Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5432_Библиотеки_им_академика_М_И_Перельмана.pdf
CHAPTER
271
25
Signals and Signaling
in the Context of Risk
Management
E
normous efforts are made, in
terms of people, time, cost, and
technology, to collect adverse
event (AE) data in companies, govern-
ments, and elsewhere. The collection
of vast amounts of data is meaningless
in and of itself. It is only when these
data are organized and analyzed for
new safety issues (which are then acted
on in the context of risk management)
that the true value of this effort
becomes apparent. The hunt for mean-
ing is known as “signaling”.
The Signal — Definition
The Uppsala Monitoring Centre defines a safety signal
as follows:
“Reported information on a possible causal
relationship between an adverse event and
a drug, the relationship being unknown or
incompletely documented previously. Usually
more than a single report is required to generate
a signal, depending upon the seriousness of the
event and the quality of the information.”
They comment further:
“This describes the first alert of a problem with
a drug. By its nature a signal cannot be regarded
272 Cobert’s Manual of Drug Safety and Pharmacovigilance
as definitive but indicates the need for further
enquiry or action. On the other hand, it is pru-
dent to avoid a multiplicity of signals based on
single case reports since follow-up of all such
would be impractical and time consuming.
The definition allows for some flexibility in
approach to a signal based on the characteris-
tics of individual problems. Some would like a
“signal” to include new information on positive
drug effects, but this is outside the scope of a
drug safety Programme.”
1
Note, however, that there may be instances when a
single, well-characterized report with high attributabil-
ity to drug exposure, e.g., Stevens-Johnson syndrome,
may be useful in alerting to a new adverse reaction (see
below). A refined definition has been proposed and is
being used by experts:
“Information that arises from one or multiple
sources (including observations and experi-
ments), which suggests a new potentially causal
association, or a new aspect of a known associ-
ation, between an intervention and an event or
set of related events, either adverse or benefi-
cial, which would command regulatory, socie-
tal or clinical attention, and is judged to be of
sufficient likelihood to justify verificatory and,
when necessary, remedial actions.”
2
The EMA delivered a definition based on the pre-
vious publication in Regulation No. 520/2012, which
has been included in guidance on Signals (GVP Mod-
ule IX — Signal Management — 2012 updated in 2017)
with the following definition:
“For the purposes of this chapter, ‘signal’
means information arising from one or multi-
ple sources, including observations and experi-
ments, which suggests a new potentially causal
association, or a new aspect of a known associ-
ation between an intervention and an event or
set of related events, either adverse or beneficial,
1
Delamothe, Br Med J 1992; 304: 465.
2
Hauben A, Defining “signal” and its subtypes, Drug Safety 2009;
32(2): 99–100.
which is judged to be of sufficient likelihood to
justify verificatory action.”
“For the purpose of monitoring data in the
Eudravigilance database, only signals related to
an adverse reaction shall be considered.”
Not everyone agrees that all signals based on single
cases should not be pursued. Sometimes, however, rare
events are picked up after a single case, and certain AEs
are almost always due to drugs, e.g., Stevens–Johnson
Syndrome, skin fixed drug reactions. Thus, a single case
can be a signal, as indicated above. More common prob-
lems, e.g., myocardial infarctions, might not be worth
pursuing as a signal if there is only a single case in a
middle-aged diabetic smoker. But in a 22-year-old, it is
worth pursuing.
Signals may be “qualitative” (based on sponta-
neously reported data) or “quantitative” (based on data
mining, epidemiologic data, or trial data). The signal
may be a new issue never seen with this product, or
it may be the worsening or changing of a known AE
or problem, e.g., a previously unaffected patient group
is experiencing this problem, or the incidence has
increased, or it is now fatal in those it attacks, whereas
before it was not. As noted above, qualitative signals
may be based on one single striking case or on a collec-
tion of cases. In addition, qualitative signals may also
be based on pre-clinical findings; experience with other
similar products in the class (“class signals”); new drug
or food interactions; confusion with a product’s name,
packaging, or use; counterfeiting issues; quality prob-
lems; and more. Thus, the meaning of “signal” is being
expanded.
“Signal” is primarily used to refer to marketed prod-
ucts, although the term is occasionally used for new
issues in clinical trials. Some people use the term poten-
tial signal to indicate an issue with minimal data (e.g.,
only one case report), whereas others use the term weak
signal. However, these are not consensus terms.
Some signals are very difficult if not impossible to
pick up. Signals with very long latencies (onset well
after the drug use has ended) or which even skip a
generation (e.g., diethylstilbestrol and vaginal cancer)
require exceedingly astute observers or great luck to be
found.
Signals and Signaling in the Context of Risk Management 273
Identifying signals, however, is not enough. The
signal must be further investigated by doing what is
variously called a “signal workup”, a “signal inquiry”,
or a “pharmacovigilance investigation”. The ultimate
goal and true raison d’être of signal discovery and inves-
tigation is to determine whether the newly identified
concern is indeed due to the drug, and is of sufficient
severity and frequency (and clinical importance) in
relation to the benefit, to require alerting physicians,
nurses, pharmacists, and patients via a change in the
product labeling, restricting distribution, television,
internet, social media, and other announcements or, in
more severe cases, recalling of the product or stopping
a clinical trial. It is quite difficult to confirm causality
with any degree of certainty and these latter extreme
steps should only be considered when there is a high
level of confidence that such interventions will posi-
tively impact public health.
Signaling is not passive. It is proactive. No longer
does one wait for AEs or SUSARs before acting. Rather
an active signaling effort must be conducted through-
out a product’s life cycle. The goal is to detect signals as
early as possible to anticipate, evaluate, and minimize
problems, not to react to them after the fact.
Notes: When the first PV rules and organizations
were set up many years ago, PV people were spending
most of their time on case management (AE collection,
analysis, data entry, etc.) and very little time on safety
signals and risk management. In recent years, this trend
has been reversed: most of the PV skills, resources and
time are now directed toward signal detection, val-
idation, prioritization, assessment, and benefit-risk
management.
Also note that when you do searches the US spelling
of signaling and the UK spelling of signalling differ.
Signal Sources
and Generation
Signals are looked for in multiple ways. The oldest
method is essentially passive and relies on the collec-
tion by pharmaceutical companies, government health
authorities, or third-party organizations (academic
centers, medical registries) of spontaneous AE reports
and aggregate analyses of these reports plus any oth-
ers picked up from other sources, such as solicited
cases, compassionate use, surveys, etc. They are then
reviewed individually and in aggregate, looking for
“striking”, “unusual”, or “unexpected” AEs or safety
issues and trends. Medically qualified people (physi-
cians, nurses, pharmacists) examine large quantities
of data, attempting to find the proverbial needle in the
haystack, and either discuss within the organization
the “potential signals” found or post them publicly (see
FDA’s potential signal website: https://www.fda.gov/
Drugs/GuidanceComplianceRegulatoryInformation/
Surveillance/AdverseDrugEffects/ucm082196.htm).
This technique is elegantly known as “global introspec-
tion”. It is quite time-consuming and laborious, but in
the hands of astute and insightful clinicians does indeed
pick up major problems and still remains, in many
respects, the cornerstone of signal generation and iden-
tification around the world. A “similar case analysis”,
which entails review of additional cases that may shed
light on the condition of interest, is often very helpful
in understanding the medical importance of a newly
emerging signal. Global introspection obviously relies
on the good will and perspicacity of the reporting phy-
sicians, nurses, pharmacists, and patients to send AE
reports into the companies or health authorities (with-
out remuneration) and on the goodwill and competence
of the data analysts.
It is most sensitive in the following instances:
The signal is very unusual and rarely seen in gen-
eral, e.g., aplastic anemia;
The signal is rarely seen with that drug class (pul-
monary fibrosis with beta-blockers, e.g., practolol);
The signal is rarely seen in that cohort of patients,
e.g., cataracts in young non-diabetic patients;
The signal is fatal, particularly in patient groups
who classically do not have high mortality rates,
e.g., deaths in 20-year-olds;
The signal is expected to be seen because it has
been reported in other drugs in the same class, e.g.,
rhabdomyolysis with a new statin;
The signal is expected because it is due to an exag-
geration of the drug’s pharmacologic effect, e.g.,
syncope in patients taking an antihypertensive;
274 Cobert’s Manual of Drug Safety and Pharmacovigilance
The AE in question is seen almost exclusively with
drugs, e.g., fixed drug reaction;
The causality is crystal clear, e.g., the tablet is large
and sticky and gets stuck in the oral pharynx,
producing obstruction; or when immediate swell-
ing and itching is seen at the site of a drug being
injected;
No other drugs, OTC products, neutraceuticals are
being taken by the patient(s) in question;
The drug is being taken for a short time, and there
are no or few confounders;
The patients are otherwise healthy and have no
other medical problems beyond the one being
treated with the drug in question;
There is a positive rechallenge, i.e., reaction reap-
pears upon drug reintroduction after a positive
dechallenge; same pharmaceutical form and same
dosage);
The AE is different from the signs, symptoms, and
problems seen with the disease being treated and
would not be confused with the disease itself.
It is less sensitive when:
The signal has a high background incidence in the
general population, e.g., headaches, fatigue;
The signal has a high background incidence in the
population being treated, e.g., myocardial infarc-
tions in middle-aged hypertensive smokers;
The signal represents a worsening of the problem
being treated, e.g., fialuridine’s effect, producing
worsening and fatal hepatitis in patients being
treated for hepatitis;
The patients are taking multiple drugs, e.g., poly-
pharmacy, intensive care unit;
The patients have major underlying medical prob-
lems producing disease, signs, and symptoms, e.g.,
oncology patients;
The drug is taken chronically, and many intercur-
rent illnesses and problems occur over time, i.e.,
confounders interfere with interpretation;
There is a negative dechallenge, i.e., the reaction
continues even after stopping drug, or the drug in
question is not stopped in the patient and the AE
disappears by itself anyway.
Increased Frequency
This is a technique that has been on-again, off-again.
It has been in favor and out of favor. It basically relies
on a statistical calculation of reporting frequency in the
current period versus a previous period (e.g., 1Q2020
versus 1Q2019) to see if there is an increase in report-
ing of a particular AE. The technique is easy to do, and
can be computerized and run for all reported AEs in
the database for a drug. It has, in practice, turned out
to be not very useful. Although signals are, by defini-
tion, generated by this process (some AEs go up, i.e.,
are more frequent and, thus, represent a signal, some go
down, and some remain the same). However, increased
frequency calculations have, in general, turned out to
be false alarms or meaningless, or were easily picked up
by other means. Note that some potential confounders,
e.g., certain background events such as symptoms of
seasonal allergies, may vary over time and could impact
assessments of increased (or decreased) frequency.
Nonetheless, it is recommended that frequency anal-
yses be done in PSURs. See: EU GVP Module VII, which
indicates that examples of new signals would include an
“identified risk for which a higher frequency or severity
of the risk is newly found (e.g., in an indicated subpopu-
lation)”. The US Food and Drug Administration (FDA)
also required frequency analysis until 1997 in its NDA
periodic reports, but ended that because it was found
to be of little practical use. The FDA has, however, in
the draft post-marketing regulations published in 2003
(“The Tome”), FDA proposed reinstating its use. The
FDA has also recently asked that frequency analyses be
done in clinical trials to see whether an SAE’s occur-
rence (incidence) is rising.
Data Mining
This term is used, sometimes somewhat pejoratively,
to describe various automated or semi-automated
techniques that generate signals from existing, large,
post-marketing databases. These techniques use raw
case report data and arrays of drug-AE combinations
to calculate “expected” versus “observed” numbers or
Signals and Signaling in the Context of Risk Management 275
reporting rates (not frequencies), and use observations
of excess reporting as signals. Various techniques exist,
including proportional reporting rate (PRR), gamma
poisson shrinker (GPS), urn-model algorithm, report-
ing odds ratio (ROR), Bayesian confidence propagation
neural network-information component (BCPNN-IC),
adjusted residual score (ARS), and others. These tech-
niques attempt to extract signals that are not obvi-
ous using global introspection. Some feel that this is
a largely futile exercise since spontaneous reports are
“dirty” data with unknown and unknowable numer-
ators and denominators and one cannot “make a silk
purse out of a sow’s ear”.
However, much work is being done on making
better use of “dirty” data. One example of a success is
called fractional or proportional reporting rates (PRR),
also known as “disproportion” reporting rates or “sig-
nals of disproportionate reporting (SDRs)”:
For each AE, the calculation of the proportion of
that AE as a function of all AEs reported for a drug is
calculated and compared with the proportion of that
AE for all other drugs in the database. This essentially
involves running large numbers of 2 × 2 tables, orga-
nized as just described (see Table 1).
For example, liver failure for drug X was reported
95 times out of the 1,418 total AEs for drug X. For
the entire AE database of all drugs (except drug X),
the score of liver failure was, for example, found to be
2,243/41,540 = 0.054.
So, liver failure with drug X is seen with a propor-
tion (or “score”, “statistic”, “disproportion”, or PRR) of
1.24 (that is, 24% more liver failure with drug X com-
pared to the rest of the drugs in the database).
Is this a signal? In theory it is, since the propor-
tion is higher than for other drugs. But at only 24%,
this is somewhat small if other AEs show a proportion
of 200% or 400%. If the proportion of the AE for the
drug in question was the same as the proportion for the
whole database, the number would be 1.00. This means
that the same reporting rate for liver failure is seen
with drug X and the rest of the drugs in the database. If
there were proportionally fewer liver cases with drug X,
the score would be <1.00. Does this mean that drug X
actually protects against this AE? In theory, this is the
logical extension of this line of reasoning, but it would
take much more than this to think there is a therapeutic
effect (of sorts) to prevent this AE.
The level at which one considers a signal to be gener-
ated could be chosen as anything >1.00, although this will
probably produce many false positives. In practice, one
might take a high score above, say, 2.0 or more before one
starts considering these to be signals. If one does this for
all 80,000 or so MedDRA
®
terms, then one might expect
about 40,000 signals (values >1.0) if the distribution was
random. This is obviously not practical. Alternatively,
one might look at the top 10 or 20 scores. Nevertheless,
such an option is strongly debatable as we could miss or
not assess signals which could also be important. The
purpose is actually to manage all signals and not a pre-
determined number of signals. Another technique would
be to use more complicated filters such as a PRR >3 and
a chi-squared test >5 and more than three cases with the
drug in question. Whatever approach is used, it should
be documented and results should be archived along with
the interpretation and disposition.
It is also useful to look at the scores periodically
to see whether a particular AE is increasing. That is,
it is becoming more disproportionate over time and,
thus, may represent a stronger signal. However, there
may be other explanations, such as media stimulation
of reporting.
To be useful, the database must be reasonably large
(though it is hard to say how large). If additional cases
are needed to expand the database, it is possible to
review redacted cases from the FDA FAERS database,
the Health Canada safety database, the MHRA inter-
active Drug Analysis Profiles (iDAP, formerly Drug
Table 1.
Calculation of a Signal of
Disproportionate Reporting for
Liver Failure Demonstrated Using
Mock Data.
Drug X All Other Drugs
Liver Failure 95 2,243
All Other AEs 1,418 41,540
Calculation
(95/1418) / (2243/41540) = 1.24
0.067/0.054 = 1.24
276 Cobert’s Manual of Drug Safety and Pharmacovigilance
Analysis Prints) reports, and the EMA EudraVigilance
database (required by legislation). There may be signifi-
cant logistical issues in uploading or manually entering
cases from these databases into another database. There
are many other issues that may make this technique less
useful. The other drugs, patients, diseases, and charac-
teristics of the rest of the database should be similar to
that of the drug in question. An extreme example would
be studying injection site reactions for drug X compared
to the rest of the drugs in the database, none of which
is given by injection. There would be no injection site
reactions for tablets. Or more subtly, if the drug in ques-
tion is given mainly to elderly diabetics, comparing it to
the AE pattern of other drugs given to children would
similarly not be very meaningful.
Even if these data mining methods become part of
the signal detection process, it should be kept in mind
that a statistical link on reported ICSRs does not mean
there is a drug-induced causal relationship.
3
Various data mining techniques are also described
in FDA’s 2005 Guidance on Good Pharmacovigilance
Practices (see below). A comprehensive assessment of
various techniques is in “Practical Aspects of Signal
Detection in Pharmacovigilance”, Report of the CIOMS
Working Group VIII (2010, CIOMS, Geneva. ISBN:
9290360828).
As in other areas of drug development, it is expected
that AI will introduce changes and (hopefully) improve-
ments in signaling. Generative AI uses extremely large
databases and may aid in signal analysis. However, not
all Generative AI systems are attached to the internet
meaning that the databases are not using new data. It is
also not clear if adverse event data are being included
in the large databases. Since we are in the very early
stages of AI applications, pharmacovigilance personnel
should pay attention to new developments and tools
being developed in the AI world.
3
For further details, see Evans, Waller, Davis, Use of proportional
reporting ratios for signal generation from spontaneous adverse
drug reaction reports, Pharmacoepidemiol Drug Safety 2001; 10:
483–486. A comparison of different techniques and thresholds is
found in Hochberg, Hauben M, Pearson RK, et al., An evaluation
of three signal-detection algorithms using a highly inclusive refer-
ence event database, Drug Safety 2009; 32(6): 509–525; Deshpande,
Gogolak, Weiss Smith, Data mining in drug safety: review of pub-
lished threshold criteria for defining signals of disproportionate
reporting, Pharm Med 2010; 24(1): 37–43.
Other Sources of Signal Data
Information should be obtained, as appropriate, from
sources other than spontaneous reports. Other sources
include non-clinical study data, such as in vitro, toxi-
cology and pharmacology data, including animal data,
the medical and scientific literature, clinical trial data
(not all of which may be found in the drug safety data
base — non-serious trial AEs are not routinely kept in
drug safety’s database), external databases (FDA, UMC,
etc.), product quality complaints and manufacturing
deviations, regulatory authority comments in PSURs or
direct communications to the company, and so on. If
a Risk Management Plan (RMP) or REMS is in place,
signaling should be done with this in mind.
Putting It All Together
After data have been found from all the sources noted
above (ICSRs, aggregate data, data mining, solicited
cases, etc.), the results should be tabulated, reviewed,
and “triaged” to determine which findings deserve fur-
ther consideration now and which go into a “holding
box” waiting for more data. This is highly dependent
on experience and medical judgment, i.e., there is no
precise formula to determine which signals should be
investigated rapidly and aggressively and which can sit.
Some factors to consider include whether the drug is
widely used, whether the signal in question is serious/
severe or not, whether the patients are seriously ill,
whether the problem is reversible, whether the inves-
tigation is easily done, whether the outcome of the
investigation can be known in a shortish time rather
than years, whether there is health authority or other
external pressure (e.g., publicity, Internet activity), and
(probably unfortunately) monetary cost.
Organizational Team
Each organization, both drug companies and health
authorities, needs to have a formal team with the remit
of evaluating safety data and managing safety signals.
This is usually a multidisciplinary team that reviews,
analyzes, and may also make recommendations on sig-
nals. It may be empowered to make decisions or it may
Signals and Signaling in the Context of Risk Management 277
function to deliver data and multiple action choices to
more senior management personnel. Members include
physicians and healthcare personnel from drug safety,
epidemiology, clinical research/development, regula-
tory affairs, biostatistics, quality, risk management, legal
(sometimes), pharmacology/toxicology (sometimes),
manufacturing (sometimes), and others as needed,
including external subject-matter experts. Marketers
and sales personnel should not be on the team.
This team should be led by a PV representative and
must deliver output against goals, specifying how they
will be achieved. Strong safety signal governance is
essential because a periodic update on signal manage-
ment is required in each PSUR/PBRER. To do so, spe-
cific tools, means, and processes must be developed; ad
hoc expertise and skills must also be included in this
team.
Signal Workup
Once the signal list has been prepared, the list needs
to be prioritized for workup according to the criticality
of the signals, i.e., potential to impact the benefit–risk
assessment, and the resources available. Of course, lack
of resources is never an acceptable excuse for not work-
ing up a signal that is important to the public health.
This will be an unacceptable reason with the HA (or
in court!) for incomplete, inadequate, or slow signal
workup, which jeopardizes public health. But realisti-
cally speaking, resources will play a role in prioritizing.
Prioritize
There are many ways to prioritize signals. Red, yellow,
green is one way, or numerical priorities on a 1–5 scale
are also used. Whatever method is chosen, though, it
should be documented end-to-end and consistently
used. Exceptions will not be well looked upon by
inspectors.
Do an initial priority assessment. Highest prior-
ity should go to drugs that are new, where the AEs are
serious or severe, where there are tampering or coun-
terfeiting issues, where the patient population is ill or
apparently at high risk, where the drug is known to
be toxic (or has a narrow therapeutic window), where
many people use the drug, and to black triangle drugs
(the designation in the EU labeling for a product that is
subject to additional monitoring), etc. If a medicine is
labelled with the black triangle, this is generally because
there is less information available on it than on other
medicines because it is new to the market or there is
limited data on its long-term use. It does not mean that
the medicine is unsafe. For better or worse, other issues
also enter into prioritization, ones that are less related
to public health, such as politics, sales volume, need to
“protect” the drug, adverse publicity, showing due dili-
gence in tracking, and others.
Conversely, drugs whose AE profile is mild and
where few adverse consequences on the public health
are seen or expected would have lower priority. Minor
AEs of toxic drugs would probably fall somewhere in
the middle of prioritization.
Although difficult to do, efficacy should also be
taken into some account when deciding on initial pri-
oritization. Drugs with minimal efficacy with potential
new, severe AEs should have a higher priority. To put
it another way, if the drug in question was “placebo”
such that no efficacy was expected (forgetting placebo
effects for the moment), then no AEs at all should be
tolerated, and this drug would get a high priority for
signal workup.
The CIOMS VIII Working Group suggests the fol-
lowing points to consider in prioritizing signals: med-
ical significance (serious, irreversible, etc.), increasing
PRR scores, an important public health impact, easily
retrievable data elements, and temporal clustering.
See “Practical Aspects of Signal Detection in Pharma-
covigilance”, Report of the CIOMS Working Group VIII
(2010, CIOMS, Geneva. ISBN: 9290360828).
Arrange and Review
Next, the drugs in question should be arranged on a
spreadsheet or put into a database. There are various
ways to do this. Some suggestions are made here.
One may create an overall summary signaling
spreadsheet and then a daughter spreadsheet for each
drug/signal combination, e.g., one sheet for Drug X and
elevated liver tests or another sheet for Drug Y and atrial
arrhythmias. Cases or case series should be arranged
278 Cobert’s Manual of Drug Safety and Pharmacovigilance
on the sheet using a simple or augmented CIOMS II
line-listing format, with “augmented” referring to add-
ing additional data to the line listing, such as a brief nar-
rative, clinical course, or causality (see below). Cases
may be arranged by date, by seriousness, or by some
other factor. Various software programs are available for
useful and creative displays of the data (see below).
Next, it is often useful to do causality assessments.
In many cases, this should be done again at the time
of signal evaluation, even if the cases have an earlier
causality from the investigator, reporter, company, or
patient. Note that many companies do not do causality
assessments on spontaneous reports on intake, as they
are presumed to be possibly related by convention. Thus,
causality on these cases should be done now. Hindsight,
time, and new data may change the original causality
determinations. There is no uniformly accepted inter-
national classification. Choose a system and stick to it,
e.g., related, possibly related, weakly related, unrelated,
insufficient information/unknown. Such categories are
more useful for assessing serious reports from clinical
trials, but, regardless, a yes or no determination must be
made at the case level to factor into regulatory report-
ing: Either related or not related. Causality in signaling
is much more nuanced.
Causality should be assigned to individual cases
and to the group of cases as a whole. In a case series,
no single case may be clearly due to the drug, but the
weight of the evidence of the sum of the cases may
strongly suggest a likely signal. FDA takes the posi-
tion that, for clinical trials, it is usually not possible to
assign causality on a single case, but rather a case series
is required.
The signal should be assessed in terms of the
following:
Magnitude and seriousness of the reaction — public
health risk;
Demographics — age, gender, ethnic background,
weight;
Effect of exposure — duration and dose — changes
in risk over time;
Concomitant medications;
Drug interactions;
Comorbid conditions and other confounders;
Biological plausibility;
Available alternative treatments and therapies;
Other issues, e.g., HA request for workup, publicity.
Next, each drug/signal combination should be
assigned a signal level based on review of the cases and
causalities. Be reasonable in terms of what constitutes
a signal. Always keep in mind the benefit–risk balance:
not all risks can be eliminated. One such classification
is as follows:
Strong: A series of well-documented cases with
no alternative causes and ideally with at least one
positive rechallenge, i.e., rechallenge criterion not
applicable in, for example, irreversible adverse
events, hepatotoxicity, etc.
Fairly strong: A series of generally well-
documented cases with few alternative causes and
ideally at least one positive dechallenge.
Average: A series of cases of variable quality.
Fairly weak: A series of cases that have signifi-
cant limitations regarding plausible temporal asso-
ciations or for which there are likely alternative
explanations.
Weak: A series of cases that are generally incom-
pletely documented, lack plausible temporal asso-
ciations, or are generally explainable by alternative
causes or similarly.
And then assign an action:
A signal warranting immediate action to protect
public health. These actions may be temporary (if
the signal is ultimately determined to be unfounded)
or permanent;
Signal warranting intensive follow-up and further
investigation in the form of a clinical trial, an epide-
miologic trial, outside consultation, and so on;
Signal warranting further investigation and fol-
low-up of the current cases, e.g., for outcomes or to
be reexamined in, e.g., 60 days;
Weak signal: continue watching, i.e., no further
action at this time;
Not a signal: no further investigation needed.
Signals and Signaling in the Context of Risk Management 279
The Workup
At this stage, the signals that have been chosen for
workup should be so designated and the workup begun.
Various steps that can be done include the following:
Search for additional cases using the appropriate
MedDRA terms (or SMQs) in the clinical trial data-
base if some cases, e.g., non-serious clinical trial
AEs, are also found in or have not been entered into
the safety database;
Search for similar or additional cases in external
databases such as EMA’s EudraVigilance database,
Health Canada’s database, FDA’s FAERS database,
and the FDA Potential Signals of Serious Risks
listing:
Consider other databases that can be used for
epidemiologic studies in addition to the spon-
taneous reporting databases noted in the pre-
vious bullet. These include Prescription Event
Monitoring Databases (Drug Safety Research
Unit in the United Kingdom), Linked Admin-
istrative Databases (US private healthcare
databases), United Kingdom General Practice
Research Database (GPRD), as well as special-
ized databases, such as teratology databases or
disease-specific databases, e.g., cystic fibrosis,
and governmental databases, e.g., Canadian
provinces. The Organization Bridge to Data has
a compilation of more than 90 worldwide data-
bases with descriptions of their characteristics,
allowing the user to find databases that may suit
the signal workup. It may be useful to engage an
expert in pharmacoepidemiology at this point
to find the right databases and assist in design-
ing the appropriate study.
Search out additional literature cases using
PubMed, Google Scholar, or other search
engines and databases. See if the signal is listed
on FDA’s potential signal database.
Consider reviewing the AE profiles and class
effects of similar drugs in that class.
Consider more complex, time-consuming, and
expensive procedures to validate, strengthen,
or refute a signal, such as epidemiologic
(observational) studies in large databases (e.g.,
claims databases), to detect or find rare AEs and
obtain information in large patient populations
(e.g., tens of millions of patients), targeted clini-
cal trials, and large simple safety studies (LSSS).
The Conclusions and Next Steps
The reviewers should come to a conclusion or conclu-
sions for recommendation to the decision maker or
safety committee (see below). As noted, many classifi-
cations are available; pick one and stick to it. The con-
clusions classifications may be simpler perhaps along
the lines of the following:
Red Signal–High Priority: SAE previously
unknown or unlabeled or inadequately labeled.
Quality issues such as adulteration or contamina-
tion. This may be accompanied by media attention
and public scrutiny despite having only weak or
incompletely documented cases. If confirmed, will
lead to a reevaluation of the benefit–risk analysis
and likely a change in labeling, product withdrawal,
and so on.
Yellow Signal–Medium Priority: Further
evaluation of the signal is required but the criteria
of the Red category are not met. If confirmed, these
signals are expected to lead to a change in the risk–
benefit analysis and, thus, will require changes in
the labeling/packaging in the AE section and possi-
bly also in the indications, contraindications, warn-
ings, and AE sections.
Green Signal–Low Priority: AEs that are
already known or labeled and felt not to be a sig-
nificant safety problem. Signal investigation at this
time may be minimal, deferred, or simply kept on a
“watch list” looking for further case reports (if any)
before re-evaluation. Workup now would not be a
good use of resources.
Note 1: Most signals can be managed by PV experts,
with support from clinical, without support from
others such as external consultants.
Note 2: Signal tracking system: Depending on the
number of signals to be managed, an IT tool can