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Risk-Based Quality Management – It’s now a Question of “how” Rather than “if”

By February 25, 2020March 9th, 2022No Comments

risk-based quality management

Risk-based quality management (RBQM) solutions allow sponsors and CROs to confidently detect, and effectively manage, risk in clinical trials. It is an evidence-backed approach with regulatory backing, meaning wide-scale adoption is becoming an increasing inevitability.

But how do organisations go about embedding this completely new way of working into their processes? Laura Trotta, R&D Manager at CluePoints, says technology is the answer, and shares her expert advice harnessing the power of Central Statistical Monitoring (CSM) to drive RBQM.

Why risk-based quality management?

At the turn of the century, drug makers found themselves faced with growing pressure from multiple directions. Highly publicized safety issues with marketed drugs, a slowing of innovation coupled with patent expirations, and a continual increase in the complexity of clinical trial designs had seen the cost and duration of clinical development steadily increase. At the same time, profit margins dwindled.

This is clearly evidenced in research published by Tufts Center for the Study of Drug Development (CSDD), which showed dramatic increase in the size and complexity of studies from 2005 to 2015. This included a 68% increase in the median number of procedures prescribed per patient, an 88% increase in the overall volume of patient data collected, and a doubling in the number of countries participating in each study.

The result is a significant uptick in the risk to the operational success of research in terms of recruiting and retaining patients, in generating the reliable results needed to support marketing approvals, and, crucially, protecting the quality of a rapidly expanding dataset.

This was born out in a review of marketing submissions to the FDA between 2000 and 2012. It found that one-third (32%) of all first-cycle review failures – or 16% of submissions overall – were driven by quality issues.

The traditional way of conducting trials, it seemed, was not fit for the 21st century.

Data quality: An indicator

There is no denying that data quality is the cornerstone of a successful clinical trial.

Data quality issues may be linked to problems trial conduct, which could have safety implications for participants. Importantly, they can also have a detrimental effect on the drug or device appraisal process, delaying or even blocking approval.

The on-site monitoring of clinical centers is one way to ensure studies reliably assess the safety and efficacy of experimental treatments. Traditionally, this has been achieved through source-data verification (SDV), or comparing the data collected in case report forms to those in the patients’ medical records.

But it is a time-consuming task, taking more than half the time of monitoring visits, and it imposes a high burden of work on site personnel. It is a problem only set to worsen in coming years, as the volume of data collected inevitably increases with the emergence of wearable technologies for continuous patient monitoring.

More time- and cost-effective ways of conducting trials and checking data quality – such as RBQM – have been proposed.

What is risk-based quality management?

Risk-based quality management (RBQM) takes the principles of risk-based monitoring and applies them to the entirety of a clinical trial. It uses technology, analytics, and statistics to detect data quality issues in real time. This enables sponsors and CROs to take corrective action before problems are able to impact on the integrity of a clinical trial.

It boosts both participant safety and the likelihood of trial success, and has been backed by the US Food and Drug Administration (FDA), the European Medicines Agency (EMA), and the International Committee on Harmonization (ICH).

RBQM can be a catalyst to risk management. By helping researchers to identify and focus on data that really matter, tech-led RBQM solutions provide the ultimate safety net, both from a patient safety and ultimate success perspective.

But while the “why” of its adoption has been well documented, the “how” has been less widely discussed.

How to implement RBQM

RBQM management applies the principles of Quality by Design (QbD) and RBM to all elements of a study, from planning right through to execution. It underpins the overall quality of the trial by identifying, controlling, and communicating risk.

ICH E6 R2 sets out what a gold standard risk-based quality management system would cover:

  • Critical process and data identification
  • Risk identification
  • Risk evaluation
  • Risk control
  • Risk communication
  • Risk review
  • Risk reporting

Within the RBQM paradigm, QbD and RBM are two phases of the same framework. Both are focused on improving the operational success of clinical research, and both apply the core process of risk assessment and risk mitigation.

QbD becomes RBM once a study protocol is finalized, at which point risk assessment is repeated with the goal of mitigating any remaining operational risks. Mitigation plans are then applied during study execution, which includes ongoing risk monitoring and a more targeted approach to site monitoring.

Risk-based approach: RBQM in clinical trials

Effective RBQM entails three elements:

  • Key risk indicators (KRIs)
  • Quality tolerance limits (QTLs)
  • Data surveillance

KRIs and QTLs are designed to monitor for pre-identified areas of risk and it’s important to remember when setting them that quality is more important than quantity. Implementing 30 or 40 KRIs for each study, for example, is not only challenging to maintain, but can also lead to duplicate risk detection and greater “signal noise”.

Rather, sponsors and CROs should use QbD to identify a core set of appropriate KRIs and QTLs and focus on ensuring these are optimized. This will enable the earliest possible risk detection and minimize the likelihood of false alerting.

CSM and data surveillance

But RBQM is about investigating all possible risks, not just the obvious threats to primary endpoints and safety data. CSM can expose forms of study misconduct that may be difficult to identify and/or characterize during pre-study risk planning.

Data surveillance, through a central statistical assessment of data quality, can provide an invaluable insurance policy by flagging anomalies that warrant further investigation.

It works on that assumption that, other than random fluctuations and natural variations, data from all centres should be comparable and statistically consistent. Unsupervised statistical monitoring, or CSM, consists of performing as many statistical tests on trial data as possible, so as to detect inconsistencies that could point to potential problems.

By running a discreet set of well-designed statistical tests across a broad swathe of study data, it can spot atypical patterns that represent potential intentional or non-intentional misconduct. It can flag issues such as fraud, sloppiness, or training needs, as well as malfunctioning or mis-calibrated study equipment.

CSM is at the heart of RBQM. It interrogates all clinical and key operational data to find discrepancies that would remain undetected by traditional techniques.

It is more than just computing statistics on a subset of key variables. It is about processing all data and guiding users to potential issues.

CluePoints’ RBQM solution

CluePoints’ solutions, all of which are cloud-based, are driven by our unique set of CSM algorithms. They interrogate clinical and operational data, centrally and in real-time, to efficiently identify outliers and anomalies in data.

We recently put them to the test using data from a large, Japanese multicentre trial in advanced gastric cancer. A blinded team was asked to use our CSM software detect intentionally contaminated datapoints within the dataset. The study, which was published in journal Clinical Trials, showed the approach could detect atypical data in a multicentre trial with better than 93% specificity.

This approach is unsupervised, meaning it requires no input from the user, and exhaustive, in that it uses all variables collected at the patient level. It eliminates the need for time-consuming SDV unless triggered by an atypical data alert, which frees up resources to be diverted into more safety- and success-critical tasks.

As such our study concluded CMS was a “cost-effective complement to other data-management and monitoring techniques”.

Goals of RBQM

The industry has not yet achieved 100% adoption of RBQM, but it is moving in the right direction.

Recent survey data presented at eClinical Forum found just 18.2% of the 210 respondents had not started a ICH E6 (R2) compliance program. More than 44% were either piloting new processes or were already fully compliant. Additionally, a 2019 roundtable organized by the CSDD,  heard that companies had made significant progress in responding to the ICH E6 addendum in the previous two years.

Amanda Hayden, Director of Global Clinical Services at Alkermes, told the meeting that the industry had experienced “significant maturation” with respect to its response to ICH E6 (R2).

“While many companies may have approached the ICH update by addressing each subpart separately, the reality is that many of the processes are interconnected,” she said.

“Advancing clinical processes in a holistic fashion and thinking of risk in all facets of study planning, execution and analysis creates synergies and efficiencies, and allows for the better use of time, resources, and study data.”

As innovative software is recognized for the essential role it plays in improving clinical trials, it looks like RBQM adoption is finally moving from “if” to “how”.  The framework for a shift from RBM to RBQM has been accepted and implemented – and the next step is to more effectively leverage the data.

This can be achieved through more advanced analytics, such as those driven by CMS, which can guide critical thinking and informed decisions.

Harnessing the power of CMS, we believe, will unlock the power of RBQM to create safer, more successful clinical trials, and shorten the pathway from candidate discovery to marketing approval.

Contact us today to find out how our solutions can help drive the quality, accuracy, and integrity of clinical trial data both during and after study conduct, improving patient safety and increasing productivity, efficiency and cost-effectiveness. Our RBQM solutions powered by CSM can help transform your trials.

Patrick Hughes

Patrick holds a Marketing degree from the University of Newcastle-upon-Tyne, UK, and a post-graduate Marketing diploma in Business-to-Business Marketing Strategy from Northwestern University - Kellogg School of Management, Chicago, Illinois. Responsible for leading global sales, product, marketing, operational and technical teams throughout his career, Patrick is a Senior Executive with over eighteen years international commercial experience within life sciences, healthcare and telecommunications. In the past, Patrick consulted on corporate and commercial strategy for various life sciences companies and was responsible for successfully positioning ClinPhone as the leading Clinical Technology Organization during his 10-year tenure with the company.