Written By: Shital Doifode, M.Pharm Pharmacology
Reviewed and Fact-Checked By: Ashish Jaydeokar (Manager, Pharmacovigilance Operations, Germany)
Pharmacovigilance is the field of detection, assessment, understanding, and prevention of adverse effects or any other drug-related problems. The field has undergone many evolutions in the 21st century, with the integration of artificial intelligence in recent times. Conventionally, it depends on randomized controlled trials (RCTs) and spontaneous adverse event reporting systems. However, these methods now fall short in capturing the complete safety profile of a drug, particularly in real-world use across diverse patient populations and disease profiles. This limitation has facilitated the integration of real-world data (RWD) and real-world evidence (RWE), active tools that offer a more inclusive, proactive approach to safety surveillance. By leveraging data from electronic health records, insurance claims, patient registries, digital health tools, and other routine clinical sources, pharmacovigilance systems can now monitor drug safety and effectiveness in real time throughout the entire life cycle of a pharmaceutical product. This shift will not only enhance signal detection and risk assessment but also support more informed regulatory and clinical decision-making in everyday healthcare practice.
What Are RWD and RWE?
Real-World Data (RWD): It refers to data related to patient health status collected from sources such as electronic health records (EHRs), insurance claims, patient registries, and even digital health tools like mobile health apps and also from social media.
Real-World Evidence (RWE) covers the clinical insights and evidence about the usage and potential safety and risk of medical products that are derived from the analysis of Real-World Data (RWD).
This change is crucial, as clinical trials during the establishment of efficacy and safety often involve highly selected populations and controlled environments, limiting their applicability to broader patient groups.
In contrast, RWD/RWE allows for the continuous and proactive assessment of medications across diverse populations, capturing long-term safety outcomes, rare adverse events, and drug performance in real-world scenarios.
Why RWD and RWE Matter in Pharmacovigilance
Clinical trials are conducted in controlled environments with carefully selected participants, generally excluding elderly patients, pregnant women, and those with multiple comorbidities. RWD and RWE help to fill these gaps by showing how drugs behave in the real world, including in these excluded populations. RWE helps to eliminate the potential bias caused in CTs. Biases are often described in 3 categories:
- Selection bias, which derives from including or selectively following subsets of the population in the study in a way that distorts the relation between the exposure and the outcome.
- Information bias, which derives from measurement errors.
- Confounding bias: this derives from noncomparability of the intervention groups in the study (27 in ->11).
Early Detection of Adverse Drug Reactions (ADRs)
By continuously analyzing data from large and diverse patient populations, pharmacovigilance teams are able to detect rare, serious, or delayed adverse drug reactions much earlier; that would not be possible through traditional clinical trial methods alone. Traditional trials are typically limited in size, duration, and participant diversity, which can make it difficult to identify side effects that only occur infrequently or in specific subgroups. In contrast, real-world data sources such as electronic health records, insurance claims, patient registries, and post-marketing surveillance reports allow pharmacovigilance experts to monitor the safety of medications across millions of users in real time.
Improved Signal Detection and Risk Management
Real-World Evidence (RWE) enables more accurate signal detection by providing an all-inclusive view of how drugs perform across diverse, real-life patient populations. The broader dataset of RWE allows researchers and regulators to correlate adverse drug events (ADEs) not just with the drug itself, but with specific patient characteristics such as age, gender, genetic factors, or underlying co-morbidities like diabetes, hypertension, or renal impairment. RWE is generated according to a research plan and interpreted accordingly.
Furthermore, RWE supports the identification of risks associated with concurrent therapies. Many patients are on multiple medications simultaneously (polypharmacy), which can lead to drug-drug interactions that are difficult to study in traditional trials. RWE can detect patterns where certain drug combinations are consistently associated with adverse outcomes, enabling more precise risk stratification.
By uncovering these population-specific safety signals, RWE enhances pharmacovigilance efforts and allows healthcare providers and regulatory agencies to implement targeted risk management strategies.
RWE allows for more accurate signal detection by correlating adverse events with specific patient populations, comorbid conditions, or concurrent therapies. This enhances the ability to manage risks in a targeted manner.
Regulatory Decision Support
Health authorities like the FDA and EMA increasingly rely on RWE to support regulatory decisions, such as label updates, post-marketing requirements, and even new indications for existing drugs.
First-ever regulatory approval of label expansion of IBRANCE (palbociclib) for male breast cancer based on RWE, have brought in a new era in the applicability of RWE in healthcare.
Overseen by the Big Data Steering Group (BDSG), EMA and the European Medicines Regulatory Network (EMRN) are working to establish a sustainable framework to enable the use and establish the value of real-world evidence (RWE) across different regulatory use cases.
Approvals where RWE was considered:
- A total of 30 FDA approvals were identified (EMA: 16).
- The number of approvals is steadily increasing.
- The approvals of new drugs only concern orphan drugs at both FDA and EMA; i.e. drugs against rare diseases.
Supporting Patient-Centred Outcomes
Real-World Data (RWD) captures the patient experience in a more holistic and meaningful way, as compare to traditional clinical trials. While randomized controlled trials (RCTs) focus primarily on efficacy under ideal conditions, RWD reflects how treatments perform in everyday clinical settings, where patients may have diverse backgrounds, health conditions, and behaviours.
One of the key advantages of RWD is its ability to provide insights into treatment adherence, how consistently patients follow prescribed therapies. Non-adherence is a major factor affecting treatment outcomes and safety, yet it’s often underreported in clinical trials. RWD can identify adherence patterns, uncover barriers (e.g., side effects, cost, complex regimens), and inform interventions to improve long-term medication use.
FDA recognizes the potential utility of using RWD in interventional studies; for example, to identify potential participants for a randomized controlled trial, to ascertain endpoints or outcomes (e.g., occurrence of stroke or other discrete events, hospitalization, survival) in a randomized controlled trial, or to serve as a comparator arm in an externally controlled trial, including historically controlled trials
Real-World Applications of RWD and RWE in Pharmacovigilance
Post-Marketing Surveillance: After a pharmaceutical product is approved and enters the market, continuous monitoring is essential to ensure its long-term safety and effectiveness. Companies now increasingly rely on electronic health records (EHRs) and insurance claims data to conduct this post-marketing surveillance. These real-world data sources enable the identification of safety trends, rare adverse events, and patterns of use that may not have emerged during clinical trials.
EHRs provide detailed clinical information, such as lab results, diagnoses, and physician notes, while claims data offer insights into medication usage, healthcare utilization, and treatment costs across large patient populations.
Risk Evaluation and Mitigation Strategies (REMS): Real-world evidence (RWE) plays a crucial role in both the design and assessment of REMS programs, which are mandated by regulatory agencies like the FDA to ensure that the benefits of certain high-risk medications outweigh their potential risks.
Adaptive Pharmacovigilance Systems: Advances in artificial intelligence (AI) and machine learning (ML), when applied to real-world data (RWD) such as electronic health records, claims databases, and patient-reported outcomes, are transforming traditional pharmacovigilance into a more dynamic, automated, and predictive system.
These technologies enable the development of adaptive pharmacovigilance systems, which can continuously analyze large and complex datasets to automatically detect safety signals such as unusual patterns of adverse events, drug interactions, or shifts in usage trends. Unlike traditional, passive reporting systems, these AI-driven tools can flag potential risks in near real-time, improving both the speed and sensitivity of signal detection.
Tools and Technologies Powering RWD and RWE
1. Electronic Health Records (EHR) Systems
EHRs are one of the primary sources of RWD. Tools like Allscripts collect detailed patient-level clinical information, including diagnoses, treatments, lab results, and adverse events. Integration of EHR data into pharmacovigilance platforms helps identify safety signals in near real-time.
2. Claims and Billing Databases
Administrative claims databases such as Optum offer large-scale, longitudinal patient information. These are particularly useful for tracking healthcare utilization, medication adherence, and long-term safety outcomes.
3. Patient Registries
Disease-specific and product-specific registries collect structured data over time from patients with defined characteristics. Tools like OpenClinica are commonly used to manage registry data. Registries help monitor rare adverse events and long-term safety in real-world populations.
4. Mobile Health (mHealth) and Wearables
Apps and devices like Fitbit, Apple Watch, and mobile symptom trackers generate real-time data on patient activity, heart rate, medication intake, and other health metrics. This type of continuous monitoring offers valuable insights into drug effects outside clinical settings.
5. Natural Language Processing (NLP) Tools
NLP tools can extract relevant information from unstructured data such as clinical notes, discharge summaries, and patient forums. Examples include Amazon Comprehend Medical NLP pipelines, which can help identify adverse drug reactions from text sources.
6. Artificial Intelligence and Machine Learning Platforms
AI-powered platforms like SAS, IBM Watson Health, and Google Cloud AI are used to detect patterns in large datasets, support predictive analytics, and improve the accuracy of signal detection. Machine learning algorithms can also classify and prioritize adverse events based on severity and novelty.
7. Data Integration and Analytics Platforms
Platforms like OMOP (Observational Medical Outcomes Partnership) and the Sentinel initiative by the FDA standardize and harmonize data from multiple sources. These tools enable scalable and reproducible analysis of RWD to support RWE generation.
8. Social Listening and Digital Epidemiology Tools
Mining social media and online patient communities through platforms like Brandwatch or MedWatcher Social can reveal emerging drug safety concerns from patients themselves, often before they are formally reported.
Conclusion
The integration of real-world data and real-world evidence into pharmacovigilance marks a more inclusive and responsive drug safety system. By bridging the gap between controlled clinical environments and the complexity of everyday healthcare, RWD and RWE enable a more nuanced, proactive, and patient-centric approach to monitoring drug safety. As regulatory frameworks and technological tools continue to evolve, their role in modern pharmacovigilance will only become more beneficial.
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