Editorial - IngentaConnect

Chemoinformatics Models for Pharmaceutical Design, Part 1

Current Pharmaceutical Design, 2016, Vol. 22, No. 33

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Editorial Chemoinformatics Models for Pharmaceutical Design, Part 1 Humberto González-Díaz, Anuraj Nayarisseri, Danail Bonchev and Subhash C. Basak Cheminformatics, Complex Networks, and Data Science models in general are of utmost importance in pharmaceutical design to predict and well comprehend the biological activity of novel compounds. This special thematic issue is a collection of review papers and novel results obtained from new trended applications and developing methods. In this regard, the issue include papers on Quantitative Structure-Activity/ Property Relationship (QSAR/QSPR) models, Perturbation theory, Markov chains theory, Graph and Complex Networks theory, Molecular Mechanics and Molecular Dynamics (MM/MD) calculations, Molecular Docking to name a few. The issue will be published in two parts: part 1 which includes 8 articles and part 2 having 7 articles. In the first paper of the part 1, Cruz-Monteagudo et al. [1] reported the first attempt to study the effect of activity cliffs over the generalization ability of machine learning (ML) based QSAR classifiers, using as study case a previously reported diverse and noisy dataset focused on drug induced liver injury (DILI) and more than 40 ML classification algorithms. Here, the hypothesis of structureactivity relationship (SAR) continuity restoration by activity cliffs removal was tested as a potential solution to overcome such limitations. Previously, a parallelism was established between activity cliffs generators (ACGs) and instances that should be misclassified (ISMs), a related concept from the field of machine learning. Based on this concept, comparative study was undertaken for classification performance of multiple machine learning classifiers as well as the consensus classifier derived from predictive classifiers obtained from training sets including or excluding ACGs. The influence of the removal of ACGs from the training set over the virtual screening performance was also studied for the respective consensus classifiers algorithms. This work represents the novel step towards the application of a remedial solution to the activity cliffs problem in QSAR studies. In the second paper, Camps et al. [2] focused on the de novo design of new inhibitors of mutated tyrosine-kinase for the treatment of myeloid leukemia. The aim of this work was to obtain new tyrosine-kinase inhibitors using in silico tools like de novo drug design, docking and ab- sorption, distribution, metabolism and excretion studies. The focus of this research is on chronic myelogenous leukemia. Since 2011, there are several tyrosine-kinase inhibitors in the market. Due to mutations in the tyrosine-kinase domain, these inhibitors are becoming less effective in the leukemia treatment, and then there is a need for new more effective inhibitors. Using the computational tools, authors obtained a set of new molecules with better inhibition capabilities and with better drug-like physicochemical descriptors than some of the reference drugs in the market. In the third paper, Agüero-Chapin et al. [3] reviewed the new trends on the computational study of Ribonuclease III (RNase III). The Ribonuclease III enzymatic class is involved in many important biological processes from bacteria to higher eukaryotes. Consequently, they have been useful as drug-target candidates for drug development. Despite their high molecular diversity, RNases III share common structural and catalytic features and some degree of enzymatic activity. However, the role of accessory domains as key determinants of substrate selectivity and over the biological function of each RNase III type is still under study. The new biochemical data were generated and integrated with previous available information affirmed that RNases III substrate specificity as well as their cellular biological role is highly influenced by its protein structure architecture. In the fourth paper, Praseetha et al. [4] discussed about the deregulation of HDACs; which has been associated with tumour development and therefore inhibiting HDAC’s have surfaced as promising therapeutic strategy in malignancy. Vorinostat analogues with different biological activities were investigated for underlying structure-activity relationship. They concluded that molecular descriptors derived from 3-D Morse and Radial Distribution Function indices were found to be selective in all the models. These molecular descriptors encode common SAR among Vorinostat derivatives were evaluated for their potent HDAC inhibition activity. Next, Macías Farrera et al. [5] discussed about clinical failure of treatment due to the emergence of reduced susceptibility to fluoroquinolones among Salmonella sp serotypes. The study was focused on the evaluation of the minimum inhibitory concentration (MIC) of quinolones in strains, obtained from pigs from the State of Mexico. On the other hand, the GyrA gene was sequenced. The present study was undertaken to describe the resistance profiles and fluoroquinolone resistance mechanism of Salmonella Typhimurium. The DNA sequence of the gyrA genes from Salmonella enterica serovar Typhimurium revealed strong similarity between gyrA and its counterpart in Escherichia coli. The sequencing of quinolone resistance-determining region (QRDR) of the gyrA gene showed the presence of mutation at either S83 or at D87 in almost all the Salmonella Typhimurium isolates. The authors discussed the used of sequence alignment Bioinformatics methods in this context. Castillo-Garit et al. [6] discussed about the use of atom-based quadratic indices to obtain QSAR models for the prediction of aquatic toxicity in agreement with the principles required by the OECD for QSAR models to regulatory purposes. Moreover, the author reported new models and defined a domain of applicability for the best models. The achieved results demonstrated that, the atom-based 1381-6128/16 $58.00+.00

© 2016 Bentham Science Publishers

5042 Current Pharmaceutical Design, 2016, Vol. 22, No. 33

Chemoinformatics Models for Pharmaceutical Design, Part 1

quadratic indices could provide an attractive alternative to the experiments currently used for determining toxicity, which are costly and time- consuming. Martínez-Santiago et al. [7] presented a structural and physico-chemical interpretation study of the Graph Derivative Indices (GDIs) from three different points of view. Authors found that the vertex own frequencies can be expressed in terms of geometrical and electronic reactivity of the atoms and bonds, respectively. It was analyzed for codification gotten by the GDIs with structural specters, such as: size, ramification, electronic richness, conjugation effects and molecule´s symmetry. Taking into consideration the regularity and direct coherence founded among GDIs and each one of the structures described by this method is possible to say that GDIs possess an interpretation in structural terms. The authors concluded that the frequency and derivative in electronic terms allowed understanding the GDIs as electronic and geometric reactivity. In the last paper, but not the least, González-Díaz et al. [8] discussed an experimental-theoretic approach to drug-lymphocyte interactome networks with flow cytometry and spectral moments perturbation. Target elements of the Interactome (protein interaction networks) of the Immune System (cell lineages, metabolic reactions, proteins, etc.) are involved on the essential function of protection. Different organic compounds and drugs can interact with these networks of targets forming drug-target interactome; which are also complex networks. Conse- quently, it is of the major importance to carry out an experimental inference and/or theoretic prediction of the networks formed by the interaction of drugs with multiple targets in the immune system. Authors demonstrated that experimental techniques like Flow Cytometry Analysis (FCA) or chemoinformatics methods with this this given aim. The public database ChEMBL lists a very large number of experimental results about drug-target interactions useful for these studies. In this work, authors determined experimentally, by the first time, the values of EC50 and Cytotoxicity for the anti-microbial / anti-parasite drug Dermofural over Balb/C CD9 lymphocytes using flow cytometry. After that, they developed a new Perturbation-theory model for Drug-Cell Target Interactome in Lymphocytes based on dispersion-polarization moments of drug structure with respect to boundary conditions of a complex biomolecular system. This work generates new knowledge and models that allow predicting effects on humoral and cellular response on others drugs. The authors show that the models proposed can correctly classify perturbations in assay endpoints of heterogeneous series of drugs in many different assays, molecular and cellular targets, standard type measures, and organisms. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8]

Cruz-Monteagudo M, Medina-Franco JL, Perera-Sardiña Y, et al. Probing the hypothesis of SAR continuity restoration by the removal of activity cliffs generators in QSAR. Cur Pharm Des 2016; 22(33): 5043-56. Pereira W, Camps I. De novo design of new inhibitor of mutated tyrosine-kinase for the myeloid leukemia treatment. Cur Pharm Des 2016; 22(33): 5057-64. Agüero-Chapin G, Pérez-Machado G, Collí Mull J, Ancede-Gallardo E, Antunes A, de la Riva de la Riva GA. How the protein architecture of RNases III influences their substrate specificity? Cur Pharm Des 2016; 22(33): 5065-71. Praseetha S, Bandaru S, Yadav M, Nayarisseri A, Sureshkumar S. Common SAR derived from multiple QSAR models on Vorinostat derivatives targeting HDACs in tumor treatment. Cur Pharm Des 2016; 22(33): 5072-8. Macías Farrera1 GP, Tenorio Borroto E, Rivera Ramírez F, et al. Detection of quinolone resistance in Salmonella Typhimurium pig isolates determined by gyrA gene mutation using PCR- and sequence-based techniques within the gyrA gene. Cur Pharm Des 2016; 22(33): 5079-84. Castillo-Garit JA, Abad C, Casañola-Martin GM, Jones-Barigye S, Torrens F, Torreblanca A. Prediction of aquatic toxicity of benzene derivatives to tetrahymena pyriformis. Cur Pharm Des 2016; 22(33): 5085-94. Martínez-Santiago O, Millán Cabrera R, Marrero-Ponce Y, et al. Generalized molecular descriptors derived from event-based discrete derivative. Cur Pharm Des 2016; 22(33): 5095-113. González-Díaz H, Tenorio-Borroto E, Rivera Ramírez F, Vázquez Chagoyán JC, Roberto Montes de Oca Jimé. Experimental-theoretic approach to drug-lymphocyte interactome networks with flow cytometry and spectral moments perturbation theory. Cur Pharm Des 2016; 22(33): 5114-9.

Subhash C. Basak

Anuraj Nayarisseri

Natural Resources Research Institute and

Bioinformatics Research Laboratory,

Department of Chemistry & Biochemistry,

Eminent Biosciences, Vijaynagar, Indore - 452010, India.

University of Minnesota Duluth, Duluth MN 55811 USA.

E-mail: [email protected]


Humberto González-Díaz

Danail Bonchev

Department of Organic Chemistry II, Faculty of Science and Technology,

Center for the Study of Biological Complexity,

University of the Basque Country UPV/EHU, 48080, Leioa, Spain.

Virginia Commonwealth University, Richmond,

IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain

Virginia, United States of America.

