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Pharmacovigilance and the WHO Collaborating Centre for International Drug Monitoring in Uppsala Technical Briefing Seminar in Essential Medicines Policies, Geneva, October 2007. Ronald Meyboom, MD, PhD The Uppsala Monitoring Centre www.who-umc.org.

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Pharmacovigilance and the WHO Collaborating Centre for International Drug Monitoring in UppsalaTechnical Briefing Seminar in Essential Medicines Policies, Geneva, October 2007

Ronald Meyboom, MD, PhD

The Uppsala Monitoring Centre

www.who-umc.org

slide2

16% of hospital admissions are drug-related (medical ward). Nelson KM, Talbert RL. Pharmacotherapy 1996;16:701-7.Adverse drug reactions are the 5th leading cause of death in a hospital. Lazarou J. Pomeranz BH, Corey PN. JAMA 1998;279:1200-5.Avoidable in ca. 50 %

definition of pharmacovigilance who 2002 isbn 9241590157
Definition of Pharmacovigilance: (WHO, 2002, ISBN 9241590157)
  • The science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other drug-related problems
  • Treatment evaluation science
why pharmacovigilance
Why pharmacovigilance?
  • Clinical trials focus on demonstrating efficacy and tolerability (selected ‘healthy’ patients, limited in number and duration)
  • Incomplete knowledge, e.g. effectiveness, rare but serious adverse reactions, interactions, ‘real-live’ patients, sub-populations
  • Do not produce all the information needed for the balance of benefit and harm
how pharmacovigilance
How pharmacovigilance?
  • Spontaneous Reporting
  • Intensive monitoring (hospital)
  • Prescription Event Monitoring
  • Case Control Surveillance
  • Comprehensive population databases, data-mining
  • Patient series
  • Observational studies
formal studies vigilance
Defined aim (identified problem)

Hypothesis testing (problem solving)

Established methods (clinical trial, case control, cohort)

Comparison

Limited in time, drugs, population, place

Open question: looking for the unexpected

Hypothesis generation (‘problem raising’)

Exploratory, controversial (SR, PEM, CCS)

No comparison

Continuous, all drugs, total population

Formal studies Vigilance
emphasis on
Emphasis on
  • Early warning
  • Generation of knowledge
  • Dissemination of information
  • Rational and safe use of medicines
    • Benefit and harm together
slide10
Type A

(pharmacological)

Type B

(hypersensitivity)

Type C

(more frequent in exposed than in unexposed)

Clinical trial

Spontaneous reporting

Pharmacoepidemiologychallenge

Three categories of adverse drug reactionsNeed different methods for detectionAn ABC of drug-related problems. Drug Saf 2000;22:415-23
spontaneous reporting

Spontaneous Reporting

A country-wide system for the reporting of suspected adverse reactions to drugs

A case report is a notification from a health care professional, describing the history of a patient with a disorder that is suspected to be drug-induced

Limitations because of privacy protection and medical secrecy

spontaneous reporting1

Spontaneous Reporting

When different doctors independently report the same unknown and unexpected adverse experience with a drug, this may be a valid early signal

Quantitative: more frequently reported than expected from the background

advantages of spontaneous reporting
Advantages of Spontaneous Reporting
  • Effective
  • ‘All’ patients; ‘all’ drugs; many ADRs
  • Continuous
  • Rapid
  • Cheap
  • Not much health care infrastructure needed
limitations of spontaneous reporting
Limitations of Spontaneous Reporting
  • Suspicions, incomplete, uncertain
  • Underreporting is vast but unknown and variable
  • Exposure data available?
  • No frequency measurement
  • Comparison of drugs difficult
  • Insensitive to type C adverse effects
  • Further study for signal testing and explanation
data assessment in pharmacovigilance

Data assessment in pharmacovigilance

Individual case report assessment

Interest, relevance (new, serious?)

Medical, pharmacological; coding

Follow-up

Causality assessment

Aggregated study and interpretation

Signal detection

Risk factors, interactions

Serial (clinicopathological) study

Frequency estimation

general design of systems for causality assessment drug safety 1997 17 374 389
General design of systems for causality assessmentDrug Safety 1997;17:374-389
  • Basic questions
    • Sub-questions
      • Scores
  • Overall score
  • Causality category,

e.g. possible, probable, etc

slide17

None of the available systems has been validated (i.e. shown to consistently and reproducibly gives a reasonable approximation of the truth)

  • Validation = ‘proving that a procedure actually leads to the expected results’
  • No gold standard
  • Causality category definitions
slide18
What causality assessment can do

Decrease disagree-ment between assessors

Classify relationship likelihood (semi-quantitative)

Mark individual case reports

Education / improve-ment of scientific assessment

What causality assessment cannot do

Give accurate quantitative measurement of relationship likelihood

Distinguish valid from invalid cases

Prove the connection between drug and event

Quantify the contribution of a drug to the development of an adverse event

Change uncertainty into certainty

underreporting
Underreporting
  • Vast (> 90%)
  • Unknown
  • Variable
  • Biased
  • Difficult to adjust for
  • No frequency calculation
  • Delays signal detection
slide20
A signal is a set of data constituting a hypothesis that is relevant to the rational and safe use of a medicine

Hypothesis, data, arguments

  • Pharmacological
  • Clinical/pathological
  • Epidemiological
  • Quantitative / qualitative
  • Dynamic; develops over time

Drug Safety 1997;17:355-65.

the balance of evidence in a signal
The balance of evidence in a signal
  • Quantitative strength of the association
    • number of case reports
    • statistical disproportionality
    • drug exposure
  • Consistency of the data (pattern)
  • Exposure-response relationship
    • site, timing, dose, reversibility
  • Biological plausibility of hypothesis
    • pharmacological, pathological
  • Experimental findings
    • e.g. dechallenge, rechallenge, blood levels, metabolites, drugdependent antibodies
  • Analogies
  • Nature and quality of the data
    • objectivity, documentation, causality assessment
slide23
Signal detection is searching for the unknown. The same data can lead to different conclusions. Since the truth is unknown it is uncertain who is right, but nobody is wrong!
  • Dilemma: a signal should be early and credible at the same time
  • Signals may consist of only a few cases and may not be statistically prominent
  • A signal is a snapshot and changes over time
  • Signal testing and explanation require further study
  • Many signals remain unconfirmed
    • scientific limitations
    • no funding
who collaborating centre for international drug monitoring
WHO Collaborating Centre for International Drug Monitoring

The Uppsala Monitoring Centre

Stora Torget 3, 75320 Uppsala, Sweden

www.who-umc.org

the uppsala monitoring centre
The Uppsala Monitoring Centre
  • 1968 - WHO Collaborating Centre for International Drug Monitoring, Geneva
  • 1978 - Moved to Uppsala after agreement between Sweden and WHO
  • Non-profit foundation with international administrative board
  • WHO Headquarters responsible for policy
  • Self-financing
  • Global pharmacovigilance
the uppsala monitoring centre1
The Uppsala Monitoring Centre
  • Director: Prof Ralph Edwards
  • Deputy director: Dr Marie Lindquist
  • International affairs: Sten Olsson
  • Pharmacists
  • (Bio)medics
  • IT specialists
  • Financing (‘Products and Services’)
  • Administrative
  • Together 45
aims and activities
Aims and activities
  • Collaboration with National Centres
  • World-wide collection, analysis and distribution of data
    • Signal detection and analysis
    • Pooling of data, comparing experiences
  • Communication, exchange of information
  • Technical support
  • Development of methods and tools
  • Improvement of pharmacovigilance around the world
world wide accumulation and assessment of data
World-wide accumulation and assessment of data
  • 80 participating National Pharmaco-vigilance Centres around the world
  • 3.5 million case reports
  • Early warning - acceleration of signal detection
  • Early signal strengthening by comparing countries
automated quantitative signal detection
Automated quantitative signal detection
  • Extremely large numbers of drug - adverse reaction combinations
  • Selects automatically high-interest combinations, using quantitative disproportionality
  • Manageable subsets of data
  • No human time needed
  • No investigators bias
  • Objective, transparent, reproducible
  • Flexible / adjustable
  • Explorative
signal detection at the uppsala monitoring centre eur j clin pharmacol 1998 54 315 321
Signal detection at the Uppsala Monitoring CentreEur J Clin Pharmacol 1998;54:315-321

A combination of

  • Automated quantitative data mining, using Bayesian statistics and a neural network architecture (Information Component – ‘IC value’)
  • ‘Triage’
  • Human assessment
    • National Centres
    • Review Panel
    • UMC staff
slide35
Triage filter, combining quantitative and qualitative criteria; automatic selection of associations that
  • IC025 > 0; two or more countries
  • Quarterly IC increase of 2 or more
  • New and serious (WHOART Critical Terms)
  • Target reaction terms (e.g. SJS), two or more reports, irrespective of IC value
  • Literaturecheck
example of results in one quarter 2004
Example of results in one Quarter (2004)

Total number of combinations: 60000

No. of associations IC025 > 0: 2300

Triage selection: 560

No. of signals for SIGNAL: 28

signal review panel
Signal Review Panel
  • 40 Experts around the world
  • Evaluate signals, together with UMC staff and National Centres
  • Select associations for follow-up
  • Write signals in the SIGNAL document
  • Preference for System Organ Class or drug group (ATC)
the signal document
The SIGNAL document
  • Sent to all National Centres (national distribution)
  • Individualized section available to industry
  • All recipients encouraged to comment on topics presented
presentations at isop annual meeting 2006
Presentations at ISOP annual meeting 2006
  • HMG-CoA inhibitors and pulmonary fibrosis
  • β-2-Adrenoceptor agonists and nocturnal enuresis
  • Systemic effects of intranasal cortico-steroids (neuropsychiatric reactions, spontaneous abortion)
  • Taxoids (paclitaxel and docetaxel) and myocardial infarction
  • Hypersensitivity reactions to Umckaloabo (Pelargonium sidoides andP. reniforme)
  • Potentiation of warfarin by glucosamine
examples of articles
Examples of articles
  • Clark DW, Strandell J. Myopathy including polymyositis: a likely class adverse effect of proton pump inhibitors? Eur J Clin Pharmacol 2006;62:473-9.
  • Sanz E, et al. Selective serotonin reuptake inhibitors in pregnant women and neonatal withdrawal syndrome. Lancet 2005;365: 482-7.
  • Coulter D, et al. Antipsychotic drugs and heart muscle disorders in international pharmacovigilance: data mining study. BMJ 2001;322:1207-9.
support to national centres
Support to National Centres
  • Methodology
  • Terminologies, guidelines
  • Software (VIGIFLOW)
  • Harmonisation, standardisation
  • VIGIMED email discussion group
  • Annual meetings
  • Training
  • Books and brochures
terminologies guidelines links with who geneva cioms ich
Terminologies, guidelinesLinks with WHO Geneva, CIOMS, ICH
  • WHOART
  • Drug Dictionary
  • Guidelines for setting up and running of a Pharmacovigilance Centre www.who-umc.org/DynPage.aspx?id=13136&mn=1512#8
  • Herbal ATC
  • Accepted scientific names of therapeutic plants. 2005, ISBN 91 974750 3 3.
  • WHO guidelines on safety monitoring of herbal medicines
harmonisation standardisation
Harmonisation, standardisation
  • Definitions (Biriell C, Edwards IR. Drug Safety 1994;23:95-9)
  • WHO causality categories
  • Reporting adverse drug reactions. Definitions of terms and criteria for their use (CIOMSCouncil for International Organizations of Medical Sciences. WHO, 1999, Geneva. ISBN 92 9036 071 2.)
herbal and traditional medicines
Herbal and traditional medicines
  • UMC Herbals database (Dr Mohamed Farah)
  • Herbal reviewers panel
  • Collaboration with
    • Uppsala University, Sweden
    • WHO Collaborating Centre, Cape Town, South Africa
    • Royal Botanical Garden, Kew, UK
    • University of Exeter, UK
    • Harvard, US
new development areas
New development areas
  • Integrate Chinese ADR database
  • Patient safety focus including medication errors
    • World Alliance for Patient Safety
  • Improved reporting and analysis of vaccine reactions (AEFI)
    • Flu pandemic planning
  • Safety surveillance for other Public Health Programmes
  • Involvement in active surveillance
    • Cohort Event Monitoring
  • Data mining analysis of longitudinal patient records
thank you for your attention
Thank you for your attention

info@who-umc.org

www.who-umc.org