Companies in the life sciences industry don't routinely consider GxP compliance in the initial stages of installing statistical computing environments. 

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This results in common GxP compliance complications such as delays in drug development, regulatory challenges, and high operational costs.

In this article, we will look at how Appsilon and ERA Sciences collaborated to implement a GxP-compliant Posit* environment on AWS. In their new environment, they can develop complex and reproducible statistical models for pharmacometric studies in clinical pharmacology, and it is set up to reduce overall validation workload when submitting to regulatory bodies like the FDA and EU.

*Please note that Posit is the rebranded name of RStudio. Although the installation was technically RStudio, we will refer to it as Posit in the article.

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Challenges with Traditional Statistical Software Implementation

When installing statistical computing environments, it is very common for IT teams to focus on the technical deployment, and secondarily consider GxP compliance for the data scientists and delivery teams.

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Historically, the environments were often implemented on local hardware, such as laptops or on-premise servers, with nominal GxP validation. This approach presents significant challenges:

• Compliance Risks: Non-compliant installations make it difficult to meet regulatory requirements, particularly for submissions under the FDA's 21 CFR Part 11 and the EU Annex 11.
• Delays and High Costs: Retrospective validation to address compliance gaps is time-consuming, resource intensive, costly, and prone to errors.
• Operational Inefficiency: Fear of disrupting existing models leads to reluctance in updating software and infrastructure, resulting in outdated systems and technical debt.
• Accuracy Concerns: Lack of validation across the software stack raises questions about the reliability of statistical results, which can undermine the credibility of regulatory submissions.

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A typical traditional stack and related associated issues:

1. Hardware: Local machines and servers can limit scalability and cause redundancies.
2. Operating System: Vendor-provided OS is often installed to meet minimum software requirements rather than planning for compliance, scaling, and future-proofing.
3. Statistical Software: Often installed without GxP qualification.
4. Statistical Models: Building on non-GxP validated platforms increases risks around accuracy and reproducibility.

Moving to the Cloud: Why and Considerations

Cloud-based solutions are efficient and highly scalable compared to traditional implementations.

Cloud-based features enhance computing power, which makes it possible to run complex models more efficiently, while the centralized access allows distributed teams to collaborate across a unified environment. Additionally, built-in redundancy ensures reliability and continuous availability, addressing challenges related to hardware failures or downtime that are common in on-premise systems.

These features also accelerate development cycles and submission processes to reduce time-to-market over traditional installations.

Challenges and Mitigation Strategies

While cloud environments offer significant advantages, they also introduce complexities:

• Frequent Updates: Cloud platforms are updated regularly, requiring ongoing validation to ensure continued compliance and functionality. This challenge can be mitigated by employing Infrastructure as Code (IaC) and coordinating the data science team with the platform team.
• Abstracted Infrastructure: Infrastructure in cloud deployments is more abstracted than using an installer on a local computer. This is mitigated via thoughtful and deliberate configuration. 
• Compliance Across the Stack: Ensuring compliance across all layers of the stack requires a coordinated approach. To adhere to GxP standards, robust validation protocols and careful execution are necessary.

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Case Study: Implementing a GxP-Compliant Posit Environment on AWS

ERA Sciences is a mid-size life sciences company that required a validated and GxP-compliant Posit environment to support pharmacometric modeling.

The main objective was to provide a fully qualified Posit platform on AWS to enhance reproducibility and compliance for clinical modeling and more streamlined GxP validation processes and documentation. 

The secondary objective was to implement IaC, as it tracks stack versions and configurations accurately to enable future updates efficiently. This is based on the inevitability of infrastructure stack changes over time.

Appsilon and ERA Sciences used the AWS cloud to address the client’s challenges. The new solution stack was made up of:

• Cloud Infrastructure: Deployed and managed on AWS using Infrastructure as Code (IaC), ensuring precise control and configuration.
• Operating System: Delivered and maintained through AWS’s secure and compliant environment.
• Posit Statistical Software Environment: Installed on an AWS instance.
• Statistical Models: Accurate and reproducible, with controls and safety mechanisms for storage.

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Steps to Implementation

Implementing a GxP-compliant Posit environment involves a structured approach to ensure compliance, reliability, and efficiency. Each step plays a role in laying the foundation for a robust and validated system, tailored to meet both technical and regulatory regulatory requirements and meet future changes.

Below are the critical steps outlined for planning and executing the AWS implementation for ERA Sciences:

1. Platform Categorization: The platform was labeled as GAMP Category 1 to ensure alignment with GxP guidelines.
2. Validation Planning: A comprehensive validation plan was defined to outline platform requirements and validation steps.
3. Infrastructure Deployment: AWS infrastructure was configured using IaC to ensure consistency and traceability.
4. Installation: The Posit environment was installed on a Kubernetes cluster within AWS, following controlled installation procedures.
5. Operational Qualification (OQ): Testing was conducted to confirm that the platform met all functional and compliance requirements.
6. Release to Developers: The qualified Posit environment was made available to the pharmacometric team for model development.

Results and Outcomes

The results of implementing the GxP-compliant environment were a success for ERA Sciences.

By separating platform qualification from model validation, the team established a scalable approach to compliance. The enablement project was completed efficiently within three to five months, significantly reducing the overall validation workload. With a fully qualified platform in place, the clinical team could shift their focus to validating statistical models at the appropriate time, enabling precise and reproducible results to meet regulatory standards. Additionally, the use of IaC streamlined updates and reduced long-term maintenance efforts, creating a more agile and future-proof system.

Conclusion

Moving from traditional on-premise environments to cloud-based solutions like AWS offers life sciences companies a transformative approach to managing statistical computing environments. With a GxP-compliant Posit platform, companies can lower validation workload, accelerate development cycles, and ensure compliance with regulatory standards.

Appsilon’s expertise in implementing and qualifying cloud-based statistical environments positions us as a valuable partner for life sciences organizations aiming to enhance their modeling capabilities. Contact us today to learn how we can help deploy a GxP-compliant environment tailored to your needs.

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