ANN multiscale model of anti-HIV Drugs activity vs AIDS prevalence in the US at county level based on information indices of molecular graphs and social networks

[Abstract] This work is aimed at describing the workflow for a methodology that combines chemoinformatics and pharmacoepidemiology methods and at reporting the first predictive model developed with this methodology. The new model is able to predict complex networks of AIDS prevalence in the US counties, taking into consideration the social determinants and activity/structure of anti-HIV drugs in preclinical assays. We trained different Artificial Neural Networks (ANNs) using as input information indices of social networks and molecular graphs. We used a Shannon information index based on the Gini coefficient to quantify the effect of income inequality in the social network. We obtained the data on AIDS prevalence and the Gini coefficient from the AIDSVu database of Emory University. We also used the Balaban information indices to quantify changes in the chemical structure of anti-HIV drugs. We obtained the data on anti-HIV drug activity and structure (SMILE codes) from the ChEMBL database. Last, we used Box-Jenkins moving average operators to quantify information about the deviations of drugs with respect to data subsets of reference (targets, organisms, experimental parameters, protocols). The best model found was a Linear Neural Network (LNN) with values of Accuracy, Specificity, and Sensitivity above 0.76 and AUROC > 0.80 in training and external validation series. This model generates a complex network of AIDS prevalence in the US at county level with respect to the preclinical activity of anti-HIV drugs in preclinical assays. To train/validate the model and predict the complex network we needed to analyze 43,249 data points including values of AIDS prevalence in 2,310 counties in the US vs ChEMBL results for 21,582 unique drugs, 9 viral or human protein targets, 4,856 protocols, and 10 possible experimental measures.Ministerio de Educación, Cultura y Deportes; AGL2011-30563-C03-0

Exploring the anti-proliferative activity of Pelargonium sidoides DC with in silico target identification and network pharmacology

Pelargonium sidoides DC (Geraniaceae) is a medicinal plant indigenous to Southern Africa that has been widely evaluated for its use in the treatment of upper respiratory tract infections. In recent studies, the anti-proliferative potential of P. sidoides was shown, and several phenolic compounds were identified as the bioactive compounds. Little, however, is known regarding their anti-proliferative protein targets. In this study, the anti-proliferative mechanisms of P. sidoides through in silico target identification and network pharmacology methodologies were evaluated. The protein targets of the 12 phenolic compounds were identified using the target identification server PharmMapper and the server for predicting Drug Repositioning and Adverse Reactions via the Chemical–Protein Interactome (DRAR-CPI). Protein–protein and protein–pathway interaction networks were subsequently constructed with Cytoscape 3.4.0 to evaluate potential mechanisms of action. A total of 142 potential human target proteins were identified with the in silico target identification servers, and 90 of these were found to be related to cancer. The protein interaction network was constructed from 86 proteins involved in 209 interactions with each other, and two protein clusters were observed. A pathway enrichment analysis identified over 80 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched with the protein targets and included several pathways specifically related to cancer as well as various signaling pathways that have been found to be dysregulated in cancer. These results indicate that the anti-proliferative activity of P. sidoides may be multifactorial and arises from the collective regulation of several interconnected cell signaling pathways.https://link.springer.com/journal/110302018-11-18hj2017AnatomyBiochemistr

Modeling Antibacterial Activity with Machine Learning and Fusion of Chemical Structure Information with Microorganism Metabolic Networks

Predicting the activity of new chemical compounds over pathogenic microorganisms with different metabolic reaction networks (MRNs) is an important goal due to the different susceptibility to antibiotics. The ChEMBL database contains >160 000 outcomes of preclinical assays of antimicrobial activity for 55 931 compounds with >365 parameters of activity (MIC, IC50, etc.) and >90 bacteria strains of >25 bacterial species. In addition, the Leong and Barabàsi data set includes >40 MRNs of microorganisms. However, there are no models able to predict antibacterial activity for multiple assays considering both drug and MRN structures at the same time. In this work, we combined perturbation theory, machine learning, and information fusion techniques to develop the first PTMLIF model. The best linear model found presented values of specificity = 90.31/90.40 and sensitivity = 88.14/88.07 in training/validation series. We carried out a comparison to nonlinear artificial neural network (ANN) techniques and previous models from the literature. Next, we illustrated the practical use of the model with an experimental case of study. We reported for the first time the isolation and characterization of terpenes from the plant Cissus incisa. The antibacterial activity of the terpenes was experimentally determined. The more active compounds were phytol and α-amyrin, with MIC = 100 μg/mL for Vancomycin-resistant Enterococcus faecium and Acinetobacter baumannii resistant to carbapenems. These compounds are already known from other sources. However, they have been isolated and evaluated for the first time here against several strains of multidrug-resistant bacteria including World Health Organization (WHO) priority pathogens. Last, we used the model to predict the activity of these compounds versus other microorganisms with different MRNs in order to find other potential targets.Ministerio de Economía y Competitividad (CTQ2016-74881-P) // Gobierno Vasco (IT1045-16

El efecto de los aceites de oleaginosas en rumen en el sistema de fermentación in vitro

Los subproductos de plantas oleaginosas han sido utilizadas para mejorar el desempeño productivo en ruminates ya sea por la alta energía que proporciona o por su composición proteica, sin embargo, pocos artículos reportan la cantidad que debe proporcionarse en forma adecuada en la dieta de ruminates como suplemento. Es por lo anterior, que el trabajo fue elaborado para evaluar el efecto de cártamo (Carthamus tinctorius L., SFM)/canola (Brassica napus, CAS) con semilla de sorgo en una proporción (0%, 25%, 50%, 75%, 100%, respectivamente) en una proporción concentrado-forraje (1:1) en una prueba in vitro de fermentación ruminal de ovino, como es la cinética de producción de gas, nitrógeno amoniacal (NH3-N), pH, producción de metano (CH4) y la desaparición de materia seca in vitro (IVDMD) de rastrojo de maíz. Los resultados mostraron que la producción de gas, y de metano significativamente disminuyeron, pero IVDMD y pH incrementaron con el incremento de ambas proporciones de SFM y CAS en alimentación a base de concentrado. Para tomar en consideración el desempeño de fermentación y ambiente, nuestros resultados sugieren que la suplementación más adecuada de canola y cartamo en alimentación a base de concentrado son de 25% a 50%, y de 25% a 75%, respectivamente. Modificar el metabolismo microbiano en rumen a través de la adición de aceites derivados de plantas es una manera efectiva de aumentar los ácidos grasos funcionales de los productos derivados de ruminates. Poco es conocido de la influencia de ácidos grasos exogenos en los procesos del metabolismo de lípidos en las membranas de bacterias y protozoarios. Es por lo anterior, que el presente trabajo se enfocó en investigar los ácidos grasos de cadena larga (LCFA), ácidos grasos volétiles (VFA) y metano (CH4) a las 48 horas de suplemtento exógeno de aceites principalmente conteniendo ácidos grasos poli/mono insaturados, PUFA/MUFA, (C18:3, C18:2 y C18:1 de aceites de linaza, cártamo y canola respectivamente) y ácidos grasos de cadena mediana MCFA, (C12:0 de aceite de coco). Los resultados mostraron que la composición de ácidos grasos entre bacteria y protozoarios fueron diferentes. La suplementación de aceite de linaza, principalmente ácido linoleico (C18:3), al incrementar las proporciones de C18:2 n6c, C18:2 n6t, C18:1 n9c, C18:1 n9t y cis- ácidos grasos en la membrane de bacterias y protozoarios en diferente medida, mejora ligeramente las concentraciones de ácido acético y propiónico pero no tiene impacto en CH4. La suplementación con aceite de coco, aumenta la composición de MCFA, y por lo tanto aumenta la biosíntesis de MCFA en fracciones tanto de bacterias como de protozoarios (del C12:0 al C14:0), para inhibir la actividad de metanogénesis en cierta medida. Los mayores ácidos grasos saturados, saturados/insaturados, trans-, even-carbon insaturados fueron obtenidos de membranas de bacterias cuando se suplementaba con aceites de cártamo y canola, principalmente C18:2 y C18:1, respectivamente, pero más bajo que aceite de coco. Sin embargo, en ambos con cártamo y canola aumentaron las concentraciones de ácido acético y propiónico, disminuye la Ac/Pro ratio pero no el impacto de la actividad de metanogénesis. En resumen, los resultados implican que diferentes grados de ácidos grasos insaturados de cadena corta o larga puede impactar en la fermentación ruminal

ANN Multiscale Model of Anti-HIV Drugs Activity vs AIDS Prevalence in the US at County Level Based on Information Indices of Molecular Graphs and Social Networks

This work is aimed at describing the workflow for a methodology that combines chemoinformatics and pharmacoepidemiology methods and at reporting the first predictive model developed with this methodology. The new model is able to predict complex networks of AIDS prevalence in the US counties, taking into consideration the social determinants and activity/structure of anti-HIV drugs in preclinical assays. We trained different Artificial Neural Networks (ANNs) using as input information indices of social networks and molecular graphs. We used a Shannon information index based on the Gini coefficient to quantify the effect of income inequality in the social network. We obtained the data on AIDS prevalence and the Gini coefficient from the AIDSVu database of Emory University. We also used the Balaban information indices to quantify changes in the chemical structure of anti-HIV drugs. We obtained the data on anti-HIV drug activity and structure (SMILE codes) from the ChEMBL database. Last, we used Box-Jenkins moving average operators to quantify information about the deviations of drugs with respect to data subsets of reference (targets, organisms, experimental parameters, protocols). The best model found was a Linear Neural Network (LNN) with values of Accuracy, Specificity, and Sensitivity above 0.76 and AUROC > 0.80 in training and external validation series. This model generates a complex network of AIDS prevalence in the US at county level with respect to the preclinical activity of anti-HIV drugs in preclinical assays. To train/validate the model and predict the complex network we needed to analyze 43,249 data points including values of AIDS prevalence in 2,310 counties in the US vs ChEMBL results for 21,582 unique drugs, 9 viral or human protein targets, 4,856 protocols, and 10 possible experimental measures

ANN Multiscale Model of Anti-HIV Drugs Activity vs AIDS Prevalence in the US at County Level Based on Information Indices of Molecular Graphs and Social Networks

This work is aimed at describing the workflow for a methodology that combines chemoinformatics and pharmacoepidemiology methods and at reporting the first predictive model developed with this methodology. The new model is able to predict complex networks of AIDS prevalence in the US counties, taking into consideration the social determinants and activity/structure of anti-HIV drugs in preclinical assays. We trained different Artificial Neural Networks (ANNs) using as input information indices of social networks and molecular graphs. We used a Shannon information index based on the Gini coefficient to quantify the effect of income inequality in the social network. We obtained the data on AIDS prevalence and the Gini coefficient from the AIDSVu database of Emory University. We also used the Balaban information indices to quantify changes in the chemical structure of anti-HIV drugs. We obtained the data on anti-HIV drug activity and structure (SMILE codes) from the ChEMBL database. Last, we used Box-Jenkins moving average operators to quantify information about the deviations of drugs with respect to data subsets of reference (targets, organisms, experimental parameters, protocols). The best model found was a Linear Neural Network (LNN) with values of Accuracy, Specificity, and Sensitivity above 0.76 and AUROC > 0.80 in training and external validation series. This model generates a complex network of AIDS prevalence in the US at county level with respect to the preclinical activity of anti-HIV drugs in preclinical assays. To train/validate the model and predict the complex network we needed to analyze 43,249 data points including values of AIDS prevalence in 2,310 counties in the US vs ChEMBL results for 21,582 unique drugs, 9 viral or human protein targets, 4,856 protocols, and 10 possible experimental measures

Modelos multi-escala de inteligencia artificial para diseño quimio-informático y fármaco-epidemiológico de terapias anti-VIH en Condados de Estados Unidos

[Resumen]Los métodos que relacionan la estructura química con la actividad biológica se conocen como “relaciones cuantitativas estructura-actividad” (en adelante QSAR). Es fundamental entender y cuantificar la relación entre la estructura y la actividad biológica de los potenciales fármacos para realizar su estudio eficiente. Este tipo de estudio consiste en correlacionar, por medio de descriptores moleculares, distintas propiedades químicas o fisicoquímicas de las moléculas en cuestión con valores de actividad biológica. Actualmente, el desarrollo de medicamentos más seguros y efectivos en el tratamiento de enfermedades como el SIDA es un objetivo que requiere del esfuerzo de un elevado número de especialistas en diferentes campos de la Ciencia, y donde el azar ha tenido un gran protagonismo. Sin embargo, parece razonable pensar que nunca se obtendrán medicamentos eficaces y seguros con sólo acudir al azar. Para ser más eficientes en el desarrollo de nuevos fármacos, la investigación en el tratamiento de las enfermedades requiere poseer mecanismos predictivos de algunas actividades. Los modelos basados en “redes de neuronas artificiales” (en adelante RRNNAA) son un ejemplo de modelos teóricos de predicción, ampliamente utilizados en muchas áreas de la Ciencia, como medicina, química, bioquímica…, así como también en el desarrollo de medicamentos. En esto último, son muy útiles para la predicción de propiedades de los potenciales fármacos. Las RRNNAA se aproximan a la forma de operar que usa el cerebro humano, con habilidad para abordar con éxito los datos, las informaciones y los conocimientos naturales, o del mundo real, que están afectados por lo que se conoce como la “maldición de la cuádruple I”, por ser datos: inciertos, inconsistentes, incompletos e imprecisos. Esta particularidad hace que sean difíciles de gestionar adecuadamente por las técnicas computacionales convencionales, haciendo precisa la utilización de técnicas de Inteligencia Artificial, como son las ya citadas RRNNAA. La mayor ventaja de estos modelos inteligentes de predicción es que permiten evitar costes innecesarios producidos por desarrollos de nuevos compuestos con potencialidad terapéutica que resultarán estériles.Por lo tanto, el objetivo principal de la tesis aquí presentada es el desarrollo, con técnicas de inteligencia artificial, de una metodología “quimioinformática multi-escala” que permita relacionar cuantitativamente datos químicos y pre-clínicos con datos epidemiológicos, para llevar a cabo predicciones “fármaco-epidemiológicas”, teniendo en cuenta la imposibilidad práctica y legal de obtener datos experimentales, en la fase IV del proceso de desarrollo de nuevos compuestos[Resumo]Os métodos que relacionan a estrutura química coa actividade biolóxica son chamados “relacións cuantitativas estrutura – actividade” (en adiante QSAR). É esencial para entender e cuantificar a relación entre a estrutura e a actividade biolóxica dos potenciais fármacos para realizar o seu estudio eficiente. Este tipo de estudo consiste en correlacionar, a través de descritores moleculares, distintas propiedades químicas ou fisicoquímicas de las moleculas en cuestión, con valores de actividade biolóxica. Actualmente, o desenvolvemento de medicamentos máis seguros e efectivos no tratamento de enfermidades como o SIDA é un obxectivo que require do esforzo de un gran número de especialistas en diferentes campos da ciencia, e onde o azar tivo un gran protagonismo. Nembergantes, parece razoable pensar que nunca se obterían medicamentos eficaces e seguros con só acudir ao azar. Para ser máis eficaces no desenvolvemento de novos farmacos, a investigación para o tratamento de enfermidades require mecanismos preditivos de algunhas actividades. Os modelos baseados en redes neurais artificiais (en adiante RRNNAA) son un exemplo de modelos teóricos de predición amplamente utilizado en moitas áreas da ciencia, como medicina, química, bioquímica..., así como tamén no desenvolvemento de medicamentos. Nesto último, son moi útiles para a predición de propiedades dos potenciais medicamentos. As RRNNAA achegánse ao xeito de funcionar do cerebro humano, coa capacidade para abordar con éxito los datos, las informaciones y los conocimientos naturales, o del mundo real, que están afectados polo que se coñece como a “maldición da cuadrúple I”, por ser dados: incertos, inconsistentes, incompletos e imprecisos. Esta particularidade fai que sexan díficiles de xestionar axeitadamente coas técnicas computacionais convencionais, facendo preciso o uso de técnicas de Intelixencia Artificial, como son as xa citadas RRNNAA. A maior vantaxe destes modelos preditivos intelixentes é que permiten evitar custos innecesarios producidos polos desenvolvementos de novos compostos con potencial terapéutico que resultaran esteriles. Polo tanto o obxectivo principal da tese aquí presentada é o desenvolvemento, con tecnicas de intelixencia artificial dunha metodoloxía “quimioinformática multi-escala” que permita relacionar cuantitativamente datos químicos e pre-clínicos con datos epidemiolóxicos, para levar a cabo predicións fármaco-epidemiolóxicas, tendo en conta a imposibilidade práctica e legal de obter datos experimentais na fase IV do proceso de desenvolvemento de novos compostos.[Abstract]The methods relating chemical structure to biological activity are called “Quantitative Structure Activity Relationships” (QSAR). It is essential to understand and quantify the relationships between the structure and biological activity of potential drugs to develop an efficient study on them. This kind of study consists of the correlation of the molecular descriptors based on several chemical or physicochemical properties with biological activity. Currently, the development of safer and more effective drugs in the treatment of diseases such as AIDS is a goal that requires a joint effort of a large number of specialists from different fields of science, and where chance also has a major role. However, it seems reasonable that no effective and safe drugs will be obtained based on chance only. To be more efficient in developing new drugs, the research for the treatment of diseases requires predictive mechanisms of some biological activities. The models based on "Artificial Neural Networks" (ANNs) are an example of theoretical prediction models, widely used in many areas of science such as Medicine, Chemistry, Biochemistry, etc. as well as in Drug Development. In the latter, they are very useful for predicting properties of potential drugs. ANNs approach the modus operandi used by the human brain, being able to successfully manage data, information and natural knowledge, or from the real world, which are affected by the so-called "curse of the fourfold I", dealing with information which is uncertain, inconsistent, incomplete and inaccurate. This feature makes it difficult to properly manage by conventional computational techniques, making the use of Artificial Intelligence (AI) techniques necessary, such as the above-mentioned ANNs. The most important advantage of these intelligent prediction models is the fact that they avoid unnecessary production costs associated with the development of new compounds with therapeutic potential which proved to be inactive. Therefore, the main objective of the thesis is the development of a chemoinformatics multi-scale methodology using artificial intelligence techniques to quantitatively relate chemical and pre-clinical data with epidemiological data, with the aim of performing "drug - epidemiological" predictions, taking into account the practical and legal impossibility of obtaining experimental data in Phase IV of the development process of new compounds