Open-source tools are growing in popularity within the pharmaceutical industry. Pharmaceutical companies such as Roche, Novo Nordisk, GSK, Johnson & Johnson, Novartis, Gilead, and Pfizer have all made significant contributions to open-source by providing insights into their integration of open-source technologies through shared open repositories. One of the examples are Boehringer Ingelheim’s DaVinci project and Roche’s teal tools that allow customization for specific trials, by providing modular components for data visualization and clinical insights.

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In this blog post, we’ll explore how the pharmaceutical industry is leveraging open-source tools (especially in R), the challenges companies face in adoption, and key considerations for pharma leaders. 

Why the Shift Towards Open Source in Pharma?

Pharma has traditionally relied on proprietary tools for tasks ranging from drug discovery to regulatory submissions. While these tools have served the industry well, they come with limitations such as high costs, vendor lock-in, and limited adaptability. 

Open source, on the other hand, offers several advantages:

• Open-source tools can be tailored to specific needs, allowing organizations to build custom solutions. 
• R's ability to integrate with other tools and handle diverse data formats allows it to adapt to both small and large-scale clinical studies. 
• Open-source platforms enable collaboration across companies, academic institutions, and regulatory bodies, speeding up innovation and the spread of reliable information, as we’ve seen with initiatives like Pharmaverse and R Validation Hub.
• Open-source tools often implement new statistical methods more quickly than proprietary software. This allows pharmaceutical companies to leverage the latest advancements in data science for better outcomes.
• While not the primary benefit, reducing costs on software licensing for statistical tools like SAS and SPSS can free up budgets for innovation.

Moreover, the FDA permits the use of open-source software for analyzing clinical trial data, which encourages pharmaceutical companies to explore R as a viable option for regulatory submissions. For example, there are initiatives like the R Consortium's Submission Working Group that focus on improving practices of R-based clinical trial regulatory submissions.

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Challenges of Open-Source Adoption in Pharma

While open-source tools offer significant benefits, their adoption in the pharmaceutical industry presents unique challenges that companies navigate:

1. Regulatory compliance & validation – Ensuring that open-source tools meet the stringent requirements of regulatory bodies like the FDA and EMA requires robust validation frameworks and documentation.
2. Security & risk management – Open-source solutions must align with industry security standards, incorporating encryption, access controls, and regular audits.
3. Long-term viability & maintenance – Open-source tools rely on community support, requiring organizations to establish internal expertise or partner with vendors to ensure continued development and maintenance.
4. Integration with existing systems – Many pharma companies operate legacy systems that rely on proprietary software. Transitioning to open-source requires careful planning and phased adoption.
5. Skill gaps & training needs –  Current data science teams need structured training and upskilling to effectively transition to R and Python, though more and more team members are already proficient in these languages. 
6. Change management – Team members accustomed to proprietary tools may be hesitant to adopt open-source alternatives, requiring leadership and clear communication about the benefits of adoption.

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Key Considerations for Pharma Leaders When Adopting Open-Source

Adopting open-source solutions is not just a technical shift, it requires strategic planning, skills development, and a supportive organizational culture. 

Here are some key considerations:

1. Build Skills & Knowledge Base

To ensure a smooth transition to open-source tools, leaders should assess current team skill levels in R, Python, Git, and other relevant technologies. Identifying gaps and implementing structured training sessions, hands-on workshops, and mentorship programs can facilitate adoption. Equally important is ensuring readiness for change through transparent communication about the transition plan and the long-term benefits of open-source adoption.

Example: GSK targets 50% open-source code adoption by 2025, retraining staff in R and other tools to ensure compliance and efficiency. To support this transition, GSK has invested in structured programs, ensuring teams are proficient in R and other open-source tools while maintaining compliance and reliability. Another example is Pfizer’s R Center of Excellence, where teams are supported with R expertise and training.

2. Develop a Validation Framework

Regulatory compliance is a top priority in the pharmaceutical industry. Open-source adoption requires well-defined validation procedures, including:

• Documenting testing protocols to align with FDA and EMA guidelines.
• Dual programming to validate outputs in both proprietary and open-source tools.
• Engaging regulatory agencies early to build trust and ensure alignment with expectations.

Example: Roche’s submission of R-generated outputs to the FDA serves as a strong example, demonstrating that open-source tools can meet regulatory standards. On a different angle but aligned with the same goal is Novo Nordisk’s OpenStudyBuilder, which is designed to build end-to-end consistency and more efficient processes to facilitate faster reporting and submissions. 

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3. Pilot Projects for Controlled Adoption

A strategic way to implement open-source tools is through pilot projects, focusing on specific studies with standardized designs. Oncology trials, where processes are well established, often serve as an ideal starting point. By adopting a phased approach, you can minimize disruption and ensure a controlled rollout. These initial projects allow for iterative improvements, helping teams fine-tune processes before scaling up. Monitoring the outcomes and gathering feedback during these pilot phases can also provide valuable insights, setting the stage for broader adoption with minimal risk. To ensure success, consider these steps:

• Monitor Outcomes – Compare time savings, automation impact, and error rates between open source and proprietary tools.
• Run Parallel Analyses –  For example, use SAS alongside R to validate results and build confidence in new approaches.
• Refine and Scale –Gather feedback from pilot projects to develop best practices for broader adoption.

Example: Novo Nordisk’s first R-based submission to the FDA serves as a strong example of how a controlled pilot can pave the way for broader adoption. By initially submitting selected R-generated outputs while maintaining SAS for statistical analysis, they were able to test regulatory acceptance, identify challenges, and refine their submission strategy for future trials.

4. Address Data Security & Compliance

Adopting open-source tools requires security measures tailored to the unique challenges of clinical research. This includes not only protecting sensitive patient data but also ensuring traceability, auditability, and compliance with industry regulations. Thoughtful planning and execution of security strategies can foster trust and regulatory confidence, essential for widespread adoption of open-source solutions. Key measures to consider:

• Data encryption & access controls – Safeguard sensitive patient information with strong encryption protocols and role-based access controls.
• Version control systems – Use tools like Git to track code changes, ensure auditability, and maintain transparency in analyses.
• Modern infrastructure – Implement containerized environments (e.g., Docker) to enhance reproducibility and security while adhering to established cybersecurity best practices. 

Example: The FDA’s precisionFDA platform demonstrates how open-source tools can enable secure collaboration in pharma. This cloud-based system allows researchers to upload, analyze, and share data while ensuring compliance with regulatory standards, proving that open-source solutions can meet industry security and compliance needs.

5. Foster Collaboration Internally & Externally

Effective adoption of open-source tools requires strong cross-functional collaboration among internal teams. This alignment ensures smoother integration, faster problem-solving, and more effective implementation strategies.

Beyond internal efforts, actively participating in the broader open-source community can further drive innovation and foster alignment with industry best practices. Engaging with initiatives such as PHUSE, Pharmaverse, and the R Consortium allows organizations to tap into collective expertise, contribute to shared solutions, and stay ahead of emerging trends. These communities foster collaboration across the industry, accelerating innovation, driving the development of best practices, and ensuring alignment with evolving regulatory and technological standards.

Example: Pfizer’s Open Repositories highlights how a pharmaceutical company can contribute to and leverage open-source collaborations for statistical computing and automation. 

Other examples include:

• GSK Biostatistics
• Novo Nordisk
• OpenSource Novartis
• Gilead BioStats 
• Pharmaverse

Example Case Study: Roche’s End-to-End Submission Using R

Roche’s successful end-to-end submission using R stands as an example of how open-source tools can be effectively integrated into clinical trials data management and analytics. These tools were essential in Roche’s submission of a breast cancer trial to regulatory bodies such as the FDA, EMA, and NMPA. The trial, which utilized standardized designs and analyses, was structured to meet the rigorous requirements of these agencies. To streamline the process, Roche utilized a suite of open-source tools:

• {admiral} for ADaM datasets, 
• {admiral.onco} for oncology-specific datasets, 
• {teal} for Shiny applications
• {rtables} for creating clinical tables, 
• {oak} for SDTM mapping, and 
• Roche proprietary admiral package for aligning with internal data standards. 

Roche’s infrastructure, built on the Ocean platform (an AWS-hosted system), utilized containerized environments to ensure both reproducibility and security. To maintain compliance, they adhered to internal validation processes that mirrored the guidelines set by the R Validation Hub. Dual programming with SAS provided an added layer of confidence in the accuracy of the results. Tools like {pkgdown} and {pkgload} were used to streamline documentation and package validation. The final submission package included detailed installation instructions and validation reports, ensuring alignment with regulatory expectations.

Key Insights on Open Source Adoption in Pharma

Adopting open-source solutions in the pharmaceutical industry requires a thoughtful, long-term strategy, significant internal investment, and collaboration across the sector. Pharmaceutical companies looking to leverage open-source tools should focus on:

1. Talent development: Building a team with expertise in open-source tools like R and fostering internal capabilities to drive innovation and productivity.
2. Measuring ROI for long-term sustainability: Establishing clear metrics to evaluate the financial and operational impact of open-source tools, ensuring their continued benefit over time.
3. Strategic implementation & regulatory alignment: Ensuring that tools meet both operational needs and evolving regulatory standards.
4. Active engagement in the open-source ecosystem: Contributing to and benefiting from the broader open-source community by sharing best practices, tools, and datasets while staying ahead of industry advancements.

Final Thoughts

By taking a proactive approach, pharmaceutical companies can unlock new avenues for growth, streamline operations, and gain a competitive edge in an industry marked by constant innovation and regulatory oversight. The next step involves incorporating these principles into broader strategic planning, ensuring open-source adoption aligns with both short-term goals and long-term vision. 

A structured approach includes assessing regulatory requirements, internal capabilities, and the potential for collaboration within the open-source community. This ensures that adoption doesn’t disrupt existing workflows while also fostering innovation. By leveraging open-source solutions, pharmaceutical companies can improve efficiency in data analysis, enhance reproducibility, and reduce costs in areas such as clinical trials, data management, and regulatory submissions.

The key takeaway? Open-source adoption in pharma presents both immediate benefits and long-term value, but it requires a well-thought-out strategy to achieve success.

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