Esvieta Tenorio-Borroto

Entropy Model for Multiplex Drug-Target Interaction Endpoints of Drug Immunotoxicity

Entropy measures are universal parameters useful to codify biologically-relevant information in many systems. In our previous work, (Gonzalez-Diaz, H., et al. Chem. Res. Toxicol. 2003, 16, 1318-1327), we introduced the molecular structure information indices called 3D-Markovian electronic delocalization entropies (3D-MEDNEs) to study the quantitative structure-toxicity relationships (QSTR) of drugs. In a second part, (Cruz-Monteagudo, M. et al. Chem. Res. Toxicol., 2008, 21 (3), 619-632), we extended 3D-MEDNEs to numerically encode toxicologically-relevant information present in Mass Spectra of the serum proteome. These works demonstrated that the idea behind classic drug QSTR models can be extended to solve more general problems in toxicological chemical research. For instance, there are not many reports of multi-target QSTR (mt-QSTR) models useful to predict multiplexed endpoints of drugs in a high number of cytotoxicity assays. In this work, we train and validate for the first time a QSTR model that correctly classifies 8,806 out of 9,001 (Accuracy = 91.1%) multiplexing assay endpoints of 7903 drugs (including both training and validation series). Each endpoint corresponds to one out of 1443 assays, 32 molecular and cellular targets, 46 standard type measures, in two possible organisms (human and mouse). We have also determined experimentally, for the first time, the values of EC50 = 8.21 μg/mL and Cytotoxicity = 26.25 % for the antimicrobial / antiparasitic drug G1 on Balb/C mouse thymic macrophages using flow cytometry. In addition, we have used the new model to predict G1 endpoints in 1,283 assays finding a low average probability of p(1) = 0.50% in 152 cytotoxicity assays. Last, we have used the model to predict average probability of the interaction of G1 with different proteins in macrophages. Interestingly, the Macrophage colony-stimulating factor receptor, the Macrophage colony-stimulating factor 1 receptor, the Macrophage migration inhibitory factor, Macrophage scavenger receptor types I and II, and the Macrophage-stimulating protein receptor, have also very low average predicted probabilities of p(1) = 0.092, 0.038, 0.077, 0.026, 0.2, 0.106, respectively. Both experimental and theoretical results show a moderate thymic macrophage cytotoxicity of G1. The obtained results are significant because they complement the immunotoxicology studies of this important drug.

QSPR and Flow Cytometry Analysis (QSPR-FCA): Review and New Findings on Parallel Study of Multiple Interactions of Chemical Compounds with Immune Cellular and Molecular Targets

The immune system helps to halt the infections caused by pathogenic microbial and parasitic agents. The ChEMBL database lists very large datasets of cytotoxicity of organic compounds but notably, a large number of compounds have unknown effects over molecular and cellular targets in the immune system. Flow Cytometry Analysis (FCA) is a very important technique to determine the effect of organic compounds over these molecular and cellular targets in the immune system. In addition, multi-target Quantitative Structure- Property Relationship (mt-QSPR) models can predict drug-target interactions, networks. The objectives of this paper are the following. Firstly, we carried out a review of general aspects and some examples of applications of FCA to study the effect of drugs over different cellular targets. However, we focused more on methods, materials, and experimental results obtained in previous works reported by our group in the study of the drug Dermofural. We also reviewed different mt-QSPR models useful to predict the immunotoxicity and/or the effects of drugs over immune system targets including immune cell lineages or proteins. Secondly, we included new results not published before. Initially, we used ChEMBL data to train and validate a new model but with emphasis in the effect of drugs over lymphocytes. Lastly, we report unpublished results of the computational and FCA study of a new nitro-vinyl-furan compound over thymic lymphocytes T helpers (CD4+) and T cytotoxic (CD8+) population.

Immunotoxicity, Flow Cytometry, and Chemoinformatics: Review, Bibliometric Analysis, and New QSAR Model of Drug Effects Over Macrophages

Bibliometric methods for analyzing and describing research output have been supported internationally by the establishment and operation of organizations such as the Institute for Scientific Information (ISI) or Scimago Ranking Institutions (SRI). This study provides an overview of the research performance of major World countries in the field cytokines, Citometric bead assays and QSAR, the most important journals in which they published their research articles, and the most important academic institutions publishing them. The analysis was based on Thomson Scientifics Web of Science (WoS), and Scimago group calculated bibliometric indicators of publication activity and actual citation impact. Studying the time period 2005-2010, and shows the visibility of Medicinal Chemistry Bioorganic in this thematic noting that the visibility of a journal must take into account not only the impact factor, but the prestige, popularity and representativeness of the theme that addresses the same making a comprehensive assessment of bibliometric indicators.

Experimental-Theoretic Approach to Drug-Lymphocyte Interactome Networks with Flow Cytometry and Spectral Moments Perturbation Theory.

We can combine experimental techniques like Flow Cytometry Analysis (FCA) with Chemoinformatics methods to predict the complex networks of interactions between organic compounds and targets in the immune system. In this work, we determined experimentally the values of EC50 = 17.82 μg/mL and Cytotoxicity = 20.6 % for the anti-microbial / anti-parasite drug Dermofural over Balb/C CD9 lymphocytes using flow cytometry. After that, we developed a new Perturbation-theory model for Drug-Cell Target Interactome in Lymphocytes based on dispersion-polarization moments of drug structure. The models correctly classifies 34591 out of 42715 (Accuracy = 80.9%) cases of perturbations in assay endpoints of 11492 drugs (including both train and validation series). Each endpoint correspond to one out of 2616 assays, 38 molecular and cellular targets, 77 standard type measures, in four possible (human and rodents).