New similarity-based algorithm and its application to classification of anticonvulsant compounds

A similarity-based algorithm based on a previously developed model is applied in the classification of two sets of anticonvulsant and non-anticonvulsant drugs. Each set is composed of a) anticonvulsant compounds that have shown moderate to high activity in the Maximal Electroshock Seizure (MES) test and b) drugs with other biological activities or poor activity in the MES test. The results from the analysis of variance (ANOVA) indicate that the proposed algorithm is able to differentiate anticonvulsant from non-anticonvulsant drugs. The proposed model may then be useful in the identification of new anticonvulsant agents through virtual screening of large virtual libraries of chemical structures.Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicada

Aplicación de la topología molecular en la búsqueda de nuevos compuestos derivados del 4-nitro-imidazol activos frente al Tripanosoma brucei

Human African Trypanosomiasis, caused by the protozoan parasite Trypanosoma brucei, is characterized by a disabling chronic infectious process affecting millions of people worldwide. The therapeutic arsenal against this disease usually requires intravenous suministración, hindering accessibility and adherence to therapy. It has developed a topological mathematical model aimed to finding new compounds derived from 1-aryl-4-nitro-1H-imidazol with potential anti-trypanosome activity. Using linear discriminant analysis (LDA) was obtained a model capable of predicting correctly the activity of 93% of the studied compounds. The model has been subjected to an internal validation using the jack-knife test or leave-one-out and an internal cross-validation. Following a virtual sweep or virtual screening ten new imidazole derivatives are proposed, with potential anti-trypanosome activity.La Tripanosomiasis Humana Africana, causada por el parásito protozoario de la especie Trypanosoma brucei, está caracterizada por un proceso infectivo crónico discapacitante que afecta a millones de personas en todo el mundo. El arsenal terapéutico frente a esta enfermedad, requiere generalmente suministración por vía parenteral, lo que dificulta la adhesión y accesibilidad del paciente al tratamiento. Se ha desarrollado un modelo topológico-matemático encaminado a buscar nuevos compuestos derivados del 1-aril-4-nitro-1H-imidazol con potencial actividad anti-tripanosómica. Utilizando el análisis lineal discriminante se ha obtenido un modelo capaz de predecir correctamente la actividad del 93% de los compuestos estudiados. Se ha sometido al modelo a una validación interna por medio del test de Jack-knife y de una validación cruzada. Tras realizar un cribado molecular virtual se proponen diez nuevos derivados imidazólicos con potencial actividad anti-tripanosómica

Modeling complex metabolic reactions, ecological systems, and financial and legal networks with MIANN models based on Markov-Wiener node descriptors

[Abstract] The use of numerical parameters in Complex Network analysis is expanding to new fields of application. At a molecular level, we can use them to describe the molecular structure of chemical entities, protein interactions, or metabolic networks. However, the applications are not restricted to the world of molecules and can be extended to the study of macroscopic nonliving systems, organisms, or even legal or social networks. On the other hand, the development of the field of Artificial Intelligence has led to the formulation of computational algorithms whose design is based on the structure and functioning of networks of biological neurons. These algorithms, called Artificial Neural Networks (ANNs), can be useful for the study of complex networks, since the numerical parameters that encode information of the network (for example centralities/node descriptors) can be used as inputs for the ANNs. The Wiener index (W) is a graph invariant widely used in chemoinformatics to quantify the molecular structure of drugs and to study complex networks. In this work, we explore for the first time the possibility of using Markov chains to calculate analogues of node distance numbers/W to describe complex networks from the point of view of their nodes. These parameters are called Markov-Wiener node descriptors of order kth (Wk). Please, note that these descriptors are not related to Markov-Wiener stochastic processes. Here, we calculated the Wk(i) values for a very high number of nodes (>100,000) in more than 100 different complex networks using the software MI-NODES. These networks were grouped according to the field of application. Molecular networks include the Metabolic Reaction Networks (MRNs) of 40 different organisms. In addition, we analyzed other biological and legal and social networks. These include the Interaction Web Database Biological Networks (IWDBNs), with 75 food webs or ecological systems and the Spanish Financial Law Network (SFLN). The calculated Wk(i) values were used as inputs for different ANNs in order to discriminate correct node connectivity patterns from incorrect random patterns. The MIANN models obtained present good values of Sensitivity/Specificity (%): MRNs (78/78), IWDBNs (90/88), and SFLN (86/84). These preliminary results are very promising from the point of view of a first exploratory study and suggest that the use of these models could be extended to the high-throughput re-evaluation of connectivity in known complex networks (collation)

New similarity-based algorithm and its application to classification of anticonvulsant compounds

A similarity-based algorithm based on a previously developed model is applied in the classification of two sets of anticonvulsant and non-anticonvulsant drugs. Each set is composed of a) anticonvulsant compounds that have shown moderate to high activity in the Maximal Electroshock Seizure (MES) test and b) drugs with other biological activities or poor activity in the MES test. The results from the analysis of variance (ANOVA) indicate that the proposed algorithm is able to differentiate anticonvulsant from non-anticonvulsant drugs. The proposed model may then be useful in the identification of new anticonvulsant agents through virtual screening of large virtual libraries of chemical structures.Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicada

Non-Neuroleptic Antitubercular and Anticancer Therapeutics through Rational Drug Remodelling of Phenothiazines and Related Antipsychotics

In light of shrinking pharmaceutical drug pipelines and drug resistance, innovative drug discovery strategies are of imperative need. Drug repurposing and related strategies such as drug rescue and drug remodelling have garnered significant research interest. Various clinically approved non-antibiotics including phenothiazines hold promise as novel classes of therapeutics in other indications. However, in addition to inherent neuroleptic properties, phenothiazines and related antipsychotics elicit adverse side effects at clinically relevant doses thus precluding their extensive clinical application. Herein, it was postulated that the selectivity of phenothiazines and related drugs for nonneuroleptic indications could be enhanced through rationalized structural remodelling. Phenothiazine and related neuroleptics are known to obey a lipophilic chromophore/basic side chain paradigm. Deviation from this paradigm is expected to decrease potential for neuroleptic effects. Therefore, the remodelling strategies involved introduction of novel functionalities that are dissimilar to native phenothiazine structures. Prior to chemical synthesis, drug metabolism and pharmacokinetic related properties were predicted in silico to assess drug-likeness of the new chemical entities derived from phenothiazines and related antipsychotics. The in silico profiling also included prediction of blood/brain partition coefficients and CNS activity to determine their likelihood of exhibiting neuroleptic effects. The new chemical entities were then evaluated against drug-susceptible Mycobacterium tuberculosis-H37Rv. Furthermore, a selected series was screened for binding to dopamine and serotonin receptors to corroborate in silico CNS activity predictions. Moreover, pharmacokinetic studies were conducted with the selected series to determine in vitro microsomal stability, kinetic solubility and in vivo toxicity profiles. Another objective of this study was to evaluate the new chemical entities for their potential as anticancer agents. The key findings herein demonstrated that it is possible to abolish neuroleptic effects through rationalized structural manipulation and still retain bio-activities of interest. Several new chemical entities including N-alkylsulfonates (DS0031, DS0032, DS0034, DS0035, DS00366) and nitrobenzenesulfonamides (DS00325, DS00326, DS00329) of phenothiazines, displayed notable antitubercular (GAST/Fe MIC90 range: 9.9-125 µM; 7H9 MIC range 12.5- 25 µg/mL) and anticancer (IC50 range 4.51-12.43 µM) activities in comparison to native phenothiazine drugs. Furthermore, in vitro and in vivo preclinical evaluation revealed favourable pharmacokinetic profiles. Overall, this study presents novel subclasses of phenothiazines that hold promise for further development as non-neuroleptic agents in either tuberculosis or cancer treatment regimens

Systems approaches to drug repositioning

PhD ThesisDrug discovery has overall become less fruitful and more costly, despite vastly increased biomedical knowledge and evolving approaches to Research and Development (R&D). One complementary approach to drug discovery is that of drug repositioning which focusses on identifying novel uses for existing drugs. By focussing on existing drugs that have already reached the market, drug repositioning has the potential to both reduce the timeframe and cost of getting a disease treatment to those that need it. Many marketed examples of repositioned drugs have been found via serendipitous or rational observations, highlighting the need for more systematic methodologies. Systems approaches have the potential to enable the development of novel methods to understand the action of therapeutic compounds, but require an integrative approach to biological data. Integrated networks can facilitate systems-level analyses by combining multiple sources of evidence to provide a rich description of drugs, their targets and their interactions. Classically, such networks can be mined manually where a skilled person can identify portions of the graph that are indicative of relationships between drugs and highlight possible repositioning opportunities. However, this approach is not scalable. Automated procedures are required to mine integrated networks systematically for these subgraphs and bring them to the attention of the user. The aim of this project was the development of novel computational methods to identify new therapeutic uses for existing drugs (with particular focus on active small molecules) using data integration. A framework for integrating disparate data relevant to drug repositioning, Drug Repositioning Network Integration Framework (DReNInF) was developed as part of this work. This framework includes a high-level ontology, Drug Repositioning Network Integration Ontology (DReNInO), to aid integration and subsequent mining; a suite of parsers; and a generic semantic graph integration platform. This framework enables the production of integrated networks maintaining strict semantics that are important in, but not exclusive to, drug repositioning. The DReNInF is then used to create Drug Repositioning Network Integration (DReNIn), a semantically-rich Resource Description Framework (RDF) dataset. A Web-based front end was developed, which includes a SPARQL Protocol and RDF Query Language (SPARQL) endpoint for querying this dataset. To automate the mining of drug repositioning datasets, a formal framework for the definition of semantic subgraphs was established and a method for Drug Repositioning Semantic Mining (DReSMin) was developed. DReSMin is an algorithm for mining semantically-rich networks for occurrences of a given semantic subgraph. This algorithm allows instances of complex semantic subgraphs that contain data about putative drug repositioning opportunities to be identified in a computationally tractable fashion, scaling close to linearly with network data. The ability of DReSMin to identify novel Drug-Target (D-T) associations was investigated. 9,643,061 putative D-T interactions were identified and ranked, with a strong correlation between highly scored associations and those supported by literature observed. The 20 top ranked associations were analysed in more detail with 14 found to be novel and six found to be supported by the literature. It was also shown that this approach better prioritises known D-T interactions, than other state-of-the-art methodologies. The ability of DReSMin to identify novel Drug-Disease (Dr-D) indications was also investigated. As target-based approaches are utilised heavily in the field of drug discovery, it is necessary to have a systematic method to rank Gene-Disease (G-D) associations. Although methods already exist to collect, integrate and score these associations, these scores are often not a reliable re flection of expert knowledge. Therefore, an integrated data-driven approach to drug repositioning was developed using a Bayesian statistics approach and applied to rank 309,885 G-D associations using existing knowledge. Ranked associations were then integrated with other biological data to produce a semantically-rich drug discovery network. Using this network it was shown that diseases of the central nervous system (CNS) provide an area of interest. The network was then systematically mined for semantic subgraphs that capture novel Dr-D relations. 275,934 Dr-D associations were identified and ranked, with those more likely to be side-effects filtered. Work presented here includes novel tools and algorithms to enable research within the field of drug repositioning. DReNIn, for example, includes data that previous comparable datasets relevant to drug repositioning have neglected, such as clinical trial data and drug indications. Furthermore, the dataset may be easily extended using DReNInF to include future data as and when it becomes available, such as G-D association directionality (i.e. is the mutation a loss-of-function or gain-of-function). Unlike other algorithms and approaches developed for drug repositioning, DReSMin can be used to infer any types of associations captured in the target semantic network. Moreover, the approaches presented here should be more generically applicable to other fields that require algorithms for the integration and mining of semantically rich networks.European and Physical Sciences Research Council (EPSRC) and GS

Desarrollo y utilización de métodos computacionales en la mejora del proceso de obtención de nuevos fármacos

Tesis doctoral inédita. Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Biología Molecular. Fecha de lectura: 19-02-201