In the pharmaceutical industry, GxP, or Good “x” Practices (e.g., x = clinical, laboratory, manufacturing), govern the safety, integrity, and reproducibility of clinical data. As such, they place tight restrictions on clinical data access and system modifications.
Pharmaceutical developers often work within the boundaries of existing infrastructure for clinical trial analytics, limiting the ability to integrate new systems or access certain types of data. The high validation requirements inherent to GxP further complicate the introduction of new systems. This often means attempting to innovate using existing tools, while also ensuring compliance.
Developing and managing applications in this environment comes with a high validation burden and significant constraints. So how can developers manage these limitations without giving up flexibility and scalability? Let’s dive in.
The Challenges of Copying and Pasting in Clinical Trial Analytics
Imagine you’re a biometrics lead managing multiple studies. You have a successful Shiny application developed for Study A, and you want to adapt it for Study B. The most straightforward approach might seem to be copying and pasting the app from Study A, then adjusting the necessary details. However, this method is fraught with inefficiencies and potential errors.
Perhaps, after copying an app for Study B, you discover a bug. Not only will the bug need to be fixed in Study B, but the fix would also need to be copied back into the original Study A app and other apps created for Studies C, D, and beyond. The more studies you add, the more difficult managing changes and ensuring consistency across applications becomes. This quickly becomes a maintenance nightmare, increasing the risk of discrepancies in compliance and validation.
Generalizing Applications for Clinical Trial Analytics: A Modular Approach
The solution? A modular, reusable framework — a “parent” application packaged with reusable components that “child” apps can depend on. This modular system allows you to make global updates in the parent app, automatically propagating those changes across all child apps. Furthermore, the child apps inherit the parent’s core functionality, ensuring consistency and reducing the risk of errors.
Each child app can also be configured slightly differently, using the parent package’s modules. The value of versioning within this framework is clear — different child apps can rely on different versions of the parent app. This is particularly useful in regulated environments, such as GxP, where validation of every individual app is costly. Instead, you validate the parent app, ensuring compliance for all child applications.
This method also supports:
Optimizing Data Processing Outside the App
To maximize performance and reduce complexity, separating data processing from the app itself is important. By scheduling scripts to handle data wrangling outside the app, you can ensure the app remains lean and efficient. This method aligns with the Posit Connect scheduling feature, allowing data processing to occur in line with updates.
Additionally, by caching data in access-controlled directories, apps can eliminate lengthy startup times, enhancing performance while ensuring data security and compliance with GxP’s strict requirements for controlled data access.
Flexible Configuration With YAML Files
No two studies are identical, meaning apps need to be flexible enough to operate different configurations. To handle this, we recommend using YAML files. These files allow you to specify study-specific variables without hardcoding them into each app. For example, one study might use “USUBJID” as a subject identifier, while another uses “PatientID.” By relying on the YAML configuration, the child app can adjust automatically to different data formats while maintaining consistent functionality.
Version Control and Validation
Versioning plays a critical role in maintaining compliance and ensuring consistent performance across apps. The Atorus team developed a package called {matte} that exemplifies this modular approach. By versioning the parent app, different child apps can operate on different versions, ensuring they remain validated and up to date. This methodology streamlines the validation process, allowing teams to focus their efforts on the parent package rather than revalidating each child app separately.
The Benefits of Generalized Apps for Biostatistics in Clinical Research
Generalized applications offer several key advantages for teams working within GxP constraints:
- Efficiency — Eliminating redundant copy and pasting reduces the time and effort required to maintain apps; updates are made centrally and automatically propagate across all child apps
- Flexibility —By using YAML configuration files, apps can be customized for different studies without altering the core functionality
- Validation — Centralizing validation efforts around the parent app significantly reduces the cost and complexity of maintaining compliance across multiple apps
- Scalability —The modular approach allows for scalable application development, as new studies can easily be added without duplicating effort
Streamlining Clinical Trial Analytics With Modular, GxP-Compliant Apps
Building reusable applications that meet GxP requirements is challenging, but our modular approach offers a practical solution. By developing a parent-child app architecture that leverages file systems, YAML configuration files, and robust version control, you can build flexible, scalable applications that are compliant with regulatory standards. This strategy not only improves efficiency but also streamlines validation, making it a valuable tool for pharmaceutical software development.
If you’re managing multiple studies and seeking a way to improve your app development process within GxP constraints, exploring modular architectures could significantly enhance both your efficiency and compliance.
Want to take your clinical trial analytics even further? Get in touch with Atorus today.