Yan A. Ivanenkov

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.

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.

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.

Histone Deacetylase Inhibitors in Cancer Therapy: Latest Developments, Trends and Medicinal Chemistry Perspective

Regulation of gene expression is mediated by several mechanisms such as DNA methylation, ATP-dependent chromatin remodeling, and post-translational modifications of histones. The latter mechanism includes dynamic acetylation and deacetylation of epsilon-amino groups of lysine residues present in the tail of the core histones. Enzymes responsible for the reversible acetylation/deacetylation processes are histone acetyltransferases (HATs) and histone deacetylases (HDACs), respectively. There are three mammalian HDAC families, namely HDACs I, II and III based on their sequence homology. Inhibitors of HDACs induce hyperacetylation of histones that modulate chromatin structure and gene expression resulting in growth arrest, cell differentiation, and apoptosis of tumor cells. In addition, HDAC inhibitors enhance efficacy of anticancer agents that target DNA. Several formidable challenges associated with their development include non-specific toxicity and poor PK properties, including cell permeability. In this review, we comment on the current progress in design, discovery, in vitro/ex vivo activity and clinical potential of the synthetic modulators of HDACs.

QUANTITATIVE STRUCTURE-METABOLISM RELATIONSHIP MODELING OF METABOLIC N-DEALKYLATION REACTION RATES

It is widely recognized that preclinical drug discovery can be improved via the parallel assessment of bioactivity, absorption, distribution, metabolism, excretion, and toxicity properties of molecules. High-throughput computational methods may enable such assessment at the earliest, least expensive discovery stages, such as during screening compound libraries and the hit-to-lead process. As an attempt to predict drug metabolism and toxicity, we have developed an approach for evaluation of the rate of N-dealkylation mediated by two of the most important human cytochrome P450s (P450), namely CYP3A4 and CYP2D6. We have taken a novel approach by using descriptors generated for the whole molecule, the reaction centroid, and the leaving group, and then applying neural network computations and sensitivity analysis to generate quantitative structure-metabolism relationship models. The quality of these models was assessed by using the cross-validated correlation coefficients of 0.82 for CYP3A4 and 0.79 for CYP2D6 as well as external test molecules for each enzyme. The relative performance of different neural networks was also compared, and modular neural networks with two hidden layers provided the best predictive ability. Functional dependencies between the neural network input and output variables, generalization ability, and limitations of the described approach are also discussed. These models represent an initial approach to predicting the rate of P450-mediated metabolism and may be applied and integrated with other models for P450 binding to produce a systems-based approach for predicting drug metabolism.

In Silico Estimation of DMSO Solubility of Organic Compounds for Bioscreening

Solubility of organic compounds in DMSO is an important issue for commercial and academic organizations handling large compound collections or performing biological screening. In particular, solubility data are critical for the optimization of storage conditions and for the selection of compounds for bioscreening compatible with the assay protocol. Solubility is largely determined by the solvation energy and the crystal disruption energy, and these molecular phenomena should be assessed in structure-solubility correlation studies. The authors summarize our long-term experimental observations and theoretical studies of physicochemical determinants of DMSO solubility of organic substances. They compiled a comprehensive reference database of proprietary data on compound solubility (55,277 compounds with good DMSO solubility and 10,223 compounds with poor DMSO solubility), calculated specific molecular descriptors (topological, electromagnetic, charge, and lipophilicity parameters), and applied an advanced machine-learning approach for training neural networks to address the solubility. Both supervised (feed-forward, back-propagated neural networks) and unsupervised (Kohonen neural networks) learning methods were used. The resulting neural network models were validated by successfully predicting DMSO solubility of compounds in independent test selections. (Journal of Biomolecular Screening 2004:22-31)

5-hydroxytryptamine subtype 6 receptor modulators: a patent survey.

Importance of the field: Among the GPCR subclasses that have been discovered to date, 5-HT receptors are especially attractive as key biological targets with enormous clinical importance. In particular, during the last decade, the 5-HT6 receptor has gained increasing attention due to extensive cellular functions. It has also been suggested that its activity can be mediated by inverse agonists. Areas covered in this review: Summarizing the points listed above, the current review primarily focuses on patent literature within the title field, evolution and trends that have not yet been covered in such depth in other published papers. What the reader will gain: To obtain a clear understanding of the situation and dynamics within the field of 5-HT6 ligands, having an obvious pharmaceutical potential in terms of related patents, we provide a comprehensive search through several key patent collections. We have covered promising small molecule compounds which are being evaluated in different clinical trials as well as drugs currently available in the pharmaceutical market. In addition, readers will gain a deep insight into the patent specification, geographic distribution, tendency and patent holders presented. Take home message: Several of 5-HT6-targeted compounds are reasonably regarded as powerful drug candidates for the treatment of a range of neuropathological disorders, including Alzheimers disease and Huntingtons disease.

Novel aryl and heteroaryl substituted N-[3-(4-phenylpiperazin-1-yl)propyl]-1,2,4-oxadiazole-5-carboxamides as selective GSK-3 inhibitors.

Synthesis, biological evaluation, and SAR dependencies for a series of novel aryl and heteroaryl substituted N-[3-(4-phenylpiperazin-1-yl)propyl]-1,2,4-oxadiazole-5-carboxamide inhibitors of GSK-3beta kinase are described. The inhibitory activity of the synthesized compounds is highly dependent on the character of substituents in the phenyl ring and the nature of terminal heterocyclic fragment of the core molecular scaffold. The most potent compounds from this series contain 3,4-di-methyl or 2-methoxy substituents within the phenyl ring and 3-pyridine fragment connected to the 1,2,4-oxadiazole heterocycle. These compounds selectively inhibit GSK-3beta kinase with IC(50) value of 0.35 and 0.41 microM, respectively.