Computational identification of structural factors affecting the mutagenic potential of aromatic amines: study design and experimental validation.

A grid-based, alignment-independent 3D-SDAR (three-dimensional spectral data-activity relationship) approach based on simulated 13C and 15N NMR chemical shifts augmented with through-space interatomic distances was used to model the mutagenicity of 554 primary and 419 secondary aromatic amines. A robust modeling strategy supported by extensive validation including randomized training/hold-out test set pairs, validation sets, "blind" external test sets as well as experimental validation was applied to avoid over-parameterization and build Organization for Economic Cooperation and Development (OECD 2004) compliant models. Based on an experimental validation set of 23 chemicals tested in a two-strain Salmonella typhimurium Ames assay, 3D-SDAR was able to achieve performance comparable to 5-strain (Ames) predictions by Lhasa Limited's Derek and Sarah Nexus for the same set. Furthermore, mapping of the most frequently occurring bins on the primary and secondary aromatic amine structures allowed the identification of molecular features that were associated either positively or negatively with mutagenicity. Prominent structural features found to enhance the mutagenic potential included: nitrobenzene moieties, conjugated π-systems, nitrothiophene groups, and aromatic hydroxylamine moieties. 3D-SDAR was also able to capture "true" negative contributions that are particularly difficult to detect through alternative methods. These include sulphonamide, acetamide, and other functional groups, which not only lack contributions to the overall mutagenic potential, but are known to actively lower it, if present in the chemical structures of what otherwise would be potential mutagens.

Why are most phospholipidosis inducers also hERG blockers?

Recent reports have noted that a number of compounds that block the human Ether-à-go-go related gene (hERG) ion channel also induce phospholipidosis (PLD). To explore a hypothesis explaining why most PLD inducers are also hERG inhibitors, a modeling approach was undertaken with data sets comprised of 4096 compounds assayed for hERG inhibition and 5490 compounds assayed for PLD induction. To eliminate the chemical domain effect, a filtered data set of 567 compounds tested in quantitative high-throughput screening (qHTS) format for both hERG inhibition and PLD induction was constructed. Partial least squares (PLS) modeling followed by 3D-SDAR mapping of the most frequently occurring bins and projection on to the chemical structure suggested that both adverse effects are driven by similar structural features, namely two aromatic rings and an amino group forming a three-center toxicophore. Non-parametric U-tests performed on the original 3D-SDAR bins indicated that the distance between the two aromatic rings is the main factor determining the differences in activity; at distances of up to about 5.5 Å, a phospholipidotic compound would also inhibit hERG, while at longer distances, a sharp reduction of the PLD-inducing potential leaves only a well-pronounced hERG blocking effect. The hERG activity itself diminishes after the distance between the centroids of the two aromatic rings exceeds 12.5 Å. Further comparison of the two toxicophores revealed that the almost identical aromatic rings to amino group distances play no significant role in distinguishing between PLD and hERG activity. The hypothesis that the PLD toxicophore appears to be a subset of the hERG toxicophore explains why about 80% of all phospholipidotic chemicals (the remaining 20% are thought to act via a different mechanism) also inhibit the hERG ion channel. These models were further validated in large-scale qHTS assays testing 1085 chemicals for their PLD-inducing potential and 1570 compounds for hERG inhibition. After removal of the modeling and experimental inconclusive compounds, the area under the receiver-operating characteristic (ROC) curve was 0.92 for the PLD model and 0.87 for the hERG model. Due to the exceptional ability of these models to recognize safe compounds (negative predictive values of 0.99 for PLD and 0.94 for hERG were achieved), their use in regulatory settings might be particularly useful.

3D-SDAR modeling of hERG potassium channel affinity: A case study in model design and toxicophore identification.

A dataset of 237 human Ether-à-go-go Related Gene (hERG) potassium channel inhibitors (180 of which were used for model building and validation, whereas 57 constituted the "true" external prediction set) collected from 22 literature sources was modeled by 3D-SDAR. To produce reliable and reproducible classification models for hERG blocking, the initial set of 180 chemicals was split into two subsets: a balanced modeling set consisting of 118 compounds and an unbalanced validation set comprised of 62 compounds. A PLS bagging-like algorithm written in Matlab was used to process the data and assign each compound to one of the two (hERG+ or hERG-) activity classes. The best predictive model evaluated on the basis of a fully randomized hold-out test set (comprising 20% of the modeling set) used 4 latent variables and a grid of 6ppm×6ppm×1Å in the C-C region, 6ppm×30ppm×1Å in the C-N region, and 30ppm×30ppm×1Å in the N-N region. An overall accuracy of 0.84 was obtained for both the hold-out test set and the validation set. Further, an external prediction set consisting of 57 drugs and drug derivatives was used to estimate the true predictive power of the reported 3D-SDAR model - a slight reduction of the overall accuracy down to 0.77 was observed. 3D-SDAR map of the most frequently occurring bins and their projection on the standard coordinate space of the chemical structures allowed identification of a three-center toxicophore composed of two aromatic rings and an amino group. A U test along the distance axis of the most frequently occurring 3D-SDAR bins was used to set the distance limits of the toxicophore. This toxicophore was found to be similar to an earlier reported phospholipidosis (PLD) toxicophore.

Alignment-independent technique for 3D QSAR analysis.

Molecular biochemistry is controlled by 3D phenomena but structure-activity models based on 3D descriptors are infrequently used for large data sets because of the computational overhead for determining molecular conformations. A diverse dataset of 146 androgen receptor binders was used to investigate how different methods for defining molecular conformations affect the performance of 3D-quantitative spectral data activity relationship models. Molecular conformations tested: (1) global minimum of molecules' potential energy surface; (2) alignment-to-templates using equal electronic and steric force field contributions; (3) alignment using contributions "Best-for-Each" template; (4) non-energy optimized, non-aligned (2D > 3D). Aggregate predictions from models were compared. Highest average coefficients of determination ranged from R Test (2) = 0.56 to 0.61. The best model using 2D > 3D (imported directly from ChemSpider) produced R Test (2) = 0.61. It was superior to energy-minimized and conformation-aligned models and was achieved in only 3-7 % of the time required using the other conformation strategies. Predictions averaged from models built on different conformations achieved a consensus R Test (2) = 0.65. The best 2D > 3D model was analyzed for underlying structure-activity relationships. For the compound strongest binding to the androgen receptor, 10 substructural features contributing to binding were flagged. Utility of 2D > 3D was compared for two other activity endpoints, each modeling a medium sized data set. Results suggested that large scale, accurate predictions using 2D > 3D SDAR descriptors may be produced for interactions involving endocrine system nuclear receptors and other data sets in which strongest activities are produced by fairly inflexible substrates.

Partial least square and k-nearest neighbor algorithms for improved 3D quantitative spectral data-activity relationship consensus modeling of acute toxicity.

A diverse set of 154 chemicals that included US Food and Drug Administration-regulated compounds tested for their aquatic toxicity in Daphnia magna were modeled by a 3-dimensional quantitative spectral data-activity relationship (3D-QSDAR). Two distinct algorithms, partial least squares (PLS) and Tanimoto similarity-based k-nearest neighbors (KNN), were used to process bin occupancy descriptor matrices obtained after tessellation of the 3D-QSDAR space into regularly sized bins. The performance of models utilizing bins ranging in size from 2 ppm × 2 ppm × 0.5 Å to 20 ppm × 20 ppm × 2.5 Å was explored. Rigorous quality-control criteria were imposed: 1) 100 randomized 20% hold-out test sets were generated and the average R(2) test of the respective models was used as a measure of their performance, and 2) a Y-scrambling procedure was used to identify chance correlations. A consensus between the best-performing composite PLS model using 0.5 Å × 14 ppm × 14 ppm bins and 10 latent variables (average R(2) test = 0.770) and the best composite KNN model using 0.5 Å × 8 ppm × 8 ppm and 2 neighbors (average R(2) test = 0.801) offered an improvement of about 7.5% (R(2) test consensus = 0.845). Projection of the most frequently occurring bins on the standard coordinate space indicated that the presence of a primary or secondary amino group-substituted aromatic systems-would result in an increased toxic effect in Daphnia. The presence of a second aromatic ring with highly electronegative substituents 5 Å to 7 Å apart from the first ring would lead to a further increase in toxicity.

Application of the QSPR approach to the boiling points of azeotropes.

CODESSA Pro derivative descriptors were calculated for a data set of 426 azeotropic mixtures by the centroid approximation and the weighted-contribution-factor approximation. The two approximations produced almost identical four-descriptor QSPR models relating the structural characteristic of the individual components of azeotropes to the azeotropic boiling points. These models were supported by internal and external validations. The descriptors contributing to the QSPR models are directly related to the three components of the enthalpy (heat) of vaporization.

QSAR modeling, synthesis and bioassay of diverse leukemia RPMI-8226 cell line active agents.

A rigorous QSAR modeling procedure employing CODESSA PRO descriptors has been utilized for the prediction of more efficient anti-leukemia agents. Experimental data concerning the effect on leukemia RPMI-8226 cell line tumor growth of 34 compounds (treated at a dose of 10 µM) was related to their chemical structures by a 4-descriptor QSAR model. Four bis(oxy)bis-urea and bis(sulfanediyl)bis-urea derivatives (4a, 4b, 8, 11a) predicted as active by this model, together with 11b predicted to be of low activity, were synthesized and screened for anti-tumor activity utilizing 55 different tumor cell lines. Compounds 8 and 11a showed anti-tumor properties against most of the adopted cell lines with growth inhibition exceeding 50%. The highly promising preliminary anti-tumor properties of compounds 8 and 11a, were screened at serial dilutions (10(-4)-10(-8) µM) for determination of their GI(50) and TGI against the screened human tumor cell lines. Compound 11a (GI(50) = 1.55, TGI = 8.68 µM) is more effective than compound 8 (GI(50)=58.30, TGI = > 100 µM) against the target leukemia RPMI-8226 cell line. Compound 11a also exhibits highly pronounced anti-tumor properties against NCI-H226, NCI-H23 (non-small cell lung cancer), COLO 205 (colon cancer), SNB-75 (CNS cancer), OVCAR-3, SK-OV-3 (ovarian cancer), A498 (renal cancer) MDA-MB-231/ATCC and MDA-MB-468 (breast cancer) cell lines (GI(50) = 1.95, 1.61, 1.38, 1.56, 1.30, 1.98, 1.18, 1.85, 1.08, TGI = 8.35, 6.01, 2.67, 8.59, 4.01, 7.01, 5.62, 6.38, 5.63 µM, respectively). Thus 11a could be a suitable lead towards the design of broad spectrum anti-tumor active agents targeting various human tumor cell lines.

A computational study of the binding of 3-(arylidene) anabaseines to two major brain nicotinic acetylcholine receptors and to the acetylcholine binding protein.

Nicotinic acetylcholine receptors (nAChRs) have become targets for drug development in recent years. 3-(2,4-dimethoxybenzylidene)-anabaseine (DMXBA), which selectively stimulates the alpha7 nAChR, has been shown to alleviate some cognitive deficits associated with schizophrenia. In this paper we report an analysis of the interactions between 47 arylidene-anabaseines (including 45 benzylidene-anabaseines) and rat brain alpha7 and alpha4beta2 nicotinic acetylcholine receptors, using three different modeling techniques, namely 2D-QSAR, 3D-QSAR and molecular docking to the Aplysia californica acetylcholine binding protein (AChBP), a water soluble, homomeric nAChR surrogate receptor with a known crystal structure. Our investigation indicates the importance of: (1) the nitrogen atom of the tetrahydropyridyl (THP) ring for hydrogen bond formation; (2) pi-pi interactions between the aromatic rings of the ligands and the nAChBP binding site; (3) molecular surface recognition expressed in terms of steric complimentarity. On the basis of the 3D-QSAR results, bulky substituents at positions 2 (and due to the rotational freedom also at position 6) and 4 of the benzylidene moiety, with highly electronegative atoms projecting approximately 3-3.5A away from the benzylidene ring at position 4 seem optimal for enhancing binding affinity to the alpha7 nAChR.

Correlation of the photolysis half-lives of polychlorinated dibenzo-p-dioxins and dibenzofurans with molecular structure.

The photolysis half-lives of 70 polychlorinated dibenzo-p-dioxins and dibenzofurans are correlated with their molecular structures by a QSPR model (R(2) = 0.72) comprising three bond-energy-related descriptors. The photodegradation depends on the stability of the aromatic system and the C-O and C-C bond strengths. Model validation utilized leave-one-out (R(2) = 0.69), leave-many-out (R(2) = 0.72), and scrambling (R(2) = 0.19) procedures. Our results allow estimation of the photolysis half-lives of the remaining possible 140 PCDDs and PCDFs congeners.

Quantitative structure-activity relationship (QSAR) modeling of EC50 of aquatic toxicities for Daphnia magna.

The experimental EC(50) toxicities toward Daphnia magna for a series of 130 benzoic acids, benzaldehydes, phenylsulfonyl acetates, cycloalkane-carboxylates, benzanilides, and other esters were studied using the Best multilinear regression algorithm (BMLR) implemented in CODESSA. A modified quantitative structure-activity relationships (QSAR) procedure was applied guaranteeing the stability and reproducibility of the results. Separating the initial data set into training and test subsets generated three independent models with an average R(2) of .735. A five-descriptor general model including all 130 compounds, constructed using the descriptors found effective for the independent subsets, was characterized by the following statistical parameters: R(2) = .712; R(2)(cv) = .676; F = 61.331; s(2) = 0.6. The removal of two extreme outliers improved significantly the statistical parameters: R(2) = .759; R(2)(cv) = .728; F = 77.032; s(2) = 0.499. The sensitivity of the general model to chance correlations was estimated by applying a scrambling procedure involving 20 randomizations of the original property values. The resulting R(2) = .192 demonstrated the high robustness of the model proposed. The descriptors appearing in the obtained models are related to the biochemical nature of the adverse effects. An additional study of the EC(50)/LC(50) relationship for a series of 28 compounds (part of our general data set) revealed that these endpoints correlated with R(2) = .98.

Novel computational models for predicting dopamine interactions.

Dopamine is a crucial neurotransmitter responsible for functioning and maintenance of the nervous system. Dopamine has also been implicated in a number of diseases including schizophrenia, Parkinson's disease and drug addiction. Dopamine agonists are used in early Parkinson's disease treatment. Dopamine antagonists suppress schizophrenia. Therefore, molecules modulating dopamine receptors activity are vastly important for understanding the nervous system functioning and for the treatment of neurological diseases. In this study we describe novel computational models that efficiently predict binding affinity of the existing small molecule dopamine analogs to dopamine receptor. The model provides the set of molecular descriptors that can be used for the development of new small molecule dopamine agonists.

QSPR modeling of flash points: an update.

Quantitative structure-property relationship (QSPR) models for the flash points of 758 organic compounds are developed using geometrical, topological, quantum mechanical and electronic descriptors calculated by CODESSA PRO software. Multilinear regression models link the structures to their reported flash point values. We also report a nonlinear model based on an artificial neural network. The results are discussed in the light of the main factors that influence the property under investigation and its modeling.

Antimalarial activity: a QSAR modeling using CODESSA PRO software.

A quantitative structure-activity relationship (QSAR) modeling of the antimalarial activity of two diverse sets of compounds for each of two strains D6 and NF54 of Plasmodium falciparum is presented. The molecular structural features of compounds are presented by molecular descriptors (geometrical, topological, quantum mechanical, and electronic) calculated using the CODESSA PRO software. Satisfactory multilinear regression models were obtained for data sets of the D6 and NF54 strains, with R2 = 0.84 and 0.89, respectively. The models were also satisfactorily validated internally. The descriptors involved in these equations were related to the mechanism of antimalarial protection.