (XP) Glide against the target protein hKAT-1 on IAC, famotidine, and bonactin were - 6.581, - 6.500 and - 7.730 kcal/mol where Glide energies were - 29.84, - 28.391, and - 47.565 kcal/mol, respectively. Bonactin is known as an antibacterial and antifungal compound being extracted from a marine Streptomyces sp. Comparing tested compounds against the drug target hKAT-1, bonactin alone showed the best Glide score and Glide energy on the target protein hKAT-1.

Integration of in silico methods and computational systems biology to explore endocrine-disrupting chemical binding with nuclear hormone receptors

Thousands of potential endocrine-disrupting chemicals present difficult regulatory challenges. Endocrine-disrupting chemicals can interfere with several nuclear hormone receptors associated with a variety of adverse health effects. The U.S. Environmental Protection Agency (U.S. EPA) has released its reviews of Tier 1 screening assay results for a set of pesticides in the Endocrine Disruptor Screening Program (EDSP), and recently, the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) data. In this study, the predictive ability of QSAR and docking approaches is evaluated using these data sets. This study also presents a computational systems biology approach using carbaryl (1-naphthyl methylcarbamate) as a case study. For estrogen receptor and androgen receptor binding predictions, two commercial and two open source QSAR tools were used, as was the publicly available docking tool Endocrine Disruptome. For estrogen receptor binding predictions, the ADMET Predictor, VEGA, and OCHEM models (specificity: 0.88, 0.88, and 0.86, and accuracy: 0.81, 0.84, and 0.88, respectively) were each more reliable than the MetaDrug™ model (specificity 0.81 and accuracy 0.77). For androgen receptor binding predictions, the Endocrine Disruptome and ADMET Predictor models (specificity: 0.94 and 0.8, and accuracy: 0.78 and 0.71, respectively) were more reliable than the MetaDrug™ model (specificity 0.33 and accuracy 0.4). A consensus approach is proposed that reaches general agreement among the models (specificity 0.94 and accuracy 0.89). This study integrates QSAR, docking, and systems biology approaches as a virtual screening tool for use in risk assessment. As such, this systems biology pathways and network analysis approach provides a means to more critically assess the potential effects of endocrine-disrupting chemicals.

QSAR modeling for predicting reproductive toxicity of chemicals in rats for regulatory purposes

The experimental determination of multi-generation reproductive toxicity of chemicals involves high costs and a large number of animal studies over a long period of time. Computational toxicology offers possibilities to overcome such difficulties. In this study, we have established ensemble machine learning (EML) based quantitative structure-activity relationship models for predicting the reproductive toxicity potential (LOAEL) of structurally diverse chemicals in accordance with the OECD guidelines. Accordingly, decision tree forest (DTF) and decision tree boost (DTB) QSAR models were developed using a novel dataset composed of the toxicity endpoints for 334 chemicals. Relevant structural features of chemicals responsible for toxicity potential were identified and used in QSAR modeling. The generalization and prediction abilities of the constructed QSAR models were evaluated by internal and external validation procedures and by deriving several stringent statistical criteria parameters. In the test set, the two models (DTF and DTB) yielded R2 of 0.856 and 0.945, between the experimental and predicted endpoint toxicity values. The models were also evaluated for predictive use through the most recent criteria based on root mean squared error (RMSE) and mean absolute error (MAE). The values of various statistical validation coefficients derived for the test data were above their respective threshold limits and thus put a high confidence in this analysis. The applicability domains of the constructed QSAR models were defined using the leverage and standardization approaches. The results suggest that the proposed QSAR models can reliably predict the reproductive toxicity potential of diverse chemicals and can be useful tools for screening new chemicals for safety assessment.

Conformal prediction to define applicability domain - A case study on predicting ER and AR binding

A fundamental element when deriving a robust and predictive in silico model is not only the statistical quality of the model in question but, equally important, the estimate of its predictive boundaries. This work presents a new method, conformal prediction, for applicability domain estimation in the field of endocrine disruptors. The method is applied to binders and non-binders related to the oestrogen and androgen receptors. Ensembles of decision trees are used as statistical method and three different sets (dragon, rdkit and signature fingerprints) are investigated as chemical descriptors. The conformal prediction method results in valid models where there is an excellent balance in quality between the internally validated training set and the corresponding external test set, both in terms of validity and with respect to sensitivity and specificity. With this method the level of confidence can be readily altered by the user and the consequences thereof immediately inspected. Furthermore, the predictive boundaries for the derived models are rigorously defined by using the conformal prediction framework, thus no ambiguity exists as to the level of similarity needed for new compounds to be in or out of the predictive boundaries of the derived models where reliable predictions can be expected.

QSAR screening of 70,983 REACH substances for genotoxic carcinogenicity, mutagenicity and developmental toxicity in the ChemScreen project

The ChemScreen project aimed to develop a screening system for reproductive toxicity based on alternative methods. QSARs can, if adequate, contribute to the evaluation of chemical substances under REACH and may in some cases be applied instead of experimental testing to fill data gaps for information requirements. As no testing for reproductive effects should be performed in REACH on known genotoxic carcinogens or germ cell mutagens with appropriate risk management measures implemented, a QSAR pre-screen for 70,983 REACH substances was performed. Sixteen models and three decision algorithms were used to reach overall predictions of substances with potential effects with the following result: 6.5% genotoxic carcinogens, 16.3% mutagens, 11.5% developmental toxicants. These results are similar to findings in earlier QSAR and experimental studies of chemical inventories, and illustrate how QSAR predictions may be used to identify potential genotoxic carcinogens, mutagens and developmental toxicants by high-throughput virtual screening.

3D pharmacophoric similarity improves multi adverse drug event identification in pharmacovigilance

Adverse drugs events (ADEs) detection constitutes a considerable concern in patient safety and public health care. For this reason, it is important to develop methods that improve ADE signal detection in pharmacovigilance databases. Our objective is to apply 3D pharmacophoric similarity models to enhance ADE recognition in Offsides, a pharmacovigilance resource with drug-ADE associations extracted from the FDA Adverse Event Reporting System (FAERS). We developed a multi-ADE predictor implementing 3D drug similarity based on a pharmacophoric approach, with an ADE reference standard extracted from the SIDER database. The results showed that the application of our 3D multi-type ADE predictor to the pharmacovigilance data in Offsides improved ADE identification and generated enriched sets of drug-ADE signals. The global ROC curve for the Offsides ADE candidates ranked with the 3D similarity score showed an area of 0.7. The 3D predictor also allows the identification of the most similar drug that causes the ADE under study, which could provide hypotheses about mechanisms of action and ADE etiology. Our method is useful in drug development, screening potential adverse effects in experimental drugs, and in drug safety, applicable to the evaluation of ADE signals selected through pharmacovigilance data mining.

Toxicants inhibiting anaerobic digestion: a review

Anaerobic digestion is increasingly being used to treat wastes from many sources because of its manifold advantages over aerobic treatment, e.g. low sludge production and low energy requirements. However, anaerobic digestion is sensitive to toxicants, and a wide range of compounds can inhibit the process and cause upset or failure. Substantial research has been carried out over the years to identify specific inhibitors/toxicants, and their mechanism of toxicity in anaerobic digestion. In this review we present a detailed and critical summary of research on the inhibition of anaerobic processes by specific organic toxicants (e.g., chlorophenols, halogenated aliphatics and long chain fatty acids), inorganic toxicants (e.g., ammonia, sulfide and heavy metals) and in particular, nanomaterials, focusing on the mechanism of their inhibition/toxicity. A better understanding of the fundamental mechanisms behind inhibition/toxicity will enhance the wider application of anaerobic digestion.

Comparison of photocatalytic degradation of dyes in relation to their structure

The photocatalytic degradation of a series of six acid dyes (Direct Red 80, Direct Red 81, Direct Red 23, Direct Violet 51, Direct Yellow 27, and Direct Yellow 50) has been tested compared in terms of color removal, mineralization, and toxicity (Lactuca sativa L. test) after photocatalysis on immobilized titanium dioxide. The dyes were examined at their natural pH and after hydrolysis at pH 12. Results show that hydrolysis decreases strongly the efficiency of color removal, that full mineralization takes much longer reaction time than color removal, and that toxicity is only very partially reduced. Some structural parameters, related to the structure and the topology of the dye molecules, could be correlated with the apparent color removal rates at natural pH.

QSAR classification models for the screening of the endocrine-disrupting activity of perfluorinated compounds

Perfluorinated compounds (PFCs) are a class of emerging pollutants still widely used in different materials as non-adhesives, waterproof fabrics, fire-fighting foams, etc. Their toxic effects include potential for endocrine-disrupting activity, but the amount of experimental data available for these pollutants is limited. The use of predictive strategies such as quantitative structure-activity relationships (QSARs) is recommended under the REACH regulation, to fill data gaps and to screen and prioritize chemicals for further experimentation, with a consequent reduction of costs and number of tested animals. In this study, local classification models for PFCs were developed to predict their T4-TTR (thyroxin-transthyretin) competing potency. The best models were selected by maximizing the sensitivity and external predictive ability. These models, characterized by robustness, good predictive power and a defined applicability domain, were applied to predict the activity of 33 other PFCs of environmental concern. Finally, classification models recently published by our research group for T4-TTR binding of brominated flame retardants and for estrogenic and anti-androgenic activity were applied to the studied perfluorinated chemicals to compare results and to further evaluate the potential for these PFCs to cause endocrine disruption.

Evaluation of the OECD (Q)SAR Application Toolbox for the profiling of estrogen receptor binding affinities

The determination of binding affinities for the estrogen receptor (ER) is used extensively to assess potential hazards to human health and the environment arising from chemicals that can interfere with natural hormone homeostasis. Given the great number of chemicals to which humans and wildlife are exposed, (quantitative) structure-activity relationship (Q)SAR models for the characterization of ER disruptors represent a fast and cost-efficient alternative to experimental testing. In this toxicological context, the freely available Organisation for Economic Co-operation and Development (OECD) (Q)SAR Application Toolbox provides a profiler for the categorical profiling of chemicals according to their ER binding propensities. The aim of this study was to evaluate the predictive performances of this profiler. To achieve such a purpose, prediction results with the ER-profiler were compared with experimental binding affinities relative to two large datasets of chemicals (rat and human). The resulting Cooper statistics indicated that the binding affinities of the majority of chemicals included in the retained datasets could be correctly predicted.

Real external predictivity of QSAR models: how to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient

The main utility of QSAR models is their ability to predict activities/properties for new chemicals, and this external prediction ability is evaluated by means of various validation criteria. As a measure for such evaluation the OECD guidelines have proposed the predictive squared correlation coefficient Q(2)(F1) (Shi et al.). However, other validation criteria have been proposed by other authors: the Golbraikh-Tropsha method, r(2)(m) (Roy), Q(2)(F2) (Schüürmann et al.), Q(2)(F3) (Consonni et al.). In QSAR studies these measures are usually in accordance, though this is not always the case, thus doubts can arise when contradictory results are obtained. It is likely that none of the aforementioned criteria is the best in every situation, so a comparative study using simulated data sets is proposed here, using threshold values suggested by the proponents or those widely used in QSAR modeling. In addition, a different and simple external validation measure, the concordance correlation coefficient (CCC), is proposed and compared with other criteria. Huge data sets were used to study the general behavior of validation measures, and the concordance correlation coefficient was shown to be the most restrictive. On using simulated data sets of a more realistic size, it was found that CCC was broadly in agreement, about 96% of the time, with other validation measures in accepting models as predictive, and in almost all the examples it was the most precautionary. The proposed concordance correlation coefficient also works well on real data sets, where it seems to be more stable, and helps in making decisions when the validation measures are in conflict. Since it is conceptually simple, and given its stability and restrictiveness, we propose the concordance correlation coefficient as a complementary, or alternative, more prudent measure of a QSAR model to be externally predictive.

Application of an integrated testing strategy to the U.S. EPA endocrine disruptor screening program

New approaches to generating and evaluating toxicity data for chemicals are needed to cope with the ever-increasing demands of new programs. One such approach involves the use of an integrated testing and evaluation strategy based on the specific properties and activities of a chemical. Such an integrated strategy, whether applied to existing or future programs, can promote efficient use of resources and save animals. We demonstrate the utility of such a strategy by applying it to the current U.S. Environmental Protection Agency Endocrine Disruptor Screening Program (EDSP). Launched in October 2009, the EDSP utilizes a two-tiered approach, whereby each tier requires a battery of animal-intensive and expensive tests. Tier 1 consists of five in vitro and six in vivo assays that are intended to determine a chemical's potential to interact with the estrogen (E), androgen (A), or thyroid (T) hormone pathways. Tier 2 is proposed to consist of multigenerational reproductive and developmental toxicity tests in several species and is intended to determine whether a chemical can cause adverse effects resulting from E, A, or T modulation. In contrast to the existing EDSP structure, we show, using the pesticide atrazine as an example, that a multilevel testing framework combined with an integrated evaluation process would significantly increase efficiency by minimizing testing.

Alternative (non-animal) methods for cosmetics testing: current status and future prospects-2010

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.

A human approach to drug development: opportunities and limitations

The pharmaceutical industry is failing in its primary function, with increasing expenditure and decreased output in terms of new medicines brought to market. It cannot carry on as it is, without sliding into a terminal decline. It must, therefore, take some positive steps toward addressing its problems. We do not have to look far to see one very obvious problem, namely, the industry's continuing reliance on nonhuman biology as the basis of its evaluation of potential safety and efficacy. The time has come to focus on the relevant, and to realise that more human-based testing is essential, if the industry is to survive as a source of innovation in drug therapy. This can incorporate earlier clinical testing, in the form of microdosing, and promotion of the development of more-powerful computational approaches based on human information. Fortunately, headway is being made in both approaches. However, a problem remains in the lack of functional evaluation of human tissues, where the lack of commitment, and the inadequacy of the tissue resource itself, are hampering any serious developments. An outline of a collaborative scheme is proposed, that will address this issue, central to which is improved access to research tissues from heart-beating organ donors.

QSAR models for anti-androgenic effect--a preliminary study

Three modelling systems (MultiCase®, LeadScope® and MDL® QSAR) were used for construction of androgenic receptor antagonist models. There were 923-942 chemicals in the training sets. The models were cross-validated (leave-groups-out) with concordances of 77-81%, specificity of 78-91% and sensitivity of 51-76%. The specificity was highest in the MultiCase® model and the sensitivity was highest in the MDL® QSAR model. A complementary use of the models may be a valuable tool when optimizing the prediction of chemicals for androgenic receptor antagonism. When evaluating the fitness of the model for a particular application, balance of training sets, domain definition, and cut-offs for prediction interpretation should also be taken into account. Different descriptors in the modelling systems are illustrated with hydroxyflutamide and dexamethasone as examples (a non-steroid and a steroid anti-androgen, respectively). More research concerning the mechanism of anti-androgens would increase the possibility for further optimization of the QSAR models. Further expansion of the basis for the models is in progress, including the addition of more drugs.

Environmental chemical mediated male reproductive toxicity: Drosophila melanogaster as an alternate animal model

Industrialization and indiscriminate use of agrochemicals have increased the human health risk. Recent epidemiological studies raised a concern for male reproduction given their observations of reduced sperm counts and altered semen quality. Interestingly, environmental factors that include various metals, pesticides and their metabolites have been causally linked to such adversities by their presence in the semen at levels that correlate to infertility. The epidemiological observations were further supported by studies in animal models involving various chemicals. Therefore, in this review, we focused on male reproductive toxicity and the adverse effects of different environmental chemicals on male reproduction. However, it is beyond the scope of this review to provide a detailed appraisal of all of the environmental chemicals that have been associated with reproductive toxicity in animals. Here, we provided the evidence for reproductive adversities of some commonly encountered chemicals (pesticides/metals) in the environment. In view of the recent thrust for an alternate to animal models in research, we subsequently discussed the contributions of Drosophila melanogaster as an alternate animal model for quick screening of toxicants for their reproductive toxicity potential. Finally, we emphasized the genetic and molecular tools offered by Drosophila for understanding the mechanisms underlying the male reproductive toxicity.

QSAR models for reproductive toxicity and endocrine disruption activity

Reproductive toxicity is an important regulatory endpoint, which is required in registration procedures of chemicals used for different purposes (for example pesticides). The in vivo tests are expensive, time consuming and require large numbers of animals, which must be sacrificed. Therefore an effort is ongoing to develop alternative In vitro and in silico methods to evaluate reproductive toxicity. In this review we describe some modeling approaches. In the first example we describe the CAESAR model for prediction of reproductive toxicity; the second example shows a classification model for endocrine disruption potential based on counter propagation artificial neural networks; the third example shows a modeling of relative binding affinity to rat estrogen receptor, and the fourth one shows a receptor dependent modeling experiment.

Prediction of acute mammalian toxicity from QSARs and interspecies correlations

With the ever-growing number of xenobiotics that can potentially contaminate the environment, the determination of their mammalian toxicity is of prime importance. In this context, LD50 tests on rats and mice have been used for a long time to express the relative hazard associated with the acute toxicity of inorganic and organic chemicals. However, these laboratory tests encounter important hurdles. They are costly, time consuming and actively opposed by animal rights activists. Moreover, new legislation policies, such as REACH (Registration, Evaluation, Authorization and Restriction of Chemicals), aim at reducing the use of toxicity tests on vertebrates. Consequently, there is a need to find alternative methods for estimating the acute mammalian toxicity of chemicals. The quantitative structure-activity relationships (QSARs) and interspecies correlations appear particularly suited to reaching this goal. In this context, this paper reviews more than 150 models aiming at predicting rat and mouse LD50 values from molecular descriptors or (and) ecotoxicity data. The interest of these computational tools is discussed.