Engaging Users to Support the Modernization of ClinicalTrials.gov

Guest post by Rebecca Williams, PharmD, MPH, acting director of ClinicalTrials.gov at the National Library of Medicine, National Institutes of Health.

ClinicalTrials.gov is the largest public clinical research registry and results database in the world – providing patients, health care providers, and researchers with information on more than 300,000 clinical studies of a wide range of diseases and conditions. More than 145,000 unique visitors use the public website daily to find and learn about clinical studies, resulting in an average of 215 million pageviews each month.

Recognizing the value of ClinicalTrials.gov to millions of users, the Board of Regents of the National Library of Medicine (NLM) described in the 2017-2027 strategic plan the importance of ensuring the long-term sustainability of this resource. NLM is committed to this goal and aims to modernize ClinicalTrials.gov to deliver a modern user experience on a flexible, extensible, scalable, and sustainable platform that will accommodate growth and enhance efficiency.

We are undertaking this effort to make ClinicalTrials.gov an even more valuable resource with a renewed commitment to engage with and serve the people who rely on it.

These users include the sponsors and investigators who submit clinical trial information for inclusion on the site through the submission portal. They also include patients, health care providers, and researchers who access listed information on ClinicalTrials.gov, whether directly or indirectly through other sites and services that use the ClinicalTrials.gov application programming interface.

Over the past several years, we have conducted testing with users and have already made some improvements in response to this feedback. With modernization, we will continue to support key functions identified by users of ClinicalTrials.gov while also seeking ways to make it an even more valuable resource.

To continue the modernization process, we are now seeking broader engagement with users to further help us determine how to evolve ClinicalTrials.gov. We are spending this summer looking inward by engaging our fellow National Institutes of Health Institutes and Centers to understand how ClinicalTrials.gov could better help in fulfilling NIH’s goals of clinical trial stewardship and transparency

This fall, we plan to expand our reach outward and are proposing to establish a working group of the NLM Board of Regents to focus on the modernization of ClinicalTrials.gov. This working group will provide a transparent forum for communicating and receiving input about efforts to enrich and modernize ClinicalTrials.gov. We want to ensure that we understand and consider changing needs while simultaneously maximizing the value of the growing amount of available information and preserving the integrity of ClinicalTrials.gov as a trusted resource.

We’ve already taken some steps to be more proactive in communicating with our users. We just launched “Hot Off the PRS!” (sign up to receive email announcements), a new informational bulletin for users of the ClinicalTrials.gov Protocol Registration and Results System (PRS). These updates provide timely announcements about new PRS features, relevant regulations (42 CFR Part 11) and policies, and information about other offerings such as the PRS Guided Tutorials (BETA), a new training resource with step-by-step instructions for submitting results information.

We’re excited about how greater user engagement will enrich and modernize ClinicalTrials.gov, improving its value for everyone throughout the clinical research lifecycle.

Please let us know what else we can do to make ClinicalTrials.gov the best it can be.

Photo of Rebecca Williams, PharmD, MPH

Rebecca Williams, PharmD, MPH, oversees the technical, scientific, policy, regulatory and outreach activities related to the operation of ClinicalTrials.gov. Her research interests relate to improving the quality of reporting of clinical research and evaluating the clinical research enterprise.

The Significance of Network Biology

Guest post by Teresa Przytycka, PhD, Senior Investigator, Computational and Systems Biology section of the Computational Biology Branch at the National Library of Medicine’s National Center for Biotechnology Information, National Institutes of Health.

The functioning of any complex system involves interactions between elements of that system. This is true at the cellular level, the macro level, and every point in between.

For example, within a cell, diverse molecules coordinate their activities and work together to carry out specific cellular functions. Cells then interact with each other to shape an organism’s development, tissue-level organization, and immune response. In turn, the organisms themselves interact to form various types of connections. In the case of people, those connections form the backbone of social systems.

These many interactions, from the microscopic to the macroscopic, can be described as networks, which comprise nodes connected via links that designate the relationships between nodes.

But how can we discover which nodes are connected? And how can we learn about the nature of those connections?

My research group and I try to answer these questions using network analysis, that is, working at the network level to uncover insights about the underlying system.

We can trace the beginnings of network analysis (also known as graph theory in mathematical circles) to Gottfried Leibniz’s geometria situs (“geometry of position”), a mathematical discipline focused on the relationship between positions and objects. The first recorded application of this new way of thinking was the famous solution to the problem known as “The Bridges of Königsberg,” published by Leonhard Euler in 1736.

Königsberg (now Kaliningrad) straddles the Pregel River. In Euler’s time, seven bridges connected the various parts of the city, including two islands in the middle of the river. The question asked was whether one could chart a walk through the city that required crossing each bridge only once and return to the start.

Euler tackled this question using what we today call network theory. If the regions of Königsberg are nodes and the bridges are links, Euler showed that, for such a walk to be possible, each node must have an even number of links. Why an even number? Because if we cannot cross the same bridge twice, then for every way into each region of the city there must be a new way out. Because that property did not hold for Königsberg, Euler concluded that no such walk could be devised.

As Euler’s argument shows, representing a complex system as a network of nodes and links can help uncover properties of the system that might have otherwise been obscured.

In biology and medicine, such network-centric approaches coincided with the emergence of high-throughput experimental techniques and advanced methods for collecting and storing diverse biomedical data. The protein interaction network for yeast became one of the first large biological networks obtained from high-throughput experiments. Analyzing that network revealed that a small fraction of proteins interacted with a disproportionately large number of other proteins. Additional research showed that these “hub” proteins are essential to the cell’s survival.

This intriguing relationship between a network property and a biological property begged for an explanation.

Our 2008 paper demonstrated that the majority of protein hubs are essential because of their involvement in complex, densely connected modules that carry out functions essential for cell survival. These results illustrate that, in addition to reporting binary relationships between individual nodes, interaction networks encode hidden higher-level organization.

In many networks, including biological and social ones, groups of nodes that interact with each other more tightly than with the rest of the network can be identified. We call these groups “modules.” In the context of biological networks, modules are often associated with groups of genes that work together to perform a specific biological function. At the same time, we’re beginning to see that complex diseases, such as cancer, are more likely caused by the dysregulation of a specific functional module than a dysfunction of an individual gene. That’s why, in recent years, cancer research has turned its attention to identifying dysregulated modules.

This effort includes several methods developed by our research group—Module Cover, Mutual Exclusivity Module Cover, and BeWith. These methods combine information from the human-interaction network with disease-specific information, such as abnormally expressed or mutated genes, to identify disease-associated modules. These modules can shed light on the mechanism of the disease, suggesting areas for further study and possible means of intervention.

We also use networks to discover how information flows between individual nodes. For example, by applying the principles of current flow within an electric circuit, we have been able to identify the causal (altered) disease genes and the pathways they dysregulate, providing another way to discover groups of genes involved in a disease.

Unfortunately, most currently available interaction networks are static depictions of a dynamically changing system. They typically do not account for tissue types, developmental stages, disease status, and other factors. As a result, these networks cannot tell us the full story.

To consider these and other factors, we need a context-specific network. But to build one, we must use context-specific data. How can we do that, given the myriad conditions we must consider? Our new method, NetREX, moves us in that direction.

Network biology has facilitated progress in many areas of biomedical science. This simple, yet powerful, concept allows us to abstract the essence of relations between genes and proteins, predict interactions between drugs, study disease comorbidity, and discover important associations. Of course, discovering an association is just the first step in uncovering a mechanism, but it is often a crucial step.

headshot of Dr. Teresa Przytycka

Teresa M. Przytycka, PhD, leads the Algorithmic Methods in Computational and Systems Biology section at the National Center for Biotechnology Information. Dr. Przytycka is particularly interested in the dynamical properties of biological systems, including spatial, temporal and contextual variations, and exploring how such variations impact gene expression, the functioning of biological pathways, and the phenotype of the organism.