Aplicación de métodos topológicos y de inteligencia artificial a la selección de nuevos antibacterianos

RESUMEN El presente trabajo constituye una aplicación de la topología molecular, que tiene por objetivo la obtención de nuevos compuestos con actividad antibacteriana aplicando un modelo basado en redes neuronales artificiales combinado con análisis lineal discriminante, para realizar la selección molecular, empleando como descriptores un conjunto de Índices Topológico-Estructurales desarrollados en nuestra unidad de investigación, caracterizados por ser números enteros sencillos y cumplir una de las características fundamentales de los índices topológicos que es la de ser invariantes de grafo. En primer lugar se eligió el grupo farmacológico sobre el que trabajaremos que en nuestro caso fueron los antibacterianos. El interés por dicho grupo terapéutico se basa fundamentalmente en su gran importancia clínica, unido a la creciente aparición de resistencias a los antibióticos habitualmente empleados, lo que hace necesaria la búsqueda de nuevas estructuras químicas que posean dicha propiedad farmacológica. Una vez elegido el grupo terapéutico el siguiente paso es seleccionar las moléculas que van a constituir los grupos de discriminación, es decir, activos e inactivos. Las moléculas pertenecientes a ambos grupos fueron obtenidas de la base de datos de la decimosegunda edición en CD-Rom del Merck Index. El grupo de activos está compuesto por 217 antibacterianos pertenecientes a distintos grupos químicos como son aminoglicósidos, anfenicoles, ansamicinas, β-lactámicos, lincosamidas, macrólidos, polipéptidos, tetraciclinas, nitrofuranos, quinolonas, sulfonamidas, sulfonas y otros. El grupo de inactivos está compuesto por 216 moléculas pertenecientes a distintas categorías terapéuticas como son analgésicos narcóticos, analgésicos no-narcóticos, antidepresivos, antihistamínicos, hipolipemiantes, antihipertensivos, hipoglucemiantes y sedantes e hipnóticos. Tanto en el grupo de activos como en el de inactivos se realiza una división estableciendo grupos de aprendizaje o entrenamiento y de test compuestos por el 70 % y el 30 % de moléculas respectivamente. A partir de la descripción en formato SMILES de las moléculas obtenemos una estructura de datos en forma de grafo, que mediante algoritmos de recorrido en profundidad y recorrido en anchura nos permiten calcular sus correspondientes índices topológico-estructurales, que nos proporcionan información sobre el número y tipo de átomos, número y tipo de enlaces, valencia topológica y distancias entre pares de átomos de la molécula. El siguiente paso es el procesado de estos índices mediante la red neuronal artificial, que tras un proceso de entrenamiento determinaremos cuáles serán las características más idóneas de la misma para realizar un adecuado proceso de discriminación. De forma paralela al procesado de índices mediante la red neuronal artificial, se realiza un análisis lineal discriminante, tras el cual obtendremos una función discriminante, que permita realizar la clasificación de los compuestos en activos e inactivos. Posteriormente mediante la función discriminante obtenida realizaremos una búsqueda guiada de estructuras con teórica actividad antibacteriana, que cumplan los requisitos topológicos marcados por dicha función, de entre los compuestos de la base de datos del módulo CS ChemFinder 5.1, perteneciente al paquete CS ChemOffice, una de las herramientas más útiles en el sector de la química computacional. La base de datos contiene catálogos de moléculas de distintos laboratorios como son Sigma-Aldrich, Fluka, Fisher, Avocado, etc. Una vez seleccionados distintos compuestos mediante la búsqueda guiada anteriormente citada, debemos calcular los índices topológico-estructurales de los mismos y posteriormente se procede a procesar estas moléculas, introduciéndolas en la red neuronal artificial entrenada, obteniendo una clasificación de estos compuestos en activos e inactivos. Los compuestos que mostraron teórica actividad antibacteriana fueron sometidos en primer lugar a ensayos de susceptibilidad antimicrobiana frente a cuatro cepas de microorganismos de referencia, dos gram-negativos y dos gram-positivos, para poner de manifiesto su actividad y la capacidad predictiva del método propuesto. Posteriormente los compuestos que se mostraron activos en estos ensayos fueron sometidos a ensayos de toxicidad aguda en animales de experimentación con objeto de determinar la Dosis Letal 50 de los mismos. De los compuestos seleccionados como teóricos antibacterianos, cuatro de ellos confirmaron dicha actividad, el compuesto N-[4-(2-Benzoxazolil)fenil]maleimida mostró actividad simultáneamente frente a Escherichia coli, Staphylococcus aureus y Enterococcus faecalis, obteniendo valores de CMI que mejoran claramente los que presentan Cefalosporina C y Ácido nalidíxico, empleados como fármacos de referencia; el compuesto 1,1'-(Metilene-di-4,1-fenilen)bismaleimida lo hizo frente a Staphylococcus aureus y Enterococcus faecalis; el producto o-Cresolftaleina complexona se mostró activo frente a Escherichia coli y Pseudomonas aeruginosa; mientras que el compuesto Dianhidrido etilendiaminotetraacético presentó actividad frente a Escherichia coli y Enterococcus faecalis. La determinación de la DL50 intraperitoneal en ratón para los cuatro productos que mostraron actividad antibacteriana, ha puesto de manifiesto la escasa toxicidad de los mismos, obteniéndose márgenes terapéuticos de considerable amplitud para dos de ellos, pues el compuesto N-[4-(2-Benzoxazolil)fenil]maleimida presenta valores de DL50 entre diez y veinte veces superior a los de CMI obtenidos frente a Enterococcus faecalis y Staphylococcus aureus; mientras que el compuesto 1,1'-(Metilene-di-4,1-fenilen)bismaleimida posee valores de DL50 entre tres y seis veces superiores a sus valores de CMI frente a Enterococcus faecalis y Staphylococcus aureus. Los resultados obtenidos confirman plenamente la validez del método topológico empleado, en la búsqueda y selección de nuevas estructuras con actividad antibacteriana, tanto por la idoneidad de los índices topológico-estructurales como por la utilización combinada de análisis lineal discriminante y redes neuronales artificiales. __________________________________________________________________________________________________ SUMARY In this work we use an interesting approach to QSAR analysis, namely the use of a set of Topological Indices as simple integers applied to individual atoms and bonds in molecules. The important common feature of all those descriptors is the independence of their numerical values on renumbering atoms in a chemical structure. These descriptors encode information about atom type, bonds, degree vertex, distances between pairs of atoms, etc. and so constitute an alternative to the use of molecular descriptors in QSAR studies, not only for the calculation process but also for a simpler interpretation in the prediction of the biological properties for a homogeneous collection of chemicals, so that such models are generally applicable. Finding structure-activity relationships is essentially a pattern recognition process, and historically, QSAR models have been developed using linear methods such as Linear Discriminant Analysis, however, several nonlinear QSAR methods have been proposed in recent years. Artificial Neural Networks is one group of methods that are increasingly being used in drug design to QSAR studies. This method is capable of recognize highly nonlinear structure-activity relationships, in contrast, LDA approaches can capture only linear relationships between molecular characteristics and structural or functional features to be predicted. The aim of this work was to discriminate between antibacterial and non-antibacterial compounds by topological methods and demonstrate the discriminative ability of the group of simple topological descriptors above mentioned in order to select new antibacterial agents from among new structures. For this purpose, the methods used for antibacterial activity discrimination were linear discriminant analysis and artificial neural networks. In the current study a total of 217 molecules with well-known antibacterial activity and 216 compounds without this activity were used. Finally, pharmacological and toxicological tests were carried out to determine the antibacterial activity and toxicity respectively of the compounds selected

GWAS Meta-Analysis of Suicide Attempt: Identification of 12 Genome-Wide Significant Loci and Implication of Genetic Risks for Specific Health Factors

Objective: Suicidal behavior is heritable and is a major cause of death worldwide. Two large-scale genome-wide association studies (GWASs) recently discovered and crossvalidated genome-wide significant (GWS) loci for suicide attempt (SA). The present study leveraged the genetic cohorts from both studies to conduct the largest GWAS metaanalysis of SA to date. Multi-ancestry and admixture-specific meta-analyses were conducted within groups of significant African, East Asian, and European ancestry admixtures. Methods: This study comprised 22 cohorts, including 43,871 SA cases and 915,025 ancestry-matched controls. Analytical methods across multi-ancestry and individual ancestry admixtures included inverse variance-weighted fixed-effects meta-analyses, followed by gene, gene-set, tissue-set, and drug-target enrichment, as well as summary-data-based Mendelian randomization with brain expression quantitative trait loci data, phenome-wide genetic correlation, and genetic causal proportion analyses. Results: Multi-ancestry and European ancestry admixture GWAS meta-analyses identified 12 risk loci at p values &lt;5×10-8. These loci were mostly intergenic and implicated DRD2, SLC6A9, FURIN, NLGN1, SOX5, PDE4B, and CACNG2. The multi-ancestry SNP-based heritability estimate of SA was 5.7% on the liability scale (SE=0.003, p=5.7×10-80). Significant brain tissue gene expression and drug set enrichment were observed. There was shared genetic variation of SA with attention deficit hyperactivity disorder, smoking, and risk tolerance after conditioning SA on both major depressive disorder and posttraumatic stress disorder. Genetic causal proportion analyses implicated shared genetic risk for specific health factors. Conclusions: This multi-ancestry analysis of suicide attempt identified several loci contributing to risk and establishes significant shared genetic covariation with clinical phenotypes. These findings provide insight into genetic factors associated with suicide attempt across ancestry admixture populations, in veteran and civilian populations, and in attempt versus death.</p

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

BACKGROUND: Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. METHODS: We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. RESULTS: Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. CONCLUSIONS: Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders

GWAS Meta-Analysis of Suicide Attempt:Identification of 12 Genome-Wide Significant Loci and Implication of Genetic Risks for Specific Health Factors

Objective: Suicidal behavior is heritable and is a major cause of death worldwide. Two large-scale genome-wide association studies (GWASs) recently discovered and crossvalidated genome-wide significant (GWS) loci for suicide attempt (SA). The present study leveraged the genetic cohorts from both studies to conduct the largest GWAS metaanalysis of SA to date. Multi-ancestry and admixture-specific meta-analyses were conducted within groups of significant African, East Asian, and European ancestry admixtures. Methods: This study comprised 22 cohorts, including 43,871 SA cases and 915,025 ancestry-matched controls. Analytical methods across multi-ancestry and individual ancestry admixtures included inverse variance-weighted fixed-effects meta-analyses, followed by gene, gene-set, tissue-set, and drug-target enrichment, as well as summary-data-based Mendelian randomization with brain expression quantitative trait loci data, phenome-wide genetic correlation, and genetic causal proportion analyses. Results: Multi-ancestry and European ancestry admixture GWAS meta-analyses identified 12 risk loci at p values &lt;5×10-8. These loci were mostly intergenic and implicated DRD2, SLC6A9, FURIN, NLGN1, SOX5, PDE4B, and CACNG2. The multi-ancestry SNP-based heritability estimate of SA was 5.7% on the liability scale (SE=0.003, p=5.7×10-80). Significant brain tissue gene expression and drug set enrichment were observed. There was shared genetic variation of SA with attention deficit hyperactivity disorder, smoking, and risk tolerance after conditioning SA on both major depressive disorder and posttraumatic stress disorder. Genetic causal proportion analyses implicated shared genetic risk for specific health factors. Conclusions: This multi-ancestry analysis of suicide attempt identified several loci contributing to risk and establishes significant shared genetic covariation with clinical phenotypes. These findings provide insight into genetic factors associated with suicide attempt across ancestry admixture populations, in veteran and civilian populations, and in attempt versus death.</p