The Database for Aggregate Analysis of ClinicalTrials.gov (AACT) and Subsequent Regrouping by Clinical Specialty

BACKGROUND: The ClinicalTrials.gov registry provides information regarding characteristics of past, current, and planned clinical studies to patients, clinicians, and researchers; in addition, registry data are available for bulk download. However, issues related to data structure, nomenclature, and changes in data collection over time present challenges to the aggregate analysis and interpretation of these data in general and to the analysis of trials according to clinical specialty in particular. Improving usability of these data could enhance the utility of ClinicalTrials.gov as a research resource. METHODS/PRINCIPAL RESULTS: The purpose of our project was twofold. First, we sought to extend the usability of ClinicalTrials.gov for research purposes by developing a database for aggregate analysis of ClinicalTrials.gov (AACT) that contains data from the 96,346 clinical trials registered as of September 27, 2010. Second, we developed and validated a methodology for annotating studies by clinical specialty, using a custom taxonomy employing Medical Subject Heading (MeSH) terms applied by an NLM algorithm, as well as MeSH terms and other disease condition terms provided by study sponsors. Clinical specialists reviewed and annotated MeSH and non-MeSH disease condition terms, and an algorithm was created to classify studies into clinical specialties based on both MeSH and non-MeSH annotations. False positives and false negatives were evaluated by comparing algorithmic classification with manual classification for three specialties. CONCLUSIONS/SIGNIFICANCE: The resulting AACT database features study design attributes parsed into discrete fields, integrated metadata, and an integrated MeSH thesaurus, and is available for download as Oracle extracts (.dmp file and text format). This publicly-accessible dataset will facilitate analysis of studies and permit detailed characterization and analysis of the U.S. clinical trials enterprise as a whole. In addition, the methodology we present for creating specialty datasets may facilitate other efforts to analyze studies by specialty groups

DOMINE: a comprehensive collection of known and predicted domain-domain interactions

DOMINE is a comprehensive collection of known and predicted domain–domain interactions (DDIs) compiled from 15 different sources. The updated DOMINE includes 2285 new domain–domain interactions (DDIs) inferred from experimentally characterized high-resolution three-dimensional structures, and about 3500 novel predictions by five computational approaches published over the last 3 years. These additions bring the total number of unique DDIs in the updated version to 26 219 among 5140 unique Pfam domains, a 23% increase compared to 20 513 unique DDIs among 4346 unique domains in the previous version. The updated version now contains 6634 known DDIs, and features a new classification scheme to assign confidence levels to predicted DDIs. DOMINE will serve as a valuable resource to those studying protein and domain interactions. Most importantly, DOMINE will not only serve as an excellent reference to bench scientists testing for new interactions but also to bioinformaticans seeking to predict novel protein–protein interactions based on the DDIs. The contents of the DOMINE are available at http://domine.utdallas.edu

DOMINE: a database of protein domain interactions

DOMINE is a database of known and predicted protein domain interactions compiled from a variety of sources. The database contains domain–domain interactions observed in PDB entries, and those that were predicted by eight different computational approaches. DOMINE contains a total of 20 513 unique domain–domain interactions among 4036 Pfam domains, out of which 4349 are inferred from PDB entries and 17 781 were predicted by at least one computational approach. This database will serve as a valuable resource to those working in the field of protein and domain interactions. DOMINE may not only serve as a reference to experimentalists who test for new protein and domain interactions, but also offers a consolidated dataset for analysis by bioinformaticians who seek to test ideas regarding the underlying factors that control the topological structure of interaction networks. DOMINE is freely available at http://domine.utdallas.edu

BIOINFORMATICS ORIGINAL PAPER

doi:10.1093/bioinformatics/btl009 COCO-CL: hierarchical clustering of homology relations based on evolutionary correlation

Recent Clinical Trials in Osteoporosis: A Firm Foundation or Falling Short?

<div><p>The global burden of osteoporotic fractures is associated with significant morbidity, mortality, and healthcare costs. We examined the ClinicalTrials.gov database to determine whether recently registered clinical trials addressed prevention and treatment in those at high risk for fracture. A dataset of 96,346 trials registered in ClinicalTrials.gov was downloaded on September 27, 2010. At the time of the dataset download, 40,970 interventional trials had been registered since October 1, 2007. The osteoporosis subset comprised 239 interventional trials (0.6%). Those trials evaluating orthopedic procedures were excluded. The primary purpose was treatment in 67.0%, prevention in 20.1%, supportive care in 5.8%, diagnostic in 2.2%, basic science in 3.1%, health services research in 0.9%, and screening in 0.9%. The majority of studies (61.1%) included drug-related interventions. Most trials (56.9%) enrolled only women, 38.9% of trials were open to both men and women, and 4.2% enrolled only men. Roughly one fifth (19.7%) of trials excluded research participants older than 65 years, and 33.5% of trials excluded those older than 75 years. The funding sources were industry in 51.0%, the National Institutes of Health in 6.3%, and other in 42.7%. We found that most osteoporosis-related trials registered from October 2007 through September 2010 examined the efficacy and safety of drug treatment, and fewer trials examined prevention and non-drug interventions. Trials of interventions that are not required to be registered in ClinicalTrials.gov may be underrepresented. Few trials are specifically studying osteoporosis in men and older adults. Recently registered osteoporosis trials may not sufficiently address fracture prevention.</p></div

The State of Infectious Diseases Clinical Trials: A Systematic Review of ClinicalTrials.gov

<div><p>Background</p><p>There is a paucity of clinical trials informing specific questions faced by infectious diseases (ID) specialists. The ClinicalTrials.gov registry offers an opportunity to evaluate the ID clinical trials portfolio.</p><p>Methods</p><p>We examined 40,970 interventional trials registered with ClinicalTrials.gov from 2007–2010, focusing on study conditions and interventions to identify ID-related trials. Relevance to ID was manually confirmed for each programmatically identified trial, yielding 3570 ID trials and 37,400 non-ID trials for analysis.</p><p>Results</p><p>The number of ID trials was similar to the number of trials identified as belonging to cardiovascular medicine (n = 3437) or mental health (n = 3695) specialties. Slightly over half of ID trials were treatment-oriented trials (53%, vs. 77% for non-ID trials) followed by prevention (38%, vs. 8% in non-ID trials). ID trials tended to be larger than those of other specialties, with a median enrollment of 125 subjects (interquartile range [IQR], 45–400) vs. 60 (IQR, 30–160) for non-ID trials. Most ID studies are randomized (73%) but nonblinded (56%). Industry was the funding source in 51% of ID trials vs. 10% that were primarily NIH-funded. HIV-AIDS trials constitute the largest subset of ID trials (n = 815 [23%]), followed by influenza vaccine (n = 375 [11%]), and hepatitis C (n = 339 [9%]) trials. Relative to U.S. and global mortality rates, HIV-AIDS and hepatitis C virus trials are over-represented, whereas lower respiratory tract infection trials are under-represented in this large sample of ID clinical trials.</p><p>Conclusions</p><p>This work is the first to characterize ID clinical trials registered in ClinicalTrials.gov, providing a framework to discuss prioritization, methodology, and policy.</p></div