Modeling Oral Rat Chronic Toxicity

Risk assessment of feed components of botanical origin - Approaches taken in the European Union

In view of a continuous trend in replacing synthetic feed additives and especially flavouring compounds by botanical preparations, different aspects of the safety evaluations of plants and plant-derived preparations and components in feed are discussed. This includes risk assessment approaches developed by the European Food Safety Authority (EFSA) for phytotoxins regarding unintentional exposure of target animals and of consumers to animal derived food via carry-over from feed. Relevant regulatory frameworks for feed additives and feed contaminants in the European Union are summarised and the essentials of existing guidelines used in the safety evaluation of botanicals and their preparations and components in feed are outlined. The examples presented illustrate how the safety of the botanicals, their preparations and components present in feed is assessed. An outlook on possible future developments in risk assessment by applying new in vitro and in silico methodologies is given.

Dopaminergic Modulation and Computational ADMET Insights for the Antidepressant-like Effect of N-(3-(Phenylselanyl)prop-2-yn-1-yl)benzamide

The compound N-(3-(phenylselanyl)prop-2-yn-1-yl)benzamide (SePB), which combines a selenium atom and a benzamide nucleus in an organic structure, has demonstrated a fast antidepressant-like effect in mice. This action is influenced by the serotonergic system and represents a promising development in the search for novel antidepressant drugs to treat major depressive disorder (MDD), which often resists conventional treatments. This study aimed to further explore the mechanism underlying the antidepressant-like effect of SePB by investigating the involvement of the dopaminergic and noradrenergic systems in the tail suspension test (TST) in mice and evaluating its pharmacokinetic profile in silico. Preadministration of the dopaminergic antagonists haloperidol (0.05 mg/kg, intraperitoneally (i.p.)), a nonselective antagonist of dopamine (DA) receptors, SCH23390 (0.01 mg/kg, subcutaneously (s.c.)), a D1 receptor antagonist, and sulpiride (50 mg/kg, i.p.), a D2/3 receptor antagonist, before SePB (10 mg/kg, intragastrically (i.g.)) prevented the anti-immobility effect of SePB in the TST, demonstrating that these receptors are involved in the antidepressant-like effect of SePB. Administration of the noradrenergic antagonists prazosin (1 mg/kg, i.p.), an α1-adrenergic antagonist, yohimbine (1 mg/kg, i.p.), an α2-adrenergic antagonist, and propranolol (2 mg/kg, i.p.), a β-adrenergic antagonist, did not block the antidepressant-like effect of SePB on TST, indicating that noradrenergic receptors are not involved in this effect. Additionally, the coadministration of SePB and bupropion (a noradrenaline/dopamine reuptake inhibitor) at subeffective doses (0.1 and 3 mg/kg, respectively) produced antidepressant-like effects. SePB also demonstrated good oral bioavailability and low toxicity in computational absorption, distribution, metabolism, excretion, and toxicity (ADMET) analyses. These findings suggest that SePB has potential as a new antidepressant drug candidate with a particular focus on the dopaminergic system.

Chronic aquatic toxicity assessment of diverse chemicals on Daphnia magna using QSAR and chemical read-across

We have modeled here chronic Daphnia toxicity taking pNOEC (negative logarithm of no observed effect concentration in mM) and pEC50 (negative logarithm of half-maximal effective concentration in mM) as endpoints using QSAR and chemical read-across approaches. The QSAR models were developed by strictly obeying the OECD guidelines and were found to be reliable, predictive, accurate, and robust. From the selected features in the developed models, we have found that an increase in lipophilicity and saturation, the presence of electrophilic or electronegative or heavy atoms, the presence of sulphur, amine, and their related functionality, an increase in mean atomic polarizability, and higher number of (thio-) carbamates (aromatic) groups are responsible for chronic toxicity. Therefore, this information might be useful for the development of environmentally friendly and safer chemicals and data-gap filling as well as reducing the use of identified toxic chemicals which have chronic toxic effects on aquatic ecosystems. Approved classes of drugs from DrugBank databases and diverse groups of chemicals from the Chemical and Product Categories (CPDat) database were also assessed through the developed models.

Uncertainty in the results from oral repeated dose toxicity tests: Impact on regulatory classifications

The Lowest Observed (Adverse) Effect Level (LO(A)EL) values are point-of-departure (PoD) values that quantify repeat dose toxicity (RDT). Here, the uncertainty in the regulatory classification of these PoDs is investigated. In the application stage, the dose-response was approximated for a large set of series, giving an account of the possible presence of a hormesis zone. The minimal effect dose (MED) or dose was computed, and the ratio MED/LO(A)EL was used to represent the two components of the experimental uncertainty. The uncertainty estimations were calculated for any combination of gender and reported examination item. Subsequently, how this uncertainty affects the possible classifications was analyzed, and the percentage of the chemicals receiving ambiguous classification was determined. It was shown that more than 40% of the investigated chemicals cannot be classified unambiguously in the Globally Harmonized System (GHS) classification scheme and bear a potential for misclassification when a regulatory decision is based on a single LO(A)EL value. A table containing grey zones for different risk levels and a table with GHS classification distributions for various LO(A)EL values were prepared to facilitate the use of the RDT uncertainty in the practice.

Reproducibility of organ-level effects in repeat dose animal studies

This work estimates benchmarks for new approach method (NAM) performance in predicting organ-level effects in repeat dose studies of adult animals based on variability in replicate animal studies. Treatment-related effect values from the Toxicity Reference database (v2.1) for weight, gross, or histopathological changes in the adrenal gland, liver, kidney, spleen, stomach, and thyroid were used. Rates of chemical concordance among organ-level findings in replicate studies, defined by repeated chemical only, chemical and species, or chemical and study type, were calculated. Concordance was 39 - 88%, depending on organ, and was highest within species. Variance in treatment-related effect values, including lowest effect level (LEL) values and benchmark dose (BMD) values when available, was calculated by organ. Multilinear regression modeling, using study descriptors of organ-level effect values as covariates, was used to estimate total variance, mean square error (MSE), and root residual mean square error (RMSE). MSE values, interpreted as estimates of unexplained variance, suggest study descriptors accounted for 52-69% of total variance in organ-level LELs. RMSE ranged from 0.41 - 0.68 log10-mg/kg/day. Differences between organ-level effects from chronic (CHR) and subchronic (SUB) dosing regimens were also quantified. Odds ratios indicated CHR organ effects were unlikely if the SUB study was negative. Mean differences of CHR - SUB organ-level LELs ranged from -0.38 to -0.19 log10 mg/kg/day; the magnitudes of these mean differences were less than RMSE for replicate studies. Finally, in vitro to in vivo extrapolation (IVIVE) was employed to compare bioactive concentrations from in vitro NAMs for kidney and liver to LELs. The observed mean difference between LELs and mean IVIVE dose predictions approached 0.5 log10-mg/kg/day, but differences by chemical ranged widely. Overall, variability in repeat dose organ-level effects suggests expectations for quantitative accuracy of NAM prediction of LELs should be at least ± 1 log10-mg/kg/day, with qualitative accuracy not exceeding 70%.

ProfhEX: AI-based platform for small molecules liability profiling

Off-target drug interactions are a major reason for candidate failure in the drug discovery process. Anticipating potential drug's adverse effects in the early stages is necessary to minimize health risks to patients, animal testing, and economical costs. With the constantly increasing size of virtual screening libraries, AI-driven methods can be exploited as first-tier screening tools to provide liability estimation for drug candidates. In this work we present ProfhEX, an AI-driven suite of 46 OECD-compliant machine learning models that can profile small molecules on 7 relevant liability groups: cardiovascular, central nervous system, gastrointestinal, endocrine, renal, pulmonary and immune system toxicities. Experimental affinity data was collected from public and commercial data sources. The entire chemical space comprised 289'202 activity data for a total of 210'116 unique compounds, spanning over 46 targets with dataset sizes ranging from 819 to 18896. Gradient boosting and random forest algorithms were initially employed and ensembled for the selection of a champion model. Models were validated according to the OECD principles, including robust internal (cross validation, bootstrap, y-scrambling) and external validation. Champion models achieved an average Pearson correlation coefficient of 0.84 (SD of 0.05), an R² determination coefficient of 0.68 (SD = 0.1) and a root mean squared error of 0.69 (SD of 0.08). All liability groups showed good hit-detection power with an average enrichment factor at 5% of 13.1 (SD of 4.5) and AUC of 0.92 (SD of 0.05). Benchmarking against already existing tools demonstrated the predictive power of ProfhEX models for large-scale liability profiling. This platform will be further expanded with the inclusion of new targets and through complementary modelling approaches, such as structure and pharmacophore-based models. ProfhEX is freely accessible at the following address: https://profhex.exscalate.eu/ .

Identification of Potential Antimalarial Drug Candidates Targeting Falcipain-2 Protein of Malaria Parasite-A Computational Strategy

Falcipain-2 (FP-2) is one of the main haemoglobinase of P. falciparum which is an important molecular target for the treatment of malaria. In this study, we have screened alkaloids to identify potential inhibitors against FP-2 since alkaloids possess great potential as anti-malarial agents. A total of 340 alkaloids were considered for the study using a series of computational pipelines. Initially, pharmacokinetics and toxicity risk assessment parameters were applied to screen compounds. Subsequently, molecular docking algorithms were utilised to understand the binding efficiency of alkaloids against FP-2. Further, oral toxicity prediction was done using the pkCSM tool, and 3D pharmacophore features were analysed using the PharmaGist server. Finally, MD simulation was performed for Artemisinin and the top 3 drug candidates (Noscapine, Reticuline, Aclidinium) based on docking scores to understand the functional impact of the complexes, followed by a binding site interaction residues study. Overall analysis suggests that Noscapine conceded good pharmacokinetics and oral bioavailability properties. Also, it showed better binding efficiency with FP-2 when compared to Artemisinin. Interestingly, structure alignment analysis with artemisinin revealed that Noscapine, Reticuline, and Aclidinium might possess similar biological action. Molecular dynamics and free energy calculations revealed that Noscapine could be a potent antimalarial agent targeting FP-2 that can be used for the treatment of malaria and need to be studied experimentally in the future.

cropCSM: designing safe and potent herbicides with graph-based signatures

Herbicides have revolutionised weed management, increased crop yields and improved profitability allowing for an increase in worldwide food security. Their widespread use, however, has also led to a rise in resistance and concerns about their environmental impact. Despite the need for potent and safe herbicidal molecules, no herbicide with a new mode of action has reached the market in 30 years. Although development of computational approaches has proven invaluable to guide rational drug discovery pipelines, leading to higher hit rates and lower attrition due to poor toxicity, little has been done in contrast for herbicide design. To fill this gap, we have developed cropCSM, a computational platform to help identify new, potent, nontoxic and environmentally safe herbicides. By using a knowledge-based approach, we identified physicochemical properties and substructures enriched in safe herbicides. By representing the small molecules as a graph, we leveraged these insights to guide the development of predictive models trained and tested on the largest collected data set of molecules with experimentally characterised herbicidal profiles to date (over 4500 compounds). In addition, we developed six new environmental and human toxicity predictors, spanning five different species to assist in molecule prioritisation. cropCSM was able to correctly identify 97% of herbicides currently available commercially, while predicting toxicity profiles with accuracies of up to 92%. We believe cropCSM will be an essential tool for the enrichment of screening libraries and to guide the development of potent and safe herbicides. We have made the method freely available through a user-friendly webserver at http://biosig.unimelb.edu.au/crop_csm.

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.

The effect of noise on the predictive limit of QSAR models

A key challenge in the field of Quantitative Structure Activity Relationships (QSAR) is how to effectively treat experimental error in the training and evaluation of computational models. It is often assumed in the field of QSAR that models cannot produce predictions which are more accurate than their training data. Additionally, it is implicitly assumed, by necessity, that data points in test sets or validation sets do not contain error, and that each data point is a population mean. This work proposes the hypothesis that QSAR models can make predictions which are more accurate than their training data and that the error-free test set assumption leads to a significant misevaluation of model performance. This work used 8 datasets with six different common QSAR endpoints, because different endpoints should have different amounts of experimental error associated with varying complexity of the measurements. Up to 15 levels of simulated Gaussian distributed random error was added to the datasets, and models were built on the error laden datasets using five different algorithms. The models were trained on the error laden data, evaluated on error-laden test sets, and evaluated on error-free test sets. The results show that for each level of added error, the RMSE for evaluation on the error free test sets was always better. The results support the hypothesis that, at least under the conditions of Gaussian distributed random error, QSAR models can make predictions which are more accurate than their training data, and that the evaluation of models on error laden test and validation sets may give a flawed measure of model performance. These results have implications for how QSAR models are evaluated, especially for disciplines where experimental error is very large, such as in computational toxicology.

An integrated virtual screening and drug repurposing strategy for the discovery of new antimalarial drugs against Plasmodium falciparum phosphatidylinositol 3-kinase

The emergence and spread of drug resistance in Plasmodium falciparum, the parasite causing the most severe form of human malaria, is a major threat to malaria control and elimination programs around the globe. With P. falciparum having evolved widespread resistance against a number of previously widely used drugs, currently, artemisinin (ART) and its derivatives are the cornerstones of first-line treatments of uncomplicated malaria. However, growing incidences of ART failure reflect the spread of ART-resistant P. falciparum strains. Despite current efforts to understand the primary cause of ART resistance due to mutations in the Kelch 13 gene (PfK13), the mechanism underlying ART resistance is still not completely unclear and no feasible strategies to counteract the causes and thereby restoring the efficiency of ART have been developed. We use a polypharmacology approach to identify potential drugs that can be used for the novel purpose (target). Of note, we have designed a multimodal stratagem to identify approved drugs with a potential antimalarial activity using computational drug reprofiling. Our investigations suggest that oxetacaine, simvastatin, repaglinide, aclidinium, propafenone, and lovastatin could be repurposed for malaria control and prevention.

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.

Variability in in vivo studies: Defining the upper limit of performance for predictions of systemic effect levels

New approach methodologies (NAMs) for chemical hazard assessment are often evaluated via comparison to animal studies; however, variability in animal study data limits NAM accuracy. The US EPA Toxicity Reference Database (ToxRefDB) enables consideration of variability in effect levels, including the lowest effect level (LEL) for a treatment-related effect and the lowest observable adverse effect level (LOAEL) defined by expert review, from subacute, subchronic, chronic, multi-generation reproductive, and developmental toxicity studies. The objectives of this work were to quantify the variance within systemic LEL and LOAEL values, defined as potency values for effects in adult or parental animals only, and to estimate the upper limit of NAM prediction accuracy. Multiple linear regression (MLR) and augmented cell means (ACM) models were used to quantify the total variance, and the fraction of variance in systemic LEL and LOAEL values explained by available study descriptors (e.g., administration route, study type). The MLR approach considered each study descriptor as an independent contributor to variance, whereas the ACM approach combined categorical descriptors into cells to define replicates. Using these approaches, total variance in systemic LEL and LOAEL values (in log₁₀-mg/kg/day units) ranged from 0.74 to 0.92. Unexplained variance in LEL and LOAEL values, approximated by the residual mean square error (MSE), ranged from 0.20-0.39. Considering subchronic, chronic, or developmental study designs separately resulted in similar values. Based on the relationship between MSE and R-squared for goodness-of-fit, the maximal R-squared may approach 55 to 73% for a NAM-based predictive model of systemic toxicity using these data as reference. The root mean square error (RMSE) ranged from 0.47 to 0.63 log₁₀-mg/kg/day, depending on dataset and regression approach, suggesting that a two-sided minimum prediction interval for systemic effect levels may have a width of 58 to 284-fold. These findings suggest quantitative considerations for building scientific confidence in NAM-based systemic toxicity predictions.

Modeling Chronic Toxicity: A Comparison of Experimental Variability With (Q)SAR/Read-Across Predictions

This study compares the accuracy of (Q)SAR/read-across predictions with the experimental variability of chronic lowest-observed-adverse-effect levels (LOAELs) from in vivo experiments. We could demonstrate that predictions of the lazy structure-activity relationships (lazar) algorithm within the applicability domain of the training data have the same variability as the experimental training data. Predictions with a lower similarity threshold (i.e., a larger distance from the applicability domain) are also significantly better than random guessing, but the errors to be expected are higher and a manual inspection of prediction results is highly recommended.

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.

CORAL: model for no observed adverse effect level (NOAEL)

The in vivo repeated dose toxicity (RDT) test is intended to provide information on the possible risk caused by repeated exposure to a substance over a limited period of time. The measure of the RDT is the no observed adverse effect level (NOAEL) that is the dose at which no effects are observed, i.e., this endpoint indicates the safety level for a substance. The need to replace in vivo tests, as required by some European Regulations (registration, evaluation authorization and restriction of chemicals) is leading to the searching for reliable alternative methods such as quantitative structure-activity relationships (QSAR). Considering the complexity of the RDT endpoint, for which data quality is limited and depends anyway on the study design, the development of QSAR for this endpoint is an attractive task. Starting from a dataset of 140 organic compounds with NOAEL values related to oral short term toxicity in rats, we developed a QSAR model based on optimal descriptors calculated with simplified molecular input-line entry systems and the graph of atomic orbitals by the Monte Carlo method, using CORAL software. Three different splits into the training, calibration, and validation sets are studied. The mechanistic interpretation of these models in terms of molecular fragment with positive or negative contributions to the endpoint is discussed. The probabilistic definition for the domain of applicability is suggested.

QSAR as a random event: a case of NOAEL

Quantitative structure-activity relationships (QSAR) for no observed adverse effect levels (NOAEL, mmol/kg/day, in logarithmic units) are suggested. Simplified molecular input line entry systems (SMILES) were used for molecular structure representation. Monte Carlo method was used for one-variable models building up for three different splits into the "visible" training set and "invisible" validation. The statistical quality of the models for three random splits are the following: split 1 n = 180, r (2) = 0.718, q (2) = 0.712, s = 0.403, F = 454 (training set); n = 17, r (2) = 0.544, s = 0.367 (calibration set); n = 21, r (2) = 0.61, s = 0.44, r m (2) = 0.61 (validation set); split 2 n = 169, r (2) = 0.711, q (2) = 0.705, s = 0.409, F = 411 (training set); n = 27, r (2) = 0.512, s = 0.461 (calibration set); n = 22, r (2) = 0.669, s = 0.360, r m (2) = 0.63 (validation set); split 3 n = 172, r (2) = 0.679, q (2) = 0.672, s = 0.420, F = 360 (training set); n = 19, r (2) = 0.617, s = 0.582 (calibration set); n = 21, r (2) = 0.627, s = 0.367, r m (2) = 0.54 (validation set). All models are built according to OCED principles.

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.

pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures

Drug development has a high attrition rate, with poor pharmacokinetic and safety properties a significant hurdle. Computational approaches may help minimize these risks. We have developed a novel approach (pkCSM) which uses graph-based signatures to develop predictive models of central ADMET properties for drug development. pkCSM performs as well or better than current methods. A freely accessible web server (http://structure.bioc.cam.ac.uk/pkcsm), which retains no information submitted to it, provides an integrated platform to rapidly evaluate pharmacokinetic and toxicity properties.

Automated and reproducible read-across like models for predicting carcinogenic potency

Several qualitative (hazard-based) models for chronic toxicity prediction are available through commercial and freely available software, but in the context of risk assessment a quantitative value is mandatory in order to be able to apply a Margin of Exposure (predicted toxicity/exposure estimate) approach to interpret the data. Recently quantitative models for the prediction of the carcinogenic potency have been developed, opening some hopes in this area, but this promising approach is currently limited by the fact that the proposed programs are neither publically nor commercially available. In this article we describe how two models (one for mouse and one for rat) for the carcinogenic potency (TD50) prediction have been developed, using lazar (Lazy Structure Activity Relationships), a procedure similar to read-across, but automated and reproducible. The models obtained have been compared with the recently published ones, resulting in a similar performance. Our aim is also to make the models freely available in the near future thought a user friendly internet web site.

REACH and in silico methods: an attractive opportunity for medicinal chemists

REACH, the most ambitious chemical legislation in the world, provides unprecedented opportunities for medicinal chemists. Companies must report (eco)toxicological information about their chemicals, disseminated to the public domain by the European Chemicals Agency after their registration. The availability of this wealth of new toxicological data, together with the promotion of REACH of in silico methods, appoints medicinal chemists to a leading role in the regulatory hazard assessment process. In fact, Quantitative Structure-Activity Relation ship (QSAR) models and predictive toxicology have been applied in drug design and development for decades. Here, we discuss toxicological endpoints and areas where further development is needed to provide an enlightened appraisal of this attractive new opportunity.

Establishing the level of safety concern for chemicals in food without the need for toxicity testing

There is demand for methodologies to establish levels of safety concern associated with dietary exposures to chemicals for which no toxicological data are available. In such situations, the application of in silico methods appears promising. To make safety statement requires quantitative predictions of toxicological reference points such as no observed adverse effect level and carcinogenic potency for DNA-reacting chemicals. A decision tree (DT) has been developed to aid integrating exposure information and predicted toxicological reference points obtained with quantitative structure activity relationship ((Q)SAR) software and read across techniques. The predicted toxicological values are compared with exposure to obtain margins of exposure (MoE). The size of the MoE defines the level of safety concern and should account for a number of uncertainties such as the classical interspecies and inter-individual variability as well as others determined on a case by case basis. An analysis of the uncertainties of in silico approaches together with results from case studies suggest that establishing safety concern based on application of the DT is unlikely to be significantly more uncertain than based on experimental data. The DT makes a full use of all data available, ensuring an adequate degree of conservatism. It can be used when fast decision making is required.

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.

QSAR modelling toxicity toward rats of inorganic substances by means of CORAL

CORAL (‘CORrelation And Logic’) is freeware available on the Internet www.insilico.eu/coral The aim of this program is to establish a correlation between an endpoint and descriptors calculated with a simplified molecular input line entry system (SMILES). Three models calculated by CORAL for toxicity towards rat (-pLD50) of inorganic substances (three random splits) have shown that CORAL could be a good tool to model this endpoint. The average statistical characteristics for the CORAL models are the following: n=38, r2=0.8461, q2=0.8298, s=0.273, F=198 (subtraining set); n=37, r2=0.8144, s=0.322, F=154 (calibration set); and n=10, r2=0.8004, Rm (test)2 =0.7815, s=0.240, F=32 (validation set).