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In this study, a methodology is proposed, combining ligand- and structure-based virtual screening tools, for the identification of phosphorus-containing compounds as inhibitors of zinc metalloproteases. First, we use Dragon molecular descriptors to develop a Linear Discriminant Analysis classification model, which is widely validated according to the OECD principles. This model is simple, robust, stable and has good discriminating power. Furthermore, it has a defined applicability domain and it is used for virtual screening of the DrugBank database. Second, docking experiments are carried out on the identified compounds that showed good binding energies to the enzyme thermolysin. Considering the potential toxicity of phosphorus-containing compounds, their toxicological profile is evaluated according to Protox II. Of the five molecules evaluated, two show carcinogenic and mutagenic potential at small LD50, not recommended as drugs, while three of them are classified as non-toxic, and could constitute a starting point for the development of new vasoactive metalloprotease inhibitor drugs. According to molecular dynamics simulation, two of them show stable interactions with the active site maintaining coordination with the metal. A high agreement is evident between QSAR, docking and molecular dynamics results, demonstrating the potentialities of the combination of these tools.

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Las Miopatías Inflamatorias Idiopáticas (MII) son un grupo heterogéneo de enfermedades que se caracterizan por debilidad muscular e inflamación subyacente en la biopsia muscular. Los principales órganos afectados son el músculo, la piel y también puede afectarse el pulmón. Se distinguen dentro de los subtipos clínicos como Polimiositis (PM), Dermatomiositis (DM), DM con la variante Dermatomiositis Clínicamente Amiopática (DMCA), el Síndrome Antisintetasa (SAS), la Miositis Necrotizante Inmunomediada, la Miositis por Cuerpos de Inclusión (MCI) y la Miositis Asociada a Neoplasia. La presencia de ciertos anticuerpos específicos y asociados predispone al desarrollo de manifestaciones clínicas, determinando el pronóstico de la enfermedad. Se presentan 4 pacientes del Registro de MII de la Sociedad Argentina de Reumatología (SAR) con estas características: un paciente con PM y anti Jo-1 positivo y tres pacientes con DM (uno con DMCA y anti-RO 52 y dos pacientes con anti-PL7 y anti-TIF1γ respectivamente).

Idiopathic Inflammatory Myopathies (MII) are a heterogeneous group of diseases characterized by muscle weakness and inflammation underlying muscle biopsy. The main organs affected are muscle, skin and the lung can also be affected. They are distinguished within clinical subtypes such as Polymyositis (PM), Dermatomyositis (DM), DM with the variant Clinically Amiopathic Dermatomyositis (DMCA), the Syndrome Antisynthetase (SAS), Immune-mediated Necrotizing Myositis, Body Myositis Inclusion (MCI) and Neoplasia-Associated Myositis. The presence of certain specific and associated antibodies predisposes to the development of clinical manifestations, determining the disease prognosis. 4 patients from the Registry of MII of the Argentine Society of Rheumatology (SAR) are presented with these characteristics: one patient with PM and anti Jo-1 positive and three patients with DM (one with DMCA and anti-RO 52 and two patients with anti-PL7 and anti-TIF1γ respectively).

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RESUMO

Las Miopatías Inflamatorias Idiopáticas (MII) son un grupo heterogéneo de enfermedades que se caracterizan por debilidad muscular e inflamación subyacente en la biopsia muscular. Los principales órganos afectados son el músculo, la piel y también puede afectarse el pulmón. Se distinguen dentro de los subtipos clínicos como Polimiositis (PM), Dermatomiositis (DM), DM con la variante Dermatomiositis Clínicamente Amiopática (DMCA), el Síndrome Antisintetasa (SAS), la Miositis Necrotizante Inmunomediada, la Miositis por Cuerpos de Inclusión (MCI) y la Miositis Asociada a Neoplasia. La presencia de ciertos anticuerpos específicos y asociados predispone al desarrollo de manifestaciones clínicas, determinando el pronóstico de la enfermedad. Se presentan 4 pacientes del Registro de MII de la Sociedad Argentina de Reumatología (SAR) con estas características: un paciente con PM y anti Jo-1 positivo y tres pacientes con DM (uno con DMCA y anti- RO 52 y dos pacientes con anti-PL7 y anti-TIF1γ respectivamente).

Idiopathic Inflammatory Myopathies (MII) are a heterogeneous group of diseases characterized by muscle weakness and inflammation underlying muscle biopsy. The main organs affected are muscle, skin and the lung can also be affected. They are distinguished within clinical subtypes such as Polymyositis (PM), Dermatomyositis (DM), DM with the variant Clinically Amiopathic Dermatomyositis (DMCA), the Syndrome Antisynthetase (SAS), Immune-mediated Necrotizing Myositis, Body Myositis Inclusion (MCI) and Neoplasia-Associated Myositis. The presence of certain specific and associated antibodies predisposes to the development of clinical manifestations, determining the disease prognosis. 4 patients from the Registry of MII of the Argentine Society of Rheumatology (SAR) are presented with these characteristics: one patient with PM and anti Jo-1 positive and three patients with DM (one with DMCA and anti-RO 52 and two patients with anti-PL7 and anti-TIF1γ respectively).

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Graph derivative indices (GDIs) have recently been defined over N-atoms (N = 2, 3 and 4) simultaneously, which are based on the concept of derivatives in discrete mathematics (finite difference), metaphorical to the derivative concept in classical mathematical analysis. These molecular descriptors (MDs) codify topo-chemical and topo-structural information based on the concept of the derivative of a molecular graph with respect to a given event (S) over duplex, triplex and quadruplex relations of atoms (vertices). These GDIs have been successfully applied in the description of physicochemical properties like reactivity, solubility and chemical shift, among others, and in several comparative quantitative structure activity/property relationship (QSAR/QSPR) studies. Although satisfactory results have been obtained in previous modelling studies with the aforementioned indices, it is necessary to develop new, more rigorous analysis to assess the true predictive performance of the novel structure codification. So, in the present paper, an assessment and statistical validation of the performance of these novel approaches in QSAR studies are executed, as well as a comparison with those of other QSAR procedures reported in the literature. To achieve the main aim of this research, QSARs were developed on eight chemical datasets widely used as benchmarks in the evaluation/validation of several QSAR methods and/or many different MDs (fundamentally 3D MDs). Three to seven variable QSAR models were built for each chemical dataset, according to the original dissection into training/test sets. The models were developed by using multiple linear regression (MLR) coupled with a genetic algorithm as the feature wrapper selection technique in the MobyDigs software. Each family of GDIs (for duplex, triplex and quadruplex) behaves similarly in all modelling, although there were some exceptions. However, when all families were used in combination, the results achieved were quantitatively higher than those reported by other authors in similar experiments. Comparisons with respect to external correlation coefficients (q2ext) revealed that the models based on GDIs possess superior predictive ability in seven of the eight datasets analysed, outperforming methodologies based on similar or more complex techniques and confirming the good predictive power of the obtained models. For the q2ext values, the non-parametric comparison revealed significantly different results to those reported so far, which demonstrated that the models based on DIVATI's indices presented the best global performance and yielded significantly better predictions than the 12 0-3D QSAR procedures used in the comparison. Therefore, GDIs are suitable for structure codification of the molecules and constitute a good alternative to build QSARs for the prediction of physicochemical, biological and environmental endpoints.

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Novel N-tuple topological/geometric cutoffs to consider specific inter-atomic relations in the QuBiLS-MIDAS framework are introduced in this manuscript. These molecular cutoffs permit the taking into account of relations between more than two atoms by using (dis-)similarity multi-metrics and the concepts related with topological and Euclidean-geometric distances. To this end, the kth two-, three- and four-tuple topological and geometric neighbourhood quotient (NQ) total (or local-fragment) spatial-(dis)similarity matrices are defined, to represent 3D information corresponding to the relations between two, three and four atoms of the molecular structures that satisfy certain cutoff criteria. First, an analysis of a diverse chemical space for the most common values of topological/Euclidean-geometric distances, bond/dihedral angles, triangle/quadrilateral perimeters, triangle area and volume was performed in order to determine the intervals to take into account in the cutoff procedures. A variability analysis based on Shannon's entropy reveals that better distribution patterns are attained with the descriptors based on the cutoffs proposed (QuBiLS-MIDAS NQ-MDs) with regard to the results obtained when all inter-atomic relations are considered (QuBiLS-MIDAS KA-MDs - 'Keep All'). A principal component analysis shows that the novel molecular cutoffs codify chemical information captured by the respective QuBiLS-MIDAS KA-MDs, as well as information not captured by the latter. Lastly, a QSAR study to obtain deeper knowledge of the contribution of the proposed methods was carried out, using four molecular datasets (steroids (STER), angiotensin converting enzyme (ACE), thermolysin inhibitors (THER) and thrombin inhibitors (THR)) widely used as benchmarks in the evaluation of several methodologies. One to four variable QSAR models based on multiple linear regression were developed for each compound dataset following the original division into training and test sets. The results obtained reveal that the novel cutoff procedures yield superior performances relative to those of the QuBiLS-MIDAS KA-MDs in the prediction of the biological activities considered. From the results achieved, it can be suggested that the proposed N-tuple topological/geometric cutoffs constitute a relevant criteria for generating MDs codifying particular atomic relations, ultimately useful in enhancing the modelling capacity of the QuBiLS-MIDAS 3D-MDs.

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The QuBiLs-MAS approach is used for the in silico modelling of the antifungal activity of organic molecules. To this effect, non-stochastic (NS) and simple-stochastic (SS) atom-based quadratic indices are used to codify chemical information for a comprehensive dataset of 2478 compounds having a great structural variability, with 1087 of them being antifungal agents, covering the broadest antifungal mechanisms of action known so far. The NS and SS index-based antifungal activity classification models obtained using linear discriminant analysis (LDA) yield correct classification percentages of 90.73% and 92.47%, respectively, for the training set. Additionally, these models are able to correctly classify 92.16% and 87.56% of 706 compounds in an external test set. A comparison of the statistical parameters of the QuBiLs-MAS LDA-based models with those for models reported in the literature reveals comparable to superior performance, although the latter were built over much smaller and less diverse datasets, representing fewer mechanisms of action. It may therefore be inferred that the QuBiLs-MAS method constitutes a valuable tool useful in the design and/or selection of new and broad spectrum agents against life-threatening fungal infections.

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Quantitative structure-activity relationship models for the prediction of mode of toxic action (MOA) of 221 phenols to the ciliated protozoan Tetrahymena pyriformis using atom-based quadratic indices are reported. The phenols represent a variety of MOAs including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles and soft electrophiles. Linear discriminant analysis (LDA), and four machine learning techniques (ML), namely k-nearest neighbours (k-NN), support vector machine (SVM), classification trees (CTs) and artificial neural networks (ANNs), have been used to develop several models with higher accuracies and predictive capabilities for distinguishing between four MOAs. Most of them showed global accuracy of over 90%, and false alarm rate values were below 2.9% for the training set. Cross-validation, complementary subsets and external test set were performed, with good behaviour in all cases. Our models compare favourably with other previously published models, and in general the models obtained with ML techniques show better results than those developed with linear techniques. We developed unsupervised and supervised consensus, and these results were better than our ML models, the results of rule-based approach and other ensemble models previously published. This investigation highlights the merits of ML-based techniques as an alternative to other more traditional methods for modelling MOA.

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Versatile event-based approaches for the definition of novel information theory-based indices (IFIs) are presented. An event in this context is the criterion followed in the "discovery" of molecular substructures, which in turn serve as basis for the construction of the generalized incidence and relations frequency matrices, Q and F, respectively. From the resultant F, Shannon's, mutual, conditional and joint entropy-based IFIs are computed. In previous reports, an event named connected subgraphs was presented. The present study is an extension of this notion, in which we introduce other events, namely: terminal paths, vertex path incidence, quantum subgraphs, walks of length k, Sach's subgraphs, MACCs, E-state and substructure fingerprints and, finally, Ghose and Crippen atom-types for hydrophobicity and refractivity. Moreover, we define magnitude-based IFIs, introducing the use of the magnitude criterion in the definition of mutual, conditional and joint entropy-based IFIs. We also discuss the use of information-theoretic parameters as a measure of the dissimilarity of codified structural information of molecules. Finally, a comparison of the statistics for QSPR models obtained with the proposed IFIs and DRAGON's molecular descriptors for two physicochemical properties log P and log K of 34 derivatives of 2-furylethylenes demonstrates similar to better predictive ability than the latter.

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Se reporta 16 casos de pénfigo foliáceo endémico en el departamento de Ucayali, en su mayoría procedentes de la provincia de Coronel Portillo (94 por ciento); el 50 por ciento corresponde al distrito de Campo Verde. El 80 por ciento de los pacientes provenían de un área rural y el 56 por ciento de los casos fueron de sexo femenino. Afecta a la población pediátrica en un 44 por ciento (7 casos); y el compromiso de las lesiones cutáneas fue generalizado en un 100 por ciento de los casos; sin compromiso de mucosas. El signo de Nikolsky estuvo presente en el 50 por ciento. En todos los casos se encontró acantólisis subcórnea y sólo el 40 por ciento tuvo inmunofluorescencia positiva para pénfigo.

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