AI-powered eCTD analysis that maps every module against ICH CTD guidelines and FDA reviewer expectations. Identify gaps, inconsistencies, and compliance risks in real time.
Section 2.5.6 requires an integrated benefit-risk assessment. Current draft lacks quantitative benefit-risk framework that FDA reviewers expect per CDER Manual of Policies and Procedures.
Primary endpoint described as "overall response rate" in §2.5.4 but "objective response rate" in §2.7.3. FDA reviewers flag terminology mismatches as a deficiency.
Table 2.5-1 uses non-standard column layout. Consider aligning to FDA's preferred tabular format for clinical study summaries to improve reviewer readability.
Continuous, intelligent oversight at every stage of your regulatory submission.
Upload your eCTD modules — complete or in progress. Leaf AI parses every document, table, cross-reference, and data point across all five modules in seconds.
Every section is mapped against ICH CTD structure, FDA reviewer checklists, and known deficiency patterns from thousands of historical review cycles.
Receive a prioritised gap analysis showing exactly what's missing, what's inconsistent, and what FDA reviewers will flag — with ICH references and remediation guidance.
Leaf AI analyses your regulatory filing the way a senior FDA reviewer would — systematically, comprehensively, and with zero tolerance for gaps.
Talk to our team →Systematic review of every eCTD module and section against ICH CTD M4 guidelines. Missing content, incomplete data tables, absent cross-references — all flagged with severity ratings.
Cross-references every claim, data point, and endpoint across all five modules. Catches terminology mismatches, conflicting statistics, and broken internal references that trigger review deficiencies.
Models how CDER, CBER, and CDRH reviewers evaluate submissions. Trained on thousands of complete response letters, refusal-to-file actions, and information requests to predict reviewer behaviour.
Real-time submission readiness score that updates as you write. Track compliance progress across modules, view severity distributions, and benchmark against successful submissions.
Every gap comes with specific ICH and FDA guidance references, recommended language, and examples from successful submissions. Your team knows exactly what to fix and how.
Continuous monitoring as your submission is written — not a one-time check at the end. Catch issues early when they're easy to fix, not days before your filing deadline.
Full eCTD analysis for all major FDA submission types.
eCTD submission intelligence is AI-powered analysis of electronic Common Technical Document (eCTD) filings that maps every module and section against ICH CTD guidelines and FDA reviewer expectations. It identifies gaps, inconsistencies, and compliance risks before submission, reducing the likelihood of complete response letters, refusal-to-file actions, and information requests.
Leaf Intelligence supports all major FDA submission types: New Drug Applications (NDA), Abbreviated New Drug Applications (ANDA), Investigational New Drug applications (IND), Biologics License Applications (BLA), and 505(b)(2) abbreviated pathway submissions. Each is analysed against the full ICH CTD M4 structure.
Leaf Intelligence parses every document, table, cross-reference, and data point across all five eCTD modules. Each section is then mapped against ICH CTD structure, FDA reviewer checklists, and known deficiency patterns from thousands of historical review cycles. The result is a prioritised gap report with severity ratings, ICH references, and specific remediation guidance.
The FDA issues a Complete Response Letter (CRL) when a submission cannot be approved in its current form. Common reasons include insufficient clinical data, manufacturing deficiencies, incomplete benefit-risk assessment, labelling issues, and inadequate statistical analysis. Leaf Intelligence's gap analysis is designed to catch these issues before filing.
Manual reviews typically focus on individual sections and rely on a reviewer's memory to catch cross-module inconsistencies. Leaf Intelligence analyses all five modules simultaneously, cross-referencing every data point, endpoint, and claim in seconds. It also benchmarks against patterns from thousands of historical FDA review cycles — something no manual process can replicate. Book a demo to see it in action.