Highlights
- Metadata collected from multiple sources is non-standardized and fragmented, stored in disparate locations and systems that is difficult to track and maintain. MDR provides a one-stop solution to overcome these inconsistencies and streamline clinical trial setup process from data collection to submission.
- Realizing the necessity to accelerate clinical trials, leveraging modern technologies, pharma organizations are now veering towards the creation of AI-powered metadata repository (MDR) platforms.
- A metadata repository can significantly accelerate clinical study setup by providing a single, unified, and centralized location for storing and organizing trial metadata, and by helping to streamline and standardize metadata management and processes across studies and teams.
Worldwide, pharma organizations are exploring novel avenues to reduce the overall cycle time and bring drugs faster to the patient.
However, this is far from a straightforward process, involving complex functions and systems making the entire clinical trial process difficult and cumbersome.
The need for a metadata repository
A clinical trial begins with the creation of a metadata that defines the trial and ensures that the data is collated in an organized, consistent and accurate manner. But the metadata collected from multiple sources is non-standardized and fragmented, stored in disparate locations and systems that is difficult to track and maintain. To overcome these inconsistencies, regulatory standards, industry-specific standards, and organizational custom standards are defined, such as the Clinical Data Interchange Standards Consortium (CDISC), Study Data Tabulation Model (SDTM), Analysis Data Model (ADaM), Clinical Data Acquisition Standards Harmonization (CDASH), etc. Post adhering to these standards, data is standardized and submitted to the regulatory authorities. However, since the standards are many, maintained separately, and suffer from unsynchronized versioning, their analysis is tedious and time-consuming.
A second challenge with metadata standardization is the lack of reusability and governance. Reusability of standards can significantly impact study setup time by referencing the already available information and literature. Also, in the absence of streamlined governance processes, maintaining clinical standards can be difficult as users may deviate from the standard set of steps.
Attributes for envisioning a next-gen metadata repository
Realizing the necessity to accelerate clinical trials leveraging modern technologies, pharma organizations are now veering towards the creation of AI-powered metadata repository (MDR) platforms. However, let us first understand as to how MDR accelerates speed to market in clinical trials. Essentially, MDR provides a one-stop solution to streamline the clinical trial setup process from data collection, generating submission-ready datasets, and facilitating actual submission ensuing faster study setup and increased regulatory compliance. This is enabled through a single, centralized location acting as a single source of truth for storing and accessing information about a trial. Few unique ways in which the MDR accelerates clinical study setup are listed below:
Enhanced data management and quality: A major challenge in clinical trial setup is integrating and managing data from multiple sources, such as electronic health records, wearable devices, and patient questionnaires. A metadata repository can help by providing a central location for storing and organizing data from these sources as well as defining standards. By using a metadata repository, sponsors and Contract Research Organizations (CROs) can quickly and efficiently set up their trial and ensure high-quality data availability.
Standardized data definitions and terminologies: Data definitions and terminologies need to be standardized across different study sites and teams. A metadata repository can define and store standard terms, codes, and formats that are used across the trial. By using a metadata repository to standardize data definitions and terminologies, organizations can easily ensure that data from different study sites is consistent and comparable, which can help to improve the overall quality and consistency of the data.
Standards versioning: Standards versioning helps users to track changes to the study over time, ensuring that all trial stakeholders are working with the most updated standards. This reduces errors and inconsistencies, and ensures the study is conducted according to the recent and most updated protocols and guidelines.
Automated SDTM generation: A metadata repository can streamline and automate SDTM generation by storing standard definitions and mapping rules for variables and datasets. During the data processing phase, this information can be used to generate auto SDTM datasets. This significantly reduces the time and resources required to create the submission-ready datasets and the manual errors incurred during the process.
Improved collaboration and communication: Clinical trials often involve complex networks of stakeholders, including sponsors, CROs, regulatory agencies, and study sites. A metadata repository can help facilitate collaboration and communication among these stakeholders by providing a single, centralized location for storing and accessing information about the trial.
Metadata reuse: A validated and standardized metadata can be reused in the future for quickly and accurately building a new study. This is possible due to the centralized metadata repository’s ability to allow instant search for metadata and standards from the standardized library. This not only enhanced trial quality and consistency but also provisions accelerated study setup.
Data integration and analysis: Clinical trials generate large amounts of data that need to be analyzed and interpreted to generate insights. A metadata repository ensures that the data and data definitions are consistent, reduces the errors in analysis, and provides a clear understanding of how data is transformed from source to target using data lineage, thereby making it easier to integrate and analyze the data.
Robust governance: An efficient metadata repository enables robust and flexible workflows and smart governance ensuring accurate data lineage essentially providing clear and accurate information about the origin and history of data. This can ensure the accuracy, reliability, and quality of data that can make it easier to design and execute the study. In addition, it can quickly troubleshoot any issues and address them quickly, thereby minimizing delays and disruptions in the study.
Leveraging AI: Metadata repository integrated with artificial intelligence (AI) has the potential to accelerate clinical trials by automating various aspects of the trial process, such as data analysis and electronic data capture, using the latest techniques and technologies including predictive analytics and Natural Language Processing (NLP). The direct impact of automation results in reduced time and effort required to complete the tasks manually.
Conclusion
Overall, a metadata repository can significantly accelerate clinical study setup by providing a single, unified, and centralized location for storing and organizing trial metadata, and by helping to streamline and standardize metadata management and processes across studies and teams. This can help to improve the efficiency and effectiveness of the clinical study setup process and can ultimately lead to more successful and impactful clinical trials.
