Computational Models for Human and Animal Hepatotoxicity with a Global Application Scope.

Hepatic toxicity is a key concern for novel pharmaceutical drugs since it is difficult to anticipate in preclinical models, and it can originate from pharmacologically unrelated drug effects, such as pathway interference, metabolism, and drug accumulation. Because liver toxicity still ranks among the top reasons for drug attrition, the reliable prediction of adverse hepatic effects is a substantial challenge in drug discovery and development. To this end, more effort needs to be focused on the development of improved predictive in-vitro and in-silico approaches. Current computational models often lack applicability to novel pharmaceutical candidates, typically due to insufficient coverage of the chemical space of interest, which is either imposed by size or diversity of the training data. Hence, there is an urgent need for better computational models to allow for the identification of safe drug candidates and to support experimental design. In this context, a large data set comprising 3712 compounds with liver related toxicity findings in humans and animals was collected from various sources. The complex pathology was clustered into 21 preclinical and human hepatotoxicity endpoints, which were organized into three levels of detail. Support vector machine models were trained for each endpoint, using optimized descriptor sets from chemometrics software. The optimized global human hepatotoxicity model has high sensitivity (68%) and excellent specificity (95%) in an internal validation set of 221 compounds. Models for preclinical endpoints performed similarly. To allow for reliable prediction of "truly external" novel compounds, all predictions are tagged with confidence parameters. These parameters are derived from a statistical analysis of the predictive probability densities. The whole approach was validated for an external validation set of 269 proprietary compounds. The models are fully integrated into our early safety in-silico workflow.

Predicting Michael-acceptor reactivity and toxicity through quantum chemical transition-state calculations.

The electrophilic reactivity of Michael acceptors is an important determinant of their toxicity. For a set of 35 α,ß-unsaturated aldehydes, ketones and esters with experimental rate constants of their reaction with glutathione (GSH), k(GSH), quantum chemical transition-state calculations of the corresponding Michael addition of the model nucleophile methane thiol (CH(3)SH) have been performed at the B3LYP/6-31G** level, focusing on the 1,2-olefin addition pathway without and with initial protonation. Inclusion of Boltzmann-weighting of conformational flexibility yields intrinsic reaction barriers ΔE(‡) that for the case of initial protonation correctly reflect the structural variation of k(GSH) across all three compound classes, except that they fail to account for a systematic (essentially incremental) decrease in reactivity upon α-substitution. By contrast, the reduction in k(GSH) through ß-substitution is well captured by ΔE(‡). Empirical correction for the α-substitution effect yields a high squared correlation coefficient (r(2) = 0.96) for the quantum chemical prediction of log k(GSH), thus enabling an in silico screening of the toxicity-relevant electrophilicity of α,ß-unsaturated carbonyls. The latter is demonstrated through application of the calculation scheme for a larger set of 46 Michael-acceptor aldehydes, ketones and esters with experimental values for their toxicity toward the ciliates Tetrahymena pyriformis in terms of 50% growth inhibition values after 48 h exposure (EC(50)). The developed approach may add in the predictive hazard evaluation of α,ß-unsaturated carbonyls such as for the European REACH (Registration, Evaluation, Authorization and Restriction of Chemicals) Directive, enabling in particular an early identification of toxicity-relevant Michael-acceptor reactivity.

The borderline range of toxicological methods: Quantification and implications for evaluating precision.

Test methods to assess the skin sensitization potential of a substance usually use threshold criteria to dichotomize continuous experimental read-outs into yes/no conclusions. The threshold criteria are prescribed in the respective OECD test guidelines and the conclusion is used for regulatory hazard assessment, i.e., classification and labelling of the substance. We can identify a borderline range (BR) around the classification threshold within which test results are inconclusive due to a test method's biological and technical variability. We quantified BRs in the prediction models of the non-animal test methods DPRA, LuSens and h-CLAT, and of the animal test LLNA, respectively. Depending on the size of the BR, we found that between 6% and 28% of the substances in the sets tested with these methods were considered borderline. When the results of individual non-animal test methods were combined into integrated testing strategies (ITS), borderline test results of individual tests also affected the overall assessment of the skin sensitization potential of the testing strategy. This was analyzed for the 2-out-of-3 ITS: Four out of 40 substances (10%) were considered borderline. Based on our findings we propose expanding the standard binary classification of substances into "positive"/"negative" or "hazardous"/"non-hazardous" by adding a "borderline" or "inconclusive" alert for cases where test results fall within the borderline range.

Model Suite for Predicting the Aquatic Toxicity of α,ß-Unsaturated Esters Triggered by Their Chemoavailability.

For a Michael-acceptor set of 45 α,ß-unsaturated esters, the 2(nd) -order rate constant of reaction with glutathione, log kGSH , was modeled through the quantum chemical reaction barrier (ΔE(≠) ) employing methane thiol as model nucleophile. Regression of their 48-h toxicity toward the ciliates Tetrahymena pyriformis (log EC50 , 50 % growth inhibition) on log Kow (octanol/water partition coefficient) and log kGSH revealed a variation in the relative weights of hydrophobicity and electrophilic reactivity as determinants of the aquatic toxicity. The difference DKk =log Kow -log kGSH turned out as a suitable means for predictively discriminating between narcosis-level (DKk >3.0) and excess-toxic (DKk <2.0) compounds. In the intermediate DKk range (2.0≤DKk ≤3.0), both narcosis-level and reactive-toxicity models are applicable for predicting aquatic toxicity. As such, DKk represents the chemoavailability of Michael-acceptor esters, characterizing their likelihood for undertaking covalent reactions with thiol sites of endogenous peptides and proteins. At the same time, DKk introduces a straightforward way for characterizing the applicability domain of QSAR (quantitative structure-activity relationship) models for predicting the toxicity of Michael-acceptor esters. The resultant model suite comprising QSARs for reactive toxicity and baseline narcosis is triggered by the compounds' chemoavailability, and yields predictions superior to existing approaches.