Bringing a drug, biologic, or medical device to market is one of the most document-intensive processes in any industry. A single New Drug Application (NDA) can contain hundreds of thousands of pages spanning clinical trial data, chemistry and manufacturing controls (CMC), preclinical studies, and proposed labeling. An Investigational New Drug (IND) application, a Clinical Trial Authorization (CTA), a Premarket Approval (PMA), or an Investigational Device Exemption (IDE) — each carries its own labyrinth of regulatory requirements, formatting rules, and evidentiary standards set by agencies such as the FDA, EMA, PMDA, and others.
The consequences of a poorly prepared submission are significant: a Refuse to File (RTF) letter from the FDA, a Day 120 List of Questions from the EMA, or an outright rejection can delay market entry by 12 to 18 months or more, costing sponsors tens of millions of dollars and — critically — delaying patient access to potentially life-saving therapies. Against this backdrop, artificial intelligence is emerging as a transformative force, offering pharmaceutical and medical device companies an intelligent, automated co-pilot for submission preparation and review.
Before examining what AI can do, it is worth understanding why traditional submission review is so prone to error and delay.
Regulatory dossiers are assembled by large, cross-functional teams — regulatory affairs specialists, medical writers, biostatisticians, clinical scientists, and CMC experts — often working across time zones and organizational boundaries. Despite rigorous internal quality systems, the manual nature of the work creates fertile ground for inconsistencies. A dosing value stated in a clinical study report may not align with the corresponding table in Module 2 of the Common Technical Document (CTD). A reference to a guideline may cite an outdated version. A required section may be missing entirely. A statistical summary may contradict the raw data tables it purports to summarize.
These are not hypothetical scenarios. Industry surveys consistently show that incomplete or inconsistent content is among the top reasons for FDA Refuse to File actions and EMA validation failures. The traditional countermeasure — layers of manual review — is expensive, slow, and itself error-prone. Even experienced regulatory professionals can miss subtle cross-document inconsistencies across a 300,000-page NDA.
An AI regulatory review agent is a purpose-built system that reads, understands, cross-references, and validates submission documents against established regulatory standards. These systems combine several layers of technology: natural language processing (NLP) for understanding unstructured regulatory text, large language models (LLMs) for contextual reasoning, knowledge graphs for mapping regulatory relationships, and rule-based engines for deterministic checklist validation.
When applied to regulatory submissions, such an agent can perform a range of functions that previously required weeks of manual effort — in a fraction of the time.
The first task of an AI agent is to ingest and parse the submission in its entirety. For CTD-formatted submissions, this means understanding the hierarchical structure of Modules 1 through 5 and verifying that required sections are present, correctly formatted, and populated with substantive content rather than placeholder text. The agent checks eCTD backbone integrity, verifies that document hyperlinks resolve correctly, confirms that file formats comply with agency specifications (PDF version, bookmarking, font embedding), and validates that all required appendices are present.
This structural validation alone can catch a significant proportion of the administrative deficiencies that trigger RTF letters — errors that have nothing to do with the science but can delay a program by months.
One of the most powerful capabilities of an AI review agent is its ability to hold the entire dossier in context simultaneously and flag inconsistencies across documents that human reviewers might never catch. Consider the following scenarios:
An AI agent trained on the logical and factual relationships between CTD modules can identify all of these inconsistencies systematically, producing a prioritized findings report that guides human reviewers to exactly where attention is needed.
Every major regulatory agency publishes detailed guidance documents, checklists, and templates that define what a complete submission must contain. The FDA's Content of and Format for Investigational New Drug Applications, its NDA guidance documents, and the ICH guidelines (E6, M4, Q1-Q12, and others) collectively define thousands of requirements. The EMA's equivalent guidance ecosystem is equally extensive.
An AI agent maintains a continuously updated knowledge base of these requirements and can automatically validate a submission against the applicable checklist. For an IND, it checks whether the pharmacology and toxicology section contains the required acute toxicity studies, whether GLP compliance statements are included, and whether the proposed clinical protocol meets the elements specified in 21 CFR 312.23. For a PMA, it verifies that the summary of safety and effectiveness data (SSED) addresses all required performance testing standards. For a CTA under the EU Clinical Trials Regulation, it confirms that the protocol follows the required structure and that the Investigator's Medicinal Product Dossier (IMPD) is complete.
This automated validation transforms compliance checking from a manual, interpretive exercise into a rapid, reproducible, and auditable process.
Regulatory guidance is not static. The FDA and EMA regularly issue, update, and retire guidance documents, and submissions that reference outdated guidelines can draw agency scrutiny. An AI agent connected to regulatory intelligence feeds can flag instances where a submission cites a superseded guidance, alert the team to new or draft guidance that may be relevant to the application, and recommend updates to the submission strategy accordingly.
This is particularly valuable in fast-moving therapeutic areas — oncology, gene therapy, mRNA-based medicines — where regulatory frameworks are evolving rapidly and staying current requires constant vigilance.
Not all deficiencies are created equal. A missing administrative form in Module 1 is serious but fixable quickly. An inconsistency in the primary efficacy endpoint across the statistical analysis plan, the clinical study report, and the integrated summary of efficacy is a substantive scientific problem that may require extensive remediation. An AI agent can categorize and score deficiencies by type, severity, and regulatory impact, allowing the submission team to triage their remediation effort and focus first on the issues most likely to trigger a major agency action.
For sponsors preparing to initiate first-in-human or Phase II/III studies, the IND (in the US) and CTA (in Europe) represent the gateway to clinical development. An AI agent reviewing an IND can validate that the proposed clinical protocol contains all elements required under 21 CFR 312.23(a)(6), confirm that the Investigator's Brochure is structured per ICH E6(R3), cross-check preclinical safety data for consistency with the proposed starting dose, and flag any gaps in the CMC data that might trigger a clinical hold.
For CTAs submitted under EU CTR 536/2014, the AI can validate the structured data elements required in the EU portal, check protocol compliance against the applicable ICH E8 and E9 guidance, and confirm that the risk-benefit discussion in the clinical development plan is internally consistent.
The NDA and its European equivalent, the Marketing Authorization Application (MAA), are the largest and most complex submission types. A complete NDA can easily exceed 500,000 pages across all modules. An AI review agent provides value at every level of this complexity: from validating the eCTD structure and checking that the proposed label is consistent with the clinical data, to performing a line-by-line comparison of efficacy results across Module 2 summaries and Module 5 study reports. The agent can also analyze the proposed risk evaluation and mitigation strategy (REMS) for completeness and internal consistency, and validate that the benefit-risk framework in the integrated summaries aligns with the data presented.
Medical device submissions carry their own unique requirements. A PMA must demonstrate reasonable assurance of safety and effectiveness through valid scientific evidence, and the submission must address applicable FDA-recognized consensus standards. An AI agent reviewing a PMA can check that all relevant performance testing standards (e.g., ISO 10993 biocompatibility series, IEC 60601 electrical safety) are addressed, flag gaps in the clinical data relative to the predetermined submission requirements agreed with FDA, and validate that design verification and validation documentation is complete and cross-referenced correctly.
For IDEs, the AI can confirm that the investigational plan addresses all elements of 21 CFR 812.25 and that the risk analysis is consistent with the device description.
The most compelling value proposition of AI in regulatory submissions is the ability to function as a rigorous pre-submission quality gate. Before a dossier is submitted to any agency, the AI agent conducts a comprehensive review that simulates — as closely as possible — the scrutiny the agency itself will apply.
Industry data suggests that a meaningful proportion of FDA RTF actions and EMA Day 120 questions could be avoided through more thorough pre-submission review. By systematically catching structural deficiencies, content gaps, cross-document inconsistencies, and guideline deviations before submission, an AI agent materially reduces the probability of a formal agency rejection, compressing the overall timeline from IND to approval and reducing the cost of rework.
This is not merely a quality improvement — it is a strategic business advantage. In competitive therapeutic areas, getting to market six months ahead of a competitor can represent hundreds of millions of dollars in revenue. Pre-submission AI review is an investment that pays for itself many times over.
Beyond catching errors, AI agents contribute to a fundamentally higher standard of regulatory compliance. When every submission passes through a consistent, rules-based validation engine, compliance becomes less dependent on the knowledge and attentiveness of individual reviewers and more a property of the process itself. This is especially valuable for organizations that submit globally across multiple jurisdictions, where the regulatory requirements of the FDA, EMA, PMDA, Health Canada, ANVISA, and others must all be satisfied simultaneously.
An AI system that maintains parallel knowledge bases for each jurisdiction's requirements can generate jurisdiction-specific compliance reports from a single submission review cycle, identifying where the global dossier meets all requirements and where jurisdiction-specific adaptations are needed. This supports the ICH Common Technical Document initiative while accommodating the inevitable regional variations in requirement.
A substantial portion of the time regulatory affairs professionals spend on submission preparation involves tasks that are repetitive, rule-based, and cognitively demanding but not strategically meaningful: cross-referencing document numbers, checking that table titles match their table of contents entries, verifying that all cited references appear in the bibliography, confirming that unit conversions are consistent across tables, and so on. These tasks are important — errors here can trigger agency queries — but they do not require regulatory expertise to perform. They require precision and patience.
AI automation of these tasks frees regulatory professionals to focus on the work that genuinely requires human judgment: crafting the regulatory strategy, developing the benefit-risk narrative, responding to agency questions, and engaging in scientific dialogue with reviewers. The result is not just faster submissions — it is smarter submissions, because the humans involved are spending their time where they add the most value.
Organizations that have piloted AI-assisted submission review report reductions in pre-submission review time of 40 to 60 percent, with corresponding improvements in document quality scores. As these tools mature and accumulate institutional knowledge from prior submissions, performance continues to improve.
Deploying AI in regulatory submission review is not without challenges. The quality of AI outputs depends heavily on the quality and currency of the regulatory knowledge base underpinning the system. Guidance documents must be continuously updated, new requirements must be incorporated promptly, and jurisdiction-specific nuances must be carefully maintained. A system that validates against outdated requirements may provide a false sense of security.
There is also the question of interpretive ambiguity. Many regulatory requirements are qualitative rather than quantitative — "adequate" evidence of safety, a "sufficient" number of patients — and an AI system must be calibrated to recognize where human judgment is required rather than generating false-positive or false-negative findings on genuinely ambiguous points.
Data privacy and information security are critical considerations, particularly when submissions contain confidential clinical data and proprietary manufacturing information. AI review platforms must meet stringent security standards, and deployment within validated, 21 CFR Part 11-compliant environments is essential for regulated life sciences organizations.
Finally, regulatory agencies themselves are still developing their frameworks for understanding and accepting AI-assisted submission preparation. Sponsors should be prepared to explain and defend their use of AI tools if asked, and should ensure that human expert review remains part of the process — AI as a powerful co-pilot, not an autonomous decision-maker.
The integration of AI into regulatory submission review represents one of the most significant shifts in regulatory operations in a generation. As large language models become more capable, as regulatory knowledge bases become more comprehensive, and as the industry accumulates experience with these tools, their role will expand from reactive quality-checking to proactive submission strategy support — anticipating agency concerns, modeling likely questions, and helping sponsors build submissions that are not just complete but compelling.
The agencies themselves are investing in AI to improve the efficiency of their own review processes. An ecosystem in which AI-assisted submission preparation and AI-assisted agency review are developed in parallel — with shared standards for data formats, terminology, and evidence presentation — could ultimately compress drug development timelines in ways that seemed impossible just a few years ago.
For life sciences companies, the question is no longer whether to adopt AI in regulatory operations, but how quickly and how well. The organizations that invest thoughtfully in this capability today will be better positioned to bring safe and effective therapies to patients faster — which is, ultimately, the purpose the entire regulatory system exists to serve.