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Clinical Trial Risk-Based Monitoring with Excel

Current regulatory and economic incentives are prompting clinical research organizations to integrate risk management strategies into clinical operations and to adopt technological enablers for the efficient monitoring of clinical trials.

Today’s drug discovery is meeting difficulties as R&D investments are being restricted and an ever-longer and more costly drug development process faces the clinical trial enterprise. Approximately two-thirds of the total drug R&D costs are associated with clinical phases of development, of which 70% are allocated to Phase II/III activities. The traditional process of sponsor oversight over clinical trials is currently being challenged as research suggests that certain monitoring practices are not optimal at  the clinical phase of development and represent non-value added activities, which unnecessarily increase the costs of clinical studies. Keeping in mind that the financial burden of drug development is ultimately transferred to patients and society, the importance of performing clinical research more efficiently while insuring data integrity is an objective.

Monitoring, specifically, refers to the act of overseeing the progress of a clinical trial to ensure that it is conducted, recorded, and reported in accordance with the protocol, standard operating procedures (SOPs), good clinical practice (GCP), and the applicable regulatory requirements. Monitoring can represent up to one-third of trial costs. Interestingly, it has recently been recognized that electronic case report form (eCRF) data are actually “not that dirty” and that 100% source data verification (SDV), which consumes a large amount of monitoring time, has a negligible impact on data quality and subject safety. Moreover, it has been recognized that data reach a point at which it can be considered “good enough” and at which more verification will not affect a study’s statistical conclusions. Amid the industry’s gradual adoption of eSource technology that further reduces the requirement to perform SDV, the focus of monitoring is thus shifting away from overdoing evaluation of data cleanliness and towards building quality into trials.

The adoption of risk management practices to monitor what matters and perform site visits when it matters could reduce overall costs by more than 20% in large, Phase III trials, and save the industry billions of dollars per year without compromising data quality. Accordingly, there is currently a movement within the industry driven by health authorities to adopt to risk-based/intelligent monitoring practices supported by centralized monitoring to exert oversight over trials and improve resources utilization.

Building quality into clinical trials and realizing continuous improvement can be achieved with a plan-do-check-act (PDCA) approach to quality management. In the context of clinical research, the different steps of the PDCA framework include various activities that can be considered under the methods of quality by design (QbD), centralized monitoring and risk-based monitoring (RBM).

A risk assessment performed prior to the start of a study is an indispensable instrument in the implementation of QbD since it allows identification of the key risk indicators (KRIs), determination of their relative importance during the different phases of a study, and development of the strategies designed to eliminate or mitigate risks. Risk assessment should focus on data and processes critical to subject safety, data quality, and trial integrity and the following questions should be addressed to orient the activities which will be performed in the subsequent steps of the PDCA process:

  • What factors represent a significant risk to quality in the context of subject safety, data quality, and trial integrity?
  • What proactive steps can be taken to avoid quality problems?
  • What ongoing checks can be performed to detect problems?
  • What type of signal will trigger corrective actions?
  • What steps can be taken to ensure that corrective and preventive actions are focused, sustainable, and efficient?

Different approaches may be used to assess a given protocol’s risks. One method is covered in a position paper published by the TransCelerate BioPharma Initiative, which has also developed a risk assessment categorization tool (RACT) to facilitate risk assessment. Similarly, the Clinical Trial Transformation Initiative (CTTI) has produced a non-exhaustive list of critical to quality (CTQ) factors that should be considered in a risk assessment exercise. The principles of risk management and the overview of the process are outlined in ICH Q9, which also provides references to various tools that can be used for risk management, including ISO 31010 standards.

FDA’s risk-based monitoring guidance stipulates that monitoring findings should be evaluated to determine whether additional actions (e.g., training of clinical investigator and site staff, clarification of protocol requirements, etc.) are necessary to ensure human subject protection and data quality across sites. European Medicines Agency (EMA) guidance also states that it is an essential part of the risk-based quality management system that review should take place as additional information becomes available. As regulators are specifically looking for evidence of actions taken to manage identified risks, centralized monitoring reports should be produced periodically to document the analysis of outliers and specify what actions, if any, were performed to mitigate risks, in order to demonstrate oversight of participating investigators. Periodic reporting serves to communicate findings and to follow-up on issues with pertinent parties, including data management, clinical operations, and QA.

The pilot study using the RI model has shown a high level of control over quality attributable to the early detection and early communication of issues. High enrollers were closely followed. Issues related to data entry errors were quickly detected and resolved with supplemental site training before requiring extensive corrective actions. Abnormal AE rates led to additional medical reviews to ensure that the safety of subjects was not compromised and that under/over-reporting was not at play. Some performance issues led to the modification of processes at the sites and, in some cases, to the scheduling of audits and the initiation of CAPAs. Documentation of site quality metrics further provided measures on which to base site selection for subsequent studies.

Periodic risk analysis and reports kept monitors informed about the status of their sites and provided them with information concerning site’s needs, which was hardly available from other systems. Calculated risk signals matched monitoring reports findings, which confirmed their validity. Importantly, risk signals were often available ahead of significant monitoring report findings. The verification task of monitors was also decreased by the requirement to perform partial SDV in a targeted risk-based manner. 

An Excel workbook used in conjunction with the RI model was shown to constitute a controlled, reliable, flexible, and low-cost tool suitable to support a centralized monitoring approach for the implementation of QbD. Since the MS office suite represents a platform that is already an integral part of most companies, it offers the flexibility needed to integrate QbD with a sponsor’s pipeline, processes, and systems.

Since quality management shifts from on-site monitoring to centralized monitoring, the new clinical trial monitoring paradigm includes the role of data management for computing KRI, their ongoing analysis, and their reporting to the appropriate stakeholders. The RI model represents a method to guide a risk-based/intelligent monitoring approach using simple statistics as relevant site-specific quality metrics. The present approach efficiently supports the objective to build quality into trials in a cost-conscious manner.

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