Svetoslav H. Slavov

CERAPP : Collaborative Estrogen Receptor Activity Prediction Project

Background: Humans are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Most of these chemicals have never been tested for their ability to interact with the estrogen receptor (ER). Risk assessors need tools to prioritize chemicals for evaluation in costly in vivo tests, for instance, within the U.S. EPA Endocrine Disruptor Screening Program. Objectives: We describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) and demonstrate the efficacy of using predictive computational models trained on high-throughput screening data to evaluate thousands of chemicals for ER-related activity and prioritize them for further testing. Methods: CERAPP combined multiple models developed in collaboration with 17 groups in the United States and Europe to predict ER activity of a common set of 32,464 chemical structures. Quantitative structure–activity relationship models and docking approaches were employed, mostly using a common training set of 1,677 chemical structures provided by the U.S. EPA, to build a total of 40 categorical and 8 continuous models for binding, agonist, and antagonist ER activity. All predictions were evaluated on a set of 7,522 chemicals curated from the literature. To overcome the limitations of single models, a consensus was built by weighting models on scores based on their evaluated accuracies. Results: Individual model scores ranged from 0.69 to 0.85, showing high prediction reliabilities. Out of the 32,464 chemicals, the consensus model predicted 4,001 chemicals (12.3%) as high priority actives and 6,742 potential actives (20.8%) to be considered for further testing. Conclusion: This project demonstrated the possibility to screen large libraries of chemicals using a consensus of different in silico approaches. This concept will be applied in future projects related to other end points. Citation: Mansouri K, Abdelaziz A, Rybacka A, Roncaglioni A, Tropsha A, Varnek A, Zakharov A, Worth A, Richard AM, Grulke CM, Trisciuzzi D, Fourches D, Horvath D, Benfenati E, Muratov E, Wedebye EB, Grisoni F, Mangiatordi GF, Incisivo GM, Hong H, Ng HW, Tetko IV, Balabin I, Kancherla J, Shen J, Burton J, Nicklaus M, Cassotti M, Nikolov NG, Nicolotti O, Andersson PL, Zang Q, Politi R, Beger RD, Todeschini R, Huang R, Farag S, Rosenberg SA, Slavov S, Hu X, Judson RS. 2016. CERAPP: Collaborative Estrogen Receptor Activity Prediction Project. Environ Health Perspect 124:1023–1033; http://dx.doi.org/10.1289/ehp.1510267

Synthesis and bioassay of improved mosquito repellents predicted from chemical structure

Mosquito repellency data on acylpiperidines derived from the U.S. Department of Agriculture archives were modeled by using molecular descriptors calculated by CODESSA PRO software. An artificial neural network model was developed for the correlation of these archival results and used to predict the repellent activity of novel compounds of similar structures. A series of 34 promising N-acylpiperidine mosquito repellent candidates (4a–4q′) were synthesized by reactions of acylbenzotriazoles 2a–2p with piperidines 3a–3f. Compounds (4a–4q′) were screened as topically applied mosquito repellents by measuring the duration of repellency after application to cloth patches worn on the arms of human volunteers. Some compounds that were evaluated repelled mosquitoes as much as three times longer than N,N-diethyl-m-toluamide (DEET), the most widely used repellent throughout the world. The newly measured durations of repellency were used to obtain a superior correlation equation relating mosquito repellency to molecular structure.

Novel Carboxamides as Potential Mosquito Repellents

ABSTRACT A model was developed using 167 carboxamide derivatives, from the United States Department of Agriculture archival database, that were tested as arthropod repellents over the past 60 yr. An artificial neural network employing CODESSA PRO descriptors was used to construct a quantitative structure-activity relationship model for prediction of novel mosquito repellents. By correlating the structure of these carboxamides with complete protection time, a measure of repellency based on duration, 34 carboxamides were predicted as candidate mosquito repellents. There were four additional compounds selected on the basis of their structural similarity to those predicted. The compounds were synthesized either by reaction of 1-acylbenzotriazoles with secondary amines or by reaction of acid chlorides with secondary amines in the presence of sodium hydride. The biological efficacy was assessed by duration of repellency on cloth at two dosages (25 and 2.5 µmol/cm2) and by the minimum effective dosage to prevent Aedes aegypti (L.) (Diptera: Culicidae) bites. One compound, (E)-N-cyclohexyl-N-ethyl-2-hexenamide, was superior to N,N-diethyl-3-methylbenzamide (deet) at both the high dosage (22 d versus 7 d for deet) and low dosage (5 d versus 2.5 d for deet). Only one of the carboxamides, hexahydro-1-(1-oxohexyl)-1H-azepine, had a minimum effective dosage that was equivalent or slightly better than that of deet (0.033 µmol/cm2 versus 0.047 µmol/cm2).

QSAR modeling of the antifungal activity against Candida albicans for a diverse set of organic compounds.

The molecular structures of 83 diverse organic compounds are correlated by a quantitative structure-activity relationship (QSAR) to their minimum inhibitor concentrations (MIC expressed as log(1/MIC)), involving 6 descriptors with R(2)=0.788, F=47.140, s(2)=0.130. A novel QSAR development technique is utilized combining advantages of the two frequently applied methods. The topological, electronic, geometrical, and hybrid type descriptors for the compounds were calculated by CODESSA PRO software.

Quantitative Structure–Activity Relationship (QSAR) Modeling of EC50 of Aquatic Toxicities for Daphnia magna

The experimental EC50 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; s2 = 0.6. The removal of two extreme outliers improved significantly the statistical parameters: R 2 = .759; R2 cv = .728; F = 77.032; s2 = 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 EC50/LC50 relationship for a series of 28 compounds (part of our general data set) revealed that these endpoints correlated with R 2 = .98.

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.

QSPR study of the first and second critical micelle concentrations of cationic surfactants

Abstract Linear and nonlinear predictive models are derived for 50 ammonium and quaternary pyridinium cationic surfactants. Multilinear models were developed for both the first and second critical micelle concentrations (CMCs). Additionally, a general ANN model was deduced for the first CMC of all 50 cationic surfactants. Most of the descriptors in these models are related to the size and charge of the hydrophobic tail and to the size of the head. The multilinear model for the second CMC was more closely related to the hydrophobic domain of the surfactant than that of the first CMC. The QSPR models (linear and nonlinear) for the first CMC in this work could provide useful predictions of the CMC of structurally similar cationic surfactants.

Estimating the toxicities of organic chemicals in activated sludge process

The experimental logEC50 toxicity values of 104 compounds causing bioluminescent repression of the bacterium strain Pseudomonas isolated from an industrial wastewater were studied. Using the Best Multilinear Regression method implemented in CODESSA PRO, models with up to 8 theoretical descriptors were obtained. Utilizing a rigorous descriptor selection and validation procedure a reliable QSAR model with four parameters was selected as best. The proposed model emphasizes the importance of the halogen atoms presented in each compound, the possibility of H-bond formation and the flexibility and degree of branching of the molecules. As pointed out by many researchers, the contribution of the octanol-water partition coefficient to the explanation of the toxicity effect was also found to be significant. In addition, the model currently proposed was compared to those reported earlier and its advantages were discussed in detail.

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.

4-Fluoro-3',4',5'-trimethoxychalcone as a new anti-invasive agent. From discovery to initial validation in an in vivo metastasis model.

Invasion and metastasis are responsible for 90% of cancer-related mortality. Herein, we report on our quest for novel, clinically relevant inhibitors of local invasion, based on a broad screen of natural products in a phenotypic assay. Starting from micromolar chalcone hits, a predictive QSAR model for diaryl propenones was developed, and synthetic analogues with a 100-fold increase in potency were obtained. Two nanomolar hits underwent efficacy validation and eADMET profiling; one compound was shown to increase the survival time in an artificial metastasis model in nude mice. Although the molecular mechanism(s) by which these substances mediate efficacy remain(s) unrevealed, we were able to eliminate the major targets commonly associated with antineoplastic chalcones.