1.1 Credits

The EMA is acknowledged as a source of information used here and should also be acknowledged by users.

1.2 Disclaimer

The correctness of information in the Markdown files is not guaranteed to be correct. Conversion of and extraction from PDF files and webpages can have errors. Users have to verify the information they use.

When using any Markdown file from this repository or offering, please include the source credits and the citation

Herold, R. (2026). Regulatory documents as Markdown files [Data set]. https://github.com/rfhb/emamds

Contact me for additional types of regulatory documents (e.g., nationally authorised medicinal products, paediatric investigation plans, medicines supply shortages, orphan designation), with the use case of interest.

2 Repository

The repository is located at https://github.com/rfhb/emamds. It contains Markdown files and (in the future) helper scripts. The files in the repository are versioned using git. Users can create a local copy of the repository (git clone ...) and efficiently keep it updated (git pull). The size of the repository is about 1.8 GB, compressed 350 MB, for the more than 15,000 included files. Note the following folder structure of the repository.

user@machine:~/localdir$ tree -d emamds
emamds
└── en
    ├── documents
    │   ├── all-authorised-presentations
    │   ├── assessment-report
    │   ├── procedural-steps
    │   ├── procedural-steps-after
    │   ├── product-information
    │   ├── referral
    │   ├── scientific-conclusion
    │   ├── scientific-discussion
    │   ├── scientific-discussion-variation
    │   ├── steps-after-cutoff
    │   └── variation-report
    └── medicines
        └── human
            └── EPAR

3 Use cases

Here is a first set of example use cases for Markdown files from regulatory documents. The use cases show how to extract sections of interest from Markdown files. These can then be used for aggregations and further analyses.

Such use cases and analyses can be part of regulatory science research based on regulatory documents (Barbier et al. 2025; Ladanie et al. 2018; Papathanasiou et al. 2016).

The examples also showcase command line tools which allow rapid processing of a large number of documents.

mdqJq() {
  mdq --output json '# Effects Table | :-: * :-: ' ${1} | \
  jq --compact-output --arg FN ${1} '{fn: $FN, tbl: [.items[].table.rows[]]}'
}
export -f mdqJq
find . -name '*-assessment-report*_en.md' -exec sh -c 'mdqJq "$0"' {} \;

{"fn":"./zynteglo-epar-public-assessment-report_en.md","tbl":[["Effect","Short Description","Unit","Treatment","Control","Uncertainties/ Strength of evidence","References"],["Favourable Effects","Favourable Effects","Favourable Effects","Favourable Effects","Favourable Effects","Favourable Effects","Favourable Effects"],["Transfusion Independence (TI) for evaluable non- β 0 /β 0 patients","Proportion of patients with a weighted average haemoglobin (Hb) ≥ 9 g/dL without any pRBC transfusions for a continuous period of ≥ 12 months at any time during the study after Zynteglo infusion","n/N (%)","15/19 (79%; 95% CI: 54% to 94%)","NA","longer authorised The single patient in Study HGB-207 who has not achieved TI exhibited a substantial decline from DP VCN to PB VCN, resulting in low HbA T87Q production Study HGB-205 was performed with the base manufacturing process, HGB-204 with the original manufacturing process, HGB-207 with the refined or commercial manufacturing process.","Studies HGB-204, HGB-205, HGB-207, LTF-303."],["Observed duration of TI for non- β 0 /β 0 patients","","min, max","no 12.0+ to 56.3+ months. * median duration of TI not reached","","All non- β 0 /β 0 patients who have achieved TI at any time have maintained their TI status through all Hb assessments. True duration of TI is unknown as follow up time is limited.","Studies HGB-204, HGB-205, HGB-207, LTF-303."],["Transfusion Reduction (TR) in non- β 0 /β 0 patients that did not achieve TI at any time (n=4)","Medicinal % change in annualized transfusion volume from the period from 6 months post- DPI through Month 24 as compared to annualized baseline pretreatment transfusion requirements","%","product -26.8%, -71.4% -86.9%, -100%","NA","Small number of patients","Studies HGB-204, HGB-205, HGB-207."],["Unfavourable Effects","Unfavourable Effects","Unfavourable Effects","Unfavourable Effects","Unfavourable Effects","Unfavourable Effects","Unfavourable Effects"],["AE for all patients with TDT (ITT)","Patients with at Least 1 AE","% (n)","98.2% (n=56)","NA","Limited safety database, contribution of Zynteglo to the overall safety profile cannot be distinguished from that of concomitant treatment/HSCT procedure",""],["Effect","Short Description","Unit","Treatment","Control","Uncertainties/ Strength of evidence","References"],["SAE for all patients with TDT (ITT)","Patients with at Least 1 SAE","% (n)","50.9% (n=29)","NA","Limited safety database, one AE was assessed as possibly related",""],["Common AE for all patients with TDT (ITT)","AEs occurring in at least 50% of patients","%","Thrombocytopenia (82.5%), Anaemia (71.9%), Stomatitis (64.9%), Vomiting (50.9.%), Neutropenia (57.9%), Alopecia (52.6%)","NA","authorised to drug product Limited safety database, contribution of Zynteglo to the overall safety profile cannot be distinguished from that of concomitant treatment/HSCT procedure",""],["Delayed platelet engraftment for all patients with TDT","Time to platelet values ≥ 20 ×10 9 /L on 3 consecutive days","Median, days","41.5 (min 19, max 191).","< 30 days","contribution of Zynteglo to the delayed platelet engraftment remains unexplained",""]]}
{"fn":"./kalydeco-h-c-002494-x-0114-g-epar-assessment-report-extension_en.md","tbl":[["Effect","Short Description","Unit","Treatment ELX/TEZ/IVA","Control -","Uncertainties/ Strength of evidence","References"],["Favourable Effects","Favourable Effects","Favourable Effects","Favourable Effects","Favourable Effects","Favourable Effects","Favourable Effects"],["SwCl","Change 0-24 wks LS mean (95% CI) from baseline","Mmol/l","-57.9 (-61.3, - 54.6)","-","Unc : open-label, single-arm trial","Study 111"],["LCI2.5","Change 0-24 wks LS mean (95% CI) from baseline","number","-0.83 (-1.02, - 0.66)","","Unc : open-label, single-arm trial","Study 111"],["PEx","Event rate/year","number","0.32","-","Unc : open-label, single-arm trial Unc : 24 week trial","Study 111"],["Unfavourable Effects","Unfavourable Effects","Unfavourable Effects","Unfavourable Effects","Unfavourable Effects","Unfavourable Effects","Unfavourable Effects"],["Elevated Transaminase Events","Events of increased transaminases (Part B)","%","10.7%","-","Unc : Open-label, single arm study,","Study 111"],["Rash","Events (Part B)","%","20.0%","-","Unc : Open-label, single arm study","Study 111"]]}
{"fn":"./abecma-epar-public-assessment-report_en.md","tbl":[["Effect","Short Description","Treatment","Result","Uncertainties/ Strength of evidence"],["Favourable effects","Favourable effects","Favourable effects","Favourable effects","Favourable effects"],["ORR (%)","Percentage of subjects who achieved PR or better as assessed by an IRC according to IMWG uniform response criteria for multiple myeloma.","150 x 10 6 CAR+T cells n=4 300 x 10 6 CAR+T cells n=70 450 x 10 6 CAR+T cells n=54 150-450 x 10 6 CAR+T cells n=128 Ide-cel treated pop. Enrolled pop. n= 140","2 (50.0%) 48 (68.6%) 44 (81.5%) 94 (73.4%) (95% CI: 65.8, 81.1) * 94 (67.1%) (95% CI: 59.4, 74.9)","No control arm. Patients were not randomised to the different dose cohorts. *p < 0.0001, 1- sample binomial test rejecting the null hypothesis of ≤ 50% for ORR [and ≤ 10% for CR rate]"],["CR (%)","Percentage of subjects who achieved CR or sCR as assessed by an IRC according to IMWG uniform response criteria for multiple myeloma.","150-450 x 106 CAR+T cells n=128 Ide-cel treated pop. Enrolled pop.","42 (32.8%)* (95% CI 24.7, 40.9) 42 (30.0%)",""],["DOR, (median, months) -EMA censoring","Time from first documentation of response of PR or better to first documentation of disease progression or death from any cause, whichever occurred first.","150-450 x 10 6 CAR+T cells n=128 Idecel treated pop. Enrolled pop. n= 140","10.6 (95% CI 8.0, 11.4) 10.6 (95% CI 8.0, 11.4)",""],["Effect","Short Description","Treatment","Result","Uncertainties/ Strength of evidence"],["Favourable effects","Favourable effects","Favourable effects","Favourable effects","Favourable effects"],["CRS","","150 - 450 x 10 6 CAR+T cells N=184","81.0% Grade ≥ 5.4 %","Few subjects with dose 150 x 10 6 CAR+T cells"],["Neurologic toxicity - 'focused'","","150 x 450 10 6 CAR+T cells N=184","41.8%","Few subjects with dose 150 x 10 6 CAR+T cells"],["Neurologic toxicity - 'broad'","","150-450 x 10 6 CAR+T cells N=184","73.4%","Two other ways of recording neurotoxicity have been used"],["Cytopenias","","150 - 450 x 10 6 CAR+T cells n=184","95.7% Grade ≥ 3: 95.1%","Few subjects with dose 150 x 10 6 CAR+T cells"],["Infections","","150 - 450 x 10 6 CAR+T cells n=184","71.2% Grade ≥ 3: 23.4%","Few subjects with dose 150 x 10 6 CAR+T cells"],["Secondary malignancy","","150 - 450 x 10 6 CAR+T cells n=184","8.7%",""]]}

mq --aggregate '
import "section"
| nodes | section::split(1) + section::split(2) + section::split(3)
| section::title_match("herapeutic indic")
| self[0]["children"]
| try: join(self, "\\n\\n") catch: ""
| gsub(self, "<div style=\\\\\"page-break-after: always\\\\\"></div>", "")
| let pn = gsub(s"${__FILE__}", "-epar-product-information_en[.]md", "")
| s"\{ \"productName\": \"${pn}\", \"section41\": \"${self}\" \}"
' *-epar-product-information_en[.]md \
| jq --compact-output '.'

{"productName":"abevmy","section41":"Abevmy in combination with fluoropyrimidine-based chemotherapy is indicated for treatment of adult patients with metastatic carcinoma of the colon or rectum.\n\nAbevmy in combination with paclitaxel is indicated for first-line treatment of adult patients with metastatic breast cancer. For further information as to human epidermal growth factor receptor 2 (HER2) status, please refer to section 5.1.\n\nAbevmy in combination with capecitabine is indicated for first-line treatment of adult patients with metastatic breast cancer in whom treatment with other chemotherapy options including taxanes or anthracyclines is not considered appropriate. Patients who have received taxane and anthracyclinecontaining regimens in the adjuvant setting within the last 12 months should be excluded from treatment with Abevmy in combination with capecitabine. For further information as to HER2 status, please refer to section 5.1.\n\nAbevmy, in addition to platinum-based chemotherapy, is indicated for first-line treatment of adult patients with unresectable advanced, metastatic or recurrent non-small cell lung cancer other than predominantly squamous cell histology.\n\n\n\nAbevmy, in combination with erlotinib, is indicated for first-line treatment of adult patients with unresectable advanced, metastatic or recurrent non-squamous non-small cell lung cancer with Epidermal Growth Factor Receptor (EGFR) activating mutations (see section 5.1).\n\nAbevmy in combination with interferon alfa-2a is indicated for first line treatment of adult patients with advanced and/or metastatic renal cell cancer.\n\nAbevmy, in combination with carboplatin and paclitaxel is indicated for the front-line treatment of adult patients with advanced (International Federation of Gynecology and Obstetrics [FIGO] stages III B, III C and IV) epithelial ovarian, fallopian tube, or primary peritoneal cancer (see section 5.1).\n\nAbevmy, in combination with carboplatin and gemcitabine or in combination with carboplatin and paclitaxel, is indicated for treatment of adult patients with first recurrence of platinum-sensitive epithelial ovarian, fallopian tube or primary peritoneal cancer who have not received prior therapy with bevacizumab or other VEGF inhibitors or VEGF receptor -targeted agents.\n\nAbevmy in combination with paclitaxel, topotecan, or pegylated liposomal doxorubicin is indicated for the treatment of adult patients with platinum-resistant recurrent epithelial ovarian, fallopian tube, or primary peritoneal cancer who received no more than two prior chemotherapy regimens and who have not received prior therapy with bevacizumab or other VEGF inhibitors or VEGF receptor -targeted agents (see section 5.1).\n\nAbevmy, in combination with paclitaxel and cisplatin or, alternatively, paclitaxel and topotecan in patients who cannot receive platinum therapy, is indicated for the treatment of adult patients with persistent, recurrent, or metastatic carcinoma of the cervix (see section 5.1)."}
{"productName":"zytiga","section41":"ZYTIGA is indicated with prednisone or prednisolone for:\n\n-  the treatment of newly diagnosed high risk metastatic hormone sensitive prostate cancer (mHSPC) in adult men in combination with androgen deprivation therapy (ADT) (see section 5.1)\n\n-  the treatment of metastatic castration resistant prostate cancer (mCRPC) in adult men who are asymptomatic or mildly symptomatic after failure of androgen deprivation therapy in whom chemotherapy is not yet clinically indicated (see section 5.1)\n\n-  the treatment of mCRPC in adult men whose disease has progressed on or after a docetaxel-based chemotherapy regimen."}

for thisMd in *-assessment-report*_en.md; do
  mq '
    import "section"
    | nodes 
    | var scts = section::split(1) + section::split(2)
    | var out = []
    | foreach (sct, scts): 
        var stt = section::title(sct) |
        if (test(stt, "Clinical safety")): break
        if (or(test(stt, "Clinical efficacy"), out != [])): out = out + sct
    end
    section::collect(out)
  ' "${thisMd}" \
  | sed 's/\s*<div style=\\"page-break-after: always\\"><\/div>//g' \
  > "${thisMd%-epar(|-public)-assessment-report*_en.md}_clinical_efficacy.md" 
done
wc -l *_clinical_efficacy.md

    1514 abecma-epar-public-assessment-report_en.md_clinical_efficacy.md
     835 kalydeco-h-c-002494-x-0114-g-epar-assessment-report-extension_en.md_clinical_efficacy.md
     989 zynteglo-epar-public-assessment-report_en.md_clinical_efficacy.md
    3338 total

find . -type f -name "*report*md" -exec \
yq --front-matter="extract" --indent=0 --output-format="json" \
  '{"time": .processing_time, "pages": .document_pages}' {} \; \
  | Rscript -e \
  "jsonlite::stream_in(file('stdin'), verbose = FALSE) |> lm()"

Call:
lm(formula = jsonlite::stream_in(file("stdin"), verbose = FALSE))

Coefficients:
(Intercept)        pages  
    -126.75         1.96  

4 Technical aspects

This offering is based on consecutive steps:

• obtaining medicines spreadsheet, select links to recent updates
• mirroring efficiently with wget2 PDF files and webpages
• setting file dates and times of webpages from their metadata
• converting updates using docling to Markdown
• adding YAML with metadata to Markdown files
• adding, committing and pushing into the repository

wget2 \
  --accept-regex='^.+/en/documents/(all-auth|scientific-[dc]|steps|assessment|procedur|variation|referral|product).+_en[.]pdf$' \
  --adjust-extension \
  --filter-urls \
  --header="Accept: text/html" \
  --ignore-tags=img,link,script \
  --input-file='emaepars.links' \
  --max-threads=10 \
  --mirror \
  --no-follow-sitemaps \
  --random-wait \
  --retry-on-http-error=429 \
  --robots \
  --stats-site=csv:emaeparsstats.csv \
  --tries=20 \
  --wait=3 \
  --waitretry=150

user@machine:~/localdir$ tree -d www*
www.ema.europa.eu
└── en
    ├── documents
    │   ├── all-authorised-presentations
    │   ├── assessment-report
    │   ├── procedural-steps
    │   ├── procedural-steps-after
    │   ├── product-information
    │   ├── referral
    │   ├── scientific-conclusion
    │   ├── scientific-discussion
    │   ├── scientific-discussion-variation
    │   ├── steps-after-cutoff
    │   └── variation-report
    └── medicines
        └── human
            └── EPAR

# find HTML files
dir(
  path = mirrorPrefix,
  pattern = "[.]html$",
  recursive = TRUE,
  full.names = TRUE
)

# get content modification time from HTML files
rvest::read_html() |>
  rvest::html_elements(
    xpath = '/html/head/meta[@property="article:modified_time"]'
    # for some, not-yet-modified HTML files use
    # '/html/head/meta[@property="article:published_time"]'
  ) |>
  rvest::html_attr("content") |>
  # e.g. "2017-09-20T13:54:00+0200"
  lubridate::as_datetime()

# set date and time of files
Sys.setFileTime()

DOCLING_SERVE_MAX_SYNC_WAIT=600 \
DOCLING_NUM_THREADS=16 \
DOCLING_PERF_PAGE_BATCH_SIZE=8 \
docling-serve run

curl -sS -X 'POST' \
  -F files="@www.ema.europa.eu/en/documents/assessment-report/tepezza-epar-public-assessment-report_en.pdf" \
  -F do_ocr="true" \
  -F do_table_structure="true" \
  -F force_ocr="false" \
  -F image_export_mode="placeholder" \
  -F include_images="true" \
  -F md_page_break_placeholder='<div style="page-break-after: always"></div>' \
  -F ocr_engine="rapidocr" \
  -F ocr_lang="en" \
  -F pdf_backend="dlparse_v4" \
  -F pipeline="standard" \
  -F table_mode="accurate" \
  -F table_cell_matching="true" \
  -F to_formats="md" \
  'http://localhost:5001/v1/convert/file' \ 
> emamds/en/documents/assessment-report/tepezza-epar-public-assessment-report_en.md

document_datetime: 2026-01-10 11:55:33.574005
document_pages: 33
document_pathfilename: www.ema.europa.eu/en/documents/procedural-steps-after/foclivia-epar-procedural-steps-taken-scientific-information-after-authorisation-archive_en.pdf
document_name: foclivia-epar-procedural-steps-taken-scientific-information-after-authorisation-archive_en.pdf
version: success
processing_time: 17.5662868
conversion_datetime: 2026-01-10 11:57:58.57619
docling_version:
  docling-serve: 1.9.0
  docling-jobkit: 1.8.1
  docling: 2.67.0
  docling-core: 2.58.0
  docling-ibm-models: 3.10.3
  docling-parse: 4.7.2
  python: cpython-313 (3.13.11)
  plaform: macOS-26.2-arm64-arm-64bit-Mach-O

5 Contributing

In the future, to update the source repository, pull requests (PRs) may be accepted if users generate Markdown files following the guidance as per the technical aspects described above; scripts that users can use for these steps will be added to the repository shortly.

Webapp

Maximise

References

This webapp has been used for unpublished work and for:

• Grupstra RJ et al. More Than a Question of Correlation: Characterization of the Evidentiary Basis for Biomarker Surrogates Used in European Marketing Authorizations. Clinical Pharmacology & Therapeutics 2026 https://doi.org/10.1002/cpt.70229

• Ferreira FS. Reporte de redes de investigação nos ensaios clínicos pediátricos registados no EudraCT. Master thesis 2024 http://hdl.handle.net/10400.5/101030

• Klinglmueller F et al. A Review of EMA Public Assessment Reports Where Non-Proportional Hazards Were Identified. arXiv 2024 https://doi.org/10.48550/arXiv.2406.12492

• Hooker A et al. (CONFIRMS consortium) Uncertainty quantification methods for complex models used in drug development and/or regulatory approval. Presentation 2026 event

Webapp

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Changes

• 2026-02-23 revised calculating procedure_number in assessment analysis (thanks for the inquiry)
• 2026-02-13 post-authorisation studies limited to authorised medicinal products
• 2025-11-06 improved assessment analysis (thanks for the suggestion), bookmarkable
• 2025-10-26 preparing major change in update mechanism
• 2025-10-25 may not be able to update more often than once weekly
• 2025-10-12 removed adapting to old bookmarks, please bookmark newly
• 2025-08-08 improve daily ingest and post-authorisation digestion
• 2025-07-31 add advanced therapy column, correct MeSH mangling
• 2025-07-26 expand daily data updates
• 2025-05-11 disable http/2 when retrieving documents from source
• 2025-04-28 improve restoring old bookmarks
• 2025-04-21 addressing issues to obtain all source documents
• 2025-03-30 catering for more than one post-authorisation steps document
• 2024-08-14 adding querying from URL query string (see https://paediatricdata.eu/emapips/)
• 2024-05-04 adding document-level assessment analysis (thanks for the suggestion)
• 2024-03-28 accommodated source XLS file outdated on EMA website
• 2023-12-10 update to changed EMA website
• 2023-07-30 correct post-authorisation extraction, improve guessing paediatric indication
• 2022-10-09 small changes to assessment analysis query syntax (after replacing Lucene with SQLite)
• 2022-10-08 re-introduced text search in images of SmPCs and assessment reports
• 2022-10-01 updated paediatric indication algorithm; faster webapp start
• 2021-10-24 steps after authorisation now finds most assessment reports
• 2021-10-03 new: steps after authorisation (further functions in the works)
• 2021-09-14 correct MeSH root term finding
• 2021-05-17 correct indexing PDF files (tab “Assessment analysis”)
• 2021-05-04 start OCRing images in PDF files (- 2021-09-25 temporarily disabled)
• 2021-05-04 delete remnants renamed products
• 2021-04-22 fix when several SmPCs are found for one product
• 2020-09-26 state now authorised, withdrawn, suspended, refused
• 2020-09-09 adding fulltext assessment report query
• 2020-05-17 improved paediatric indication detection
• 2020-05-10 fix indication extraction
• 2020-03-14 speed up
• 2020-02-20 only show indications from SmPC PDF
• 2019-12-03 enable regex filtering on empty columns
• 2019-11-17 adding calculating paediatric indication (in testing; uses section 4.1 information)
• 2019-09-14 adding imposed post-authorisation studies
• 2019-08-31 more indications from PDF, MeSH
• 2019-06-21 improve user interface functions and bookmarking
• 2019-06-11 generate MeSH trees and root terms for each medicine (see new column)
• 2018-10-31 initial release

Getting started

Package ctrdata is on CRAN: https://cran.r-project.org/package=ctrdata. Within R, package ctrdata can be installed with install.packages("ctrdata"). See code below for how to install the latest working development version. The documentation shows a full simple workflow and has articles with detailed trial analysis, see https://rfhb.github.io/ctrdata/. Issues, feature requests and bugs reports are most welcome at https://github.com/rfhb/ctrdata/issues or through links in the top right corner of this page.

Milestones

• January 2026, more robustly throttle and retry downloading of data and documents
• July 2025: save EUCTR documents into folders by trial, and optionally load just one protocol of multi-country trials
• Version 1.23.0 got a technology upgrade with speed-ups including for EUCTR with its much structured results data
• March 2025: new functions for easy analyses of trial concepts across registers and finding trials from simple input
• In November 2024, accelerated database and trial data functions by contributions to RSQLite 2.3.8 and nodbi 0.11.0
• Since 2024-06-30, support for CTIS as relaunched on 2024-06-17 and translation of queries to classic CTGOV (retired 2024-06-25) to the current CTGOV2
• Version 1.18.0 of 2024-05-13 retrieves historic versions of studies as structured data from CTGOV2 (example)
• By November 2023, removed all dependencies on command line tools of the operating system
• The European Union’s new Clinical Trials Information System (CTIS) is supported since March 2023
• Since 2019, ctrdata supports several databases (through package nodbi, now maintained by the same author)
• Results data are supported from the EU Clinical Trials Register since 2017, from ClinicalTrials.Gov since 2015
• On 15 September 2015, package ctrdata was first published on CRAN.

Disclaimer

When using package ctrdata, the registers’ terms and conditions need to be respected and are shown with ctrOpenSearchPagesInBrowser(copyright = TRUE). Please cite the package (citation("ctrdata"), style = "text")) in any publication as:

Herold R (2026). “Aggregating and analysing clinical trials data from multiple public registers using R package ctrdata.” Research Synthesis Methods. 17(3):624-656. https://doi.org/10.1017/rsm.2025.10061 or Herold R (2025). ctrdata: Retrieve and Analyze Clinical Trials Data from Public Registers. doi:10.32614/CRAN.package.ctrdata, R package version 1.25.1, https://CRAN.R-project.org/package=ctrdata

References

Package ctrdata has been used for unpublished work and for:

• Machado et al. (2026) The road for developing new pharmacological therapies for Parkinson’s disease: Current trends and targets in clinical trials https://doi.org/10.1177/1877718X251412671
• Jong et al. (2025) Experiences with Low-Intervention Clinical Trials – the New Category under the European Union Clinical Trials Regulation https://doi.org/10.1177/17407745241309293
• Lopez-Rey et al. (2025) Use of Bayesian Approaches in Oncology Clinical Trials: A Cross-Sectional Analysis https://doi.org/10.3389/fphar.2025.1548997
• Russek et al. (2025) Supplementing Single-Arm Trials with External Control Arms—Evaluation of German Real-World Data https://doi.org/10.1002/cpt.3684
• Clinical Studies Sweden (2025) National Summary of Clinical Trials in Human Medicines Based on CTIS Data link
• Alzheimer’s disease Horizon Scanning Report (2024) link
• Kundu et al. (2024) Analysis of Factors Influencing Enrollment Success in Hematology Malignancy Cancer Clinical Trials (2008-2023) https://doi.org/10.1182/blood-2024-207446
• Sood et al. (2022) Managing the evidence infodemic: Automation approaches used for developing NICE COVID-19 living guidelines https://doi.org/10.1101/2022.06.13.22276242
• Lasch et al. (2022) The Impact of COVID‐19 on the Initiation of Clinical Trials in Europe and the United States https://doi.org/10.1002/cpt.2534
• Blog post (2018) Innovation coming to paediatric research https://paediatricdata.eu/innovation-coming-to-paediatric-research/
• Cancer Research UK (2017) The impact of collaboration: The value of UK medical research to EU science and health link
• EMA (2017) Results of juvenile animal studies (JAS) and impact on anti-cancer medicine development and use in children PDF file, p 34

Webapp

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Changes

• 2026-01-14 expanded links to EMA documents and case section
• 2025-10-26 mechanism to update at least once weekly
• 2025-10-18 added FDA’s Qualified Medical Device Development Tools (MDDTs)
• 2025-06-22 added FDA’s CDRH peer-reviewed regulatory science tools catalogue
• 2025-04-21 implement more robust approach
• 2025-03-16 added explanations and latest FDA status
• 2025-01-13 updated to changes on FDA and EMA websites
• 2024-05-05 updated to changed FDA website
• 2023-12-07 updated to new EMA website
• 2023-11-18 add proposers for EMA, complete for FDA
• 2023-09-23 list all DDT document types
• 2023-08-20 added FDA’s Fit for purpose outputs
• 2023-05-13 initial release, covering five different sourceTypes

Methods

The webapp shows information that is automatically extracted at least once a week from decisions made publicly available by the EMA. Users can track PIPs of interest to be presented in tables and visualisations. Available information concerns study designs, progress to completion, discontinuations, overall timelines, modifications, therapeutic area, indications, compliance opinions, links to search for paediatric data in marketing authorisations.

For more insights and technical access, an application programming interface (API) can be provided upon request. The webapp takes several seconds to fully load. See the “About” tab for how to credit the EMA and for how to cite the webapp.

Webapp

Maximise

Changes

• 2026-03-17 removed rare duplicate decisions (thanks for anonymous notifications)
• 2026-01-14 address some aberrant source documents
• 2025-11-08 further adaptation to new numbering
• 2025-10-18 first adaptation to new numbering (EMA/PE/…, EMADOC-…)
• 2025-04-18 adapt to EMA webserver change
• 2025-02-02 update to EMA website changes
• 2024-08-14 added searching EPARs for PIP Decision numbers
• 2024-08-14 added cases missing due to partial information
• 2024-03-28 accommodated source XLS file outdated on EMA website
• 2023-12-10 updated to new EMA website
• 2023-07-13 mitigate PDF metadata errors
• 2023-05-13 system update
• 2022-10-01 faster webapp start
• 2021-06-11 better aligning conditions and indications
• 2021-05-30 harmonised PIP completion plots
• 2020-04-26 updated to EMA website changes
• 2019-12-07 improved discontinuation date detection
• 2019-06-29 improved user interface
• 2019-06-06 improved extracting PDF contents
• 2018-11-25 revised into Shiny app
• 2018-11-01 adapted to revised source website
• 2018-09-09 divided by condition, column selection
• 2018-08-15 added access to decision files
• 2018-08-12 added summary and completion tabs
• 2018-07-11 show studies by modification
• 2018-07-07 show applicant
• 2018-01-07 initial release

Start of trials with children in relation to when medicine was authorised in Europe

The figure shows all cancer medicines that are centrally authorised in Europe.

Dots indicate the date of the authorisation. Green dots marks cancer medicines that are authorised for children (full list is in another post). Whether the trial is part of a paediatric development plan (PIP) is not visualised here.

# Code by ralf.herold@gmx.net

# last updates:
# - 2019-12-31 to match ctrdata 1.1 package
# - 2022-04-23 to match ctrdata 1.9 package
# - 2025-10-14 to Quarto document
# - 2026-01-25 to ctrdata 1.26.0 package

### Options for subsequent chunks

# knitr
knitr::opts_chunk$set(
  dev = 'svglite', 
  fig.width = 6, 
  fig.height = 12,
  out.width = '100%', 
  out.height = '1200px',
  width = '100%',
  eval = FALSE,
  echo = TRUE, 
  results = "asis",
  warning = FALSE, 
  message = FALSE,
  cache = FALSE
)

# Helper function
getAnnotationElements <- function(x, element = "AS") {
  
  # example from annotation.text: 
  # x <- "|AS:tisagenlecleucel| |AS:tisagenlecleucel|"
  
  x <- strsplit(x = x, split = "\\|")
  x <- unlist(x)
  x <- x[grepl(paste0("^", element, ":"), x)]
  x <- strsplit(x = x, split = ":")
  x <- unlist(x)
  x <- x[!grepl("AS", x)]
  sort(unique(x))
  
}

# Initialise
library(ggplot2)
library(stringr)
library(tidyr)
library(ctrdata)
library(dplyr)
library(stopwords)
library(glue)
library(ggiraph)

# Get centrally authorised medicines

# https://www.ema.europa.eu/en/about-us/about-website/download-website-data-json-data-format
emaCapsJson <- "https://www.ema.europa.eu/en/documents/report/medicines-output-medicines_json-report_en.json"

# Read and transform
emaCapsList <- 
  url(emaCapsJson) |>
  jqr::jq(x = _, ".data") |>
  jsonlite::fromJSON() |>
  as_tibble()

# Select cancer medicines
emaAntiCancerList <- 
  emaCapsList |>
  filter(
    category == "Human",
    grepl("[Tt]reatment|[Tt]herapy", therapeutic_indication),
    generic_or_hybrid == "No",
    biosimilar == "No", 
    (grepl("ntineoplas", pharmacotherapeutic_group_human) |
       grepl("eoplasms|arcoma|ymphoproliferative", therapeutic_area_mesh))
  )

# Indication includes paediatric population
emaAntiCancerList <- 
  emaAntiCancerList |> 
  mutate(
    paediatricIndication = grepl(
      "children|adolescent|paediatric",
      therapeutic_indication
    )
  ) 

# Dates to date formats
emaAntiCancerList <- 
  emaAntiCancerList |> 
  mutate(
    across(
      ends_with("date"), 
      ~ as.Date(.x, format = "%d/%m/%Y")
    )
  ) 

# Selectively reword active substance 
# to more specifically identify trials
emaAntiCancerList$international_non_proprietary_name_common_name[
  emaAntiCancerList$name_of_medicine == "Spectrila"
] <- "recombinant asparaginase"
emaAntiCancerList$international_non_proprietary_name_common_name[
  emaAntiCancerList$name_of_medicine == "Abraxane"
] <- "nabpaclitaxel"


# Normalise active substances 
emaAntiCancerList$activeSubstancesToDo <- 
  emaAntiCancerList |> 
  pull(international_non_proprietary_name_common_name) |>
  stringi::stri_replace_all_regex(str = _, "[();,]", " ") |>
  stringi::stri_replace_all_regex(
    str = _, pattern = paste0(" ", stopwords("en", "smart"), " "), 
    replacement = " ", vectorize_all = FALSE) |>
  stringi::stri_replace_all_regex(str = _, " +", " ") 

# Keep earliest authorisation
emaAntiCancerList <- 
  emaAntiCancerList |> 
  filter(grepl("Authorised|Opinion", medicine_status)) |>
  group_by(activeSubstancesToDo) |>
  slice_min(marketing_authorisation_date, n = 1L) |>
  ungroup()

# Extract for search
activeSubstancesToDo <- 
  emaAntiCancerList |> 
  filter(activeSubstancesToDo != "") |>
  pull(activeSubstancesToDo) |>
  unique() |>
  sort()

# Connect trial database
db <- nodbi::src_sqlite(
  # dbname = "cancertrials.sqlite",
  dbname = "posts/cancertrialschildren/trials.sqlite",
  collection = "emaAntiCancerList"
)

# Get any active substances already in 
# database as per the annotation field
if (nrow(dbQueryHistory(db)) > 0L) {
  
  # get active substances from annotations  
  asDone <- dbGetFieldsIntoDf(c("annotation"), con = db)
  asDone <- getAnnotationElements(x = asDone$annotation, element = "AS")
  
  # remove already downloaded active substances
  activeSubstancesToDo <- activeSubstancesToDo[
    !(activeSubstancesToDo %in% asDone)]
}

# Download trials by active substance
# this will take around 10 seconds per
# active substance, ca. 30 mins for all
for (thisAs in activeSubstancesToDo) {
  
  # - user info  
  message(rep("-", 80), "\n", thisAs)
  
  # - generate queries for all registers
  query <- ctrGenerateQueries(intervention = thisAs)
  
  # - download from relevant registers
  results <- suppressMessages(lapply(
    query[-4], 
    ctrLoadQueryIntoDb, 
    annotation.text = paste0("|AS:", thisAs, "|"),
    con = db))
  
  # - extract from results
  print(sapply(results, "[[", "n"))
  
}

# Database size approximately 2.5 GB

# get trials
trialsSet <- dbGetFieldsIntoDf(
  fields = c(
    "annotation"
  ),
  calculate = c(
    "f.startDate",
    "f.trialTitle",
    "f.trialPopulation",
    "f.isUniqueTrial"
  ), 
  con = db)

# saveRDS(trialsSet, "trialsSet.rds")
# trialsSet <- readRDS("trialsSet.rds")

# deduplicate trials, keep
# trials with single-agent, 
# keep earliest trial that
# includes paediatric pop
trialsSet <- 
  trialsSet |>
  filter(.isUniqueTrial) |>
  filter(grepl("P", .trialPopulationAgeGroup)) |>
  rowwise() |>
  mutate(
    activeSubstancesToDo = list(getAnnotationElements(annotation)),
    activeSubstancesNumber = length(activeSubstancesToDo))  |>
  filter(activeSubstancesNumber == 1L) |> 
  mutate(activeSubstancesToDo = unlist(activeSubstancesToDo)) |>
  group_by(activeSubstancesToDo) |>
  slice_min(.startDate, n = 1L) |>
  ungroup() 

# merge trials and medicines
plotSet <- full_join(
  emaAntiCancerList,
  trialsSet,
  multiple = "all",
  relationship = "many-to-many",
  by = join_by(activeSubstancesToDo)
)

# inspect and handle duplicate cases
plotSet <- 
  plotSet |>
  filter(
    name_of_medicine != "Evotopin",
    `_id` != "NCT00042354"
  )

# format plot data
plotSet <- 
  plotSet |>
  arrange(marketing_authorisation_date) |>
  mutate(
    activeSubstance = str_trunc(
      activeSubstancesToDo, 20L),
    activeSubstance = factor(activeSubstance, levels = activeSubstance), 
    tooltip = glue(
      "{active_substance} ({name_of_medicine}):
       - authorised on: {marketing_authorisation_date}
       - authorised for children: {paediatricIndication}
       - first trial with children start: {.startDate}
       - first trial with children ID: {`_id`}"),
    timetrialma = difftime(
      marketing_authorisation_date, 
      .startDate, units = "days"),
    dotcolor = case_when(
      paediatricIndication ~ "green", 
      .default = "red"
    ), 
    segmentcolor = case_when(
      timetrialma > 5 * 365.25 ~ "green", 
      .default = "red"
    )
  )

# Plot
trialMaPlot <- 
  ggplot(data = plotSet) +
  scale_color_identity() + 
  geom_point_interactive(
    aes(
      x = marketing_authorisation_date, 
      y = activeSubstance, 
      color = dotcolor,
      tooltip = tooltip,
      data_id = activeSubstance
    )
  ) + 
  geom_segment(
    aes(
      x = .startDate, 
      y = activeSubstance,
      xend = marketing_authorisation_date,
      color = segmentcolor, 
      group = activeSubstance
    )
  ) +
  xlim(as.Date(c("1988-01-01", "2030-01-01"))) + 
  labs(
    title = "Time relation of EU cancer medicines authorisation
             and start of first trial including children with cancer", 
    subtitle = paste0(
      "Data from EUCTR, CTIS, CTOV, ISRCTN\n", 
      "Line colour green if more than 5 years, otherwise red\n",
      "https://regulatorysciencedata.eu, ", format(Sys.Date(), format = "%Y-%m-%d"))
  ) +
  theme_gray(base_size = 8L)

# Interactive
iaPlot <- girafe(
  ggobj = trialMaPlot, 
  height_svg = 12L)

# Save for later static production
saveRDS(iaPlot, file = "./posts/cancertrialschildren/iaPlot.rds")

Lines indicate the time of start of a trial of the respective active substance that included the paediatric population (irrespective of subset, such as adolescents or broader paediatric age ranges) to initial marketing authorisation for the active substance. Green lines indicate that this trial was started at least 5 years before the marketing authorisation.

Hover over the symbols to see details.

readRDS(file = "iaPlot.rds")