Network reconstruction of platelet metabolism identifies metabolic signature for aspirin resistance.

Recently there has not been a systematic, objective assessment of the metabolic capabilities of the human platelet. A manually curated, functionally tested, and validated biochemical reaction network of platelet metabolism, iAT-PLT-636, was reconstructed using 33 proteomic datasets and 354 literature references. The network contains enzymes mapping to 403 diseases and 231 FDA approved drugs, alluding to an expansive scope of biochemical transformations that may affect or be affected by disease processes in multiple organ systems. The effect of aspirin (ASA) resistance on platelet metabolism was evaluated using constraint-based modeling, which revealed a redirection of glycolytic, fatty acid, and nucleotide metabolism reaction fluxes in order to accommodate eicosanoid synthesis and reactive oxygen species stress. These results were confirmed with independent proteomic data. The construction and availability of iAT-PLT-636 should stimulate further data-driven, systems analysis of platelet metabolism towards the understanding of pathophysiological conditions including, but not strictly limited to, coagulopathies

Advances in Developing Therapies to Combat Zika Virus: Current Knowledge and Future Perspectives

Zika virus (ZIKV) remained largely quiescent for nearly six decades after its first appearance in 1947. ZIKV reappeared after 2007, resulting in a declaration of an international “public health emergency” in 2016 by the World Health Organization (WHO). Until this time, ZIKV was considered to induce only mild illness, but it has now been established as the cause of severe clinical manifestations, including fetal anomalies, neurological problems, and autoimmune disorders. Infection during pregnancy can cause congenital brain abnormalities, including microcephaly and neurological degeneration, and in other cases, Guillain-Barré syndrome, making infections with ZIKV a substantial public health concern. Genomic and molecular investigations are underway to investigate ZIKV pathology and its recent enhanced pathogenicity, as well as to design safe and potent vaccines, drugs, and therapeutics. This review describes progress in the design and development of various anti-ZIKV therapeutics, including drugs targeting virus entry into cells and the helicase protein, nucleosides, inhibitors of NS3 protein, small molecules, methyltransferase inhibitors, interferons, repurposed drugs, drugs designed with the aid of computers, neutralizing antibodies, convalescent serum, antibodies that limit antibody-dependent enhancement, and herbal medicines. Additionally, covalent inhibitors of viral protein expression and anti-Toll-like receptor molecules are discussed. To counter ZIKV-associated disease, we need to make rapid progress in developing novel therapies that work effectually to inhibit ZIKV

Computing Network of Diseases and Pharmacological Entities through the Integration of Distributed Literature Mining and Ontology Mapping

The proliferation of -omics (such as, Genomics, Proteomics) and -ology (such as, System Biology, Cell Biology, Pharmacology) have spawned new frontiers of research in drug discovery and personalized medicine. A vast amount (21 million) of published research results are archived in the PubMed and are continually growing in size. To improve the accessibility and utility of such a large number of literatures, it is critical to develop a suit of semantic sensitive technology that is capable of discovering knowledge and can also infer possible new relationships based on statistical co-occurrences of meaningful terms or concepts. In this context, this thesis presents a unified framework to mine a large number of literatures through the integration of latent semantic analysis (LSA) and ontology mapping. In particular, a parameter optimized, robust, scalable, and distributed LSA (DiLSA) technique was designed and implemented on a carefully selected 7.4 million PubMed records related to pharmacology. The DiLSA model was integrated with MeSH to make the model effective and efficient for a specific domain. An optimized multi-gram dictionary was customized by mapping the MeSH to build the DiLSA model. A fully integrated web-based application, called PharmNet, was developed to bridge the gap between biological knowledge and clinical practices. Preliminary analysis using the PharmNet shows an improved performance over global LSA model. A limited expert evaluation was performed to validate the retrieved results and network with biological literatures. A thorough performance evaluation and validation of results is in progress

Formulation and Evaluation of Methylphenidate Hydrochloride Extended Release Capsules

The treatment of acute diseases or chronic illness was achieved by delivery of drugs to patients from many years. These drug delivery systems include tablets, injectables, capsules, suspensions, creams, ointments, liquids and aerosols. Nowadays these drug delivery systems are widely used. The term drug delivery system can be defined as techniques which are used for getting therapeutic agents inside the human body. The formulation containing 40mg of Methylphenidate Hydrochloride was prepared as extended release capsules.These techniques are particularly useful for patients who should not administer the drug in repeated intervals. The optimized formulation have consistent release profile to provide the drug release for longer duration of 10 hours. FTIR studies have shown that there was no considerable interactions between drug and excipients.The short term stability study also indicates no change in the physical characteristic of drug content. The comparision of dissolution profiles between the Methylphenidate Hydrochloride extended release capsules 40mg and the reference drug, showed no major changes in the dissolution profiles. Hence, it can be concluded that the Methylphenidate Hydrochloride extended release capsules were successfully developed and evaluated

Electrokinetic treatment of environmental matrices. Contaminants removal and phosphorus recovery

There is a need to develop viable techniques for removal and recovery organic and inorganic compounds from environmental matrices, due to their ecotoxicity, regulatory obligations or potential supplies as secondary materials. In this dissertation, electro –removal and –recovery techniques were applied to five different contaminated environmental matrices aiming phosphorus (P) recovery and/or contaminants removal. In a first phase, the electrokinetic process (EK) was carried out in soils for (i) metalloids and (ii) organic contaminants (OCs) removal. In the case of As and Sb mine contaminated soil, the EK process was additionally coupled with phytotechnologies. In a second phase, the electrodialytic process (ED) was applied to wastes aiming P recovery and simultaneous removal of (iii) toxins from membrane concentrate, (iv) heavy metals from sewage sludge ash (SSA), and (v) OCs from sewage sludge (SS). EK enhanced phytoremediation showed to be viable for the remediation of soils contaminated with metalloids, as although remediation was low, it combines advantages of both technologies while allowing site management. EK also proved to be an effective remediation technology for the removal and degradation of emerging OCs from two types of soil. Aiming P recovery and contaminants removal, different ED cell set-ups were tested. For the membrane concentrates, the best P recovery was achieved in a three compartment (3c) cell, but the highest toxin removal was obtained in a two compartment (2c) cell, placing the matrix in the cathode end. In the case of SSA the best approach for simultaneous P recovery and heavy metals removal was to use a 2c-cell placing the matrix in the anode end. However, for simultaneous P recovery and OCs removal, SS should be placed in the cathode end, in a 2c-cell. Overall, the data support that the selection of the cell design should be done case-by-case

PROBABILISTIC LATENT FACTOR MODELS FOR TRANSFORMATIVE DRUG DISCOVERY

The cost of discovering a new drug has doubled every 9 years since the 1950s. This can change by using machine learning to guide experimentation. The idea I have developed over the course of my PhD is that using latent factor modeling (LFM) of the drug-target interaction network, we can guide drug repurposable efforts to achieve transformative improvements. By better characterizing the drug-target interaction network, it is possible to use currently approved drugs to achieve therapies for diseases that currently are not optimally treated. These drugs might be directly used through repurposing, or they can serve as a starting point for new drug discovery efforts where they are optimized through medicinal chemistry methods. To achieve this goal, I have developed LFM-based techniques applicable to existing databases of drug-target interaction networks. Specifically, I have started out by establishing that probabilistic matrix factorization (PMF; one type of LFM algorithm) can be used as descriptors by showing they capture therapeutic function similarities that state-of-the-art 3D chemical similarity methods could not capture. Then I have shown that PMF can effectively predict unknown drug-target interactions. Furthermore, I have used newly developed computational techniques for discovering repurposable drugs for two diseases, α1 antitrypsin (1-AT) deficiency (ATD) and Huntington’s disease (HD) leading to successful discoveries in both. For ATD, two sets of data generated by the David Perlmutter and Gary Silverman laboratories have been used as input to deduce potential targets and repurposable drugs: (i) a high throughput screening data from a genome-wide RNAi knockdown in a C. elegans model for studying ATZ (Z-allele of 1-AT), and (ii) data from Prestwick library screen for the same model. We have predicted that the antidiabetic drug glibenclamide would be beneficial against ATZ aggregation, and data collected to date in Mus musculus models are promising. We have worked on HD with the Robert Friedlander lab, by examining the potential drugs and implicated pathways for 15 neuroprotective (repurposable) drugs that they have identified in a two-stage screening study. Based on LFM-based analysis of the targets of these drugs, we have developed a number of hypotheses to be tested. Among them, the antihypertensive drug sodium nitroprusside appears to be effective against HD based on neuronal cell death inhibition experiments that were conducted at the University of Pittsburgh Drug Discovery Institute as well as the Friedlander lab. Finally, we have built a web server, named BalestraWeb, for facilitating the use of PMF in repurposable drug identification by the broader community. BalestraWeb enables users to extract information on known and potential targets (or drugs) for any approved drug (or target), simply by entering the name of the query drug (or target). I have also laid out the framework for developing an integrated resource for quantitative systems pharmacology, Balestra toolkit (BalestraTK), which would take advantage of existing databases such as STITCH, UniProt, and PubChem. Collectively, our results provide firm evidence for the potential utility of machine learning techniques for assisting in drug discovery

Uso de cocaína o estimulantes de tipo anfetamínico y riesgo de crisis convulsivas o de enfermedad cerebrovascular. Revisiones sistemáticas

Los psicoestimulantes son un grupo de sustancias neurofarmacológicamente muy similares cuyo uso se encuentra muy extendido en todo el mundo. En este trabajo nos centramos en el uso de Cocaína y Estimulantes de Tipo Anfetamínico, que son los de consumo más frecuente y causantes desde los años 70 verdaderas epidemias de consumo problemático, conquistando regiones enteras y subgrupos de población. En los últimos años su consumo ha experimentado un importante aumento contribuyendo de manera significativa a la carga global de enfermedades y representa un reto para la salud pública en varias zonas del mundo. No se trata solo del incremento en el consumo de sustancias ilegales, determinadas variedades de estimulantes tipo anfetamínico son medicamentos recetables, lo cual comporta nuevas implicaciones. Entre las complicaciones médicas con las que se relaciona su uso, destacan las complicaciones neurológicas agudas, en concreto las crisis convulsivas y las enfermedades cerebrovasculares. No obstante, la mayoría de las publicaciones científicas que se refieren a la asociación entre el consumo de psicoestimulantes y la aparición de enfermedades cerebrovasculares o crisis convulsivas, se basan en evidencia procedente de estudios con diseños metodológicos poco adecuados o de muy baja calidad. Existen tantos estudios que se limitan a describir casos o series de casos como escasez de otros con metodologías adecuadas para establecer relaciones de causalidad. Por ese motivo es necesario conocer el estado de la situación de las complicaciones neurológicas agudas en relación al uso de psicoestimulantes siguiendo una metodología sistemática que permita llegar a conclusiones sobre el estado de la cuestión y poder marcar el camino para futuras investigaciones. Objetivos: En este trabajo se pretende compilar, revisar y resumir la evidencia científica disponible sobre la relación entre el uso de psicoestimulantes y la ocurrencia de complicaciones neurológicas agudas. Concretamente evaluar la evidencia científica disponible para las siguientes tres asociaciones: 1. En estudios clínico-epidemiológicos sobre la relación entre el consumo de cocaína y la ocurrencia de crisis convulsivas. 2. En estudios clínico-epidemiológicos sobre la relación entre el consumo de cocaína y la ocurrencia de enfermedad cerebrovascular. 3. En estudios clínico-epidemiológicos sobre la relación entre el consumo de ETA y la ocurrencia de enfermedad cerebrovascular..

A machine learning and network framework to discover new indications for small molecules.

Drug repurposing, identifying novel indications for drugs, bypasses common drug development pitfalls to ultimately deliver therapies to patients faster. However, most repurposing discoveries have been led by anecdotal observations (e.g. Viagra) or experimental-based repurposing screens, which are costly, time-consuming, and imprecise. Recently, more systematic computational approaches have been proposed, however these rely on utilizing the information from the diseases a drug is already approved to treat. This inherently limits the algorithms, making them unusable for investigational molecules. Here, we present a computational approach to drug repurposing, CATNIP, that requires only biological and chemical information of a molecule. CATNIP is trained with 2,576 diverse small molecules and uses 16 different drug similarity features, such as structural, target, or pathway based similarity. This model obtains significant predictive power (AUC = 0.841). Using our model, we created a repurposing network to identify broad scale repurposing opportunities between drug types. By exploiting this network, we identified literature-supported repurposing candidates, such as the use of systemic hormonal preparations for the treatment of respiratory illnesses. Furthermore, we demonstrated that we can use our approach to identify novel uses for defined drug classes. We found that adrenergic uptake inhibitors, specifically amitriptyline and trimipramine, could be potential therapies for Parkinson's disease. Additionally, using CATNIP, we predicted the kinase inhibitor, vandetanib, as a possible treatment for Type 2 Diabetes. Overall, this systematic approach to drug repurposing lays the groundwork to streamline future drug development efforts

A machine learning and network framework to discover new indications for small molecules.

Drug repurposing, identifying novel indications for drugs, bypasses common drug development pitfalls to ultimately deliver therapies to patients faster. However, most repurposing discoveries have been led by anecdotal observations (e.g. Viagra) or experimental-based repurposing screens, which are costly, time-consuming, and imprecise. Recently, more systematic computational approaches have been proposed, however these rely on utilizing the information from the diseases a drug is already approved to treat. This inherently limits the algorithms, making them unusable for investigational molecules. Here, we present a computational approach to drug repurposing, CATNIP, that requires only biological and chemical information of a molecule. CATNIP is trained with 2,576 diverse small molecules and uses 16 different drug similarity features, such as structural, target, or pathway based similarity. This model obtains significant predictive power (AUC = 0.841). Using our model, we created a repurposing network to identify broad scale repurposing opportunities between drug types. By exploiting this network, we identified literature-supported repurposing candidates, such as the use of systemic hormonal preparations for the treatment of respiratory illnesses. Furthermore, we demonstrated that we can use our approach to identify novel uses for defined drug classes. We found that adrenergic uptake inhibitors, specifically amitriptyline and trimipramine, could be potential therapies for Parkinson's disease. Additionally, using CATNIP, we predicted the kinase inhibitor, vandetanib, as a possible treatment for Type 2 Diabetes. Overall, this systematic approach to drug repurposing lays the groundwork to streamline future drug development efforts