Download Division of Clinical Pharmacology Department of Medicine and Health Sciences Linköping University

Division of Clinical Pharmacology
Department of Medicine and Health Sciences
Linköping University
Sweden
Drug interaction surveillance
using individual case safety reports
Johanna Strandell
Linköping 2011
I
© Johanna Strandell, 2011
Division of Clinical Pharmacology
Department of Medicine and Health Sciences
Faculty of Health Sciences
Linköping University
SE-581 85 Linköping
Sweden
ISBN: 978-91-7393-106-9
ISSN: 0345-0082
Linköping University Medical Dissertations No. 1252
Printed in Sweden by Liu-Tryck, Linköping, 2011
II
Abstract
Background: Drug interactions resulting in adverse drug reactions (ADRs) represent a major
health problem both for individuals and society in general. Post-marketing
pharmacovigilance reporting databases with compiled individual case safety reports (ICSRs)
have been shown to be particularly useful in the detection of novel drug - ADR
combinations, though these reports have not been fully used to detect adverse drug
interactions.
Aim: To explore the potential to identify drug interactions using ICSRs and to develop a
method to facilitate the detection of adverse drug interaction signals in the WHO Global
ICSR Database, VigiBase.
Methods: All six studies included in this thesis are based on ICSRs available in VigiBase. Two
studies aimed to characterise drug interactions reported in VigiBase. In the first study we
examined if contraindicated drug combinations (given in a reference source of drug
interactions) were reported on the individual reports in the database, and in the second
study we examined the scientific literature for interaction mechanisms for drug
combinations most frequently co-reported as interacting in VigiBase. Two studies were case
series analyses where the individual reports were manually reviewed. The two remaining
studies aimed to develop a method to facilitate detection of novel adverse drug interactions
in VigiBase. One examined what information (referred to as indicators) was reported on
ICSRs in VigiBase before the interactions became listed in the literature. In the second
methodological study, logistic regression was used to set the relative weights of the
indicators to form triage algorithms. Three algorithms (one completely data driven, one
semi-automated and one based on clinical knowledge) based on pharmacological and
reported clinical information and the relative reporting rate of an ADR with a drug
combination were developed. The algorithms were then evaluated against a set of 100
randomly selected case series with potential adverse drug interactions. The algorithm’s
performances were then evaluated among DDAs with high coefficients.
Results: Drug interactions classified as contraindicated are reported on the individual
reports in VigiBase, although they are not necessarily recognised as interactions when
reported. The majority (113/123) of drug combinations suspected for being responsible for
an ADR were established drug interactions in the literature. Of the 113 drug interactions 46
(41%) were identified as purely pharmacodynamic; 28 (25%) as pharmacokinetic; 18 (16%)
were a mix of both types and for 21 (19%) the mechanism have not yet been identified.
Suspicions of a drug interaction explicitly noted by the reporter are much more
common for known adverse drug interactions than for drugs not known to interact.
The clinical evaluation of the triage algorithms showed that 20 were already known in the
literature, 30 were classified as signals and 50 as not signals. The performance of the semiautomated and the clinical algorithm were comparable. In the end the clinical algorithm was
chosen. At a relevant level, 38% were of the adverse drug interactions were already known
in the literature and of the remaining 80% were classified as signals for this algorithm.
Conclusions: This thesis demonstrated that drug interactions can be identified in large postmarketing pharmacovigilance reporting databases. As both pharmacokinetic and
pharmacodynamic interactions were reported on ICSRs the surveillance system should aim
to detect both. The proposed triage algorithm had a high performance in comparison to the
disproportionality measure alone.
Key words: adverse drug reactions, adverse drug interaction surveillance, drug interactions,
individual case safety reports, postmarketing pharmacovigilance, signal detection
III
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Populärvetenskaplig sammanfattning
Läkemedelsinteraktioner som resulterar i biverkningar är ett betydande hälsoproblem för
såväl enskilda individer som för samhället i stort. Misstänkta läkemedelsinteraktioner nämns
sällan i enskilda biverkningsrapporter, vilket medför att oönskade interaktioner kan vara
svåra att upptäcka. Samtidigt är det sedan länge känt att enskilda biverkningsrapporter med
fördel kan användas för att identifiera signaler om nya läkemedelsbiverkningar.
Syftet med denna avhandling är dels att undersöka möjligheten att identifiera
läkemedelsinteraktioner i WHO:s globala biverkningsdatabas, VigiBase, dels att utveckla en
metod som underlättar upptäckten av okända läkemedelsinteraktioner.
De studier som ingår i denna avhandling är samtliga baserade på biverkningsrapporter som
förekommer i databasen VigiBase. Studierna undersöker bland annat huruvida
läkemedelsinteraktioner omnämns i de enskilda rapporterna samt hur identifieringen av nya
oönskade läkemedelsinteraktioner i VigiBase kan underlättas.
Resultatet från denna avhandling visar att läkemedelsinteraktioner klassificerade som
kontraindicerade, förekommer i rapporterna i VigiBase även om de inte alltid anges som
interagerande. Farmakodynamiska och farmakokinetiska mekanismer är involverade i de
läkemedelskombinationer som misstänks vara orsaken till att en läkemedelsbiverkning
uppkommer. Baserat på dessa resultat så utvecklades en strategi för att hitta nya okända
läkemedelsinteraktioner i VigiBase.
Denna avhandling visar på möjligheten att identifiera okända interaktioner med hjälp av
biverkningsrapporter. Eftersom sjukvården rapporterar biverkningar som ett resultat av
både farmakokinetiska och farmakodynamiska interaktioner bör övervakningssystemet
sträva efter att upptäcka båda dessa typer. Den presenterade strategin är mer effektiv än
nuvarande metoder, vilket är lovande i arbetet med att identifiera problematiska
läkemedelskombinationer.
Nyckelord: biverkningar, monitorering av läkemedelsinteraktioner, läkemedelsinteraktioner,
individuella biverkningsrapporter, farmakovigilans, signaldetektion
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List of Papers
This thesis is based on the following papers that will be referred to according to their
Roman numerals:
I.
Strandell J, Bate A, Lindquist M, Edwards IR; Swedish, Finnish, Interaction Xreferencing Drug-drug Interaction Database (SFINX Group). Drug-drug
interactions - a preventable patient safety issue? Br J Clin Pharmacol 2008;
65(1):144-6.
II.
Strandell J, Bate A, Hägg S, Edwards IR. Rhabdomyolysis a result of
azithromycin and statins: an unrecognized interaction. Br J Clin Pharmacol
2009;68(3):427-34.
III.
Strandell J, Wahlin S. Pharmacodynamic and pharmacokinetic drug
interactions reported to VigiBase, the WHO Global Individual Case Safety
Report Database. Eur J Clin Pharmacol 2011;67(6):633-41.
IV.
Strandell J, Caster O, Bate A, Norén GN, Edwards IR. Reporting patterns
indicative of adverse drug interactions – a systematic evaluation. Drug Saf
2011;34(3):253-266.
V.
Strandell J, Norén GN, Hägg S. Key Elements in Adverse Drug Interaction
Safety Signals. Submitted to Drug Safety.
VI.
Strandell J, Caster O, Hopstadius J, Edwards IR, Norén GN. Triage algorithms
for early discovery of adverse drug interactions. Submitted to Drug Safety.
The published papers are reprinted with permission of the copyright holders.
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Abbreviations and Acronyms
ADR
AERS
ASA
ATC
CYP
DDA
DIPS
HCP
IC
ICSR
INR
LHR
MAH
MAO
MedDRA
MPA
NSAID
OTC
PD
PK
PMS
RCT
SPC
SWEDIS
UMC
US FDA
VigiBase
WHO
WHO-ART
WHO DDE
Ω
Adverse Drug Reaction
US Food and Drug Administration’s Adverse Event Reporting System
Acetylsalicylic acid
Anatomical Therapeutic Chemical Classification
Cytochrome P450
Drug-Drug-Adverse Drug Reaction
Drug interaction probability scale
Health Care Professional
Information Component
Individual Case Safety Report
International Normalized Ratio
Longitudinal health care records
Marketing Authorisation Holder
Monoamine oxidase
Medical Dictionary for Regulatory Activities
Swedish Medical Product Agency
Non-Steroidal Anti-Inflammatory Drug
Over the counter
Pharmacodynamic
Pharmacokinetic
Post-Marketing Surveillance
Randomised Clinical Trial
Summary of Product Characteristics
Swedish Drug Information System
Uppsala Monitoring Centre
US Food and Drug Administration
WHO Global Individual Case Safety Report Database
World Health Organisation
WHO Adverse Reaction Terminology
WHO Drug Dictionary Enhanced
Omega, a three-way measure of disproportionality
IX
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Definitions and terminology
ADR (WHO)
A response to a drug which is noxious and unintended, and
which occurs at doses normally used in man for the
prophylaxis, diagnosis, or therapy of disease, or for the
modification of physiological function.[1]
ADR(Edwards and Aronson) An appreciably harmful or unpleasant reaction, resulting from
an intervention related to the use of a medicinal product,
which predicts hazard from future administration and
warrants prevention or specific treatment, or alteration of the
dosage regimen, or withdrawal of the product.[2]
Drug
Any substance or combination of substances presented as
having properties for treating or preventing disease in human
beings; or any substance or combination of substances which
may be used in or administered to human beings either with a
view to restoring, correcting or modifying physiological
functions by exerting a pharmacological, immunological or
metabolic action, or to making a medical diagnosis.[3]
Drug Interaction
The effects of one drug are changed in the presence of
another drug, herbal medicine, food, drink or by some
environmental chemical agent.[4]
Pharmacovigilance
The science and activities relating to the detection,
assessment, understanding and prevention of adverse effects
or any other drug-related problems.[5]
Signal (WHO)
Reported information on a possible causal relationship
between an adverse event and a drug, the relationship being
unknown, or incompletely documented previously.
Note: A signal is an evaluated combination which is considered
important to investigate further. A signal may refer to new
information on an already known combination. 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.[1]
Signal (CIOMS VIII)
Information that arises from one or multiple sources (including
observations and experiments), which suggests a new
potentially causal association, or a new aspect of a known
association, between an intervention and an event or a set of
related events, either adverse or beneficial, that is judged to
be of sufficient likelihood to justify verificatory action.[6]
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Contents
BACKGROUND ............................................................................................................................... 1
DRUG SAFETY ............................................................................................................................................................. 2
Historic background ............................................................................................................................................. 2
Adverse drug reactions ........................................................................................................................................ 3
Pharmacovigilance ................................................................................................................................................ 4
Individual case safety reports .............................................................................................................................. 5
WHO Programme for International Drug Monitoring ................................................................................. 6
Signal detection ..................................................................................................................................................... 7
DRUG INTERACTIONS................................................................................................................................................ 8
Historic background ............................................................................................................................................. 9
Pharmacology ........................................................................................................................................................ 9
Epidemiology ....................................................................................................................................................... 10
Drug interaction surveillance ............................................................................................................................ 12
Drug interaction surveillance using individual case safety reports ............................................................. 13
AIMS OF THIS THESIS .................................................................................................................15
MATERIAL AND METHODS .......................................................................................................16
DATA SOURCES ......................................................................................................................................................... 17
VigiBase ................................................................................................................................................................ 17
Reference sources of drug interactions ........................................................................................................... 21
METHODS................................................................................................................................................................... 23
Causality assessment method ............................................................................................................................ 23
Statistical methods .............................................................................................................................................. 23
RESULTS ....................................................................................................................................... 25
CHARACTERISTICS OF SUSPECTED/POTENTIAL DRUG INTERACTIONS .......................................................... 26
SIGNAL DETECTION AND INFORMATION STRENGTHENING CAUSALITY ....................................................... 27
REPORTING RATE OF ADVERSE DRUG INTERACTIONS ...................................................................................... 29
INDICATORS OF ADVERSE DRUG INTERACTIONS ............................................................................................... 30
TRIAGE ALGORITHMS FOR DETECTION OF ADVERSE DRUG INTERACTIONS ................................................ 31
DISCUSSION ................................................................................................................................. 33
CONCLUSIONS ............................................................................................................................ 37
FUTURE PERSPECTIVES ............................................................................................................................................ 38
ACKNOWLEDGEMENTS ............................................................................................................ 39
REFERENCES .............................................................................................................................. 40
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Background
Drugs have been used successfully to treat and prevent illnesses for long time and
have revolutionised health care. Today drug treatment is the most important
intervention for curing diseases and maintaining mankind’s well being. The number
of drugs used per individual has successively increased over time. For many
individuals the numerous drugs used are necessary, with undisputable benefits,
though for some patients multiple drug therapies (polypharmacy) are a result of
irrational and excessive drug use. No drug is absolutely free from harmful effects and
polypharmacy increases the risk of reactions related to drug use, adverse drug
reactions (ADRs), and ADRs as consequence of drug-drug interactions (adverse drug
interactions).[7-10]
This thesis focuses on early detection and surveillance of drug interactions in postmarketing pharmacovigilance reporting databases. For the early detection of novel
ADRs related to single drugs in large post-marketing pharmacovigilance reporting
databases computerised screening including measures of disproportionality and
selection strategies have been recognised as being essential.[11-13] However, for drug
interactions there are no systems in place currently (at least not published) that
apply computerised methods that incorporates other information than measures of
disproportionality, to detect adverse drug interaction signals.
1
Drug Safety
Historic background
Over the past centuries many drug related problems have been discovered and some
have changed our view on drugs. Two major drug disasters, sulfanliamide and
thalidomide (described in more detail below), have played a key role for the
awareness of ADRs as a real threat and have had a major impact on social guidelines
and drug regulation. All drug related incidents have contributed to the development
and processes of drug surveillance, though a more recent example, cerivastatin, is
described to show that there is still need for development of pharmacovigilance
world-wide, including surveillance of drug interactions.
Sulfanliamide
The liquid form of sulfanliamide (a sulfonamide indicated for streptococcal
infections) entered the drug market in 1937 in United States (US).[14] The drug had
previously been distributed in tablet and powder form without serious reactions. The
liquid formulation of sulfanilamide was reported to have caused deaths in more than
100 people in United States in 1937. The reason for problems with the liquid form
and not tablets were that sulfanilamide was dissolved in diethylene glycol (an
antifreeze agent used in windscreen washer fluid) which is deadly poison. The
disaster led to the passage of the 1938 Food, Drug, and Cosmetic Act, which
dramatically increased the US Food and Drug Administration's (FDAs) authority to
regulate drugs.
Thalidomide
In 1961 it was reported by William McBride that women who ingested thalidomide, a
non-barbiturate hypnotic agent indicated for nausea (morning sickness), gave birth
to children with skeletal malformations (phocomelia).[15] More than 10 000 children
worldwide (46 countries) have been affected by this very rare form of limb
reduction. Thalidomide was withdrawn from the global market during the early
1960s. Following the thalidomide disaster the need of closer drug monitoring to
detect novel adverse reactions was recognised and the incident led to the systematic
collection of suspected reports of adverse drug reactions on national and global
level. It also led to the development of other registers of birth defects for example.
Cerivastatin
In August 2001, cerivastatin, a HMG-CoA-reductase inhibitor (referred to as statin),
was voluntarily withdrawn by the market authorisation holder (MAH) from the global
market because of fatal reports of rhabdomyolysis.[16] The risk of rhabdomyolysis, a
rare and dose dependent reaction was therefore higher in patients using high
dosages (0.8 mg/day), or other drugs that potentially could increase the plasma
concentration of cerivastatin such as gemfibrozil. Even though the drug combination
was contraindicated,[17] 12 out of 31 deceased cerivastatin patients in United States
had received gemfibrozil concurrently.[16]
2
Adverse drug reactions
An ADR is defined as “a response to a drug which is noxious and unintended, and
which occurs at doses normally used in man for the prophylaxis, diagnosis, or
therapy of disease, or for the modification of physiological function” by the World
Health Organisation (WHO).[1] This definition has been widely used, although it does
not include errors in drug use or intoxications, or reactions related to contaminants
or inactive ingredients. Therefore, Edwards and Aronson definition of an adverse
reaction is used in this thesis: “an appreciably harmful or unpleasant reaction,
resulting from an intervention related to the use of a medicinal product, which
predicts hazard from future administration and warrants prevention or specific
treatment, or alteration of the dosage regimen, or withdrawal of the product”.[2]
Furthermore, adverse reaction is synonymously used with ADR in this thesis.
Incidence of adverse drug reactions
ADRs have been reported to account for 2.4% - 13.8% of all hospital admissions[18-22]
and been described as the fourth to seventh leading cause of death in Sweden and
the United States.[18,23] Drug-related hospital admissions are reported to lead to a
mean length of hospital stay of 6-13 days,[19,21-22] which is longer than for typical
medical admission[19,22] and therefore also more expensive.[22] In Germany, drugrelated hospital admissions were estimated to cost an average of 3700 Euros per
stay with annual costs of 400 million Euros.[22] A single drug-related hospital
admission was calculated to cost 2200 Euros in Sweden in 2002.[19]
Risk factors of adverse drug reactions
Factors that may influence the risk of experiencing an ADRs are drug dosage, drug
formulation, pharmacological properties of the drug, phenotype of the user affecting
the pharmacokinetics and pharmacodynamics of the drug, use of multiple drugs, and
drug-drug interactions.[24] Furthermore, females have been reported to experience
more ADRs than males.[19,21,25] Specific groups such as elderly, elderly with cognitive
impairment and individuals with specific diseases such as renal failure are also more
likely to experience ADRs.[24,26]
Type of adverse drug reactions
ADRs are commonly divided into type A and type B reactions.[2,24] Type A reactions
are characterised as being an augmented pharmacologic effect of the drug. These
effects are dose dependent and common (representing approximately 80% of all
ADRs).[18,22] Type A reactions are in theory preventable, as they can be predicted
from the pharmacological properties of the drug. It has been suggested that 18-73%
of these are preventable.[20,27-29] Type B reactions are unexpected as they are not
related to the pharmacological properties of the drug. Type B reactions are often
serious, occurring in a minority of patients and are often allergic or idiosyncratic
reactions.[30] In addition to type A and B reactions, ADRs have been further
categorised as C (chronic) that are relatively uncommon and related to the
cumulative dose (for example analgesic nephropathy), D (delayed reactions) are
3
uncommon and usually dose-related, occurring after some time of usage (for
example teratogenous effects), E (end of use) occur shortly after the drug is
withdrawn (for example opiate withdrawal syndrome), F (unexpected failure of
therapy) reactions, are common, dose-related and often caused by drug interactions
(for example inadequate effect of oral contraceptives during concurrent use of
enzyme inducers).[2]
Therapeutic ineffectiveness was not included in WHOs definitions of an ADR,[1-2]
although the lack of effect is reported as one of the most common drug related
problems.[31] Therapeutic ineffectiveness of a medicinal product may be the result of
pharmaceutical defects such as substandard and counterfeit drugs, resistance,
inappropriate use, tolerance or drug interactions.[32]
Adverse drug reactions and drugs involved in drug related admissions
Among the most commonly observed ADRs for patients with drug related admissions
are gastrointestinal complications including gastrointestinal bleedings, central
nervous system complications, cardiovascular disorders and hemorrhages.[19,21] Nonsteroidal anti-inflammatory drugs (NSAIDs), antithrombotic drugs, sedatives,
cardiovascular agents including cardiac stimulants and antiarrhythmics are some of
the pharmacological classes responsible for these drug-related admissions.[19,21,27]
Pharmacovigilance
Pharmacovigilance is usually described as ‘the science and activities relating to the
detection, assessment, understanding and prevention of adverse effects or any other
drug-related problem’.[5] This scope involves both pre and post marketing activities
with the primary endpoint to improve patient care and safety.
Before marketing a drug general pharmacology, efficacy and safety are tested. Premarketing studies include animal experiments (studying acute toxicity, dose
dependence, carcinogenicity and mutagenicity/teratogenicity), and three phases of
clinical testing on humans. Phase I is based on a small group of healthy volunteers to
gather preliminary data, whereas phase II includes patients to study efficacy, dosage
recommendations, and collect early safety data. In phase III, most often undertaken
as Randomised Clinical Trials (RCT), a group of patients are randomly assigned to the
drug of interest, or to placebo or a comparator. Because of the limited and restricted
study populations exhibited for a short study period in RCTs, only common ADRs and
ADRs occurring in the recent time frame are detected. ADRs occurring more rarely,
after a long time, or in populations previously excluded in RCTs (such as children,
elderly, pregnant women or patients with co-morbid conditions) will therefore not
be known at the time of marketing. Since the drug’s usage may evolve during the
drug’s life-time, post marketing studies (experimental studies (continuous RCTs) or
non-experimental pharmacoepidemiological studies, case reports (one patient), case
series (a collection of patients); case control studies; cohort studies and metaanalyses) will be essential to detect drug related problems including new ADRs,
frequency of ADRs, and to identify risk factors for developing ADRs for drugs after
approval.
4
Individual case safety reports
Following the thalidomide disaster world-wide national systems (referred postmarketing pharmacovigilance reporting databases in this thesis) were set up to
collect reports of suspected ADRs. These reports are referred to as spontaneous
reports or individual case safety reports (ICSRs), and synonymously referred to as
reports in this thesis.
ICSRs represent alerts of clinical concerns of drug related problems, occurring in
individuals in the real-world use of drugs. These reports have large population
coverage including patients with certain pre-dispositions, and patient groups that are
excluded from clinical trials such as pregnant women or children. ICSRs have been
described as a cornerstone in the early detection of the previously novel ADRs, postmarketing[33] and they are particularly useful in the detection of rare ADRs.
Furthermore, the post-marketing pharmacovigilance reporting system is an
inexpensive source of ADR information and is simple to manage.
National databases
During 1961-1965 the first countries Australia, Italy, Netherlands, New Zealand,
Sweden, United Kingdom and USA, started to systematically collect national reports
including suspected ADRs.[34] The reports are assessed, and stored in the individual
countries databases. The collection, assessment and regulatory action are often
maintained by the regulatory authority within that country. However in some
countries such as Netherlands is data collection separate from regulation. The
national reports and systems vary between countries in other aspects. For instance
the reporting requirements vary for individual countries for example in terms of who
is allowed to/should report (reporter). Some countries only allow reports from
physicians while others allow reports from all types of health care professionals
(physicians, pharmacists, nurses, and dentists) and/or reports from non health care
professionals such as consumers and lawyers. The nature of reports in a national
database may also vary if the reports are submitted via pharmaceutical companies,
or directly reported to the authority. There are also variations between national
databases in terms of drugs that are included in these collections. Some countries
include all drugs (traditional drugs, herbals and vaccines) in the same database, while
others separate vaccines, traditional drugs and herbals. Furthermore, some of the
national centres focus the analyses on aggregated data, while countries perform
manual causality assessments (assessments examining the relationship between a
drug and an adverse reaction) of all reports received. There is a range of operational
causality algorithms (Naranjo’s Scale,[35] WHO causality criteria,[36] French
algorithm[37]) available which results in variations of causality outcome. There are
also variations between national databases in terms of the level of suspicion that the
drug caused the ADR. The majority of databases include reports concerning ADRs
where the drug is believed to have caused the unpleasant reaction. While some
national databases include reports of adverse events[38] which are an adverse
outcome that occurs when a patient is taking a drug, though the adverse event is not
necessarily related to drug use.[2]
5
International databases
There are three large international post-marketing pharmacovigilance reporting
databases. The EudraVigilance (the European Medical Agency’s database) including 4
million reports (March 2011) with reports from countries within European Unions,[39]
the US Food and Drug Administration’s Adverse Event Reporting System (AERS)
including more than 4 million public reports (August 2011)[38] of which around one
third are foreign reports submitted from companies around the world, and the WHO
Global ICSR Database, VigiBase containing more than 6.6 million reports (August
2011) from 106 countries worldwide.[40] For country members of the European
Union (EU) reporting to EudraVigilance is mandatory, while the reporting to VigiBase
is not regulated by law for the countries which are members of the WHO Programme
for International Monitoring (see section WHO Programme for International
Monitoring).
When interpreting data from international databases one should consider that there
might be national variations in terms of who is allowed to report, the type of reports,
language (the national reports are usually provided in the official language/s of the
country), causality outcome, as well as the level of suspicion that the drug caused
the ADR (adverse reaction vs. adverse event). For the latter, reactions reported on
ICSRs are referred to as ADRs for simplicity in this thesis.
Limitations
ICSRs and the system maintaining these reports have well recognised disadvantages.
The dataset is often heterogeneous (for the reasons mentioned above) and these
reports sometimes lack clinical information which can make the causality assessment
difficult. Furthermore, underreporting together with increased reporting of known
associations (sometimes referred to as reporting bias), are two fundamental
challenges to effective ADR surveillance.[41-42] Another fundamental problem for
collections of ADR reports is the presence of duplicate reports that may lead to
inflated disproportionality measures[43] or distort the manual analysis. Because of the
influence of different sources of biases ICSRs are primarily useful for hypothesis
generation, in contrast to case-control, or cohort designs that are used to test
hypothesis.
WHO Programme for International Drug Monitoring
In 1968, ten countries with developed national reporting systems from Europe,
North America and Oceania1 agreed to compile their national data in an international
database with the intention to detect rare and serious ADRs as early as possible in an
international perspective.[44] This international collaboration was initiated by the
WHO and later formed the WHO Programme for International Drug Monitoring.
Since 1978 the WHO Collaborating Centre for International Drug Monitoring has
been responsible for the Programme. The centre is localised in Uppsala, Sweden and
operates under the name Uppsala Monitoring Centre (UMC). On behalf of the WHO
1 Australia, Canada, Czechoslovakia, Germany (the Federal Republic of Germany), Ireland,
Netherlands, New Zealand, Sweden, United Sates of America and United Kingdom.
6
Programme UMC is responsible for maintaining and analysing the global database,
WHO Global ICSR Database, VigiBase. As of August 2011, 106 countries were full
members and forwarded reports to VigiBase. Figure 1 shows countries members of
the WHO Programme for International Drug Monitoring (August 2011).
Figure 1. August 2011, WHO Programme for International Drug Monitoring member countries: full member
states (dark blue) and associated member countries (light blue). Associated member countries have not yet
successfully transmitted ICSRs to the international database and fulfilled other requirements.
Signal detection
The early discovery of ADRs for which the causality related to drug use has previously
not been established is referred to as signal detection. It should be stressed that a
signal in this thesis is a clinically evaluated association (drug-ADR, drug-drug or drugdrug-ADR), which is considered important, though it is preliminary and therefore
needs further investigation. With the exception for study VI (used CIOMS
definition)[6] WHO’s signal definition “reported information on a possible causal
relationship between an adverse event and a drug, the relationship being unknown
or incompletely documented previously”[1] has been used in this thesis.
Signal detection of single drug-ADRs in VigiBase
Data is collected in national as well as the international databases to detect drug
related problems. For this purpose some national centres perform case- by- case
analysis while others focus the analyses on aggregated data. The latter approach is
7
often done in large national databases and also in VigiBase. Because of the large
amount of data more advanced methods to facilitate detection of ADRs is needed.
Historically all drug-ADR combinations reported during the past quarter were
reviewed.[13] For obvious reasons this approach was not efficient, and to improve the
systematic detection of new signals in VigiBase quantitative signal detection was
implemented in 1998.[45] A measure of disproportionality referred to as the
Information Component (IC)[11] is used in VigiBase. The measure indicates how
frequently a single drug-ADR combination is reported in relation to the background
of the dataset. The IC is based on the observed and the expected reporting of a drugADR pair. The IC measure reflects the relative reporting rate of the drug-ADR in the
database and a positive IC value indicates that a particular drug-ADR pair is reported
more often than expected, based on all the reports in the database. The higher the
value of the IC, the more the combination stands out from the background. The IC025
is the lower limit of IC’s 95% credibility interval and IC025 used as the threshold in the
routine screening of VigiBase.
After implementation of the IC measure, the systematic surveillance of single drugADR combinations was even further improved by introducing selection strategies
(Triage algorithms) in 2001.[46] The current routine signal detection process in
VigiBase involves systematic screening of drug-ADR combinations listed on at least
one report entered into the database during the last quarter and having an IC025
above zero. After the initial screening are two Triage algorithms applied.[47] These
triage algorithms contain pre-defined criteria that need to be fulfilled on the case
series level. One of the algorithms examines drug-ADR combinations reported from
at least two countries and involving new drugs (defined as drugs entered into WHO
DD during the past five years), and serious terms (defined as critical terms according
to WHO-ART). A second algorithm focuses on drug-ADR combinations reported from
at least two countries and where the IC has increased with one unit (the ratio of the
observed to the expected number of reports has been doubled) since the last
quarter. After the systematic screening, literature sources[48-50] are reviewed to
assess if the ADR has been described for the drug of interest. If not, the individual
VigiBase reports are then examined for the relationship between a drug and an
adverse reaction. If more information is required for a thorough case analysis, the
original files (that generally provide more detailed information in the form of
narrative text for the individual report) are requested from the national authority
and reviewed. Topics that fall under the signal definition are then presented in the
SIGNAL document, which is circulated to the national centres.
Drug Interactions
A general description of a drug - drug interaction is when “the effects of one drug
are changed in the presence of another drug, herbal medicine, food, drink or by some
environmental chemical agent”.[4] The effects of the drug combination may be:
synergistic or additive; antagonistic or reduced; or altered or idiosyncratic, and it
may result in beneficial effects or adverse reactions.
8
This thesis focuses on drug interactions which have negative effects: adverse
reactions or failure of the therapeutic effects in humans. Subsequently interactions
as a result of pharmaceutical incompatibility are not covered. Furthermore, within
the scope of this thesis a drug interaction refers to the combination of two drugs,
and an adverse drug interaction is a drug-drug combination resulting in an ADR or
therapeutic failure of either drug.
In the general description of a drug interaction is a drug interaction with additive
pharmacodynamic effects excluded as the outcome of the two drugs is not more
than a direct result of their individual effects.[4,51] However, the risk of an ADR during
concurrent use of two drugs with additive effect may be synergistic. Since our
intention is to detect drug combinations that are of particular concern in health care,
are drug combinations with additive pharmacodynamic effects, but with synergistic
risks of ADRs during concurrent use included in our concept of drug interactions and
adverse drug interactions.
Historic background
The first reports on drug interactions in the literature concern the potential to
enhance or reduce the drug/s effect,[52] though it was not until the 1960s that
reports of clinically significant drug interactions began to appear in the literature.
Among the first clinically important interactions discovered were hypertensive crises
in patients who had taken concurrently certain cheeses and were using monoamine
oxidase (MAO) inhibitors.[53-56] During the 1960s the first drug interaction tabulations
appeared in journals and methods for systematically addressing drug interactions
when dispensing or prescribing were developed, including checklists of factors to be
asked, wall-chart systems, and monographs of drug interactions.[57] In 1970 the
regulatory agencies began to require pharmaceutical companies to issue annual
reviews of drug interactions in the national formularies.[56]
Pharmacology
Drug interaction mechanisms are categorised into two main groups, pharmacokinetic
and pharmacodynamic, depending on the principles that determine the drug’
behaviour in the human body.[4,51,58]
Pharmacokinetic interactions include mechanisms where the absorption,
distribution, metabolism or excretion of one drug is altered by a second drug, and
results in changes in the drug concentration.[4,51,58] A large proportion of potentially
clinically significant drug interactions are reported to occur by alterations in the drug
metabolism through inhibition and induction of enzymes and drug transport proteins
in the liver.[59] The outcome of changed metabolism depends on the drug, for
instance inhibition of an active drug can lead to rises in the concentration to toxic
levels, while for a pro-drug that is activated via the enzyme inhibition can lead to
reduced efficacy.[60] Among the most important enzymes involved in the metabolism
are cytochrome P450 enzymes (CYP). They are responsible for the metabolism in
approximately 50% of drugs used clinically. CYP3A4 is by far the most abundant
isoform accountable for most cytochrome P450-related metabolism of all marketed
9
drugs. Other isoforms often involved in drug metabolism are CYP1A2, CYP2C9, 2C19,
2D6 and CYP2E1. Inhibition of CYP enzymes is more common than induction.
Amongst other pharmacokinetic mechanisms are absorption which may be affected
via changes in the gastrointestinal pH or motility, damage in the gastrointestinal
tract, alterations in intestinal flora, and drug binding in the gastrointestinal tract;
[4,51,60-61]
distribution that may be changed via displacement of plasma proteins; and
excretion that primarily occurs in the kidney[58] and involves changes in the urinary
pH or renal blood flow including passive tubular re-absorption, glomerular filtration
and active tubular secretion.[4]
Pharmacodynamic interactions include mechanisms where the effect of one drug is
altered by a second drug at its site of action without changes in the drug
concentration.[4,51,61] These interactions can result in antagonistic, synergistic or
additive effects.
Time course
A drug interaction can occur within a couple minutes while others can take several
weeks to develop.[58] Although the time course of drug interactions may be relatively
consistent within a group of patients, there may be significant variations between
individuals. There are numerous factors explaining variations in the time course of
drug interactions e.g. half-life time, dosages, route of administration and whether
the active substance is the metabolite or the parent drug. For instance long half-life
time of the drug inducing the interaction means that it takes a longer period to reach
steady-state; whereas high doses of the affected drug during administration of
another drug that inhibits its elimination and intravenous administration result in a
shorter time period before the upper end of the therapeutic range is reached. The
time course varies also according to mechanism. For instance enzyme induction
occurs gradually and it can take several days up to weeks for the affected drug to
accumulate toxic levels.[4,58] In contrast, enzyme inhibition of CYP enzymes is rapid in
development and occurs within 2-3 days. Enzyme inhibition also dissipates more
rapidly than enzyme induction.[4,58] Interactions via renal excretion are similar to
enzyme inhibition as they often fairly rapid when occurring and dissipating and the
elimination is usually back to normal after 2-3 half-lives after the drug inducing the
interaction is discontinued. Pharmacodynamic interactions have in general a rapid
onset, though there are some more complex pharmacodynamic interactions which
are developed during a longer period.[58]
Epidemiology
Incidence of potential drug interactions
In primary health care, 4% to 70% of the patients are exposed to potential drug
interactions, of these are 1% to 26% considered clinically relevant.[10,62-68] However,
these figures may be under-estimated since traditional drugs provided for selfmedication and herbals also increase the risk of drug interactions, but are usually not
included in dispensed prescriptions.[69]
10
Incidence of adverse drug interactions
The magnitude of ADRs related to drug interactions in the existing literature is
inconsistent, reflecting the variety of settings, populations studied and the
methodology used and definitions applied. For instance results from a postmarketing pharmacovigilance reporting database suggest that 22% of patients
exposed to a potential drug interaction experienced an associated ADR,[70] while in
specific hospital settings drug interactions have been reported to cause from 1-21%
of all ADRs.[20,65]
Pharmacodynamic and pharmacokinetic adverse drug interactions
Few studies have examined drug interaction categories responsible for causing ADRs.
In a review of drug related reactions occurring during hospital stay, the majority
were pharmacodynamic (91.7%), pharmacokinetic (5.3%) and had both
pharmacodynamic-pharmacokinetic mechanisms (3%).[71] In another small study
investigating ADRs leading to hospital admissions, all drug interactions assessed as
responsible for the ADR were pharmacocodynamic.[68]
Risk factors of adverse drug interactions
An increased number of dispensed drugs have been reported to increase the risk of
experiencing adverse reactions related to drug interactions.[62] Other risk factors for
drug interactions related to the drug involved are high doses, route of
administration, long time drug therapies, drugs with self-induced or saturable
metabolism, substances with identical or similar pharmacological profile and drugs
with steep dose-response curves for which moderate changes in plasma
concentration may lead to significant increases in the drug effect.[58,72] Furthermore
substances with narrow therapeutic windows are more likely, in comparison to those
with broad therapeutic window, to be involved in adverse drug interactions.[58] In
addition, for many new drugs the risk of adverse drug interactions is increased as
they have complex mechanisms of action and multiple effects.[72]
Patients with particular risks of experiencing an ADR as a result of a drug interaction
are elderly (as they are often exposed to multiple drugs, have underlying diseases
and impaired homeostatic systems), individuals with hepatic or renal disease,
patients in intensive care (not only due to the number of drugs, but also because of
impaired homeostatic mechanisms), patients who undergo complicated surgical
procedures, transplant recipients, patients with more than one prescribing doctor.
Furthermore, there is also a risk of overdoses and augmented toxic effects in
patients whereas the CYP system is induced via genetically and/or environmental
factors such as chronic use of alcohol or nicotine.[24,73]
Problematic drug interactions in clinical practice
Drugs that have been reported to be involved in potentially serious drug interactions
are cardiovascular agents (including enalapril, digoxin, ramipril, furosemide and
11
spironolactone),[62,70] anti-inflammatoric drugs (acetylsalicylic acid and other NSAIDs
(diclofenac, naproxen, ibuprofen)) and anticoagulants (such as warfarin).[25,62,74]
Anticoagulants and antiplatelets have been reported as responsible for the greatest
number of fatal and serious reactions.[70]
Drug interaction surveillance
It is well established that polypharmacy increases the risk of adverse reactions
related to drug use, and ADRs as consequence of drug interactions. However,
polypharmacy including potentially interacting drug combinations will not lead to an
adverse outcome in every patient. The clinical impact of an adverse drug interaction
on the population level does not only depend on the seriousness of harm, but also
on the risk that the adverse drug interaction actually occurs, which is dependent on
to what extent the two drugs are co-prescribed, the existence of risk factors, and the
incidence of the adverse reaction. The risk of serious drug interactions is of concern
for the patient, the physician, the regulatory authority and society at large. It is also a
concern for the pharmaceutical companies marketing the drug since great economic
value may be at stake. Table I shows some examples of drugs that have been
withdrawn because of serious ADRs partly due to drug interactions. Since drug’s
usage may evolve during the drug’s life-time, new indications and new patient
groups will be exposed, post-marketing studies are essential in the process of
identifying new potential drug interactions.
Clinical surveillance
Many drug interactions are predictable as they are related to the pharmacokinetic
and pharmacodynamic effects of the drugs. To prevent unnecessary interactions
information should be available for the prescriber so that he or she can take action
to minimise the risk of adverse reactions or therapeutic failure by using an
alternative drug, making dose adjustment, or to monitor the patient. However, one
problem regarding information available for drug interactions today is the overload
of information and it can be difficult to retrieve, sort and incorporate all available
information in clinical decision making.[75] To improve the practitioners’ prescribing
habits computerised decision systems that prompt the physicians when prescribing
have been integrated in dispensing software, and these systems’ (when sending
relevant alerts) have been reported as successful.[76]
12
Table I. Examples of drugs that have been withdrawn worldwide or in some part of the world
because of serious ADRs partly related to drug interactions
Year
approved
1984
Year
withdrawn
1999
Affected drug (ATC class*)
1985
1997
Astemizole (other
antihistamines for systemic
use)
Terfenadine (other
antihistamines for systemic
use)
1993
2000
1997
2001
1997
1998
1997
2010
Drug/s inducing
the interaction
CYP3A4 inhibitors
ADRs
Torsade de
pointes
CYP3A4 inhibitors
Torsades de
Pointes and
cardiac
arrhythmias
Cisapride (propulsive)
CYP3A4 inhibitors
Torsade de
pointes
Cerivastatin (HMG CoA
reductase inhibitor)
Mibefradil** (other calcium
channel blockers with mainly
vascular effects)
Gemfibrozil and
other fibrates
CYP3A substances
Rhabdomyolysis
Sibutramine (centrally acting
antiobesity product)
CYP3A4 inhibitors.
MAO inhibitors
and other centrally
active drugs.
Cardiovascular
effects and
strokes.
CNS active drugs
increase the risk
for serotonin
syndrome.
Torsade de
pointes,
rhabdomyolysis
*Pharmacological subgroup
**Mibefradil (CYP 3A4 inhibitor) was withdrawn for its plausibility to affect other agents
Drug interaction surveillance using individual case safety reports
Even though post-marketing pharmacovigilance reporting system’s have been shown
to be particularly useful in detection of novel drug - ADRs combinations,[33] ICSRs
have not been fully used to detect adverse drug interactions. One explanation could
be that it can be difficult to interpret whether an ADR arise from a single drug or a
combination of two or more drugs in individual patients, and particularly in patients
with underlying risk factors such as multiple drugs and other diseases.
Among published drug interaction signals the majority have been generated from
regular case-by-case analysis performed by national or regional centres worldwide.
For instance, the Swedish post-marketing pharmacovigilance reporting system has
provided a range of signals about drug interactions with St John’s wort (hypericum
perforatum), between warfarin and tramadol, between warfarin and noscapine, and
between warfarin and tetracyclines.[77-81] The Swedish examples show that postmarketing pharmacovigilance reporting system’s can be a valuable source to detect
clinically relevant drug interactions. However this approach is not feasible in a large
database such as VigiBase where additional methods to facilitate the detection of
potential adverse drug interactions are needed.
13
In the beginning of this project (2008) we found that a disproportionality measure
Omega (Ω) (see section Statistical methods Omega (Ω)) used to detect adverse drug
interactions in VigiBase[82] highlighted several false positive adverse drug interactions
due to clusters of reports.[83] We also had examples of signals, found by manual
analysis (for instance warfarin - noscapine and bleeding[80]), that were not
highlighted with the measure of disproportionality. At that point the need for
efficient algorithms for detection of adverse drug interactions were clear, thus such a
method could not rely on Ω025 alone, as in the single drug – ADR surveillance in
VigiBase (see section Signal detection of single drug-ADRs in VigiBase). We therefore
we hypothesised that adverse drug interaction surveillance would be much more
effective if clinical information and the disproportionate reporting for adverse drug
interactions were combined.
14
Aims of this thesis
The overall purpose of this thesis was to explore the potential to identify drug
interactions using ICSRs and to develop a method to facilitate the detection of
adverse drug interactions signals in VigiBase. Specific objectives were:
I
To establish in what form known drug interactions are identified on ICSRs
and to determine whether these can give insight into the inappropriate
co-prescribing of drugs.
II
To examine if the case reports of the potential association of
azithromycin - statins and rhabdomyolysis is suggestive of a drug
interaction and how the reporting of this potential interaction has
changed over time.
III
To explore those drug combinations most frequently co-reported as
interacting in VigiBase, and categorise them with respect to the drug
interaction mechanisms.
IV
To systematically examine a set of indicators’ (information supportive of
drug interactions) propensity of highlighting suspected adverse drug
interactions in the time period before the interaction became known in
the literature.
V
To identify what reported information may support the identification of a
drug interaction safety signal, and to what extent this information is
available in structured format.
VI
To design triage algorithms for adverse drug interaction surveillance in
VigiBase, and to evaluate the algorithms prospectively relative to clinical
assessment.
15
Material and Methods
All studies in this thesis were based on ICSRs in VigiBase. Studies II and V also
included original reports from each respective country. Studies I, IV and VI used
information from drug interaction databases (Swedish, Finnish, INteraction Xreferencing drug-drug interaction database (SFINX database)[75] and Stockley’s Drug
Interactions[4].) Studies II, IV and VI applied a measure of three-way
disproportionality referred to as Ω (Omega).[82] In study V an operational algorithm
for causality assessment of drug interactions (Drug Interaction Probability Scale
(DIPS))[84] was applied. The sources and methods used are described in more detail
further down in this chapter.
Below are short summaries of data and methods used in each study (I-VI).
I
An explorative study where all drug combinations classified as
‘established’ and ‘clinically important’ drug interactions (given as D4) in
the SFINX database[75] were examined for their co-reporting on reports in
Vigibase.
II
A case series study where all reports in VigiBase, and the original files,
including azithromycin co-reported with any statin and rhabdomyolysis
were reviewed. The reporting over time in VigiBase was investigated by
generating Ω values retrospectively for rhabdomyolysis with
azithromycin and statins.
III
A descriptive study where drug combinations co-reported as interacting
in at least 20 reports in VigiBase during the past 20 years were examined.
Each drug combination was reviewed in the literature to identify if the
drug combination was known to interact and the mechanism of
interaction. Report characteristics were also examined.
IV
This study examined the reporting patterns for 322 known adverse drug
interactions in the time period before the adverse drug interactions
become known in the literature. The patterns for these known adverse
drug interactions were compared to group of 6440 drug-drug-ADR (DDA)
triplets where the drug combinations were not known to interact (the set
of interacting and non interacting DDAs was referred to as the reference
set). A reference set were created from information in Stockley’s Drug
Interaction Alerts. VigiBase reports including known adverse drug
interactions and non interacting drug combinations were screened for
indicators of drug interactions (for example pharmacological properties
such as common CYP metabolism, reported clinical information
suggestive of a drug interaction and a positive Ω025 measure indicating an
excessive reporting of the ADR and the drug combination) in the time
period before the drug interaction became established in the literature.
The results for known adverse drug interactions were compared to the
results for non interacting drug combinations.
V
The reports in VigiBase and original files referred to in three published
drug interaction signals were assessed using the causality assessment
16
tool DIPS.[84] Each DIPS element was evaluated for being available on the
VigiBase report and its corresponding original file. The retrieved case
information was specified as being listed in the structured fields, free text
and, in total.
VI
The reference set generated in study IV was used to develop a predictive
model for detection of novel adverse drug interactions in VigiBase. In
study IV individual indicators were studied, although in this study
additional indicators were introduced by combining the unique indicators
tested previously. Logistic regression was used to set the relative weights
of the indicators to form triage algorithms. Three algorithms (one
completely data driven, one semi-automated and one based on clinical
knowledge) were then evaluated against a set of 100 randomly selected
case series including potential adverse drug interactions. The algorithm’s
performances were then evaluated by comparing true positive rates for
drug-drug-ADR (DDA) triplets with high coefficients.
Data sources
VigiBase
VigiBase is a vast resource of medicine safety information and consists of more than
6.6 million ICSRs reported from 1968 and onwards by health care professionals and
non-health professionals from 106 countries worldwide. The volume of reports in
VigiBase varies greatly between countries and twelve countries contribute with over
two percent and in total 87%. See Figure 2.
Total proportion of reports per country in VigiBase
United States; 50%
All others;
13%
United
Kingdom; 9%
Germany; 6%
Netherlands; 2%
Canada; 5%
Italy; 2%
Sweden; 2%
Thailand; 2%
Spain; 3%
Australia; 4%
France;
4%
Figure 2. The proportion of reports in VigiBase per country (including countries contributing with at least 2%)
as of July 2011.
17
An individual report in VigiBase includes at least one drug suspected of causing the
adverse reaction, at least one suspected ADR, country of origin and an identification
number. Drugs and ADRs listed on the reports are mapped with standard
terminologies. Drugs are coded with WHO Drug Dictionary Enhanced and ADRs are
mapped with WHO-Adverse Reaction Terminology (ART)[41] or Medical Dictionary for
Regulatory Activities (MedDRA).[85] Drugs listed on the individual case reports are
assigned to one of the following categories: 1) “suspected” (drugs suspected of causing
the reaction, but not explicitly due to a drug interaction) or, 2)”interacting” (if an
adverse drug reaction is suspected of being related to a drug interaction between two
or more drugs) or 3) “concomitant” (drugs used concurrently but not suspected by the
reporter to have caused the ADR). In addition to the mandatory information (drug,
ADR, country of origin and identification number), the reports can also include more
detailed clinical information such as therapy dates, date of reaction (onset date),
dose information, route of administration and information regarding the outcome of
drug withdrawal and/or drug re-introduction. In 2003 the first reports in ICH/E2B
format was entered in VigiBase. This reporting format allows the reporter/sender
(the person at the national or regional centre sending the report) to include more
detailed and specific information such as case narrative and diagnostic tests which
permits a thorough case analysis.
Drug Interactions in VigiBase
On the 6.6 million case reports in VigiBase 27 million drug-drug-ADR triplets have
been reported at least once. These triplets could potentially represent suspected
adverse drug interactions. There are three alternatives of how a drug interaction can
be recorded on a report:
•
•
•
two drugs assigned as interacting,
drug interaction terms, or
a case narrative including the word stem ‘interact’ or ‘interact’.
A drug interaction is most explicitly noted when the drugs are listed as interacting. A
drug interaction term is in general less informative than when two drugs are
assigned as interacting, as the interacting effect cannot be directly related to the
specific drugs involved. Since interaction terms were recently (2010) added to the
WHO-ART terminology the following MedDRA preferred terms to define drug
interactions: Drug interaction, Labelled drug-drug interaction medication error,
Inhibitory drug interaction and Potentiating drug interaction. Information regarding
a suspected drug interaction is sometimes also available only in the form of free text.
To capture this interaction information free text searches including the individual
wording ‘interact’ or ‘interact’ were used. Furthermore, a drug interaction ascribed
in the free text is more difficult to manage in the systematic screening as some level
of text screening needs to be made, and the current method results in some false
positives (around 14%) interactions.[86] In this thesis an interaction report is defined
to include any of the three elements mentioned above.
In total, approximately 52 000 (0.8%) of VigiBase reports contains any of the three
above elements signifying a drug interaction. Figure 3 shows the proportion of
18
interaction reports per year. It should be noted that interaction reports up to 2002
only include reports having two drugs assigned as interacting. MedDRA interaction
terms have been reported since 2002 and reports including interaction in the free
text since 2003. As seen in Figure 3 the proportion of interaction reports are much
greater during 1985 and 1986. During these two years nearly 5000 reports were
incorrectly categorised as interacting. To avoid potential biases related to this
identified quality issue studies III-VI include reports from 1st of January 1990
onwards.
Proportion of interacting reports per year
5,0%
4,5%
4,0%
3,5%
3,0%
2,5%
2,0%
1,5%
1,0%
0,5%
0,0%
Figure 3. Percentage of reports with any indication of a drug interaction, including reports having two drugs
assigned as interacting, or a MedDRA interaction term or a drug interaction noted in the case narrative.
Of all interacting reports, two drugs assigned as interacting were reported on 0.5% of
the reports, MedDRA interaction term and Narrative both were listed on 0.2% of the
reports. There is an overlap between these variables i.e. some reports include
several of these variables. However the majority of reports include just one (around
90%). Figure 4 illustrates the overlap interacting variables on interaction reports
entered from January 1990 to October 2009 in VigiBase.[86]
The country distribution for suspected drug interaction reports in VigiBase contrasts
general distribution of reports in VigiBase.
VigiBase. In the general distribution contributes
USA with 50%, while for drug interactions the distribution is spread between top 10
reporting countries. See Figure 2 and Figure 5. 61 countries have submitted at least
one report containing a suspected drug interaction, though the majority of such
reports (84%) has been received from the top ten countries.
19
Narrative
20.7
6.0
3.4
1.9
MedDRA 4.9
term 29.3
Interacting
62.4
Figure 4. The overlap (in proportion)between interacting variables on interaction reports entered between
[86]
January 1990 to October 2009 in VigiBase.
Proportion of interaction reports per country
excluding reports entered during 1985-86
SWE
4%
FRA
15%
All others
16%
USA
4%
ESP
14%
NLD
4%
GBR
6%
CAN
7%
CHE DEU
7% 11%
AUS
12%
Figure 5. Top 10 countries having reported any indication of a drug interaction including reports with two drugs
assigned as interacting, MedDRA ADR term interaction or specified in the case narrative. Reports from 1985
and 1986 are excluded to avoid an identified quality issue in the categorisation of drugs as interacting among
these reports.
Data management
External drug interaction reference sources have been used in studies I, IV and VI. To
systematically access the information available in these sources the xml format was
used. The standard drug and ADR terminologies used in VigiBase permitted us to
automatically link the data in these external sources to VigiBase. Drugs within
literature references were mapped to their substance name in the WHO Drug
20
Dictionary Enhanced[41] and adverse reactions in the reference set used in studies IV
and VI were mapped to the WHO-ART terminology.
As mentioned above one of the fundamental problems for collections of ICSRs is the
presence of duplicate reports. VigiBase is therefore regularly screened for suspected
duplicate reports.[43] In the case analysis, study II, suspected duplicates reports were
highlighted with the automatic method and excluded from the further analysis when
confirmed via clinical evaluation. In the large, systematic screenings of VigiBase,
studies IV and VI, suspected duplicates reports were automatically identified and
only one report in a group of suspected duplicates were retained in the analysis (the
report with the greatest amount of information).
Ethical considerations
As all studies in this thesis are based on ICSRs which are anonymised no ethical
approvals were needed.
Reference sources of drug interactions
Swedish, Finnish, INteraction X-referencing drug-drug interaction (SFINX) database (Study I)
SFINX is a drug-drug interaction database containing potential interactions for
substances registered in Sweden and Finland.[75] The database is developed by staff
at the Department of Clinical Pharmacology, Karolinska Institute, Stockholm,
Sweden, Drug Interaction Unit at University Hospital in Turku, Finland, and the
Division of Drug Management and Informatics at Stockholm County Council,
Stockholm, Sweden. In Sweden, SFINX is integrated in the Janus medical journal
system in Stockholm county council, and accessible through the Internet[75,87] and in
Finland it is published in the largest medical portal.[75]
At the time of paper I, the SFINX database included around 5500 drug interactions
supported by scientific literature (Medline, Drugline, DrugDex, Stockley’s Drug
Interactions 6th ed 2002, Hansten and Horns Drug Interactions Analysis and
Management, European Public Assessment Report (EPARs), Swedish SPC and
Pharmaca Fennica). The drug interactions included were primarily pharmacokinetic,
but there were some pharmacodynamic interactions (unless they were apparent
from the main pharmacological action of the substances).
The drug interactions are classified according to clinical significance (A-D) and
documentation level (0-4). In study I we examined interactions classified as D4,
where clinical significance of D represents: “the interaction may result in serious
clinical consequences in terms of adverse drug reactions, therapeutic failure and is
difficult to master by individual dosages. The combination therefore ought to be
avoided” and a documentation level of 4 informs that: “the interaction has been
documented in controlled studies of the relevant patient material”. These drug
interactions were referred to as ‘established and clinically important’ in study I and
in this thesis.
21
Stockley’s Drug Interactions (Studies IV, VI)
Stockley’s Drug Interactions[4] is well-renowned and the most complete
international listing of drug interactions.[88] Stockley’s Drug Interaction Alerts,[89] is
derived from the text book/electronic publication. It is a web interface designed to
be used for reference at the point of prescribing or dispensing. It categorises and
summarises information of more than 40000 clinically evaluated drug–drug, drugherbal, drug–alcohol and drug–food pairs. Each of the alerts are rated and formed
into three separate categories: 1) action (describes whether or not any action
needs to be taken to accommodate the interaction and this category ranges from
‘avoid’ to ‘no action needed’); 2) severity (describes the likely effect of an
unmanaged interaction on the patient and this category ranges from ‘severe’ to
‘nothing expected’); 3) evidence (describes the weight of evidence behind the
interaction and this category ranges from ‘extensive’ to ‘theoretical’). For each
interaction a short summary is also provided describing the interaction and
indicating whether the drugs can safely be taken together, if they may alter the
therapeutic effect of one another, or may result in ADRs. Information in Stockley’s
Drug Interaction Alerts was used to create the reference set used in studies IV and
VI.
22
Methods
Causality assessment method
Drug interaction probability scale (DIPS) (Study V)
Causality assessment of suspected drug interactions is in general even more difficult
than that of single drug-ADR associations due to the complexity of having to consider
two drugs in parallel.
In study V we used a causality assessment algorithm for suspected drug interactions,
the drug interaction probability scale (DIPS)[84] which in general is intended to serve
as a reference for scientific publication of clinical case reports with large amount of
clinical information. The assessment includes general information whether there is
support in the literature that the drugs of interest may indeed interact, and whether
there is a plausible pharmacological basis for the interaction. It also evaluates
relevant properties of the clinical case at hand: whether the time course of the ADR
is consistent with the suspected drug interaction, whether there is information on
the effect on the drug interaction of withdrawing the drug inducing the interaction,
and whether the adverse reaction reappeared when drug inducing the interaction
was re-administered in the presence of the affected drug.
Statistical methods
The raw number of reports of a single drug-ADR combination or an adverse drug
interaction in post-marketing pharmacovigilance reporting databases cannot be
interpreted as an estimate of incidence in the population as there is no direct
information that describes the usage of drugs, frequency of ADRs, drug-ADR
combination or drug interactions in the general population. However, measures of
disproportionality can be used to gauge the relative reporting rates within the
dataset and to highlight for which drug-ADR combinations or adverse drug
interactions the reporting rate is greater than expected in relation to the
background. Studies II, IV and VI used the three-way disproportionality measure
Omega (Ω) which is described in more detail below.
Logistic regression[90] was used to set the relative weights of the indicators
(information supportive of a drug interaction) to form Triage algorithms in study VI.
Omega (Ω) (Studies II, IV, VI)
Omega (Ω) is an observed-to-expected measure of disproportionate reporting for a
drug-drug-ADR (DDA) triplet whose ultimate endpoint is to highlight potential
adverse drug interaction signals. The measure indicates how frequently a DDA is
represented in the dataset in comparison to what is expected based on the relative
reporting in the dataset. The observed counts are based on the relative frequency of
the ADR with both drugs together (i.e. the DDA). The expected reporting is based on
the relative frequencies for the ADR) without the two drugs (f00), 2) with drug1 but
not drug2 (f10), and 3) with drug2 but not drug1 (f01).
23
Figure 6 is a schematic figure of the concept of Ω. It’s an additive model where two
drugs causing the same ADR add to the risk of the ADR, independently of each other.
The method is described in more detail in Norén et al.[82] Loosely, a positive Ω
indicates that the risk of the ADR is greater if the two drugs are used together than if
they are taken one at a time. The formula for Ω is as follows:
Ω log observed 0.5
expected 0.5
Observed = the relative reporting rate of the ADR with both drugs
Expected = is based on relative frequencies for the ADR 1) without the two drugs (f00), 2) with drug1
but not drug2 (f10), and 3) with drug2 but not drug1 (f01)
The relative
frequency of the
ADR with drug1
without drug2 (f10)
ADR
The relative
frequency of the
ADR without drug1
or drug2 (f00)
The relative
frequency of the
ADR with drug2
without drug1 (f01)
Drug2
Drug1
The relative
frequency of the
ADR with drug1
and drug2 (f11 or
observed value)
Figure 6. Illustration of the principle for relative ADR frequencies on which the Ω measure is based. The small
pie in the middle f11 is the observed relative reporting rate for the DDA that is compared to expected relative
reporting rate that is based on f00, f10 and f01 and the number of reports with the drug combination (drug1 and
drug2) in the dataset.
In the formula above 0.5 is given. 0.5 is the shrinkage (a dampening value) which is
applied to stabilise the estimated value, in particular when there are few observed
reports (i.e. the number of reports for the DDA triplet) in the database. For example,
without the shrinkage 2 observed reports and 0.1 expected reports would lead to a
ratio of 20, but when the shrinkage is applied the ratio is 4.2. While applying the
shrinkage to 200 observed reports observed and 10 expected reports it has limited
impact as it gives a ratio of 19.1 which is almost the same as without the shrinkage
(20). The logarithm is applied to centralise the quotient of Ω around zero. Usually the
lower limit of the 95% credibility interval for Ω, Ω025, is used in order to increase
further statistical support.
24
Results
Below are short summaries of results presented in each study (I-VI).
I
This study showed that well established drug interactions are reported,
though not necessarily recognised as such on the reports in VigiBase. The
results from this study illustrated a long-standing international problem
of co-medication of contraindicated drugs.
II
The case reports of the potential association of azithromycin - statins and
rhabdomyolysis was suggestive of a drug interaction. The
reporter/sender assessments suspecting a possible interaction and
plausible time lapses between the drug interaction and reaction and
strengthened the causality. Azithromycin – statins - rhabdomyolysis was
reported more often than expected (in comparison to general reporting
to VigiBase) from 2000 and onwards as indicated with positive Ω025
values.
III
Drug interactions reported on globally collected ICSRs cover both
pharmacodynamic, specifically additive pharmacological effects, and
pharmacokinetic mechanisms, where the drugs involved have the same
metabolic pathway.
IV
Information that were reported on the ICSRs for known adverse drug
interactions to greater extent than for non interacting drug combinations
in the time period before a drug interaction became listed in the
literature are: suspicions of a drug interaction by the reporter as noted in
a case narrative, as an ADR term, or the assignment of the two drugs as
interacting, an increased effect, and a positive Ω025.
V
Plausible time lap between the drug interaction and reaction and
resolution of the ADR upon withdrawal of the drug inducing the
interaction were reported information that strengthens the causality of a
drug interaction on individual reports. For two of the three case series
provided the original files more complete information. While for one
case series there were small differences in the DIPS assessment between
VigiBase reports and original files.
VI
The clinical evaluation of the 100 randomly selected case series showed
that 20% were already known in the literature, 30% were classified as
signals and 50% as not signals. Even though the performance of the semiautomated and the clinical algorithm were comparable the clinical
algorithm was chosen as it is less complex, uses a broader basis of
qualitative information, and promotes CYP mediated interactions to a
less extent as well as incorporates Ω025. Among DDA triplets with high
coefficients (a true positive rate of ≥0.15): 38% were already known in
the literature and 80% of the remaining DDAs were classified as signals
worthy of further follow-up.
25
Study I showed that drug interactions are a longstanding (including reports from
1968 an onwards) and international problem (including reports from 50 countries).
However, only 14 countries2 had reported an interaction term in the time period
before an adverse drug interaction became established in the literature (unpublished
data from study IV). Demographics from the descriptive studies (I, II, III and V) are
summarised in Table II.
Table II. Summary of results from descriptive studies (I, II, III and V). Rhabdomyolysis as a result of
azithromycin and statins are presented in study II. In study V the series of coagulation disorders due to
concurrent use of omeprazole and coumarine derivates referred is to as series I and changes in international
normalized ratio (INR) related to the concurrent use of glucosamine and warfarin as series II.
Study
I
Total
number
of ICSRs
9547
Countries
reporting
Age distribution
Sex
distribution
Reporter
50 countries
-
Males 56%
Females 44%
41-85 years
(Median 60
years)
Males 58%
Females 42%
≤17: 3%
18-44: 14%
45-64: 21%
65-74: 21%
≥75: 33%
38-95 years
(Median 72
years)
Males 48%
Females 52%
HCP 50%
(Physicians 38%
Pharmacists 8%
Nurses and dentists 4%)
HCP 79%
(Physicians 51%
Pharmacists 19%
Nurses and dentists 9%)
HCP 83%
(Physicians 78%
Pharmacists 4%
Nurses and dentists 1%)
66% from USA
II
53
5 countries
87% from USA
III
3766
47 countries
67% from
Europe*
V.I
Males 53%
8 countries
HCP 93%
Netherlands 21%
Females 47%
Physicians 74%
Pharmacists 17%
Switzerland 17%
Nurses and dentists 2%
Germany 16%
V.II
33
6 countries
33-99 (Median
Male 61%;
HCP 81%
USA (33%)
70 years)
Female 39%
(Physicians 59%
AUS (27%)
Pharmacists 11%
UK (15%)
Nurses and dentists11%)
*In study III proportions for regions ere examined, therefore are data not provided for individual
countries
51
HCP= health care professionals
Characteristics of suspected/potential drug interactions (Studies I, III)
The majority (113/123) of drug combinations co-reported as interacting in study III,
were established to interact in the literature. Of the 113 drug interactions 46 (41%)
were identified as purely pharmacodynamic; 28 (25%) as pharmacokinetic; and 18
(16 %) were a mix of both types, and for 21 (19%) the mechanism have not yet been
identified. In this subset of drug combinations the most common mechanisms were
additive pharmacological effects and inhibition of metabolic pathway. Of the
2 Australia, Bulgaria, Canada, France, Germany, Ireland, Netherlands, New Zealand, Norway,
Spain, Sweden, Switzerland, United Kingdom, United States.
26
interactions acknowledged to a specific enzyme, CYP3A4 and CYP2C9 were
particularly frequent and these accounted for 42% and 24%, respectively. Other
enzymes involved were CYP1A2, CYP2B6, CYP2C19, CYP2C8 and epoxide hydrolase.
Despite different study designs in studies I and III, the results from these studies
cover a similar set of drugs were identified. A large proportion of the reports
involved drugs with a serious ADR profile and drugs with narrow therapeutic range
such as anticoagulants including warfarin, and anticonvulsants (carbamazepine and
phenytoin). A large proportion of reports also concerned drugs that have been
available on the market for more than 10 years.
Table III shows top 15 ADR terms for drug pairs reported ≥20 times as interacting in
VigiBase (study III).
Table III. Top 15 ADR terms for drug pairs reported ≥20 times as interacting in VigiBase (study III).
ADR Term
No of Reports
Critical Term
Prothrombin level decreased
402
x
Drug level increased
305
Haematoma
201
Therapeutic response increased
180
Anaemia
177
Rhabdomyolysis
172
Drug interaction
168
x
Melaena
163
x
Renal failure acute
152
x
Vomiting
130
INR increased
129
Gastrointestinal haemorrhage
127
Bradycardia
122
Drug toxicity
119
Nausea
110
x
INR: International Normalised Ratio.
Signal detection and information strengthening causality (Studies II, IV, V)
In study II a potential adverse drug interaction between azithromycin – statins had
been suspected by the reporter/sender in 11 of the 53 reports. The drugs were
assigned as interacting in three reports, and the interaction was noted in narrative
text in another eight cases. In total, azithromycin and a statin were both reported as
suspected or as interacting in 45% of the reports. Furthermore, the long term use of
statins with a rapid onset within 10 days of rhabdomyolysis from initiation of
azithromycin was also indicative of a drug interaction.[91-92] Since the majority of
reported statin doses were within the recommended daily doses[48-49] this suggests
that an external factor may have induced the interaction.
27
Similarly to what was reported in study II, study V showed that consistent time
lapses between the drug interaction and the ADR strengthened the suspected
causality of a drug interaction on the individual report. The strength of this
information varied with duration and initiation of the affected drug and the drug
inducing the interaction. Case causality was particularly strong when the drug
expected to induce the interaction, altered the effect of the affected drug shortly
after its initiation. Study V also showed that resolution of the ADR upon withdrawal
of the drug inducing the interaction (referred to as positive dechallenge)
strengthened the suspected causality of a drug interaction. Figure 7 shows one of
the reports included in study V; it exemplifies how re-exposure of glucosamine
affected warfarin’s expected outcome.
Figure 7. A sample of an original individual case safety report showing repeatedly raised INR values related to
re-introduction of glucosamine. The case report is reprinted with permission from Health Canada, Canada.
28
Study V also showed that the level of information provided on VigiBase reports and
original files varies. For two case series were the information given in the original
files more complete. While for the remaining case series there was small differences
in the DIPS assessment between VigiBase reports and original files. The variations
between the case series were primarily explained by three factors: country
submitting the report, reporting format, and when report was submitted.
Reporting rate of adverse drug interactions (Studies II, IV and VI)
Within the scope of this thesis the Ω (Omega) measure has been tested in practice
for detection of novel adverse drug interactions. The first prospective screening of
VigiBase using the Ω measure was done in July 2008. That screening highlighted an
increased relative reporting (Ω025 >0) of rhabdomyolysis under the concurrent use of
azithromycin and the individual statin: atorvastatin, lovastatin and simvastatin. This
resulted in a study II. To study the reporting over time in VigiBase Ω025 values was
retrospectively calculated. The retrospective analysis of rhabdomyolysis with
azithromycin – statins showed that the association was reported more often than
expected in comparison to general reporting to VigiBase from 2000 and onwards.
The results suggest that association could not be explained by high reporting of
statins observed since the withdrawal of cerivastatin in 2001.
Study IV was the first large scale evaluation relative to a comprehensive references
set that demonstrate the value of the Ω025 to highlight adverse drug interactions.
This study showed that positive Ω025 values are much more common for known
adverse drug interactions than for drugs not known to interact. Study IV also
demonstrated that reporting patterns based on excess reporting rates tend to
highlight other adverse drug interactions than those highlighted by detailed clinical
information. Although, the results in study VI show that Ω025>0 in isolation has a
more limited value than an algorithm based on broader scope of information (see
section Triage algorithms for detection of adverse drug interactions).
Underreporting of interacting drugs on the individual report
To increase our understanding of how adverse drug interactions are reported in
VigiBase a complementary analysis including DDAs highlighted in study I were done.
One of the examples was insulin-rosiglitazone-cardiac failure of which the US FDA
issued a warning in 2004.[93] Figure 8 shows the absolute number of reports
(including all drugs listed on the reports i.e. reported as suspected, interacting or
concomitant) of cardiac failure with rosiglitazone, insulin and the combination of the
two drugs. Both drugs are individually reported more frequently with cardiac failure
than the combination. Since the number of reports with cardiac failure for insulin
raised at the same time as rosigliatzone was launched, it may well be that
rosiglitazone is co-administered but not listed on insulin and cardiac failure reports.
If so, Figure 8 is an example of under-reporting of concurrently used agents.
However, this increase could also be related to the enhanced focus and reporting on
cardiac ADRs during the past decade, which occasionally would include cardiac
failure and also insulin.
29
Figure 8. Reporting trend including absolute numbers for rosiglitazone-cardiac failure, insulin-cardiac failure
and rosiglitazone-insulin- cardiac failure in VigiBase. 21 percent of all reports of insulin and cardiac failure also
had rosiglitazone co-reported.
Indicators of adverse drug interactions (Study IV)
Study IV showed that suspicions of a drug interaction noted by the reporter/sender
in a case narrative, as an ADR term, or the assignment of the two drugs as interacting
are much more common for known adverse drug interactions than for drugs not
known to interact. Excessive co-reporting of an ADR together with two drugs as
measured by the Ω025 and the co-reporting of enhanced therapeutic effect (effect
increased), were also much more frequent for known adverse drug interactions than
for drugs not known to interact. The study also demonstrated that reporting
patterns based on detailed clinical information tend to highlight other adverse drug
interactions than those with Ω025.
Figure 9 shows the proportion of DDAs occurring with the primary indicators
(indicators that may independently drive suspicion of an adverse drug interaction as
they provide clinical information to suggest that a suspected drug interaction has
occurred) among DDAs (known adverse drug interactions and DDAs where the drug
combinations is not known to interact) in the reference set developed in study IV.
The ratio between the groups and the numbers behind the proportions are given in
text.
30
Figure 9. Shows the proportion (with 95% confidence intervals) of DDAs occurring with the primary indicators,
among DDAs in the reference set developed in study IV. The ratio between the groups and the numbers behind
the proportions are given in text.
Triage algorithms for detection of adverse drug interactions (Study VI)
Three algorithms (a full data driven, a semi-automated (referred to as lean data
driven in paper VI) and a clinical algorithm (referred to as lean clinical in paper VI))
were examined. Though in practice the semi-automated and a clinical algorithm
were tested, since the full data driven algorithm was excluded as it was believed to
be over-adjusted to the reference set. The algorithms used a broader range of
information than what is customary in first-pass screening of adverse drug
interactions. Detailed reported information such as notes of suspected interactions
and indications of altered therapeutic effect, and case series driven by individual
case reports with strong support of an adverse drug interaction was utilised. The
algorithms also took into account whether the drugs are metabolised via the same
cytochrome P450 enzyme, and if so, if their activity may affect the metabolism of the
other drug involved.
The clinical evaluation included case series for 100 randomly selected adverse drug
interactions. Of these 100 were 20 already known adverse drug interactions in the
literature. Of the remaining 80 DDAs, 30 were classified as signals and 50 as not
signals. The performance of the semi-automated and the clinical algorithm were
comparable for DDAs with high coefficients. Furthermore, both algorithms out
perform the Ω025 measure when applied alone. See Figure 10, whereas the
algorithms show a higher true positive rate (more to the left) than the Ω025
(represented as the dotted line).
Even though the algorithms’ performances were comparable (see Figure 10) there
were clear advantages of choosing the clinical algorithm rather than the semi31
automatic: it is less complex, uses a broader basis of qualitative information, and
promotes CYP mediated interactions to a less extent as well as incorporates Ω025. To
estimate how well the clinical algorithm would work in practice we hypostasized that
we will be able to yearly assess around 2000 DDAs for the next 7 years (in total
14000). DDAs with a true positive rate of 0.15 or greater met this threshold. Of the
100 DDAs in the clinical evaluation, 16 cases series had a true positive rate of 0.15 or
greater, 6 (38%) were identified already known in the literature and 8 of the
remaining 10 (80%) were classified as signals worthy of further follow-up.
Figure 10. Receiver operating curves (ROC curves) comparing the triage algorithms to a pure disproportionality
triage based on Ω025 only, relative to the manual clinical review of the 80 DDAs in the evaluation data set not
found in the literature. The circle corresponds to a threshold of 0 for Ω025. The region to the left of the vertical
line is considered to be the one relevant in practical use. The clinical algorithm is referred to as the lean clinical
and the semi-automated algorithm as the lean data driven in study VI.
32
Discussion
Drug interactions represent an important drug health issue and if known they could
at least in theory be avoided. This thesis has demonstrated that post-marketing
pharmacovigilance reporting databases are a valuable source for surveillance of drug
interactions. The reported results within this thesis showed some important aspects
concerning drug interactions. In study I we found a continuing reporting of seemingly
well established interactions, these results suggests that individuals regularly take
high risk drugs together. The results in study I may also well be a reflection of real life
where physicians fail to recognise drug information and continue to prescribe
interacting drugs.[63-66,94-96] Furthermore, the results of drug combinations with
additive effects in study III could indicate that the pharmacological mechanism is not
apparent or understood by the prescriber[97] and perhaps a reflection of the
prescribers’ general knowledge of drug interactions. These results suggests that
multidisciplinary strategies including increased openness and understanding of
ADRs[98] and enhanced pharmacological knowledge among prescribers, as well as
enhanced routine prescribing practices are required to reduce future harm and costs
for preventable ADRs related to drug interactions.
We found in study III that the interactions were of both pharmacodynamic and
pharmacokinetic nature. In contrast to these results, results from hospital settings
report that the majority of drug interactions responsible for ADRs are
pharmacodynamic.[68,71] Most likely are our results a reflection of different treatment
strategies and drug usages in various settings (i.e. hospital versus primary care),
wherefore the heterogeneity sometimes regarded as a disadvantage of postmarketing pharmacovigilance reporting system (in comparison to other
epidemiological studies), was a clear advantage in study III. It is likely that both
pharmacodynamic and pharmacokinetic interactions are responsible for interactions
occurring in clinical practise, although the prominence of CYP mediated in study III
may have been influenced by selective reporting related to drug withdrawals (see
Table I) or the emphasis on these interactions in the literature.
A new drug interaction is often suspected when other substances within the same
pharmacological class have been shown to cause an interaction, in particular if the
drugs have the same pharmacokinetic properties. However, the azithromycin-statin
example in study II shows that potential drug interactions should not be completely
disregarded if a drug does not hold the same (or to the same extent)
pharmacokinetic properties as the others within the same pharmacological class.
There can be more mechanisms involved, even though they are not known or less
well understood at the time being. Furthermore the risk of a drug interaction may be
increased in particularly vulnerable patients.
Even if the regulatory post-marketing surveillance focuses on new drugs[99] the
results in studies I and III and data presented previously by Johansson and
Meyboom[100-101] jointly emphasise the importance of continuous monitoring of well
established drugs with a serious ADR profile. This is particularly important for drug
interactions, as problems related to the concurrent use of new drugs are constantly
being discovered.[79-81,102] Since post-marketing pharmacovigilance reporting
33
databases are representative of drug safety problems occurring in the entire
population for all drugs during their entire life span these reports will be a good costefficient alternative in the surveillance of adverse drug interactions.
For obvious reasons 100% reporting can never be achieved. The reporting of ADRs
relies on the recognition of drug induced effects and that the information regarding
the ADR is being transferred successfully from the patient via health care
professionals, sometimes via the market authorization holder, to the national
centres, and then to VigiBase. Some of the reasons for under-reporting[103] could be
that the patient may not recognise and report the adverse reaction; or the health
care system may fail to recognise the symptoms as a drug induced effect from one
single drug or from the combination of two drugs. Under-reporting in terms of drug
interactions have additional implications, for instance not all potential drug
interactions are recognised as interactions (studies I, II and V) or that not all
medicines taken by a patient who has experienced a suspected ADR is reported on
the individual reports (illustration of rosiglitazone and insulin in figure 8). These
findings were expected as the rarity of drug interactions on individual reports have
been described previously by van Puijenbroek,[12] and other studies have shown a
much higher rate of ADRs related to drug interactions (around 20%),[20,65,70] than
what is reported in VigiBase(0.8%).
Even if a drug interaction is rarely listed on the individual reports in VigiBase the
results in study IV showed that when an adverse drug interaction is recognised (by
the person providing the information to the national authority or by staff at the
national authority) it may be a good indicator of what becomes acknowledged as
adverse drug interactions in the future. The strengths and merits of ICSRs are
dependent on the quality of information provided. The possibility for thorough case
assessments of ICSRs is often influenced by the quality of the documentation of the
observations, and the amount of uncertainty in a report is often determined by the
absence of data. The absence of data is particularly a problem for adverse drug
interactions for which the assessments are in general more complex than the
assessment for single drug-ADR associations, as more data is needed to be able to
evaluate the reports. The reports in VigiBase are have sometimes been mentioned
to be of poor quality, however the results presented within this thesis demonstrates
that VigiBase reports contain sufficient qualitative information which can be used to
generate drug interaction signals.
The triage algorithm that we propose in study VI incorporates the knowledge that we
have gained through the individual projects in this thesis. For instance studies I and II
fed ideas on what information on an individual report supports an adverse drug
interaction and study III addressed the importance of detecting any drug
combination with an increased risk of an ADR during concurrent use independently if
the two drugs have additive or synergistic pharmacological effects. Figure 10 shows
that the algorithm is superior to Ω025 alone, which probably reflects its broad basis
using clinical and pharmacological information as well as the relative reporting rate.
In comparison to the first-pass screening of single drug-ADR in VigiBase (around
50%), the drug interaction triage has a lower proportion (38%) of already known
associations. It also highlights a greater proportion (80%) of the remaining
associations as potential signals. The relative figure for the pair-wise drug-ADR triage
34
strategy is around 20%. These results indicate that the proposed triage algorithm will
work more efficiently than the current triage algorithms for pair-wise associations.
To our knowledge clinical information listed on the case reports has not yet been
used in the automatic detection of ADRs ascribed one single drug and a similar
approach has therefore also the potential to improve pair-wise drug-ADR
surveillance. Though, it should be stressed that it is not necessarily an advantage
with a high proportion of signals. It is more important that signals have clinical value
and consist of some level of support. For instance the rising number of statistics of
disproportionate reporting (SDR)[6] (sometimes referred to as signals of
disproportionality i.e. signals that have not been evaluated clinically) (for single drugADRs) in the literature is a problem as it causes an overload of information, which is
difficult to retrieve, sort and interpret in clinical decision making. Furthermore, to
the best of our current knowledge, the interaction algorithm does not appear to
include associations confounded by data quality issues in the top ranked associations
as reported for pair-wise disproportionality measures by Bate et al.,[11] and
mentioned previously as a potential problem for disproportionality measures used
for adverse drug interactions detection (see section Drug interaction surveillance
using individual case safety reports).[83] The reason for this is probably that the
disproportionality measures exclusively rely on raw numbers of reports, which
makes the measures particularly vulnerable to selective reporting.[42] Selective
reporting was clearly a risk in the analysis of statins and rhabdomyolysis[104-105] (with
azithromycin), because of the world wide withdrawal of cerivastatin in 2001.
However the results in study II showed that the reporting of this potential
interaction had changed over time and the relative reporting rate was higher than
expected already before the statin publicity (2001). This shows that the association
could not be explained by the peculiarities of statin reporting observed in recent
years.
The results in studies IV and VI clearly shows that the systematic screening benefit
when only suspected and interacting drugs are included i.e. concomitant drugs does
not improve the systematic screening for potential drug interactions. The descriptive
analysis (I, II and V) on the other hand, show that there is a clear risk to miss
important cases if concomitant drugs are excluded. Because of the conflicting
information, our approach will therefore be to include drugs reported as suspected
and interacting in the first-pass screening, while the following case analyses should
include all reports with both drugs listed independently of how they are
characterised.
The reference literature used within the scope of this thesis have been essential.
These sources have had an apparent clinical and practical value on the conducted
studies. For example the evaluation of VigiBase has been more efficient. Without the
reference literature would the results in this thesis been more limited, and
presumably the methodological development wouldn’t had come as far. Since drug
interaction reference databases are often differently structured and use different
classification systems[4,58,75] the results could potentially vary with the reference
source. However, in study I it was verified that the majority of drug interactions
defined as contraindicated in SFINX, were also considered as severe in the
international comprehensive drug interaction source,[88] Stockley’s Drug
35
Interactions.[4] We can therefore assume that the subset of contraindicated drug
combinations is generalisable (these are drug combinations should be avoided in
clinical practice) and not specific to the developers of SFINX.
The main advantage of using causality algorithms in general rather than explorative
case analysis is to reduce the subjectivity. However, the main limitation with
causality methods is that there are no methods general enough to be applicable for
all types of problems. Using DIPS (in study V) was an important step towards a better
understanding of causality assessment for suspected adverse drug interactions.
However, it is clear that DIPS was originally developed for well-documented clinical
case reports rather than for those with scarcer information, which sometimes is the
issue for ICSRs. To apply DIPS in the context of broad adverse drug interaction
surveillance, the threshold for what constitutes a potentially interesting report
probably needs to be lowered. For example, de- and rechallenge interventions of the
drug believed to have induced the interaction without change to the affected drug
can be useful for causality assessment, but are feasible primarily for interactions
with limited clinical impact. For the large proportion of ICSRs that refer to serious
reactions, it would not be defensible to only withdraw the drug assumed to have
induced the interaction, and in practice both drugs are withdrawn simultaneously in
order to reduce toxicity as rapidly as possible. DIPS de- and rechallenge interventions
are appropriate in terms of the strength of causality but not by factoring in a
patient’s safety. Similarly, DIPS places great emphasis on established mechanisms for
the suspected interaction, but post-marketing safety surveillance must be able to
detect unexpected ADRs, also in the absence of well-understood mechanisms of the
interaction.
Finally, because of the influence of different sources of biases the results within the
scope of this thesis should be interpreted with caution. The raw number of reports
presented within these studies, per drug-drug combination, adverse drug
interactions or mechanism for drug interactions, should not be interpreted as an
estimate of incidence or frequency outside the database. Similarly, the
disproportionality measures (IC and Ω) describe the relative reporting rates within
the database and these measures are not representative of the true occurrence of a
drug - ADR or an adverse drug interaction. The triage strategy should be used for
hypothesis generation of adverse drug interaction signals, and not for signal testing,
where other methods are superior.
36
Conclusions
•
•
•
•
•
•
Drug interactions can be identified in large post-marketing
pharmacovigilance reporting databases.
ADRs related to drug interactions are a global problem. Many of the
interactions analysed and assessed within the scope of this thesis are
preventable. They include well established interactions with documented
risks as well as those that are expected from the drugs pharmacological
mechanism.
Drug interactions reported on globally collected ADR reports covers both
pharmacodynamic, specifically additive pharmacological effects, and
pharmacokinetic mechanisms primarily accredited inhibition of hepatic
CYP enzymes.
The results also demonstrate important differences in the reporting
patterns for adverse drug interactions and those of drugs not known to
interact. Suspicions of a drug interaction by the reporter as explicitly
noted in a case narrative, as an ADR term, or the assignment of the two
drugs as interacting are much more common for known adverse drug
interactions than for drugs not known to interact. Also, excessive coreporting of an ADR together with two drugs as measured by the Ω025
and the co-reporting of enhanced therapeutic effect were also much
more frequent for known adverse drug interactions than for non
interacting combinations.
Beyond the reporter’s notification of a suspected drug interaction,
positive dechallenge from the drug inducing the interaction and plausible
time lapses between the drug interaction and the ADR are clinical
information that strengthen the causality of an adverse drug interaction
signal.
A triage algorithm, promoting strong case reports, pharmacological
information more than relative reporting rates is developed. The
proposed triage algorithm is superior to the disproportionality measure,
Ω, alone.
37
Future perspectives
This thesis has shown that the post-marketing pharmacovigilance reporting system is
an important source in the future surveillance of adverse drug interactions. This is
important as often only combinations of two drugs are examined with respect to
interactions, although patients are often treated with several drugs in health care
today. However, to improve the international surveillance of drug interactions even
further, guidelines to harmonise the reporting of drug interactions are needed.
The algorithm for surveillance of adverse drug interactions will soon be put into
routine use at the UMC. This routine screening can hopefully aid on a global level to
detect novel adverse drug interactions that are important for public health.
However, the first-pass screening could be improved even further if one could
automatically identify strong cases including likely time lapses between the drug
interactions and the ADR as well as positive dechallenge of the drug inducing the
interaction.
The results in study VI clearly demonstrate the value of incorporating clinical
information and pharmacological information in first-pass screening, rather than just
using measures of disproportionality. The first-pass screening for single drug-ADR
combinations or in groups that are particularly vulnerable, such as children, elderly,
or patients with decreased renal or hepatic function, will also most likely benefit
from this approach. The systematic screening and clinical case analysis can be further
improved by having automatic access to clinical factors that are important for ADR
outcome: dosage, time to onset,[106-108] therapeutic window, half-life time, receptor
activity and specific chemical structures. However, more work needs to be done in
this field.
Electronic medical records which have potential for detecting new signals signal have
not yet been fully established.[109-110] Though these data sets have clear potential to
be used as background information and for signal strengthening for adverse drug
interaction surveillance as recently showed by Tatonetti et al.[111]
In Sweden there are exceptional opportunities to follow up patients via different
registers because of the personal identification numbers. Since its introduction in
2005 the Prescription register has been much used. This register has to some extent
been used for research on drug interactions[62,112-114] although much more
knowledge can be gained by linking this register with others (SWEDIS, forensic
databases, and hospital discharge registers) and drug-drug interaction databases.
38
Acknowledgements
I wish to express my warm and sincere gratitude to supervisors, colleagues, family,
and friends who have helped and supported me during the preparation of this thesis.
In particular I would like to thank the following:
Staffan Hägg, my main supervisor, for giving me the possibility to perform my PhD
studies under your supervision, for broadening my perspective of my work within
clinical pharmacology, and also for all support throughout the whole process!
Niklas Norén, my co-supervisor, for your day to day support, for challenging
questions and discussions, and for helping me finalise this thesis.
Andrew Bate, my former supervisor, for your enthusiasm, for believing in me and
inspiring me to pursue my PhD studies.
Marie Lindquist and Ralph Edwards for making me realise that pharmacovigilance is
a very important field of research and making it possible for me to perform my PhD
studies within this area: Marie, for being a role model and always sharing your
expertise on all various aspects of VigiBase; Ralph, for your wisdom and constructive
feedback.
Kristina Star, for broadening the scope of drug safety in my early days within
pharmacovigilance, for many interesting discussions, for being a great friend for
many years, and for being there for me when I needed it!
Ola Caster, for statistical support, for your efforts developing models, and thinking of
how to present our results most optimally.
Johan Hopstadius, for computational support, for your engagement and
encouragement during this work.
All my colleagues at the Uppsala Monitoring Centre for providing an excellent work
environment, particularly the members of past Signal Detection and Analysis team
who introduced me to pharmacovigilance: Anne Kiuru, Jenny Bate, Monica Plöen and
Ronald Meyboom; and my current colleagues at the Research department: Ghazaleh
Khodabakhshi, Tomas Bergvall and Kristina Juhlin for showing interest in our
research and support on various issues.
Colleagues at Linköping University and the Sahlgrenska Academy in Gothenburg.
The SFINX group for giving us access to your data, and particularly Birgit Eiermann
for making it possible. Pharmaceutical Press, for giving us the possibility to use
Stockley’s Drug Interactions as reference source.
My family and friends in Falköping, Gothenburg, Skåne, Uppsala and elsewhere. In
particular: my parents, for your longstanding support; my brother Fredrik and sister
in law Charlotta, for heaps of good fun and making my stay in Gothenburg during the
mandatory course very pleasant.
Finally I would like to thank Peter, my love and happiness, for all your love, and for
all patient support during this period; and Arvid, my little sunshine, for boosting me
with energy!
39
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