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182
P. Biswas et al.
Table 7.4 Integrated safety summary-related sections
with corresponding regulations
US
CTD section
2.5 Clinical Overview
2.5.5 Overview of
Safety
2.7 Clinical Summary
2.7.4 Summary of
Clinical Safety
5.3 Clinical Study
Reports
5.3.5.3 Reports of
Analyses of Data
from More than One
Study (Including Any
Formal Integrated
Analyses, Meta
Analyses, and
Bridging Analyses)
Regulation Comment
N/A Not a US
requirement, but
recommended by
ICH M4E
21 CFR
314.50(c)
(2)(viii)
21CFR
314.50(d)
(5)(vi)
US requirement
for a clinical
summary
Integrated
summary of
safety
• Evaluation of AE effects in various subgroups
of the variable patient base including impact of
concomitant medications’ safety and efcacy.
• Analysis of adverse effects relating to dose–
response information—this includes any evidence of dose–response variations relating to
age, sex, or any other subpopulations
– Drug–drug, drug–demographic, and drug–
disease interactions
– Any other pharmacologic properties—
including effects on liver, kidneys, etc.
– Long-term adverse effects and withdrawal
effects
• The extent of exposure—the number of
patients exposed (by gender and other demographic subgroups) and number exposed to
various doses for dened durations.
• Demographic and other characteristics of
study population—age, gender, race, body
weight, primary and secondary outcomes, and
other relevant prognostic variables.
• Laboratory assessments.
• Summary of animal data important to human
safety.
In an ISS, for analysis of the AEs, it must
clearly dene the treatment-emergent adverse
events (TEAE) consistently across studies. It is
important to specify the analysis rules used to
identify TEAE. Also, the analysis rules for
assigning AEs in crossover studies (e.g. an AE
that occurs during washout between treatment
periods 1 and 2) should be specied and stay as
consistent as possible with individual study analyses. Finally, it is necessary to clearly specify all
analysis rules in the ISS, statistical analysis plan,
so regulatory agencies can provide input on the
proposed approach ahead of time.
9 Aggregate Safety Data in
Clinical Development and
Timelines for Submissions to
Regulatory Authorities
9.1 Development Safety Update
Report
A Development Safety Update Report (DSUR)
is a premarketing aggregate periodic report that
covers safety information of drugs, biological,
vaccines, and combination products under
development (including marketed drugs that
are under further study) among the ICH regions
[26].
DSURs are internationally harmonised, safety
documents which became mandatory in EU
member states in September 2011. It covers the
safety summary of medicinal products during
their development or clinical trial phase.
The DSUR format replaced the previous EU
Annual Safety Report (EU-ASR) and the United
States IND Annual Report and is based heavily
on the Periodic Safety Update Report (PSUR)
format that is used for post-marketing products.
The main objective of a DSUR is to present a
comprehensive annual review and evaluation of
pertinent safety information collected during the
reporting period related to a drug under investigation, whether or not it is marketed, by:
• Examining whether the information obtained
by the sponsor during the reporting period is
in accord with previous knowledge of the
investigational drug’s safety.
7 Clinical Trials Safety Data
183
• Describing new safety issues that could have
an impact on the protection of clinical trial
subjects.
• Summarising the current understanding and
management of identied and potential risks.
• Providing an update on the status of the clinical investigation/development programme and
study results.
A DSUR should be concise and provide infor-
mation to assure regulators that sponsors are adequately monitoring and evaluating the evolving
safety prole of the investigational drug. All
safety issues obtained during the reporting period
should be discussed in the DSUR; however, it
should not be used to provide the initial notication of signicant new safety information or provide how new safety issues are detected.
9.1.1 DSUR Reporting
andSubmission Timelines
Sponsors are required to submit a DSUR within
1year of the Development International Birth Date
(DIBD)—the date of rst authorisation of a clinical
trial in any country worldwide and provide annual
DSUR submissions until all open clinical studies
have ended (the nal clinical study is completed,
and its study report has been submitted). Thus, the
DIBD is used to determine the start of the annual
reporting period for the DSUR.The data lock point
of the DSUR is the last day of the 1-year reporting
period. The start of the annual period for the DSUR
is the month and date of the DIBD.The DSUR is
submitted to all applicable regulatory competent
authorities in countries where the trial is being conducted and approving ResearchEthicsCommittes
(RECs) no later than 60 calendar days after the
DSUR data lock point. A DSUR must be submitted
during every 12-month reporting period until the
End of Trial Notication has been submitted to the
competent authority of the member state where the
trial is being conducted.
9.1.2 Reference Safety Information
Used forDSUR
From start of the reporting period, the IB serves
as the Reference Safety Information (RSI) to
determine whether the information received dur-
ing the reporting period remains consistent with
previous knowledge of the safety prole of the
investigational drug. Section 7.1 of the DSUR
should clearly indicate the version number and
date of the IB used for this purpose. The IB is a
compilation of the clinical and non-clinical data
on the investigational drug that are relevant to the
study of the drug in human subjects. When an IB
is not required by national or regional laws or
regulations, the applicable national or regional
product label serves as the RSI.Usually, a single
document should serve as the RSI, but in certain
circumstances, it might be appropriate to use
more than one reference document to support the
DSUR (e.g. for a DSUR providing information
on an investigational drug used in combination
and as monotherapy) [26].
If the IB has been revised during the reporting
period and not previously submitted to the relevant regulatory authority, the sponsor should provide a copy of the current version of the IB as an
attachment to the DSUR.If the IB or Summary of
Product Characteristics (SmPC) has been revised
during the reporting period, the sponsor should
provide a copy of the current version of the IB or
SmPC as an attachment to the DSUR, listing any
signicant safety-related changes in the relevant
section of the DSUR.Despite the change in the
RSI, the RSI in effect at the start of the reporting
period serves as the RSI during the reporting
period.
10 Signal Detection andData
Management inClinical
Trials
Signal detection and data management is an
important activity that takes place continuously
throughout the entire life cycle of the drug development process, where related and not related
AEs to drugs are anticipated during drugdevelopment programmes. An increase of AEs associated with a product’s use compared with the
expected rate is referred to as a safety signal.
Signals are detected at any time during the lifespan of a drug, from the pre-clinical phase through
the post- marketing phase.
184
P. Biswas et al.
An important factor in signal detection for a
medicinal product during the premarketing
period is the availability of denominator data and
the ability to compare AE incidence rates between
two or more selected populations. This is possible due to structured data collection under strictly
controlled conditions in accordance with
GCP. The blinding of investigators and study
subjects to therapy assignment in randomised
controlled studies also helps reduce bias in ascertaining AEs. This contrasts with spontaneous
reporting systems during post-marketing period,
which rely on spontaneous reports where the
quality of data received are not complete. A
detailed discussion of evaluating safety from
clinical trials, and identication of emerging
safety signals, is included in the report of CIOMS
Working Group VI (Management of Safety
Information from Clinical Trials) [27].
There are several methodologies for detecting
signals in clinical trials. These include quantitative signal detection, which is most useful on
large pivotal studies or pooled studies, and qualitative methods, which remain the basis of signal
detection in early stages. Signal detection methods are ranked according to their performance
with regard to positive predictive value (PPV),
specicity, and sensitivity [28]. The EMA has
published scientic guidance on routine signal
detection methods in EudraVigilance for use by
the agency, national competent authorities, and
Marketing Authorisation Holders(MAHs).
CIOMS VI includes the following key
sources of new safety information:
1. Evaluation of serious individual case safety
reports.
2. Periodic aggregate assessment of available
clinical safety data (including clinical AEs
and laboratory parameters) without regard to
seriousness or causality.
3. Evaluation of unblinded studies including
individual study results and pooled analyses
where appropriate.
While randomised clinical trial data are generally thought to accrue from the pre-approval
period, there is often a substantial body of randomised clinical trial safety data that accumu-
lates after approval. The data from both pre- and
post-approval randomised clinical trials can be
pooled in a cumulative meta-analysis to understand the previously unrecognised ADRs that the
pre-approval human safety database was unable
to detect because of insufcient sample size due
to the lack of adequate statistical power.
11 Clinical Trials Safety Data
Reconciliation
As already discussed, SAE information is continuously collected throughout the life cycle of
the clinical trials. Usually, SAEs are captured in
two different databases—a clinical database that
includes the AE information recorded in the
CRFs and a safety database that contains information collected on the SAE form and their
respective follow-ups. It is a challenge to continuously match these two databases, making sure
that the information pertaining to the SAEs collected during the clinical trial, particularly at the
time of study database lock, does not impact the
data quality, operations performance, and compliance. The parallel processing of SAEs in two
different databases may create discrepancies that
need to be claried and reconciled before the
clinical database is locked.
Reconciliation is therefore dened as a process of comparing key safety data variables
between the drug or device safety SAE database
and the clinical database in order to identify any
discrepancy, determine whether a discrepancy is
acceptable or not and, if acceptable, document
the discrepancy [29]. It is an iterative process and
an important activity that occurs regularly
throughout and during the entire study. The timeline for reconciliation is determined by the frequency of data receipt, scheduling of safety
updates, and timing of interim and nal reports.
The objective is to reconcile all discrepancies
before nal clinical database lock to submit validated data to regulatory authorities.
The SAE reconciliation process is undertaken
on a periodic basis, where data is reviewed in the
clinical database and compared with the corresponding records in the safety database. Several
departments are involved in this activity, includ-
7 Clinical Trials Safety Data
185
ing drug safety and clinical research departments,
but also data management and clinical operations. During the reconciliation process, verbatim
descriptions, coding terms, treatment and event
onset/resolution dates, causality assessment, seriousness criteria, SAE outcome, and other information that is found to be different are rst
matched and then corrected. Based on each company’s standards and processes, the discrepancies
are resolved and corrected in either dataset before
the clinical database lock at the end of the trial.
Therefore, a mismatch between the safety database and clinical databaseis identied during the
reconciliation process. A discrepancy can be for
example:
• SAE present in one database but missing in
the other one
• Inconsistent SAE associated data between the
databases
• Mismatched SAE preferred term
Once the discrepancies are identied, actions
are then taken to address the discrepancies, and the
status relevant to the action taken are recorded in
the SAE manual or electronic reconciliation tool.
The SAE data listing is generally in Excel format
and is used to review and document all the discrepancies found during SAE data reconciliation.
After the database has been corrected and
updated, the evidence of SAE reconciliation and
the resulting outcome is documented and led in
the Trial Master File (TMF) for future inspection
by health authorities.
12 Clinical Trial Safety Data
Integrity
As clinical trial methodologies and new technologies are deployed, data integrity, and the safety
of clinical trial participants remain at the forefront of regulatory oversight. Ofcials from the
US FDA and the UK MHRA have written an
article published in Clinical Pharmacology &
Therapeutics [30], on this topic.
Data integrity as dened by the WHO “is the
degree to which data are complete, consistent,
accurate, trustworthy and reliable”. The data in
good practice environments is collected and
maintained in a secure manner, so that they are
attributable, legible, contemporaneously
recorded, original (or a true copy), and accurate.
In the USA, the FDA uses the ALCOA acronym [31] to dene expectations with respect to
data integrity as stated below:
• Accurate: Recorded data should be correct,
complete, valid, reliable, and free from errors.
• Legible: The record that is created, especially
paper-based records should be legible, should
be permanent, and not erasable so that they are
reliable throughout the data life cycle.
• Contemporaneous: The evidence of actions,
events, or decisions should be recorded as it is
generated.
• Original: The record should be a true copy,
and the data is to be used or presented as it was
created.
• Attributable: The evidence or every piece of
data entered into the record must be capable of
being traced back to the person collecting the
data.
12.1 Ensuring andMonitoring Data
Integrity inClinical Trials
To ensure that the data that is collected is t for
purpose, data is monitored by keeping a close eye
on the following areas:
• Source data verication (SDV)
• Validation of computer systems supporting
data collection, data processing, data report-
ing, etc.
• Data access and control
• Training of personnel involved in data collec-
tion, data processors, analysts, site staff, and
report writers
• Data monitoring: onsite, centralised, and risk-
based monitoring
• Clinical trial quality assurance and quality
control: ensuring vendors and Contract
ResearchOrganisations(CROs) have efcient
quality management system in place with
Standard Operating Procedures (SOPs) and
controlled records
186
P. Biswas et al.
As clinical trials become more complex and
multinational, involving multiple sites vendor
protecting the data becomes immensely critical.
Collection of accurate clinical trial data is essential for compliance with good clinical practice
(ICH GCP E6 R2), regulatory compliance (FDA21 CFR part 11, EMA, etc.), and clinical research
ethical principles [31]. Worldwide regulatory
authorities, especially the FDA and EMA, are
particularly interested in ensuring data integrity
in clinical trials to maintain their ability to not
only accurately evaluate safety and efcacy of
the medical product that is in clinical development but also protect the patient and the public,
as a lack of acceptable data integrity practices in
clinical research can lead to serious regulatory
and nancial consequences.
In October 2018, FDA and MHRA held their
rst joint GCP workshop to discuss data integrity
in clinic trials. Several examples were cited by the
FDA and MHRA on data integrity issues, including data integrity issues in some of the largest biopharma companies. A joint article written by the
FDA and MHRA cited several concerns with
audit trails, blinding, and data management in
addition to sponsor oversight of electronic systems and electronic health records used at sites,
electronic source data, protocol deviations and
management of these deviations, and novel clinical trial designs and the challenges in ensuring the
quality and reliability of study data [30].
13 Summary andConclusions
ical trials is to translate clinically signicant safety
information to the product label under development and protect research subjects from experiencing ADRs. The last few decades have changed
the regulatory landscape of safety data monitoring
in clinical trials and the guidelines issued by regulatory authorities specically for the collection of
appropriate safety data during clinical trials, documentation and reporting of serious events have
made a signicant impact to ensure the safety of
clinical trial subjects. With the advent of new technology and articial intelligence, it remains to be
seen if there can be a harmonised and structured
way of data collection and analysis worldwide that
would be acceptable to the regulatory authorities
that would help detect early safety signals from the
enhanced safety data, thus protecting the trial subjects and patients from serious risks.
14 Case Studies
Clinical trial AEs referred to as safety data are
required to be collected in all clinical trials. The
data collected consists of non-SAEs and SAEs
and are collected separately as two separate data
sets. One dataset collected by the data management team consists of both non-SAEsand SAEs,
while the safety database managed by the pharmacovigilance/drug safety team comprises of
SAEs only. The safety analysis is conducted
using only the AE dataset from the data management which has all safety events reported in the
clinical trial.
Patient safety monitoring is a critical component
of the drug development process. The main
objective of collecting safety data from clinical
trials is the early detection of important safety
signals to protect subjects in clinical trials and
provide information about new risks, assess the
potential risk to future patients, and develop
safety prole/product label of the drug contributing to its benet–risk assessment.
Safety data, obtained from ongoing clinical trials, has a direct effect on the safety and clinical
care of research subjects enrolled in these trials.
The ultimate goal of safety signal detection in clin-
14.1 Case Study 1: Clinical Trial
Data Review for Risk of
Respiratory Adverse Events
for Treatment of Hepatorenal
Syndrome
A pharmaceutical company evaluates clinical
trial data for the treatment of Hepatorenal
Syndrome to determine why a drug was linked to
a high number of respiratory AEs.
Literature search for articles on the active
moiety were conducted as the product was mar-
7 Clinical Trials Safety Data
187
keted in countries outside of the USA as well of
the EudraVigilance database for AEs reported in
the post-marketing arena. A dataset was created
for all respiratory-reported AEs by standardising
the MedDRA coding with the AEs reported in the
clinical trials in the respiratory system organ
classication (SOC). A combined dataset of all
unique safety events (whether reported in the
post-marketed or clinical trial dataset) was created and safety analyses re- performed. To evaluate if these were true ADRs, all data from time to
onset of the AE, to start and stop date of the AEs,
concomitant medications, medical history had to
be determined to assess if the injury was causally
related to the drug.
In-depth analysis of the post-marketing data
showed that there was an increase in the respiratory disorders in patients with decompensated
cirrhosis and the clinical safety data showed that
the subjects that experienced respiratory failure
had a history of cardiorespiratory, recent upper
GI haemorrhage, or increasing hepatic encephalopathy, and it was advised that the drug should
be used with caution in these patients.
1. What are the major lessons learnt from the
above case study?
Pharmacovigilance personnel need to
gather literature articles and ICSRs for health
authority databases for a marketed product
used for other indications and determine if the
product is being used off-label and if yes, is it
the indication of interest. This needs to be performed earlier on in the clinical trial.
2. What signicant data was obtained from in-
depth analysis of post-marketing and clinical
safety data and what safety measure was
taken with the obtained data?
Post-marketing data demonstrated an
increase in respiratory disorders in patients
with decompensated cirrhosis. Clinical trial
data identied history of cardiorespiratory
disorders, recent GI haemorrhage, or increasing hepatic encephalopathy as the potential
predisposing factors for the development of
respiratory disorders as an adverse effect in
the patients on the drug in question.
14.2 Case Study 2: Clinical Trial
Data Review for Risk of Lung
Cancer with Inhaled Insulin
A small pharmaceutical company evaluating an
inhaled drug/device combination product consisting of Technosphere insulin powder (known
as TI), the inhaler, and the cartridges containing
TI. TI consists of recombinant human insulin
and fumaryl diketopiperazine (FDKP an inert
excipient). The insulin powder particles are
adsorbed onto uniform-sized (approximately 2
μm) carrier Technosphere particles that contain
mostly crystallised FDKP and when inhaled, the
Technosphere particles carry the insulin into the
alveoli where the particles dissolve. The insulin
and the FDPK are rapidly absorbed across the
alveolar walls independently of each other,
leading to insulin uptake into the blood stream
faster than that of any other approved insulin.
The risk of lung cancer was raised as a safety
concern to the use of inhaled insulin. In the registration trials, two heavy smokers developed
lung cancer—one while in a controlled comparative clinical trial and one while in an extension.
Pharmacoepidemiology was the safety tool used
to compare the frequency risks in the general
patient population with lung malignancies to the
patient population in the clinical trials. The
results showed that incidence of lung cancer
(0.8 cases per 1000 patient-years) was felt by
the company to be within the range of lung cancer observed in the general population (approximately 0.23–1.22 cases per 1000 patient-years,
according to the American Lung Association).
1. What are the major lessons learnt from the
above-mentioned case?
The incidence rate of the AEof interest
in the clinical trial vs. incidence rate of the
AE of interest in the general patient population needs to be determined proactively to
mitigate any risks seen in the trial. If there
is an imbalance where the risk is associated
with the drug, the pharmacovigilance team
can then proactively mitigate the risk by
putting a strategy in place for a successful
ling.
188
P. Biswas et al.
References
1. Medicine and Healthcare products Regulatory
Agency. NIBSC. TGN1412 – learning from a clinical
trials disaster. https://www.nibsc.org/about_us/world-
wide_impact/tgn1412.aspx.
2. Gibb F. Victim Ryan Wilson in “elephant man” drug
trial to get £2m. The Times, London. 2008.
3. Expert Group on Phase One Clinical Trials. Final
report. 2006. https://webarchive.nationalarchives.
gov.uk/ukgwa/+/dh.gov.uk/en/publicationsandstatistics/publications/publicationspolicyandguidance/
dh_063117.
4. Medicines and Healthcare Products Regulatory
Agency (MHRA). Clinical trial suspension: latest
ndings, 5 Apr 2006. https://www.ema.europa.eu/
en/news/rst- man- clinical- trials- guideline- releasedpublic- consultation. http://www.mhra.gov.uk/
NewsCentre/Pressreleases/CON2023515.
5. WHO International Drug Monitoring Centre
(Uppsala, Sweden), Edwards IR, et al.
Harmonisation in pharmacovigilance. Drug Saf.
1994;10(2):93–102.
6. Regulation (EU) No. 536/2014 of the European
Parliament and of the council of 16 April 2014 on
clinical trials on medicinal products for human use,
and repealing Directive 2001/20/EC.
7. Medicines and Healthcare products Regulatory
Agency. Clinical trials for medicines: apply for
authorisation in the UK. 2014. https://www.gov.uk/
guidance/clinical- trials- for- medicines- apply- forauthorisation- in- the- uk.
8. Center for Drug Evaluation and Research (CDER).
Investigator responsibilities–safety reporting for
investigational drugs and devices. 2021. https://www.
fda.gov/regulatory- information/search- fda- guidancedocuments/investigator- responsibilities- safetyreporting- investigational- drugs- and- devices.
9. Government of Canada. Adverse drug reactions (ADR) reporting. https://www.canada.ca/
en/health- canada/services/drugs- health- products/
drug- products/applications- submissions/guidancedocuments/clinical- trials/post- authorizationrequirements.html#adr.
10. U.S. Department of Health and Human Services.
Guidance for industry and investigators safety
reporting requirements for INDs and BA/BE
studies. 2012. https://www.fda.gov/les/drugs/
published/Safety- Reporting- Requirementsfor- INDs- %28Investigational- New- DrugApplications%29- and- BA- BE- %28BioavailabilityBioequivalence%29- Studies.pdf.
11. Pharmaceutical Regulations in Japan. Pharmaceutical
Laws and Regulations (Chapter 12). 2020.
https://www.jpma.or.jp/english/about/parj/
eki4g6000000784o- att/2020e_ch02.pdf.
12. Ministerial Ordinance on Good clinical practice for
drugs. https://www.pmda.go.jp/les/000152996.pdf.
13. Song H, Pei X, Liu Z, Shen C, Liu Y, Zhou L, Sun
F, Xiao X, Sun J. Pharmacovigilance in China:
Evolution and future challenges. Br J Clin Pharmacol.
2023;89:510–22.
14. Central Drugs Standard Control Organisation. Good
clinical practices for clinical research in India. 2001.
Available from: http://cdsco.nic.in/html/GCP.htm.
15. Central Drugs Standard Control Organisation.
Schedule Y (Amended version). 2005. Available
from: http://cdsco.nic.in/html/GCP1.html.
16. International conference on harmonization of technical requirements for registration of pharmaceuticals
for human use. Development safety update report
E2F. 2010. Available from: http://www.ich.org/LOB/
media/MEDIA4727.pdf.
17. European Commission. Detailed guidance on the
collection, verication and presentation of adverse
reaction reports arising from clinical trials on
medicinal products for human use. 2006. Available
from: http://ec.europa.eu/health/les/eudralex/
vol- 10/21_susar_rev2_2006_04_11_en.pdf.
18. National Health and Medical Research Council.
Guidance: safety monitoring and reporting in
clinical trials involving therapeutic goods. 2016.
https://www.nhmrc.gov.au/sites/default/files/
images/NHMRC- guidance- safety- monitoring- andreporting.pdf.
19. European Medicines Agency. ICH guideline E19 on a
selective approach to safety data collection in specic
late-stage pre-approval or post-approval clinical trials.
2022.
https://www.ema.europa.eu/en/ich- guideline-
e19- optimisation- safety- data- collection- step- 2b.
20. European Medicines Agency. Clinical safety data
management: denitions and standards for expedited reporting. 1995. https://www.ema.europa.eu/
en/documents/scientific- guideline/internationalconference- harmonisation- technical- requirementsregistration- pharmaceuticals- human- use_en- 15.pdf.
21. U.S. Department of Health and Human Services.
Guidance for industry: computerized systems
used in clinical investigations. 2007. https://
www.fda.gov/files/drugs/published/Guidance- forIndustry%2D%2DComputerized- Systems- Used- inClinical- Investigations.pdf.
22. Monaghan TF, Agudelo CW, Rahman SN,
Wein AJ, Lazar JM, Everaert K, Dmochowski
RR. Blinding in clinical trials: seeing the big picture. Medicina. 2021;57:647. https://doi.org/10.3390/
medicina57070647.
23. National Institute on Deafness and Other
Communication Disorders (NIDCD). NIDCD guidelines for data and safety monitoring of clinical trials.
https://www.nidcd.nih.gov/research/clinical- studies/
nidcd- guidelines- data- and- safety- monitoringclinical- trials.
24. Ellenberg SS, Fleming TR, DeMets DL.Data monitoring committees in clinical trials: a practical perspective. Chichester: John Wiley; 2002. Also, see
Ellenberg, S.S. Independent data monitoring com-
7 Clinical Trials Safety Data
189
mittees: rationale, operations and controversies. Stat
Med. 20:2573–2583, 2001.
25. Center for Drug Evaluation and Research. Integrated
summaries of effectiveness and safety: location within
the common technical document. 2009. https://www.
fda.gov/regulatory- information/search- fda- guidancedocuments/integrated- summaries- effectiveness- andsafety- location- within- common- technical- document.
26. European Medicines Agency. ICH guideline E2F on
development safety update report. 2011. https://www.
ema.europa.eu/en/documents/scientific- guideline/
international- conference- harmonisation- technicalrequirements- registration- pharmaceuticals- humanuse_en- 26.pdf.
27. Council for International Organizations of Medical
Sciences. Management of safety information from
clinical trials—report of CIOMS working group VI.
2005. https://cioms.ch/wp- content/uploads/2017/01/
Mgment_Safety_Info.pdf.
28. Spontaneous reporting systems and pharmacovigilance. WHO programme perspective. imi-protect.eu.
29. Burnstead B, Furlan G. Unifying drug safety and
clinical databases. Curr Drug Saf. 2013;8(1):56–62.
https://doi.org/10.2174/1574886311308010008.
30. Khin NA, Francis G, Mulinde J, Grandinetti C, Skeete
R, Yu B, Ayalew K, Cho S-J, Fisher A, Kleppinger C,
Ayala R, Bonapace C, Dasgupta A, Kronstein PD,
Vinter S.Data integrity in global clinical trials: discussions from Joint US Food and Drug Administration
and UK Medicines and Healthcare Products
Regulatory Agency Good Clinical Practice Workshop.
Clin Pharmacol Ther. 2020;108(5):949–63.
31. U.S. Department of Health and Human Services.
Data integrity and compliance with CGMP guidance
for industry. 2016. https://www.fda.gov/les/drugs/
published/Data- Integrity- and- Compliance- WithCurrent- Good- Manufacturing- Practice- Guidance- forIndustry.pdf.
Causality Assessment
inPharmacovigilance
MadhanRamesh andAnandHarugeri
8
Abstract
Causality assessment is an essential componentof, and routine procedure in, anypharmacovigilance system. Establishing a causal
relationship between the suspected drug and
the adverse event is a complex process and
should consider both patient- and medicationrelated factors. There are three broad
approaches, or categories, of causality assessment adopted for establishing causation in
pharmacovigilance. The methods adopted to
assess the causal relationship generally consider a few criteria in common. There are multiple criteria or algorithms available for
establishing a causal relationship in cases of
adverse events. The causality assessment tools
help clinicians in identifying the culprit drugs.
Establishing acausal relationship is important
to incorporate the individual case safety
M. Ramesh (*)
Department of Pharmacy Practice, JSS College of
Pharmacy, JSS Academy of Higher Education &
Research, Mysuru, Karnataka, India
e-mail: mramesh@jssuni.edu.in
A. Harugeri
PV Projects Leadership and Strategic Solutions,
Lifecycle Safety, IQVIA RDS Inc., Durham, NC,
USA
reports in benet–harm analysis, identication of new adverse drug reactions, and regulatory decision-making process. Causality
assessment is a continuous process and has
wider applications across various elds
including academicresearch, clinical practice,
sponsored clinical research, and regulatory activity. Therefore, knowledge of its
applications, and associated challenges, is
important in enabling stakeholders to play
their role at all levels to improve patient safety.
Keywords
Causality assessment · Causality assessmentalgorithms · Pharmacovigilance ·
Adverse drug reactions · Adverse events ·
Articial intelligence
Learning Objectives
• Understand the importance and basic princi-
ples of causality assessment.
• Describe the categories of causality
assessment.
• Apply various methods of causality
assessment.
• Understand the clinical, industry, and regula-
tory perspectives of causality assessment.
• Appreciate the applications of causality
assessment.
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024
J. Jose et al. (eds.), Principles and Practice of Pharmacovigilance and Drug Safety,
https://doi.org/10.1007/978-3-031-51089-2_8
191
192
Key Points
• Causality assessment is one of the
essential elements and challenging
aspects of pharmacovigilance.
• Causality in pharmacovigilance should
be viewed considering both patientrelated and medication-related factors.
• WHO probability scale and Naranjo’s
algorithm are widely accepted and used
across the globe.
• Pharmaceutical industry uses structured
causality assessment methods for individual case safety reports to facilitate
case processing.
• Causality assessment is important for
inclusion of reports into benet–harm
analysis, identication of new adverse
drug reactions, and regulatory decisionmaking process.
• Although causal association is established, there exists some degree of
uncertainty.
• There is a need to develop a high-quality
assessment tool which can meticulously,
accurately, and consistently establish suitable diagnostic criteria for adverse drug
reactions with universal acceptance.
M. Ramesh and A. Harugeri
Although the term ‘adverse event’ (AE) and
‘adverse drug reaction’ are often used interchangeably in clinical practice, the term adverse
event refers to reported safety information, mainly
in clinical trials, where the causality may or may
not exist. If causality is established, then these
events are termed as an ADR. Causality assessment is an essential component and routine procedure in pharmacovigilance system. Causality
assessment is a standardised method for establishing the causal relationship between suspect drug
and observed adverse event. Itcontributes to the
assessment of pharmacovigilance signals, better
knowledge of benet–harm proles of medicines,
and regulatory decision-making process. In clinical settings, it is useful in understanding the possible causal relationship between the drug and the
event and accordingly deciding the future course
of action. Recognising ADRs at their early stage
of manifestations and establishing a likelihood
relationship between the drug and the adverse
event in order help ensure early and proactive
management to avoid further, or more serious,adverse consequences [5, 6].
2 Importance ofCausality
Assessment
1 Introduction
Adverse drug reactions (ADRs) are inevitablein
clinical practice and if unrecognised and not adequately managed may cause prolonged harm,
additional medical costs, and hospitalisation or
prolongation of hospitalisation. As ADRs can
mimic naturally occurring disease process, it is
often difcult to distinguish them from the
patient’s disease condition. Nevertheless, early
detection of ADRs is important in their management and to minimise the human sufferings.
Epidemiological studies have reported that ADRs
are an important cause of morbidity, hospital
admission, and even death [1–4]. Establishing
arelationship likelihood (causal association) for
case reports of suspected ADRs is important for
decision-making at a regulatory and clinical level.
Causality assessment aims to reduce data ambiguity and plays a role in data exchange and preventing erroneous conclusions. This is especially
of signicance when reports are submitted to
apharmacovigilance system since it aids in the
regulatory decision-making process. Use of
standardised causality assessment system in clinical practice may also reduce the ambiguity in the
process of evaluation of ADRs. Furthermore,
causality assessment contributes to the efcient
evaluation of the benet–harm prole of the drug
and serves as an essential part of evaluating ADR
reports in early warning systems and for regulatory reporting purposes [7]. In regular clinical
practice, it is an important element to understand
the possible causal relationship between the
observed adverse event and the suspected drug/s.
Causality assessment of ADRs supports prevention and management of ADRs, aids in differential diagnosis, helps manage patient’s drug
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