Rare Disease Cures Accelerator-Data and Analytics Platform

Overview

The Problem

Over 350 million people in the world have been diagnosed with a rare disease. In the United States, a rare disease is defined as affecting fewer than 200,000 people, and there are over 10,000 classified rare diseases. Only about 600, or 10%, of rare diseases have an FDA-approved treatment available, and drug development is frequently slowed by the low numbers of patients and limited understanding of the variability and progression of each disease.

The Solution

Critical Path Institute’s Rare Disease Cures Accelerator-Data and Analytics Platform (RDCA-DAP®) is an FDA-funded initiative that provides a centralized and standardized infrastructure to support and accelerate rare disease characterization, with the goal of accelerating the development of treatments and cures for rare diseases. RDCA-DAP creates the collaborative, non-competitive space to share existing patient-level data and encourages the standardization of new data collection. RDCA-DAP accelerates the understanding of disease progression (including sources of variability to optimize the characterization of subpopulations), clinical outcome measures and biomarkers, and facilitates the development of mathematical models of disease and innovative clinical trial designs. RDCA-DAP is positioned to generate solutions to drug development bottlenecks through program-led solutions and facilitation of C-Path Rare and Orphan diseases consortia activities.

The Impact

The RDCA-DAP platform continues to expand since going live September 2021, and now contains data for 34 different rare disease areas, including Polycystic Kidney Disease, Duchenne Muscular Dystrophy, mitochondrial diseases, neurodevelopmental disorders, rare epilepsies and rare neurodegenerative disorders, including Friedreich ataxia. More data will be added and made accessible as outreach efforts continue.

Since the platform’s launch, we’ve seen engagement from 325 approved platform requests and 35 approved workspaces for external users and research.

A New Data and Analytics Platform

The Rare Disease Cures Accelerator-Data and Analytics Platform (RDCA-DAP®) is an FDA-funded initiative that provides a centralized and standardized infrastructure to support and accelerate rare disease characterization, with the goal of accelerating therapy development across rare diseases. This platform is made possible through a collaborative grant from the FDA [Critical Path Public-Private Partnerships Grant Number U18 FD005320 from the U.S. Food and Drug Administration].

RDCA-DAP promotes the sharing of existing patient-level data and encourages the standardization of new data collection. By integrating such data in a regulatory-grade format suitable for analytics, RDCA-DAP accelerates the understanding of disease progression (including sources of variability to optimize the characterization of subpopulations), clinical outcome measures and biomarkers, and facilitates the development of mathematical models of disease and innovative clinical trial designs. RDCA-DAP is positioned to generate solutions to drug development bottlenecks. As such, the utility of the patient-level data is maximized and data may be used to develop tools that will be accessible to the community in order to optimize and accelerate drug development across rare diseases.

How RDCA-DAP Works

RDCA-DAP houses integrated patient-level data from diverse sources, including clinical trials, longitudinal observational studies, patient registries and real-world data (e.g. electronic health records) across a multitude of rare diseases. Data are contributed from different organizations and companies around the world. C-Path has extensive experience in building such integrated databases for many diseases, including existing rare disease databases (in Duchenne muscular dystrophy, Huntington’s disease, Friedreich’s ataxia and polycystic kidney disease). C-Path has partnered with the National Organization for Rare Disorders (NORD) to leverage its IAMRARE® registry platform and extensive expertise to help identify data contributors and establish contacts with the contributing organizations. C-Path will negotiate data contribution and use agreements to allow patient-level data to be transferred to the RDCA-DAP, standardize and integrate the data with other contributed data, and make it available to the degree agreed to by the data contributors.

For questions or additional information about participating in RDCA-DAP, please email rdcadap@c-path.org.

If you’re a patient or a patient organization looking to start a registry, visit https://rarediseases.org/rdca-dap/.

Learn more about RDCA-DAP and stay connected by subscribing to our updates here.

Critical Path Institute is supported by the Food and Drug Administration (FDA) of the Department of Health and Human Services (HHS) and is 54% funded by the FDA/HHS, totaling $19,436,549, and 46% funded by non-government source(s), totaling $16,373,368. The contents are those of the author(s) and do not necessarily represent the official views of, nor an endorsement by, FDA/HHS or the U.S. Government.  

Diseases FAQ Icon
  • Angelman Syndrome
  • Congenital Hyperinsulinism*
  • Desmoid Tumor*
  • Duchenne Muscular Dystrophy*
  • Facioscapulohumeral muscular dystrophy (FSHD)*
  • Friedreich’s Ataxia
  • GNE Myopathy
  • hnRNP related disorders*
  • Kidney Transplant
  • KIF1A Associated Neurological Disorder*
  • Lennox-Gastaut Syndrome
  • Mitochondrial Disease
  • Necrotizing Enterocolitis*
  • Niemann-Pick Disease
  • Pemphigus & Pemphigoid*
  • Phenylketonuria (PKU)*
  • Polycystic Kidney Disease
  • Prader-Willi Syndrome*
  • Progressive Supranuclear Palsy*
  • Rare Epilepsies*
  • RYR-1 gene mutation*
  • Spinal Muscle Atrophy with Respiratory Distress*
  • Spinocerebellar ataxias type 1, 2, 3 & 6
  • Sturge-Weber Syndrome
  • Tuberous Sclerosis

*Indicates disease with datasets that are currently discoverable on the platform

Collaborators

RDCA-DAP Data Contributors

RDCA-DAP® Collaborators

Founding Partners

Critical Path Institute is supported by the Food and Drug Administration (FDA) of the Department of Health and Human Services (HHS) and is 54% funded by the FDA/HHS, totaling $19,436,549, and 46% funded by non-government source(s), totaling $16,373,368. The contents are those of the author(s) and do not necessarily represent the official views of, nor an endorsement by, FDA/HHS or the U.S. Government.  

Data Use

The contribution of data and subsequent access and use of the data by external users of the RDCA-DAP platform are governed by processes and agreements. These processes and documents are established to ensure patient privacy, data security, and respect data contributors’ conditions of use of their data on the platform. The Data Governance summarizes how the data provided by our contributors may be requested and accessed by users, how the decision on granting access is determined, and what is requested from users before accessing the data. In particular, once access to one or multiple datasets is authorized, users are required to sign the Data Use Agreement, a document defining the conditions, limitations and obligations for use of the data. RDCA-DAP strongly encourages data contributors to make their datasets available to users after review by our Data Use Committee. The five-member Data Use Committee is comprised of representatives of C-Path, NORD and other interested parties in the rare disease community, including patient advocacy, academic research and industry. As explained in the data governance, the committee examines users’ requests for access based on the scientific merit of the research project, educational reasons or public interest rationale, and votes as a majority to determine approval or rejections of the users’ applications.

Data Use Agreement FAQ Icon

Access here.

Data Governance FAQ Icon

Access here

Data Contribution Agreement, GDPR SCCs FAQ Icon

According to the General Data Protection Regulation (GDPR), contractual clauses ensuring appropriate data protection safeguards can be used as a grounds for data transfers from the EU to third countries. This includes model contract clauses – so-called standard contractual clauses (SCCs) – that have been “pre-approved” by the European Commission.

Data Use Committee

Edward Neilan, MD, PhD
Data Use Committee

Steve Roberds, PhD
Data Use Committee

James R. Rusche, PhD
Data Use Committee

Theresa V. Strong, PhD
Data Use Committee

Resources

Webinars

2023 Webinars FAQ Icon

VCP Disease(s), an Integrated Approach to Neurodegenerative Disorders

In this webinar from Critical Path Institute’s Rare Disease Cures Accelerator-Data and Analytics Platform (RDCA-DAP), speaker addresses different aspects of the generation, starting from presenting the prints of Proteus to think about heterogeneity in an unconventional manner.

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Teaching Patients and Caregivers: The Role of Education in Research and Data Sharing

This webinar will focus on the role of foundational education in research concepts, such as data literacy and the research process, in creating a culture of research participation and data sharing in the rare disease space.

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GUIDs and De-Identification Tools for Rare Diseases

This webinar discusses the need for rare diseases communities to adopt common GUIDs and why it helps maximize the use of patient data integrated in RDCA-DAP. It will provide an overview of the de-identification solutions available to Foundations and registries.

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Innovative Trial Design Approaches: Model Informed Drug Development in Rare & Neurological Diseases

Are you passionate about making a difference in the fields of neurology and rare diseases? Do you want to explore new methods that can revolutionize drug development in these critical areas? This enlightening webinar delves into the latest advancements and strategies.

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CP-RND: An Introduction to the Patient Community

C-Path’s Critical Path for Rare Neurodegenerative Diseases public-private partnership on held an introductory webinar on Wednesday, March 15. Attendees of this webinar were introduced to the CP-RND strategic plan, its stake holder groups and deliverables, the envisioned impact of CP-RND.

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Clinical Outcome Assessments: Does one size fit all?

This will discuss the complexity of identifying appropriate clinical outcome assessments (COAs) for rare disease clinical trials, provide an overview of the Rare Disease Clinical Outcome Assessment Consortium, and highlight the synergy with RDCA-DAP and data aggregation.

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2022 Webinars FAQ Icon

Shared Stewardship in Collaborative Curation of Rare Disease Datasets

Patient data is a key asset in understanding the progression of rare diseases and may it be provided in a variety of forms from a multitude of sources. The Rare Disease Cures Accelerator-Data and Analytics Platform serves as a catalyst for gathering and organizing this information. This requires coordination and diligence to ensure data integrity, security, and privacy are not compromised in any part of the process. In this talk, we will discuss some of the best practices and collaborative approaches utilized by NORD and C-Path. From collection through curation, we will demonstrate the importance of data stewardship and its consistency throughout.  View Now 

The Role of Integrated Datasets in Addressing Rare Diseases Research Challenges

Gaining knowledge on rare diseases is limited by the scarcity and dispersity of patient data. Sequencing technologies have greatly contributed to elucidating the genetic components of these conditions, but a more comprehensive molecular picture remains to be uncovered. In this talk, we will discuss how digitalization can foster data collaboration and integrative analysis of multi-omics data across various diseases. We will show how to characterize rare disorders and build disease predictive models guiding the diagnosis and treatment of yet unknown conditions.

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Addressing the Gaps in Clinical Trial Readiness for FSHD

With increasing interest by biopharmaceuticals in Facioscapulohumeral muscular dystrophy, the FSHD Society is leveraging existing clinical data to help chart the natural history of the disease, promote the identification of suitable clinical endpoints and enhance patient stratification for upcoming trials. In this webinar, we will discuss the perceived gaps in clinical trial readiness and the various efforts the FSHD Society is taking to help accelerate clinical development.

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Using Ontologies to Standardize Rare Disease Data Collection presented by Monarch Initiative

This webinar will provide an overview of biomedical ontologies and demonstrate how they can be used for standardizing and integrating data and downstream analyses. In addition, we will discuss why you should contribute to ontology development efforts and how to do it.

The Monarch Initiative is an integrative data and analytic platform connecting phenotypes to genotypes across species, bridging basic and applied research with semantics-based analysis.

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Generating Synthetic Longitudinal Data presented by Replica Analytics

In this webinar, Replica Analytics’ Dr. Khaled El Emam is joined by Director of Data Science Lucy Mosquera to provide a general introduction to synthetic data generation (SDG), explaining what it means, how it works and what technologies are used, as well as an overview of the use cases where synthetic data can provide value in the context of real-world data and clinical trial data in collaboration with C-Path. Given the paucity of data that exists for rare disease drug development, synthetic data may augment the limited data that does exist to support patient selection, patient phenotype characterization and clinical trial design. This presentation will also address the value of synthetic data in the context of rare disease drug development.

Replica Analytics, a pioneer in using artificial intelligence (AI) for synthetic health data generation, is part of Aetion, the leading regulatory-grade real-world evidence (RWE) technology provider. Its AI technology generates synthetic, privacy-protected copies of real-world data (RWD) that preserve the integrity and utility of source data.

View Now

2021 Webinars FAQ Icon

AI-powered Real-world Simulations for Faster and Value-based Rare Disease Drug Development

On March 16 Quinten Health CEO Billy Amzal, PhD, MBA, presented AI-powered Real-world Simulations for Faster and Value-based Rare Disease Drug Development.

This presentation showed how Quinten Health can use a data-integrative, value-based and disease-centric analytic approach to support RDCA-DAP’s mission to accelerate and de-risk trials, while maximizing the real-world value of rare disease products in development.

This approach relies on real-world disease modelling designed to simulate patient journeys in terms of both disease progression and care pathways in the real-life practice. A mix of real-world data science, Bayesian modelling, interpretable machine learning, advanced statistics and predictive analytics can be deployed to inform the various components of RDCA-DAP. For example, real-world simulations can be used to demonstrate effectiveness from efficacy, or long-term outcomes from short-term ones, in the context of trial design. Both the general approach and proven examples in rare diseases will be presented to demonstrate the current value of such an approach, and to illustrate how Quinten tools can support and inform RDCA-DAP’s mission.

RDCA-DAP Team: Jeff Barrett, PhD, FCP, Senior Vice President, RDCA-DAP Lead; Alexandre Bétourné, PhD, PharmD, Scientific Director, RDCA-DAP; Megan Cala Pane, PhD, Quantitative Scientific Director, RDCA-DAP

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Glossary

Glossary of Terms FAQ Icon

Administrator – A person who is responsible for the upkeep, configuration, and reliable operation of a data collection (registry or clinical trial database). There are varying levels of administrator roles: system administrator, data engineer, data curator.

Anonymized Data – Previously identifiable data (indirectly or individually identifiable) that have been de-identified and for which a code or other link no longer exists. An investigator has NO means for linking anonymized data back to a specific subject. (See also: de-identified data)

Assent – A process used when patients are below the age of consent for the patient to actively show willingness to participate in the research and understanding about the research to the degree they are capable.

Biomarker – A defined characteristic that is measured as an indicator of normal biological processes, pathogenic processes, or responses to an exposure or intervention, including therapeutic interventions

Clinical Outcome Assessment – A measure that describes or reflects how a patient feels, functions, or survives

Clinical Trial Simulation Tool – A computer program, based on mathematical models of disease progression built from existing data, that allows users to test different trial designs in silico to determine the more efficient trial design for a proposed trial.

Data Contributor – A data contributor (also known as data custodian) willing and able to share data with RDCA-DAP. The contributor retains ownership of the data and tells RDCA-DAP how the data may be shared or used in the RDCA-DAP platform.

Data Contribution Agreement – A legal document signed by a data custodian (also known as Data Contributor) for a specific dataset and by C-Path that defines how the data will be used within RDCA-DAP. The contributor states that the data was collected and shared ethically and C-Path agrees to keep the data secure and share the data only as agreed within the document.

Data Curation – The organization and integration of data collected from various sources. It may involve annotation, publication or presentation of the data such that the value of the data is maintained over time, and the data remains available for reuse and preservation.

Data Custodian – The person or entity that has collected data in a registry, study, clinic or other process and is legally able to share data with RDCA-DAP. The data custodian is responsible for ethical collection and sharing of data, using appropriate consent documents and ethics approvals for the study. (Also known as Data Contributor)

Data Engineer – A person who sets up and maintains the data infrastructures that support information systems and applications. Data engineers are responsible for building and maintaining pipelines that feed data to data scientists.

Data Governance – The process of creating and maintaining mechanisms for responsibly acquiring, storing, safeguarding, and using data in a way that demonstrates good stewardship.

Data Integration – Combining data from different sources and providing users with a unified view of them.

Data Lake – A system or repository of data stored in its natural/raw format, usually object blobs or files that can include structured data from relational databases, semi-structured data (CSV, logs, XML, JSON), unstructured data and binary data.

Data Silo – A data store or repository that is isolated from other data sources due to lack of access or shared standards, metadata, and formats.

Data Standard – The rules by which data are described and recorded.

Common Data Model – Common Data Models are used to integrate data that come from multiple different sources in a standardized format using a commonly defined structure and relationships between the data. An example of a common data model is the Study Data Tabulation Model.

Data Use Committee – RDCA-DAP has established a data use committee that reviews research applications from users who wish to access and use data from the platform. This committee consists of representatives from NORD, C-Path, the rare disease community and academia. The committee will review all ethical research requests that can be completed by the proposed user with available data. Aka: data standards and monitoring board, data access committee.

Database – A structured set of data held in a computer or cloud environment, especially one that is accessible in various ways. Database structures can be as simple as a spreadsheet or as complex as a complex relational or graph model.

Datamart – Subset of data extracted from all the data within RDCA-DAP to be used for a specific analysis.

De-Identified Data – Also known as: anonymized data, pseudonomyzed data: A record in which identifying information is removed so that the data cannot be traced back to an individual.

  • Under the HIPPA Privacy Rule, data are de-identified if either:
    • an experienced expert determines that the risk that certain information could be used to identify an individual is “very small” and documents and justifies the determination, or
    • the data do not include any of the 18 identifiers (of the individual or his/her relatives, household members, or employers) which could be used alone or in combination with other information to identify the subject. Note that even if these identifiers are removed, the Privacy Rule states that information will be considered identifiable if the covered entity knows that the identity of the person may still be determined.
  • Under GDPR all direct and indirect identifiers must be removed from the data.

Federated data – A virtual database or data system that aggregates data that are stored in multiple physical locations by providing a shared data model and access method.

IRB – Institutional Review Board (IRB)/Independent Ethics Committee (IEC) – An independent body constituted of medical, scientific, and nonscientific members whose responsibility it is to ensure the protection of the rights, safety, and well-being of human subjects involved in a trial or other study by, among other things, reviewing, approving, and providing continuing review of protocols and amendments, and of the methods and material to be used for obtaining and documenting informed consent of the trial participant.

Informed Consent – A process by which a participant or legal guardian voluntarily confirms his or her willingness to participate in a particular trial, after having been informed of all aspects of the trial that are relevant to the participant’s decision to take part in the clinical trial. Informed consent is usually documented by means of a written, signed, and dated informed consent form, which has been approved by an IRB/IEC.

Individually Identifiable Data – Any information that includes personal identifiers (18 HIPAA Identifiers or any subset of health information that identifies the individual or can reasonably be used to identify the individual).

Indirectly Identifiable – Data that do not include personal identifier but link the identifying information to the data through use of a code. These data are still considered identifiable by the Common Rule. To determine what data may be considered identifiable, please see de-identified.

Medical Product Development Tool – Methods, materials, or measurements used to assess the effectiveness, safety, or performance of a medical product. In a regulatory context, examples of MPDTs are clinical outcome assessments, assessments of biomarkers, and non-clinical assessment methods or models.

Metadata – Data that provides information about other data. Includes descriptive metadata, structural metadata, and administrative metadata.

Natural History Study – A study that collects information about the natural history of a disease (I.e. disease course) in the absence of an intervention, from the disease’s onset until either its resolution or the individual’s death

Ontology – A representation, formal naming and definition of the categories, properties and relations between the concepts, data and entities that substantiate one, many, or all. More simply, an ontology is a way of showing the properties of a subject area and how they are related, by defining a set of concepts and categories that represent the subject.

Study Participant/Subject – A person taking part in a study of a disease (clinical trial, registry or natural history study) who has given consent for data to be collected.

Patient-Reported Outcome Instrument – Any report of the status of a patient’s health condition that comes directly from the patient, without interpretation of the patient’s response by a clinician or anyone else

P.I. – Principal Investigator – The person who is responsible for the scientific and technical direction of the entire clinical study or other data collection.

Pseudonymized Data – Previously identifiable data (indirectly or individually identifiable) that have been de-identified and for which a code or other link still exists but is kept separately from the data. An investigator can only link pseudonymized anonymized data back to a specific subject by going back to the original source of data.

Query – A request for information from a database. Queries can be conducted in the database by selecting parameters from a pre-determined menu and specifying certain fields and values that define that query to produce tailored results.

Real-World Data – Real-world data are the data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources (for example: electronic health records, claims and billing activities, product and disease registries, patient-generated data including in home-use settings, data gathered from other sources that can inform on health status, such as mobile devices).

Registry – A registry is simply a database that collects and stores specified types of information that are usually related in some way. In the context of the therapy development pathway, a registry usually collects information about patients who have a specific disease or condition and may be referred to as a patient registry. However, other registries may seek participants who are healthy and are interested in volunteering for phase 1 clinical trials. Registries may contain information that is reported by patients, by clinicians or researchers, or a combination. The goals of registries vary as does the information being collected

Registry Platform – An existing IT platform designed to host and run a registry in a consistent way across multiple disease areas or types.

Reporter – The individual who is entering data into a registry system. For patient-reported registries this may be the individual themselves or a legally authorized representative. For clinical registries this may be a doctor, other health professional or a member of the clinical staff. (Also known as Respondent)

Respondent – The individual who is entering data into a registry system. For patient-reported registries this may be the individual themselves or a legally authorized representative. For clinical registries this may be a doctor, other health professional or a member of the clinical staff. (Also known as Reporter)

Sponsor – The organization or individual that sponsors or funds a clinical trial or study including physicians, foundations, medical institutions, voluntary groups, and pharmaceutical companies, as well as Federal agencies such as NIH, FDA, the Department of Defense, and the Department of Veterans Affairs.

Data User – A person (from academia, industry, patient group or other researcher) who wishes to access data within RDCA-DAP to answer specific research questions related to rare diseases. The data user must request access to the data of interest using a standardized research request, be approved for access and sign terms and conditions for use of the data.

Data Use Agreement – A legal document signed by the data user prior to gaining access to patient-level data in RDCA-DAP. Explaining the conditions requiring ethical use of data, protection of data, and acknowledgement of the source of the data etc.

Workshops

2019-2023 Workshops FAQ Icon

 

 

C-Path Rare & Orphan Disease Conference

 

 

 

 

 

2022 RDCA-DAP Workshop

 

 

 

 

 

 

 

2021 RDCA-DAP Workshop Agenda

 

 

 

 

 

 

 

 

 

2021 RDCA-DAP Workshop Bios

 

 

 

 

 

2021 RDCA-DAP Patient Profiles:

2020 RDCA-DAP Annual Workshop

Opening RemarksTheresa Mullin (FDA/CDER)PDF version
The Rare Disease Cures Accelerator- how the data and analytics platform accelerates rare disease drug developmentMichelle Campbell (FDA/CDER)PDF version
RDCA-DAP: Progress to date and future plansJane Larkindale (C-Path)PDF version
Using RDCA-DAP- searchability and data analyticsAmanda Borens (C-Path)PDF version
Case Study 1: Role of integrated data and advanced analytics to accelerate medical product development: A Case Study in Polycystic Kidney DiseasePravin Jadhav (OTSUKA)PDF version
Case Study 2: Integration and application of phenylketonuria patient registry data in RDCA-DAPVanessa Boulanger (NORD)PDF version
Case Study 3: RDCA-DAP drug development tool prototypes- examples of the use of integrated rare disease data to accelerate drug developmentKlaus Romero (C-Path)PDF version
Data ingestion and data governanceJane Larkindale (C-Path)PDF version
Closing RemarksBilly Dunn (FDA, CDER)

Additional 2020 Workshop In-Depth Presentations

RDCA-DAP Platform Preview

RDCA-DAP: Data Standards and Integration

Optimization of clinical trial design for rare disease through modeling: example in Duchenne’s

RDCA-DAP: Prototype 3: Polycystic Kidney Disease Modeling

RDCA-DAP: Searchability and Standardized Ontologies

Workshop Panel Presentations

Panel 1: The Platform and how it may be used

Panel 2: Sharing data with RDCA-DAP & Panel Discussion #2- Moderated discussion about data sharing

Panel 3: Panel Discussion #3- Moderated discussion about RDCA-DAP and its intersection with other data platforms

RDCA-DAP 2020 Workshop FAQs – read here

 

0RDCA-DAP Launch, September 2019  – watch here

Videos

5 Steps to Drug Development Video Series FAQ Icon

There are five steps in the drug development process, which are designed to help ensure that potential new therapies are both safe and effective. The Rare Disease Cures Accelerator-Data and Analytics Platform (RDCA-DAP) will accelerate some of these steps and therefore contribute to faster development of treatments for rare diseases, 90% of which are still without an FDA-approved treatment.

Together, C-Path and NORD have created a three-part video series, “Accelerating Rare Disease Drug Development,” which describes this process for the lay person. The first video in the series, addresses the basics of the drug development process, the second examines the challenges of developing therapies for rare diseases at each step of the drug development process and the third video highlights the ways RDCA-DAP can streamline and speed the journey to rare disease cures and treatments throughout the five stages of the drug development process.

Video 1: The Drug Development Process

Video 2: Rare Disease Challenges in Each Step of the Drug Development Process

Video 3: RDCA-DAP: Shortening the Timeline for Developing New Treatments for Rare Diseases

Data Literacy

FAQs

What is the Rare Disease Cures Accelerator-Data and Analytics Platform? FAQ Icon

The Rare Disease Cures Accelerator–Data and Analytics Platform (RDCA-DAP®) is a database containing information about many rare diseases, coupled with an analytical framework to help understand that data. RDCA-DAP helps us to understand how rare diseases progress and how to best assess patient progression to inform the effects of future therapies with the goal of accelerating drug development for rare diseases.

Why is RDCA-DAP important to the rare disease community? FAQ Icon

RDCA-DAP is a resource through which researchers and drug developers can access rare diseases data to analyze that data and develop new insights and discoveries about the diseases and how they progress. It also provides a way to develop new tools and methodologies to improve clinical trial design and empower the rare disease community. This will result in faster, more effective clinical trials and more rapid (and cheaper) development of new drugs.

Each rare disease, and even subsets of rare diseases, is different. What is the value in aggregating data from multiple rare diseases? FAQ Icon

Each rare disease is unique, and it is important to understand the progression of each one individually. However, there are many aspects of rare diseases that may be common to several disease states and learnings can be applied across diseases. For example, tools, such as biomarkers, endpoints, outcome measures, etc., developed for a given disease, might be adapted and applied to a different but related rare disease even with less supportive data at our disposal. This is of particular value in disease areas where there are very few patients and limited knowledge, and where there is no precedent in terms of what to measure and when.

What is the best way for someone with an interest in a disease area to initiate sharing data with the platform? FAQ Icon

Please contact rdcadap@c-path.org and we will guide you through the process. The RDCA-DAP team is always willing to discuss how companies or other researchers can engage with the initiative, what data you may be willing to contribute and what your data and analytics needs are.

Does this compete with existing observational (natural history) studies, clinical data collections or patient registries? FAQ Icon

Because RDCA-DAP aggregates data already collected but does not collect data, it works in tandem with these efforts, including through collaboration with patient groups collecting prospective data through NORD’s IAMRARE® platform. Working with such groups ensures the highest possible data quality in new prospective studies, the ability to integrate such data in the future, and to ensure common use of data standards. RDCA-DAP can also convert existing data from prospective studies into regulatory-ready formats and share that information back with those collecting the data.

What analytic tools are provided by the platform? FAQ Icon

If request for data access is approved, users will utilize secure and private workspaces where advanced analysis can be completed. Pre-existing statistical and analytical visualization tools are available to users within these workspaces. Also included is an R console where users are able to develop and/or import their own code and algorithms. A mini-app function enables the development of custom R Shiny apps for deeper analysis along with several other default tools. Windows or Linux virtual machines can be added for more complex use cases. The platform will also incorporate a dataset cohort builder which will enable users to explore the datasets content without accessing patient-level data for early data exploration (e.g., simple visualization of patients’ demographics distribution within a dataset). This will help the user explore the data and identify datasets of interest and request access to patient-level data (anticipated deployment of the cohort builder is forthcoming).

Who owns the data contributed to RDCA-DAP? FAQ Icon

The original custodian of the data (the person or institution that shares the data with RDCA-DAP) retains ownership. They dictate how RDCA-DAP may use the data through a legal agreement signed with the platform called a Data Contribution Agreement. When users of the platform are authorized to access the patient data, they need to sign and obey the terms and conditions of the Data Use Agreement.

Who is responsible for the development of RDCA-DAP? How is it funded? FAQ Icon

RDCA-DAP is an FDA-funded initiative that provides a centralized and standardized infrastructure to support and accelerate rare disease characterization, with the goal of accelerating therapy development across rare diseases. This platform is made possible through a collaborative grant to the Critical Path Institute (C-Path) from the U.S. Food and Drug Administration (FDA), in partnership with the National Organization for Rare Disorders (NORD) [Critical Path Public-Private Partnerships Grant Number U18FD005320].

How will the FDA be reviewing and utilizing the data coming out of RDCA-DAP? FAQ Icon

FDA will have input into the development of the platform to assure that the aggregated data and the interpretation of those data will have the most impact on innovating therapy development for rare diseases. FDA will have access to analyses of the data as will other researchers according to the governance procedures. FDA will also potentially be asked to review select data sets as evidence packages for the evaluation of new tools (e.g., quantitative models, biomarkers, clinical outcome assessment instruments, etc.)

How does this project interact with other global data aggregation initiatives such as the European Platform on Rare Disease Registration (EU-RD)? FAQ Icon

C-Path and NORD are reaching out to existing initiatives to ensure collaboration with established platforms, to avoid duplicating efforts and to promote the interoperability of data.

Additional FAQ Icon

Access two additional FAQ documents here: Accessing Data and Sharing Data.
For more FAQs specific to patients and patient organizations, click here.

FA-ICD Database

The platform for FA-ICD is hosted by the Critical Path Institute Online Data Repository (CODR) and by the Rare Disease Cures Accelerator – Data and Analytics Platform. For more information and for any questions, please email rdcadap@c-path.org.


C-Path has a decade of experience in data standards development, platform development and hosting, patient-level data privacy stewardship, data platform security, and controlled access methodology.

C-Path currently provides secure hosting for data collected from more than 100 clinical trials, over 60,000 subjects, and nine different therapeutic areas, totaling more than 200 million data points.

Important information about FA-ICD content and access FAQ Icon
  • The data platform contains, but is not limited to:
    • Demographic data
    • Friedreich’s Ataxia Rating Scale (FARS)
    • International Co-operative Ataxia Rating Scale (ICARS)
    • Activities of Daily Living Scale
    • Functional Disability Scale
    • 25-Foot Walk
    • 9-Hole Peg Test
    • Modified Fatigue Impact Scale (MFIS)
    • MOS Pain Effects Scale (PES)
    • Bladder Control Scale (BLCS)
    • Bowel Control Scale (BWCS)
    • Impact of Visual Impairment Scale (IVIS)
    • Sloane low contrast letter acuity scale. (LCLA)
    • SF-10 and SF-36
    • Vital signs
    • ECG
    • Echocardiogram
    • Genetic mutation
  • C-Path has fully anonymized all data.
  • Researchers must agree to the Terms and Conditions for Use of the FA-ICD data platform  and submit an online application form to request access to the data platform.

    The FA-ICD Steering Committee approves data access for external users.

    The Resources tab within FA-ICD contains information to help users understand and make use of the platform capabilities.

Important information about data standardization FAQ Icon
  • C-Path has normalized all data to the CDISC Study Data Tabulation Model (CDISC SDTM) to enable researchers to analyze the data in aggregate.
  • FA-ICD provides basic information on how data are structured using CDISC. Knowledge of SDTM is required for effective use of the data. Information and training about SDTM are available through the CDISC website; researchers who receive access to FA-ICD will find a link to the CDISC website on the Resources tab.
A summary of detailed concepts captured by SDTM domains contained in the FA-ICD is provided in the table below FAQ Icon
CDISC Domain Contents
CE Clinical events
CM Medications
CV LVEF, LVSF, LVMass, LVIDD, LVIDS, IVS, ejection fraction, fractional shortening, valve regurgitation, wall motion, wall thickness, LVOT, LVIT, interpretation
DD Age at death, autopsy indicator, death certificate obtained, hospital medical record obtained, cause of death
DM Age, gender, race, ethnicity, trial arm, country
DS Withdrawal, death, lost to follow up, reconsent
DU Assistive walk device indicator, type, age
EG Mean heart rate, PR, QRS duration, QT, QTc, interpretation
FA Occurrence and completion indicators, reason for missing visit
FT FARS*, 25-Foot Walk, 9-Hole Peg Test, Functional Staging for Ataxia
LB ALT, AST, creatinine, corrected leukocytes, ferritin, glucose, hemoglobin, neutrophils, neutrophils/leukocytes, platelets, nucleated erythrocytes/leukocytes, leukocytes, zinc
MH Medical history events
OE Letter eye chart, cataract surgery laterality, require correction for vision indicator
PE Physical exam
PR Scoliosis surgery, cardiac procedures
QS  Activities of Daily Living, SF-10, SF-36, PGI, BLCS, BWCS, MFIS, IVIS, PedsQL, PES
RE FEV1, FEV1/FVC, FVC, FEF25-75, percent predicted, indication, interpretation
RP Pregnancy confirmed, birth control method, pregnancy outcomes
RS International Co-operative Ataxia Rating Scale (ICARS)*
SC Level of education, living status, marital status, occupation
SS Change in ambulation status
VS Height, weight, BMI, pulse rate, DBP, SBP, heart rate

*Note: The FARS is located in the FT domain because the answers to the questions are governed by the duration/number of times a patient could perform the task. The ICARS is located in the RS domain because the answers are more subjectively answered by the clinician/technician who is observing the patient performing the task.

Task Forces

FA-ICD Task Force FAQ Icon

Friedreich’s Ataxia Integrated Clinical Database (FA-ICD)

Friedreich’s ataxia (FA) is a debilitating, life-shortening, degenerative neuromuscular disorder. It is the most common form of hereditary ataxia, affecting approximately 1 in every 50,000 people in the United States and Europe (FA is primarily found in white, Hispanic, and Southeast Asian populations; incidence is very rare in other racial groups). FA is an autosomal recessive, single gene disorder, caused by mutations in the FXN gene. Loss of balance and coordination is the most common presenting symptom typically beginning between the ages of 5 and 15 years with progression of symptoms leading to loss of ambulation and independence of all activities of daily living. Adult or late onset FA is less common, affecting <25% of diagnosed individuals, and can occur anytime during adulthood. While neurological features of the disease are fully penetrant, affecting 100% of those diagnosed, it is a multi-system disease. Two thirds of patients also develop cardiomyopathy, more than half develop severe scoliosis, and 10-20% develop diabetes. The mean age at death is 35 years due to cardiac complications in greater than 50% of individuals.

More information can be found at https://curefa.org

FA-ICD Mission

Launched in February 2018, the Friedreich’s Ataxia Integrated Clinical Database (FA-ICD) is designed to catalyze and accelerate Friedreich’s ataxia (FA) research and drug development by curating and standardizing FA clinical trial and natural history data into CDISC format and making this data publicly available to qualified researchers. These researchers can access and analyze data in aggregate, or filter and view individual de-identified patient-level data from four clinical trials and a large FA natural history study. Additional data may be available in the future.

FA-ICD Partnership

This initiative represents a collaborative partnership between the Friedreich’s Ataxia Research Alliance (FARA) and the Rare Diseases Cures Accelerator Data and Analytic platform (RDCA-DAP) of the Critical Path Institute (C-Path), with a goal of expanding the FA-ICD platform by engaging with other data contributors to secure additional datasets.

FA-ICD Content

FA-ICD Access and Data Contribution

FA-ICD catalogs completed FA clinical trials and natural history data and makes it available to qualified researchers. Access to the patient level data is by request only and subject to review and approval by the FA-ICD Steering Committee. To request access, you must agree to the Terms and Conditions for Use and submit a Request for Access application detailing how the data will be used, who will access the data and any plans for publishing work informed by the data. The Terms and Conditions can be accessed here.

FA-ICD encourages data contributions from interventional and non-interventional studies and is always willing to discuss how companies or other researchers can engage with the initiative. For more information on FA-ICD, including “Frequently Asked Questions”  and how your organization can contribute data, please contact Alexandre Betourne, abetourne@c-path.org or rdcadap@c-path.org.

FA-ICD Solutions

The FA-ICD is currently being utilized to develop nonlinear mixed effects-based models of disease progression in FA to investigate and compare available outcome measures collected in interventional and non-interventional studies. Additionally, a placebo effect model may be incorporated to quantify the magnitude, onset, and offset of the placebo response for control arm subjects included in the FA-ICD. These models are intended to provide the foundation for a downstream clinical trial simulation tool.

Mitochondrial and Inherited Metabolic Diseases Task Force FAQ Icon

Read about this recently-developed task force here.

Progressive Supranuclear Palsy Task Force FAQ Icon

Read about this recently-developed task force here.

Team

C-Path Team

Klaus Romero, MD, MS, FCP
Chief Executive Officer, Chief Science Officer, Principal Investigator, RDCA-DAP

Collin Hovinga, PharmD, MS
FCCP,
Vice President, Rare and Orphan Disease Programs

Alexandre Bétourné, PhD
PharmD,
Executive Director, RDCA-DAP

Ramona Walls, PhD
Executive Director, Data Science, DCC

Heidi Grabenstatter, PhD, MS
Scientific Director, RDCA-DAP

Laura Hopkins, MS, MLS
Associate Director, Rare and Orphan Disease Programs

Dominique Cruz, MPH
Project Manager II, RDCA-DAP

Danisha Green
Project Coordinator II, RDCA-DAP

William Roddy
Data Engineer Team Lead, DCC

Diane Corey
Data Team Manager, DCC

Vicki Theurer Crider
Sr. Project Manager, DCC

Kashawna Orme
Senior Project Coordinator, DCC

NORD Team

Edward Neilan, MD, PhD
Chief Medical and Scientific Officer

Pam Gavin, MBA
Chief Strategy Officer

Aliza Fink, DSc
Director of Research Programs

Prashant R. Goel
Vice President, Information Technology

Rebecca Aune, MPA
Director of Education Programs

Amanda Scull, MLIS
Research Project Manager

For questions or additional information about participating in RDCA-DAP, please email rdcadap@c-path.org.

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