941 resultados para drug-drug interactions (DDI)


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Virtual screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface in order to find new hotspots, where ligands might potentially interact with, and which is implemented in last generation massively parallel GPU hardware, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods and concretely BINDSURF is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to improve accuracy of the scoring functions used in BINDSURF we propose a hybrid novel approach where neural networks (NNET) and support vector machines (SVM) methods are trained with databases of known active (drugs) and inactive compounds, being this information exploited afterwards to improve BINDSURF VS predictions.

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Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface to find new hotspots, where ligands might potentially interact with, and which is implemented in massively parallel Graphics Processing Units, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to solve this problem, we propose a novel approach where neural networks are trained with databases of known active (drugs) and inactive compounds, and later used to improve VS predictions.

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Purpose: To construct a cluster model or a gene signature for Stevens-Johnson syndrome (SJS) using pathways analysis in order to identify some potential biomarkers that may be used for early detection of SJS and epidermal necrolysis (TEN) manifestations. Methods: Gene expression profiles of GSE12829 were downloaded from Gene Expression Omnibus database. A total of 193 differentially expressed genes (DEGs) were obtained. We applied these genes to geneMANIA database, to remove ambiguous and duplicated genes, and after that, characterized the gene expression profiles using geneMANIA, DAVID, REACTOME, STRING and GENECODIS which are online software and databases. Results: Out of 193 genes, only 91 were used (after removing the ambiguous and duplicated genes) for topological analysis. It was found by geneMANIA database search that majority of these genes were coexpressed yielding 84.63 % co-expression. It was found that ten genes were in Physical interactions comprising almost 14.33 %. There were < 1 % pathway and genetic interactions with values of 0.97 and 0.06 %, respectively. Final analyses revealed that there are two clusters of gene interactions and 13 genes were shown to be in evident relationship of interaction with regards to hypersensitivity. Conclusion: Analysis of differential gene expressions by topological and database approaches in the current study reveals 2 gene network clusters. These genes are CD3G, CD3E, CD3D, TK1, TOP2A, CDK1, CDKN3, CCNB1, and CCNF. There are 9 key protein interactions in hypersensitivity reactions and may serve as biomarkers for SJS and TEN. Pathways related gene clusters has been identified and a genetic model to predict SJS and TEN early incidence using these biomarker genes has been developed.

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Police services in a number of Australian states and overseas jurisdictions have begun to implement or consider random road-side drug testing of drivers. This paper outlines research conducted to provide an estimate of the extent of drug driving in a sample of Queensland drivers in regional, rural and metropolitan areas. Oral fluid samples were collected from 2657 Queensland motorists and screened for illicit substances including cannabis (delta 9 tetrahydrocannibinol [THC]), amphetamines, ecstasy, and cocaine. Overall, 3.8% of the sample (n = 101) screened positive for at least one illicit substance, although multiple drugs were identified in a sample of 23 respondents. The most common drugs detected in oral fluid were ecstasy (n = 53), and cannabis (n = 46) followed by amphetamines (n = 23). A key finding was that cannabis was confirmed as the most common self-reported drug combined with driving and that individuals who tested positive to any drug through oral fluid analysis were also more likely to report the highest frequency of drug driving. Furthermore, a comparison between drug vs. drink driving detection rates for one region of the study, revealed a higher detection rate for drug driving (3.8%) vs. drink driving (0.8%). This research provides evidence that drug driving is relatively prevalent on Queensland roads, and may in fact be more common than drink driving. This paper will further outline the study findings’ and present possible directions for future drug driving research.

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Single nucleotide polymorphisms (SNPs) are unique genetic differences between individuals that contribute in significant ways to the determination of human variation including physical characteristics like height and appearance as well as less obvious traits such as personality, behaviour and disease susceptibility. SNPs can also significantly influence responses to pharmacotherapy and whether drugs will produce adverse reactions. The development of new drugs can be made far cheaper and more rapid by selecting participants in drug trials based on their genetically determined response to drugs. Technology that can rapidly and inexpensively genotype thousands of samples for thousands of SNPs at a time is therefore in high demand. With the completion of the human genome project, about 12 million true SNPs have been identified to date. However, most have not yet been associated with disease susceptibility or drug response. Testing for the appropriate drug response SNPs in a patient requiring treatment would enable individualised therapy with the right drug and dose administered correctly the first time. Many pharmaceutical companies are also interested in identifying SNPs associated with polygenic traits so novel therapeutic targets can be discovered. This review focuses on technologies that can be used for genotyping known SNPs as well as for the discovery of novel SNPs associated with drug response.

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Typically adolescents' friends are considered a risk factor for adolescent engagement in risk-taking. This study took a more novel approach, by examining adolescent friendship as a protective factor. In particular it investigated friends' potential to intervene to reduce risk-taking. 540 adolescents (mean age 13.47 years) were asked about their intention to intervene to reduce friends' alcohol, drug and alcohol-related harms and about psychosocial factors potentially associated with intervening. More than half indicated that they would intervene in friends' alcohol, drug use, alcohol-related harms and interpersonal violence. Intervening was associated with being female, having friends engage in overall less risk-taking and having greater school connectedness. The findings provide an important understanding of increasing adolescent protective behavior as a potential strategy to reduce alcohol and drug related harms.

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Introduction and Aims: Since the 1990s illicit drug use death rates in Australia have increased markedly. There is a notable gap in knowledge about changing socio-economic inequalities in drug use death rates. Some limited Australian and overseas data point to higher rates of drug death in the lowest socio-economic groups, but the paucity of available studies and their sometimes conflicting findings need to be addressed. Design and Methods: This paper uses data obtained from the Australian Bureau of Statistics (ABS) to examine changes in age-standardised drug-induced mortality rates for Australian males over the period 1981 – 2002. Socio-economic status was categorised as manual or non-manual work status. Results: With the rapid increase in drug-induced mortality rates in the 1990s, there was a parallel increase in socio-economic inequalities in drug-induced deaths. The decline in drug death rates from 2000 onwards was associated with a decline in socio-economic inequalities. By 2002, manual workers had drug death rates well over twice the rate of non-manual workers. Discussion: Three factors are identified which contribute to these socio-economic inequalities in mortality. First, there has been an age shift in deaths evident only for manual workers. Secondly, there has been an increase in availability until 1999 and a relative decline in the cost of the drug, which most often leads to drug death (heroin). Thirdly, there has been a shift to amphetamine use which may lead to significant levels of morbidity, but few deaths. [Najman JM, Toloo G, Williams GM. Increasing socio-economic inequalities in drug-induced deaths in Australia: 1981–2002.

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In December 2007, random roadside drug testing commenced in Queensland, Australia. Subsequently, the aim of this study was to explore the preliminary impact of Queensland’s drug driving legislation and enforcement techniques by applying Stafford and Warr’s [Stafford, M. C., & Warr, M. (1993). A reconceptualization of general and specific deterrence. Journal of Research in Crime and Delinquency, 30, 123-135] reconceptualization of deterrence theory. Completing a comprehensive drug driving questionnaire were 899 members of the public, university students, and individuals referred to a drug diversion program. Of note was that approximately a fifth of participants reported drug driving in the past six months. Additionally, the analysis indicated that punishment avoidance and vicarious punishment avoidance were predictors of the propensity to drug drive in the future. In contrast, there were indications that knowing of others apprehended for drug driving was not a sufficient deterrent. Sustained testing and publicity of the legislation and countermeasure appears needed to increase the deterrent impact for drug driving.

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Alginate microspheres are considered a promising material as a drug carrier in bone repair due to excellent biocompatibility, but their main disadvantage is low drug entrapment efficiency and non-controllable release. The aim of this study was to investigate the effect of incorporating mesoporous bioglass (MBG), non-mesoporous bioglass (BG) or hydroxyapatite (HAp) into alginate microspheres on their drug-loading and release properties. X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and atomic emission spectroscopy (AES) were used to analyse the composition, structure and dissolution of bioactive inorganic materials and their microspheres. Dexamethasone (DEX)-loading and release ability of four microspheres were tested in phosphate buffered saline with varying pHs. Results showed that the drug-loading capacity was enhanced with the incorporation of bioactive inorganic materials into alginate microspheres. The MBG/Alginate microspheres had the highest drug loading ability. DEX release from alginate microspheres correlated to the dissolution of MBG, BG and HAp in PBS, and that the pH was an efficient factor in controlling the DEX release; a high pH resulted in greater DEX release, whereas a low pH delayed DEX release. In addition, MBG/alginate, BG/alginate and HAp/alginate microspheres had varying apatite-formation and dissolution abilities, which indicate that the composites would behave differently with respect to bioactivity. The study suggests that microspheres made of a composite of bioactive inorganic materials and alginate have a bioactivity and degradation profile which greatly improves their drug delivery capacity, thus enhancing their potential applications as bioactive filler materials for bone tissue regeneration.

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Alcohol and drug dependency is a widespread health and social issue encountered by registered nurses in contemporary practice. A study aiming to describe the experiences of registered nurses working in an alcohol and drug unit in South East Queensland was implemented. Data were analysed via Giorgi’s phenomenological method and an unexpected but significant finding highlighted the frustration felt by registered nurses regarding experiences of stigma they identified in their daily work encounters. Secondary analysis confirmed the phenomenon of stigma with three themes: (1) inappropriate judgement; (2) advocacy; and (3) education. Resultantly, findings concluded registered nurses’ working in this field need to become advocates for their clients, ensuring professional conduct is upheld at all times. This paper recommends that stigma could be addressed by incorporating alcohol and other drug dependency subjects and clinical placements into the curriculum of the Bachelor of Nursing degrees, and in-services for all practising registered nurses.