Safety and effectiveness of low-dose amikacin in nontuberculous mycobacterial pulmonary disease treated in Toronto, Canada

Amikacin; NTM lung disease; Nontuberculous mycobacteriaAmikacina; Malaltia pulmonar per micobacteri no tuberculós; Micobacteri no tuberculósAmikacina; Enfermedad pulmonar por micobacteria no tuberculosa; Micobacteria no tuberculosaBACKGROUND: Treatment guidelines suggest either a low-dose or high-dose approach when prescribing amikacin for nontuberculous mycobacterial pulmonary disease (NTM PD), but data supporting the low-dose approach are limited. The purpose of this study was to describe the safety and efficacy of the use of a low-dose of intravenous amikacin in a cohort of patients with NTM PD. METHODS: We retrospectively reviewed all patients with NTM PD who received amikacin at our institution between July 1, 2003 and February 28, 2017. Demographics, clinical, microbiological and radiological data, indication and dose of amikacin, and adverse drug effects were recorded. RESULTS: A total of 107 patients received a regimen containing amikacin for a median (IQR) of 7 (4-11) months. Seventy (65.4%) were female and the mean age (SD) was 58.3 (14.9) years. Amikacin was started at a median dose of 9.9 (2.5) mg/kg/day. Ototoxicity was observed in 30/77 (39%) patients and it was related to female sex (OR 4.96, 95%CI 1.24-19.87), and total dose of amikacin per bodyweight (OR 1.62, 95%CI 1.08-2.43). Patients of East Asian ethnicity were less likely to develop ototoxicity (0.24, 95%CI 0.06-0.95). Out of 96 patients who received amikacin for more than 3 months, 65 (67.7%) experienced symptom improvement and 30/62 (49.2%) converted their sputum to culture negative within a year. CONCLUSIONS: Patients with NTM PD treated with low-dose intravenous amikacin frequently developed ototoxicity, which was associated with female sex, and total dose of amikacin per bodyweight. Physicians should carefully consider dose, treatment duration, and long term prognosis in balancing risks and benefits of intravenous amikacin in NTM PD

Safety and effectiveness of low-dose amikacin in nontuberculous mycobacterial pulmonary disease treated in Toronto

Treatment guidelines suggest either a low-dose or high-dose approach when prescribing amikacin for nontuberculous mycobacterial pulmonary disease (NTM PD), but data supporting the low-dose approach are limited. The purpose of this study was to describe the safety and efficacy of the use of a low-dose of intravenous amikacin in a cohort of patients with NTM PD. We retrospectively reviewed all patients with NTM PD who received amikacin at our institution between July 1, 2003 and February 28, 2017. Demographics, clinical, microbiological and radiological data, indication and dose of amikacin, and adverse drug effects were recorded. A total of 107 patients received a regimen containing amikacin for a median (IQR) of 7 (4-11) months. Seventy (65.4%) were female and the mean age (SD) was 58.3 (14.9) years. Amikacin was started at a median dose of 9.9 (2.5) mg/kg/day. Ototoxicity was observed in 30/77 (39%) patients and it was related to female sex (OR 4.96, 95%CI 1.24-19.87), and total dose of amikacin per bodyweight (OR 1.62, 95%CI 1.08-2.43). Patients of East Asian ethnicity were less likely to develop ototoxicity (0.24, 95%CI 0.06-0.95). Out of 96 patients who received amikacin for more than 3 months, 65 (67.7%) experienced symptom improvement and 30/62 (49.2%) converted their sputum to culture negative within a year. Patients with NTM PD treated with low-dose intravenous amikacin frequently developed ototoxicity, which was associated with female sex, and total dose of amikacin per bodyweight. Physicians should carefully consider dose, treatment duration, and long term prognosis in balancing risks and benefits of intravenous amikacin in NTM PD

An Expanded Evaluation of Protein Function Prediction Methods Shows an Improvement In Accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent

An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent. Keywords: Protein function prediction, Disease gene prioritizationpublishedVersio

Biomedical Text Mining and Genomic Data Fusion for Disease-Gene Discovery

Our genome is an amazing sequence of three billion chemical letters (DNA nucleotides) that is present in almost every cell inside the human body. This sequence contains fragments called genes that encode proteins with a wide diversity of functions. Any mutation in the gene sequence might result in an alteration of these functions, which sometimes is undesirable and contributes to disease. Hence identifying which genes are associated with which disease is of great medical importance. It is a key step to diagnosing and curing diseases, and hence plays a key role in many critical applications such as personalized medicine and early prediction, and drug design and repurposing. However this task is not trivial, especially with the exponential growth of genomic data that makes it challenging for the geneticists to explore all possible hypotheses in a reasonable amount of time. In this thesis, we propose Beegle, an online search and discovery engine, which allows geneticists to explore possible hypotheses about links between genes and diseases in a fast and easy way. It starts from text mining to quickly present the user with an ordered list of genes that have been reported in the literature to be linked with the query in question. Then it integrates genomic data fusion techniques to learn a model and generate novel gene hypothesis. In this work, we analysed over 20 million biomedical abstracts to extract relevant links between genes and diseases. We tested different statistical measures to decide on the degree of relevance of such links, which ranged from co-occurrence to cosine similarities. We experimented with two biomedical text taggers, which are quite diverse in tagging the biomedical text with the different biomedical concepts. We also investigated the application of topic modelling, where we relied on a latent Dirichlet allocation model, to infer a latent set topics that better model our text data. Finally, we integrated state-of-the-art learning methodologies to analyse and fuse over 70 genomic data sources and compute gene similarity scores to eventually present the user with one final ranked hypothesis. We release Beegle at http://beegle.esat.kuleuven.be/, where we welcome our users to start their disease-gene discovery experience with an introductory video tutorial. We validated Beegle in multiple experimental setups, which we partly created in-house based on public genetic databases. We mainly designed the validation process such that it mimics real discovery, where we limited information in our data sets up to a certain date, then we used test sets of disease-gene links that were only reported after this date. Hence, our hypotheses were not contaminated with novel information. In one setup, our results show that Beegle recommends on average 41.2% true novel hypotheses in the top 5% ranking genes. In another setup, our results show that Beegle recommends at least one true novel hypothesis in the top 20 ranking genes. Our methodology increases the true positive rate of manual approaches by 44%, and reduces the error of automatic approaches by 50%. We believe Beegle is an interesting tool to quickly explore all the gene hypotheses related to any query of interest. These can further be assessed and filtered by the geneticist who can carry out the necessary validation experiments. This motivates us to extend Beegle such that it additionally explores similar drug hypotheses, which we believe is a potential future work given the availability of the relevant data sets.nrpages: 175status: publishe

Biomedical text mining for disease-gene discovery (poster)

status: publishe

Biomedical Text Mining for Disease Gene Discovery

status: publishe

Gene Prioritization Through Geometric-Inspired Kernel Data Fusion

© 2015 IEEE. In biology there is often the need to discover the most promising genes, among a large list of candidate genes, to further investigate. While a single data source might not be effective enough, integrating several complementary genomic data sources leads to more accurate prediction. We propose a kernel-based gene prioritization framework using geometric kernel fusion which we have recently developed as a powerful tool for protein fold classification [I]. It has been shown that taking more involved geometry means of their corresponding kernel matrices is less sensitive in dealing with complementary and noisy kernel matrices compared to standard multiple kernel learning methods. Since genomic kernels often encodes the complementary characteristics of biological data, this leads us to research the application of geometric kernel fusion in the gene prioritization task. We utilize an unbiased and prospective benchmark based on the OMIM [2] associations. Experimental results on our prospective benchmark show that our model can improve the accuracy of the state-of-the-art gene prioritization model.status: publishe

Beegle: from literature mining to disease gene discovery (poster)

status: publishe