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Dario Lirio

FDA Compliance

Staying Ahead of ICH E6(R3) Good Clinical Practice Guidelines

Modernization is critical to enhance patient experience and boost clinical trial productivity

Published: Tuesday, May 17, 2022 - 11:03

By now, it’s no secret that good clinical practice guidelines used by FDA inspectors are expanding. These GCP guidelines are developed by the International Conference on Harmonization. The ICH last revised its GCP document, called ICH E6(R2), in 2016. It will be releasing a new version in August 2023. The FDA calls these “Guidances” once they are implemented.

The primary reason for this revision by ICH is to ensure patient safety and data integrity amid the growing volume of diverse data sources and study types, such as adaptive clinical trials, trials using master protocols, and decentralized clinical trials. Health authorities are concerned that this diversity introduces new risks. They want to ensure these risks don’t outweigh the benefits of new drugs and medical devices.

Some leaders in clinical research have already adopted the cutting-edge technology needed for clinical teams to optimize the drug development process and meet the guidelines articulated by ICH E6(R2). Others still struggle to get a comprehensive view of clinical trial risk. A modern clinical trial management system (CTMS) is pivotal to helping clinical teams make operational changes to follow the GCP guidance.

Clinical leaders using an advanced CTMS can make changes in accordance with ICH E6(R2) to modernize clinical operations with intelligent technology and approachable automation. Their leadership has positioned their organizations to prepare for the next round of FDA guidance, known as E6(R3), and to overcome obstacles such as poor data infrastructures and risk-averse clinical culture.

Clinical technology is leading the way

In a world forever altered by Covid-19, clinical leaders are now pushing for greater adoption of advancements in clinical research such as telemedicine and remote operations, and applications using artificial intelligence (AI) such as natural language processing (NLP). As clinical teams become tech leaders, they enhance clinical trial productivity, increase the amount and quality of data collected in trials, and ultimately improve the patient experience.

According to a recent ArisGlobal report, 75 percent of clinical teams rely on Microsoft Office apps for vendor oversight. While spreadsheets and other common applications suit basic needs, they aren’t built to handle clinical research processes, and due to security shortcomings, they pose major concerns to trial outcomes and compliance. So, as some technology has already been embraced by the industry, there are opportunities to select, onboard, and optimize specific clinical software to drive innovation forward.

When it comes to the high-risk management of study blinding, investigational product logistics, endpoint selection, and other clinical operations tasks, spreadsheets are usually not up to the job. In fact, they can introduce greater levels of risk and costly delays during study startup when risk planning takes place.

There’s a better way to identify and manage clinical trial risk. It’s by using a modern CTMS that includes advanced frameworks for making decisions about risk. The best frameworks are based on the guidance set down in ICH E6(R2). Using a modern CTMS with this framework built in, teams of functional leaders work with a project manager to proactively identify critical risks that may affect trial quality or participant safety. From there, they create cross-functional plans for mitigating risks.

A CTMS with this framework helps answer key questions, such as:
• What risks do we have?
• How large are the risks?
• How can we reduce risks?
• How well can we detect it if things go wrong?
• If they do, how will we respond?

Clinical teams can uncover hidden study risks with industry-standard questions, and score risk based on likelihood, impact, and detectability. The result is a more airtight risk plan that makes for a stronger clinical study report filed with regulators.

Modern clinical trial technology creates significant opportunities for industry advancement. Consider that digital transformation supports more diversity in trials, including decentralized designs and new-outcomes data types. Operational tech like CTMS improves collaboration between teams and creates more efficient clinical data processing. The case for onboarding niche clinical technology speaks for itself.

According to McKinsey, automation can bring medicines to the market 500 days faster and reduce drug safety costs up to 25 percent. Automated technology also reduces delays in reporting events and enables proactive risk management. Because fast time-to-market is essential to compete in the market and offer the best possible services, clinical leaders need to define operational success metrics and chart out a path of technology adoption.

Overcoming obstacles

The two largest and most common obstacles to implementing new technologies in clinical operations are immature data infrastructure (data security) and internal risk aversion for high-priority clinical trial processes. In other words, many organizations balk at the complexity of implementing a digital strategy, as a delay in productivity could put them at a competitive disadvantage. While investing time and resources in critical clinical tech might seem daunting or unappealing, the result pays dividends. Combine the clinical software with complementary platforms of the research and development journey, and you’ve unlocked time, cost, and productivity savings that were unimaginable just a few years ago.

With proper planning, strategy, and execution, clinical teams can continue driving digital transformation by looking at these obstacles as opportunities.

Staying ahead of digital innovation

In modern drug development, technology isn’t a choice but a fundamental strategy. After all, digital innovation can corroborate and curate accurate data insights that not only accelerate clinical trials and other R&D areas, but also drive business decisions.

Adopting technology such as a modern CTMS is only one step of bringing digital transformation to your organization. Quite simply, the best technology doesn’t do much if your team doesn’t know how to implement or use it. Use these methods to increase the adoption of clinical trial technology, and allow clinical teams to be tech leaders and continue to drive the industry forward:
• Define operational success metrics that show how automation accelerates time-to-market and reduces costs
• Upgrade in-house technology and analytics expertise
• Ensure technology pilot programs include funding for full implementation

As organizations begin to onboard automation, it’s critical to upgrade in-house technology and analytics expertise. Many companies start their analytics journey eagerly but without a clear strategy. These companies quickly become frustrated when they see their efforts falling short. To avoid this spiral, the entire clinical team should be trained on new technologies and feel confident using them.

Before piloting new technology, teams should ensure funding for full implementation and emphasize the risk that pilot projects promise to fulfill. Organizations using a pilot program should also remember to identify implementation challenges early, allowing larger-scale adoption to run more smoothly. This also allows for clear communication of technology expectations.

A transition from traditional, manual processes to technology-led initiatives doesn’t happen overnight. Nor does it happen once in a company’s history. Investing in intelligent technology and approachable automation can enhance the patient experience, boost clinical trial productivity, and streamline data collection. These improvements come together to help clinical leaders continue to accomplish their core mission—to positively affect patients’ lives through safer and more efficient treatments.


About The Author

Dario Lirio’s picture

Dario Lirio

Dario Lirio is the senior director, LifeSphere Clinical at ArisGlobal, where he applies his unique 18 years of experience in the enterprise cloud services industry working with pharmaceutical, biotech, large banking, and financial institution customers to successfully design, develop, and deploy business solutions.