By Deborah Borfitz 

October 25, 2021 | The Critical Path Institute (C-Path) recently debuted a first-of-its-kind Rare Disease Cures Accelerator-Data and Analytics Platform (RDCA-DAP) to encourage data-sharing and collaboration among investigators to accelerate treatment innovation for rare diseases. The ultimate value of the platform will be as “an ambassador for getting drugs approved quicker,” according to Jeff Barrett, Ph.D., F.C.P., C-Path senior vice president and RDCA-DAP lead. 

Since RDCA-DAP is an initiative funded by the U.S. Food and Drug Administration (FDA), critical stakeholders—including academic, clinical, regulatory, and patient communities—can be assured it houses highly curated, regulatory-grade data that can be used to fill in some of the knowledge gaps.  The expectation is that researchers will utilize the platform to integrate patient-level data from multiple sources, including clinical trials and patient registries, and apply it to a secondary scientific question that couldn’t be answered by any one data source on its own, says Amanda Borens, M.S., executive director of data science at C-Path.

The platform hosts, standardizes, and makes accessible rare disease data as part of its functionality. Since the public premiere of RDCA-DAP on Sept. 14, attracting 900 registrants to a day-long online workshop, C-Path has already approved nearly 40 new platform users, Borens reports. The event focused on the overall launch of the platform, including the user interface.

RDCA-DAP is a disease-agnostic platform, intended to provide an actionable solution for data integration across rare diseases, including those data integrated by C-Path consortia that focus on specific diseases, she says. That’s important, given that more than 7,000 rare diseases are known to exist and many of them have not had any formal clinical trials. Data on the disease in question might be combined with data on other rare diseases to, for example, find a medication to treat a common symptom. 

Data Provenance

For end users, C-Path endeavors to make the data access request process as seamless as possible, says Borens. Investigators can request access to multiple datasets, with the interface facilitating the process to satisfy specific provisions across data use agreements. 

One key goal is to meet the needs of researchers with a single request and avoid unwelcome surprises, such as desired variables being unpopulated in a dataset, by doing the data quality management work behind the scenes, she adds. This added value lifts the burden on users with the often-Herculean task of combining raw data that may be in various formats and structures with varying levels of provenance. 

More than 18 organizations have signed a data contribution agreement to share data with RDCA-DAP and over 10 organizations have each signed a memorandum of understanding to collaborate in some way, says Borens, who directs data cleaning and curation efforts. 

The National Organization for Rare Disorders (NORD), C-Path’s partner on the initiative, has worked with data custodians to contribute datasets from six different disease states with surveys housed in NORD’s IAMRARE registry platform, to be integrated in the platform. NORD continues to work on contributing data in additional disease areas, she reports.

Metadata for 54 datasets are now being shared through a catalogue that RDCA-DAP maintains, or are in the queue to be added, Borens says, including existing disease datasets from C-Path’s rare disease consortia. Data from many more conditions are at an intermediate stage of the contribution process. 

In keeping with best practices for regulatory submissions, C-Path has “full data provenance” so everything done to the data is transparently tracked. “Everything that we do is in compliance with the best practices for regulatory submissions,” Borens stresses. 

Real-world data has come mostly from patient registries. This fall, C-Path plans to publish its “lessons learned” in collecting patient registry data for maximum regulatory impact, says Borens. Patient advocacy groups are generally motivated to share data by the possibility of helping themself, their child, or someone in the future with a new therapeutic, so they would be receptive to the perspectives of industry partners developing those drugs.

Only about 10% of rare diseases currently have an FDA-approved treatment available, says Barrett. To that end, RDCA-DAP seeks to enable “ecosystem-driven output” by breaking down the data silos that fragment the knowledge base and empowering stakeholders to be “part of the solution.”

Customized Workflow 

The platform vendor for RDCA-DAP is Aridhia Informatics (Scotland), which has been engaged in several high-profile projects, says Borens. Among them is AMYPAD (amyloid imaging to prevent Alzheimer’s disease), an ongoing study funded by the Innovative Medicines Initiative.  

Aridhia has a long history of working with medical data as well as experience with public-private partnerships and understands healthcare-related privacy and security concerns, she continues. The company’s data-sharing services and virtual desktop provides RDCA-DAP with robust base functionality.  

Collaborative development work with Aridhia has been focused on customized workflow for the curation, mapping, and aggregation activities of C-Path, says Borens. The resulting tools and tracking mechanisms have been critical to enabling C-Path to have full data provenance. 

This will allow researchers to develop advanced models to quantitatively describe relevant aspects of disease progression within and across diseases, capturing relevant sources of variability, she says. The underlying data provenance will facilitate the submission of such models for potential regulatory review. And once completed, the models can be hosted in the platform, and researchers will be able to run simulations based on the models, for the purposes of informing and optimizing clinical trial design. 

Pre-Competitive Use Case 

At the virtual workshop, RDCA-DAP use cases were presented for Duchenne muscular dystrophy and Friedreich’s ataxia. In both cases, data from multiple studies were aggregated to maximize their utility in answering research questions beyond the primary analyses of each individual dataset, Borens points out. 

The Friedreich's ataxia example is a natural history study centered on the endpoints used in clinical trials—neurological exams, functional disability staging, patient-reported outcomes, and direct functional measures. Over the past two decades, it has grown into a multinational program for understanding the impact of new treatments as they become approved.

Learnings from the pre-competitive collaborative effort could be passed on to FDA reviewers at key timepoints as the aggregated data is being explored, says Michelle Campbell, Ph.D., who leads stakeholder engagement and clinical outcomes for the Office of Neuroscience in the FDA’s Center for Drug Evaluation and Research (CDER). The agency is as interested as anyone in what the aggregated data has to say, and the learnings from this data could inform the advice it gives study sponsors about the outcomes they should be assessing. 

RDCA-DAP will increase its “applicability and availability” for different end users, including the FDA, over time, Campbell says. The agency intends to leverage the data for individual drug development programs in the future.

Megan Cala, Ph.D., with C-Path’s Quantitative Medicine Program, says the goal is to “quantitatively understand the natural history and clinical endpoints well enough to run trials at different stages of [Friedreich's ataxia],” as has been done with Duchenne muscular dystrophy. As the ongoing natural history study has revealed, certain endpoints only work in certain phases of the disease. 

C-Path has worked with several rare disease consortia in recent years and those partnerships provide additional “proven examples” of how clinical data sharing and standardization support the development of modeling tools, she says. These include biomarkers and clinical outcome assessments that have been endorsed by the FDA and European Medicines Agency. 

As an example, Cala cites C-Path’s Polycystic Kidney Disease Outcomes Consortium that led the integration of patient-level data from multiple data sources to generate the quantitative evidence that supported the qualification of total kidney volume (TKV) as an imaging biomarker to optimize patient selection for trials, and the eventual designation of TKV as a reasonably likely surrogate endpoint for clinical trials in this disease. 

Experience across disease areas has been leveraged to build RDCA-DAP into a platform that can be used by researchers regardless of their preferred statistical analysis tool, Borens says. Those that aren’t available directly in the platform can be developed independently by users or in collaboration with C-Path experts. 

Data Use Agreements 

Data contributors vary widely in their data-sharing policies, says Borens, the least restrictive being confirmation that the end user is an actual person with a research purpose. Sometimes, contributing organizations require the university or industry email address of the data requester. More restrictive policies might involve submission of the statistical analysis plan to ensure the researcher is “asking appropriate questions of the data.” Working out the particulars and remaining flexible with each data contributor is vital to the initiative, she adds. 

To keep data use agreements as simple as possible, says Barrett, C-Path and Aridhia have made substantial investments in the development of customized workflows that allow data owners to manage the stewardship process. Properly vetted individuals can build accounts and essentially go “shopping” in the metadata catalogue. 

Protection of patient privacy is a key principle that RDCA-DAP upholds, he adds. “We adhere to the highest level of compliance in that regard.” Data contributors have the ultimate say about how their data is used and can rescind a decision to share. 

Had RDCA-DAP been an available resource back when he was working at Sanofi Pharmaceuticals (as vice president of translational informatics), Barrett says, it would have been one of the first places he would have looked for data. Many companies generate registries of their own and eventually share them more broadly with organizations like C-Path, but data-sharing is not yet a ubiquitous practice. “Creating a culture of sharing is a key milestone for this entire ecosystem and… hopefully this platform will be spurring people to do it more [often]. 

Part of the opportunity is providing data owners with the resources to share their datasets “without any strings attached,” says Barrett. “We have the ability to maintain complex heuristics, so if a company, for instance, wants to provide data but release it with certain timing in mind, we can adhere to that. That is something that has to be made clear to us when the data are first ingested.” 

The data science team at C-Path has focused their efforts on making the data request process user-friendly, notes Barrett. Researchers can see summary data about clinical studies in the metadata catalogue, including how often certain variables are answered, and pull histograms to look at the distribution of trial participants. “When you get to the point of requesting data, you know those data are going to be helpful to your question.” 

Expansion Plans

In his closing remarks at the online workshop, Barrett reported that C-Path will continue to enhance RDCA-DAP and develop user-customized dashboards as well as take in more data from the rare disease community. More tools will also be developed that touch on commonalities across disease areas, and C-Path will be forging expanded global partnerships. 

The list of technology partners will also enlarge to expand the platform’s functionality. A memorandum of understanding is already in place with Clinerion. 

The “positive feedback loop” from research conducted using RDCA-DAP will undoubtedly serve the “larger scale needs of rare disease drug development,” Billy Dunn, director of CDER’s Office of Neuroscience, commented during closing remarks of the RDCA-DAP workshop. Specifically, the platform may have opportunity to improve how rare disease patient populations are defined as well as the powering of clinical trials and selection of clinical and biomarker-based outcomes. 

“This is a non-exclusive but definitive repository for data generation in rare diseases,” notes Dunn, with 32,000 patients’ worth of data as a starting point. “Every patient is a critically valuable resource,” referencing the ability of those who don’t qualify for a clinical trial to nonetheless contribute data in some way. 

Sustainability Plans

“Beyond [grant] funding to answer a specific scientific question for a particular disease, we are working on identifying opportunities to support the development and maintenance of the platform’s technology and infrastructure,” says Borens. 

Optimizing the bandwidth and resources remains a key component of RDCA-DAP’s strategy, in keeping with the goal of making the data as widely accessible as possible. “RDCA-DAP is positioned to blaze the trail for a new paradigm of sustainability.”

“The entire ecosystem is going to have to weigh in on,” adds Barrett, speaking to both the potential of RDCA-DAP to expedite drug approvals as well as how to sustain the platform over the long term.