AI in Regulatory Operations
Regulatory Operations professionals are at the forefront of AI transformation in drug development. AI can be integrated to simplify the complexity of pharmaceutical regulatory affairs through automating regulatory processes such as administrative work, dossier filling, data extraction, auditing, the implementation of regulations, and quality management. The current drug development landscape is plagued by costly, time-intensive processes that often yield suboptimal results, with artificial intelligence suggested as a potential solution for these challenges. By automating time-intensive tasks, these technologies streamline workflows, accelerate result generation, and shorten the product approval timeline. This transformation was significantly accelerated in June 2025 when the FDA launched "Elsa," a generative AI tool designed to help agency employees work more efficiently across clinical reviews, regulatory assessments, and compliance operations. This guide explores evidence-based AI applications across the drug development lifecycle, from preclinical planning through post-marketing compliance, incorporating both industry solutions and the FDA's own AI capabilities that are reshaping regulatory interactions.
Preclinical Studies
Current Challenges
During preclinical phases, regulatory operations teams must establish the foundational systems and processes that will support the entire drug development program. Key challenges include setting up document management systems across multiple jurisdictions, creating regulatory strategy documents, managing early regulatory intelligence gathering, coordinating with regulatory agencies for pre-IND meetings, and establishing compliance frameworks that will scale through clinical development. The complexity is compounded when planning for global submissions requiring different regulatory pathways (FDA, EMA, PMDA, Health Canada, etc.).
AI Applications and Implementation
Document Management and Organization: AI-powered document management systems can automatically classify, tag, and organize preclinical study documents according to regulatory requirements. Research demonstrates that AI tools can be applied to automate regulatory processes including data extraction and administrative work. Natural language processing (NLP) algorithms can extract key information from study protocols, toxicology reports, and manufacturing documents, automatically populating regulatory databases and ensuring compliance with eCTD structure requirements. Machine learning models can learn organizational patterns and suggest optimal document hierarchies for different jurisdictions.
Regulatory Intelligence and Competitive Analysis: AI systems can continuously monitor regulatory databases, patent filings, and scientific literature to provide real-time intelligence on competitive landscapes and regulatory precedents. Advanced analytics can identify patterns in regulatory approvals for similar compounds, predict potential regulatory hurdles, and suggest optimal regulatory strategies based on historical data from comparable programs.
Timeline and Resource Planning Predictive analytics can analyze historical preclinical programs to forecast timelines, resource requirements, and potential bottlenecks. AI models can incorporate jurisdiction-specific variables, seasonal regulatory agency patterns, and complexity factors to provide more accurate project planning and resource allocation recommendations.
Readiness Assessment
Available Now: Document classification and management systems, eCTD formatting automation with AI/ML technologies, basic regulatory intelligence platforms
Emerging (1-2 years): Advanced competitive intelligence with real-time market analysis, integrated cross-jurisdictional planning tools
Experimental (3+ years): Fully automated regulatory strategy generation, AI-driven regulatory pathway optimization
Note: Current readiness assessments are based on available research literature and may vary by organization and specific implementation requirements.
Investigational New Drug (IND) Application
Current Challenges
IND preparation represents the first major regulatory milestone, requiring coordination of complex, multi-disciplinary information into a cohesive submission package. Regulatory operations teams must ensure all sections meet FDA formatting requirements, coordinate with international teams for parallel submissions (Clinical Trial Applications in EU, Clinical Trial Notifications in Canada), manage version control across multiple contributors, ensure data integrity and completeness, and coordinate submission timelines with clinical trial startup activities.
AI Applications and Implementation
Automated Submission Package Assembly: AI systems can automatically compile IND submissions by pulling relevant information from various source documents, ensuring proper eCTD formatting, and cross-referencing requirements across different modules. The electronic Common Technical Document (eCTD) is a standardized format used by pharmaceutical companies to file regulatory submissions with authorities. Artificial intelligence and machine learning technologies make it possible to obtain data directly from its source and automate the regulatory publishing process. The FDA supports electronic submission of eCTD v4.0 to CBER and CDER for new NDA, BLA, ANDA, IND, and MFs beginning September 16, 2024. Natural language generation can create standardized summary documents, while quality control algorithms verify completeness and consistency across sections.
Cross-Jurisdictional Alignment: Advanced AI platforms can simultaneously prepare submission packages for multiple jurisdictions, automatically adjusting content, formatting, and requirements for FDA IND, EMA CTA, and other international applications. Machine learning models can identify jurisdiction-specific requirements and flag potential issues that could delay approvals.
Quality Assurance and Error Detection: AI-powered quality control systems can scan entire submission packages for inconsistencies, missing information, formatting errors, and regulatory compliance issues. These systems can compare information across different sections, verify data integrity, and ensure all regulatory requirements are met before submission.
Submission Timeline Optimization: Predictive models can analyze agency review patterns, seasonal variations, and submission complexity to recommend optimal submission timing. AI can also coordinate complex submission sequences across multiple countries, considering interdependencies and resource constraints.
Readiness Assessment
Available Now: eCTD formatting automation, basic quality control checking, submission timeline tracking
Emerging (1-2 years): Advanced cross-jurisdictional package generation, AI-powered quality assurance systems
Experimental (3+ years): Fully automated submission preparation with minimal human oversight, real-time regulatory requirement updates
Clinical Trials (Phases 1-3)
Current Challenges
During clinical phases, regulatory operations becomes increasingly complex as teams must manage ongoing regulatory maintenance, safety reporting obligations, protocol amendments, regulatory agency interactions, and preparation for multiple global submissions. Challenges include coordinating safety updates across jurisdictions, managing protocol amendment processes, maintaining regulatory intelligence on evolving guidelines, coordinating with clinical operations and data management teams, and preparing for regulatory meetings and inspections.
AI Applications and Implementation
Protocol Amendment Management: AI systems can analyze proposed protocol changes, automatically assess regulatory impact across different jurisdictions, generate amendment documents in appropriate formats, and predict approval timelines. Natural language processing can identify substantive changes requiring regulatory notification versus administrative updates, streamlining the amendment process.
Safety Reporting Automation: Advanced AI can process safety data from clinical trials, automatically generate regulatory safety reports (IND Safety Reports, Development Safety Update Reports), and ensure timely submission to appropriate regulatory agencies. Machine learning algorithms can identify safety signals requiring expedited reporting and ensure compliance with varying international reporting requirements.
Regulatory Intelligence and Guideline Monitoring: AI platforms can continuously monitor regulatory agency websites, guidance documents, and industry communications for updates relevant to ongoing clinical programs. Advanced analytics can assess the impact of new guidelines on existing studies and recommend necessary compliance actions.
Meeting Preparation and Management: AI systems can analyze regulatory agency feedback patterns, prepare briefing documents for regulatory meetings, and generate response strategies based on historical interaction data. Natural language processing can extract key themes from agency communications and suggest optimal approaches for future interactions.
Inspection Readiness: AI-powered systems can continuously monitor compliance status, identify potential inspection triggers, and maintain inspection-ready documentation packages. Predictive models can forecast inspection likelihood and recommend proactive compliance measures.
Readiness Assessment
Available Now: Safety report generation, protocol amendment tracking, basic compliance monitoring
Emerging (1-2 years): Advanced safety signal detection, automated meeting preparation, predictive inspection readiness
Experimental (3+ years): Fully integrated regulatory operations platforms with real-time compliance optimization
New Drug Application (NDA) / Biologics License Application (BLA) Submission
Current Challenges
NDA/BLA preparation represents the culmination of regulatory operations efforts, requiring assembly of comprehensive submission packages containing years of development data. Key challenges include coordinating massive document compilation across multiple teams, ensuring consistency and quality across all modules, managing parallel submissions for international markets (Marketing Authorization Applications for EMA, New Drug Submissions for Health Canada), coordinating with regulatory agencies during pre-submission meetings, and maintaining submission readiness while managing last-minute data updates.
AI Applications and Implementation
Comprehensive Submission Assembly AI platforms can orchestrate the entire NDA/BLA assembly process, automatically pulling data from clinical databases, manufacturing records, and preclinical studies to populate submission modules. Advanced algorithms ensure data consistency across sections, automatically generate cross-references, and maintain version control throughout the compilation process.
Global Submission Coordination Sophisticated AI systems can simultaneously prepare submission packages for multiple global markets, automatically adapting content for different regulatory requirements while maintaining data integrity. These systems can optimize submission sequences to maximize approval efficiency across key markets.
Quality Control and Review Management AI-powered quality assurance systems can perform comprehensive reviews of entire submission packages, identifying inconsistencies, data gaps, and potential regulatory concerns. Advanced analytics can compare submissions against regulatory requirements and historical approval patterns to predict potential review issues.
Regulatory Response Management AI systems can analyze regulatory agency questions and information requests, suggest response strategies based on historical data, and coordinate response preparation across multiple functional teams. Natural language processing can identify key regulatory concerns and recommend evidence-based responses.
Readiness Assessment
Available Now: Document assembly automation, basic quality control systems, submission tracking platforms
Emerging (1-2 years): Advanced global submission coordination, AI-powered regulatory response generation
Experimental (3+ years): Fully automated submission preparation with predictive regulatory review simulation
FDA Review Process
Current Challenges
During FDA review, regulatory operations teams must manage complex interactions with the agency while maintaining submission integrity and coordinating potential approval preparations. Challenges include managing FDA information requests and responses, coordinating Advisory Committee preparations when required, maintaining communication with international regulatory agencies for aligned reviews, preparing for FDA inspections of clinical and manufacturing sites, and coordinating internal teams for potential approval activities. The regulatory landscape changed significantly in June 2025 with the FDA's launch of Elsa, their internal AI tool, which is now being used by FDA reviewers to accelerate clinical protocol reviews, streamline safety profile assessments, and facilitate label comparisons.
AI Applications and Implementation
FDA's Elsa AI Tool Impact on Industry Interactions: The FDA's launch of Elsa, a large language model-powered AI tool, represents a paradigm shift in regulatory review processes. Elsa is designed to assist FDA staff with reading, writing, and summarizing regulatory documents, which directly impacts how regulatory operations teams should prepare submissions. The tool can summarize adverse events to support safety profile assessments, perform faster label comparisons, and generate code to help develop databases for nonclinical applications. Built in Amazon Web Services' secure GovCloud environment, Elsa ensures data security while enabling FDA staff to work more efficiently. This means regulatory operations teams may experience faster review timelines but should also prepare more comprehensive, AI-readable documentation.
Information Request Management: AI systems can analyze FDA information requests, categorize questions by regulatory theme, and coordinate response development across appropriate functional teams. With the FDA now using AI tools like Elsa for faster document processing, regulatory operations teams should anticipate more rapid information request cycles and prepare AI-optimized response strategies. Advanced analytics can predict information request likelihood based on submission characteristics and historical review patterns, enabling proactive preparation of supporting materials.
Advisory Committee Preparation: When Advisory Committee meetings are required, AI platforms can analyze historical committee patterns, voting trends, and discussion themes to inform presentation strategies. Natural language processing can analyze committee member backgrounds and previous statements to predict potential concerns and questions.
Review Timeline Prediction: Predictive models can analyze FDA review patterns, submission characteristics, and external factors to forecast review timelines and identify potential delays. With Elsa's implementation potentially accelerating FDA review processes, these predictive models need recalibration to account for AI-enhanced review capabilities. These systems can recommend proactive actions to maintain review momentum and optimize approval timing.
Inspection Coordination: AI systems can coordinate FDA inspection preparations, automatically compile inspection-ready documentation packages, and predict inspection focus areas based on submission content and historical inspection patterns. Real-time monitoring can track inspection scheduling and coordinate across multiple sites.
Readiness Assessment
Available Now: Information request tracking, FDA Elsa AI tool operational (June 2025), basic timeline prediction, inspection preparation tools
Emerging (1-2 years): Advanced Advisory Committee analytics, AI-optimized submission strategies for FDA's AI-enhanced review process
Experimental (3+ years): Real-time FDA review simulation, automated regulatory strategy optimization aligned with FDA AI capabilities
Note: FDA's Elsa deployment represents the initial phase of the agency's broader AI integration strategy, with future enhancements expected to include advanced data analytics and expanded generative AI applications.
Approval & Post-Marketing
Current Challenges
Post-approval regulatory operations involves managing ongoing compliance obligations while supporting commercial activities and potential lifecycle management initiatives. Key challenges include managing post-marketing study commitments, coordinating global labeling updates and variations, maintaining pharmacovigilance compliance across markets, supporting lifecycle management regulatory activities, and managing regulatory inspections of commercial operations.
AI Applications and Implementation
Post-Marketing Commitment Management: AI systems can track post-marketing study commitments across multiple jurisdictions, monitor milestone compliance, and coordinate reporting activities. Predictive analytics can forecast commitment completion timelines and identify potential compliance risks requiring proactive management.
Global Labeling Management: Advanced AI platforms can manage complex global labeling requirements, automatically propagate approved changes across multiple markets while ensuring local regulatory compliance. Natural language processing can identify labeling discrepancies and recommend harmonization strategies.
Lifecycle Management Support: AI systems can identify lifecycle management opportunities by analyzing market data, competitive intelligence, and regulatory precedents. Advanced analytics can optimize regulatory strategies for supplemental applications, line extensions, and indication expansions.
Regulatory Intelligence and Compliance Monitoring: Continuous AI monitoring can track evolving regulatory requirements, industry guidance updates, and compliance obligations across global markets. Predictive models can assess compliance risk and recommend proactive actions to maintain regulatory good standing.
Readiness Assessment
Available Now: Commitment tracking systems, basic labeling management, compliance monitoring tools
Emerging (1-2 years): Advanced lifecycle management analytics, automated global labeling coordination
Experimental (3+ years): Fully integrated post-marketing regulatory operations with predictive compliance management
Implementation Considerations
Technology Infrastructure Requirements
Successful AI implementation in regulatory operations requires robust data management systems, secure cloud computing infrastructure, and integration capabilities with existing regulatory information management systems. Organizations should ensure data security compliance with FDA 21 CFR Part 11 and international equivalents, while maintaining audit trails for all AI-assisted regulatory activities.
Change Management and Training
Regulatory operations teams will require comprehensive training on AI tool capabilities and limitations. Change management strategies should emphasize AI as augmenting rather than replacing human expertise, with clear protocols for AI-assisted decision making and human oversight requirements.
Regulatory Compliance Considerations
All AI applications in regulatory operations must maintain compliance with current regulatory requirements for data integrity, documentation, and submission standards. Organizations should establish clear validation protocols for AI systems and maintain human oversight for all regulatory decisions.
References
Sahoo, A. P., Pradhan, S. K., Nayak, B. P., & Behera, R. K. (2023). Artificial intelligence in pharmaceutical regulatory affairs. Drug Discovery Today, 28(8). Available at: https://www.sciencedirect.com/science/article/abs/pii/S1359644623002167 | PubMed: https://pubmed.ncbi.nlm.nih.gov/37442291/
Bansal, A., Sharma, A., Kumar, A., et al. (2025). Innovative Approaches in Regulatory Affairs: Leveraging Artificial Intelligence and Machine Learning for Efficient Compliance and Decision-Making. PubMed. https://pubmed.ncbi.nlm.nih.gov/39776314/
Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2024). Evolution of Drug Development and Regulatory Affairs: The Demonstrated Power of Artificial Intelligence. Clinical Therapeutics. https://www.clinicaltherapeutics.com/article/S0149-2918(24)00138-3/fulltext | PubMed: https://pubmed.ncbi.nlm.nih.gov/38981791/
U.S. Food and Drug Administration. (2025). FDA Launches Agency-Wide AI Tool to Optimize Performance for the American People. Press Release. https://www.fda.gov/news-events/press-announcements/fda-launches-agency-wide-ai-tool-optimize-performance-american-people
U.S. Food and Drug Administration. (2025). Considerations for the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological Products - Draft Guidance for Industry. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/considerations-use-artificial-intelligence-support-regulatory-decision-making-drug-and-biological
U.S. Food and Drug Administration. (2025). Artificial Intelligence for Drug Development. Center for Drug Evaluation and Research. https://www.fda.gov/about-fda/center-drug-evaluation-and-research-cder/artificial-intelligence-drug-development
Federal Register. (2025). Considerations for the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological Products; Draft Guidance for Industry; Availability; Comment Request. https://www.federalregister.gov/documents/2025/01/07/2024-31542/considerations-for-the-use-of-artificial-intelligence-to-support-regulatory-decision-making-for-drug
CNN Politics. (2025). FDA's artificial intelligence is supposed to revolutionize drug approvals. https://www.cnn.com/2025/07/23/politics/fda-ai-elsa-drug-regulation-makary
Interesting Engineering. (2025). Meet Elsa: FDA's most advanced AI tool to streamline drug reviews. https://interestingengineering.com/health/fda-deploys-ai-tool-elsa
DLRC Group. (2023). Artificial Intelligence – Transforming Regulatory Affairs. White Paper. https://www.dlrcgroup.com/wp-content/uploads/2023/09/Whitepaper-Artificial-Intelligence-%E2%80%93-Transforming-Regulatory-Affairs-2023.pdf
Current AI Solutions
Veeva Vault RIM: Comprehensive regulatory information management platform offering document management, submission coordination, and regulatory tracking capabilities. Note: Specific AI capabilities should be verified directly with vendor as features evolve rapidly.
IQVIA RIM SmartSuite: Regulatory operations platform featuring submission preparation tools and compliance management capabilities with documented experience in eCTD automation and AI/ML integration for regulatory publishing processes.
Disclaimer
Vendor capabilities and specific AI features are subject to rapid change. Organizations should conduct thorough due diligence and validation before implementing AI solutions in regulated environments. This guide represents current understanding based on available research literature as of 2025.
This guide represents current understanding of AI applications in regulatory operations as of 2025. Regulatory professionals should consult with legal and compliance teams before implementing AI solutions in regulated activities.
