Quality by Design (QbD) is now a foundational element in our industry, and it’s the premise of Risk-Based Quality Management methodologies.  This presentation will cover the main aspects of QbD, and then explore the possibilities of 1) monitoring for study quality, and 2) assessing the quality of a study’s monitoring activities.   Current industry standards will be examined and recommendations for leveraging QbD in monitoring strategies will be provided.

Yes, you are in the right session! We will discuss considerations to maintain quality and oversight of clinical trials while balancing new challenges and options associated with the wide variety of decentralized clinical trial approaches now available.

As the industry adapts to new ways of working, RbQM processes are driving quality in our clinical trials.  In evaluating your business’ direction forward, reviewing the infrastructure and defining new responsibilities and accountabilities is key to ensuring efficient ways of working and reducing potential duplication of efforts in cross functional teams. In this session we will explore the different functions that have key accountabilities and offer insight into division of tasks, proper change management and effective implementation plans.

Implementing RBQM principles into existing clinical trial processes has proven to be challenging in supporting complex study designs. In addition, there is an impetus for the industry to explore whether RBQM can be leveraged in addressing the lack of clinical trial diversity. Can this be accomplished via industry-wide Key Risk Indicators?

While RBM originally was focused on identifying site- and study-level risks, expanding our risk analyses beyond trial level gave us the opportunity to identify broader systemic issues that may be caused by external and internal processes. Conducting a proper root cause analysis and ensuring feedback loops have led to process improvement and educational efforts. During the session, I will share examples of how we at Janssen - JnJ are using beyond trial level risk analyses to support process improvement and educational efforts.

The Quality Management System provides a framework for defining and delivering quality outcomes across R&D.  Hallmarks of an effective QMS are enhanced patient safety, assurance of data integrity, and minimized delays in trials and filings. Though the overall framework for an effective QMS may vary, it must contain these three elements: 1) achieving a state of control 2) active risk management, and 3) a culture where quality is everyone’s responsibility.

As clinical trials become more complex, so has the technology that could identify realized risks. There is much value to be extracted and exploited from clinical and operational data repositories, utilizing analytics with incredibly nuanced capabilities. This presentation will touch on certain elements of monitoring where technology can be a help or a hindrance, to identifying realized risks and communicating their impact to protocol teams. Is simple better?

ICH E6 and E8 require sponsors to focus their oversight activities on the aspects of studies which are critical for ensuring study integrity and protecting patient safety. This requirement results in prioritisation of data and processes ensuring clinical and  scientific integrity of the study and makes them the primary area of focus for multiple parties involved in study oversight and delivery, including centralized monitoring and medical monitoring. In this talk I’ll discuss the challenges and opportunities resulting from looking at the same set of data and study activities from different perspectives but with risk-based quality management in mind.

Sponsors review monitoring trip reports as part of effective oversight, but the process can be very manual. Cerevel worked with two vendors to design and develop an Artificial Intelligence tool to scan reports for key data points, flag reports for review based on business criteria, and built a user interface that allows for quick insight into important report content. 

It is well recognized that errors in trial execution lead to audit, inspection findings. However, can a trial be done so badly that it produces a totally false answer to the scientific question?In the exercise we will try to model such situation.  The participants will be conducting a simple trial checking if glasses improve vision of near-sighted people (true answer: they do). We will introduce errors to the trial execution and evaluate the impact on the answer to the scientific question.

We will review current practice in error detection and risked based practices. Then talk about how critical thinking is needed to move from a check box, expensive and not effective way to detect errors, to a lower cost, lower risk way to solve problems. We will address people, process and the necessary technology.