cune-Distiller

Content analysis for regulatory data mining and beyond

Designed specifically for Life Sciences, cune-Distiller is built on our Content Analysis Framework and is optimized to enable comprehensive data extraction from large volumes of documents.

For IDMP readiness, we have a configuration designed to automatically extract data from SmPCs and xEVPRM messages.

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Extract Data Elements

Map Vocabularies

Highly Configurable

Infinite possibilities

  • Extract data from SmPC’s & xEVPRM’s
  • Automatically maps MedDRA, GSRS, WHO codes
  • Generate IDMP ready data
  • Data quality assurance workflows
  • Easy to configure
  • Export your data into your RIM or IDMP system
cune-Distiller Main Page

Best performance by combined strategies

cune-Distiller uses many techniques including artificial intelligence (AI) to reach its full potential. Depending on the specific needs for each task, one or multiple techniques get applied.

AI technology is very complex and difficult to develop. Cunesoft invested many resources to develop an algorism which serves the needs of the life science industry. There are many possible use cases for this multipurpose tool. Below you see the six steps during the data mining process applicable in IDMP iteration 1.

IDMP Artificial inteligence

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Data Categories

*click to see the extracted Information

Clinical Particulars

Extracted Information:
Indication

Medicinal Product Name

Extracted Information:

Name

ATC Code

Strength

Product Form

Package Description

Package Medicinal Product

Extracted Information:
Dosage Forms
Shelf Life
Storage Conditions
Active Substance
Excipient
Package Item

Marketing Authorization

Extracted Information:
MA Holder
MA Number
Authorization Procedure
MRP#
EU Authorization#
Legal Basis
Intensive Monitoring
Orphan Drug
QPPV Code
Enquiry E-Mail
Enquiry Phone
MFL Code
Product Type
Authorization Date

Extracted Data Elements

cune-Distiller automatically extracts various data types from PDF documents including unstructured information such as clinical particulars.

  1. Medicinal Product Name data entities
  2. Clinical Particulars data entities
  3. Package Medicinal Product data entities
  4. Marketing Authorization data entities

Actual Data Mining POC Summary report (fully redacted):

In this 26-page report you will learn about the overall project approach as well as the results. To receive the full POC summary report please apply here.

  • Combine SmPC and xEVPRM data into one set of data elements
  • Multiple products per SmPC are extracted into separate data elements
  • Powerful quality assurance capabilities
  • Review data and make manual modifications if necessary
cune-Distiller Review Data Extraction Result

Controlled Vocabulary Mapping (EV Codes, GSRS) and MedDRA Coding Concept

The system automatically maps extracted data entities with regulatory data bases GiNAS or EV Codes. Furthermore identified indications are being mapped with MedDRA codes automatically. At any point in time users can manually override the systems suggestions.

cune-Distiller - Annotated Text
  • Reduce data extraction work from hours to minutes
  • Increase data accuracy and data quality
  • Reuse data within your RIM or IDMP system

Self-learning natural language processing (NLP) approach

The platform can be configured to extract information from additional document types other than those found in module 3 of the eCTD. Find out how Cunesoft can help you find more possibilities in your content.

Contact us for further information