Technology.
12 articles on technology — analysis from the Asthra team and our advisor network.
2026
One Batch, a Dozen Tables: The Consistency Problem in CMC Module 3
A CMC / Module 3 dossier is mostly interlocking tables, and the same batch, impurity, and limit turn up across many of them. Keeping those appearances consistent, and traceable, is what makes Module 3 hard to automate — not the writing. Here's how we think about it.
2026
Regulatory Review: An FDA/ICH Audit Inside the Draft, Grounded in Real openFDA Precedent
Asthra's Regulatory Review runs a section-by-section FDA/ICH audit right inside the document — grounded in curated ICH E3 expectations and real approved-product labeling from openFDA — and writes specific, actionable regulator flags inline where the writer works.
2026
The Safety Analysis Moved to ITT. Now Reconcile 200 Pages.
A late change to the safety analysis population recomputes every adverse-event table, exposure summary, and benefit-risk paragraph in a CSR. Here's what that costs by hand — and what it costs with an agent that re-grounds every number in the source.
2026
From a One-Line Indication to a Cited Protocol Synopsis
Asthra's seed-then-expand authoring flow turns a bare indication into a complete, standards-aligned protocol synopsis — drafted end-to-end, grounded in registered trial evidence, and surfaced as cited decisions the writer can steer.
2026
Every Run, Every Quality Flag, in One Report — Asthra's End-of-Run QC
When the agent finishes drafting, Asthra runs a structured QC pass — cross-reference integrity, statistical-claim sourcing, citation density, gap detection — and ships a quality_report.json alongside the draft. Here is what it checks and why.
2026
Tables and Figures in the Chat — Agent-Driven Python You Can Audit
Adverse-event line listings, sales-exposure data, lab parameter shifts — the data behind a CSR or PSUR runs into thousands of rows. Asthra's data analysis agent handles the analysis inside the chat with deterministic Python, every step audit-logged. No manual filtering, no LLM-fabricated numbers.
2026
Walk-Away Drafting: How to Generate a CSR Overnight
Initiate the run, step away, and come back to the studio fully briefed. Asthra lands a first draft + bibliography + hyperlinks + QC report in 1–2 hours; the writer reviews and refines in the studio with the full agent history in view — nothing is lost between the run and your review.
2026
What's in a Regulatory-Grade Run Bundle
Every Asthra run ships four artifacts side by side — the .docx, traceability.json, quality_report.json, and run_manifest.json. Each one answers a specific question a regulator or auditor would ask, and together they make the deliverable portable, reproducible, and inspector-ready.
2026
Hyperlinks Belong in the Draft, Not the Publishing Phase
Cross-references in regulatory submissions usually get wired up weeks after the draft is locked. We moved that work into the draft itself — anchors at write time, hyperlinks before publishing.
2026
Literature Search Belongs Inside the Regulatory Draft
PubMed, ClinicalTrials.gov, and bioRxiv are now reachable from inside Asthra's chat — without the browser-tab detour that used to break a writer's flow and let evidence go stale.
2026
Beyond Frontier Models — Why Regulatory AI Needs a Purpose-Built Stack
Frontier LLMs and generic RAG aren't enough for regulatory writing. A purpose-built stack — document model, retrieval, planning, and verification — is what separates a demo from a draft a regulator will accept.
2025
Writer-Defined Provenance: Why Source Control Matters in AI-Assisted Regulatory Writing
How writer-defined provenance ensures that every piece of AI-generated regulatory content is traceable to its specific source document, section, table, or image.
See it on a real CSR, PSUR, or CER.
Asthra's five-step loop — Plan, Retrieve, Draft, Review & refine, Hand off — running on real regulatory documents, not slides.