A safety signal suggests a possible causal relationship between an adverse event (AE) and a drug, or a new aspect of a known AE and a drug, which requires some type of further investigation. **Disclaimer: This article was previously published. Sciformix is now a Covance company.

1. Volume 8 Issue 218 Journal for Clinical Studies
Regulatory
Pharmacovigilance (PV) and risk management are
essential in pharmaceutical product development and
commercialisation, and include many activities which are
highly regulated across the world. Documentation and
communication of a product’s safety profile, which usually
focuses on adverse drug reactions, is the responsibility
of the pharmaceutical company. The product’s safety
profile is rarely static and often progresses through many
changes due to increasing exposure, different styles
of practice, new or untested drug-drug interactions,
and pharmacogenetics variations. Additionally, rare
adverse events (AEs) may not be identified until large
numbers of patients receive a drug, therefore, PV and
risk management is a continual process throughout a
product’s life cycle.
A safety signal suggests a possible causal relationship
between an adverse event (AE) and a drug, or a new
aspect of a known AE and a drug, which requires some
type of further investigation. When a signal is detected,
further investigation is warranted to determine whether
an actual causal relationship exists1
. The entire process
of signal management is one of the most crucial steps
in PV and is defined in numerous guidelines to various
degrees. These include the EMA Guideline on Good
Pharmacovigilance Practices (GPV) Module IX – Signal
Management2
, FDA Guidance for Industry, GPV and
Pharmacoepidemiology Assessment3
, reports of CIOMS
Working Group VIII,4
and ICH E2E5
.
Signal management includes many processes,
such as identifying sources of data, signal detection,
prioritisation, evaluation, analysis, and assessment with
recommendations for action, and remains at the centre
of PV and drug safety. These processes are required for
patient safety and by drug regulatory agencies. Tracking
and documentation of the activities, decisions, and results
is also required. The outcome of the signal management
process is directly dependent on the quality of the safety
data and, as a result, improvements in the quality of safety
data will have a significant impact on the effectiveness of
signal management and pharmacovigilance as a whole.
The area of safety signal detection has come into focus
over the past few years and is growing in importance.
It is well accepted that statistical methods of signal
detection can flag certain drug-event combinations for
in-depth analysis from a medical perspective, potentially
leading to confirmation of evidence and identification
of a signal. Signal detection can be completed using
many methods, each of which has inherent advantages
and limitations. New and improved computer-aided
statistical methodologies are under evaluation with the
anticipation of offering improved sensitivity, specificity
and predictive value.
What is Signal Management?
The signal management process is a set of activities
including signal detection, prioritisation, validation,
analysis, and assessment with recommendation for
actions, and is completed to identify any new risks
associated with a drug, or whether known risks have
changed. The signal management must be performed
on all safety data, individual case safety reports (ICSRs),
aggregated data from active surveillance systems or
studies, literature information or other data sources, and
all activities, results, and decisions must be tracked and
recorded.
Types of Signal Detection Methods
Signal detection in spontaneous reporting systems can
fall into two different categories: traditional methods
and enhanced quantitative (statistical or automated
signal detection methods). The traditional methods
encompass manual medical review of individual cases,
case series, and reporting rates, whereas the enhanced
quantitative methods include computer-aided statistical
methodologies and data mining algorithms (DMAs).
Signal detection is just an initial step in the signal
management process and cannot be used in place of an
entire signal management process.
Traditional - Case and Case Series Review
The “index case” reviews of spontaneous reports and
other post-marketing AEs are carried out in order to
identify safety concerns which can be found in a single
case, cluster of cases, case trends or cases which strongly
support causality between the drug and the AE. Cases
which strongly support causality include positive de-
challenge / re-challenge and close temporal association
between the drug and the AE. In practice, two lists of
AEs are often assessed in case and case series review;
designated medical events (DME) and targeted medical
events (TMEs). DMEs represent a single, predefined list
of AEs in a case and case series review across a wide
range of products and therapeutic classes. These DMEs
represent AEs which are rare, serious, and have high drug
attributable risk. The identification of a single or a small
number of a particular DME in a case or case series review
will likely identify a signal. Unfortunately, neither an
absolute definition of DMEs, nor a broadly accepted list
of DMEs, exists across regulatory agencies.
In contrast, TMEs are associated with a particular
product, group of products or patient population. DMEs
are the same list of AEs across many therapeutic classes,
whereas TMEs are specific and tailored to a drug, group
of drugs or patient population. The TMEs may be treated
in a similar manner to DMEs, but most often do not have
the same strength of drug attributability.
The Growing Role of Signal Management in the Product
Life Cycle and How Optimised Pharmacovigilance
Operations Can Help

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Regulatory
Simple Analysis of Larger Datasets
Simple analysis of safety reports, periodic benefit risk
evaluation reports (PBRERs), annual safety reports
(ASRs), periodic adverse drug experience reports (PADERs)
and investigational new drug (IND) safety reports, are
commonly employed for signal detection. Using these
methods, a signal can be detected through review of
the 1) absolute number of a specific AE, 2) proportion
of a specific AE versus the total number of AEs reported
for the drug, 3) proportion of a specific AE versus the
estimated exposure, 4) clustering of an event over time,
geography, and population, and 5) emergence of a new
event. Various statistical methods can be employed to
evaluate the significance of AEs in these larger datasets.
Enhanced Quantitative Signal Detection Methods
These methods usually include computer-aided statistical
methods and data-mining algorithms (DMA) based on
2×2 contingency tables producing disproportionality
analysis (DA). DMAs offer advantages for large data sets
containing multiple products, since automated methods
drastically reduce time and expert resource, as well as
providing a reproducible and auditable methodology.
Measures of association used in DA fall into two general
groups, frequentist and Bayesian, although all methods
can be evaluated on sensitivity, specificity and predictive
value.
The Stages of Signal Management
Signal Prioritisation
Often the signal detection process generates a
considerable number of signals that must be managed.
As a result, prioritisation of the data is required, since not
all signals can be evaluated or validated simultaneously
due to resource limitations. In order to identify signals
needing immediate attention, an impact analysis can
be carried out, which explores the strength of evidence,
medical significance, and potential impact on public
health4
. Additionally, further signal prioritisation may
include criteria such as reports in a vulnerable population,
including children and pregnant women, life cycle of the
drug, and whether the signal is based on more than one
data source.
Signal Evaluation or Validation
The basic goal of signal evaluation or validation is to
determine whether sufficient evidence exists to identify
a causal association between the adverse event-drug
pair identified in the detected signal. Additionally, the
evaluation or validation can be focused on a new aspect
of a known adverse event-drug pair association. Signal
evaluation or validation has three potential outcomes:
the signal can be 1) verified or validated, 2) refuted, or 3)
remain indeterminate. A clear case definition of the AE(s)
is required in order to construct a case series which will
contain identical or similar AEs encompassed within the
signal. Following the identification of the cases series,
key criteria are employed to evaluate the signal. These
criteria include consistency between cases, positive re-
challenge / de-challenge, lack of alternate explanations
and a known mechanism, to name a few.
Signal Analysis
For signal analysis, validated or verified signals are
assessed to determine their impact on the benefit:risk
profile and public health. Signals that alter the benefit:risk
profile of a drug may represent a significant medical
impact on the population using the drug. Important
criteria for signal analysis can include severity, potential
for prevention, and medical necessity of the drug.
Signal Assessment and Recommendations for Action
The assessment step identifies appropriate activities
for all signals, especially validated or verified signals.
Refuted and indeterminate signals should be documented
in a tracking sheet as future AEs could upgrade a refuted
signal to a validated signal. All aspects of the signal
management process should be tracked and documented,
such as strategy, actions, and decisions which need to
be recorded in a consistent and formal manner with
appropriate sign-off. Possible action might include:
reporting to regulatory agencies, updating product
information, and public statements, to name a few.
Challenges and Solutions
Throughout the entire signal management process,
multiple challenges can be encountered. During the initial
data-sourcing step, capturing the entire global safety
data accurately and completely is quite complicated and
can be fraught with error. Cases with inaccurate terms
or scant data will lead to missed or erroneous signals.
By employing good pharmacovigilance practice in data
collection, medical review, and query resolution combined
with a well-managed database, organisations can ensure
quality and complete data sets. Signal detection requires
the appropriate choice of a methodology as well as
the selection of both the DA and criteria to identify a
signal. The selection of a signal detection methodology
is often based on the database size, type of product(s)
and the resources available, while appropriate sensitivity,
specificity and predictive value dominate the choice for
DA and signal criteria. For example, computer-aided
statistical methods and data-mining algorithms (DMA)
might not be the optimal choice for a small dataset from
a statistical and economic point of view, as traditional
methods may offer advantages.
Complexities are often seen in the signal validation
process, as this process requires the review of numerous
individual cases to validate or verify a signal, and is
usually the most labour-intensive part of the entire
process. At present few solutions exist to remedy the
significant resource required, however clearly-written
SOPS (standard operating processes) and having well-

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Regulatory
trained PV scientists on staff will help streamline the
activities and minimise the introduction of errors.
To minimise risks associated with approved medical
products, PV professionals must decide whether their
current systems for detecting and adjudicating safety
information rely too heavily on manual methods,
which may be time-consuming, more prone to human
error, and lack the enhanced statistical methodologies.
Advantages can be seen with automated processes
allowing organisations to reap benefits including time,
cost, enhanced statistical methodologies, and a reduced
rate of human error. In the end, good signal management
depends on an accurate and thorough decision-making
process that must be logical, based on medical and
epidemiological principles, and clearly documented.
Future Trends in Signal Management
Sincethesignalmanagementprocessesmustbecompliant,
rapid, and efficient, automated signal management
processes that ensure high-quality outcomes will likely
become a future trend. In an automated process,
computerised algorithms will be able to complete many of
the labour-intensive steps, in particular signal evaluation
and validation, involving activities at the case and AE
level. As a result, these computerised algorithms will
improve quality and significantly reduce time, resources
and expenses for signal management.
PV organisations need a consistent and centralised
system to track signals, ensuring all safety actions are
reconciled in a standardised manner, independent of
geography or therapeutic category. Having the ability to
customise a work automation solution based on specific
processes and procedures will allow organisations to
have the flexibility and agility required to make global
safety decisions. The risks and cost associated with
an inefficient and highly-siloed PV infrastructure are
enormous. Companies need to examine their safety
signal information systems and question whether these
systems provide needed information in a timely manner,
to minimise the risk to the patients and to avoid the
chance of a failed audit. Automation can dramatically
improve tracking of drug safety and risk management
information. PV teams can rapidly detect and resolve
safety signals, and provide regular updates to regulatory
authorities on the safety of approved products. Process
managers and executives can spot trends and raise alerts
quickly, and companies can produce accurate safety
reports that stand up to intense scrutiny from regulatory
bodies.
Summary
Identifying new potential risks and developing risk
minimisation action plans is at the heart of all PV activities
throughout a product life cycle. It is essential to partner
with a company that has the knowledge, technology
and expertise to qualitatively and quantitatively assess
safety data, identify new safety signals, develop risk
management plans (RMPs) for healthcare products and
have capabilities that include screening, data-mining
and frequency tabulations for potential signals. A signal
management partner can provide in-depth evaluation
of potential signals by further medical analysis of case
series, targeted literature search, and review of data from
external databases.
By taking advantage of end-to-end safety and
risk management services, companies can utilise
tools to allow superior, efficient signal detection by
incorporating computational algorithms for all methods
of signal detection, including the recent algorithms which
outperform previous versions. This type of automation,
with a user-friendly interface for navigation, graphical
depiction and drill-down analysis of signal data, can
dramatically improve drug safety and risk management
information tracking across PV activities, enabling the
proper, rapid movement of information while maximising
collaboration across the entire bio-pharmaceutical
enterprise.
References
1. Gagnon, S. et al. (2012) Global Clinical Trials Playbook. Academic Press.
2. EMA Guideline on good pharmacovigilance practices, EMA Module IX – Signal
Management. 2012
3. FDA Guidance for Industry: good pharmacovigilance practices and pharmacovigilant
assessment, March 2005
4. CIOMS Working Group VIII. Practical aspects of signal detection in
pharmacovigilance. Geneva, CIOMS, 2010.
5. ICH E2E. Harmonization tripartite guideline on pharmacovigilance planning, 2004.
6. EMA Guideline on good pharmacovigilance practices, EMA Module VI – Reporting
AEs. 2014
7. Pierce, J. (2014) Four Ways to Improve Pharmacovigilance Processes with
Automation. Applied Clinical Trials.
Dr Mitchell Gandelman, Principal, Global
Consultancy Services, Safety and Risk
Management, Sciformix Corporation. Dr
Gandelman has 20 years of pharmaceutical
industry experience in pharmacovigilance,
medical affairs and international clinical
development. As Principal, Global
Consultancy Services, Safety and Risk Management at
Sciformix, Mitch is responsible for planning and building
our Consultancy Services business. He spent most of his
career at Pfizer, holding numerous positions of increasing
importance. He led risk management activities as Vice
President, Global Safety and Risk Management including
the REMS subcommittee. He has also been employed at
Johnson and Johnson as Vice President, Global Safety and
Risk Management where he managed the PV Analytics
and Insight Group. Finally at Alexion Pharmaceuticals, he
was Vice President and Head of the Pharmacovigilance
Group. Prior to joining Pfizer, Mitch was on the faculty at
Yale in the Department of Psychiatry and was involved
in Brain Imaging Research. He received his MD from the
University of Connecticut, a PhD in Chemistry from the
University of Colorado, and a BS in Chemistry from Trinity
College.