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Life Sciences

Life Sciences-01

Automating eCTD Data Classification & Extraction:

Leverage Pre-Trained AI to Achieve Risk-Free Submissions by Eliminating Incomplete or Inconsistent eCTD Information

The Roots: What spurred this on?

70% of all rejected IND Applications were due to 1734 Error,
which centers around a dataset named ts.xpt with information on study start date must be present for each study in required sections.

Report period of Sept 2021 - Jan 2023


How do these errors germinate and why are they so difficult to eradicate?


1. Incomprehensible Volumes

Organizations managing clinical trials are tasked with an enormous endeavor to bring order to millions of documents, spanning 300+ nested categories, which is critical for a new drug submission to regulatory bodies, such as the FDA.

earthquake (1)

2. Gaps In eCTD Automation

The industry made strides in taming the chaos with robust RIM solutions on the market today. But, the process of classifying, formatting and enriching clinical & regulatory documentation is still a human-centric process.


3. Difficult to Transfer Knowledge

AI models have been getting increasingly more intelligent over the recent past. But they were not smart enough yet understand highly nuanced and contextualized clinical documentation workflows reliant heavily on unstructured data. Up until now, bringing order to clinical data has only been achieved by human professionals with large expertise in this field.


4. No Room For Technical Errors

Global shift to standardized eCTD format helped bring much needed efficiency to the approval process, which also helped automate submission's technical format review. This means there is no room for error in file naming conventions, font sizing, document navigation, date formats, and other technical requirements. While the review process is mechanized, the submission preparation process is still largely human-driven and is ultimately error-prone.

What can Adlib do to help?

Automating the submission process with machine-like precision and human-like intelligence can eliminate rejections based on technicalities, like Validation Errors 1734, 1735 & 1736. New AI capabilities can now make this possible.

Collaborate with us as we harness the power of pre-trained AI to overcome these challenges. 

Stemming business challenges

Inefficiencies & Delays

Manual eCTD submission preparation and validation is time-consuming, demanding on resources, and ultimately delays time to market.

Increased Chance of Rejection

Varying approaches and training across business units may cause errors in eCTD submission increasing rework, demand on resources and delays.

Misfiling & Retrieval Issues

Human errors may cause misplaced or incomplete documentation, complicating future retrieval, audits and rejection risk.

Scalability & Cost Concerns

Staff shortages, increased turnover and expertise gaps hinder growth and elevate operational costs.     

Benefits of automating with Adlib's Pre-trained AI

Happier, More Productive Staff

Automated assembly of compliant, validated and submission-ready eCTD documentation frees up time, empowering your staff to get more done.

Faster Approval & Launch

More efficient eCTD prep and reduced chance of regulatory rejection enable your organization to expedite Go-To-Market action plan knowing that your submission will get approved on time.

Proven Compliance & Accuracy

Pre-trained AI gets the work done accurately and in adherence with regulatory requirements every single time, eliminating technical rejection risk.

Adlib AI Grows With Your Needs

The elasticity of Adlib's cloud-based solution can support spikes in demand or flourishing growth without resource constraints.


Forecast outcomes


reduction in FDA submission rejections
due to misclassified documentation or missing metadata.


classification & extraction accuracy
within first 6 months of deployment.

See How Things Are Growing...


How It Works:

Leverage custom-built and pretrained AI model to drive eCTD document classification 


Classification eCTD-01-2
Extraction eCTD_Extraction eCTD


How It Works:

Leverage custom-built and pretrained AI model to facilitate extraction of CDEs from clinical trial documents that have been classified using the eCTD schema.



How It Works:

Annotation module tracks low confidence classification and data extraction entries enabling the human in the loop to correct & modify the class assigned to each document or extracted CDEs.

Validation eCTD_Validtion eCTD


How it Works:

Automated retraining capability enables the underlying models to continually improve knowledge libraries for even better performance.



How It Works:

The systems seamlessly plugs into existing DMS, eTMF, RIM , eQMS systems and other systems via API integration.


Integration eCTD_Integration eCTD
Standardized eCTD


How It Works:

Advanced document assembly functionalities automatically standardize 300+ document types, including PDF, Word, TIFFs, Images and Excel, into conformant, submission- and archive-ready formats reducing technical rejections.



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