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

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.

Compliance with results reporting at ClinicalTrials.gov.

BACKGROUND: The Food and Drug Administration Amendments Act (FDAAA) mandates timely reporting of results of applicable clinical trials to ClinicalTrials.gov. We characterized the proportion of applicable clinical trials with publicly available results and determined independent factors associated with the reporting of results. METHODS: Using an algorithm based on input from the National Library of Medicine, we identified trials that were likely to be subject to FDAAA provisions (highly likely applicable clinical trials, or HLACTs) from 2008 through 2013. We determined the proportion of HLACTs that reported results within the 12-month interval mandated by the FDAAA or at any time during the 5-year study period. We used regression models to examine characteristics associated with reporting at 12 months and throughout the 5-year study period. RESULTS: From all the trials at ClinicalTrials.gov, we identified 13,327 HLACTs that were terminated or completed from January 1, 2008, through August 31, 2012. Of these trials, 77.4% were classified as drug trials. A total of 36.9% of the trials were phase 2 studies, and 23.4% were phase 3 studies; 65.6% were funded by industry. Only 13.4% of trials reported summary results within 12 months after trial completion, whereas 38.3% reported results at any time up to September 27, 2013. Timely reporting was independently associated with factors such as FDA oversight, a later trial phase, and industry funding. A sample review suggested that 45% of industry-funded trials were not required to report results, as compared with 6% of trials funded by the National Institutes of Health (NIH) and 9% of trials that were funded by other government or academic institutions. CONCLUSIONS: Despite ethical and legal obligations to disclose findings promptly, most HLACTs did not report results to ClinicalTrials.gov in a timely fashion during the study period. Industry-funded trials adhered to legal obligations more often than did trials funded by the NIH or other government or academic institutions. (Funded by the Clinical Trials Transformation Initiative and the NIH.).

Clinical trials in peripheral vascular disease: pipeline and trial designs: an evaluation of the ClinicalTrials.gov database.

BACKGROUND: Tremendous advances have occurred in therapies for peripheral vascular disease (PVD); until recently, however, it has not been possible to examine the entire clinical trial portfolio of studies for the treatment of PVD (both arterial and venous disease). METHODS AND RESULTS: We examined interventional trials registered in ClinicalTrials.gov from October 2007 through September 2010 (n=40,970) and identified 676 (1.7%) PVD trials (n=493 arterial only, n=170 venous only, n=13 both arterial and venous). Most arterial studies investigated lower-extremity peripheral artery disease and acute stroke (35% and 24%, respectively), whereas most venous studies examined deep vein thrombosis/pulmonary embolus prevention (42%) or venous ulceration (25%). A placebo-controlled trial design was used in 27% of the PVD trials, and 4% of the PVD trials excluded patients >65 years of age. Enrollment in at least 1 US site decreased from 51% of trials in 2007 to 41% in 2010. Compared with noncardiology disciplines, PVD trials were more likely to be double-blinded, to investigate the use of devices and procedures, and to have industry sponsorship and assumed funding source, and they were less likely to investigate drug and behavioral therapies. Geographic access to PVD clinical trials within the United States is limited to primarily large metropolitan areas. CONCLUSIONS: PVD studies represent a small group of trials registered in ClinicalTrials.gov, despite the high prevalence of vascular disease in the general population. This low number, compounded by the decreasing number of PVD trials in the United States, is concerning and may limit the ability to inform current clinical practice of patients with PVD.

The landscape of clinical trials in nephrology: a systematic review of Clinicaltrials.gov.

BACKGROUND: Well-designed trials are of paramount importance in improving the delivery of care to patients with kidney disease. However, it remains unknown whether contemporary clinical trials within nephrology are of sufficient quality and quantity to meet this need. STUDY DESIGN: Systematic review. SETTING & POPULATION: Studies registered with ClinicalTrials.gov. SELECTION CRITERIA FOR STUDIES: Interventional (ie, nonobservational) studies (both randomized and nonrandomized) registered between October 2007 and September 2010 were included for analysis. Studies were reviewed independently by physicians and classified by clinical specialty. PREDICTOR: Nephrology versus cardiology versus other trials. OUTCOMES: Select clinical trial characteristics. RESULTS: Of 40,970 trials overall, 1,054 (2.6%) were classified as nephrology. Most nephrology trials were for treatment (75.4%) or prevention (15.7%), with very few diagnostic, screening, or health services research studies. Most nephrology trials were randomized (72.3%). Study designs included 24.9% with a single study group, 64.0% that included parallel groups, and 9.4% that were crossover trials. Nephrology trials, compared with 2,264 cardiology trials (5.5% overall), were more likely to be smaller (64.5% vs 48.0% enrolling≤100 patients), phases 1-2 (29.0% vs 19.7%), and unblinded (66.2% vs 53.3%; P<0.05 for all). Nephrology trials also were more likely than cardiology trials to include a drug intervention (72.4% vs 41.9%) and less likely to report having a data monitoring committee (40.3% vs 48.5%; P<0.05 for all). Finally, there were few trials funded by the National Institutes of Health (NIH; 3.3%, nephrology; 4.2%, cardiology). LIMITATIONS: Does not include all trials performed worldwide, and frequent categorization of funding source as university may underestimate NIH support. CONCLUSIONS: Critical differences remain between clinical trials in nephrology and other specialties. Improving care for patients with kidney disease will require a concerted effort to increase the scope, quality, and quantity of clinical trials within nephrology.

The state of infectious diseases clinical trials: a systematic review of ClinicalTrials.gov.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: This work is the first to characterize ID clinical trials registered in ClinicalTrials.gov, providing a framework to discuss prioritization, methodology, and policy.

Portfolio of clinical research in adult cardiovascular disease as reflected in ClinicalTrials.gov.

BACKGROUND: Cardiovascular medicine is widely regarded as a vanguard for evidence-based drug and technology development. Our goal was to describe the cardiovascular clinical research portfolio from ClinicalTrials.gov. METHODS AND RESULTS: We identified 40 970 clinical research studies registered between 2007 and 2010 in which patients received diagnostic, therapeutic, or other interventions per protocol. By annotating 18 491 descriptors from the National Library of Medicine's Medical Subject Heading thesaurus and 1220 free-text terms to select those relevant to cardiovascular disease, we identified studies that related to the diagnosis, treatment, or prevention of diseases of the heart and peripheral arteries in adults (n = 2325 [66%] included from review of 3503 potential studies). The study intervention involved a drug in 44.6%, a device or procedure in 39.3%, behavioral intervention in 8.1%, and biological or genetic interventions in 3.0% of the trials. More than half of the trials were postmarket approval (phase 4, 25.6%) or not part of drug development (no phase, 34.5%). Nearly half of all studies (46.3%) anticipated enrolling 100 patients or fewer. The majority of studies assessed biomarkers or surrogate outcomes, with just 31.8% reporting a clinical event as a primary outcome. CONCLUSIONS: Cardiovascular studies registered on ClinicalTrials.gov span a range of study designs. Data have limited verification or standardization and require manual processes to describe and categorize studies. The preponderance of small and late-phase studies raises questions regarding the strength of evidence likely to be generated by the current portfolio and the potential efficiency to be gained by more research consolidation.

Using ClinicalTrials.gov to understand the state of clinical research in pulmonary, critical care, and sleep medicine.

RATIONALE: ClinicalTrials.gov is the largest trial registry in the world. Strengthened registration requirements, including federal mandates in 2007, have increased study representation. A systematic evaluation of all registered studies has been limited by the absence of an aggregate data set and specialty-specific search terms. OBJECTIVES: We leveraged a newly transformed database containing annotated data from ClinicalTrials.gov to define the portfolio of interventional clinical research in pulmonary, critical care, and sleep medicine. METHODS: Analysis was restricted to studies registered after September 2007 through September 2010 and defined as "interventional" (n = 40,970). A specialty-specific study data set (n = 2,226) was created using disease condition terms provided by data submitters and medical subject heading terms generated by a National Library of Medicine algorithm. Trial characteristics were extracted and summarized using descriptive statistics. MEASUREMENTS AND MAIN RESULTS: Pulmonary, critical care, and sleep medicine trials composed 5.4% of all interventional studies registered over the 3-year period. In contrast, oncology and cardiovascular disease composed 21.9 and 8.4% of trials, respectively. Within pulmonary trials, asthma and chronic obstructive pulmonary disease were the most studied conditions (27.4 and 21.8% of studies, respectively), and measures of lung function or safety were the most frequent primary outcomes. Nearly two-thirds of trials indicated enrollment of 100 patients or fewer, and a majority of studies were phase II or III trials. The single largest funding source (43.5%) was industry, and study characteristics varied by funding source. CONCLUSIONS: We applied a novel approach to describe the portfolio of interventional clinical research in pulmonary medicine. Our results indicate a disparity between trial representation and the burden of respiratory disease. Resources should be targeted across the spectrum of pulmonary research to address this discrepancy.

Characteristics of oncology clinical trials: insights from a systematic analysis of ClinicalTrials.gov.

IMPORTANCE: Clinical trials are essential to cancer care, and data about the current state of research in oncology are needed to develop benchmarks and set the stage for improvement. OBJECTIVE: To perform a comprehensive analysis of the national oncology clinical research portfolio. DESIGN: All interventional clinical studies registered on ClinicalTrials.gov between October 2007 and September 2010 were identified using Medical Subject Heading terms and submitted conditions. They were reviewed to validate classification, subcategorized by cancer type, and stratified by design characteristics to facilitate comparison across cancer types and with other specialties. RESULTS: Of 40 970 interventional studies registered between October 2007 and September 2010, a total of 8942 (21.8%) focused on oncology. Compared with other specialties, oncology trials were more likely to be single arm (62.3% vs 23.8%; P < .001), open label (87.8% vs 47.3%; P < .001), and nonrandomized (63.9% vs 22.7%; P < .001). There was moderate but significant correlation between number of trials conducted by cancer type and associated incidence and mortality (Spearman rank correlation coefficient, 0.56 [P = .04] and 0.77 [P = .001], respectively). More than one-third of all oncology trials were conducted solely outside North America. CONCLUSIONS AND RELEVANCE: There are significant variations between clinical trials in oncology and other diseases, as well as among trials within oncology. The differences must be better understood to improve both the impact of cancer research on clinical practice and the use of constrained resources.

Characteristics of clinical trials registered in ClinicalTrials.gov, 2007-2010.

CONTEXT: Recent reports highlight gaps between guidelines-based treatment recommendations and evidence from clinical trials that supports those recommendations. Strengthened reporting requirements for studies registered with ClinicalTrials.gov enable a comprehensive evaluation of the national trials portfolio. OBJECTIVE: To examine fundamental characteristics of interventional clinical trials registered in the ClinicalTrials.gov database. METHODS: A data set comprising 96,346 clinical studies from ClinicalTrials.gov was downloaded on September 27, 2010, and entered into a relational database to analyze aggregate data. Interventional trials were identified and analyses were focused on 3 clinical specialties-cardiovascular, mental health, and oncology-that together encompass the largest number of disability-adjusted life-years lost in the United States. MAIN OUTCOME MEASURES: Characteristics of registered clinical trials as reported data elements in the trial registry; how those characteristics have changed over time; differences in characteristics as a function of clinical specialty; and factors associated with use of randomization, blinding, and data monitoring committees (DMCs). RESULTS: The number of registered interventional clinical trials increased from 28,881 (October 2004-September 2007) to 40,970 (October 2007-September 2010), and the number of missing data elements has generally declined. Most interventional trials registered between 2007 and 2010 were small, with 62% enrolling 100 or fewer participants. Many clinical trials were single-center (66%; 24,788/37,520) and funded by organizations other than industry or the National Institutes of Health (NIH) (47%; 17,592/37,520). Heterogeneity in the reported methods by clinical specialty; sponsor type; and the reported use of DMCs, randomization, and blinding was evident. For example, reported use of DMCs was less common in industry-sponsored vs NIH-sponsored trials (adjusted odds ratio [OR], 0.11; 95% CI, 0.09-0.14), earlier-phase vs phase 3 trials (adjusted OR, 0.83; 95% CI, 0.76-0.91), and mental health trials vs those in the other 2 specialties. In similar comparisons, randomization and blinding were less frequently reported in earlier-phase, oncology, and device trials. CONCLUSION: Clinical trials registered in ClinicalTrials.gov are dominated by small trials and contain significant heterogeneity in methodological approaches, including reported use of randomization, blinding, and DMCs.

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.

CDD: specific functional annotation with the Conserved Domain Database.

NCBI's Conserved Domain Database (CDD) is a collection of multiple sequence alignments and derived database search models, which represent protein domains conserved in molecular evolution. The collection can be accessed at http://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml, and is also part of NCBI's Entrez query and retrieval system, cross-linked to numerous other resources. CDD provides annotation of domain footprints and conserved functional sites on protein sequences. Precalculated domain annotation can be retrieved for protein sequences tracked in NCBI's Entrez system, and CDD's collection of models can be queried with novel protein sequences via the CD-Search service at http://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi. Starting with the latest version of CDD, v2.14, information from redundant and homologous domain models is summarized at a superfamily level, and domain annotation on proteins is flagged as either 'specific' (identifying molecular function with high confidence) or as 'non-specific' (identifying superfamily membership only).

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.

Co-evolutionary analysis of domains in interacting proteins reveals insights into domain-domain interactions mediating protein-protein interactions.

Recent advances in functional genomics have helped generate large-scale high-throughput protein interaction data. Such networks, though extremely valuable towards molecular level understanding of cells, do not provide any direct information about the regions (domains) in the proteins that mediate the interaction. Here, we performed co-evolutionary analysis of domains in interacting proteins in order to understand the degree of co-evolution of interacting and non-interacting domains. Using a combination of sequence and structural analysis, we analyzed protein-protein interactions in F1-ATPase, Sec23p/Sec24p, DNA-directed RNA polymerase and nuclear pore complexes, and found that interacting domain pair(s) for a given interaction exhibits higher level of co-evolution than the non-interacting domain pairs. Motivated by this finding, we developed a computational method to test the generality of the observed trend, and to predict large-scale domain-domain interactions. Given a protein-protein interaction, the proposed method predicts the domain pair(s) that is most likely to mediate the protein interaction. We applied this method on the yeast interactome to predict domain-domain interactions, and used known domain-domain interactions found in PDB crystal structures to validate our predictions. Our results show that the prediction accuracy of the proposed method is statistically significant. Comparison of our prediction results with those from two other methods reveals that only a fraction of predictions are shared by all the three methods, indicating that the proposed method can detect known interactions missed by other methods. We believe that the proposed method can be used with other methods to help identify previously unrecognized domain-domain interactions on a genome scale, and could potentially help reduce the search space for identifying interaction sites.

COCO-CL: hierarchical clustering of homology relations based on evolutionary correlations.

MOTIVATION: Determining orthology relations among genes across multiple genomes is an important problem in the post-genomic era. Identifying orthologous genes can not only help predict functional annotations for newly sequenced or poorly characterized genomes, but can also help predict new protein-protein interactions. Unfortunately, determining orthology relation through computational methods is not straightforward due to the presence of paralogs. Traditional approaches have relied on pairwise sequence comparisons to construct graphs, which were then partitioned into putative clusters of orthologous groups. These methods do not attempt to preserve the non-transitivity and hierarchic nature of the orthology relation. RESULTS: We propose a new method, COCO-CL, for hierarchical clustering of homology relations and identification of orthologous groups of genes. Unlike previous approaches, which are based on pairwise sequence comparisons, our method explores the correlation of evolutionary histories of individual genes in a more global context. COCO-CL can be used as a semi-independent method to delineate the orthology/paralogy relation for a refined set of homologous proteins obtained using a less-conservative clustering approach, or as a refiner that removes putative out-paralogs from clusters computed using a more inclusive approach. We analyze our clustering results manually, with support from literature and functional annotations. Since our orthology determination procedure does not employ a species tree to infer duplication events, it can be used in situations when the species tree is unknown or uncertain. CONTACT: jothi@mail.nih.gov, przytyck@mail.nih.gov SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.

Identification of the prokaryotic ligand-gated ion channels and their implications for the mechanisms and origins of animal Cys-loop ion channels.

BACKGROUND: Acetylcholine receptor type ligand-gated ion channels (ART-LGIC; also known as Cys-loop receptors) are a superfamily of proteins that include the receptors for major neurotransmitters such as acetylcholine, serotonin, glycine, GABA, glutamate and histamine, and for Zn2+ ions. They play a central role in fast synaptic signaling in animal nervous systems and so far have not been found outside of the Metazoa. RESULTS: Using sensitive sequence-profile searches we have identified homologs of ART-LGICs in several bacteria and a single archaeal genus, Methanosarcina. The homology between the animal receptors and the prokaryotic homologs spans the entire length of the former, including both the ligand-binding and channel-forming transmembrane domains. A sequence-structure analysis using the structure of Lymnaea stagnalis acetylcholine-binding protein and the newly detected prokaryotic versions indicates the presence of at least one aromatic residue in the ligand-binding boxes of almost all representatives of the superfamily. Investigation of the domain architectures of the bacterial forms shows that they may often show fusions with other small-molecule-binding domains, such as the periplasmic binding protein superfamily I (PBP-I), Cache and MCP-N domains. Some of the bacterial forms also occur in predicted operons with the genes of the PBP-II superfamily and the Cache domains. Analysis of phyletic patterns suggests that the ART-LGICs are currently absent in all other eukaryotic lineages except animals. Moreover, phylogenetic analysis and conserved sequence motifs also suggest that a subset of the bacterial forms is closer to the metazoan forms. CONCLUSIONS: From the information from the bacterial forms we infer that cation-pi or hydrophobic interactions with the ligand are likely to be a pervasive feature of the entire superfamily, even though the individual residues involved in the process may vary. The conservation pattern in the channel-forming transmembrane domains also suggests similar channel-gating mechanisms in the prokaryotic versions. From the distribution of charged residues in the prokaryotic M2 transmembrane segments, we expect that there will be examples of both cation and anion selectivity within the prokaryotic members. Contextual connections suggest that the prokaryotic forms may function as chemotactic receptors for low molecular weight solutes. The phyletic patterns and phylogenetic relationships suggest the possibility that the metazoan receptors emerged through an early lateral transfer from a prokaryotic source, before the divergence of extant metazoan lineages.