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Guide for Risk-based Monitoring Technologies

By Ramin-Wright, Randy, Director of Quality Risk Management, Clinerion Ltd., Basel Switzerland

and Andrianov, Artem, Ph.D., Managing Director, Cyntegrity Germany GmbH, Frankfurt Germany

 

Efficient risk management becomes more than advice today, it becomes part of a survival kit for a modern pharmaceutical company. Due to increasing trial complexity, regulatory scrutiny, competition for patients and high quality sites, the R&D returns in pharma have halved in the past 10 years

[1].

FDA’s monitoring guideline advises: “monitoring should be tailored to your organization, the study protocol, and the product being tested” [2]. This implies that the selection of monitoring methods should involve a thorough analysis of the study protocol, its execution, the contributing parties as well as the associated risks. Only after the analysis of information critical to the success and quality of the study is one prepared to define a Risk-based Monitoring (RbM) strategy that is commensurate with the study risk profile (See Figure 1).

What is RBM Comparing to Traditional Montioring Approach

Figure 1. Comparison of the Traditional Monitoring approach and RbM.

Initially GCP referred to RbM indirectly in §5.18.1 [3], although the upcoming GCP E6R2 addendum (undergoing regulatory review currently) puts stronger emphasis on this procedure. In accordance with the addendum, a sponsor should develop an approach to monitoring of clinical trials, which is systematic, prioritized and risk-based. The addendum advises that a combination of on-site and centralized monitoring activities is appropriate. Additionally, it points out that emerging advances in technology may facilitate the remote monitoring of source data. Thus, in this article we will categorize available RbM technologies and see how they can support clinical operations.

RbM technology can be categorized by different factors:

By IT-Infrastructure: Cloud-based vs. On-Site Solutions

  1. Cloud-based. Software as a Service (SaaS) approach makes RbM available via a web-portal. Using these solutions, organizations do not need to take care of IT infrastructure.
    1. Commodity Service. These systems use the commodity cloud solutions and share resources with other services. Sometimes concerns about data security keep some companies from using this infrastructure.
    2. Private Cloud – A dedicated infrastructure for each customer is allocated so that computing resources and a higher level of stability and availability are assured. As such data security is generally not a concern.
  2. On-Site Solutions. These solutions are located on servers of consumers of the RbM solution. An important advantage of this solution is nearness of data sources and as a result, high speed of data access.

Major influencing factors regarding IT infrastructure are location of service, skill specialization and scalability. Location strongly influences the price. As a result, cloud solutions are cost efficient. Traditionally onsite solutions have been implemented but cloud-based solutions are now more in demand as they also provide high levels of service but at significantly lower cost and more flexibility than traditional internal IT departments can generally provide.

By Data Sourcing

Data sourcing capabilities of RbM technology can differ by its data sources (e.g. EDC, CTMS) and by its data acquisition method: push vs. pull. Most of the RbM solutions today focus on EDC, as EDC can deliver many risk-relevant parameters, e.g., the number of enrolled patients, visit schedules, etc. Some technology providers consolidate data sources in a data warehouse and others apply an elastic network approach enabling configurable data source interfaces. Data warehouse: data gets stored at one central location, whereas networks crawl different clinical recording systems and capture risk-relevant information.

By Assessment Frequency

The risk assessment frequency is an important criterion, some solutions offer quarterly assessments, others conduct periodic automatic or semi-automatic assessments. The advantage of the first approach is the assessment may be done deeper with preliminary data cleaning and preparation. In the latter the RbM operator can observe the development of risk dynamics: speed and direction.

By Risk Areas

  1. Basic Risk Areas (Patient Safety, Site Performance, Data Quality, Fraud Detection)
  2. Protocol specific Risk-areas (Protocol Compliance)
  3. Therapy-specific Risk Areas (ECG, Spirometry, Imaging, ePro)
  4. Resource Availability
  5. Vendor Oversight

Technology choice should be driven by requirements and risk tolerance. For larger trials with extensive requirements a solution with a broad feature-set and predictive analytics is suitable. For smaller trials a cloud solution, with an elastic network is usually well suited.

By Functions

  1. Risk Detection
  2. Issue Management
  3. Risk Mitigation Process
  4. Predictive Analytics, Heuristics

Each solution differs in the provided feature set (see Figure 2). Among the most universal features are risk detection, risk dashboards and reporting, issue management and risk mitigation process. More advanced solutions provide predictive analytics and heuristics to identify residual risks.

RBM Technologies Graph

Figure 2. Technology Landscape of major RbM Providers

RbM Implementation

Experience shows that a staged approach is most effective with RbM [4]. It provides opportunities to reflect and adjust the process. Successful RbM includes both an appropriate governance and enabling e-tools. In other words, People, Process and Technology are key to success. The stages follow:

  1. Proof of Concept. Execute workshops in order to determine what kind of RbM organization, process and technology are appropriate. A suitable trial shall be chosen. Simple e-tools are applied and an RbM Core Team is established and trained on the RbM process and tools.
  2. Here e-tools play a stronger role. The pilot team gains experience, so that an informed decision can be made regarding how to proceed.
  3. Lessons Learned. During the pilot, the RbM Governance Team and the Study Management Team provide feedback regarding the process and technology. Upon completion of the Pilot, the results are analyzed, consolidated and presented to major stakeholders.
  4. Adjust RbM. The RbM approach and technology are adapted consistent with change requests.
  5. RbM Rollout. RbM process and technology are fully integrated for the whole trial portfolio, progressively involving more studies.

Summing up, RbM is a journey of continuous improvement requiring mechanisms for process change and a new way of working and thinking within the organization.

Key take-away Messages:

  • Tools are essential for implementing RbM successfully as they enable recurring evaluation of risks and sites profiles.
  • Today’s technology solutions vary significantly and their suitability depends strongly on your situation and risk tolerance.
  • When implementing RbM, apply a staged approach.
  • Plan on bringing external expertise if needed, it will reduce risks introduced by RbM itself.

References

[1]KPMG, “Future Pharma,” Aug-2011. [Online]. Available: http://www.biodeutschland.org/tl_files/content/dokumente/biothek/Future%20Pharma.pdf. [Accessed: 03-Jul-2015].[2]FDA, “Guidance for Industry Oversight of Clinical Investigations — A Risk-Based Approach to Monitoring,” Aug-2013. [Online]. Available: http://www.fda.gov/downloads/Drugs/…/Guidances/UCM269919.pdf. [Accessed: 15-Aug-2014].[3]International Conference on Harmonisation (ICH), “Guideline for Good Clinical Practice.” Jul-2002.[4]B. Widler, J. Schenk, P. Schiemann, A. Andrianov, M. Proupín-Pérez, and M. Alsumidaie, “RbM Guidance Document: Ten Burning Questions about Risk-Based Study Management,” Applied Clinical Trials, vol. Risk Based Monitoring, May-2015.

By | 2017-04-28T09:18:25+00:00 October 8, 2015|White Paper|1 Comment

About the Author:

Professional in the integration of data-driven Risk-based Monitoring (RbM) process in international clinical trials of pharmacology. Speaker at regional and global conferences such as: DIA, PharmaForum, PharmaDay, DGGF, etc. 10+ years of experience in data quality projects and biostatistics for the pharmaceutical industry. Life passion: improving clinical research with RbM, driving the RbM research to new frontiers for CROs, pharma and biotech companies.

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