Self-Hosted AI Deployment in Barcelona, Spain

We deployed a self-hosted AI platform over 14 weeks — GMP-validated, EU Annex 11-compliant, and purpose-built for pharmaceutical operations. (1) Barcelona-resident pharmaceutical-grade infrastructure. Hardware: 24 NVIDIA H100 80GB GPUs across 3 server nodes, deployed in the company's Barcelona data centre (a validated pharmaceutical computing facility meeting EU GMP Annex 11 requirements — controlled access, environmental monitoring, UPS-protected, and equipped with validated backup procedures). Validation: the AI platform validated as a GMP computerised system following EU Annex 11 and GAMP 5 methodology. Validation documentation including: User Requirements Specification (URS), Functional Specification (FS), Design Qualification (DQ), Installation Qualification (IQ), Operational Qualification (OQ), and Performance Qualification (PQ). Every model deployment, update, and configuration change documented in the company's change control system — meeting GMP requirements for validated system management.

Data integrity: all AI inputs and outputs meeting ALCOA+ requirements — Attributable (linked to the requesting user and system), Legible (human-readable outputs), Contemporaneous (timestamped at creation), Original (tamper-evident storage), and Accurate (validated against known-good test cases). (2) Manufacturing quality AI: optimising 35 years of plasma fractionation. Batch record analysis: Llama 3 405B fine-tuned on 420,000 historical batch records (35 years of manufacturing data). The model learning correlations between process parameters and quality outcomes that traditional statistical analysis had missed — particularly multi-parameter interactions (the effect of pH adjustment timing combined with specific ethanol concentrations at specific temperatures) that were invisible to individual parameter analysis. Process parameter optimisation: the AI providing recommendations for process parameter adjustments — not controlling manufacturing equipment directly (GMP requiring human decision-making for production parameters) but presenting optimised parameter sets to manufacturing scientists with predicted outcomes and confidence intervals.

For each batch, the AI recommended: optimal parameter targets (temperature setpoints, pH targets, centrifugation parameters), critical process parameters to monitor most closely (based on the specific plasma lot characteristics), and predicted yield and quality outcomes (enabling manufacturing scientists to set appropriate in-process testing schedules). Batch outcome prediction: before each batch began, the AI predicting: expected yield (protein recovery per litre of plasma), potential quality issues (based on input plasma characteristics and planned process parameters), and risk factors (parameters where the specific batch was most likely to deviate from specification). Quality deviation investigation: when batches deviated from specification, the AI analysing all available data to identify root cause — comparing the deviation batch's parameters against historical batches with similar outcomes, identifying the parameter deviations most likely responsible.

Deviation investigation time: from 5 days average to 8 hours (AI providing root cause hypothesis within 2 hours, manufacturing scientists validating within 6 hours). (3) R&D AI: accelerating drug development. Formulation screening: AI analysing the company's formulation database (4,200 historical formulations with stability and performance data) to predict: drug-excipient compatibility for new molecules, optimal formulation composition (vehicle, preservative, buffer, tonicity agent), and predicted stability profile (shelf life estimation based on molecular properties and formulation composition). Formulation screening reducing physical experiments by 62 percent — the AI narrowing candidate formulations from 200+ to 20-30 for physical testing, dramatically reducing development time and cost. Literature intelligence: Llama 3 70B processing scientific literature — 12,000+ papers annually across the company's therapeutic areas (plasma therapies, hospital injectables, dermatology).

The AI extracting: competitive intelligence (competitor pipeline updates, regulatory submissions, clinical trial results), scientific advances (new purification techniques, novel excipients, stability enhancement methods), and safety signals (published adverse events relevant to the company's products — feeding into pharmacovigilance). Literature processing: from 3-week review cycle (team of 8 scientists) to continuous AI processing with weekly synthesised reports reviewed by scientific team in 4 hours. Clinical trial analysis: AI processing interim clinical trial data — identifying enrollment trends, protocol deviations, and early efficacy signals. The model understanding clinical trial terminology and regulatory reporting requirements (ICH E6 R2, EU CTR 536/2014). (4) Pharmacovigilance AI: safety signal detection. Case processing: AI processing incoming adverse event reports (ICSRs — Individual Case Safety Reports) from multiple sources: spontaneous reports from healthcare professionals and patients, literature case reports, clinical trial adverse events, and post-marketing study reports.

The AI extracting MedDRA-coded events, assessing causality, and populating the company's safety database — reducing case processing time from 45 minutes to 12 minutes per case while maintaining data quality validated through parallel processing (AI and human processing 500 cases independently, comparing outputs). Signal detection: LLM-enhanced signal detection analysing narrative case descriptions — identifying safety patterns from free-text descriptions that statistical methods (which operated on coded data only) missed. The AI identifying 8 potential safety signals in the first 12 months that traditional statistical methods had not detected — 3 of which were subsequently confirmed as valid signals requiring regulatory action. PSUR automation: AI generating draft periodic safety update reports — compiling case data, benefit-risk assessment summaries, and regulatory context. PSUR drafting time: from 6 weeks to 8 days (AI generating comprehensive draft in 2 days, medical writers editing and finalising in 6 days).

(5) GMP model governance: pharmaceutical-grade AI management. Validated system lifecycle: every aspect of the AI platform managed through pharmaceutical validation lifecycle — requirements, specifications, qualifications, ongoing monitoring, periodic review, and decommissioning procedures. Change control: any model update, parameter change, or configuration modification processed through the company's pharmaceutical change control system — risk assessment, impact analysis, testing, approval, and implementation documentation. Audit trail: complete audit trail meeting AEMPS inspection requirements — every AI interaction logged with: user identification, timestamp, input data reference, AI output, human decision (accept/modify/reject), and rationale. AEMPS inspection readiness: documentation organised for regulatory inspection — the company able to demonstrate to AEMPS inspectors exactly how AI was used in manufacturing decisions, what controls were in place, and how the system was validated.