Vladyslav Kholodovych

Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information.

The Online Chemical Modeling Environment is a web-based platform that aims to automate and simplify the typical steps required for QSAR modeling. The platform consists of two major subsystems: the database of experimental measurements and the modeling framework. A user-contributed database contains a set of tools for easy input, search and modification of thousands of records. The OCHEM database is based on the wiki principle and focuses primarily on the quality and verifiability of the data. The database is tightly integrated with the modeling framework, which supports all the steps required to create a predictive model: data search, calculation and selection of a vast variety of molecular descriptors, application of machine learning methods, validation, analysis of the model and assessment of the applicability domain. As compared to other similar systems, OCHEM is not intended to re-implement the existing tools or models but rather to invite the original authors to contribute their results, make them publicly available, share them with other users and to become members of the growing research community. Our intention is to make OCHEM a widely used platform to perform the QSPR/QSAR studies online and share it with other users on the Web. The ultimate goal of OCHEM is collecting all possible chemoinformatics tools within one simple, reliable and user-friendly resource. The OCHEM is free for web users and it is available online at http://www.ochem.eu.

Human Pregnane X Receptor Antagonists and Agonists Define Molecular Requirements for Different Binding Sites

The pregnane X receptor (PXR) is an important transcriptional regulator of the expression of xenobiotic metabolism and transporter genes. The receptor is promiscuous, binding many structural classes of molecules that act as agonists at the ligand-binding domain, triggering up-regulation of genes, increasing the metabolism and excretion of therapeutic agents, and causing drug-drug interactions. It has been suggested that human PXR antagonists represent a means to counteract such interactions. Several azoles have been hypothesized to bind the activation function-2 (AF-2) surface on the exterior of PXR when agonists are concurrently bound in the ligand-binding domain. In the present study, we have derived novel computational models for PXR agonists using different series of imidazoles, steroids, and a set of diverse molecules with experimental PXR agonist binding data. We have additionally defined a novel pharmacophore for the steroidal agonist site. All agonist pharmacophores showed that hydrophobic features are predominant. In contrast, a qualitative comparison with the corresponding PXR antagonist pharmacophore models using azoles and biphenyls showed that they are smaller and hydrophobic with increased emphasis on hydrogen bonding features. Azole antagonists were docked into a proposed hydrophobic binding pocket on the outer surface at the AF-2 site and fitted comfortably, making interactions with key amino acids involved in charge clamping. Combining computational and experimental data for different classes of molecules provided strong evidence for agonists and antagonists binding distinct regions on PXR. These observations bear significant implications for future discovery of molecules that are more selective and potent antagonists.

Shape Signatures: New Descriptors for Predicting Cardiotoxicity In Silico

Shape Signatures is a new computational tool that is being evaluated for applications in computational toxicology and drug discovery. The method employs a customized ray-tracing algorithm to explore the volume enclosed by the surface of a molecule and then uses the output to construct compact histograms (i.e., signatures) that encode for molecular shape and polarity. In the present study, we extend the application of the Shape Signatures methodology to the domain of computational models for cardiotoxicity. The Shape Signatures method is used to generate molecular descriptors that are then utilized with widely used classification techniques such as k nearest neighbors ( k-NN), support vector machines (SVM), and Kohonen self-organizing maps (SOM). The performances of these approaches were assessed by applying them to a data set of compounds with varying affinity toward the 5-HT(2B) receptor as well as a set of human ether-a-go-go-related gene (hERG) potassium channel inhibitors. Our classification models for 5-HT(2B) represented the first attempt at global computational models for this receptor and exhibited average accuracies in the range of 73-83%. This level of performance is comparable to using commercially available molecular descriptors. The overall accuracy of the hERG Shape Signatures-SVM models was 69-73%, in line with other computational models published to date. Our data indicate that Shape Signatures descriptors can be used with SVM and Kohonen SOM and perform better in classification problems related to the analysis of highly clustered and heterogeneous property spaces. Such models may have utility for predicting the potential for cardiotoxicity in drug discovery mediated by the 5-HT(2B) receptor and hERG.

Computational Discovery of Novel Low Micromolar Human Pregnane X Receptor Antagonists

Very few antagonists have been identified for the human pregnane X receptor (PXR). These molecules may be of use for modulating the effects of therapeutic drugs, which are potent agonists for this receptor (e.g., some anticancer compounds and macrolide antibiotics), with subsequent effects on transcriptional regulation of xenobiotic metabolism and transporter genes. A recent novel pharmacophore for PXR antagonists was developed using three azoles and consisted of two hydrogen bond acceptor regions and two hydrophobic features. This pharmacophore also suggested an overall small binding site that was identified on the outer surface of the receptor at the AF-2 site and validated by docking studies. Using computational approaches to search libraries of known drugs or commercially available molecules is preferred over random screening. We have now described several new smaller antagonists of PXR discovered with the antagonist pharmacophore with in vitro activity in the low micromolar range [S-p-tolyl 3′,5-dimethyl-3,5′-biisoxazole-4′-carbothioate (SPB03255) (IC50, 6.3 μM) and 4-(3-chlorophenyl)-5-(2,4-dichlorobenzylthio)-4H-1,2,4-triazol-3-ol (SPB00574) (IC50, 24.8 μM)]. We have also used our computational pharmacophore and docking tools to suggest that most of the known PXR antagonists, such as coumestrol and sulforaphane, could also interact on the outer surface of PXR at the AF-2 domain. The involvement of this domain was also suggested by further site-directed mutagenesis work. We have additionally described an FDA approved prodrug, leflunomide (IC50, 6.8 μM), that seems to be a PXR antagonist in vitro. These observations are important for predicting whether further molecules may interact with PXR as antagonists in vivo with potential therapeutic applications.

Neural Network Studies. 3. Variable Selection in the Cascade-Correlation Learning Architecture

Pruning methods for feed-forward artificial neural networks trained by the cascade-correlation learning algorithm are proposed. The cascade-correlation algorithm starts with a small network and dynamically adds new nodes until the analyzed problem has been solved. This feature of the algorithm removes the requirement to predefine the architecture of the neural network prior to network training. The developed pruning methods are used to estimate the importance of large sets of initial variables for quantitative structure−activity relationship studies and simulated data sets. The calculated results are compared with the performance of fixed-size back-propagation neural networks and multiple regression analysis and are carefully validated using different training/test set protocols, such as leave-one-out and full cross-validation procedures. The results suggest that the pruning methods can be successfully used to optimize the set of variables for the cascade-correlation learning algorithm neural networks. Th...

Sugar-based amphiphilic nanoparticles arrest atherosclerosis in vivo

Significance Lipid-rich plaques in major blood vessels recruit macrophages that further exacerbate the lipid burden and risk of heart attacks or stroke. A local approach to prevent plaque growth has yet to be successfully deployed. In this study, we examine how synthetic ligands counteract macrophage atherogenesis and de-escalate plaque burden. Using a library of sugar-based amphiphilic core-shell layered nanoparticles, we demonstrate the design principles necessary to prevent oxidized lipid uptake and suppress scavenger receptor expression in macrophages, switching them to an “atheroprotective” phenotype. When administered in vivo, nanoparticles were retained at atherosclerotic lesions, where they mitigated cholesterol clefts, inflammation, and artery occlusion. Thus, synthetic nanomedicines could be used to potentially treat acute coronary syndrome, a major unmet need in cardiovascular diseases. Atherosclerosis, the build-up of occlusive, lipid-rich plaques in arterial walls, is a focal trigger of chronic coronary, intracranial, and peripheral arterial diseases, which together account for the leading causes of death worldwide. Although the directed treatment of atherosclerotic plaques remains elusive, macrophages are a natural target for new interventions because they are recruited to lipid-rich lesions, actively internalize modified lipids, and convert to foam cells with diseased phenotypes. In this work, we present a nanomedicine platform to counteract plaque development based on two building blocks: first, at the single macrophage level, sugar-based amphiphilic macromolecules (AMs) were designed to competitively block oxidized lipid uptake via scavenger receptors on macrophages; second, for sustained lesion-level intervention, AMs were fabricated into serum-stable core/shell nanoparticles (NPs) to rapidly associate with plaques and inhibit disease progression in vivo. An AM library was designed and fabricated into NP compositions that showed high binding and down-regulation of both MSR1 and CD36 scavenger receptors, yielding minimal accumulation of oxidized lipids. When intravenously administered to a mouse model of cardiovascular disease, these AM NPs showed a pronounced increase in lesion association compared with the control nanoparticles, causing a significant reduction in neointimal hyperplasia, lipid burden, cholesterol clefts, and overall plaque occlusion. Thus, synthetic macromolecules configured as NPs are not only effectively mobilized to lipid-rich lesions but can also be deployed to counteract atheroinflammatory vascular diseases, highlighting the promise of nanomedicines for hyperlipidemic and metabolic syndromes.

The Major Human Pregnane X Receptor (PXR) Splice Variant, PXR.2, Exhibits Significantly Diminished Ligand-Activated Transcriptional Regulation

The pregnane X receptor (PXR; PXR.1) can be activated by structurally diverse lipophilic ligands. PXR.2, an alternatively spliced form of PXR, lacks 111 nucleotides encoding 37 amino acids in the ligand binding domain. PXR.2 bound a classic CYP3A4 PXR response element (PXRE) in electrophoretic mobility shift assays, but transfected PXR.2 failed to transactivate a CYP3A4-promoter-luciferase reporter plasmid in HepG2 cells treated with various PXR ligands. Cotransfection experiments showed that PXR.2 behaved as a dominant negative, interfering with PXR.1/rifampin activation of CYP3A4-PXRE-LUC. In HepG2 and LS180 cells stably transduced with PXR.1, PXR target genes (CYP3A4, MDR1, CYP2B6, and UGT1A1) were higher than mock-transduced cells in the absence of ligand and were further induced in the presence of rifampin. In contrast, PXR.2 stably introduced into the same host cells failed to induce target genes over levels in mock-transfected cells after drug treatment. Our homology modeling suggests that ligands bind PXR.1 more favorably, probably because of the presence of a key disordered loop region, which is missing in PXR.2. Yeast two-hybrid assays revealed that, even in the presence of ligand, the corepressors remain tightly bound to PXR.2, and coactivators are unable to bind at helix 12. In summary, PXR.2 can bind to PXREs but fails to transactivate target genes because ligands do not bind the ligand binding domain of PXR.2 productively, corepressors remain tightly bound, and coactivators are not recruited to PXR.2.

Using surrogate modeling in the prediction of fibrinogen adsorption onto polymer surfaces.

We present a Surrogate (semiempirical) Model for prediction of protein adsorption onto the surfaces of biodegradable polymers that have been designed for tissue engineering applications. The protein used in these studies, fibrinogen, is known to play a key role in blood clotting. Therefore, fibrinogen adsorption dictates the performance of implants exposed to blood. The Surrogate Model combines molecular modeling, machine learning and an Artificial Neural Network. This novel approach includes an accounting for experimental error using a Monte Carlo analysis. Briefly, measurements of human fibrinogen adsorption were obtained for 45 polymers. A total of 106 molecular descriptors were generated for each polymer. Of these, 102 descriptors were computed using the Molecular Operating Environment (MOE) software based upon the polymer chemical structures, two represented different monomer types, and two were measured experimentally. The Surrogate Model was developed in two stages. In the first stage, the three descriptors with the highest correlation to adsorption were determined by calculating the information gain of each descriptor. Here a Monte Carlo approach enabled a direct assessment of the effect of the experimental uncertainty on the results. The three highest-ranking descriptors, defined as those with the highest information gain for the sample set, were then selected as the input variables for the second stage, an Artificial Neural Network (ANN) to predict fibrinogen adsorption. The ANN was trained using one-half of the experimental data set (the training set) selected at random. The effect of experimental error on predictive capability was again explored using a Monte Carlo analysis. The accuracy of the ANN was assessed by comparison of the predicted values for fibrinogen adsorption with the experimental data for the remaining polymers (the validation set). The mean value of the Pearson correlation coefficient for the validation data sets was 0.54 +/- 0.12. The average root-mean-square (relative) error in prediction for the validation data sets is 38%. This is an order of magnitude less than the range of experimental values (i.e., 366%) and compares favorably with the average percent relative standard deviation of the experimental measurements (i.e., 17.9%). The effects of each of the user-defined parameters in the ANN were explored. None were observed to have a significant effect on the results. Thus, the Surrogate Model can be used to accurately and unambiguously identify polymers whose fibrinogen absorption is at the limits of the range (i.e., low or high) which is an essential requirement for assessing polymers for regenerative tissue applications.

Self-assembled cationic amphiphiles as antimicrobial peptides mimics: Role of hydrophobicity, linkage type, and assembly state.

Inspired by high promise using naturally occurring antimicrobial peptides (AMPs) to treat infections caused by antimicrobial-resistant bacteria, cationic amphiphiles (CAms) were strategically designed as synthetic mimics to overcome associated limitations, including high manufacture cost and low metabolic stability. CAms with facially amphiphilic conformation were expected to demonstrate membrane-lytic properties and thus reduce tendency of resistance development. By systematically tuning the hydrophobicity, CAms with optimized compositions exhibited potent broad-spectrum antimicrobial activity (with minimum inhibitory concentrations in low μg/mL range) as well as negligible hemolytic activity. Electron microscope images revealed the morphological and ultrastructure changes of bacterial membranes induced by CAm treatment and validated their membrane-disrupting mechanism. Additionally, an all-atom molecular dynamics simulation was employed to understand the CAm-membrane interaction on molecular level. This study shows that these CAms can serve as viable scaffolds for designing next generation of AMP mimics as antimicrobial alternatives to combat drug-resistant pathogens.

Specific Interactions between the Viral Coreceptor CXCR4 and the Biguanide-Based Compound NB325 Mediate Inhibition of Human Immunodeficiency Virus Type 1 Infection

ABSTRACT The present studies were conducted to better define the mechanism of action of polyethylene hexamethylene biguanide (PEHMB) (designated herein as NB325), which was shown in previous studies to inhibit infection by the human immunodeficiency virus type 1 (HIV-1). Fluorescence-activated flow cytometric analyses of activated human CD4+ T lymphocytes exposed to NB325 demonstrated concentration-dependent reductions in CXCR4 epitope recognition in the absence of altered recognition of selected CD4 or CD3 epitopes. NB325 also inhibited chemotaxis of CD4+ T lymphocytes induced by the CXCR4 ligand CXCL12. However, NB325 did not cause CXCR4 internalization (unlike CXCL12) and did not interfere with CXCL12 binding. Additional flow cytometric analyses using antibodies with distinct specificities for extracellular domains of CXCR4 demonstrated that NB325 specifically interfered with antibody binding to extracellular loop 2 (ECL2). This interaction was confirmed using competitive binding analyses, in which a peptide derived from CXCR4 ECL2 competitively inhibited NB325-mediated reductions in CXCR4 epitope recognition. Collectively, these results demonstrate that the biguanide-based compound NB325 inhibits HIV-1 infection by specifically interacting with the HIV-1 coreceptor CXCR4.