Use of historic metabolic biotransformation data as a means of anticipating metabolic sites using MetaPrint2D and Bioclipse.

BACKGROUND: Predicting metabolic sites is important in the drug discovery process to aid in rapid compound optimisation. No interactive tool exists and most of the useful tools are quite expensive. RESULTS: Here a fast and reliable method to analyse ligands and visualise potential metabolic sites is presented which is based on annotated metabolic data, described by circular fingerprints. The method is available via the graphical workbench Bioclipse, which is equipped with advanced features in cheminformatics. CONCLUSIONS: Due to the speed of predictions (less than 50 ms per molecule), scientists can get real time decision support when editing chemical structures. Bioclipse is a rich client, which means that all calculations are performed on the local computer and do not require network connection. Bioclipse and MetaPrint2D are free for all users, released under open source licenses, and available from http://www.bioclipse.net.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching

open access articleBackground: The Chemistry Development Kit (CDK) is a widely used open source cheminformatics toolkit, providing data structures to represent chemical concepts along with methods to manipulate such structures and perform computations on them. The library implements a wide variety of cheminformatics algorithms ranging from chemical structure canonicalization to molecular descriptor calculations and pharmacophore perception. It is used in drug discovery, metabolomics, and toxicology. Over the last 10 years, the code base has grown significantly, however, resulting in many complex interdependencies among components and poor performance of many algorithms. Results: We report improvements to the CDK v2.0 since the v1.2 release series, specifically addressing the increased functional complexity and poor performance. We first summarize the addition of new functionality, such atom typing and molecular formula handling, and improvement to existing functionality that has led to significantly better performance for substructure searching, molecular fingerprints, and rendering of molecules. Second, we outline how the CDK has evolved with respect to quality control and the approaches we have adopted to ensure stability, including a code review mechanism. Conclusions: This paper highlights our continued efforts to provide a community driven, open source cheminformatics library, and shows that such collaborative projects can thrive over extended periods of time, resulting in a high-quality and performant library. By taking advantage of community support and contributions, we show that an open source cheminformatics project can act as a peer reviewed publishing platform for scientific computing software

Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms.

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure-activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein-ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.JK, MJW, JT, PJB, AB and RCG thank Unilever for funding

Development of in silico models for the prediction of toxicity incorporating ADME information

Drug discovery is a process that requires a significant investment in both time and resources. Although recent developments have reduced the number of drugs failing at the later stages of development due to poor pharmacokinetic and/or toxicokinetic profiles, late stage attrition of drug candidates remains a problem. Additionally, there is a need to reduce animal testing for toxicological risk assessment for ethical and financial reasons. In silico methods offer an alternative that can address these challenges. A variety of computational approaches have been developed in the last two decades, these must be evaluated to ensure confidence in their use. The research presented in this thesis has assessed a range of existing tools for the prediction of toxicity and absorption, distribution, metabolism and elimination (ADME) parameters with an emphasis on absorption and xenobiotic metabolism. These two ADME properties largely determine bioavailability of a drug and, in turn, also influence toxicity. In vitro (Caco-2 cells and the parallel artificial membrane permeation assay) and in silico approaches, such as various druglikeness filters, can be used to estimate human intestinal absorption; a comparison between different methods was performed to identify relative strengths and weaknesses of the approaches. In terms of xenobiotic metabolism it is not only important to predict metabolites correctly, but it is also crucial to identify those compounds that can be biotransformed into species that can covalently bind to biomolecules. Structural alerts are routinely used to screen for such potential reactive metabolites. The balance between sensitivity and specificity of such reactive metabolite alerts has been discussed in the context of correctly predicting reactive metabolites of pharmaceuticals (using data available from DrugBank). Off-target toxicity, exemplified by human Ether-à-go-go-Related Gene (hERG) channel inhibition, was also explored. A number of novel structural alerts for hERG toxicity were developed based on groups of structurally similar compounds. Finally, the importance of predicting potential ecotoxicological effects of drugs was also considered. The utility of zebrafish embryos to distinguish between baseline and excess toxicity was investigated. In evaluating this selection of existing tools, improvements to the methods have been proposed where possible

Defining molecular initiating events in the adverse outcome pathway framework for risk assessment.

Consumer and environmental safety decisions are based on exposure and hazard data, interpreted using risk assessment approaches. The adverse outcome pathway (AOP) conceptual framework has been presented as a logical sequence of events or processes within biological systems which can be used to understand adverse effects and refine current risk assessment practices in ecotoxicology. This framework can also be applied to human toxicology and is explored on the basis of investigating the molecular initiating events (MIEs) of compounds. The precise definition of the MIE has yet to reach general acceptance. In this work we present a unified MIE definition: an MIE is the initial interaction between a molecule and a biomolecule or biosystem that can be causally linked to an outcome via a pathway. Case studies are presented, and issues with current definitions are addressed. With the development of a unified MIE definition, the field can look toward defining, classifying, and characterizing more MIEs and using knowledge of the chemistry of these processes to aid AOP research and toxicity risk assessment. We also present the role of MIE research in the development of in vitro and in silico toxicology and suggest how, by using a combination of biological and chemical approaches, MIEs can be identified and characterized despite a lack of detailed reports, even for some of the most studied molecules in toxicology.The authors acknowledge the financial support of Unilever.This is the accepted manuscript. The final published version is available from ACS at http://dx.doi.org/10.1021/tx500345

QSAR Modeling: Where Have You Been? Where Are You Going To?

Quantitative Structure-Activity Relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss: (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists towards collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making

Structure-based Predictions for Molecular Initiating Events

Toxicity testing of chemicals is currently undergoing its largest ever paradigm shift, moving towards faster, cheaper and more human-relevant methods which focus on mechanistic understanding. An AOP provides a framework for organising biological knowledge and data. The gateway to an AOP is the MIE, and chemistry is key to predicting which chemicals can undergo a MIE. In silico predictions of MIEs are a vital tool in a modern, mechanism-focused approach to risk assessment of chemicals. In this project, new structural alert-based models for receptor binding MIEs have been constructed that create accurate, transparent and interpretable predictions. The alerts have been constructed with an automated workflow that uses Bayesian statistics to iteratively select substructures associated with activity. The models were constructed from balanced data sets taken from human in vitro¬ assays in the ChEMBL and ToxCast databases. The new models significantly improve on previous models, with performance metrics comparable to random forest models. Methods for further improving structural alert models are presented, including a method for generalising aromatic atoms in structural alerts to reduce the number of alerts in a model, and construction of a consensus model combining structural alerts with a random forest model. Structural alert models have been constructed for a wide range of biological targets of toxicological interest and the variation in performance across all targets has been explained by considering the proportion of activity cliffs in data sets. Having significantly improved structural alert models in terms of performance, new methods for assessing confidence in both active and inactive predictions have been developed. These involve considering similarity to relevant chemicals in the training set. The measure of confidence in active predictions allows for applicability of predictions to be evaluated, whilst the measure of confidence in inactive predictions is vital in risk assessment of chemicals. Moving beyond structural alerts, attempts to describe chemicals in terms of the key interactions made with the biological target have been made. This a step towards describing how the receptor binding MIEs work and then using this knowledge to make better activity predictions. The generalised aromatic structural alerts have been used to predict key receptor binding interactions, which are consistent with interactions derived from crystal structures. Using structural alerts to group chemicals, pharmacophore models have been developed, allowing for activity predictions in terms of general features in three-dimensional space instead of the specific combination of atoms and bonds described by structural alerts.Unileve

Técnicas de separación avanzada para la obtención sostenible de extractos con potencial uso cosmético o farmacéutico a partir de plantas aromático-medicinales

Uno de los mayores retos a los que actualmente los países desarrollados se enfrentan es al excesivo impacto generado sobre el medio ambiente por el ser humano. Este excesivo impacto genera tal insostenibilidad que ha provocado una crisis social y ecológica sin precedentes lo que ha hecho necesaria la búsqueda de alternativas sostenibles en la producción, fomentada por la creciente conciencia social, y las mayores exigencias legales requeridas para desarrollar procesos cada vez más sostenibles tanto para el ser humano como para el medio ambiente. Sectores como el farmacéutico, el cosmético o el agroalimentario deben lidiar con exigencias aún mayores que el resto debido a que sus productos están enfocados hacia el uso humano. En consonancia con estos hechos, existe una reorientación de las preferencias de los consumidores hacia el uso de recursos naturales como ingredientes para la fabricación de productos cosméticos o medicinales, lo que hace que el interés por los extractos vegetales siga aumentando y las industrias busquen bioactivos naturales que sean seguros, a la vez que eficaces y rentables.Las plantas aromático-medicinales, en especial las pertenecientes a las familias de las lamiáceas y asteráceas, son importantes para las industrias farmacéuticas y cosméticas por su alto contenido en metabolitos secundarios con un gran rango de bioactividades. Es por ello que, dentro del marco de la Circularidad Económica, este tipo de plantas puede ayudar a la diversificación de cultivos permitiendo un aprovechamiento máximo de recursos. Por otro lado, la aplicación de técnicas supercríticas supone sin duda una mejora en la creación de productos comerciales de corte natural y sostenible. En los últimos años, estas técnicas están siendo ampliamente desarrolladas para el procesado de plantas aromático-medicinales al objeto de obtener y concentrar metabolitos secundarios, entre los que presentan un papel muy destacado los compuestos fenólicos por su alto valor terapéutico y cosmético.En base a la problemática y a las oportunidades señaladas, el objetivo general planteado en esta tesis es estudiar la viabilidad del uso de extractos sostenibles, obtenidos mediante tecnologías supercríticas a partir de plantas aromático-medicinales que, por sus propiedades, sean susceptibles de ser incorporados en formulaciones cosméticas y que además resulten de interés en el sector farmacéutico. Para alcanzar este fin, se estableció una metodología procedimental, experimental y teórica mediante una selección adecuada de materiales, técnicas experimentales, modelos computacionales y, en su caso, adaptación de protocolos.En primer lugar se seleccionaron aquellas plantas aromático-medicinales: i) ricas en compuestos fenólicos que destaquen por su actividad antioxidante y por su alta demanda industrial, como lo son los ácidos clorogénico, cafeico, ferúlico y rosmarínico, ii) idóneas para uso cosmético y farmacéutico y iii) adaptadas al territorio. Salvia officinalis L., Calendula officinalis L., Melissa officinalis L. y Jasonia glutinosa (L.) D.C. fueron las plantas seleccionadas para elaborar este trabajo y que cumplen sobradamente los requisitos establecidos. Para garantizar el cumplimiento de las exigencias en la preparación de formulados ecológicos, se estableció un protocolo de trazabilidad basado en el Reglamento 178/2002, Sistema de Análisis de Peligros y Puntos Críticos de Control (APPCC) y la Norma ISO 22000. Además, fue necesaria la elaboración de fichas técnicas y de seguridad para los extractos obtenidos en cumplimiento de las normas UNE-EN ISO 21149:2017, ISO 17516:2014 e ISO 16212:2017.En la obtención de extractos ricos en los compuestos fenólicos mencionados, se emplearon dos tecnologías sostenibles supercríticas, a saber, extracción con CO2 supercrítico (SCE) y Fraccionamiento Supercrítico Antidisolvente (SAF), para, respectivamente, eliminar los compuestos lipídicos y concentrar los compuestos activos. Tras la deslipidización del material vegetal, la concentración de los ácidos clorogénico, cafeico, ferúlico y rosmarínico mediante SAF se apoyó en el diseño experimental para poder evaluar estadísticamente la influencia de la presión y el caudal de CO2 en el proceso. En concreto, se utilizó una Metodología de Superficie de Respuesta (RSM) basada en un Diseño Central Compuesto (CCD) que permitió estudiar y optimizar las condiciones de trabajo dentro de unos rangos prefijados de 80-160 bar y 10-60 g/min y manteniendo temperatura, concentración y flujo de la alimentación constantes (40 ¿C, 3% en peso y 0.45 mL/min, respectivamente). Además, se implementó una metodología para la identificación y cuantificación de los activos estudiados mediante HPLC-PDA que detectó ácido clorogénico en salvia, caléndula y jasonia; ácido cafeico en salvia, caléndula y melisa; ácido rosmarínico en salvia y melisa y ácido ferúlico en caléndula.En general, el proceso SAF permitió recuperar hasta el 95% del material de alimentación, del cual la mayor parte se recogió en la cámara de precipitación (valores comprendidos entre el 47% y el 77%). Esta fracción de cámara, libre de disolventes, resultó enriquecida en un 40% (valor promedio) en los activos de interés. Los ácidos rosmarínico y ferúlico fueron retenidos en esta fracción, mientras que los ácidos cafeico y clorogénico presentaron un reparto que depende ya no solo de las condiciones experimentales, sino también de la naturaleza del extracto de alimentación propio de cada planta.El análisis estadístico de los resultados de concentración mediante SAF y cuantificación de activos permitió obtener, para cada una de las especies vegetales y dentro del intervalo estudiado, las condiciones óptimas de presión y caudal de CO2, condiciones que resultaron ser próximas a 150 bar y 20 g/min. En cuanto a la metodología computacional, se seleccionaron tres herramientas: i) modelos QSAR/COSMO-RS, ii) permeabilidad en piel in silico y iii) screening inverso, a fin de, respectivamente, establecer una relación entre la estructura molecular y la actividad antioxidante, estudiar el comportamiento de los activos seleccionados en un modelo de piel computacional y localizar dianas de interés farmacológico sobre las que interaccionan estos mismos activos.Se desarrollaron dos modelos QSAR/COSMO-RS que correlacionaron el denominado perfil ¿ y la actividad antioxidante de un grupo de moléculas fenólicas seleccionadas que constituyeron el conjunto de entrenamiento. Los modelos se diferenciaron en la partición del perfil ¿ de la molécula, en 4 y 10 áreas. Se encontró un mejor ajuste para el modelo de 10 áreas, si bien ninguno de ellos consiguió ajustar suficientemente los datos del conjunto de validación consecuencia, muy posiblemente, de la escasez de datos bibliográficos con calidad contrastable. Pese a ello, la herramienta resulta prometedora para realizar una preselección de activos con actividad antioxidante.Para el estudio de permeabilidad en piel de los compuestos fenólicos se utilizó el modelo computacional propuesto por Schwöbel et al. que fue implementado con el software COSMOtherm. Las permeabilidades obtenidas para los activos de interés mostraron que todos ellos quedaban retenidos en las capas de la epidermis y por tanto resultaban aptos y seguros para su posible uso cosmético y que la metodología empleada era consistente con la de otros modelos preexistentes.La aplicación de la herramienta online Similarity Ensemble Approach (SEA), basada en el principio de similitud química, pese a su sencillez, permitió localizar posibles bioactividades asociadas a los activos y por tanto prever sus posibles usos farmacéuticos. Jasonia glutinosa, por su contenido en ácido clorogénico, se postula como una especie interesante para investigaciones futuras.En resumen, el presente estudio asienta las bases metodológicas para el desarrollo de futuros trabajos que tengan como finalidad la implementación de formulados con una perspectiva ecológica y sostenible dentro de la industria cosmética, farmacéutica o alimentaria. De hecho, gracias a las aportaciones de este trabajo se ha conseguido generar los ingredientes activos que formaron parte de la formulación final de la crema hidratante facial para el proyecto SPAGYRIA (Interreg-POCTEFA), que fue evaluada y certificada por la Agence Nationale de Sécurité du Médicament et des Produits de Santé (ANSM-Francia).<br /