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International Clinical Trials
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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.
References
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: www.transceleratebiopharmainc.com/wp-content/uploads/2013/10/2_RACT_20140411.xlsx
5.
Clinical Trials Transformation Initiative Quality by Design project –
Critical to quality factors principles document, 2015. Visit:
www.ctti-clinicaltrials.org/sites/www.ctti-clinicaltrials.org/files/QbD_
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:
www.appliedclinicaltrialsonline.com/rbmguidance-document-ten-burning-questions-about-risk-based-study-management
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