Konstantin V. Balakin

VEGF/VEGFR signalling as a target for inhibiting angiogenesis

VEGFs and a respective family of tyrosine kinases receptors (VEGFRs) are key proteins modulating angiogenesis, the formation of new vasculature from an existing vascular network. There has been considerable evidence in vivo, including clinical observations, that abnormal angiogenesis is implicated in a number of disease conditions, which include rheumatoid arthritis, inflammation, cancer, psoriasis, degenerative eye conditions and others. Antiangiogenic therapies based on inhibition of VEGF/VEGFR signalling were reported to be powerful clinical strategies in oncology and ophthalmology. Current efforts have yielded promising clinical data for several antiangiogenic therapeutics. In this review, the authors elucidate key aspects of VEGFR signalling, as well as clinically relevant strategies for the inhibition of VEGF-induced angiogenesis, with an emphasis on small-molecule VEGFR inhibitors.

Drug Discovery Using Support Vector Machines. The Case Studies of Drug-likeness, Agrochemical-likeness, and Enzyme Inhibition Predictions

Support Vector Machines (SVM) is a powerful classification and regression tool that is becoming increasingly popular in various machine learning applications. We tested the ability of SVM, in comparison with well-known neural network techniques, to predict drug-likeness and agrochemical-likeness for large compound collections. For both kinds of data, SVM outperforms various neural networks using the same set of descriptors. We also used SVM for estimating the activity of Carbonic Anhydrase II (CA II) enzyme inhibitors and found that the prediction quality of our SVM model is better than that reported earlier for conventional QSAR. Model characteristics and data set features were studied in detail.

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.

Small Molecule Inhibitors of NF-κB and JAK/STAT Signal Transduction Pathways as Promising Anti-Inflammatory Therapeutics

In the current review, we discuss the role of NF-kB and JAK/STAT signaling pathways and their small molecule regulators in the therapy of inflammatory diseases. Considering potential harmful effects directly assigned to the COX-2 inhibition, novel therapeutically-relevant biological targets such as NF-kB and JAK/STAT signaling pathways have received a growing attention. Here we summarize recent progress in the identification and development of novel, clinically approved or evaluated small molecule regulators of these signaling cascades as promising anti-inflammatory therapeutics. In addition, we illustrate key structural modifications and bioisosteric transformations among these inhibitors to provide a helpful basis for further development of novel small molecule anti-inflammatory agents.

Recent Progress in Discovery and Development of Antimitotic Agents

This review highlights structural diversity of antimitotic agents. In particular, we emphasized current antimitotic therapies based on modulation of microtubule dynamics. With several successful anticancer drugs on the market and numerous compounds in clinical developments, tubulin-binding agents remain among the most important categories of anticancer agents. Compounds targeting mitotic kinases and kinesins are also discussed.

KOHONEN MAPS FOR PREDICTION OF BINDING TO HUMAN CYTOCHROME P450 3A4

The drug development process utilizes the parallel assessment of activity at a therapeutic target as well as absorption, distribution, metabolism, excretion, and toxicity properties of molecules. The development of novel, reliable, and inexpensive computational methods for the early assessment of metabolism and toxicity is becoming increasingly an important part of this process. We have used a computational approach for the assessment of drugs and drug-like compounds which bind to the cytochromes P450 (P450s) with experimentally determined Km values. The physicochemical properties of these compounds were calculated using molecular descriptor software and then analyzed using Kohonen self-organizing maps. This approach was applied to generate a P450-specific classification of nearly 500 drug compounds. We observed statistically significant differences in the molecular properties of low Km molecules for various P450s and suggest a relationship between 33 of these compounds and their CYP3A4-inhibitory activity. A test set of additional CYP3A4 inhibitors was used, and 13 of 15 of these molecules were colocated in the regions of low Km values. This computational approach represents a novel method for use in the generation of metabolism models, enabling the scoring of libraries of compounds for their Km values to numerous P450s.

New approaches to the treatment of inflammatory disease : focus on small-molecule inhibitors of signal transduction pathways.

This‘state-of-the-art’ review specifically focuses on alternative signalling pathways deeply involved in acute and chronic inflammatory responses initiated by various pathological stimuli. The accumulated scientific knowledge has already revealed key biological targets, such as COX-2, and related proinflammatory mediators (cytokines and chemokines, interleukins [ILs], tumour necrosis factor [TNF]-α, migration inhibition factor [MIF], interferon [IFN]-ψ and matrix metalloproteinases [MMPs]) implicated in uncontrolled, destructive inflammatory reaction. A number of physiologically active agents are currently approved for market or are under active investigation in different clinical trials. However, recent findings have exposed the fatal adverse effects directly associated with drug therapy based on COX-2 inhibition. Given these possible harmful outcomes, a range of novel therapeutically relevant biological targets that include nuclear transcription factor (NF-κB), p38 mitogen-activated protein kinases (MAPK) and Janus protein tyrosine kinases and signal transducers and activators of transcription (JAK/STAT) signalling pathways has received growing attention. Here we discuss recent progress in the identification and development of novel, clinically approved or evaluated small-molecule regulators of these signalling cascades as promising anti-inflammatory drugs.

Comprehensive computational assessment of ADME properties using mapping techniques.

One strategy to potentially improve the success of drug discovery is to apply computational approaches early in the process to select molecules and scaffolds with ideal binding and physicochemical properties. Numerous algorithms and different molecular descriptors have been used for modeling ligand-protein interactions as well as absorption, distribution, metabolism and excretion (ADME) properties. In most cases a single data set has been evaluated with one approach or multiple algorithms that have been compared for a single dataset. These models have been primarily evaluated by leave-one out analysis or boot strapping with groups representing 25-50% of the training set left out of the final model. In a very few examples a test set of molecules not included in the model has been used for an external evaluation. In the present study we have applied Sammon non-linear maps, Support Vector Machines and Kohonen Self Organizing Maps to modeling numerous datasets for ADME properties including human intestinal absorption, blood brain barrier permeability, cytochrome P450 binding, plasma protein binding, P-gp inhibition, volume of distribution and plasma half life.

Computational mapping tools for drug discovery

During the past decade, computational technologies have become well integrated in the modern drug design process and have gained in influence. They have dramatically revolutionized the way in which we approach drug discovery, leading to the explosive growth in the amount of chemical and biological data that are typically multidimensional in structure. As a result, the irresistible rush towards using computational approaches has focused on dimensionality reduction and the convenient representation of high-dimensional data sets. This has, in turn, led to the development of advanced machine-learning algorithms. In this review we describe a variety of conceptually different mapping techniques that have attracted the attention of researchers because they allow analysis of complex multidimensional data in an intuitively comprehensible visual manner.

Caspase Activity Modulators as Anticancer Agents

Proteolytic caspase enzymes play a central role in cell apoptosis, or programmed cell death, often as integrating elements of different stimuli leading to the cell death. Since blockade of apoptotic pathways are fundamental for cell survival and proliferation, particularly in cancer cells, the activation of caspases is an attractive target for anticancer therapy. This review describes some of the druggable therapeutic targets thus far identified within the core apoptotic machinery, the corresponding drugs that have been developed, their effects on caspase-dependent apoptotic pathways and their potential impact on the therapy of cancer. With several successful anticancer drugs on the market and numerous compounds in preclinical and clinical developments, modulators of caspase-dependent apoptotic pathways belong to the most important category of anticancer agents.