This project originated from an exploration of EMA approval trends between 2020 and 2024. The goal was to look into the data without a predetermined narrative, allowing the figures to reveal patterns in recent pharmaceutical approvals. This project is based on a dataset manually compiled to explore EMA approval trends from 2020 through 2024. The collection process followed a multi-step verification to ensure the data is as accurate and consistent as possible.
Data Sourcing
The primary list of medicines was identified using the EMA’s annual "Human Medicines Highlights" publications for 2020 through 2024. Once the initial list was established, each entry was cross-referenced and completed with data published in the EMA’s Medicine Data Table and individual European Public Assessment Reports (EPAR).
In instances where discrepancies occurred between the annual highlights and the comprehensive data tables, records were reviewed on a case-by-case basis. In most scenarios, the status reflected in the official Medicine Data Table was given precedence.
Inclusion and Exclusion Criteria
The analysis focuses on medicines that remained authorized through August 7, 2025. Medicines withdrawn prior to this date were excluded from the dataset. A final consistency check was performed in October 2025; any medicines withdrawn between August and October were retained in the tables to maintain the structural coherence of the year-on-year analysis.
Key steps:
- Consolidated data from multiple formats (PDF, structured tables, web pages)
- Standardized company entities (aggregating Marketing Authorisation Holders at parent level)
- Harmonized regulatory classifications (e.g. novelty status, special approval pathways)
- Interpreted and categorized complex fields such as paediatric development status
- Derived analytical metrics, including estimated time to approval
Working with regulatory data required extensive interpretation and validation.
Data Challenges
- Inconsistent data across sources
Conflicting information between EMA highlights and structured datasets required case-by-case decisions. - Ambiguous classifications
Fields such as paediatric indicators and regulatory pathways are not consistently defined and required interpretation. - Non-standardized company names
Marketing Authorisation Holders were grouped at parent-company level to enable meaningful analysis. - Non-exclusive regulatory pathways
Products can qualify for multiple pathways (e.g. orphan designation, PRIME), requiring careful structuring. - Incomplete or evolving data
Certain elements (e.g. paediatric investigation plans) reflect ongoing processes rather than fixed states.
These challenges shaped both the dataset design and the final analytical views.
In particular, most impactful assumptions are:
- Approval Timelines: The time to approval was calculated as the duration between the initial dossier submission and the final European Commission decision, as documented in the EPAR.
- Marketing Authorisation Holders (MAH): For the purpose of this analysis, MAHs are grouped by their parent company. For example, approvals granted to different regional subsidiaries of the same organization are consolidated under the single parent entity.
- Paediatric Data: Due to the complexity of how paediatric information is recorded, these data points were interpreted and categorized to allow for meaningful comparison across the analysed years.
The completed work
The resulting dataset was used to build a set of interconnected Tableau dashboards for exploring the approval landscape.
Approval Landscape
- Annual approval trends (2020–2024)
- Distribution across therapeutic areas
- Drill-down to individual medicines with contextual metadata
Time to Approval
- Estimated approval timelines per product
- Breakdowns by:
- therapeutic area
- novelty status (new active substance, generic, biosimilar, other status)
- paediatric development indicators
Sponsor (MAH) Landscape
- Concentration of approvals across companies
- Activity across therapeutic areas and time
- Parent-level aggregation of Marketing Authorisation Holders
Regulatory Pathways & Novelty
- Relationship between product type and regulatory pathways
- Use of mechanisms such as:
- orphan designation
- PRIME
- accelerated or conditional approvals
Paediatric Development
- Flow from Paediatric Investigation Plans (PIP) to approvals
- Relationship between paediatric development and product novelty
- Variation across therapeutic areas
Across dashboards, a consistent interaction pattern allows users to explore high-level patterns and drill down to individual medicines.
The project demonstrates how fragmented regulatory information can be collected, structured and analysed to explore development trends, therapeutic areas, sponsor activity and approval pathways.
Outcome
I especially wanted to dig into the paediatric data as I feel this area is often overlooked. It was important to me to include this specifically to better understand how these medicines are being prioritized and developed.
