Real-world data (RWD) has gained significant importance in pharmacovigilance and regulatory decision-making processes. Real-world data refers to data collected from routine clinical practice, including electronic health records (EHRs), claims databases, registries, and other sources, outside the controlled environment of clinical trials. Here are some key impacts of real-world data in pharmacovigilance and regulatory decision-making

1. Welcome
THE IMPACT OF REAL WORLD DATA IN
PHARMACOVIGILANCE AND REGULATORY DECISION
MAKING
Student’s Name- Harika Chollangi
Student’s Qualification-
MSc.Biotechnology
Student ID – 114/062023
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2. Index
• Introduction
• What is Real World Data (RWD)
• Sources for RWD
• Why we need RWD
• Desired criteria for the acceptability of RWD
• Comparison with Randomized clinical trails (RCTs)
• Pictorial representation of use of RWD
• Challenges with the use of RWD
• Conclusion
• References
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3. Introduction
• Real world data and real world evidence are drawing ever increasing attention in
the pharmaceutical and dug regulatory authorities.
• This is due to their paramount role in supporting the Drug development and
regulatory decision making.
• However there is a little systematic way for the use of RWE by the regulatory
authorities in evaluating new treatment approaches.
• Different strategies have been applied by the FDA, EMA, and NMPA for the
development of the RWD
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4. What is Real World Data (RWD)
DEFINITION
• Data relating to the patient health status and the delivery of health acre
routinely collected from a variety of sources.
• It can also be described as the data collected during routine clinical
practice in the real-world or by the patients going about their daily lives.
REAL WORLD EVIDENCE(RWE)
• Clinical evidence regarding the usuage and potential benefits and risks of a
medical product derived from analysis of real world data(RWD)
• RWE can be generated through randomized clinical trials or observational
studies
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5. Sources for RWD
• Electronic health records
• Claims data
• Prescription data
• Patient registries
• Wearables
• Medical health apps
• Environmental data includes data on social status and other
lifestyle factors.
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6. Why we need RWD
The majority of the current literature on RWD mainly focuses on the
following aspects :
• Drug clinical development and evaluation
• Assisting drug regulatory decision making
• Pharmacovigilance
• Post marketing research
• Evaluating clinical treatment effects
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7. Desired criteria for the acceptability of
RWD
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Derived from data source of
demonstrated good quality
Valid(external and internal validity)
Consistent and adequate data

8. Comparision with Randomized
clinical trails (RCTs)
• Traditionally, regulatory approvals of of new drugs have always
been largely based on the randomized clinical trails(RCTs).
• There are some drawbacks with RCTs when compared with the
RWD.
1. Multiple comorbid conditions can be easily compromised
with the exclusion of population subsets in RCTs.
2. Developing the evidence base for the proper use of treatment
or drug is limited.
• Therefore RWD play an complementary role to RCTs with
much needed information from real life practices to support
regulatory decision making.
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9. Pictorial representation of use of RWD
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10. Challenges with the use of RWD
• By utilizing the real world data in the healthcare data brings us different challenges
due to the heterogenous nature of data.
• These are mainly classified into 3 challenges.
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Operational
Technical
Methodological

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OPERATIONAL TECHNICAL METHODOLOGIC
AL
•Feasibility (e.g.,
data access and cost,
data protection,
availability of
hospital data)
•Sustainability
(sustained data
collection &
analysis)
•Patients consent
•Governance(e.g:
data sharing policy)
•Collection of
adequate time
elements
•Data completeness
(missing data)
•Consistent,
accurate, and timely
data collection,
recording &
management
•Quality assurance
•Data audit
•Variability in results
from multi–data source
studies.
•Adequate data
collection on potential
confounders (e.g.
smoking)
•Understanding the
data source
•Management of
missing data
•Sound data analysis

12. Conclusion
• The digitalization of the health care and increasingly lifestyle data brings new
opportunities to complement and enhance the data traditionally utilized in the
regulatory decision making.
• As the standardizing and validating data retrospectively is expensive and time
consuming.
• Therefore with the combination of scientific and technological advances, the
quality of evidence generated and the consistency of regulatory decision
making can be optimized.
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13. References
• Cave, A. C., Kurz, X., & Arlett, P. (2019a, April 10). Real‐World Data for Regulatory Decision Making: Challenges and Possible Solutions for
Europe. Clinical Pharmacology & Therapeutics. https://doi.org/10.1002/cpt.1426
• Chen, W., Lin, C., Huang, W., Chao, P., Gau, C., & Hsiao, F. (2020a). Using real‐world evidence for pharmacovigilance and drug safety‐related
decision making by a resource‐limited health authority: 10 years of experience in Taiwan. Pharmacoepidemiology and Drug Safety, 29(11), 1402–
1413. https://doi.org/10.1002/pds.5084
• Lavertu, A., Vora, B., Giacomini, K. M., Altman, R. B., & Rensi, S. E. (2021b). A New Era in Pharmacovigilance: Toward Real‐World Data and
Digital Monitoring. Clinical Pharmacology & Therapeutics, 109(5), 1197–1202. https://doi.org/10.1002/cpt.2172
• Liu, L., Chen, S., Lai, Y., Liang, Z., Wang, J., Shi, J., Lin, H., Yao, D., Hu, H., & Ung, C. O. L. (2021a). Integrating Real-World Evidence in the
Regulatory Decision-Making Process: A Systematic Analysis of Experiences in the US, EU, and China Using a Logic Model. Frontiers in Medicine, 8.
https://doi.org/10.3389/fmed.2021.669509
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14. Thank You!
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(India | Canada)
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info@clinosol.com
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