Herramientas de cribado virtual aplicadas a inhibidores de tirosina quinasas. Contribución al desarrollo del programa PRALINS para el diseño de quimiotecas combinatorias

L'aplicació de mètodes de cribatge virtual adquireix cada vegada més importància en el procés de descobriment de fàrmacs, complementant a les tècniques de High-throughput screening per tal de facilitar i contribuir a la comprensió dels mecanismes bioquímics d'actuació dels fàrmacs, donar agilitat i reduir el cost del procés.Pel que fa a la present tesi, l'interès farmacològic és la inhibició de receptors de tirosina cinases. Aquests enzims participen en múltiples processos de senyalització cel·lular, fet que fa que tant la disfunció de les mateixes o el seu paper privilegiat en els mecanismes del cicle cel·lular les converteixin en diana farmacològica de malalties com el càncer i altres relacionades amb desordres hiperproliferatius, migratoris, del desenvolupament embrionari i malalties vasculars. Una de las estratègies d'inhibició més usuals és el bloqueig del lloc d'unió de l'ATP a través de molècules orgàniques com les piridopirimidines, heterocicles especialment interessants per al grup d'investigació en el qual es desenvolupa aquest treball per la seva àmplia experiència sintètica en aquests sistemes.En la present tesi s'exploren i validen gran part de les tècniques de cribatge virtual amb l'objectiu d'establir una seqüència jerarquitzada de filtres que permetin avaluar aquells compostos candidats a ser sintetitzats. Els successius passos de filtrat inclouen la selecció de compostos d'una quimioteca virtual a partir de la diversitat o representativitat de l'espai químic, l'aplicació de recerques de similitud i models farmacofòrics construïts a partir d'inhibidors coneguts, un filtrat mitjançant docking o acoblament dels inhibidors a la cavitat d'unió d'aquestes proteïnes i mètodes de predicció de l'afinitat d'unió d'una sèrie de lligands. La jerarquia d'aquestes etapes s'imposa a partir de la diferència de recursos computacionals que requereix cadascuna d'elles, sent aquests cada vegada superiors. Els mètodes han estat validats retrospectivament en bases de dades formades per compostos actius recopilades de la bibliografia. Una vegada validades, han permès la caracterització prospectiva dels candidats sintètics.S'ha dissenyat un fingerprint d'interacció estructural proteïna-lligand basat en el concepte de parells atòmics (IFbAP) destinat a facilitar el postprocessat dels resultats de docking, aplicant-se com a filtre en un cribatge virtual. La seva capacitat per a discriminar entre compostos actius e inactius s'analitza per a tres dianes: el receptor d'estrogen, el receptor del factor de creixement de fibroblasts i la transcriptasa reversa del HIV.Paral·lelament, s'ha seguit amb el desenvolupament del programa PRALINS (Program for Rational Analysis of Libraries in Silico), programa dirigit al disseny de quimioteques combinatòries virtuals que incorpora els principals criteris de selecció basats en diversitat. En el context de les quimioteques combinatòries focalitzades, es proposa un nou mètode (Direct), la capacitat de focalització del qual s'ha testat front als mètodes tradicionals, també implementats a PRALINS. Així mateix s'incorporen i analitzen mètodes d'avaluació de diversitat, suggerint-se un mètode (cell-integral-diversity criterion) destinat a superar els desavantatges dels mètodes tradicionals. S'incorporen els algoritmes genètics a PRALINS com a tècnica d'optimització, tant d'un únic criteri de diversitat/similitud com per a realitzar optimitzacions multiobjetiu.En l'àmbit d'una altra línia d'investigació del grup dirigida cap al desenvolupament d'inhibidors del procés de fusió del HIV, s'estudia el mode d'unió de dos antagonistes de CXCR4 i CCR5, receptors cel·lulars de la família de les GPCRs implicats en aquesta etapa del cicle del virus.La aplicación de métodos de cribado virtual cobra cada vez más importancia en el proceso de descubrimiento de fármacos, complementando a las técnicas de High-throughput screening con el fin de facilitar y contribuir a la comprensión de los mecanismos bioquímicos de actuación de los fármacos, agilizar y reducir el coste del proceso.En particular, el interés farmacológico de la presente tesis es la inhibición de receptores de tirosina quinasas. Estos enzimas participan en múltiples procesos de señalización celular, por lo que tanto la disfunción de las mismas o su papel privilegiado en los mecanismos del ciclo celular las convierten en diana farmacológica de enfermedades como el cáncer y otras relacionadas con desórdenes hiperproliferativos, migratorios, del desarrollo embrionario y enfermedades vasculares. Una de las estrategias de inhibición más usuales es el bloqueo del sitio de unión del ATP a través de moléculas orgánicas como las piridopirimidinas, heterociclos especialmente interesantes para el grupo de investigación en el que se desarrolla este trabajo por su amplia experiencia sintética en dichos sistemas.En la presente tesis se exploran y validan gran parte de las técnicas de cribado virtual con el objetivo de establecer una secuencia jerarquizada de filtros que permitan evaluar aquellos compuestos candidatos a ser sintetizados. Los sucesivos pasos de filtrado incluyen la selección de compuestos de una quimioteca virtual a partir de la diversidad o representatividad del espacio químico, la aplicación de búsquedas de similitud y modelos farmacofóricos construidos a partir de inhibidores conocidos, un filtrado mediante docking o acoplamiento de los inhibidores en la cavidad de unión de estas proteínas y métodos de predicción de la afinidad de unión de una serie de ligandos. La jerarquía de estas etapas se impone a partir de la diferencia de recursos computacionales que requiere cada una de ellas, siendo éstos cada vez superiores. Los métodos han sido validados retrospectivamente en bases de datos formadas por compuestos activos recopilados de la bibliografía. Una vez validadas, han permitido la caracterización prospectiva de los candidatos sintéticos.Se ha diseñado un fingerprint de interacción estructural proteína-ligando basado en el concepto de pares atómicos (IFbAP) destinado a facilitar el postprocesado de los resultados de docking, aplicándose como filtro en un cribado virtual. Su capacidad para discriminar entre compuestos activos e inactivos se analiza para tres dianas: el receptor de estrógeno, el receptor del factor de crecimiento de fibroblastos y la transcriptasa reversa del HIV.Paralelamente, se ha continuado con el desarrollo del programa PRALINS (Program for Rational Analysis of Libraries in Silico), programa dirigido al diseño de quimiotecas combinatorias virtuales que incorpora los principales criterios de selección basados en diversidad. En el contexto de las quimiotecas combinatorias focalizadas, se propone un nuevo método (Direct), cuya capacidad de focalización se ha testado frente a los métodos tradicionales, también implementados en PRALINS. Asimismo se incorporan y analizan métodos de evaluación de diversidad, sugiriéndose un método (cell-integral-diversity criterion) destinado a superar las desventajas de los métodos tradicionales. Se incorporan los algoritmos genéticos en PRALINS como técnica de optimización, tanto de un único criterio de diversidad/similitud como para realizar optimizaciones multiobjetivo.En el ámbito de otra línea de investigación del grupo dirigida hacia el desarrollo de inhibidores del proceso de fusión del HIV, se estudia el modo de unión de dos antagonistas de CXCR4 y CCR5, receptores celulares de la familia de las GPCRs implicados en dicha etapa del ciclo del virus.Virtual screening is progressively gaining importance in the drug discovery process, complementing high-throughput screening in order to facilitate and contribute to the understanding of the action mechanism of drugs, while expediting and reducing the cost of the process.The pharmacological focus of this thesis lies in the inhibition of tyrosine kinase receptors. These enzymes are the critical components of signalling pathways, so both their dysfunction and their privileged role in the cell cycle pathways make them pharmacological targets in diseases such as cancer and others related to hyperproliferative disorders, migratory disorders, embryonic development and vascular pathologies. One of the most common strategies of inhibition is the blockage of the ATP binding site by small organic molecules such as pyridopyrimidines, which are of particular interest for the research group where this project was carried out due to our wide experience in the synthesis of these heterocycles.In the present thesis, most of the techniques currently employed in virtual screening are explored and validated with the aim of establishing a hierarchical database of screening to be used in the evaluation of drug candidates to be synthesized. The successive filtering steps include compound selection from a combinatorial library based on diversity or representativity of the chemical space, pharmacophore similarity searches, docking and affinity predictions for a series of ligands. The different strategies have been retrospectively validated in databases containing active compounds compiled from literature. After validation, they have been applied in the prospective characterization of the synthetic candidates.A structural interaction fingerprint has been designed, based on the concept of atomic pairs (IFbAP), for the post processing of docking outputs as a filter step in virtual screening. Its ability to discern between active and inactive compounds has been analysed for three targets: estrogen receptor, fibroblast growth factor receptor and HIV reverse transcriptase.We have also continued developing the PRALINS program (Program for Rational Analysis of Libraries in Silico), a program for the design of combinatorial libraries, which incorporates the main diversity selection criteria. In the context of focused combinatorial libraries, we propose a new method (Direct), whose ability to focalise has been compared to the traditional methodologies also implemented in PRALINS. Moreover, different diversity evaluation criteria have been compared, introducing a new method (cell-integral-diversity criterion) aimed at surpassing the disadvantages of traditional techniques. We have implemented genetic algorithms as optimisation techniques, both for unique diversity/similarity criterion and for carrying out multiobjective optimisations.Within another research area of interest for the group, directed towards the development of inhibitors of the HIV fusion process, we study the binding mode for CXCR4 and CCR5 antagonists

Novel pharmacological maps of protein lysine methyltransferases: key for target deorphanization

Epigenetic therapies are being investigated for the treatment of cancer, cognitive disorders, metabolic alterations and autoinmune diseases. Among the diferent epigenetic target families, protein lysine methyltransferases (PKMTs), are especially interesting because it is believed that their inhibition may be highly specifc at the functional level. Despite its relevance, there are currently known inhibitors against only 10 out of the 50 SET-domain containing members of the PKMT family. Accordingly, the identifcation of chemical probes for the validation of the therapeutic impact of epigenetic modulation is key. Moreover, little is known about the mechanisms that dictate their substrate specifcity and ligand selectivity. Consequently, it is desirable to explore novel methods to characterize the pharmacological similarity of PKMTs, going beyond classical phylogenetic relationships. Such characterization would enable the prediction of ligand of-target efects caused by lack of ligand selectivity and the repurposing of known compounds against alternative targets. This is particularly relevant in the case of orphan targets with unreported inhibitors. Here, we frst perform a systematic study of binding modes of cofactor and substrate bound ligands with all available SET domain-containing PKMTs. Protein ligand interaction fngerprints were applied to identify conserved hot spots and contact-specifc residues across subfamilies at each binding site; a relevant analysis for guiding the design of novel, selective compounds. Then, a recently described methodology (GPCR-CoINPocket) that incorporates ligand contact information into classical alignment-based comparisons was applied to the entire family of 50 SET-containing proteins to devise pharmacological similarities between them. The main advantage of this approach is that it is not restricted to proteins for which crystallographic data with bound ligands is available. The resulting family organization from the separate analysis of both sites (cofactor and substrate) was retrospectively and prospectively validated. Of note, three hits (inhibition>50% at 10 µM) were identifed for the orphan NSD1

Towards the understanding of the activity of G9a inhibitors: an activity landscape and molecular modeling approach

In this work, we analyze the structure–activity relationships (SAR) of epigenetic inhibitors (lysine mimetics) against lysine methyltransferase (G9a or EHMT2) using a combined activity landscape, molecular docking and molecular dynamics approach. The study was based on a set of 251 G9a inhibitors with reported experimental activity. The activity landscape analysis rapidly led to the identifcation of activity clifs, scafolds hops and other active an inactive molecules with distinct SAR. Structure-based analysis of activity clifs, scafold hops and other selected active and inactive G9a inhibitors by means of docking followed by molecular dynamics simulations led to the identifcation of interactions with key residues involved in activity against G9a, for instance with ASP 1083, LEU 1086, ASP 1088, TYR 1154 and PHE 1158. The outcome of this work is expected to further advance the development of G9a inhibitors

Evaluation of chemical and gene/protein entity recognition systems at BioCreative V.5: the CEMP and GPRO patents tracks

This paper presents the results of the BioCreative V.5 offline tasks related to the evaluation of the performance as well as assess progress made by strategies used for the automatic recognition of mentions of chemical names and gene in running text of medicinal chemistry patent abstracts. A total of 21 teams submitted results for at least one of these tasks. The CEMP (chemical entity mention in patents) task entailed the detection of chemical named entity mentions. A total of 14 teams submitted 56 runs. The top performing team reached an F-score of 0.90 with a precision of 0.88 and a recall of 0.93. The GPRO (gene and protein related object) task focused on the detection of mentions of gene and protein related objects. The 7 participating teams (30 runs) had to detect gene/protein mentions that could be linked to at least one biological database, such as SwissProt or EntrezGene. The best F-score, recall and precision in this task were of 0.79, 0.83 and 0.77, respectively. The CEMP and GPRO gold standard corpora included training sets of 21,000 records and test sets of 9,000 records. Similar to the previous BioCreative CHEMDNER tasks, evaluation was based on micro-averaged F-score. The BeCalm platform supported prediction submission and evaluation (http://www.becalm.eu).We acknowledge the OpenMinted (654021) and the ELIXIREXCELERATE (676559) H2020 projects, and the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology for funding. The Spanish National Bioinformatics Institute (INB) unit at the Spanish National Cancer Research Centre (CNIO) is a member of the INB, PRB2-ISCIII and is supported by grant PT13/0001/0030, of the PE I+D+i 2013-2016, funded by ISCIII and ERDF.info:eu-repo/semantics/publishedVersio

The Markyt visualisation, prediction and benchmark platform for chemical and gene entity recognition at BioCreative/CHEMDNER challenge

Biomedical text mining methods and technologies have improved significantly in the last decade. Considerable efforts have been invested in understanding the main challenges of biomedical literature retrieval and extraction and proposing solutions to problems of practical interest. Most notably, community-oriented initiatives such as the BioCreative challenge have enabled controlled environments for the comparison of automatic systems while pursuing practical biomedical tasks. Under this scenario, the present work describes the Markyt Web-based document curation platform, which has been implemented to support the visualisation, prediction and benchmark of chemical and gene mention annotations at BioCreative/CHEMDNER challenge. Creating this platform is an important step for the systematic and public evaluation of automatic prediction systems and the reusability of the knowledge compiled for the challenge. Markyt was not only critical to support the manual annotation and annotation revision process but also facilitated the comparative visualisation of automated results against the manually generated Gold Standard annotations and comparative assessment of generated results. We expect that future biomedical text mining challenges and the text mining community may benefit from the Markyt platform to better explore and interpret annotations and improve automatic system predictions. Database URL: http://www.markyt.org, https://github.com/sing-group/MarkytThis work was partially funded by the [14VI05] Contract-Programme from the University of Vigo and the Agrupamento INBIOMED from DXPCTSUG-FEDER unha maneira de facer Europa (2012/273) as well as by the Foundation for Applied Medical Research, University of Navarra (Pamplona, Spain). The research leading to these results has received funding from the European Union's Seventh Framework Programme FP7/REGPOT-2012-2013.1 under grant agreement no 316265, BIOCAPS

CHEMDNER: The drugs and chemical names extraction challenge

Natural language processing (NLP) and text mining technologies for the chemical domain (ChemNLP or chemical text mining) are key to improve the access and integration of information from unstructured data such as patents or the scientific literature. Therefore, the BioCreative organizers posed the CHEMDNER (chemical compound and drug name recognition) community challenge, which promoted the development of novel, competitive and accessible chemical text mining systems. This task allowed a comparative assessment of the performance of various methodologies using a carefully prepared collection of manually labeled text prepared by specially trained chemists as Gold Standard data. We evaluated two important aspects: one covered the indexing of documents with chemicals (chemical document indexing - CDI task), and the other was concerned with finding the exact mentions of chemicals in text (chemical entity mention recognition - CEM task). 27 teams (23 academic and 4 commercial, a total of 87 researchers) returned results for the CHEMDNER tasks: 26 teams for CEM and 23 for the CDI task. Top scoring teams obtained an F-score of 87.39% for the CEM task and 88.20% for the CDI task, a very promising result when compared to the agreement between human annotators (91%). The strategies used to detect chemicals included machine learning methods (e.g. conditional random fields) using a variety of features, chemistry and drug lexica, and domain-specific rules. We expect that the tools and resources resulting from this effort will have an impact in future developments of chemical text mining applications and will form the basis to find related chemical information for the detected entities, such as toxicological or pharmacogenomic properties

Immunomodulatory properties of carvone inhalation and Its effects on contextual fear memory in mice

A complex network of interactions exists between the immune, the olfactory, and the central nervous system (CNS). Inhalation of different fragrances can affect immunological reactions in response to an antigen but also may have effects on the CNS and cognitive activity. We performed an exploratory study of the immunomodulatory ability of a series of compounds representing each of the 10 odor categories or clusters described previously. We evaluated the impact of each particular odor on the immune response after immunization with the model antigen ovalbumin in combination with the TLR3 agonist poly I:C. We found that some odors behave as immunostimulatory agents, whereas others might be considered as potential immunosuppressant odors. Interestingly, the immunomodulatory capacity was, in some cases, strain-specific. In particular, one of the fragrances, carvone, was found to be immunostimulatory in BALB/c mice and immunosuppressive in C57BL/6J mice, facilitating or impairing viral clearance, respectively, in a model of a viral infection with a recombinant adenovirus. Importantly, inhalation of the odor improved the memory capacity in BALB/c mice in a fear-conditioning test, while it impaired this same capacity in C57BL/6J mice. The improvement in memory capacity in BALB/c was associated with higher CD3+ T cell infiltration into the hippocampus and increased local expression of mRNA coding for IL-1β, TNF-α, and IL-6 cytokines. In contrast, the memory impairment in C57BL/6 was associated with a reduction in CD3 numbers and an increase in IFN-γ. These data suggest an association between the immunomodulatory capacity of smells and their impact on the cognitive functions of the animals. These results highlight the potential of studying odors as therapeutic agents for CNS-related diseases

Concomitant histone deacetylase and phosphodiesterase 5 inhibition synergistically prevents the disruption in synaptic plasticity and it reverses cognitive impairment in a mouse model of Alzheimer's disease

Background: Given the implication of histone acetylation in memory processes, histone deacetylase inhibitors (HDACIs) have been postulated as potential modulators of cognitive impairment in Alzheimer's disease (AD). However, dose-dependent side effects have been described in patients with the currently available broad-spectrum HDACIs, explaining why their therapeutic potential has not been realized for chronic diseases. Here, by simultaneously targeting two independent enzyme activities, histone deacetylase (HDAC) and phosphodiesterase-5 (PDE5), we propose a novel mode of inhibitory action that might increase the therapeutic specificity of HDACIs. Results: The combination of vorinostat, a pan-HDACI, and tadalafil, a PDE5 inhibitor, rescued the long-term potentiation impaired in slices from APP/PS1 mice. When administered in vivo, the combination of these drugs alleviated the cognitive deficits in AD mice, as well as the amyloid and tau pathology, and it reversed the reduced dendritic spine density on hippocampal neurons. Significantly, the combination of vorinostat and tadalafil was more effective than each drug alone, both against the symptoms and in terms of disease modification, and importantly, these effects persisted after a 4-week washout period. Conclusions: The results highlight the pharmacological potential of a combination of molecules that inhibit HDAC and PDE5 as a therapeutic approach for AD treatment

Discovery of first-in-class reversible dual small molecule inhibitors against G9a and DNMTs in hematological malignancies

The indisputable role of epigenetics in cancer and the fact that epigenetic alterations can be reversed have favoured development of epigenetic drugs. In this study, we design and synthesize potent novel, selective and reversible chemical probes that simultaneously inhibit the G9a and DNMTs methyltransferase activity. In vitro treatment of haematological neoplasia (acute myeloid leukaemia-AML, acute lymphoblastic leukaemia-ALL and diffuse large B-cell lymphoma-DLBCL) with the lead compound CM-272, inhibits cell proliferation and promotes apoptosis, inducing interferon-stimulated genes and immunogenic cell death. CM-272 significantly prolongs survival of AML, ALL and DLBCL xenogeneic models. Our results represent the discovery of first-in-class dual inhibitors of G9a/DNMTs and establish this chemical series as a promising therapeutic tool for unmet needs in haematological tumours.We particularly acknowledge the Biobank of the University of Navarra for its collaboration. We thank Dr Edorta Martínez de Marigorta and Dr Francisco Palacios from Departamento de Química Orgánica I, Facultad de Farmacia, Universidad del Pais Vasco for 13C NMR determination and Angel Irigoyen Barrio and Dr Ana Romo Hualde, from University of Navarra, for HRMS determination. Dr. Irene de Miguel Turrullols from Small Molecule Discovery Platform, CIMA, University of Navarra is acknowledged for NMR data interpretation. This work was funded by grants from Instituto de Salud Carlos III (ISCIII) PI10/01691, PI13/01469, PI14/01867, PI10/2983, TRASCAN (EPICA), CIBERONC, cofinanciacion FEDER, RTICC RD12/0036/0068, Fundació La Marató de TV3 (20132130-31-32) and ‘Fundación Fuentes Dutor’. B.P. is supported by a Sara Borrell fellowship CD13/00340 and X.A. is a Marie Curie researcher under contract ‘LincMHeM-330598’.S

Information retrieval and text mining technologies for chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio