Open relationship

Balancing agility with regulatory compliance

Open source programming languages present an incredible opportunity to enrich approaches to generate clinical study results and engage a larger community.

Patients could potentially receive life-saving therapies sooner, and organisations can expedite product development. However, to fully capitalise on this potential, researchers must establish guard rails for the use of open source, limiting it to an analytics environment that can be trusted.

But when you think about life-saving technology, does a statistical computing environment (SCE) come to mind? SCEs are critical in accelerating scientific discoveries by enabling researchers to manage, process, and analyse data efficiently and compliantly, maintaining the utmost regulatory integrity.

One of the most effective ways to ensure regulatory compliance is by employing an analytics framework or an SCE that can manage diverse data streams, integrate disparate sets of information, and provide auditability and traceability throughout the analytics process.

As life sciences research generates increasingly large and diverse data sets, powerful SCE’s are essential for extracting meaningful insights and advancing our understanding of biological systems.

By providing the necessary computational tools and infrastructure, SCEs empower researchers to uncover clinically relevant correlations, identify potential therapeutic targets, and drive the data-driven development of new diagnostics and treatments.

New types of data are becoming available and are integral to pharmaceutical companies. This influx of data is changing the landscape of clinical trials. Just as safety, compliance and auditability have become paramount in the era of decentralised trials, they are equally critical in the adoption of open source technology in the pharmaceutical industry.

At the same time, it is also critical to work within an environment that has a user-friendly interface and user support, which can be beneficial in a regulated environment where users may not have extensive technical backgrounds.

Trust in data quality is crucial for organisations to make informed decisions. Trust involves not only having access to data but also ensuring that data is accurate, well-curated and governed.

A controlled environment, whether on-premises or in the cloud, is essential for maintaining trust in data. While the specific use cases for open source in pharma are still evolving, several adoptions are emerging.

For instance, open source languages can be used for preparing clinical study report results, but it’s essential to embrace a truly open approach, evaluating technology based on its suitability for the objectives, rather than personal preferences. Often, a hybrid approach, integrating open source within existing frameworks, is safer than a complete overhaul, minimising the risk of failure or regulatory rejection.

By embracing open source languages within a secure framework, sponsors and study teams can leverage the benefits of open source while maintaining stringent regulatory standards.

This approach not only ensures compliance but also enhances agility, enabling pharma, biotech and clinical research teams to stay ahead of the curve, while simultaneously helping to reduce the complexity of managing multiple open source tools in disparate platforms.

It’s clear that the adoption of open source technology in the pharmaceutical industry is an inevitable evolution driven by the need for greater efficiency and innovation. Achieving a balance between agility and regulatory compliance, however, is paramount.

By carefully selecting use cases, embracing a secure hybrid approach and leveraging robust SCEs, the life sciences sector can harness the potential of open source technology to advance research faster than ever before.

Now is the time for pharma, biotech and clinical research teams to act, setting the stage for a new era of discovery and innovation.

Soundarya Palanisamy, is an Industry Advisor at SAS.
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