Consistent and reliable causality attribution at the case level is the cornerstone of confident signal detection. The current practice relies on study investigators to establish causal relationships based on their observations. The Sponsor (Company) can add their assessment based on additional information about the drug. The current industry standard, E2B (R3), accounts for multiple assessment methods and presents the data elements for each drug-event pair evaluated by multiple sources in a matrix. There are many causality assessment methods used within the industry, some universal, others more specialized. Most commonly used methods include WHO-UMC, Naranjo, Roussel-Uclaf (RUCAM) - to detect drug-associated liver injury, Karch and Lasagna, the French PV Algorithm, Bayesian Adverse Reactions Diagnostic Instrument (BARDI), MacBARDI, and Updated Logistic method. Expert judgment remains the most common method used. Serious challenges prevent the practical implementation of existing algorithms by the industry. Many of the algorithms cannot be applied rigorously because of missing data. Additionally, an accurate definition of clinical harm is often lacking (e.g., peripheral neuropathy, vasculitis). Brighton Collaboration Case Definitions partly address this component. Algorithms do not consider medication errors and are not easy to use with interactions, contributory causation, or secondary harms. Information obtained from the reporter is usually insufficient to establish a causal relationship, and follow-up requests for information must be sent, often repeatedly. The result is a very high share of unassessable reports and poor internal consistency of existing assessments. I suggest modifying the ADE reporting to incorporate components enabling structured causality assessment directly by the reporting physician (postmarket) or investigator (clinical trials). Guiding questions would assist the reporting physician in determining causal relationships and facilitate algorithmic attribution upon submission: Temporal relationship is a key component of causality assessment. Safety databases routinely calculate latency and last dose latency that feed the algorithm. Dechallenge and Rechallenge represent key concepts in pharmacovigilance. This information is typically missing from reports. A series of questions regarding Outcome and Response (Action taken with drug) guide the reporting physician through a checklist for all suspect and interacting drugs, reliably and consistently calculating dechallenge/rechallenge for each drug-event pair. Biological plausibility is a complex component requiring knowledge of the drug and the patient's medical condition. Finally, it is important to ask the reporting physician about any underlying diseases that could have contributed to the event. A clear answer to this question is an essential component of the causality assessment algorithms.

1. Problem Statement Current Practices Path forward
LET’S TALK CAUSALITY ATTRIBUTION
Current Practices and Path Forward
Let's talk causality attribution: Current Practices and Path Forward 1
9/27/2023
Veronika Valdova
Standards
Presented at WDSEC in Amsterdam on October 5, 2023

2. Let's talk causality attribution: Current Practices and Path Forward 2
9/27/2023
Veronika Valdova, DVM is a multi-disciplined professional in the pharma and medical device sectors,
with extensive knowledge of all aspects of drug safety in post-market surveillance and clinical
research. She works with global corporations, niche manufacturers, and startups as a freelance
consultant or B2B.
Current endeavors. Independent consultant for Navitas Life Sciences (since 2023), Oriel STAT A
MATRIX (since 2023), and several medical device manufacturers (since 2016). Co-founder and
Consultant at Arete-Zoe, LLC (since 2013).
Experience. Pharmacovigilance officer at IVAX Pharmaceuticals in Opava, Czech Republic (2004-6);
Senior Drug Safety Officer at Stiefel Laboratories, UK (2007-8); Pharmacovigilance Manager at Dr.
Reddy's Laboratories, UK (2009); independent consultant (2009 onwards); Inspector for medical
devices at the Czech Institute for Drug Control (2012-13); Case processor at Accenture Services
(2018-2019); Chief Scientific Officer at Veracuity (2019-2022); Co-founder and Consultant at
Arete-Zoe, LLC (since 2013), providing consultancy for drug safety and MDD to MDR transition for
device manufacturers.
Contact: veronikav@arete-zoe.com | +420-721-079-971 | +1-631-791-8129
Links: LinkedIn | Arete-Zoe, LLC | SlideShare
Veronika Valdova
Independent Consultant
Czechia
Problem Statement Current Practices Path forward
Standards

3. PART 1: Causality attribution at case level remains a major challenge in pharmacovigilance
case management, resulting in suboptimal effectiveness and reliability of safety signal
detection.
Let's talk causality attribution: Current Practices and Path Forward 3
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Information obtained
from traditional
reporting formats such
as MedWatch or
CIOMS forms is rarely
sufficient to establish a
causal relationship.
Variability in interpretation
of information on
collection forms
necessitate multiple
follow-up requests, which
are then subjectively
interpreted by the
collector, often far
removed from the patient
due to outsourcing of
data processing.
The result is a high
share of unassessable
reports compounded by
inconsistencies in
subjective information
exchanges, resulting in
low-confidence causality
attribution at case level.
Accurate, consistent
causality attribution is the
cornerstone of reliable
safety signal detection.
Attribution of causality is
central to the sponsor’s
ability to detect safety
concerns since only
SUSARs are reportable
in most jurisdictions.
Problem Statement Current Practices Path forward
Standards

4. Part 2: No causal relationship in PV databases
9/27/2023 Let's talk causality attribution: Current Practices and Path Forward 4
Causal inference from data currently
available in PV databases is not
currently computable.
Causal inference from FAERS reports
depends on many components with
complex logical relationships that are yet
to be made fully computable.
(Kreimeyer, 2021)
• FAERS. FDA does not require a proven causal
relationship. Reports often do not contain
enough detail to evaluate an event. (FDA)
• VAERS. The report is not documentation that a
vaccine caused the event. (HHS)
Data currently available in PV databases do
not document causal relationship between
drug/vaccine and an adverse event.
Problem Statement Current Practices Path forward
Standards

5. Let's talk causality attribution: Current Practices and Path Forward 5
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Accurate, consistent causality attribution is the cornerstone of reliable safety
signal detection.
Regulation (EU) No 520/2012 on the performance of pharmacovigilance activities
EU GVP Guideline - Module IX – Signal management
Problem Statement Current Practices Path forward
Standards
‘Signal’ means information arising 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 set of related events, either adverse or
beneficial, which is judged to be of sufficient
likelihood to justify verificatory action.
(Article 19 - Identification of changed risks and new risks)
‘Signal validation’ means the process of
evaluating the data supporting the detected
signal in order to verify that the available
documentation contains sufficient evidence
demonstrating the existence of a new potentially
causal association, or a new aspect of a known
association, and therefore justifies further analysis
of the signal.
(Article 21 - Signal management process)

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EXPEDITED ICSRs: ICH E2A (Clinical) & ICH E2D (Post-market)
So why the discrepancies? Remediation reduces risks and liabilities!
Problem Statement Current Practices Path forward
Standards
Clinical studies
• Single cases of serious, unexpected ADRs (SUSARs)
are subject to expedited reporting
• Causality assessment is required for clinical
investigation cases and post-study events
• All cases judged by either the reporting investigator or
the sponsor as having a reasonable suspected causal
relationship qualify as ADRs.
ICH Guidelines ICH E2A (Clinical safety data management)
Post-market surveillance
• Serious, unexpected ADRs always reportable
• Reportability of serious, expected ADRs depends on
country
• ADRs associated with marketed drugs (spontaneous
reports) usually implied causality.
• PASS Studies and solicited cases: at least a possible
causal relationship is reportable
ICH Guideline ICH E2D (Post-approval safety data management)

7. ICH E2B(R3)– ICSR Implementation Guide
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FDA - E2B(R3) Electronic Transmission of Individual Case Safety Reports (ICSRs) Implementation Guide – Data Elements and Message Specification
EMA - ICH E2B (R3) Electronic transmission of individual case safety reports (ICSRs) - data elements and message specification - implementation guide
CAUSALITY ASSESSMENT FOR EACH DRUG-EVENT PAIR – EXPECTATIONS AT
DATABASE LEVEL
Global introspection = overall impression [related / not related]
Database infrastructure is in place to accommodate requirements for Sponsor/MAH to use multiple different
algorithmic and Bayesian methods in addition to global introspection as performed by multiple sources.
So why do discrepancies persist?
Problem Statement Current Practices Path forward
Standards

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Good Pharmacovigilance
Guidelines (EU)
RELATEDNESS FOR EACH DRUG-EVENT PAIR EXPRESSED BY MULTIPLE SOURCES WITH
MULTIPLE METHODS
• The degree of suspected relatedness of each medicinal product to each reported adverse reaction can be
presented in a structured manner in the ICSR.
• It can be expressed for multiple sources (reporters, competent authorities, marketing authorization holders) while
using multiple methods of causality assessment.
• CURRENT SAFETY DATABASE SOLUTIONS SUPPORT COMPLEX CAUSALITY ATTRIBUTION REQUIREMENTS
Systems structure and regulatory expectations are unambiguous.
So why do discrepancies persist?
Problem Statement Current Practices Path forward
Standards

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• Accurate definition of clinical harm is often lacking.
• Brighton Collaboration Case Definitions define clinical harm for syndromes following immunization.
• RegiSCAR scoring system specifies criteria for severe cutaneous adverse drug reactions
• Drug labels describe expected (experienced, non-hypothetical) ADRs
• National Action Plan for ADE Prevention addresses ADEs that are common, clinically significant,
preventable, and measurable; resulting from high-priority drug classes in high-risk populations.
• REMS/RMPs exist for certain medications with serious safety concerns to help ensure the benefits of
the medication outweigh its risks.
CLINICAL HARM IN POSTMARKETING USE: One size does not fit all.
Some degree is a reasonable expectation and should be anticipated in the data collection scheme.
Problem Statement Current Practices Path forward
Standards
The data collection schemes need to reflect relevant product risks.

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REMS DRUG LABELS (SmPCs)
OZEMPIC – semaglutide drug label (DailyMed)
FDA REMS program for Zyprexa
Approved REMS for Zyprexa Relprevv (olanzapine)
ZYPREXA RELPREVV Patient Care Program Medication Guide
Drug-specific harm is often well-defined in product labeling. However, is it well understood by
clinicians, and communicated effectively to patients, so they can achieve informed consent?
Problem Statement Current Practices Path forward
Standards

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* Marketing Authorization Holder (MAH) – operated call-center or a generic form on company website
** Multiple follow-ups to determine causal relationship through data collector
HCP or
Pharmacist
Patient
MAH*
Medical Reviewer
Outsourced data processing (global)**
Database location used to synchronize
communication between participants.
Outsourced data collection,
regional by language, with
translation*
Uncoordinated collector
enquiries often conflict
with HCP’s schedule.
1) The HCP documentation on drug potential ADRs from MAH must be more comprehensive and accessible.
2) The MAH’s data collection systems must be drug-specific to account for relevant risks, including case definitions, lab thresholds, biological half-
life relevant for event timing and patient differentiation.
3) Each of the blue arrows represent a variable delay between inquiry and response.
Meanwhile, other patients continue to be treated absent pertinent emerging information.
Regulatory
Agency
Problem Statement Current Practices Path forward
Standards

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CIOMS I
Drug label information for Lamictal:
What to anticipate
≠
Steven-Johnson Syndrome / Toxic
Epidermal Necrolysis (SJS/TEN)
What are the patient’s risk factors?
• Variant pharmacogenomic profile
• HIV infection
• Associated cancer
Focus on early detection
• Was the patient informed when to seek
medical care?
• When did the medical personnel
recognize SJS/TEN?
Problem Statement Current Practices Path forward
Standards

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No gold standard exists. A variety of methods* are available, typically 2 or more are used in parallel.
WHO-UMC
• Widely adopted
• Easy to use
• Accuracy depends on expert
judgment
• Reproducibility of results tends to
be poor
• Roussel-Uclaf (RUCAM)
• Jones' algorithm
• Naranjo algorithm
• Yale algorithm
• Karch algorithm
• Begaud algorithm
• ADRAC
• Quantitative approach algorithm
• MONARCSi
• Bayesian Adverse Reactions
Diagnostic Instrument (BARDI)
• MacBARDI
• Updated Logistic method
EXPERT JUDGMENT ALGORITHMIC BAYESIAN (PROBABILISTIC)
• Algorithms do not consider medication errors
• Algorithms are less able to detect interactions, contributory causation, or secondary harms.
• Missing information at ICSR level cannot be corrected by an algorithm.
CAUSALITY ATTRIBUTION
Problem Statement Current Practices Path forward
Standards
*However, no methodology can be effective without essential relevant information gained through the collection interaction.

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WHO-UMC CAUSALITY CATEGORIES:
The use of the WHO-UMC system for standardised case causality
assessment Summary description of causality assessment
Standard protocol questions
• What are the suspect drug(s)?
• What other drugs is the patient taking?
• What are the therapy and event dates
for all drugs (temporal relationship)?
• What action was taken with drugs in
response to the event? (Drug stopped,
dose reduced, drug continued as
before)
• What was the patient’s response
(dechallenge)?
• Was/were the drug(s) reintroduced?
• What was the patient’s response
(rechallenge)?
Causality term Assessment criteria
CERTAIN • Event or laboratory test abnormality, with plausible time relationship to drug intake
• Cannot be explained by disease or other drugs
• Response to withdrawal plausible (pharmacologically, pathologically)
• Event definitive pharmacologically or phenomenologically (i.e. an objective and specific medical
disorder or a recognized pharmacological phenomenon)
• Rechallenge satisfactory, if necessary
PROBABLE /LIKELY • Event or laboratory test abnormality, with reasonable time relationship to drug intake
• Unlikely to be attributed to disease or other drugs
• Response to withdrawal clinically reasonable
• Rechallenge not required
POSSIBLE • Event or laboratory test abnormality, with reasonable time relationship to drug intake
• Could also be explained by disease or other drugs
• Information on drug withdrawal may be lacking or unclear
UNLIKELY • Event or laboratory test abnormality, with a time to drug intake that makes a relationship
improbable (but not impossible)
• Disease or other drugs provide plausible explanations
CONDITIONAL
/UNCLASSIFIED
• Event or laboratory test abnormality
• More data for proper assessment needed, or
• Additional data under examination
UNASSESSABLE
/UNCLASSIFIABLE
• Report suggesting an adverse reaction
• Cannot be judged because information is insufficient or contradictory
• Data cannot be supplemented or verified
Reporter and collector should share standard protocols to orient communications
Problem Statement Current Practices Path forward
Standards

15. Let's talk causality attribution: Current Practices and Path Forward 15
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Questions YES NO DNK
Are there previous conclusive reports on this reaction? +1 0 0
Did the adverse event appear after the suspected drug was administered? +2 -1 0
Did the adverse reaction improve when the drug was discontinued or a specific antagonist was
administered?
+1 0 0
Did the adverse event reappear when the drug was re‐administered? +2 -1 0
Are there alternative causes (other than the drug) that could on their own have caused the
reaction?
-1 +2 0
Did the reaction reappear when a placebo was given? -1 +1 0
Was the drug detected in blood (or other fluids) in concentrations known to be toxic? +1 0 0
Was the reaction more severe when the dose was increased or less severe when the dose was
decreased?
+1 0 0
Did the patient have a similar reaction to the same or similar drugs in any previous exposure? +1 0 0
Was the adverse event confirmed by any objective evidence? +1 0 0
Sponsor/MAH Knowledge
HCP Reporter /Investigator
Knowledge
*Case definition shall define objective evidence required, i.e., specific labs or tests
• Company Medical Reviewer
needs to obtain this
information via follow-up
SCORES
9 = DEFINITE ADR
5-8 = PROBABLE ADR
1-4 = POSSIBLE ADR
0 = DOUBTFUL ADR
NARANJO CAUSALITY SCALE
*
These questions should appear as a prompt at the MAH point of contact and should be
part of the initial submission.
Problem Statement Current Practices Path forward
Standards

16. • System design is adequate for its intended purpose of
controlling risk.
• Regulatory requirements are clear.
• A suite of causality assessment tools exists.
• Database infrastructure is in place.
• The only missing component is diligence prioritizing
reduction of patient risk and optimal outcome.
• Current practices deny potential AI advantages.
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Problem Statement Current Practices Path forward
Standards
CONCLUSION