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InoCure

Updating the operating model

Industry
Biotechnology
Public information as of
January 2026

A4BEE prepared this analysis from publicly available sources. It reflects our own reading of InoCure's published strategy and is not endorsed by, or produced in cooperation with, InoCure. Company website

Strategic priorities

InoCure operates across 4 stated priorities, with the most concrete near-term plan anchored on high-throughput nanofiber production.

Scaling electrospinning from benchtop research to industrial mass production with the InoSPIN MXI platform, targeting pharma, filtration, and tissue engineering markets demanding 99.9% uptime and process reproducibility.

use emulsion electrospinning and microSphere technology to create core-shell nanofibers for controlled release of APIs, immunotherapeutics, and biologics with consistent drug loading profiles.

Positioning proprietary hardware as the solution to the "home-made device" problem in academia, guaranteeing process reproducibility across client laboratories worldwide.

Challenges we see

  • Manufacturing Operational Technology

    Lab-to-Fab Scale-Up Discontinuity

    InoCure's central value proposition is bridging the gap between laboratory research and industrial production. However, the physics of electrospinning make this transition inherently non-linear - process parameters (voltage, flow rate, distance) that work on lab-scale InoSPIN units do not scale linearly to the industrial InoSPIN MXI systems due to electric field interference, solvent evaporation dynamics, and aerodynamic turbulence in larger chambers.

    Significant trial-and-error downtime when moving formulations from R&D to manufacturing; risk loss of client confidence if industrial results fail to match laboratory performance, threatening the company's core value proposition.

  • Quality Control Process Analytics

    Environmental Sensitivity and Process Variability

    Electrospinning is hypersensitive to environmental variables. Relative humidity affects solvent evaporation rate and fiber porosity, while temperature affects polymer viscosity. InoCure explicitly markets the InoCOOL add-on to address non-acclimatized laboratories, acknowledging this fundamental process constraint.

    In high-throughput industrial settings with 400mm web widths, maintaining environmental homogeneity is exponentially harder. A 5% drift in humidity can result in fiber "beading" defects, rendering entire medical-grade membrane batches non-compliant and generating expensive waste of APIs and polymers.

  • Data Integrity Digital Maturity

    Subjective Quality Assessment in R&D

    InoCure's research methodology explicitly states that "stability of the emulsion is evaluated visually, by observing the phase separation." This subjective, manual approach is characteristic of traditional laboratory practices but fundamentally incompatible with Industry 4.0 standards and pharmaceutical client expectations.

    Visual evaluation is unrecordable for audit trails, non-scalable, and introduces human error into critical process parameters. This creates serious compliance risk for pharmaceutical clients requiring GxP data integrity and ALCOA+ principles.

  • Knowledge Management Digital Infrastructure

    Joining records across systems

    InoCure manages simultaneous, complex EU-funded projects (NanoBAT for obesity, transMed for ocular delivery, NanoFEED for livestock, I-DireCT for cancer immunotherapy) involving diverse partners and distinct research domains. Data generated in each project is stored in project-specific repositories with no cross-pollination.

    No "Institutional Memory" accessible via queryable database - the transMed team's learnings about polymer behavior at specific conditions may not transfer to NanoFEED, causing repeated experiments and inefficient resource utilization. Key expert departure causes permanent knowledge loss.

  • Compliance Regulatory

    Regulatory Compliance Burden for Medical Applications

    InoCure produces GMP-compliant nanoparticles and respiratory membranes, and maintains ISO 13485 certification. As they move deeper into medical device and pharmaceutical delivery applications, the EU MDR/IVDR regulatory shift places immense pressure on all supply chain actors for better documentation and traceability.

    Maintaining ISO 13485 and GMP compliance requires rigorous Data Integrity - traceable batch histories, calibration records, operator qualifications. Paper-based or disjointed spreadsheet systems represent significant liability during FDA or Notified Body audits and create economic operator chain compliance gaps.

Opportunities, by urgency and business impact

Each bubble is one opportunity, numbered to match the list below. Further right means it bites sooner; higher means a bigger effect on the business. A bigger bubble means a bigger implementation effort.

Source: A4BEE analysis of public sources

  1. Scale-Up Parameter Optimization

    Process parameters optimized on lab-scale InoSPIN do not transfer directly to industrial InoSPIN MXI, requiring weeks of trial-and-error to achieve stable production due to non-linear physics scaling of electric fields and aerodynamics.

    Implement Digital Twin and physics-based modeling to simulate the electrostatic and aerodynamic environment of the MXI chamber, predicting parameter adjustments automatically and reducing time-to-stable-production from weeks to days while protecting expensive API materials.

  2. Real-Time Environmental Compensation

    Environmental drift in temperature and humidity causes fiber quality defects in industrial production, leading to batch failures and waste of expensive materials including APIs that cannot be recovered.

    Deploy closed-loop control systems with real-time environmental sensing that automatically adjusts process parameters (voltage, collector distance, flow rate) to compensate for environmental changes, maintaining product quality dynamically without operator intervention.

  3. Emulsion Stability Monitoring

    Emulsion electrospinning relies on thermodynamically unstable oil-water mixtures. Over time, droplets coalesce through Ostwald ripening, causing inconsistent drug loading throughout production runs. Current monitoring is "visual evaluation" which is subjective and unauditable.

    Implement computer vision and in-line optical sensors (turbidity/dynamic light scattering) to objectively quantify emulsion stability in real-time, with closed-loop feedback to adjust process parameters when instability is detected, ensuring consistent product from beginning to end of roll.

  4. Unified R&D Knowledge Platform

    Project-specific data silos prevent knowledge transfer between research streams. Experiments may be repeated unnecessarily, and insights from failures are not institutionally captured, lost forever when researchers leave.

    Build a unified R&D Data Lake that ingests experimental parameters and results from all projects, enabling AI-powered mining to surface patterns (e.g., "Show me all formulations that failed at 40% humidity") and convert past failures into future insights while retaining institutional knowledge.

  5. GxP-Compliant Digital Quality System

    Maintaining ISO 13485 and GMP compliance with paper-based or spreadsheet-driven documentation creates audit liability and operational inefficiency while failing to meet EU MDR requirements for economic operators.

    Implement an electronic Quality Management System (eQMS) integrated directly with manufacturing hardware - machines refuse to start batches without completed digital checklists and valid calibration status, providing "Poka-Yoke" mistake-proofing and complete audit trails.

What we'd propose

  • Digital CDMO

    Digital Twin for Electrospinning Scale-Up

    Physics-based simulation platform modeling the electrostatic, aerodynamic, and thermal environment of electrospinning systems to predict optimal parameters for lab-to-industrial scale transitions automatically.

    • OT/IT convergence

      Pull sensor and controller data off the line into a shared data plane in real time.

      DETAIL

    • Batch intelligence

      Golden-batch comparison and deviation detection running on the same data plane.

      DETAIL

    • Production release flow

      Closed-loop between QA, MES, and ERP so batch record review and release follow the data, not the paperwork.

      DETAIL

    • Shorter lead time from data capture to decision.
    • Records that audit on their own, not on inspection day.
    • Scale without adding the same headcount.

    We've done this before

    See how we do Digital CDMO
  • Digital Lab

    Closed-Loop Environmental Control System

    IT/OT convergence solution integrating real-time environmental sensors with adaptive process control to maintain product quality despite ambient condition variations in industrial electrospinning.

    • Unified data backbone

      Connect instruments and LIMS into a single data spine so QC and CDMO records are queryable across sites.

      DETAIL

    • Paperless workflows

      Move lab execution from paper to instrument-captured records with full audit trail.

      DETAIL

    • Continuous QC release

      Review-by-exception dashboards that flag only the records needing scientist attention.

      DETAIL

    • Shorter lead time from data capture to decision.
    • Records that audit on their own, not on inspection day.
    • Scale without adding the same headcount.

    We've done this before

    See how we do Digital Lab
  • Digital Lab

    Computer Vision Quality Monitoring Platform

    AI-powered visual inspection system replacing subjective human assessment with objective, auditable quality measurements for emulsion stability monitoring and fiber morphology classification.

    • Unified data backbone

      Connect instruments and LIMS into a single data spine so QC and CDMO records are queryable across sites.

      DETAIL

    • Paperless workflows

      Move lab execution from paper to instrument-captured records with full audit trail.

      DETAIL

    • Continuous QC release

      Review-by-exception dashboards that flag only the records needing scientist attention.

      DETAIL

    • Shorter lead time from data capture to decision.
    • Records that audit on their own, not on inspection day.
    • Scale without adding the same headcount.

    We've done this before

    See how we do Digital Lab
  • Enterprise AI

    Unified R&D Knowledge Management Platform

    Centralized data lake architecture with semantic search and AI-powered insight extraction, transforming project-specific data silos into organizational intellectual capital retained beyond individual researcher tenure.

    • Ontology layer

      A shared semantic model so lab, process, and quality data describe the same things the same way.

      DETAIL

    • Predictive models

      Models trained on the historical data plane that flag deviations before they become scrap.

      DETAIL

    • Decision surfaces

      Single pane of glass that surfaces model output to the right role at the right moment.

      DETAIL

    • Shorter lead time from data capture to decision.
    • Records that audit on their own, not on inspection day.
    • Scale without adding the same headcount.

    We've done this before

    See how we do Enterprise AI
  • Digital Lab

    Electronic Quality Management System Implementation

    Integrated digital quality infrastructure connecting manufacturing execution with compliance documentation, enabling proactive GxP enforcement through automated workflows and machine-level compliance controls.

    • Unified data backbone

      Connect instruments and LIMS into a single data spine so QC and CDMO records are queryable across sites.

      DETAIL

    • Paperless workflows

      Move lab execution from paper to instrument-captured records with full audit trail.

      DETAIL

    • Continuous QC release

      Review-by-exception dashboards that flag only the records needing scientist attention.

      DETAIL

    • Shorter lead time from data capture to decision.
    • Records that audit on their own, not on inspection day.
    • Scale without adding the same headcount.

    We've done this before

    See how we do Digital Lab

Digital maturity: today and target

Scored out of 100 across six dimensions. The target is what InoCure's own published ambition implies — not a perfect score.

Source: A4BEE analysis of public sources
Process Automation 45 → 85
Hardware is automated (InoSPIN runs recipes) but lacks adaptive closed-loop control; quality assessment remains manual/visual evaluation
Data Integration 30 → 80
Project-based data silos with no centralized knowledge platform; Wi-Fi connectivity exists but no Industrial IoT fleet management platform
Quality Systems 50 → 90
ISO 13485 certified but documentation likely spreadsheet-based; no evidence of integrated eQMS with machine-level enforcement
Analytics & AI 25 → 75
No evidence of predictive modeling or AI-driven optimization; reliance on trial-and-error for scale-up parameter discovery
Digital Twin Capability 15 → 70
No simulation capability for scale-up parameter prediction; physics-based modeling absent; Golden Batch comparison not implemented
Cybersecurity & Connectivity 40 → 80
Wi-Fi enabled devices but no evidence of secure cloud platform; project data sharing with EU partners requires robust security

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This is an independent analysis prepared by A4BEE from publicly available information as of January 2026. It reflects A4BEE's own interpretation and opinion, is not affiliated with, endorsed by, or verified with InoCure, and may be incomplete or inaccurate. All company names and trademarks are the property of their respective owners. To request a correction or removal, contact [email protected].