COSMOperm: Mechanistic Prediction of Passive Membrane Permeability for Neutral Compounds and Ions and Its pH Dependence.

We present a new and entirely mechanistic COSMOperm method to predict passive membrane permeabilities for neutral compounds, as well as anions and cations. The COSMOperm approach is based on compound-specific free energy profiles within a membrane of interest from COSMO-RS (conductor-like screening model for realistic solvation) calculations. These are combined with membrane layer-specific diffusion coefficients, for example, in the water phase, the polar head groups, and the alkyl tails of biochemical phospholipid bilayers. COSMO-RS utilizes first-principle quantum chemical structures and physically sound intermolecular interactions (electrostatic, hydrogen bond, and van der Waals). For this reason, it is unbiased toward different application scenarios, such as in cosmetics and industrial chemical or pharmaceutical industries. A fully predictive calculation of passive permeation through phospholipid bilayer membranes results in a performance of r2 = 0.92; rmsd = 0.90 log10 units for neutral compounds and anions, as compared to gold standard black lipid membrane experiments. It will be demonstrated that new membrane types can be generated by the related COSMOplex method and directly used for permeability studies by COSMOperm.

COSMOplex: self-consistent simulation of self-organizing inhomogeneous systems based on COSMO-RS.

During the past 20 years, the efficient combination of quantum chemical calculations with statistical thermodynamics by the COSMO-RS method has become an important alternative to force-field based simulations for the accurate prediction of free energies of molecules in liquid systems. While it was originally restricted to homogeneous liquids, it later has been extended to the prediction of the free energy of molecules in inhomogeneous systems such as micelles, biomembranes, or liquid interfaces, but these calculations were based on external input about the structure of the inhomogeneous system. Here we report the rigorous extension of COSMO-RS to a self-consistent prediction of the structure and the free energies of molecules in self-organizing inhomogeneous systems. This extends the application range to many new areas, such as the prediction of micellar structures and critical micelle concentrations, finite loading effects in micelles and biomembranes, the free energies and structure of liquid interfaces, microemulsions, and many more related topics, which often are of great practical importance.

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.

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.

Prediction of michael-type acceptor reactivity toward glutathione.

A model has been developed to predict the kinetic rate constants (k(GSH)) of α,ß-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 ß-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.

The in chemico-in silico interface: challenges for integrating experimental and computational chemistry to identify toxicity.

A number of toxic effects are brought about by the covalent interaction between the toxicant and biological macromolecules. In chemico assays are available that attempt to identify reactive compounds. These approaches have been developed independently for pharmaceuticals and for other nonpharmaceutical compounds. The assays vary widely in terms of the macromolecule (typically a peptide) and the analytical technique utilised. For both sets of methods, there are great opportunities to capture in chemico information by using in silico methods to provide computational tools for screening purposes. In order to use these in chemico and in silico methods, integrated testing strategies are required for individual toxicity endpoints. The potential for the use of these approaches is described, and a number of recommendations to improve this extremely useful technique, in terms of implementing the Three Rs in toxicity testing, are presented.

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.

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.

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, eta(H), which in turn was quantified through empirically defined site-specific effective donor and acceptor energies, EE(occ) and EE(vac). 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, r(2), 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 (r(2) = 0.92).