A recent study published in Clinical and Translational Science has outlined a method to develop standardised data structures based on CDISC Therapeutic Area User Guidelines, which will allow comparison of data from multiple studies. In turn, this will help facilitate a quantitative understanding of disease progression, leading to more efficient clinical trial design and, ultimately, a faster path to regulatory approval for new therapies to treat rare diseases.
Clinical Data Interchange Standards Consortium (CDISC)
In 1997 the CDISC was established to facilitate understanding of clinical data and the use of standard analytical tools to review such data. The CDISC develops controlled terminology to optimise data capture and quality. It also defines standards for structure of the data in the database. This is particularly important in clinical research and drug development for rare diseases where data is limited, due to the limited number of individuals affected, and where the wide adoption of standardised data terminology, collection, and tabulation can enable dataset comparison and accelerate regulatory review. The CDISC standards can be used to help integrate small data collections into larger, more informative datasets, which may be needed to detect variance in smaller disease populations. Overall, CDISC provides a standard terminology to harmonize the format of the data collected and its subsequent analysis.
The CDISC has developed Therapeutic Area User Guidelines (TAUGs) that describe CDISC data standards specific to therapeutic areas. TUAGs address elements that augment the core data for a specific area, which often includes the documentation of efficacy data for a clinical research study. Although the CDISC TAUGs describe how to document measurements, they do not prescribe end point or outcome measure selection. Therefore, the researchers in this study described the development of TAUGs for two rare diseases, Duchenne muscular dystrophy (DMD) and Huntington’s disease (HD), where there is significant, ongoing drug development but where standards have not yet been defined by the CDISC.
Development of TAUGs for DMD and HD
This paper addresses the key question of how to efficiently document and integrate clinical research data for DMD and HD. The researchers describe the development of the CDISC TAUG for DMD and HD and the use of these data standards in the development of databases containing patient clinical data.
The generation of TUAGs involved reviewing the National Institute of Neurological Disorders and Stroke common data elements, as well as case report forms from natural history studies and trials. This allowed identification of 10 DMD and 6 HD therapeutic elements of interest. For any areas that lacked representation or coverage within the current CDISC standards, the researchers further developed the TUAGs using dataset examples and disease specific questionnaires, ratings, scales and supplements.
The need for standardised data structures for rare diseases
The data structure developed by the researchers in this study will encourage the standardisation of data collection and will allow investigators to integrate data for rare diseases. Standardised terminology that this data structure defines for DMD and HD may also be applied or adapted to similar concepts collected in other disease areas, therefore expanding the utility of these guidelines. For example, advances in HD research, facilitated by the standardised terminology in the TAUG, may similarly inform drug development in other neurodegenerative diseases, such as Parkinson’s disease.
In addition to the benefit of standardised terminology being publicly available, clinical programs will greatly benefit from the ability to compare, contrast and aggregate data in these areas. This greatly enhances our understanding of disease natural history, and aids in the development of disease progression models and drug interaction models. This will help to optimize clinical trial design and accelerate regulatory review for rare diseases.
Summary
Standardised data structures allow data that has been collected to be standardised and integrated, and for further research data to be collected in a standardised format from the start. Therefore, this allows the data to be more readily integrated, compared, and for maximised utility of every datapoint. Researchers can thus gain a better understanding of such diseases and develop smaller, shorter and more informed clinical trials, resulting in faster progression in the development of new therapeutics for rare diseases. This is especially important considering the unmet need for treatments for rare diseases.
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