Quantitative Structure-Activity (mt-QSAR) techniques may become an important tool for prediction of cytotoxicity and High-throughput Screening (HTS) of drugs to rationalize drug discovery process. In this work, we train and validate by the first time mt-QSAR model using TOPS-MODE approach to calculate drug molecular descriptors and Linear Discriminant Analysis (LDA) function. This model correctly classifies 8258 out of 9000 (Accuracy = 91.76%) multiplexing assay endpoints of 7903 drugs (including both train and validation series). Each endpoint correspond to one out of 1418 assays, 36 molecular and cellular targets, 46 standard type measures, in two possible organisms (human and mouse). After that, we determined experimentally, by the first time, the values of EC50 = 21.58 μg/mL and Cytotoxicity = 23.6% for the anti-microbial/anti-parasite drug G1 over Balb/C mouse peritoneal macrophages using flow cytometry. In addition, the model predicts for G1 only 7 positive endpoints out 1251 cytotoxicity assays (0.56% of probability of cytotoxicity in multiple assays). The results obtained complement the toxicological studies of this important drug. This work adds a new tool to the existing pool of few methods useful for multi-target HTS of ChEMBL and other libraries of compounds towards drug discovery.

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.

Unbalanced uptake of Omega 6/Omega 3 (ω-6/ω-3) ratios could increase chronic disease occurrences, such as inflammation, atherosclerosis, or tumor proliferation, and methylation methods for measuring the ruminal microbiome fatty acid (FA) composition/distribution play a vital role in discovering the contribution of food components to ruminant products (e.g., meat and milk) when pursuing a healthy diet. Hansch's models based on Linear Free Energy Relationships (LFERs) using physicochemical parameters, such as partition coefficients, molar refractivity, and polarizability, as input variables (Vk) are advocated. In this work, a new combined experimental and theoretical strategy was proposed to study the effect of ω-6/ω-3 ratios, FA chemical structure, and other factors over FA distribution networks in the ruminal microbiome. In step 1, experiments were carried out to measure long chain fatty acid (LCFA) profiles in the rumen microbiome (bacterial and protozoan), and volatile fatty acids (VFAs) in fermentation media. In step 2, the proportions and physicochemical parameter values of LCFAs and VFAs were calculated under different boundary conditions (cj) like c1 = acid and/or base methylation treatments, c2 = with/without fermentation, c3 = FA distribution phase (media, bacterial, or protozoan microbiome), etc. In step 3, Perturbation Theory (PT) and LFER ideas were combined to develop a PT-LFER model of a FA distribution network using physicochemical parameters (V(k)), the corresponding Box-Jenkins (ΔV(kj)) and PT operators (ΔΔV(kj)) in statistical analysis. The best PT-LFER model found predicted the effects of perturbations over the FA distribution network with sensitivity, specificity, and accuracy > 80% for 407 655 cases in training + external validation series. In step 4, alternative PT-LFER and PT-NLFER models were tested for training Linear and Non-Linear Artificial Neural Networks (ANNs). PT-NLFER models based on ANNs presented better performance but are more complicated than the PT-LFER model. Last, in step 5, the PT-LFER model based on LDA was used to reconstruct the complex networks of perturbations in the FA distribution and compared the giant components of the observed and predicted networks with random Erdős-Rényi network models. In short, our new PT-LFER model is a useful tool for predicting a distribution network in terms of specific fatty acid distribution.

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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 Scientific's 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.