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EU | EMA Catalogues of Data Sources and Non-Interventional Studies Go Live in February 2024

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EU | EMA Catalogues of Data Sources and Non-Interventional Studies Go Live in February 20242024-01-09T14:12:16+00:00

EU – EMA RWE Framework to Support Regulatory Decision Making

RWE 201 – EU – EMA RWE Framework to Support Regulatory Decision Making

 

EMA RWE Framework 2023: https://www.ema.europa.eu/en/documents/report/real-world-evidence-framework-support-eu-regulatory-decision-making-report-experience-gained_en.pdf

The European Medicine Agency (EMA) is actively working on creating a framework that will facilitate the use and establishes the value of RWE in decision-making throughout the entire drug lifecycle.

The current framework includes 3 evidence generation pathways: (1) In-house database e.g., The Health Improvement Network (THIN®), (2) DARWIN-EU, and (3) ‘Traditional’ primary data studies.

80% of the database studies, also referred to as ‘rapid data analytics’ , were delivered in less than 90 days.  Whereas delivery of the DARWIN-EU studies averaged 215 days. The majority of the studies focused on safety, rare diseases, and paediatrics. 

Key Learning

  1. Suitability of RWD Sources:

– RWD aids in enhancing evidence from clinical trials, aiding EU regulatory decisions.

– Most suitable studies addressed primary care scenarios due to available databases.

  1. Regulatory Context & Timelines:

– Comprehending regulatory nuances and evidence gaps is crucial for apt data selection and study design.

– In-house studies, given their speed, fit well with research questions having tight timelines.

  1. Building Capability & Capacity:

– Familiarity with RWD concepts, methodologies, and terms is essential for RWE acceptance.

  1. Usefulness for Decision-making:

– RWD studies bolster scientific evaluations in multiple regulatory situations.

– Understanding data source attributes aids in grasping study strengths, limitations, and RWE’s value.

  1. Other Process-related Aspects:

– Standardizing RWD study approaches, through agreed processes and templates, enhances RWE efficiency.

Recommendations

  1. RWD Source Expansion: Access more diverse data sources for better representation. Retain all three RWE pathways and collaborate with NCAs to maximize RWE generation.
  2. Enhance RWE Timeliness: Adopt proactive RWD study identification methods and increase RWE generation speed, possibly via tools like DARWIN EU.
  3. Boosting Capability & Capacity: Implement the Big Data Steering Group’s curriculum specifically for regulatory stakeholders.
  4. Improving Decision-making Utility: Detail strengths and limitations in future reports for improved interpretation.
  5. Process Refinement: Encourage reflections on RWE’s potential to bridge knowledge gaps and further unify processes.

Conclusions

Over the report’s timeframe, 27 regulator-led RWD studies were finalized, aiding various regulatory assessments like PRAC and SAWP. These studies spanned across safety, drug use, disease epidemiology, and more, primarily providing descriptive analyses. Some comparative analyses were also done. The range and design of these studies highlight the vast potential of the existing RWE resources and the Agency’s adaptability in addressing diverse research inquiries. However, further enhancements can be made to fully harness the EMA RWD study framework’s potential.

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EU – EMA RWE Framework to Support Regulatory Decision Making2023-11-05T12:17:32+00:00

EU – EMA Big Data Steering Group Updates Its Workplan to Accelerate Transformation to Data-Driven Medicines Regulation

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EU – EMA Big Data Steering Group Updates Its Workplan to Accelerate Transformation to Data-Driven Medicines Regulation2023-11-05T11:05:33+00:00

Real World Evidence (RWE) 101 – The Impact of the EMAs Data Quality Framework on RWE

RWE 101 – The Impact of the EMAs Data Quality Framework on RWE

The EMA (European Medicines Agency) data quality framework provides a set of guidelines and principles for ensuring high-quality data in real-world evidence (RWE) studies in the context of EU medicines regulation. The framework aims to promote the use of RWE in the assessment of medicines, and to ensure that RWE studies are conducted in a rigorous and reliable manner.

The impact of the EMA data quality framework on RWE can be significant. By promoting high-quality data collection and analysis in RWE studies, the framework can help to ensure that the results of such studies are reliable and can be used to inform regulatory decision-making. This, in turn, can facilitate the timely access of patients to new treatments and can help to improve public health outcomes.

The framework encourages the use of transparent and reproducible methods in RWE studies, which can help to ensure that the results are credible and trustworthy. The use of standardized data collection and analysis methods can also facilitate the comparison of results across different studies and settings, which can help to build a more comprehensive understanding of the safety and efficacy of medicines.

Overall, the EMA data quality framework can help to promote the use of RWE in medicines regulation and improve the quality and reliability of RWE studies. This can have a positive impact on public health by facilitating timely access to new treatments and improving the understanding of the safety and efficacy of medicines.

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Real World Evidence (RWE) 101 – The Impact of the EMAs Data Quality Framework on RWE2023-08-07T23:06:32+00:00

Real World Evidence (RWE) 101 – EMA Good Pharmacovigilance Practices (GVPs)

RWE 101 – EMA Good Pharmacovigilance Practices (GVPs)

The European Medicines Agency’s (EMA) Good Pharmacovigilance Practices (GVPs) provide a framework for the monitoring and reporting of adverse drug reactions (ADRs) to ensure the safety and efficacy of medicines. In the context of real-world evidence, GVPs play an important role in ensuring the quality and reliability of data collected from real-world studies.

Real-world evidence refers to data collected from sources outside of traditional clinical trials, such as electronic health records, patient registries, and observational studies. This type of data is becoming increasingly important in drug development and regulatory decision-making, as it provides valuable insights into how medicines perform in real-world settings.

To ensure the quality and reliability of real-world evidence, GVPs require that data collection methods are standardized and that the data is collected in a manner that minimizes bias and confounding factors. GVPs also require that adverse events are reported in a timely and accurate manner, and that data is regularly monitored for safety signals.

In addition, GVPs require that all stakeholders involved in the collection and use of real-world evidence are trained (as appropriate) in pharmacovigilance principles and are aware of their responsibilities in ensuring the safety and efficacy of medicines.

By adhering to GVPs in the context of real-world evidence, researchers and regulatory agencies can ensure that the data collected is of high quality and can be used to inform decision-making related to the safety and efficacy of (approved) medicines.

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Real World Evidence (RWE) 101 – EMA Good Pharmacovigilance Practices (GVPs)2023-08-07T22:58:35+00:00
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