65,209 research outputs found

    A network inference method for large-scale unsupervised identification of novel drug-drug interactions

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    Characterizing interactions between drugs is important to avoid potentially harmful combinations, to reduce off-target effects of treatments and to fight antibiotic resistant pathogens, among others. Here we present a network inference algorithm to predict uncharacterized drug-drug interactions. Our algorithm takes, as its only input, sets of previously reported interactions, and does not require any pharmacological or biochemical information about the drugs, their targets or their mechanisms of action. Because the models we use are abstract, our approach can deal with adverse interactions, synergistic/antagonistic/suppressing interactions, or any other type of drug interaction. We show that our method is able to accurately predict interactions, both in exhaustive pairwise interaction data between small sets of drugs, and in large-scale databases. We also demonstrate that our algorithm can be used efficiently to discover interactions of new drugs as part of the drug discovery process

    Search algorithms as a framework for the optimization of drug combinations

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    Combination therapies are often needed for effective clinical outcomes in the management of complex diseases, but presently they are generally based on empirical clinical experience. Here we suggest a novel application of search algorithms, originally developed for digital communication, modified to optimize combinations of therapeutic interventions. In biological experiments measuring the restoration of the decline with age in heart function and exercise capacity in Drosophila melanogaster, we found that search algorithms correctly identified optimal combinations of four drugs with only one third of the tests performed in a fully factorial search. In experiments identifying combinations of three doses of up to six drugs for selective killing of human cancer cells, search algorithms resulted in a highly significant enrichment of selective combinations compared with random searches. In simulations using a network model of cell death, we found that the search algorithms identified the optimal combinations of 6-9 interventions in 80-90% of tests, compared with 15-30% for an equivalent random search. These findings suggest that modified search algorithms from information theory have the potential to enhance the discovery of novel therapeutic drug combinations. This report also helps to frame a biomedical problem that will benefit from an interdisciplinary effort and suggests a general strategy for its solution.Comment: 36 pages, 10 figures, revised versio

    Development of models for predicting Torsade de Pointes cardiac arrhythmias using perceptron neural networks

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    Blockage of some ion channels and in particular, the hERG cardiac potassium channel delays cardiac repolarization and can induce arrhythmia. In some cases it leads to a potentially life-threatening arrhythmia known as Torsade de Pointes (TdP). Therefore recognizing drugs with TdP risk is essential. Candidate drugs that are determined not to cause cardiac ion channel blockage are more likely to pass successfully through clinical phases II and III trials (and preclinical work) and not be withdrawn even later from the marketplace due to cardiotoxic effects. The objective of the present study is to develop an SAR model that can be used as an early screen for torsadogenic (causing TdP arrhythmias) potential in drug candidates. The method is performed using descriptors comprised of atomic NMR chemical shifts and corresponding interatomic distances which are combined into a 3D abstract space matrix. The method is called 3D-SDAR (3 dimensional spectral data-activity relationship) and can be interrogated to identify molecular features responsible for the activity, which can in turn yield simplified hERG toxicophores. A dataset of 55 hERG potassium channel inhibitors collected from Kramer et al. consisting of 32 drugs with TdP risk and 23 with no TdP risk was used for training the 3D-SDAR model.An ANN model with multilayer perceptron was used to define collinearities among the independent 3D-SDAR features. A composite model from 200 random iterations with 25% of the molecules in each case yielded the following figures of merit: training, 99.2 %; internal test sets, 66.7%; external (blind validation) test set, 68.4%. In the external test set, 70.3% of positive TdP drugs were correctly predicted. Moreover, toxicophores were generated from TdP drugs. A 3D-SDAR was successfully used to build a predictive model for drug-induced torsadogenic and non-torsadogenic drugs.Comment: Accepted for publication in BMC Bioinformatics (Springer) July 201

    Personalized medicine—a modern approach for the diagnosis and management of hypertension

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    The main goal of treating hypertension is to reduce blood pressure to physiological levels and thereby prevent risk of cardiovascular disease and hypertension-associated target organ damage. Despite reductions in major risk factors and the availability of a plethora of effective antihypertensive drugs, the control of blood pressure to target values is still poor due to multiple factors including apparent drug resistance and lack of adherence. An explanation for this problem is related to the current reductionist and ‘trial-and-error’ approach in the management of hypertension, as we may oversimplify the complex nature of the disease and not pay enough attention to the heterogeneity of the pathophysiology and clinical presentation of the disorder. Taking into account specific risk factors, genetic phenotype, pharmacokinetic characteristics, and other particular features unique to each patient, would allow a personalized approach to managing the disease. Personalized medicine therefore represents the tailoring of medical approach and treatment to the individual characteristics of each patient and is expected to become the paradigm of future healthcare. The advancement of systems biology research and the rapid development of high-throughput technologies, as well as the characterization of different –omics, have contributed to a shift in modern biological and medical research from traditional hypothesis-driven designs toward data-driven studies and have facilitated the evolution of personalized or precision medicine for chronic diseases such as hypertension

    Sol-Gel synthesis, spectroscopic and thermal behavior study of SiO2/PEG composites containing different amount of chlorogenic acid

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    In this work, new phenol-based materials have been synthesized by the sol-gel method, in which different amounts of the phenolic antioxidant chlorogenic acid (CGA) (from 5 wt % to 20 wt %) were embedded in two different silica matrices: pure silica and silica-based hybrids materials, containing 50 wt % of polyethylene glycol (PEG). The incorporation of CGA in different sol-gel matrices might protect them from degradation, which could cause the loss of their properties. The two series of materials were chemically characterized by Fourier transform infrared (FTIR) spectroscopy. In addition, the thermal behavior of both series of materials containing CGA was studied by thermogravimetry under both air and inert N2flowing gas atmosphere. The bioactivity was evaluated by soaking the synthesized hybrids in a simulated body fluid, showing that the bioactivity of the silica matrix is not modified by the presence of PEG and CGA

    Remotely triggered scaffolds for controlled release of pharmaceuticals

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    Fe3O4-Au hybrid nanoparticles (HNPs) have shown increasing potential for biomedical applications such as image guided stimuli responsive drug delivery. Incorporation of the unique properties of HNPs into thermally responsive scaffolds holds great potential for future biomedical applications. Here we successfully fabricated smart scaffolds based on thermo-responsive poly(N-isopropylacrylamide) (pNiPAM). Nanoparticles providing localized trigger of heating when irradiated with a short laser burst were found to give rise to remote control of bulk polymer shrinkage. Gold-coated iron oxide nanoparticles were synthesized using wet chemical precipitation methods followed by electrochemical coating. After subsequent functionalization of particles with allyl methyl sulfide, mercaptodecane, cysteamine and poly(ethylene glycol) thiol to enhance stability, detailed biological safety was determined using live/dead staining and cell membrane integrity studies through lactate dehydrogenase (LDH) quantification. The PEG coated HNPs did not show significant cytotoxic effect or adverse cellular response on exposure to 7F2 cells (p < 0.05) and were carried forward for scaffold incorporation. The pNiPAM-HNP composite scaffolds were investigated for their potential as thermally triggered systems using a Q-switched Nd:YAG laser. These studies show that incorporation of HNPs resulted in scaffold deformation after very short irradiation times (seconds) due to internal structural heating. Our data highlights the potential of these hybrid-scaffold constructs for exploitation in drug delivery, using methylene blue as a model drug being released during remote structural change of the scaffold
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