An FDA information request never arrives at a convenient time. Whether it is a mid-review information request during an NDA cycle, a set of discipline review questions, or a full deficiency letter, the pattern is the same: a dense document containing anywhere from three to sixty distinct questions, a clock that started ticking the moment it landed, and a scramble to figure out who owns what.

Teams that handle information requests well treat them as a repeatable operational process rather than a fire drill. This playbook lays out that process — and is honest about where AI genuinely helps versus where it has no business being.

Step 1: Decompose the Letter Into Atomic Questions

The single most valuable thing you can do in the first hour is break the letter into individual, atomic questions. Agency letters bundle relentlessly: a single numbered item might contain a stability question, a request for updated batch data, and a labeling implication all in one paragraph. If you track the response at the letter-item level, three different teams end up half-owning one item and nobody owns the seams.

Atomic decomposition means every distinct ask becomes its own tracked unit with its own owner, its own due date, and its own draft. It is tedious to do by hand — which is exactly why it is one of the places AI earns its keep. DnXT’s RFI Workbench accepts a pasted information request and decomposes it into atomic questions automatically, so the team starts from a structured worklist instead of a PDF and a highlighter.

Step 2: Route by Discipline

Agency questions map to disciplines with surprising consistency — our analysis of more than 1,000 real health-authority questions found that the CTD section a question targets predicts its discipline with 98–100% reliability. A question about section 3.2.P.8 is a stability question; it belongs to CMC. A question about 2.7.4 belongs to clinical safety.

That predictability means routing should be automatic. Each atomic question gets tagged to a discipline — CMC, nonclinical, clinical, biostatistics, labeling, quality — and lands in the right SME queue immediately. The routing conversation (“who should take question 14?”) is a meeting your team no longer needs to have.

Step 3: Draft From Precedent, Not From Scratch

Most companies have answered most of their questions before. Stability trends, impurity justifications, site transfer rationales — the same themes recur across programs and cycles. The problem is that past answers live in old submission archives and personal folders, effectively unsearchable when the clock is running.

This is the second place AI legitimately helps: grounded retrieval. The RFI Workbench retrieves relevant past approved responses from your own knowledge base — semantically, so a question phrased completely differently still finds the right precedent — and generates a draft response grounded in what your company has actually said and had accepted before. Every retrieved source is cited, so the SME reviewing the draft sees exactly where each claim came from. This retrieval layer is part of the same AI intelligence architecture that powers document understanding across the DnXT platform.

What AI must not do is invent data. A drafting system for regulated responses needs anti-fabrication guardrails: if the corpus contains no relevant precedent and no source document supports a claim, the correct output is a flagged gap — not a fluent paragraph of plausible fiction.

Step 4: QC the Draft Like an Auditor Would

Before any draft reaches a human approver, it should pass a rubric-based QC check: Are there placeholder phrases left in the text? Does every factual claim trace to a cited source? Does the response actually answer the question that was asked, or an adjacent easier one? Is the tone declarative where it should be and appropriately hedged where data is pending?

DnXT runs this rubric as an automated AI QC pass over every generated draft. It routinely catches the embarrassing failure modes — an unfilled “[insert batch number]”, a response that answers half of a two-part question — before a human ever spends review time on the draft.

Step 5: The Human Approval Gate — Draft vs Record

Here is the line that matters for GxP: everything AI produces is a draft until a human approves it. The draft-versus-record boundary is not a disclaimer, it is an architecture. AI-generated responses live in a draft state, visibly marked, with no pathway into the submission record that does not pass through an accountable human approval. Once approved, the response becomes a record — and the approved answer joins the knowledge base, which means the next information request starts from a stronger corpus than this one did. The system improves with every cycle precisely because humans, not models, decide what enters the record.

Step 6: Close the Loop Into the Submission

An approved response still has to become a submission. Response documents get assembled into an eCTD sequence — typically as an amendment to the application under review — with the same publishing, validation and lifecycle discipline as any other sequence. If your response tooling and your eCTD publishing platform are the same system, the handoff is a click rather than an export-import ritual. Run the sequence through full eCTD validation before it goes out; a technically rejected response sequence burns days you do not have.

What Good Looks Like

  • Every letter decomposed into atomic questions within hours of receipt
  • Every question routed to a discipline owner automatically
  • Drafts grounded in cited precedent, never freehand generation
  • Automated QC before human review
  • A hard draft/record boundary with human approval as the only crossing
  • Approved answers feeding a growing, searchable response knowledge base

Teams operating this way report the difference most visibly in review cycles: responses go out faster and come back with fewer follow-up questions, because each answer is consistent with everything the company has said before.

Frequently Asked Questions

How long do you get to respond to an FDA information request?

It varies by context — mid-review information requests often carry short informal windows (days to a few weeks), while complete response letters trigger formal resubmission timelines. Treat every request as clock-driven and confirm the expectation with your review division.

Is it acceptable to use AI to draft regulatory responses?

Using AI to decompose, route, retrieve precedent and draft is increasingly common — what matters is governance: grounded generation with citations, anti-fabrication controls, and a human approval gate before anything becomes part of the record.

What is an RTQ knowledge base?

A structured library of past agency questions and your approved responses, indexed so future questions can retrieve relevant precedent. It turns institutional memory into an operational asset instead of tribal knowledge.

Handle Your Next Letter Differently

If your last information request involved a spreadsheet, a war room, and a weekend, there is a better operating model. Book a demo and bring a past (redacted) agency letter — we will decompose it live and show you what grounded, governed response drafting looks like.