A revolutionary cure for RBM and centralized monitoring

CluePoints discuss the changes in the ICH E6 (R2) guidelines and how RBM technology can enable compliance




In the clinical trials industry there is a tendency to watch and wait, which has been the case with RBM. However, the official publication of the long-awaited ICH E6 (R2) Guidelines last June, means that this is no longer an option. Sponsor and CRO organizations that were previously delaying implementation of RBM are now poring over the updated guidelines to determine what is needed for compliance to ensure they take appropriate consideration of risk in clinical study design and management. Here, CluePoints’ Richard Davies and Suzanne Lukac, discuss the important changes in the guidance and how RBM technology can enable compliance.

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Proof that change was needed

It’s no secret that the scale, complexity and cost of clinical trials has increased significantly over recent years. With a 68% increase in the average number of procedures in a clinical trial and an 88% increase in data volume, coupled with an 100% increase in the number of countries involved, it’s easy to see how traditional methods of trial monitoring have become outdated and ineffective. 1

Clinical trials have seen a 68% increase in the average number of procedures and an 88% increase in data volume

For many years, most in the industry have adopted the same long-standing approach to monitoring, which incorporates a combination of paper-based processes and software, and involves onsite monitoring visits every four to eight weeks and 100% source data verification (SDV). SDV contributes 15% of overall clinical research costs globally but this expensive process is not yielding the expected ROI, and is not solving the quality issues currently faced by researchers. Around 50% of FDA drug submissions fail first-cycle review, with up to 16% of these due to quality-related issues.2

Although the high rate of quality-related submission failures has undercut the argument that the high cost implications of SDV is a necessary investment to ensure data quality, an analysis on clinical data in 2014 from 1168 clinical trials showed that on average SDV only drives corrections to 1.1% of site-entered clinical data, demonstrating that this exhaustive, manual, on-site review process is not only insufficient but ineffective too.

SDV is responsible for 15% of overall clinical research costs but is not yieding the expected ROI

The facts considered, improving quality and replacing this out-dated processes is a no-brainer. Evolutions in technology and risk management processes offer new opportunities to increase efficiency, so it’s no wonder that the regulators are now demanding the industry adopts a risk-based approach to trial monitoring.

 

A regulatory call-to-action

As is typical of the industry, despite the endorsement of risk-based monitoring (RBM) from both the FDA and EMA back in 2013, researchers have been very conservative with respect to adopting this proven approach as standard within their clinical trials.

However, the introduction of ICH E6 R2 last year means that waiting and watching is no longer acceptable. The updated guidelines provide an outline of the types of changes that need to be implemented in the industry, and in particular, it strongly advocates a risk-based approach to clinical trial quality, making it now a codified GCP expectation.

 

QBD and RBM – A single paradigm

The ICH E6 R2 guidance is not simply about RBM, its roots originate from Quality by Design (QBD). With the same goal of enhancing the operational outcomes of clinical research and ensuring ongoing assessment and mitigation of operational risk, RBM and QBD are two components of a single paradigm.

QBD, including a risk assessment, begins during the earliest phase of a study’s protocol design. As well as assuring operational feasibility and success, this guarantees that studies are designed to be based on the scientific merits of the clinical research. The patient perspective and the viewpoint of the research site(s) also plays a key role in the QBD approach. By considering factors including study complexity, and how much burden the study will place on sites to administer as well as patients to participate in, sponsors can create a study design that benefits both groups, leading to improved enrolment, retention, and compliance levels.

At the study execution stage, QBD effectively becomes RBM

At the study execution stage, QBD becomes RBM. At this point, risk assessment is carried out on the completed design by a cross-functional study team. Risk mitigation plans are then established by identifying and prioritizing any remaining operational risks, which will guide all downstream operational study management plans. This instigates a more targeted approach to quality management in clinical trials, in which a robust centralized statistical monitoring (CSM) and key risk indicators (KRIs) solution plays a pivotal role.

 

The Role of Centralized Monitoring

CSM is an agnostic RBM approach that uses statistical methodology to identify unexpected or unusual patterns in clinical trial databases, driving better quality outcomes. By drilling down into individual patient data and comparing the distribution of all variables in each study site with other sites, CSM solutions are able to determine the quality, accuracy and integrity of clinical trial data both during and after a clinical study in order to identify any abnormalities.

The ICH E6 R2 strongly supports the use of CSM as a core component of operational risk detection, and key to the operational success of any RBM implementation due to its ability to provide, “additional monitoring capabilities that can complement and reduce the extent and/or frequency of on-site monitoring and help distinguish between reliable data and potentially unreliable data.”3

CSM is an agnostics RBM approach which uses statistical methodology to identify unexpected or unusual patterns in clinical trial databases

CSM comprises Statistical Data Monitoring (SDM), Key Risk Indications (KRIs) and Quality Tolerance Limits (QTLs), which work together to enable more effective risk and issue detection.

Through the use of a well-designed, robust set of statistical tests, SDM is able to uncover unusual data patterns that could represent operational risks that may not have necessarily been considered during pre-study risk planning. This could range from fraud (eg, fabricated patient data, falsified eligibility data, duplicate patients), through to tampering and sloppiness (eg, propagated or fabricated vital signs, under-reporting of adverse events, procedures or assessments not performed), and systematic errors (eg, site training issues or mis-calibrated study equipment).

KRIs are operational indicators designed to provide an early signal of sites deviating from an expected norm. They tend to be operational quality metrics or measures assessing various aspects of site behavior, such as rate of protocol deviations, rate of adverse event reporting, cycle times for data entry, SAE reporting or query response etc.

KRIs provide early signals of sites deviating from an expected norm. With KRIs, it is important to focus on quality over quantity

When considering implementing central monitoring, the focus should be on selecting quality KRIs over quantity – ideally no more than 20, in order to avoid signal overlap, allow for scalability and maximize effectiveness. What’s more important is to select KRIs and configure them to ensure they are both reliable and proactive in detecting emerging risks as early as possible.

Similar to KRIs, QTLs represent metrics designed to monitor for specific operational risks. The focus with QTLs however, is on more systematic issues. While the industry is still developing an appropriate interpretation of this new ICH language, QTLs should generally be thought of as monitoring for specific thresholds beyond which the study would likely be considered an operational failure.

QTLs allow for the monitoring of thresholds, beyond which the study would be considered an operational failure

When used effectively, a combination of SDM, KRIs and QTLs will drive significantly higher quality outcomes while simultaneously improving operational resource efficiency, through an unrivalled approach to operational quality and risk monitoring, leading to a noticeable reduction in the reliance on SDV and related on-site monitoring reviews.

 

Final thoughts

The introduction of ICH E6 R2 is pushing organizations to not just review their monitoring processes, but to change their entire approach to clinical trials. A daunting task? No question. But there is no doubt that RBM represents a huge opportunity for the clinical trials industry, with CSM the lynchpin to its success. So rather than fear the change, organizations should be aware that by working collaboratively with the correct study partners to identify and implement the right tools, RBM strategies can actually be relatively straightforward to implement.

Not only that, RBM offers researchers the chance to significantly improve the data integrity and success rates of their trials quality, and do this all at significantly lower cost. So far from being considered a new regulatory hurdle to overcome, the ICH E6 R2 should be viewed as a huge opportunity and positive step forward in carrying out more efficient clinical research and development, with considerably better outcomes. 

 

References

  1. Tuffs Center for the Study of Drug Development – Tuffs University Complex and Demanding Protocols presentation
  2. http://jama.jamanetwork.com/article.aspx?articleid=1817795
  3. Evaluating Source Data Verification as a Quality Control Measure in Clinical Trials, Therapeutic Innovation & Regulatory Science 2014, Vol. 48(6) 671-680

 

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