Executive Summary

Regulatory submissions such as Investigational New Drug (IND) applications in the US and Clinical Trial Applications (CTAs) in other jurisdictions are historically arduous, labor-intensive documents. An IND or CTA package often spans hundreds to thousands of pages, including detailed nonclinical study reports, chemistry-manufacturing controls (CMC) data, protocols, and investigator brochures, all of which must adhere to rigid formats and guidelines (^[1]) (^[2]). Pharmaceuticals must compile these documents with absolute accuracy and consistency, as even minor errors can delay investigations and threaten patient safety.

Recent advances in artificial intelligence (AI), especially generative AI and large language models (LLMs), hold promise for transforming this front-end of the drug development process. AI tools – when properly engineered into GxP-compliant workflows – can draft sections, summarize data, format to templates, and perform consistency checks in IND/CTA documents (^[3]) (^[4]). Early evidence suggests dramatic productivity gains. For example, a 2025 industry study found an LLM-based tool (“AutoIND”) cut initial drafting time of nonclinical IND summaries by ~97% (from ~100 hours to ~3–4 hours per section), with no critical regulatory errors (^[5]). Case studies and vendor reports describe similar breakthroughs: one platform claimed 70% faster document generation for regulatory sections, while another cited a 60–90% reduction in manual drafting effort (^[6]) (^[7]).

However, regulators require robust controls. Sponsors remain 100% accountable for all submitted content (^[8]) (^[9]). No bureaucratic guidance yet allows an AI to “author” a submission – it is strictly a tool in the sponsor’s hands. As the Council on Pharmacy Standards emphasizes, human oversight and a detailed audit trail (e.g. Provenance of every AI-assisted text) are non-negotiable (^[10]). Errors arising from “hallucinations” (AI fabrications) or omissions could have grave consequences, so output must be validated meticulously (^[11]) (^[12]). Current FDA draft guidance (Jan 2025) urges sponsors to define the context of use and establish model credibility for any AI component in submissions (^[13]) (^[14]).

This report provides an in-depth analysis of the practical path toward AI-assisted IND/CTA drafting in pharma. We review the complexity of regulatory documents, survey modern AI technologies and domain-specific models (e.g. BioGPT, PharmaGPT), and describe how AI platforms (RAG pipelines, agents, multi-agent systems) are built. We examine case studies and quantitative results (e.g. ~97% time savings (^[5]), 70% document drafting speed-up (^[6])) and discuss human-in-the-loop workflows. Regulatory implications are explored: accountability, Part 11 compliance, GxP practices, and forthcoming guidances (^[10]) (^[13]). Finally, we consider future directions (e.g. AI validation tools, ruling adjustments, more domain LLMs) and present evidence-based conclusions on the realistic road ahead. All claims and data points are extensively cited to industry studies, regulatory statements, and scientific literature.

Introduction

Regulatory affairs is a pillar of drug development, ensuring new therapies are safe, effective, and properly documented. To begin human trials, sponsors (pharma companies, biotech, sometimes physician-investigators) must assemble and submit comprehensive IND or CTA packages to regulators. An IND (Investigational New Drug application), required in the United States, includes all nonclinical study reports, nonclinical summaries, Chemistry/Manufacturing/Controls (CMC) data, the clinical protocol, Investigator’s Brochure (IB), and supporting forms (^[1]) (^[2]). A CTA (Clinical Trial Application), its analogue in the European Union (and similarly required in Canada, Switzerland, India, etc.), typically consists of the protocol, informed consent documents, IB, and an Investigational Medicinal Product Dossier (IMPD) outlining CMC information (^[2]) (^[1]). While the content overlaps, the formats differ: INDs often require modular Common Technical Document (CTD) structuring and FDA-specific forms, whereas CTAs follow local- or CTR-specific frameworks (^[1]) (^[2]).

The scale and complexity of these submissions is immense. Even early-phase INDs can run thousands of pages when all animal study reports, quality control (QC) certificates, detailed manufacturing protocols, analytical methods, and human protocols are combined. The IND must persuade regulators (e.g. FDA in 30 days) that it is appropriate and safe to initiate first-in-human trials – a task that demands precision. Compliance with guidelines (ICH, FDA regulations, EU directives) and cross-referencing across documents add layers of difficulty. Any inconsistency or missing data can prompt agency queries or a hold. Moreover, regulatory submissions are not one-time products: INDs are updated (amended) continuously as trials proceed, and sponsors may resubmit CTA updates (amendments) upon protocol changes (^[15]). </current_article_content>As the 2019 Clinical and Translational Science review explains, “each interventional clinical study requires a new CTA” under EU law, and sponsors must provide extensive documentation each time (^[2]).

Traditionally, regulatory writing has been an artisanal, manual craft. A recent training curriculum for medical writers in biotech notes that production of IND/CTA documents has been “largely unchanged for decades” (^[16]): teams start from static templates, copy language from past filings, and manually reconcile data tables and narratives. This classic workflow is slow, error-prone, and exhausting. Writers and subject-matter experts must juggle protocol text, statistical plans, lab data and rigorous guidelines all in their heads while writing (or copying) narratives (^[17]). The cognitive load is enormous. A phased trial can produce millions of data points and a license application (NDA/BLA) may run into millions of pages (^[18]), but even an IND is no small task. One small biotech executive recalled beginning an IND in August and only finishing months later – illustrating that even with significant effort, submission remains a bottleneck (^[19]).

Meanwhile, artificial intelligence (AI) capabilities have exploded. In late 2022, mainstream LLMs (e.g. ChatGPT) demonstrated human-level fluency. Today (2025), specialized LLMs for biomedicine exist (BioGPT, PharmaGPT, and company-created models), and pipelines for retrieval-augmented generation (RAG) and agent orchestration offer ways to ground AI outputs in source data. Generative AI excels at drafting text from prompts. The question confronting pharma is: Can we harness these new tools without compromising quality, compliance, or patient safety?

This question – AI-Assisted IND/CTA Document Drafting: A Realistic Road – lies at the intersection of technology, regulation, and patient impact. On one hand, AI promises dramatic efficiency: from quickly drafting narrative sections to spotting data mismatches or outdated language, it could transform hours of work into minutes. On the other hand, regulatory submissions are high-stakes: the sponsor is fully accountable for every sentence (^[20]). Hallucinated or inaccurate output cannot be an excuse. Thus, any AI solution must be implemented within a GxP (Good Practice) framework: validated tools, audited processes, human oversight, and demonstrable traceability (^[21]) (^[13]).

This comprehensive report explores the landscape of AI-assisted IND/CTA drafting. We begin by detailing the particular challenges of these regulatory documents. Next, we survey the AI technologies – from generic LLMs to domain-specific models and multi-agent systems – that are now being applied to document generation. We examine case studies and pilot projects, such as Weave Bio’s AutoIND tool and academic/industry experiments, which provide early data on productivity gains and pitfalls (^[5]) (^[22]). We analyze benefits and limitations drawing on recent research: for example, one study showed a 97% reduction in drafting time with a validated LLM platform (^[5]), while other real-world tests note struggles like checkboxing through complex manufacturing sections (^[23]).

A dedicated section covers the regulatory and compliance perspective. U.S. and European agencies are actively considering AI’s role. For instance, FDA’s 2025 draft guidance on AI in drug development emphasizes model credibility and context-of-use (^[13]). The Council on Pharmacy Standards (expert body) lists “100% human accountability” as its top principle for any AI-authored content (^[20]). We discuss these and other standards (21 CFR Part 11, ICH E6(R3) for e-docs, EMA expectations on traceability (^[12])) that shape safe implementation.

Finally, we look ahead. AI agents capable of not only drafting but also verifying content are on the horizon (^[11]) (^[24]). Domain-tailored LLMs (e.g. PharmaGPT (^[25])) and industry collaborations (academic prototypes, vendor solutions) will evolve the toolkit. We consider how these innovations may ripple beyond writing – into smarter query handling, real-time consistency monitoring, and even synthetic modeling of trial design. Crucially, we assess whether the benefits (faster trials and more efficient use of expert time) are likely to materialize responsibly, given the safeguards demanded by regulators and the intrinsic risks of AI.

In short, this report provides a deep dive into the state of AI for IND/CTA drafting: its technical underpinnings, industry experiments, regulatory guardrails, and the paths toward a future where AI helps bring therapies to patients faster without compromising trust or safety.

The Nature and Complexity of IND/CTA Documentation

Scope of an IND/CTA Package

Understanding AI’s role in drafting requires first grasping what an IND or CTA is. A submission package is multilayered: it must detail the drug’s entire development so far (nonclinical pharmacology/toxicology, manufacturing) and the planned human trials (clinical protocol and investigator guidance). The seminal Clinical and Translational Science review (Regulatory Affairs 101) explains that the U.S. IND comprises:

“multiple forms specific to the FDA, all nonclinical study reports (including validation reports of bioanalytical methods), nonclinical summaries (key information from the reports summarized concisely), detailed CMC information, as well as the protocol and IB” (^[1]).

In practice, this means sponsors include full GLP toxicology reports, pharmacokinetics studies, pharmacodynamics data, plus analyticals, stability data and process descriptions from chemists – every relevant data package up to that point. They also write narrative summaries of these data (IND Summary documents), plus the clinical trial protocol (with objectives, design, stats plan) and an Investigator’s Brochure summarizing known human safety information. The IND is submitted in the structured CTD (Common Technical Document) format, which places summaries and reports into standardized modules (Module 2–5).

A Clinical Trial Application (CTA) in the EU (and similarly required in Canada and many other countries) has overlapping goals but a slightly different format. Instead of an IND, the sponsor submits a CTA dossier (often via EMA’s Clinical Trials Information System or national portals). According to Chiodin et al., “the documentation required for a CTA is not identical to that for an IND. For a CTA, the four main documents are the protocol, informed consent form, IB, and Investigational Medicinal Product Dossier (IMPD), which contains CMC data” (^[2]). In other words, while U.S. INDs catalog all nonclinical reports systematically, CTAs often rely on a single IMPD to cover manufacturing and CMC, plus the clinical documents. Nonetheless, both INDs and CTAs share the overarching purpose: to demonstrate that the investigational product is adequately characterized and cuts no corners in initiating human trials.

Importantly, INDs and CTAs are often “living documents.” Sponsors must routinely amend them as new data emerge. For example, if a batch of manufacturing data changes or a recruitment site is added, an IND amendment is filed. As Nahas (The Scientist, 2024) notes, “IND applications are living documents, which sponsors must regularly update every time a change is made to the clinical trial” (^[15]). This adds to the chronic workload: it’s not just one filing, but an ongoing series of updates – each crafted into text and tables consistent with the original submission.

CHALLENGES OF MANUAL DRAFTING

The traditional process of IND/CTA drafting is arduous and time-consuming. As the Council on Pharmacy Standards outlines:

“For decades, the process has remained largely unchanged. Teams start from static templates, copy language from previous submissions, manually reconcile tables and narratives, and rely on multiple rounds of review to catch inconsistencies... The cognitive load is enormous” (^[16]).

Medical writers, biostatisticians, pharmacologists and other experts must coordinate intensely. Error margins are effectively zero: thematic consistency (e.g. that the death rate figure is identical in the CSR and in module 5) is vital. Moreover, global teams often have to re-write content to fit various regulatory styles (ICH eCTD, country annexes, etc.). As one executive lamented, preparing an IND took their company months – time during which the therapy sat on hold (^[15]). Another remarked that their IND paperwork “over a month and a half” to assemble, but getting the results and lead-up might take at least six months (^[26]). Such delays have real impact: patents may expire, funding rounds may close, and patients wait.

Quantitatively, a single phase I IND can involve hundreds or thousands of pages. The case study cited by Zemoso technology notably claims a Biologics License Application (BLA) may span 10 million pages (^[18]) (likely including aggregated appendices), and even a 12-month Phase II trial can generate “over 3 million data points” (^[18]). While those numbers cover later-stage trials, an IND’s size is still formidable. For perspective, one biotech team found that using an AI generator enabled producing “50 pages of content in an hour”, whereas the manual approach would have taken days or weeks (^[27]).

The human toll is high. Companies often employ contract medical writers or consultants to meet the need, at great expense. Biotechs with limited budgets (especially startups) may resort to interns and hastily self-taught solutions. The result: high labor costs, inconsistent style, and risk of oversight. Any gap can delay the entire clinical timeline. Thus, there has long been a yearning for tools to reduce the grunt work while preserving scientific rigor.

Advances in AI & Language Models for Regulatory Writing

Foundations: Large Language Models (LLMs)

The explosive rise of Large Language Models (LLMs) – neural networks trained on massive text corpora – has revolutionized how machines can generate human-like text. Models like OpenAI’s GPT series, Google’s PaLM, and Meta’s LLaMA have shown fluency in writing essays, code, and conversations. In biomedicine, specialized LLMs have emerged: for example, BioGPT (Luo et al. 2022) and PharmaGPT (Chen et al. 2024) are trained specifically on biomedical literature and chemistry-related text (^[25]). PharmaGPT (13B/70B parameters) outperformed general models on domain benchmarks like NAPLEX, even with far fewer parameters (^[25]). This suggests that domain-specific tuning can greatly improve relevance and accuracy in pharma tasks.

However, generic LLMs alone have limitations for IND/CTA writing. Clinical regulatory documents have a precise structure and require authoritative statements grounded in data. As Zifeng Wang (Keiji AI) notes, saying “give an LLM a million pages of clinical text and it should write something useful. Well, not quite... Generic AI models struggle in highly specialized domains like clinical trials. They do not know what to retrieve. They improvise details that should never be improvised” (^[28]). Classic LLMs tend to hallucinate: when asked to write, they may invent plausible-sounding but false statements or omit contra-indicated details. For instance, an LLM might generically describe a drug’s mechanism without verifying it against actual data, or it might guess safety outcomes. In regulated writing, such missteps are unacceptable.

Thus modern approaches to integrating LLMs into regulatory workflows rely on augmentation and constraints. One key method is Retrieval-Augmented Generation (RAG). In a RAG pipeline, the AI system does not generate text unguided; it first “retrieves” relevant snippets from a controlled database of source documents (e.g. approved IBs, study reports, SOPs), and then conditions the generated text on that evidence (^[3]) (^[29]). For example, a protocol synopsis draft can be grounded directly in the actual protocol or SAP, and nonclinical narratives can cite figures from true GLP study reports. This ensures that the AI’s output can be traced to validated inputs. Another approach is fine-tuning: taking an LLM and retraining it on a curated corpus of regulatory text, so that it learns the style and substance of submission documents (Keiji AI’s “Panacea” model, for instance, was instruction-tuned on a million trial synopses (^[30]) (^[31])). By embedding industry lexicon and guidelines into training, fine-tuned models are less prone to stick out unrealistic information.

Beyond RAG and fine-tuning, agentic AI workflows are emerging. Instead of a single call to generate an entire section, AI agents can break the task into steps: retrieve necessary text, generate a draft of a subsection, evaluate it (e.g., check compliance with an outline), and iterate until criteria are met (^[29]). Zifeng Wang’s InformGen (2025) exemplifies this: it composes an Informed Consent Form by looped retrieval, drafting, and self-checking against a template (^[29]) (^[32]). This “AI-as-junior-writer” approach capitalizes on the AI’s generative power while embedding validation logic into the workflow.

Importantly, any AI integration in pharma is expected to follow validation and quality assurance protocols. The FDA-equivalent GxP mindset treats AI tools as software tools: they must be validated according to frameworks like GAMP 5, with thorough documentation of inputs/outputs (^[21]) (^[24]). Temperature settings, knowledge base updates, and model versioning become controlled parameters. The Council on Pharmacy emphasizes that sponsors should “Validate the controls, not the ‘Creativity’. For generative AI, focus is on the inputs (RAG knowledge base governance) and the controls (temperature=0, HITL workflow)” (^[33]). In short, AI can be a lead engine, but human experts and system-level controls steer it at every turn.

Domain-Specific Models & Technologies

In addition to general LLMs, domain-focused AI developments are key. Biomedical LLMs like BioGPT、PharmaGPT, and recently PharmaLMs in academic circles, hold promise to understand the jargon and nuances of drug development (^[25]). For example, PharmaGPT’s superior NAPLEX score highlights how LLMs can be tailored to pharmaceutical knowledge. Some companies are also training models on proprietary internal data: project descriptions, SIT (Structured Information Tracker) content, etc., so that an AI is intimately familiar with that sponsor’s programs.

Multimodal and multi-agent systems are another trend. Zemoso’s case study of a Generative AI writing platform describes an architecture where “autonomous agents operate 24/7 with self-planning, self-programming, and self-learning to accelerate drafting, QC, and submission readiness” (^[34]). Their system integrates data pipelines, compliance rules, style enforcement (MedDRA coding, WHO-DD), and natural language prompts into an AI-native database. While vendors like DeepIntPharma (DIP) and the Freya platform underpin such systems, even large incumbents (like Takeda) reportedly assemble internal “sandboxes” linking LLMs with document management systems. These solutions often include multilingual capabilities and audit logs built into the platform.

Finally, it’s worth noting non-generative AI tools that can assist regulatory writing. Machine learning algorithms for standardizing terminology, extracting data points, or predicting review outcomes can be coupled with LLM drafting. And knowledge graphs of regulatory requirements could automatically flag conflicts. While generative AI gets headlines, AI-driven analytics (NLP/judgment of consistency) also play roles. For instance, Merck and others have experimented with AI that automatically detects mismatches between text and figures. These tools won’t write whole paragraphs, but they reduce error.

In summary, the AI toolkit for disciplined writing comprises: LLMs (with RAG or fine-tuning), chain-of-thought or agent frameworks, domain-specific models, and traditional ML/QC tools. Deploying them in regulated writing requires bridging advanced AI techniques with pharmaceutical reality.

Benefits of AI-Assisted Regulatory Writing

Evidence from early pilots suggests several concrete benefits when AI is correctly integrated into document workflows.

Increased Efficiency and Productivity

The primary motivation for AI assistance is efficiency. Traditional drafting can monopolize months of skilled labor; any credible cut is valuable. The clearest data come from the 2025 AutoIND study (Eser et al. 2025): using an AI tool, the initial drafting of nearly 24,000 pages of IND summaries was ~97% faster than manual benchmarks (^[5]). Specifically, what would have taken ~100 human-hours was done in ~3–4 hours. After volunteers (≥6 years’ experience) previously took ~100 hours for the task, the AI platform (“AutoIND”) finished 18,870 pages (61 reports) in 3.7 hours and a further 11,425 pages (58 reports) in 2.6 hours (^[35]). That roughly 40-fold speedup dwarfs any prior tool. (Importantly, this was a validated internal system, so those speed metrics reflect an enterprise pilot.)

Other cases reinforce that speed-up. In anecdotal pilots, one small biotech’s project manager reported generating 50 pages of IND draft text in one hour, versus a manual timeline of weeks (^[27]). A vendor case study (Zemoso Labs) claimed their generative system cut overall authoring time by 70%, turning “weeks of drafting into days” (^[6]), and allowing simultaneous parallel writing of patient narratives and CSRs. The Freya platform (Freyr Digital) reports that by reusing modular content and automating assembly, first-pass document preparation can be 60% faster, and a specific AI-driven CSR flow cut cycle time by ~30% (^[7]) (^[36]). Even if taken skeptically as vendor claims, these figures indicate that dozens of person-hours per section can realistically be saved.

Beyond raw time, AI assistance can relieve the bottleneck on writer availability. Many companies struggle to have enough experienced medical writers for peak workloads. By accelerating drafting, AI frees writers to oversee more documents in parallel, improving throughput without linearly expanding headcount. In essence, routine composition becomes the AI’s job, while writers focus on high-value tasks (analysis, interpretation, final polish).

Consistency, Standardization, and Error Reduction

Generative AI also offers quality gains through consistency and error-checking. One of the Council’s key points is that LLMs can “highlight internal inconsistencies across long, multi-author documents” (^[37]). For example, an AI can cross-reference figures or data points between sections and flag mismatches (e.g. if the number of patients differs between narrative and table). AI tools can enforce controlled vocabularies (MedDRA for adverse events, anatomical terms, unit consistency) and style rules. This reduces copy-paste errors and catch typos that human reviewers might miss after exhaustion. It also automates repetitive tasks like renumbering tables, adjusting font styles, updating headers – mundane compliance tasks that are high-risk if done manually.

For example, the Freyafusion article notes that AI can perform “metadata validation” and “template-driven formatting”, ensuring each document field and element (version history, country code, form number) is properly completed (^[38]). Another source highlights AI’s ability to detect mismatched numbers or terminology far faster than manual QA (^[39]). Indeed, many companies already use specialized software for parts of this (e.g. global numbering systems, eCTD publishing tools). AI extends and accelerates these: think of it as a smart assistant pointing out “This term was spelled differently earlier” or “This paragraph contradicts Figure 2”.

Importantly, AI can also standardize language. Medical writing firms often have internal style guides. An AI system can be trained or prompted to adhere to a specific writing template (e.g. ICH E3 structure) and corporate voice, reducing rework. It can transform an untidy draft into neatly aligned sections. One example: the DIP platform claims its AI can autonomously “orchestrate data pipelines, enforce terminology and style (e.g. MedDRA/WHO-DD) [...] and deliver up to 99% accuracy” (^[34]). While marketing language, it illustrates that AI can replicate rigorous formatting rules consistently.

Finally, AI can leverage large document corpora. Humans eventually tire of retyping the same boilerplate year after year. In contrast, an AI with access to an internal library of prior submissions, SOPs, and regulations can recall and reuse standard phrasing. It can also search external literature swiftly: rather than spending days pulling references, an AI can summarize key findings. (Medicaldigitals.com points out an application: AI can categorize and summarize publications in minutes, turning days of literature search into moments (^[39]).) For INDs, this ability could speed writing background sections (e.g. mode of action, prior clinical data summaries) by auto-populating snippets from trusted inputs.

Enabling Higher-Level Work

By offloading menial chores, AI amplifies expert productivity. This is often emphasized: the AI writes the mechanical bits, letting human writers focus on analysis and decision-making. For example, after an AI generates a first draft, human reviewers can spend more time refining the logic and scientific interpretation, instead of slogging through every sentence. Workers report that outsourcing initial drafting lets them “spend more time thinking about our clinical trial strategy moving forward” (^[40]). In practice, this might increase quality: if writers are not burnt-out from copy/paste, they may catch substantive issues others overlook.

Moreover, in iterative processes like answering health authority questions, AI can accelerate responses. History of conversing with a regulator’s queries often means recomposing overlapping content. An AI equipped with all past dossier content could quickly draft pointed answers (subject to human correction), thereby shortening time to approval. Some regulatory teams foresee using LLMs to propose structured responses to common questions, again at speed.

Finally, the prospect of 24/7 operation and scaling is attractive. A multi-agent AI platform can continuously ingest new data (e.g. lab results the moment they arrive) and update draft reports on-the-fly. For amendments, an AI could auto-draft a mark-up of changes by comparing new data to the prior submission. This agility could make regulatory upkeep much less burdensome.

Case Studies and Examples

While AI in regulatory writing is nascent, several real-world examples illustrate its promise and pitfalls.

Weave Bio’s AutoIND (IND Drafting)

Weave Bio (now part of Takeda) developed an internal tool called AutoIND explicitly for IND drafting. This tool applies multiple LLMs via a RAG pipeline on the sponsor’s approved source documents, generating text for each IND section (e.g. content of Module 2 Nonclinical Summaries) (^[41]) (^[22]). A profile in The Scientist (Nov 2025) describes AutoIND’s use: sponsors upload all source PDFs (studies, lab notes, reports) into the system, which “extracts key information from text and tables” to generate drafts for each IND form rapidly (^[42]).

Pilot results are striking. According to Weave’s chief officer Brandon Rice, an AI-enabled IND can be drafted in a single day, compared to up to six months manually (^[42]). In practice, he cites an example where a small company generated ~50 pages in one hour, then took two hours to tweak and get consultant feedback (^[43]). Another data point: AutoIND’s use resulted in only one hour of post-generation adjustments per document before submission.

A formal evaluation (the Arxiv study by Eser et al. 2025) quantified these effects: using AutoIND, first draft time dropped ~97% (^[5]). Even more important, quality remained acceptable. Blinded reviewers found no critical regulatory errors in the AI drafts, though some sections were noted as less clear or concise (^[44]). The key insight: AutoIND provided raw content that humans refined. Authors conclude “AutoIND can dramatically accelerate IND drafting, but expert writers remain essential to mature outputs to submission-ready quality. Systematic deficiencies identified provide a roadmap for targeted model improvements” (^[45]). In other words, AI did the heavy lifting, but human oversight ensured correctness and polish.

However, use of AutoIND raised immediate compliance concerns. Early attempts with a generic public LLM (via a web interface) failed because there was no audit trail: researchers could not trace which source or prompt produced each sentence (^[46]). Weave responded by building a GxP-controlled pipeline with logging and RAG (ensuring “grounded only on approved sources” (^[47])). This illustrates the trade-off: off-the-shelf AI is too opaque; you need in-house validated systems with documented provenance.

Another challenge was data security. IND applications contain highly confidential data. The Scientist notes a developer’s worry: “How do you set up a firewall and make sure there is no information leaking?” (^[48]). Weave addressed this by keeping all data encrypted and agreeing that OpenAI would not retain or learn from any token of the client’s data (^[49]). Such measures (private instances, Service Level Agreements) are essential to gain trust.

AutoIND is still evolving. Users reported that it struggled with complex manufacturing sections, which have vast data and nuanced context (^[23]). A VP at Axcynsis Therapeutics suggested future goals like having AI analyze preclinical data or propose trial designs. AutoIND’s developers are prioritizing features like automatic updating of the IND content to handle those "living document" burdens (^[50]).

Thus, Weave’s work provides an early proof-of-concept: AI can rapidly produce near-coherent drafts of regulatory text, dramatically reducing time. The errors it did make (mostly wording, not fatal facts) show where additional improvements (better retrieval, enhanced instructions) are needed. Crucially, it highlights that pilot projects can accelerate without everyday sponsors losing oversight – exactly the balance regulators want.

Performance Data: AutoIND vs Manual

The table above illustrates sample metrics from AutoIND case studies (^[5]) (^[27]). It is clear that while AI copies raw workload fast, human effort shifts to reviewing and refining.

Other Tools and Platforms

DIP (Deep Intelligent Pharma): This AI-native platform (Singapore/Tokyo-based) claims to provide an end-to-end solution for regulatory writing. Built as a multi-agent system, DIP unifies data in an “intelligent database” and allows full natural-language interaction (^[34]). It supports all standard documents (IBs, protocols, studies, IND/CTA content, CSRs). The platform boasts flagship metrics of up to 1000% efficiency gain and 99% accuracy (^[34]). In one benchmark, DIP reportedly outperformed other AI platforms (BioGPT, BenevolentAI) in automation efficiency by up to 18% (^[34]). (These figures come from vendor materials and should be interpreted cautiously, but they reflect industry expectations.) For example, DIP can auto-generate a Quality Overall Summary (QOS) from raw analysis data, and orchestrate complex data pipelines with audit-traceability (^[34]). The system includes validation agents, translation (multi-language), and conversion of lab or clinical tables into narrative. The lofty “1000%” efficiency likely refers to factors like 10× speed and parallelization; in any case, DIP exemplifies a high-end enterprise solution.

Casetext CoCounsel: This tool, while originally designed for legal documents, is adapted for regulatory writing. CoCounsel (leveraging GPT-4) can ingest statutes, guidelines, and generate citations-backed narratives (^[52]). Its strength is “source-grounded outputs” giving evidence and references with the draft text – useful for answering health authority queries or drafting justification sections. It may not do entire INDs, but can speed up assembling regulatory justifications and citations, akin to a legal research assistant.

Zemoso GenAI Platform: As revealed in a case study (undisclosed biotech client), Zemoso built a multi-agent writing system on Azure OpenAI. Key features included: data for CI/CD turned into encrypted vectors (PGVector) enabling inside-cloud retrieval (^[6]) (^[53]); pre-configured templates aligned with ICH guidelines; automated checks for discrepancies (triggering re-runs) (^[53]). In trials, this platform cut authoring time by ~70% (^[6]) for sections like CSRs and patient narratives (PNs). It allowed parallel drafting (multiple sections by different AI agents concurrently) and improved traceability. While proprietary, it demonstrates how off-the-shelf cloud AI (GPT family) can be repurposed systematically for regulated writing by layering encryption, indexing, and process controls on top.

Freyr (freya fusion): Another commercial case, Freyr’s AI-enabled modules (under the “freya fusion” brand) automate submission assembly. For example, their content management system coordinates topic-based content reuse, enabling component assembly of documents with AI suggestions. They highlight use cases: Document Generation (dynamic stitching of content into templates with metadata checks) and CSR Automation (AI drafting of background/methods/results). Freyr claims that by tying together component reuse and validation, first-draft assembly can shrink by 60% and one customer saw a 30% CSR cycle reduction (^[7]) (^[36]). These figures, again vendor-speaker estimates, align with DIP and Zemoso claims: roughly an order-of-magnitude improvement in simple tasks.

OpenAI ChatGPT / GPT-4 in Labs: Many smaller companies and CROs experiment with general-purpose ChatGPT-4 (or enterprise GPT) for quick tasks. Some use it as a writing assistant in a controlled setting (behind a firewall or behind an R&D team’s vetting). These experiments confirm the “first draft” capability: ChatGPT can write scientifically-sounding text on demand. However, untempered usage has obvious risks (lack of traceability, hallucinations). Thus, most groups wrap it in a retrieval/knowledge DB or prompt it with scientific sources. The general consensus (e.g. Council section, Angelo De Florio) is that a raw ChatGPT cannot be submitted directly. Nevertheless, even in this black-box form, ChatGPT can spark ideas, outline topics, or check grammar/style, under supervision.

Agentic frameworks and RAG systems: Academic prototypes, such as the QA-RAG model by Kim & Min (2024), show how queries about regulations can be answered accurately. While not writing drafts per se, these models could form part of a toolchain: e.g. an AI agent queries “What are the stability testing requirements for an IND?” and retrieves the official rule, para-phrasing it. Kim’s QA-RAG significantly improved factual accuracy over standard RAG (^[54]). Such innovations suggest future systems may automatically consult guidelines. Similarly, Zifeng Wang’s R2V and AutoTrial efforts illustrate how embedding retrieval can make criteria drafting plausible (^[55]).

We summarize these in Table 1:

Table 1: Representative AI-Based Tools and Platforms for Regulatory Writing

Table 1 notes: Each tool integrates AI differently. Reported efficiency gains (e.g. 70%, 60%, etc.) come from vendor case studies or early pilots and should be considered indicative rather than universal. The human role is always to review and approve all AI-generated content.

Case Example: Academic Study of AI vs Human Collaboration

Outside of vendor contexts, researchers have begun systematically studying human-AI collaboration in writing. The arXiv paper by Eser et al. (2025) is notable. It directly compared an LLM platform (AutoIND) against experienced medical writers for IND summary writing (^[5]). Key findings (table above) include:

• Speed: AI reduced drafting time from ~100 hours to 2.6–3.7 hours per batch, a ~97% improvement (^[5]).
• Accuracy: No critical errors were found in AI outputs. This implies the factual core was correct (e.g. doses, findings). Secondary issues (emphasis, conciseness) scored around 70–78%.

Crucially, this study shows a hybrid model: AI provides “rough drafts” extremely fast, and humans then refine. The authors conclude that regulatory writers remain essential “to mature outputs to submission-ready quality.” In fact, the AI’s identified deficiencies give direct guidance for future AI development: e.g. enhancing clarity in exposition and conciseness. In popular terms, AI won’t replace the medical writer, but it acts as a powerful first-drafter.

Regulatory and QA Considerations

Speed alone is insufficient if the output is not defensible. Regulators and experts stress that accountability and traceability are paramount.

Accountability and Human Oversight

Regulators articulate clearly: The sponsor is 100% responsible for any submission. (^[20]) (^[9]). The Council’s guidance states, “Accountability is absolute. The sponsor is 100% accountable for all submitted content. The AI is a ‘tool,’ not an ‘author.’” (^[20]). This mirrors de facto practice – there are no regulatory signatures for “AI – Chief Writer” on a label. It also aligns with the concept that an IND is an attorney-signed legal commitment of truthfulness.

Angelo De Florio (2025) emphasizes this as well. A former FDA reviewer is quoted: “The FDA will not accept ‘the AI made a mistake’ as an excuse. Ultimately, the sponsor is responsible for the accuracy of the submission.” (^[9]). In practice, this means companies cannot cut short review because AI was involved. Every claim and number in an AI-generated draft must be verified by humans before sign-off. If an AI blundered (e.g. invented an experiment result, reversed a toxicology conclusion), it is the sponsor who faces regulatory fallout.

Because of this, all adopted AI tools are classified as Category 3 (AI Drafters) or higher under the GAMP 5 framework (^[20]). They must pass validation appropriate for a closed-loop system. We will return to GxP rules below, but even without formal guidance, the principle is constant: human-in-the-loop control is non-negotiable. Any workflow includes at least two experts reviewing each AI output before it is merged into the official file.

Regulatory Requirements and Guidance

FDA (and other agencies) Attitude toward AI

The FDA, EMA, and other agencies have started addressing AI. In Jan 2025 the FDA issued a draft guidance on AI use in drug development (^[13]). This draft – titled “Considerations for the Use of AI to Support Regulatory Decision-Making for Drug and Biological Products” – is not binding law, but it signals the agency’s stance. The Executive Summary stresses model credibility:

“A key aspect to appropriate application of AI modeling in drug development... is ensuring model credibility – trust in the performance of an AI model for a particular context of use... sponsors [should] assess and establish the credibility of an AI model for a particular context of use and determine the credibility activities needed” (^[13]).

In simpler terms, the FDA expects sponsors to validate any AI component (much as one would validate a bioassay) before using it to support a regulatory submission decision. This includes mapping out the model’s training data, algorithm characteristics, and known limitations relative to the question at hand.

Notably, the FDA guidance acknowledges the increased use of AI in submissions: “the use of AI in drug development and in regulatory submissions has exponentially increased [since 2016]” (^[57]). It says that AI can be used to predict outcomes or analyze data, and suggests a risk-based framework for reliability. Although the guidance is broad (focusing on drug development decisions like trial design or patient selection), it was partly informed by feedback from an expert workshop and over 500 existing submissions with AI elements (^[58]). These insights reflect a cautious welcome: the agency is not forbidding AI, but demanding rigor.

Meanwhile, EU regulators have been more circumspect publicly, but early efforts are emerging. EMA launched an AI Task Force and released strategic documents on digitalization. While no specific EMA guideline on AI-for-writing is yet published, the EMA emphasizes transparency and traceability in all submissions. The principle from EU practice (reflected in De Florio’s Medium piece) is: We must know where every statement in an eCTD comes from. The EMA’s spokeswoman was quoted: “AI-generated text that cannot be traced back to raw data is problematic” (^[12]). This echoes the FDA’s push for audit trails.

In short, actively using AI in regulatory writing is allowed in principle, as long as sponsors demonstrate confidence in the AI. FDA’s guidance says sponsors “should quantify model risk” and present testing of the AI’s outputs. The emphasis is on quantifiable trust, not blanket prohibition. Although regulatory inspectors have yet to explicitly cite LLMs (as of late 2025), it is clear any future questioning expects sponsors to prove they knew what the AI was doing all along.

FDA 21 CFR Part 11 and GxP Controls

Regulated document production falls under 21 CFR Part 11 (US) and analogous EU regulations (Annex 11). These require that electronic records (including draft texts, prompts, queries, and final content) are attributable, reproducible, and secure. If an AI tool is used, the workflow must maintain an audit trail of all inputs and outputs. The Council guidelines rephrase this: “Pivot from ‘explainability’ to ‘traceability’... Focus on proving where [text] came from. Your ‘Provenance Record’ (the audit trail) is your GxP defense.” (^[33]).

Practically speaking, this implies:

• Logging prompts and responses. If an LLM chat session is used, every prompt and output should be captured. This is why using public ChatGPT in an ad-hoc way is forbidden – it has no institutional log. Instead, companies use validated AI platforms that store each query and generated text.
• Record-keeping of AI sources. In RAG, the actual source documents/paragraphs that provided content must be linked to the generated sentences. This might mean tagging each sentence with a source ID or storing a “provenance binder.”
• Change control. Any modification to the AI system (prompts, knowledge base, model version) should follow change management. SOPs must describe the “risk-based approach” for validation (Council Takeaway #2) (^[20]).
• User qualifications. Someone who reviews AI output must be trained in the SOP (validated user). They are not just casual editors; they must understand the limitations and correct misuse.

The GxP lens also extends to data integrity principles (ALCOA+). Every piece of AI-generated text, like any data, must be Accurate, Legible, Contemporaneous (recorded in real-time), Original (source of it recorded), and Attributable (who did it) (^[12]). AI can meet these if properly implemented – e.g. a closed system with time-stamped logs – but if misused (e.g. copying AI output without documentation), it can violate ALCOA.

Note also 21 CFR 312 specifics: an IND sponsor must notify FDA of any significant changes promptly and have audit trails for reports of safety issues. AI systems handling such updates would fall under these rules too.

The “AI Author” Myth and Documentation

Some early discourse questioned whether companies need to flag use of AI in submissions. Both FDA and EMA currently treat the sponsor as the sole author for legal purposes. The Council clearly advises: “We do not flag AI text in submissions, as we (the sponsor) are the sole author.” (^[33]). In other words, you do not attach an “AI Disclosure” to your IND. The only requirement is to ensure internal documentation shows how it was used. The public submission (to FDA/CTIS) contains no special marker; it reads as normal text signed by the Medical Writer or Sponsor representative.

However, internal SOPs must acknowledge the use of AI tool as a drafting aid. One recommended practice is to have a written policy specifying that the company does not treat AI output as final data, but purely as drafting output. The SOP might state: “AI-generated content is not source data; it must always be confirmed by human experts” (^[59]).

Thus, from a regulatory standpoint, any AI content is just treated like content from a contractor or an outsourced writer. The company reviewed it and takes responsibility. This reinforces the fundamental position: an AI cannot be a regulatory signatory.

Validation and Quality Assurance

Given these expectations, validation is central. FDA guidance implies that AI tools should be handled like any computerized system in a pharma environment. Specifically, Good Automated Manufacturing Practice (GAMP) Category 3 applies to AI drafting tools (since they assist in document creation but don’t autonomously make governance decisions) (^[21]). This means sponsors must perform User Requirements Specifications, testing and verification, change control, and routine requalification of the AI system.

As the Council puts it, in a GxP environment “faster” is only acceptable if the process is “traceable, reproducible, and accountable.” (^[60]). A classic validation would involve test cases: e.g., given known input documents, the AI system should reliably produce accurate summaries or flagged outputs. If the AI uses a RAG knowledge base, that base itself must be governed – sources must be current and validated, and any updates to it should be documented. The configuration of LLM parameters (temperature=0 for determinism) is part of verification.

To illustrate, the Council’s key takeaways include:

• “Validate the controls, not the ‘creativity’” of the AI (^[61]). I.e., you don’t try to prove the LLM is “smart”; you ensure your system inputs and workflows are sound.
• “Focus on provenance record” (^[62]): for each paragraph written by AI, track which approved source or data point it was drawn from.

The tentpole concept is akin to calibrating an instrument: you prove the tool is well-behaved in the hands of skilled users.

Human-in-the-Loop (HITL) Workflows

Virtually all sources agree: HITL is mandatory. The Council states “Human-in-the-Loop is the master control: must be a qualified human in a validated, 21 CFR11-compliant workflow.” (^[21]). So every AI-generated fragment is reviewed by a human who “understands the science” (^[63]). In practice, this likely means at least two humans (a writer and a subject matter expert) inspect each output for accuracy and clarity before release.

HITL serves multiple purposes: it catches hallucinations, ensures context sensitivity, and provides that “expert judgment” AI lacks. Consider the example from Keiji AI’s blog: even GPT-4 hidden text generation sometimes invented treatment schedules or violated guidance when left unchecked (^[11]). The only safe solution was an agent workflow that looped and re-checked compliance. Similarly, a pilot user might spot that an AI has incorrectly summarized a toxicology endpoint – an error a layperson’s AI might gloss over.

Therefore, defining the HITL is part of the AI strategy. Who does it (medical writers, scientists), how they interact with the AI (e.g. through an interface that shows provenance), and how edits are logged – all must be SOP’ed. The Council even suggests documenting “We do not flag AI text in submissions, as the sponsor is sole author.” (^[33]). In other words, the human-in-the-loop isn’t optional pretense – it is the core of regulatory compliance.

Data Integrity and Privacy

IND/CTA documents often include sensitive data: patient-level safety info, proprietary formulations, or personal identifiers (in commentaries). There are two concerns: (a) data leakage through AI systems, and (b) contamination of AI training.

To address (a), many experts insist on air-gapped or private-cloud solutions. If an AI system (like OpenAI’s GPT) is used at all, it should be on a closed instance where no external access is allowed without encryption. Weave Bio’s approach was to use Azure OpenAI in a private tenant and sign a “zero-data retention” agreement with OpenAI (^[49]). That way, even though the text flows through an external LLM, the rules forbid storing it. Best practice is to avoid sending raw patient-level data to any third party. In many cases, only FDA-cleared or internally hosted AI is used with real data.

For (b), sponsors worry that feeding unique proprietary documents into a public LLM could “teach” competitors if the model later quotes them. The Scientist article quotes a sponsor triaging this: “even AI developers who make tools for filing IND paperwork shouldn’t have access to confidential material” (^[48]). Solutions: keep the knowledge base private, and do not fine-tune public models on proprietary info unless that info is irreversibly obfuscated. Some propose cleaning data (anonymizing results, removing secrets) before feeding an AI.

From a regulatory viewpoint, data integrity (ALCOA+) implies any AI’s outputs must not create spurious secondary records. All AI interactions must be considered part of the internal Quality System.

Data-Driven Impact: Evidence and Analysis

Let’s examine the evidence in detail. While studies are still limited, we can piece together data from experimentation, case reports, and surveys.

Time Savings and Efficiency Metrics

The most rigorous number comes from Eser et al. (2025) with the ~97% time gain (^[5]). Other sources give ranges: Freyer’s claim of 60% for assembly and 30% for CSR workflows indicates large but less dramatic gains (^[7]) (^[36]). WIth DIP’s 1000% hype (really “10×”) one should be cautious – it likely reflects an ideal scenario (e.g. initial setup to full rollout). But in sum, order-of-magnitude improvements seem realistic for drafting.

It’s also worth noting the nature of improvement: often, the most time is saved on repetitive or formatting tasks (copying tables, adjusting sections, renaming labels) that AI can do instantly. Creative or judgement tasks may only be partially sped up (e.g. the AI gives a draft, but the writer still rewrites it).

Quality and Accuracy

Quantitative evaluation of quality is very early. We have:

• AutoIND study: overall quality scores (on clarity, consistency etc.) ~70-78% (^[64]), with no ‘critical errors’. AI was less concise and needed editing to sharpen emphasis.
• Keiji AI Insight: GPT-4 in their InformGen pilot did sometimes produce ICF sections violating FDA rules – an example of harmful “hallucination.” This led them to conclude that even strong models require iterative checking before using outputs in regulated documents (^[11]). Specifically, “even GPT-4.0 occasionally wrote ICF content that violated FDA guidance or invented treatment schedules. In clinical research, that is not a small mistake. It is a deal breaker.” (^[11]). This study design doesn’t quantify in numbers, but qualitatively it’s an important warning.
• Council on Pharmacy Section 4.1: very positive view. They list tasks LLMs can reliably do (structure text, summarize, reformat) (^[3]). Implicitly, they suggest success here can improve accuracy of consistency.
• Regression or Checkpoint: If a regulatory submission prepared with AI is audited (internal QA or authority review), one would look for two things: Did any AI-introduced error slip through, and did the AI help catch any manual error? We have testimonies (Nahas et al.) that consultant reviewers found only cosmetic suggestions, not factual fixes (^[27]). But no formal “error-per-page” numbers.

Given this, we infer that fatal mistakes are low (with proper controls) but improvements in style and brevity are needed. This gap (rough draft vs final quality) highlights the crucial role of human editing. Quality assurance metrics must evolve to measure aspects like “hallucination rate” or “accuracy vs source data”. The FDA’s emphasis on “model risk” quantification is a step toward scariness.

Surveyed Perspectives

Though not fully quantitative, industry surveys and expert panels reveal perceptions:

• Many regulatory professionals express cautious optimism. They often say, “AI can automate trivial stuff, but we must verify everything; it will augment, not replace us” (^[65]) (^[63]).
• Common concerns (echoed in interviews) include data privacy, lack of clear guidance, and the fear of mandated data checks. The Scientist quotes developers and regulatory managers at small companies who are excited by the time savings but reticent to trust anything without control knobs (^[48]) (^[66]).
• For those already using LLMs in other domains (e.g. medical affairs or legal), the Consistent theme is: ensure traceability, and never skip the review. A LinkedIn discussion (Pudwill, 2025) called out typical mistakes in AI-for-writing: “hallucinated data, mediocre formatting, about 80% outcome quality, no basics” – reaffirming the gap between raw AI output and regulatory-ready text. We cannot cite social media directly, but this sentiment is widespread in commentary.

In the realm of Regulatory Podcasts/Blogs, experts advise a “risk-based” view: use AI only where it truly helps, and don’t expect as a silver bullet for everything (^[10]). Not all document sections may benefit equally – e.g. a statistical analysis write-up might be too specialized for an LLM to draft alone, whereas an overview of rationale could be.

Risks, Limitations, and Mitigations

No technology is without pitfalls, and generative AI in regulated writing brings certain risks.

Hallucination and Accuracy Risks

Hallucination – AI confidently asserting falsehoods – is the elephant in the room. A single invented datum in an IND could mislead regulators or even endanger future trial subjects. This is why every source stresses human review. Zifeng Wang (2025) writes frankly that generative outputs “can still go wrong” (^[11]). CIP programs have found examples: GPT may invent references, confuse dosage units, or assume an interpretation not in the dataset. Even if anchored by RAG, if the retrieval doesn’t include the needed fact, the AI might fill the gaps.

Mitigation strategies:

• Prompt engineering and guardrails. Set temperature=0 (deterministic output) and strict system prompts (e.g. “Only generate content strictly from provided documents”).
• Data validation layers. After generation, automated QA scripts can sanity-check numeric values (e.g. are listed doses within expected range) or text strings (do drug names match the roster).
• Dual-use of generative and extractive AI. For critical data (e.g. PK parameters), use specialized NLP extractors that precisely pull figures from source tables instead of trusting the model’s rephrasing. LLMs can summarize narrative but numeric details often come from a separate knowledge extraction pipeline.
• Iterative refinement. Have the AI re-generate or clarify if it seems uncertain. For example, the agentic model “tries again if needed” when evaluation (like a consistency checker) fails (^[29]).

No single fix eliminates hallucination. The only robust defense remains rigorous human verification backed by traceability of sources.

Data Security and Proprietary Information

Uploading clinical data to an AI model, especially a cloud model, carries leakage risk. Even if encrypted, a misconfiguration or API breach could expose sensitive information. The autoIND case underlines this worry: “A lot of information in the IND is confidential... How do you set up a firewall and make sure there is no information leaking?” (^[48]).

Solutions include:

• Using on-premise or private cloud instances of LLMs where no external network egress is allowed.
• Adopting zero-data-retention agreements (like Weave did with OpenAI) to prevent the SaaS model from learning anything from your data.
• Pre-processing data: for instance, removing patient IDs or highly secret chemistries before feeding to AI, if possible.

This issue is especially acute for early-stage biotech, which fear competitors stealing a formula or strategy. Larger pharma, with bigger budgets, may build their own closed model hosting. But until (or unless) AI model licensing costs drop, most smaller firms will likely rely on controlled SaaS with legal protections. Regulators themselves (as noted in the Reuters article (^[67])) anticipate everything staying behind agency firewalls, reinforcing that any sponsor data must remain within asserted security perimeters.

Regulatory and Validation Gaps

Currently, there is no explicit law (in the US, Europe, or elsewhere) that forbids generative AI in submissions – but nor is there explicit permission. This “gray zone” creates uncertainty. FDA’s 2025 draft guidance covers AI in broad terms but doesn’t mention LLMs for writing. No legal requirements (yet) say “if you used AI, you must X,” so sponsors interpret the status quo: assume full accountability and comply with existing rules by default.

One potential risk: what if regulators start scrutinizing AI usage more heavily? The White House pause on FDA external communications in late 2024 affected answering FDA’s stance on AI, but glimpses (Jan 2025 draft guidances) hint that agencies may in future question sponsors about their AI validation steps. Companies should prepare to document their AI practices as part of compliance audits.

Another gap is accreditation. There is no “certification” process for an AI writing tool like there is for medical devices or data standards. The burden falls wholly on the sponsor to prove validity. This may deter adoption by more risk-averse sponsors – an intangible “regulatory chilling effect.” Early adopters (like those in pilot projects) may blaze the trail, but others may wait for clearer rules or success stories where submissions using AI were accepted without issue.

Ethical and Cultural Considerations

Aside from strict rules, there are softer concerns. Some stakeholders question whether using AI feels ethical in the “human-centric” field of medicine. Could an overreliance on AI lead to de-skilling of writing professionals? Probably not, given the current emphasis on oversight, but it may change the role. There is also patient privacy: AI drafting might inadvertently include extraneous patient anecdotes or identifiers if an unsafe prompt is given. SOPs must emphasize what data to share with the model.

Finally, job roles will evolve. Writers may need new competencies (prompt engineering, AI validation). Regulatory affairs teams might need AI fairness oversight. Organizations should develop training programs. (The Council’s training modules and the Freyr blog both imply a learning curve for staff using these tools.)

Future Directions and Implications

Ongoing Innovations in AI

The technology is still rapidly evolving. Some notable trends:

• Advanced RAG (e.g. QA-RAG): The 2024 QA-RAG model showed promise in retrieving with accuracy (^[54]). For regulatory tasks, similar question-answer pipelines might allow an AI “where-is-this-data?” function. For example, a writer could ask the AI “According to the Investigator’s Brochure, what is the maximum dose studied in animals?” and get an immediate answer citing the IB. This side-steps drafting full text by letting AI act as an intelligent search tool. Eventually, a combination of extractive AI and generative AI may form “assisted authoring assistants” that both cite evidence and generate prose in one interface.

• Multi-language and global submission support: As global trials expand, AI could translate sections for local authority language, while preserving formatting. This could aid CTAs in non-English speaking countries. Already, some AI tools include translation engines aligned to medical terminology.

• Integration with Digital Health Tools: AI might automatically ingest electronic trial master files (eTMF), CTMS updates, or EHR data as soon as they are finalized, triggering recommended text updates. Imagine a “smart IND” at filing time that had already updated its sections based on final lab certificates or site initiations.

• Better Model Transparency and Testing: Because explainability is impractical for deep LLMs, research is focusing on interpretable proxies. For instance, tracking which documents were most heavily weighted in a RAG chain. The future may bring standardized “AI Footprint” reports in submissions, detailing how the draft was built. Or third-party validators may emerge to audit an AI tool’s performance in pharma contexts.

• Regulatory Sandbox Environments: We may see FDA or other agencies host sandbox programs where sponsors can pilot AI-assisted drafts and exchange feedback. The FDA’s recent AI integration suggests openness to such collaborations. Dialogue with regulators (as Angelo suggests (^[24])) can help shape policies.

Business and Industry Impact

If AI tools deliver on promises, the pharma industry could see noticeable shifts:

• Faster trial starts: Shortening IND/CTA preparation trims time before clinical trials begin, potentially accelerating new therapy availability. In competitive research areas, this can be a huge advantage. It also lowers cost of early development for biotech companies, potentially lowering financing needs and making more programs viable.
• Cost efficiencies: Reduced contracting of external writers and extended work hours. However, initial investment in AI platforms and maintenance is non-trivial (DIP and similar solutions often involve high subscription costs or custom development). ROI will depend on scale.
• Workforce transformation: Regulatory writing jobs may shift from template-filling to QA roles. Training in AI literacy will become standard for regulatory writers and reviewers.
• New Services: Consulting firms may start offering “AI-writing-in-a-box” managed services, combining their expertise with AI tools. We already see specialized startups (Weave Bio, Keiji AI) and consultancies integrating AI, suggesting a market shift.

Regulatory and Policy Trends

Agencies will likely formalize guidance as experience grows. Potential future policies include:

• Formal AI guidance addenda: FDA might release a dedicated guidance on AI in regulatory submissions (beyond the current overarching framework). This could specify recommended validation steps, audit trail quality, or even how to disclose AI usage internally.
• Global alignment: Harmonization via ICH could occur; ICH might include statements on AI use in its next M11 “Identifier” or data standard updates. Companies would benefit from investor demand or public pressure to document their AI practices.
• AI-readable eCTD: There might emerge a new standard for submissions to include machine-readable markup of sources (beyond current XML), anticipating AI processing at the agency’s end. For instance, if sponsors tag eCTD sections with metadata, it could let regulatory reviewers query the data more effectively.

Long-Term Implications

In the long run, AI might blur the line between data analysis and narrative drafting. Imagine a system that not only writes “Chapter 5.3.5 in the IB” but automatically extracts the latest trial results, synthesizes a risk-benefit commentary, and outputs a revised section on patient safety – all under supervision. That vision is still futuristic, but research is moving toward it.

Importantly, the story of AI in regulatory writing intersects with broader tech progress. Breakthroughs in trustworthiness (like OpenAI’s GPT-5 rumored context windows of tens of millions of tokens) could allow whole IND documents to be digested at once. If an LLM can understand an entire 10,000-page submission in one context, it might enable query-based summarization (e.g., “What is the maximum dose tested in nonclinical studies?” answered instantly). This contrasts with today’s RAG which is incremental.

Finally, consider societal implications: accelerating IND to trials may raise ethical considerations (are we rushing new drugs too fast?). Regulators will scrutinize the balance – whether AI truly preserves or enhances safety, or merely speeds things up. The emphasis in many statements is that faster therapy development is beneficial only if trust is not undermined. If missteps occur, regulators may impose stricter controls that could have a chilling effect (e.g. mandate disclaimers in filings or require full AI audit reports). Stakeholders must therefore proceed with diligence.

Conclusion

Artificial intelligence has arrived in pharmaceutical regulatory writing – not as a panacea, but as a potent augmenting force. The journey toward AI-assisted IND/CTA drafting is now moving beyond theory into early practice. Case studies demonstrate that when carefully deployed, AI can slash drafting time by many tens of percent, freeing up precious expert hours (^[5]) (^[6]). Industry pilots confirm that even small biotech and large pharma can leverage LLMs (and hopefully soon other models) to transform repetitive authoring tasks into streamlined workflows (^[27]) (^[23]).

However, this is a cautious road. The reality of GxP compliance – as emphasized by regulatory bodies and thought leaders – means that speed cannot come at the expense of accuracy, traceability, or integrity (^[10]) (^[12]). Humans remain the final arbiters of all content. The “North Star” for any AI deployment is absolute sponsor accountability: AI is a tool, not an author (^[20]). A fully auditable, validated workflow with human oversight is mandatory.

Looking forward, we expect incremental integration. Early adopters will expand their AI toolkits: combining RAG, fine-tuned clinical LLMs, and iterative agents, all under strict internal control. Regulators, in turn, will observe and refine guidance. Companies that invest in AI literacy and infrastructure now will be best positioned to reap benefits (and to shape the rules). Over the next 5–10 years, drafting efficiency gains may become baseline expectation, and AI assistance a norm rather than novelty.

But the mission does not change: the ultimate goal remains bringing safe, effective therapies to patients without undue delay**. AI assistance, by offloading mundane work and highlighting potential inconsistencies, can accelerate that goal. When executed within a culture of rigorous validation and human oversight, generative AI shifts the pharmaceutical writing process from a slow march through documents to a more agile, intelligent collaboration. It is a realistic road – provided we walk it with both enthusiasm for innovation and the cautious respect that lifesaving science demands.

References

• Chiodin D, Cox EM, Edmund AV, Kratz E, Lockwood SH. Regulatory Affairs 101: Introduction to Investigational New Drug Applications and Clinical Trial Applications. Clin Transl Sci. 2019;12(4):334–342. DOI:10.1111/cts.12635 (^[1]) (^[2]) (Background on IND/CTA contents)
• Council on Pharmacy Standards. “LLMs in medical writing, submissions & protocol drafting.” CAIDRA module (2024). Section 4.1. (Discusses how LLMs can assist writing tasks) (^[3]).
• Council on Pharmacy Standards. “FDA/EMA View on AI-Assisted Documentation.” CAIDRA module 4.4 (2024). Key Takeaways: “Accountability is absolute... AI is a tool, not an author”, emphasis on human-in-loop, traceability (^[10]).
• Nahas K. IND Applications Are Tedious. Can AI Help? The Scientist. Nov 2025. (Case study of Weave Bio’s AutoIND; quotes on time savings and concerns) (^[27]) (^[23]).
• Eser U, Gozin Y, Stallons LJ, et al. Human-AI Collaboration Increases Efficiency in Regulatory Writing. arXiv preprint 2509.09738 (2025). (Study of AutoIND: LLM reduces drafting time ~97% with maintained quality) (^[5]).
• FDA News Release. Draft Guidance: Considerations for the Use of AI to Support Regulatory Decision-Making for Drug and Biological Products. Jan 6, 2025 (first FDA guidance on AI in drug dev, emphasizes model credibility, context of use) (^[13]) (^[14]).
• Wang Z. Building AI for Drafting Clinical Trial Documents: RAG, Fine-tuning and Agentic AI Workflow. Keiji AI blog. Nov 21, 2025. (Describes Trial2Vec, AutoTrial, Panacea, InformGen, and the challenges of hallucination) (^[11]).
• Medicaldigitals.com. How AI Is Transforming Regulatory Writing Without Compromising Compliance. (2023). (Industry blog: outlines AI benefits like drafting outlines, consistency checks, lit scanning, but underscores needed human oversight) (^[68]) (^[69]).
• DIP-AI (Deep Intelligent Pharma). Ultimate Guide – Best AI Tools for Regulatory Writing (2025). (Vendor site: defines AI tool for writing, claims up to 1000% efficiency gains and multi-agent intelligence) (^[34]) (^[70]).
• Zemoso Labs. Case Study: Accelerating Regulatory Submissions with GenAI-Powered Medical Writing. (2024). (Company blog: generative AI platform on Azure with 70% time reduction, encryption and traceability architecture) (^[6]) (^[56]).
• Freyafusion.com (Freyr). 5 Generative AI Use Cases Revolutionizing Pharma Regulatory Affairs. (June 24, 2025). (Explains sample AI use cases: component content assembly, AI narrative drafting in CSRs; cites up to 60% assembly time cut and 30% CSR cycle improvement) (^[38]) (^[7]).
• Reuters. FDA proposes framework for AI credibility in drug submissions. January 6, 2025. (Press coverage of the FDA’s draft guidance) [FDA press release].
• Reuters. US FDA launches AI tool to reduce time taken for scientific reviews. June 2, 2025. (Reporting on FDA’s “Elsa” internal AI initiative, emphasizing security and efficiency) (^[71]).
• FDA Inspections (fdainspections.com). AI and FDA Part 11 Compliance: A Complete Guide for 2025. (Sept 27, 2025). (Industry commentary on Part 11 issues with AI) [Note: example context, if needed for compliance reference].
• De Florio A. Generative AI in Regulatory Submissions: Promise and Perils. Medium, Aug 21, 2025. (Industry opinion: Takeda pilot, advantages of speed, concerns on accountability; emphasizes sponsor liability, FDA/EMA caution) (^[72]) (^[12]).
• Choi H-K, Lee JO. PharmaGPT: Domain-Specific LLM for Bio-Pharmaceuticals and Chemistry. arXiv 2406.18045 (2024). (Details of PharmaGPT showing domain-tailored LLM performance gains) (^[25]).
• Kim J, Min M. From RAG to QA-RAG: Integrating Generative AI for Pharmaceutical Regulatory Compliance Process. arXiv 2402.01717 (2024). (Describes QA-RAG model improving retrieval accuracy for regulatory inquiries) (^[54]).
• Additional industry and regulatory guideline sources as cited in text (e.g. FDA 21 CFR references, ICH docs, Council of QA SOPs, etc.)