Production on Demand

Recent supply‑chain disruptions have highlighted the urgent need for flexible, local, and autonomous production of active pharmaceutical ingredients (APIs). In response, Novalix, Alysophil, De Dietrich Process Systems, and Bruker have joined forces within the PIPAc (Production Intelligente de Principes Actifs) consortium to redefine how APIs are produced.

PIPAc introduces a next‑generation, AI‑driven continuous manufacturing demonstrator that seamlessly integrates advanced analytics with modular process hardware. The approach combines continuous flow chemistry for precise control and a compact footprint, real‑time in‑line NMR for multi‑attribute process insight, artificial intelligence for adaptive optimization, and additively manufactured reactor hardware tailored to each reaction.

Together, these innovations deliver the first AI‑powered autonomous industrial demonstrator for API production, enabling high‑quality output that is safer by design, more efficient, and ready for the future of decentralized pharmaceutical manufacturing.

Novalix

Expertise in reaction development and optimization of continuous flow chemistry, enabling robust, efficient, and scalable synthetic pathways.

Alysophil

Development of the AI agent based on deep reinforcement learning (DRL), including digital‑twin modeling and implementation of closed‑loop, self‑optimizing process strategies.

De Dietrich Process Systems

Design and engineering of the industrial skid and reactor systems, including additively manufactured (3D‑printed) components, full automation, and integration into a restricted‑access environment with waste destruction capability.

Bruker

Delivery of the process analytical technology (PAT) backbone, centered on the Fourier 80 benchtop NMR spectrometer as the primary in‑line analytical tool, equipped with the InsightMR flow tube and integrated with synTQ software. This combination provides high specificity and quantitative accuracy, enabling direct measurement of critical quality attributes, real‑time process insight, and serving as the reference technique for calibration and validation within the autonomous manufacturing framework.