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Quality Review

The extent to which resource allocation, patient safety and data quality can be enhanced by risk-based monitoring (RBM) is highly influenced by the quality of the planning process. Regulatory guidance on risk-based quality management and monitoring from the EMA and FDA, as well as the recent draft of the addendum to the ICH Good Clinical Practice (GCP) guideline, highlight that study design and implementation procedures must be in sync with the chosen monitoring strategy (1-3).

Feasibility Analysis

When it comes to early risk evaluation, the primary objective is to determine how likely it is that the study will succeed or fail, in order to minimise critical issues. As the major source of risk information, the initial study feasibility analysis is of utmost importance. Historical information, lessons learned, input from key opinion leaders, and overall big data – nowadays available in a more structured way – are key sources for assessing risk.

As stated in the recent addendum to the ICH GCP guideline, the identification of critical risks should not be reduced to the risks associated with the facilities and personnel within a system, but extend to risks directly related to the therapeutic area, protocol, investigational product, and further studylevel design features (3). Accurate identification of the main risks is fundamental when choosing the most appropriate monitoring strategy, to ensure efficient patient safety and quality control.

Investigator responses to study feasibility questions must not be regarded as mere regulatory compliance and procedural checklists, but as a valuable indicator to identify and steer required design changes and monitoring needs. Unfortunately, however, feasibility analysis is commonly done late and succumbs to demanding milestone achievements. As a result, vital feedback on critical study design risks can be overlooked.

Complex Designs

Study risks are proportional to study complexity, which will determine the degree to which monitoring is required to ensure subject safety and data integrity. The FDA guidance mentions adaptive and stratified designs and complex dosing as complexity factors that may call for closer oversight (2). Other key risk factors, identified by the EMA, are the chosen target population, therapeutic area, eligibility criteria, study procedures, selection of endpoints, and the statistical analysis plan (1). The number of involved vendors is also highly relevant – it has increased from five to 15 per study in the past 10 years.

The challenge is how to objectively evaluate risks to optimise study design. Desirable tools are comprehensive documents summarising study design choices and their associated risks. A variety of relevant study planning techniques and document templates are available – for example, risk identification and categorisation tools, ranging from simple documents to those produced by modern software (4,5). These tools must direct the study team in how to ensure quality in view of the risks that each design item introduces to the project, and to evaluate how study control is enhanced or endangered by design choices. The whole cross-functional team – comprising, in most cases, different departments, sponsors and CROs – should participate in the early risk identification and evaluation exercise. It brings a common understanding of study design factors and their inherent risks in the shape of a reasoned decision-making tool.

Data Access

The RBM advantage, apart from improved patient safety and data quality, is the cost-efficient and adaptive lessening of on-site source data verification and review (SDV and SDR). However, it is unrealistic to expect RBM to reduce on-site SDV and SDR in a study in which incommensurable data collection requirements inflict risks for subject safety and data reliability. The monitoring process can only be streamlined if the protocol adjusts to a rational and convenient course of action for data collection. The draft addendum to the ICH GCP guideline requests easy study procedures free of unnecessary complexity (3).

Procedures that hinder timely access to study data can be critical, too. As an example, cost-effective grouped sample shipments should be limited in time, according to the value and sensibility of data obtained by the relevant assessment. As data accessibility decreases, RBM utility diminishes proportionally due to risk detectability constraints. Similar data flow discontinuations are a risk on their own.

RBM Twist

The draft of the revised ICH GCP guideline incorporates a new section dedicated to study risk management, calling for quality management systems to primarily guarantee patient safety and data reliability (3). RBM must be utilised in all clinical trials to optimise subject protection and quality control. The implication here is that, for all studies, an integrated quality and risk management plan should be prepared, comprising instructions on how to utilise risk management tools equivalent to the following:
  • Risk register: a record of risks, after identifi cation of critical and non-critical study data and processes, to assist with risk tracking during study conduct
  • Risk assessment and categorisation tool (RACT): a list of prioritised risks according to impact, likelihood and detectability scores, organised in risk categories (for example, subject enrolment, IT systems, etc) to evaluate study risks, highlight which risks are critical, and adjust monitoring to risk priorities
Before RBM implementation, the following risk management activities should be taken into consideration during project planning:

Identification of Key Risk Indicators (KRIs)
The risk register and RACT will set the basis for the selection of required risk indicators, which should provide suitable risk metrics (how to measure each risk), define the threshold limits for risk control, and determine when and how to trigger a risk alert (communication of out-of-control risks). A metric informing about a specific risk, with its relevant thresholds and alerts, as one piece, will constitute the main attributes of the KRI.

Alerts based on risk likelihood levels offer a significant advantage as they prevent threats from occurring by means of observed fundamental events, as well as by known risk recurrence. Historical information and lessons learned about responses to identified risks, together with risk mitigation strategies, will prevent the usual pitfalls due to delayed reactions. These responses should be planned to not only mitigate risk after occurrence, but also to prevent threats in the first place.

Adjustment of SDV to Study and Site Risk Levels
SDV and SDR as monitoring tools will be adjusted according to risk information obtained by means of risk levels stemming from the KRIs. How SDV will be modified against observed risk levels should be clearly specified in the clinical monitoring plan. Nonetheless, SDV adjustments should be validated in line with the established quality control (QC) system, in order to assess effectiveness.

Organisation of RBM and QC Systems
Related standard operating procedures will help to systematically determine how to adapt RBM and QC procedures to study-specific safety, data collection and quality requirements. Study requirements will drive the type and level of RBM tools needed – for instance, RBM in Phase 3 trials would require higher sophistication than Phase 2 studies, as virtual teams may require different risk communication systems to local teams (6).

Centralised Monitoring
Study control can be tightened with central monitoring through the continuous verification of study data to identify outliers, biases, and additional safety and data quality issues addressed promptly after data collection. Centralised monitoring engages data managers, statisticians and medical monitors in a cross-functional early evaluation of available data. Simple electronic case report form remote monitoring is improved by means of expert input from statisticians and medical advisors. This early analysis makes possible the identification of the majority of SDV findings prior to the conduct of site visits, assisting with the adjustment of SDV and SDR targets and extent. It also enables the continuous evaluation of sites and overall study performance with risk and quality metrics (2).

Unchecked critical data increases the risk of unnoticed safety and quality issues to be flagged up by regulatory agencies and inspectors. Therefore, serious risks should be reviewed on a regular basis to ensure that on-site SDV and SDR prevent critical study threats and complement centralised monitoring effectively.

The selected RBM system must ensure smooth risk communication and in-time risk mitigation. Nowadays, state-of-the-art technologies enable risk assessments and speedy risk communication from study to clinical site level, on top of the usual automated data querying systems. Training requirements need to be revised prior to RBM deployment, in order to help study teams adapt to procedural changes, new technologies, and updated functional roles and responsibilities.

Continuous improvement of the quality risk management system should be performed according to knowledge gained before, during and after its implementation in a clinical study. The cost of quality imposed will be compensated by enhanced compliance, lessened on-site SDV and SDR expenses, and optimised study outcomes. Taking the plunge into RBM requires some effort, but will alleviate the need for hasty and unsatisfactory study rescue measures.

1. EMA Reflection paper on risk-based quality management in clinical trials, 18 November 2013
2. FDA Guidance for Industry: Oversight of clinical investigations – A risk-based approach to monitoring, August 2013
3. Integrated Addendum to ICH E6(R1): Guideline for GCP E6(R2), draft version dated 11 June 2015
4. TransCelerate Biopharma Inc. Visit:
5. Clinical Trials Transformation Initiative Quality by Design project – Critical to quality factors principles document, 2015. Visit: toolkit/Principles%20Document_finaldraft_19MAY15.pdf
6. Widler B, Schenk J et al, RBM guidance document: Ten burning questions about risk-based study management, Applied Clinical Trials, 2015. Visit:

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Dr María Proupín-Pérez is Project Leader at PPH plus GmbH & Co. KG (Frankfurt am Main, Germany), with managerial responsibilities for clinical studies and innovative projects. She has been working in drug development since 2009. María's therapeutic areas of expertise are haemophilia, meningitis C, septic shock, atrial fibrillation, stroke and myocardial infarction. She graduated from the University of Liverpool in 2006 with a PhD in Chemistry, and is a member of the German Society of Pharmaceutical Medicine and the Project Management Institute.
Dr María Proupín-Pérez
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