Johannes Schwöbel

Measurement and Estimation of Electrophilic Reactivity for Predictive Toxicology

Measurement and Estimation of Electrophilic Reactivity for Predictive Toxicology Johannes A. H. Schw€obel, Yana K. Koleva, Steven J. Enoch, Fania Bajot,MarkHewitt, Judith C.Madden, David W. Roberts, Terry W. Schultz, and Mark T. D. Cronin* School of Pharmacy and Chemistry, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, England College of Veterinary Medicine, Department of Comparative Medicine, The University of Tennessee, 2407 River Drive, Knoxville, Tennessee 37996-4543, United States

Prediction of Michael-Type Acceptor Reactivity toward Glutathione

A model has been developed to predict the kinetic rate constants (k(GSH)) of α,β-unsaturated Michael acceptor compounds for their reaction with glutathione (GSH). The model uses the local charge-limited electrophilicity index ω(q) [Wondrousch, D., et al. (2010) J. Phys. Chem. Lett. 1, 1605-1610] at the β-carbon atom as a descriptor of reactivity, a descriptor for resonance stabilization of the transition state, and one for steric hindrance at the reaction sites involved. Overall, the Michael addition model performs well (r² = 0.91; rms = 0.34). It includes various classes of compounds with double and triple bonds, linear and cyclic systems, and compounds with and without substituents in the α-position. Comparison of experimental and predicted rate constants demonstrates even better performance of the model for individual classes of compounds (e.g., for aldehydes, r² = 0.97 and rms = 0.15; for ketones, r² = 0.95 and rms = 0.35). The model also allows for the prediction of the RC₅₀ values from the Schultz chemoassay, the accuracy being close to the interlaboratory experimental error. Furthermore, k(GSH) and associated RC₅₀ values can be predicted in cases where experimental measurements are not possible or restricted, for example, because of low solubility or high volatility. The model has the potential to provide information to assist in the assessment and categorization of toxicants and in the application of integrated testing strategies.

Prediction of the Intrinsic Hydrogen Bond Acceptor Strength of Chemical Substances from Molecular Structure

Hydrogen bonding affects the partitioning of organic compounds between environmental and biological compartments as well as the three-dimensional shape of macromolecules. Using the semiempirical quantum chemical AM1 level of calculation, we have developed a model to predict the site-specific hydrogen bond (HB) acceptor strength from ground-state properties of the individual compounds. At present, the model parametrization is confined to compounds with one HB acceptor site of the following atom types: N, O, S, F, Cl, and Br that act as lone-pair HB acceptors, and pi-electron (aromatic or conjugated) systems with the associated C atoms as particularly weak HB acceptors. The HB acceptor strength is expressed in terms of the Abraham parameter B and calculated from local molecular parameters, taking into account electrostatic, polarizability, and charge transfer contributions according to the Morokuma concept. For a data set of 383 compounds, the squared correlation coefficient r2 is 0.97 when electrostatic potential (ESP) derived net atomic charges are employed, and the root-mean-square (rms) error is 0.04 that is in the range of experimental uncertainty. The model is validated using an extended leave-50%-out approach, and its performance is comparatively analyzed with the ones of earlier introduced ab initio (HF/6-31G**) and density functional theory (B3LYP/6-31G**) models as well as of two increment methods with respect to the total compound set as well as HB acceptor type subsets. The discussion includes an explorative model application to amides and organophosphates that demonstrates the robustness of the approach, and further opportunities for model extensions.

Modeling the H bond donor strength of OH, NH, and CH sites by local molecular parameters

A quantum chemical model is introduced to predict the H‐bond donor strength of monofunctional organic compounds from their ground‐state electronic properties. The model covers OH, NH, and CH as H‐bond donor sites and was calibrated with experimental values for the Abraham H‐bond donor strength parameter A using the ab initio and density functional theory levels HF/6‐31G** and B3LYP/6‐31G**. Starting with the Morokuma analysis of hydrogen bonding, the electrostatic (ES), polarizability (PL), and charge transfer (CT) components were quantified employing local molecular parameters. With hydrogen net atomic charges calculated from both natural population analysis and the ES potential scheme, the ES term turned out to provide only marginal contributions to the Abraham parameter A, except for weak hydrogen bonds associated with acidic CH sites. Accordingly, A is governed by PL and CT contributions. The PL component was characterized through a new measure of the local molecular hardness at hydrogen, η(H), which in turn was quantified through empirically defined site‐specific effective donor and acceptor energies, EEocc and EEvac. The latter parameter was also used to address the CT contribution to A. With an initial training set of 77 compounds, HF/6‐31G** yielded a squared correlation coefficient, r2, of 0.91. Essentially identical statistics were achieved for a separate test set of 429 compounds and for the recalibrated model when using all 506 compounds. B3LYP/6‐31G** yielded slightly inferior statistics. The discussion includes subset statistics for compounds containing OH, NH, and active CH sites and a nonlinear model extension with slightly improved statistics (r2 = 0.92).

Prediction of the Intrinsic Hydrogen Bond Acceptor Strength of Organic Compounds by Local Molecular Parameters

A quantum chemical model has been developed for predicting the hydrogen bond (HB) acceptor strength of monofunctional organic compounds from electronic ground-state properties of the single molecules. Local molecular parameters are used to quantify electrostatic, polarizability, and charge transfer components to hydrogen bonding, employing the ab initio and density functional theory levels HF/6-31G** and B3LYP/6-31G**. The model can handle lone pairs of intermediate and strong HB acceptor heteroatoms (N, O, S) as well as of weak HB acceptor halogens (F, Cl, Br) and includes also olefinic, alkyne, and aromatic pi-bonds as weak HB acceptor sites. The model calibration with 403 compounds and experimental values for the Abraham HB acceptor strength B yielded squared correlation coefficients r(2) around 0.95, outperforming existing fragment-based schemes. Model validation was performed applying a leave-50%-out procedure, yielding predictive squared correlation coefficients q(2) of around 0.95 for the subsets that both cover the whole chemical domain as well as (almost) the whole target value range of the data set.

Examination of Michael addition reactivity towards glutathione by transition-state calculations

Kinetic rate constants (k GSH) for the reaction of compounds acting as Michael acceptors with glutathione (GSH) were modelled by quantum chemical transition-state calculations at the B3LYP/6-31G** and B3LYP/TZVP level. The data set included α, β-unsaturated aldehydes, ketones and esters, with double bonds and triple bonds, linear and cyclic systems, both with and without substituents in the α-position. Predicted values for k GSH were found to be in good agreement with experimental k GSH values. Factors affecting rate constants have been elucidated, especially solvent effects and the influence of steric hindrance. Solvent effects were examined by adding explicit solvent molecules to the system and by using a polarizable continuum solvent model. Detailed analysis of transition-state energies shows that the reaction is reversible. The reactive enolic intermediate plays an important role in Michael addition to GSH, while the subsequent keto-enol-tautomerism is not rate limiting.

Modelling acute oral mammalian toxicity. 1. Definition of a quantifiable baseline effect.

Quantitative structure-activity relationships (QSARs) provide a useful tool to define a relationship between chemical structure and toxicity and allow for the prediction of the toxicity of untested chemicals. QSAR models based upon an anaesthetic or narcosis mechanism represent a baseline, or minimum, toxicity, i.e. unless a chemical acts by another, more specific, mechanism, its toxicity will be predicted by such models. The aim of this investigation was to develop baseline models for the acute toxicity of chemicals to mammals (rat and mouse) following the oral route of administration. The availability of such baseline toxicity models for mammalian species can provide a probe for testing new chemicals with respect to their molecular mechanism of toxicity. Multiple-regression type structure-toxicity relationships were derived . (i.e., from oral log LD(50)(-1) data for mammalian species (rat and mouse) and the 1-octanol/water partition coefficient (log P) of classic non-polar narcotics). Subsequently, these models were used to distinguish between reactive chemicals of different mechanistic domains and baseline toxic chemicals. Comparison of measured toxicity data for oral rat and mouse LD(50) with predictions from baseline QSAR provides a means of identifying mechanistic categories and for categorising more specific acute mechanisms.

Application of a computational model for Michael addition reactivity in the prediction of toxicity to Tetrahymena pyriformis.

A computational model to predict acute aquatic toxicity to the ciliate Tetrahymena pyriformis has been developed. A general prediction of toxicity can be based on three consecutive steps: 1. Identification of a potential reactive mechanism via structural alerts; 2. Confirmation and quantification of (bio)chemical reactivity; 3. Establishing a relationship between calculated reactivity and toxicity. The method described herein uses a combination of a reactive toxicity (RT) model, including computed kinetic rate constants for adduct formation (log k) via a Michael acceptor mechanism of action, and baseline toxicity (BT), modelled by hydrophobicity (octanol-water partition coefficient). The maximum of the RT and BT values defines acute toxicity for a particular compound. The reactive toxicity model is based on site-specific steric and quantum chemical ground state electronic properties. The performance of the model was examined in terms of predicting the toxicity of 106 potential Michael acceptor compounds covering several classes of compounds (aldehydes, ketones, esters, heterocycles). The advantages of the computational method are described. The method allows for a closer and more transparent mechanistic insight into the molecular initiating events of toxicological endpoints.

Quantifying intrinsic chemical reactivity of molecular structural features for protein binding and reactive toxicity, using the MOSES chemoinformatics system

Covalent binding of xenobiotic compounds to endogenous biomolecular sites, e.g. protein residues, leads to potentially irreversible toxic effects such as enhanced acute toxicity or skin sensitization [1]. This mechanistic knowledge provides the basis for the in silico prediction of these toxicities, as required by the EU REACH legislation and the EU Cosmetics Directive. A general toxicity prediction can be based on three consecutive steps [2]: (1.) Identification of a potential reactive protein binding mechanism via a set of molecular structural features. Those structural features can be encoded by the Chemical Subgraph Representation Markup Language (CSRML), that supports a flexible annotation of meta information, including physicochemical properties as annotations. (2.) Confirmation and quantification of (bio)chemical reactivity. The potential for a chemical to be reactive can be captured by mechanistically based QSAR models. This intrinsic reactivity is calculated rapidly by descriptors of the chemoinformatics platform Molecular Structure Encoding System (MOSES) [3]. It represents electronic, steric and resonance effects in chemical structures. The performances obtained by these reactivity models are close to time-consuming quantum chemical reactivity calculations, e.g., se = 0.44 versus 0.40 for glutathione adduct formation via Michael addition, comparing predicted values to an experimental reactivity dataset [1]. (3.) Establishing a relationship between calculated reactivity and toxicity. The predicted intrinsic reactivity values were linked to the computational prediction for different modes of toxic action, with good correlations between predicted and in vitro toxicity (up to r2=0.86). The combined use of structural information and computed reactivity could assist in the non-animal based risk assessment of chemicals for regulatory purposes and in the application of integrated testing strategies (ITS). The research has received funding from the EU FP7 COSMOS Project (grant agreement n° 266835) and financing from COLIPA.

Application of a chemical reactivity database to predict toxicity for reactive mechanisms

Covalent binding of xenobiotic electrophiles to nucleophilic endogenous biomolecules, e.g. peptides or DNA, is a common molecular initiating event, leading to potentially irreversible toxic effects such as enhanced acute toxicity, skin sensitisation, or mutagenicity. This knowledge provides the basis for the in silico prediction of these toxicities. The potential for a chemical to be reactive can be determined experimentally by a number of chemical tests and therefore can be captured computationally to form (Q)SARs. Providing a source of in chemico data for the reactivity of electrophiles with reference nucleophiles could assist in the non-animal based risk assessment of chemicals for regulatory purposes and in the application of integrated testing strategies (ITS). For this reason, we have compiled a database from a full range of chemical reactivity assays containing various reactivity data of numerous electrophiles forming peptide and DNA adducts. This includes reactivity data, kinetic rate constants, and qualitative information regarding the adducts formed. The data collection facilitates the in silico profiling of toxicologically relevant compounds by grouping and category approaches, and allows for the combination of the following information: the identification of electrophilic compounds; their mechanistic applicability domain and compound class; physical-chemical properties; reactivity data; and toxicological data. These experimental reactivity data were linked to the computational prediction of reactive mechanisms for different modes of toxic action (e.g. acute aquatic toxicity), at which the results indicated that physically meaningful parameters are suitable to explain the varying behaviour of electrophiles. This could be applied for screening purposes based on structural information and the reactivity data could be used to elucidate mechanisms of toxic effects. The funding of the EU FP6 InSilicoTox Marie Curie Project (MTKD-CT-2006-42328) is gratefully acknowledged.