Current Pharmaceutical Design - Volume 16, Issue 24, 2010

Both, computer-aided Pharmaceutical Design and Drug Target Discovery using Bioinformatics are valuable tools in biomedical sciences. They may become useful in order to reduce costs in terms of material resources, personal, time and the use of animals of laboratory in the exploration of large databases. These techniques are not aimed to replace experimentation at all; we should understand these methods only as a guide to “seek the needle in the haystack”. There are many computational techniques and mathematical models useful in this sense. In particular, Graph theory is of special interest due to its high flexibility to study many types of systems ranging from drug molecules to drug target proteins and beyond. In fact, many authors have used molecular graphs to represent the structure of drugs by means of vertices (represented by dots) that represent atoms and edges that represent chemical bonds. Consequently, molecular graphs express the structure of organic compounds in terms of atom connectivity. In addition, we can associate graphs with different classes of numeric matrices to carry out computational studies. The Boolean or Adjacency matrices are perhaps the more simple to explain. These matrices are square tables with elements bij = 1 for pair of connected nodes and 0 otherwise. At one higher structural level we can use essentially the same type of graphs to study complex networks used to represent the 3D structures of proteins (enzymes, molecular targets, channels, receptors). The construction of this type of graphs and matrices is straightforward to realize in an intuitive form taking into consideration the analogy between the previous situations. In these networks, aminoacids often play the role of nodes and links express spatial contact between two aminoacids (see also, contact maps or residue networks). In the same group with proteins we can find the graphs used to represent the secondary structure of RNAs. In this last class of networks, nucleotides often play the role of nodes and links express that a pair of bases are sequence neighbors or are involved in a hydrogen bond. In parallel, many authors have been used Graph and Complex Network theory to approach very large networks with low computational cost. These large networks are graphical representations of real bio-systems with essentially two components nodes and links in a broad sense. In the case of bio-systems of certain relevance for Current Pharmaceutical Design we can name drug-target networks, protein interaction networks (PINs) used to represent proteomes, drug-tissue action networks, and drug - disease/gen-disease networks for diseasome, to cite only some examples. These are the same type of above-mentioned graphs but nodes are not atoms or aminoacids but proteins, tissues, targets, patients, diseases, population groups, disease incidence regions, etc. Node-to-node links (edges or arcs) express different types of ties or relationships between two nodes as for instance: drug-target inhibition, gen-disease regulation. In all these cases, we can easily calculate different invariant parameters of the matrices associated to the graphs that may be used to describe the structure of these objects (drugs, proteins, or large bio-systems). As this numbers are based only on connectivity information they are often named as Connectivity measures or Topological Indices (TIs). To recommended readings connecting these topics are both the comprehensive handbook in graph and complex networks [1] and the handbook of molecular descriptors [2]. In fact, in our days, there is an explosion on the use of Topological Indices (TIs) of Graphs and Complex Networks on a broad spectrum of topics related to Drug metabolism and distribution research. Using TIs as inputs we can find Quantitative Structure-Property Relationships (QSPR) models for any kind of bio-systems in principle. We see QSPR model as a function that predict the properties of the system (drug, protein, RNA, diseasome) using parameters that numerically describe the structure of the system (like TIs). There are many QSPR-like terms that fit to more specific situations, for instance Quantitative Structure-Activity Relationships (QSAR), Quantitative Structure-Toxicity Relationships (QSPR), Quantitative Proteome-Property Relationships (QPPR), Quantitative Sequence-Action Model (QSAM), or Quantitative Structure-Reactivity Relationships (QSRR), to cite a few examples. In all this cases we can find models that use the TIs of the system as input to predict the properties of this system (output), see the recent book edited by Gonzalez-Diaz and Munteanu in 2010 [3]. In a recent, preliminary review in the field published in Proteomics in 2008 Gonzalez-Diaz et al. discussed the use of these methods but only from the point of view of proteins [4]. Next we extended the discussion to a collective of authors edited a special issue on TIs but ever restricted to the field of protein and proteomics; published in Current Proteomics in December 2009 [5-11]. In other recent issue, we guestedited [12] a series of papers devoted to QSPR techniques but only from the point of view of low-molecular-weight drugs without discussion of metabolism or distribution; this issue was published in Current Topics in Medicinal Chemistry in 2008 [12-21]. Last, we guest-edited [22] an issue focused on graph TIs approach to Drug ADMET processes and Metabolomics, see the papers published in the issue of may 2010 for the journal Current Drug Metabolism [23-30]. In any case, we believe that there is necessity of a collection of manuscripts or issue more focused on QSAR, TIs and networks applied to pharmaceutical design at all structural levels. Based on all these reasons we edited the present issue including QSPR/QSAR studies with applications to Pharmaceutical Design and related areas like Microbiology, Parasitology, Pharmacology, Chemoterapy, Epidemiology, Toxicology, and others.

In this review an overview of the application of computational approaches is given. Specifically, the uses of Quantitative Structure-Activity Relationship (QSAR) methods for in silico identification of new families of compounds as novel tyrosinase inhibitors are revised. Assembling, validation of models through prediction series, and virtual screening of external data sets are also shown, to prove the accuracy of the QSAR models obtained with the TOMOCOMD-CARDD (TOpological MOlecular COMputational Design- Computer-Aided Rational Drug Design) software and Linear Discriminant Analysis (LDA) as statistical technique. Together with this, a database is collected for these QSAR studies, and could be considered a useful tool in future QSAR modeling of tyrosinase activity and for scientists that work in the field of this enzyme and its inhibitors. Finally, a translation to real world applications is shown by the use of QSAR models in the identification and posterior in-vitro evaluation of different families of compounds. Several different classes of compounds from various sources (natural and synthetic) were identified. Between them, we can find tetraketones, cycloartanes, ethylsteroids, lignans, dicoumarins and vanilloid derivatives. Finally, some considerations are discussed in order to improve the identification of novel drug-like compounds based on the use of QSAR-Ligand-Based Virtual Screening (LBVS).

Development of quantitative structure-activity relationships (QSARs) and quantitative structure-property relationships (QSPRs) has been practiced for prediction of various toxicities and other relevant properties of chemicals including drug candidates to minimize animal testing, cost and time associated with risk assessment and management processes. This communication reviews published reports of QSARs/QSPRs with Extended Topochemical Atom (ETA) indices for modeling chemical and drug induced toxicities and some physicochemical properties relevant to such toxicities. In each study, ETA models have been compared to those developed using various non-ETA models and it was found that the quality of the QSARs involving ETA parameters were comparable to those involving non-ETA parameters. ETA descriptors were also found to increase statistical quality of the models involving non-ETA parameters when used in combination. On the basis of the reported studies, it can be concluded that the ETA descriptors are sufficiently rich in chemical information to encode the structural features contributing to the toxicities and these indices may be used in combination with other topological and physicochemical descriptors for development of predictive QSAR models. Such models may be used for virtual screening and in silico prediction of toxicities, and if appropriately used, these may be proved helpful for regulatory decision support and decision making processes.

There is a need for the study of complex diseases due to their important impact on our society. One of the solutions involves the theoretical methods which are fast and efficient tools that can lead to the discovery of new active drugs specially designed for these diseases. The Quantitative Structure - Activity Relationship models (QSAR) and the complex network theory become important solutions for screening and designing efficient pharmaceuticals by coding the chemical information of the molecules into molecular descriptors. This review presents the most recent studies on drug discovery and design using QSAR of several complex diseases in the fields of Neurology, Cardiology and Oncology.

The increasing resistance of Mycobacterium tuberculosis to the existing drugs has alarmed the worldwide scientific community. In an attempt to overcome this problem computer-aided drug design has provide an extraordinary support to the different strategies in drug discovery. There are around 250 biological receptors such as enzymes that can be used in principle, for the design of antituberculosis compounds that act by a specific mechanism of action. Also, there more than 5000 compound available in the literature, and that constitute important information in order to search new molecular patterns for the design of new antituberculosis agents. The purpose of this paper is to explored the current state of drug discovery of antituberculosis agents and how the different strategies supported by computeraided drug design methods has influenced in a determinant way in the design of new molecular entities that can result the future antituberculosis drugs.

GSK-3 inhibitors are interesting candidates to develop anti-Alzheimer compounds. GSK-3β are also interesting as antiparasitic compounds active against Plasmodium falciparum, Trypanosoma brucei, and Leishmania donovani; the causative agents for Malaria, African Trypanosomiasis and Leishmaniosis. The high number of possible candidates creates the necessity of Quantitative Structure-Activity Relationship models in order to guide the GSK3 (Glycogen Synthase Kinase 3 inhibitor) synthesis. In this work, we revised different computational studies for a very large and heterogeneous series of GSK-3Is. First, we revised QSAR studies with conceptual parameters such as flexibility of rotation, probability of availability, etc. We then used the method of regression analysis and QSAR studies in order to understand the essential structural requirement for binding with receptor. Next, we reviewed 3D-QSAR, CoMFA and CoMSIA with different compounds to find out the structural requirements for GSK-3 inhibitory activity.

A topological substructural molecular design approach (TOPS-MODE) has been used to formulate structural rules for binding of substrates of P-glycoprotein (P-gp). We first review some of the models developed in the recent literature for predicting binding to Pgp. Then, we develop a model using TOPS-MODE, which is able to identify 88.4% of substrates and 84.2% of non-substrates. When the model is presented to an external prediction set of 100 substrates and 77 nonsubstrates it identifies correctly 81.8% of all cases. Using TOPS-MODE strategy we found structural contributions for binding to P-gp, which identifies 24 structural fragments responsible for such binding. We then carried out a chemico-biological analysis of some of the structural fragments found as contributing to P-gp binding of substrates. We show that in general the model developed so far can be used as a virtual screening method for identifying substrates of P-gp from large libraries of compounds.

The number of protein 3D structures without function annotation in Protein Data Bank (PDB) has been steadily increased. Many of these proteins are relevant for Pharmaceutical Design because they may be enzymes of different classes that could become drug targets. This fact has led in turn to an increment of demand for theoretical models to give a quick characterization of these proteins. In this work, we present a review and discussion of Alignment-Free Methods (AFMs) for fast prediction of the Enzyme Classification (EC) number from structural patterns. We referred to both methods based on linear techniques such as Linear Discriminant Analysis (LDA) and/or non-linear models like Artificial Neural Networks (ANN) or Support Vector Machine (SVM) in order to compare linear vs. nonlinear classifiers. We also detected which of these models have been implemented as Web Servers free to the public and compiled a list of some of these websites. For instance, we reviewed the servers implemented at portal Bio-AIMS (http://miaja.tic.udc.es/Bio- AIMS/EnzClassPred.php) and the server EzyPred (http://www.csbio.sjtu.edu.cn/bioinf/EzyPred/).

The complex diseases in the field of Neurology, Cardiology and Oncology have the most important impact on our society. The theoretical methods are fast and they involve some efficient tools aimed at discovering new active drugs specially designed for these diseases. The ontology of all the items that are linked with the molecule metabolism and the treatment of these diseases gives us the possibility to correlate information from different levels and to discover new relationships between complex diseases such as common drug targets and disease patterns. This review presents the ontologies used to process drug discovery and design in the most common complex diseases.

Quantitative Structure-Activity Relationship (QSAR) models have been used in Pharmaceutical design and Medicinal Chemistry for the discovery of anti-parasite drugs. QSAR models predict biological activity using as input different types of structural parameters of molecules. Topological Indices (TIs) are a very interesting class of these parameters. We can derive TIs from graph representations based on only nodes (atoms) and edges (chemical bonds). TIs are not time-consuming in terms of computational resources because they depend only on atom-atom connectivity information. This information expressed in the molecular graphs can be tabulated in the form of adjacency matrices easy to manipulate with computers. Consequently, TIs allow the rapid collection, annotation, retrieval, comparison and mining of molecular structures within large databases. The interest in TIs has exploded because we can use them to describe also macromolecular and macroscopic systems represented by complex networks of interactions (links) between the different parts of a system (nodes) such as: drug-target, protein-protein, metabolic, host-parasite, brain cortex, parasite disease spreading, Internet, or social networks. In this work, we review and comment on the following topics related to the use of TIs in anti-parasite drugs and target discovery. The first topic reviewed was: Topological Indices and QSAR for antiparasitic drugs. This topic included: Theoretical Background, QSAR for anti-malaria drugs, QSAR for anti-Toxoplasma drugs. The second topic was: TOMO-COMD approach to QSAR of antiparasitic drugs. We included in this topic: TOMO-COMD theoretical background and TOMO-COMD models for antihelmintic activity, Trichomonas, anti-malarials, anti-trypanosome compounds. The third section was inserted to discuss Topological Indices in the context of Complex Networks. The last section is devoted to the MARCH-INSIDE approach to QSAR of antiparasitic drugs and targets. This begins with a theoretical background for drugs and parameters for proteins. Next, we reviewed MARCH-INSIDE models for Pharmaceutical Design of antiparasitic drugs including: flukicidal drugs and anti-coccidial drugs. We close MARCH-NSIDE topic with a review of multi-target QSAR of antiparasitic drugs, MARCH-INSIDE assembly of complex networks of antiparasitic drugs. We closed the MARCH-INSIDE section discussing the prediction of proteins in parasites and MARCH-INSIDE web-servers for Protein-Protein interactions in parasites: Plasmod-PPI and Trypano-PPI web-servers. We closed this revision with an important section devoted to review some legal issues related to QSAR models.