AxiSentinel extends continuous AI compliance evaluation to pharmaceutical and biotech companies, CDMOs and CROs: the AI that inspects the vial, reviews the batch record, triages the deviation, codes the adverse event, picks the trial site, drafts the submission and answers the doctor’s question, evaluated against the GxP, AI, data-protection and medicines rules of every market where the product is developed, made and sold.
Pharma and biotech now use AI at every step from target to patient: the model that screens molecules, the camera that inspects the vial, the engine that triages the safety case, the tool that drafts the clinical study report. GxP rules were written for software that behaves the same way every time. In 2025 and 2026 regulators began writing the rules for software that learns.
AI vendors validate their own tools, and computerized-system validation captures a model once, at go-live. No one evaluates the whole stack continuously, from discovery to safety, with evidence a GxP inspector, a regulatory assessor or a licensing partner would accept.
Pharma AI sits under GxP rules, medicines law, data-protection law and AI-specific law, in every market where a product is developed, made and sold. AxiSentinel tracks every layer and builds RegDef packages the moment a rule is stable enough to evaluate against.
The first horizontal AI law. AI used solely for scientific research and development is exempt, but AI in devices, diagnostics, the workforce and general-purpose models is not.
The first GMP rules written for AI. The drafts went to consultation from 7 Jul to 7 Oct 2025, and EU inspectors are targeting a final text for the European Commission in Q4 2026.
Adopted in September 2024, the reflection paper sets EMA’s expectations for AI across the medicinal product lifecycle, backed by a multi-annual AI workplan running to 2028.
Issued in January 2025 for drugs and biological products and still in draft. Drug discovery is out of scope; everything that feeds a safety, effectiveness or quality decision is in.
Ten joint principles published on 14 Jan 2026, the first shared transatlantic baseline for AI in drug development.
Electronic records and signatures rules apply to every GxP record an AI system creates, changes or relies on.
E6(R3) took effect in the EU on 23 Jul 2025 and was published by FDA on 9 Sep 2025. M15, adopted on 29 Jan 2026, covers model-informed drug development, including AI and machine learning.
The industry reference points for validating AI in GxP work: the CIOMS Working Group XIV report on AI in pharmacovigilance (December 2025) and the ISPE GAMP Guide: Artificial Intelligence (2025).
Health and genetic data are special-category data under GDPR. The EHDS Regulation, in force since March 2025, opens secondary use of health data for research and innovation from March 2029.
The largest reform of EU medicines law in two decades, adopted by the Council and awaiting the final European Parliament vote, with full application foreseen from 2028.
Two major regulators are building AI into how medicines are developed, inspected and supervised.
General-purpose AI risk-management frameworks that already apply to every AI component in a discovery, clinical, manufacturing or safety stack.
From a single visual-inspection line to an enterprise portfolio of discovery, clinical, quality and safety models, AxiSentinel evaluates pharma AI against whichever rule actually applies, wherever the site, the study and the data sit.
Nothing about AxiSentinel’s core architecture changes for pharma and biotech. What changes is which RegDef packages are switched on and what telemetry the agents capture, from a visual-inspection line to a pharmacovigilance intake engine.
Agents capture manufacturing, quality, clinical, pharmacovigilance and medical-information telemetry on the cadence you configure, Always On or scheduled, never limited to a periodic audit cycle.
Every inspection result, triage decision and AI-generated record evaluated against whichever GxP, data-protection or AI rule set actually applies, per event.
Every audit record is linked to the one before it in a signed, tamper-evident evidence chain, the kind of record a GxP inspector expects to see behind every AI-assisted decision.
Captures each model’s question of interest, context of use, risk rating and validation results, ready for FDA’s credibility framework and EMA’s risk-based expectations.
Tests whether models in critical GMP applications are static and deterministic, whether test data stayed separate from training data, and whether confidence and explainability are logged.
Ongoing evaluation of inspection, triage, safety and recruitment models for error rates, drift and skew across products, sites and patient groups.
Source-traceability and factual-accuracy evidence for AI-drafted submissions, safety narratives, medical-information answers and promotional material.
A compliance-state change on one line, site, study or product propagates network-wide as it happens.
AXI-Node agents deploy alongside cloud quality and clinical systems, on-premises manufacturing networks and in-region environments, or fully air-gapped through the .axibatch format.
Nothing becomes a compliance finding until a qualified AxiLayer AI auditor reviews and signs it, not for a visual-inspection model and not for a safety-case triage tool.
Records who, or which model version, created or changed each GxP record and when, aligned to Part 11, Annex 11 and data-integrity expectations.
The same architecture monitoring a bank’s credit model or a hospital’s diagnostic AI now monitors a visual-inspection line or a pharmacovigilance intake engine. What changes is which RegDef packages are switched on.
Pharma already runs on validated systems and audited records, and the rules for AI inside those systems are arriving now. AxiSentinel’s architecture already does this for software AI; pharma and biotech is a new RegDef surface on the same platform, not a new product.
Whether it’s a single visual-inspection model or an enterprise portfolio of discovery, clinical and safety AI, AxiSentinel evaluates it the same way it evaluates any AI system: on the cadence you configure, Always On or scheduled, with full evidence, and with a human signature before anything counts as a finding.
