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International Clinical Trials
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Accurate reporting of serious adverse events is an essential stage in
the clinical development process. New data visualisation technology and
statistical analysis software can make the process more efficient,
reducing the cost for sponsors and increasing safety for patients.
Conducting clinical studies that are safe and effective is a goal shared
by all participants in a clinical trial, from the patients to sponsors
and regulatory bodies. One cornerstone of clinical trial safety is clear
and consistent monitoring of serious adverse events (SAEs). Over the
years, the clinical research industry has developed methods for tracking
SAEs and determining their cause. However, these traditional methods
have a number of shortcomings, including delays in identifying potential
problems and obstacles to determining causality or analysing all
relevant information. Over the past 10 years the industry has embraced
new technology, including electronic data capture (EDC) and safety
systems, which are placing growing pressure on sponsors to update their
safety procedures in order to fully comply with regulatory requirements.
This article will discuss how best practices in SAE reporting are
changing, and how companies can use modern analytics and visualisation
tools to keep their own SAE reporting systems in compliance with
regulations.
SAE Reporting Requirements
Monitoring is the most fundamental component of the safety reporting
process. Over the course of a clinical trial, the sponsor is responsible
for reviewing all information relevant to the drug, including data
derived from the study itself (1). If the sponsor identifies an adverse
event that is potentially caused by the treatment that is under
investigation, they are obliged to report the risk to the FDA and all
investigators within a predetermined period of time. These reports are
typically called investigational new drug (IND) safety reports.
The FDA has mandated which potential adverse events are and are not
necessary to report – requirements which are fully detailed in 21 CFR
Part 312. For example, the sponsor is required to report any SAE that is
unexpected if there is evidence suggesting a causal relationship
between the treatment and the adverse event, but is not required to
report a SAE that does not appear to be related to the treatment. The
key to meeting this requirement lies in determining whether or not there
is evidence of a causal relationship within the timeframe for
reporting.
Meeting the FDA reporting requirement has become more difficult for
sponsors in recent years due to mass adoption of electronic data
collection systems. Sponsors are collecting data in a format that is
immediately accessible, thus their obligation to identify reportable
events may begin much earlier in the process than they are used to with
paper-based data collection.
Moreover, while sponsors are grappling with more data, they are also now
required to analyse it with greater care. Effective from 28th March
2011, the FDA modified its rules with the primary goal of preventing
over-reporting by sponsors, who had done so in order to comply but
effectively ruined the efficacy of the process. As described by the FDA,
the modifications “improve the utility of IND safety reports, reduce
the number of reports that do not contribute in a meaningful way to the
developing safety profile of the drug, expedite the FDA’s review of
critical safety information, better protect human subjects enrolled in
clinical trials, subject bioavailability and bioequivalence studies to
safety reporting requirements, promote a consistent approach to safety
reporting internationally, and enable the agency to better protect and
promote public health” (2).
The bottom line for sponsors is that they must implement a process that
quickly alerts them to potential adverse events, allows them to see and
analyse all of the data being collected in their study, and helps them
to readily identify underlying causes and potential confounding factors.
Modern visualisation and analytics tools can address all of these
challenges.
Timely Reporting
Given the short timeframe in which sponsors are required to make an
accurate determination of whether an event needs to be reported under
the FDA rule, it is essential that the appropriate stakeholders are
aware of the event as soon as possible. Automatic alerting features in
today’s visualisation and analytics tools, and ensures this process
occurs immediately, without the need for investigator intervention. For
example, the analytics software could be configured to analyse data
collected by an EDC or safety system in real-time as the data is
uploaded. If a certain type of data designated by the sponsor is
uploaded, or a specific keyword or an adverse event, the analytics
software would automatically send an email or text message to the
sponsor’s safety team.
Electronic alerting can increase efficiency even where identifying
potential problems is less straightforward. For example, global studies
have traditionally suffered from translation issues. Before an adverse
event can be properly coded, it may need to be translated from the
language in which it was collected. There may be over ten languages used
at the various investigator sites, yet once the data is reported it
needs to be acted on within the same period of time regardless of its
source. Therefore, rather than placing untranslated, collected data on
hold for any period of time, alerts could be used to deliver new data to
an appropriate translator as soon as it is entered into the EDC or
safety system. Additional rules to increase efficiency could be placed
on the alert, including changing the recipient based on the time the
data is received so that, if translators are located throughout the
world, it always goes to a translator during their local business hours,
or if no action is taken on the data within a certain period of time,
an alert goes to a translation supervisor. A subsequent alert can be set
up after translation to go to the coding or safety compliance team.
Alerts are an extremely flexible way to meet the challenge of
identifying which events may need to be reported in a timely fashion.
They can be put in place before the first site visit of the first
patient, or they can be added as the study progresses, giving the
sponsor a better understanding of what information is relevant to them.
Alerts can be simple or follow complex rules, as demonstrated in the
previous example, and ideally they can be triggered by any data source
or from combinations of data across a range of sources. The form that
alerts can take can also be versatile, ranging from a simple SMS text
message to an interactive report accessible on a smart phone or via
email.
Analysing Relevant Data
One potential problem in the reporting process is that safety data is collected in a variety of ways during a clinical study.
Depending on the method of collection, data from the same study may end
up in different systems or formats. The challenge for sponsors is to
reconcile data across the systems.
Ideally, an investigator collects the data, recognises it as a potential
SAE, and files it with the appropriate clinical safety group so that it
is properly entered into the safety system. However, it is not uncommon
for an SAE to be captured only in the EDC system or written on a case
report form (CRF). Eventually the SAE will be discovered and properly
filed in the safety system, but in the interim the sponsor’s ability to
meet its reporting requirements, and even in extreme cases, the study
itself, can be jeopardised.
Traditionally, the process of reconciling disparate data sources to
ensure complete reporting of SAEs is a slow, expensive and error-prone
process, as it generally involves the human resource-intensive process
of regularly comparing data, often by manually evaluating printouts for
the various systems side-by-side. Such methods not only rely on a human
reviewer being sharp-eyed enough to identify issues, but also increase
inefficiency because the reviewer is dealing with a static report, and
therefore has to the take the time to request an additional report if he
or she wants to drill down on any information in order to reconcile a
piece of data.
Automated analytics tools can solve a number of these problems and
provide huge productivity gains; today’s technology can create an
automated reconciliation report that quickly points a reviewer to
potential issues and reduces the likelihood of overlooking filing the
proper procedures for a SAE. The technology that provides this
functionality comes in many forms. One solution is a data warehouse that
aggregates all data from the various systems into one place. Another
option is software capable of querying data where they reside, across
all formats or schemas.
Regardless of the form of the technology, any of today’s methods of
automatically reconciling SAEs across data systems is likely to reduce
costs for sponsors and increase safety for patients.
Understanding Causality
One of the most important aspects of safety reporting is determining
whether or not there is a potentially causal relationship between an
event and the treatment under study. As already noted, the FDA is
looking to sponsors to reduce the number of unnecessary reports that
they file in order to allow the FDA and investigators to focus on the
most important safety concerns. Doing this requires sophisticated tools
that provide reviewers with robust analytical functions.
Ideally, an analytics and visualisation tool should connect to all
relevant data sources and provide the reviewer with the ability to
explore and understand the data through a nontechnical (that is, a
non-computer programmer) interface. A tool with this capability
eliminates the downtime that can occur if data across sources needs to
be reconciled or merged, or if a reviewer needs to wait for a programmer
to create and run the reports he or she wants to see.
Such tools should contain a number of features. Ad hoc querying and
drill-down capabilities are extremely helpful when providing a reviewer
with the ability to understand causality. The reviewer must be able to
identify and compare patient subpopulations to determine whether or not
one subpopulation is at risk over another. They must be able to find
outliers and trends – potential concerns that are worth further
interrogation. They must be able to quickly look at all relevant data
from a patient, for example all of the data from that patient’s CRF, in
order to determine whether or not something else is happening.
Statistical analysis capabilities are also essential. The reviewer must
have access to statistical analysis systems (SAS) and the ability to
bring up analysis such as comparative incidence and more intense
statistical analysis across all relevant data.
Understanding causality is an analysis-driven process, so the right
tools are critical for reducing the time reviewers spend setting up and
waiting for data reports and increasing the time they have to review the
contents of those reports.
Conclusion
In today’s clinical research landscape, sponsors are under more
regulatory pressure than ever before. The use of modern analytics and
visualisation software is critical to ensuring sponsors can deliver
timely SAE reporting to regulatory bodies, making sure that all relevant
data is included in this analysis, and helping to understand whether or
not there is a causal relationship between an incoming SAE and the
treatment. Ultimately, these tools will empower sponsors with the
information they need to make the changes necessary to reduce risk, and
ultimately, save studies.
References
- US FDA, Code of Federal Regulations Title 21, available at www.
accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/CFRSearch. cfm?fr=312.32
- 21 CFR Parts 213 and 320, Federal Register, 75(188): pp59,935-
59,963, 29th September 2010, available at www.gpo.gov/fdsys/pkg/
FR-2010-09-29/pdf/2010-24296.pdf
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