Current Topics in Medicinal Chemistry - Volume 12, Issue 8, 2012

Some years ago we assembled one special issue (Curr. Topics in Medicinal Chemistry, 2008, Vol. 8, No. 18) focused on the topic: Quantitative Structure-Activity and Structure-Property Relationships (QSAR/QSPR) applied to Medicinal Chemistry. We refer to QSAR/QSPR model as any general function (not necessarily linear or simple) that links the structure of system with their external properties. In QSAR/QSPR the structure of the system is described using numerical parameters that play the role of inputs of the model. This previous issue paved the way for many colleagues worldwide that used these works as an important state-of-art collection on QSAR/QSPR modeling [1-10]. This issue also served as source of inspiration for their works about computational models applied to Medicinal Chemistry and related Bio-Medical Sciences. In fact, after this first issue more special issues appeared as a natural consequence, were many groups have explored different areas of applications of QSAR/QSPR models. In particular, we have edited different issues with review/research papers about the applications of QSAR/QSPR models combined with Chemoinformatics, Bioinformatics, and Complex Networks techniques. Some of these issues coming after our initial issue in Medicinal Chemistry in 2008 [1-10] are: Curr. Proteomics in 2009 [11-15], Curr. Drug Metabolism in 2009 [16-24], Curr. Pharm. Des., in 2010 [25-34], and Curr. Bioinformatics in 2011 [35-46]. Now, passed the 10th Anniversary of CTMC our Editor-In-Chief Prof. Allen Reitz proposed this new issue. The idea is to look back and review the past and recent tendencies on QSAR/QSPR modeling. It may become an excellent opportunity to re-think about the future trends in the development of these methods. In so doing, we should re-think QSAR/QSPR modeling technique called to become one of the more important Enabling Technologies complementary to experimental techniques in the process of drug and target discovery. In fact, all the above-mentioned issues as well as in many other works published in recent years continued the development of various strategies to characterize and classify structural patterns of low weighted drugs by means of molecular descriptors useful as inputs in QSAR/QSPR modeling. It has become possible not only to continue the efforts to assess diversity or similarity within structure databases, but molecular descriptors also facilitate the identification of potential bioactive molecules from the rapidly increasing number of compound libraries. They even allow for a controlled de-novo design of new lead structures. The number and apparent diversity of molecular descriptors developed in this sense is wide covering from constitutional and physicochemical properties to 3D descriptors. For instance, many of the works published in the previous issues are based on indices collected in the Handbook of Molecular Descriptors (HMD) published by Todeschini & Consoni describes more than 6 000 molecular descriptors grouped on more than 15 different families [47]. Anyhow, the research in this field is far from ended and more recently we have seen and explosion on the use of the class of parameters called Topological Indices (TIs). TIs are parameters used describe numerically the structure of system represented by means of a graph. In these graphs we have essentially two classes of objects nodes (parts of the system) and edges (relationships between the parts of the system). In the classic Medicinal Chemistry context use to represent molecules by graphs were the atoms are represented by nodes and the chemical bonds by means of edges. Some TIs, previously collected in HMD, has called the attention of many researchers due to their important capacity to capture biologically relevant information on many different systems (including single molecules and more large systems) but being very simple and fast to calculate. In this sense, TI-like new descriptors appeared that come to reinforce the pool of indices published in HMD. In addition, in clear advantage with respect to other indices, TIs are enabling QSAR/QSPR analysis going beyond classic frontiers and opening new possibilities such as the study of proteins, RNAs, Complex Networks represent a plethora of complex bio-systems [1-47]. In this sense is that we stated here that QSAR/QSPR analysis based on classic parameters and helped by TIs-like indices is called to become an important enabling technology. That is way, the present collection of papers pretend to humbly call the attention of experimental and theoretical authors (of different and somehow parallel areas) on the new scenarios that may be considered when we put in the same bag all together Medicinal Chemistry, Drug Design, Proteomics, QSAR, Complex Systems theory, and Bioinformatics. The present issue aims to reach this goal taking as basis all the experience accumulated in these years. As a Guest-Editor of this special issue and also as Section editor for Enabling Technologies I would like to express my sincere appreciation to the contributing authors for their prompt submission of their manuscripts for this issue. Then, we hope that this issue will not only offer useful and interesting information to scientists who are involved in the field, but, perhaps more importantly, will also serve as an inspiration for new researchers.

Hepatitis C constitutes an infectious disease that causes severe damages to the liver, and is caused by hepatitis C virus. There is no vaccine against this type of disease and the number of people infected continues to grow worldwide. The anti-viral therapy which is currently used is a mixture of interferon alpha-2a with ribavirin, but approximately half of the patients do not respond to therapy. Therefore, it is necessary to search for new compounds with anti-hepatitis C activity. Computer-aided drug design methodologies have been vital in the discovery of candidates to drugs. This review is dedicated to the role of computer-aided drug design methodologies for the development of new anti-hepatitis C agents. In addition, we introduce a QSAR model based on substructural approaches in order to model the anti-hepatitis C activity in vivo.

In this paper we applied the “macromolecular docking” procedure to perform molecular modeling with the aim of screening transcription factor sequences for possible interaction to the HIV-1 TAR-RNA, employing the software Hex version 4.2. The molecular modeling data were compared with electrophoretic mobility shift assays (EMSA) and surface plasmon resonance (SPR) based biospecific interaction analysis (BIA) using an optical biosensor. Finally the specific interactions between NF-κB and RNA have been calculated utilizing the AMBER-MM and FMO calculations. The results obtained clearly indicate that (a) NF-kB p50 transcription factor can bind TAR-RNA; (b) this binding efficiency is lower than that displayed by NF-kB factor in respect to DNA sequences; (c) other structured RNAs used as controls do not bind to NF-kB; (d) TAR-RNA is capable to bind pre-formed NF-kB/DNA complexes. Despite the fact that our data do not indicate whether NF-kB/TAR-RNA complexes play a role in the early steps of HIV-1 transcriptional activation, the results presented strongly indicate that interactions between transcription factors recruited at the level of HIV-1 LTR might interact with the TAR-RNA and deserve further studies aimed to determine its role in the HIV-1 life cycle.

Alzheimer's disease (AD) is characterize with several pathologies this disease, amyloid plaques, composed of the β-amyloid peptide and γ-amyloid peptide are hallmark neuropathological lesions in Alzheimer's disease brain. Indeed, a wealth of evidence suggests that β-amyloid is central to the pathophysiology of AD and is likely to play an early role in this intractable neurodegenerative disorder. AD is the most prevalent form of dementia, and current indications show that twenty-nine million people live with AD worldwide, a figure expected rise exponentially over the coming decades. Clearly, blocking disease progression or, in the best-case scenario, preventing AD altogether would be of benefit in both social and economic terms. However, current AD therapies are merely palliative and only temporarily slow cognitive decline, and treatments that address the underlying pathologic mechanisms of AD are completely lacking. While familial AD (FAD) is caused by autosomal dominant mutations in either amyloid precursor protein (APP) or the presenilin (PS1, PS2) genes. First, we revised Desing, synthesis, and Biological assay of β and γ-secretase inhibitors. Next, we review 2D QSAR, 3D QSAR, CoMFA, CoMSIA and Docking with different compound to find out the structural requirements. Next, we revised QSAR studies using method of Artificial Neural Network (ANN) in order to understand the essential structural requirement for binding with receptor for β and γ-secretase inhibitors.

It is known that potassium channels are important for cell proliferation. HERG, a potassium channel protein, is a transmembrane protein, which increases in concentration on the cell surface of cancer cells. Apart from cancer cells, this protein is found only in the brain & heart tissue, in very low number. The proliferation of cells in cancer is dependent on activation of this protein, and it has been noted that blocking of this protein with drug molecule, helps inhibit the proliferation of the cells further. The current work aims to study the binding potentials of κ-PVIIA, conotoxin isolated from Conus purpurascens venom with HERG K+ channel of tumor cells, where HERG mutation has been noted. The toxin under consideration i.e. κ-conotoxins-PVIIA (κ-PVIIA) is a 27 residue peptide. The docking studies suggest that the conotoxin binds stably to the HERG protein. The study shows that the peptide interacts with the charged extracellular unit of the HERG protein, i.e. the extracellular portion of the S5 domain named S5-P extracellular linker. Study of binding of toxins of similar origin, with normal potassium channels has been studied in silico. Further, wet laboratory work needs to be conducted for development of a drug molecule from this toxin, to treat some number of cancers.

The neglected tropical diseases (NTDs) affect more than one billion people (one-sixth of the world’s population) and occur primarily in undeveloped countries in sub-Saharan Africa, Asia, and Latin America. Available drugs for these diseases are decades old and present an important number of limitations, especially high toxicity and, more recently, the emergence of drug resistance. In the last decade several Quantitative Structure-Activity Relationship (QSAR) studies have been developed in order to identify new organic compounds with activity against the parasites responsible for these diseases, which are reviewed in this paper. The topics summarized in this work are: 1) QSAR studies to identify new organic compounds actives against Chaga’s disease; 2) Development of QSAR studies to discover new antileishmanial drusg; 3) Computational studies to identify new drug-like compounds against human African trypanosomiasis. Each topic include the general characteristics, epidemiology and chemotherapy of the disease as well as the main QSAR approaches to discovery/identification of new actives compounds for the corresponding neglected disease. The last section is devoted to a new approach know as multi-target QSAR models developed for antiparasitic drugs specifically those actives against trypanosomatid parasites. At present, as a result of these QSAR studies several promising compounds, active against these parasites, are been indentify. However, more efforts will be required in the future to develop more selective (specific) useful drugs.

In the last decades computer-aided drug design techniques have been successfully used to guide the selection of new hit compounds with biological activity. These methods, that include a broad range of chemoinformatic and computational chemistry algorithms, are still disciplines in full bloom. In particular, virtual screening procedures have celebrated a great popularity for the rapid and cost-effective assessment of large chemical libraries of commercial compounds. While the usage of in silico techniques promises an effective speed-up at the early-stage of the development of new active compounds, computational projects starting from scratch with raw chemical data are often associated with resource- and time-consuming preparation protocols, almost blunting the advantages of using these techniques. In order to help facing these difficulties, in the last years several chemoinformatic projects and tools have emerged in literature and have been useful in preparing curated databases of chemical compounds for high-throughput virtual screening purposes. The review will focus on the detailed analysis of free databases of commercial chemical compounds that are currently employed in virtual screening campaigns for drug design. The scope of this review is to compare such databases and suggest the reader on how and in which conditions the usage of these databases could be recommended.

Adenosine receptors (ARs) are widespread on virtually every human organ/tissue, and have long been considered promising therapeutic targets in a wide range of conditions, ranging from cerebral diseases to cancer, including inflammatory disorders. The knowledge acquired up to date in relation to ARs, in particular regarding the molecular biology of the A3 AR has provided a solid basis that led to the proposal of this receptor as a novel therapeutic target enabling the rational design and development of potent and selective A3 AR ligands. This review attempts to summarize the most recent developments in the A3 research field, focusing in particular on Quantitative Structure-Activity Relationships (QSAR) based studies that supported so far the design of new, potent and selective human A3 AR antagonists. In addition, a classical QSAR modeling study carried out on two series of pyrazolo-triazolopyrimidine derivatives is presented as a case study. Specifically, a systematic evaluation of linear and non-linear models along with a variety of structure representations and feature selection tools is reported. The combination of these techniques (neural networks to capture non-linear relationships in the data and feature selection to prevent over-fitting) was found to produce QSAR models with good overall accuracy and robustness, as well as predictivity on external data. Moreover, the study indicated that the antagonist activity of these derivatives is largely explained by electrostatic, steric and hydrogen-bonding factors, highlighting the role of the size, shape and type of inhibitor in forming effective blocking of the A3 AR subtype. The developed QSAR models could then be usefully employed to design new compounds selectively active towards the A3 adenosine receptor.

Effective as statin drugs or acids are inhibitors mevinic limiting enzyme in cholesterol biosynthesis, 3-hydroxy- 3-methyl-glutaryl coenzyme A-3-hydroxy-3-reductase (HMGR), an enzyme responsible for the reduction the double methyl-glutaryl coenzyme A. These compounds promoted the synthesis and evaluation of new inhibitors of HMGR, called HMGRIs. The high number of potential candidates need to create models of quantitative structure-activity relationship in order to guide the HMGRI (3-hydroxy-3-methyl-glutarylcoenzyme A inhibitor) synthesis. In this work, we revised different computational studies for a very large and heterogeneous series of HMGRIs. First, we revised QSAR studies with conceptual parameters how flexibility of rotation, probability of availability, etc; Next, using method of regression analysis; and QSAR studies in order to understand the essential structural requirement for binding with receptor. Next, we review 3D QSAR, CoMFA and CoMSIA with different compound to find out the structural requirements for 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR) inhibitory activity.

Lung cancer is known as the top cancer killer in most developed countries. Epidermal growth factor receptor (EGFR) is frequently found to be activated by mutation or amplification in lung cancer. MicroRNA (miRNA) is a new class of small molecules that has emerged as important markers of lung cancer development and therapeutic target. There are queries on which miRNAs can regulate EGFR and it is important to predict the candidate miRNAs that target EGFR by bioinformatics and to investigate on the availability of these candidate miRNA regulators in lung cancer. Systematic and rigorous searches for miRNAs targeting EGFR were performed on 10 representative databases. The identified miRNAs that target EGFR were formulated into a conditional regulation matrix and then hierarchical clustering algorithm was applied for the analysis. The systematic search came up with 138 miRNAs that potentially target EGFR. Among them, 11 miRNAs including miR-7 and miR-128b were confirmed by published experimental data or literatures. There were 14 candidate miRNAs predicted by at least 3 prediction pipelines in this study which have never been previously reported to target EGFR. Further studies of these novel identified miRNAs may provide insight on the regulation of EGFR in lung cancer. To the best of our knowledge, this is the first bioinformatic study applying genomic sequence analysis for the prediction of miRNAs that target EGFR in lung cancer. This new strategy that integrates computational and published data approaches provides a fast and effective prediction of miRNAs in specific target genes involved in various diseases.

Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models have been largely used for different kind of problems in Medicinal Chemistry and other Biosciences as well. Nevertheless, the applications of QSAR models have been restricted to the study of small molecules in the past. In this context, many authors use molecular graphs, atoms (nodes) connected by chemical bonds (links) to represent and numerically characterize the molecular structure. On the other hand, Complex Networks are useful in solving problems in drug research and industry, developing mathematical representations of different systems. These systems move in a wide range from relatively simple graph representations of drug molecular structures (molecular graphs used in classic QSAR) to large systems. We can cite for instance, drug-target interaction networks, protein structure networks, protein interaction networks (PINs), or drug treatment in large geographical disease spreading networks. In any case, all complex networks have essentially the same components: nodes (atoms, drugs, proteins, microorganisms and/or parasites, geographical areas, drug policy legislations, etc.) and links (chemical bonds, drug-target interactions, drug-parasite treatment, drug use, etc.). Consequently, we can use the same type of numeric parameters called Topological Indices (TIs) to describe the connectivity patterns in all these kinds of Complex Networks irrespective the nature of the object they represent and use these TIs to develop QSAR/QSPR models beyond the classic frontiers of drugs small-sized molecules. The goal of this work, in first instance, is to offer a common background to all the manuscripts presented in this special issue. In so doing, we make a review of the most used software and databases, common types of QSAR/QSPR models, and complex networks involving drugs or their targets. In addition, we review both classic TIs that have been used to describe the molecular structure of drugs and/or larger complex networks. In second instance, we use for the first time a Markov chain model to generalize Spectral moments to higher order analogues coined here as the Stochastic Spectral Moments TIs of order k (πk). Lastly, we report for the first time different QSAR/QSPR models for different classes of networks found in drug research, nature, technology, and social-legal sciences using πk values. This work updates our previous reviews Gonzalez-Diaz et al. Curr Top Med Chem. 2007; 7(10): 1015-29 and Gonzalez-Diaz et al. Curr Top Med Chem. 2008; 8(18):1676-90. It has been prepared in response to the kind invitation of the editor Prof. AB Reitz in commemoration of the 10th anniversary of this journal in 2010.