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Core Biogenesis

Plant-based growth factors, surfaced as data

Industry
Biotechnology – plant-based bioproduction
Headquarters
Strasbourg, France
Public information as of
July 2026

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

Strategic priorities

Core Biogenesis was founded in Strasbourg in 2020 and develops recombinant growth factors and skin-care actives, including Peauvita (EGF) and Peauforia (FGF-2), by expressing human proteins in the seeds of Camelina sativa. Its industrial facility in Strasbourg opened in 2023 after a $10.5M Series A in March 2022, taking the platform from research prototypes to kilogram-scale GMP-grade output. The company also claims a carbon-negative production footprint and a 30 to 50 percent cost advantage over fermentation-based alternatives.

The 2024 award of the L'Oréal Big Bang Beauty Tech Innovation Challenge put Core Biogenesis on a commercial track with tier-one cosmetic partners. CCO Tony Abboud has publicly committed to lead times under two weeks and minimum order quantities as low as 1 kg for the 2026 launch of the Peauvita and Peauforia actives. That is a high-frequency, low-volume order profile sitting on top of a manufacturing process that was run as a startup research operation two years earlier.

The parallel clinical track targets cell therapy and regenerative medicine customers, with manufacturing bound by both US and European regulatory expectations. The FDA offers expedited programs such as RMAT (Regenerative Medicine Advanced Therapy), while the EMA demands longer follow-up and decentralized pharmacovigilance; dual-market supply on a small team requires validated digital systems rather than parallel spreadsheets. The same industrial data backbone is also the route to proving the carbon-negative claim to institutional partners and beauty brands.

Three operational realities shape the digital priorities: greenhouse-to-factory data is siloed, critical process parameters are tracked manually in combination with spreadsheets, and mechanical extraction equipment lacks the integrated sensing that lets a modern fermenter flag drift before it consumes a batch. Each of these is a tractable systems problem on top of an already interesting biology.

Challenges we see

  • Operations Manufacturing

    Linking greenhouse conditions to downstream outcomes

    Core Biogenesis uses Camelina sativa cultivated in controlled greenhouses in the Strasbourg region as its biomanufacturing feedstock. Light spectrum, hydration cycles and soil nutrient levels influence protein expression in the seeds, and that signal has to be carried through to grinding, centrifugation and purification.

    Where upstream variation lands in the seed without a number attached, downstream steps have to assume the worst case. Capturing greenhouse conditions as data against the batch that follows them makes the variation legible and adjustable rather than invisible.

  • Digital Integration

    Closing the IT and OT data gap across the production cycle

    Greenhouse monitoring systems, lab proteomics and production line equipment are tracked in separate systems, with critical process parameters recorded in manual logs and isolated spreadsheets across R&D, greenhouse and industrial sites.

    Where each stage of the process is read in a separate system, the only way to understand a batch is to reconcile what those systems say about it. Bringing the stages under one model turns that reconciliation into a single query.

  • Operations Manufacturing

    Adding sensing to mechanical extraction stages

    The protein recovery process relies on mechanical grinding of lipid-rich oleosomes and subsequent centrifugation. These mechanical stages carry temperature, vibration and pressure signals that current instrumentation does not surface.

    Where a grinding or centrifugation step is run on a fixed recipe rather than on a live signal, the process has to assume the equipment is in its last known good state. Reading the signal at the machine makes that assumption testable.

  • Compliance Regulatory

    Sustaining dual-market regulatory and biosafety evidence

    Core Biogenesis must meet USDA and FDA biosafety rules for genetically engineered crops as well as GMP-grade (Good Manufacturing Practice) data-integrity expectations for clinical cell therapy partners, including GAMP5 (Good Automated Manufacturing Practice) and 21 CFR Part 11, the US rule on electronic records and signatures.

    Where the same process feeds two regulatory regimes, each with its own evidence chain, the documentation work compounds with the market count. Producing evidence from the source systems rather than compiling it for each submission keeps the work proportionate to the science.

  • Operations Operations

    Producing high-mix, low-volume orders on schedule

    The 2024 L'Oréal Big Bang Beauty Tech Innovation Challenge led to a public commitment to lead times under two weeks and 1 kg minimum order quantities for Peauvita and Peauforia. The Strasbourg facility must switch between different protein products to meet that order profile.

    A line configured for a single product serves a single cadence. A line that has to move between products on a short clock needs the changeover time, the recipe and the lot state to be readable by the line rather than held in the team's heads.

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. Reading greenhouse-to-purification as one continuous model

    Greenhouse sensor data, lab proteomics and production line records are not joined together, so operators cannot adjust purification settings to the actual qualities of the incoming seed harvest.

    An ontology-based data platform that names each entity — seed lot, cultivation batch, harvest, purification run — once, and then streams greenhouse, lab and line data into that model, lets a single query relate upstream conditions to downstream outcomes.

    • Core Biogenesis Company Cartography, 2024
    • Core Biogenesis Industrial Data Platform reference architecture

  2. Capturing process parameters as data rather than as log entries

    Critical process parameters in the Strasbourg plant are tracked in manual logs and isolated spreadsheets, which slows batch release and multiplies the surface area for human error in a GMP-grade operation.

    A validated electronic batch record that captures process parameters, timestamps and operator actions with a full audit trail shortens the release path and produces evidence that an inspection can read on its own.

    • Core Biogenesis operational friction analysis, 2024
    • FDA 21 CFR Part 11, US rule on electronic records and signatures

  3. Monitoring grinding and centrifugation equipment in real time

    Grinding and centrifugation stages lack real-time sensing for vibration, thermal fluctuation and equipment health, so temperature spikes during grinding and calibration drift on centrifuges are only noticed after the batch is off the line.

    Retrofitting those lines with vibration, temperature and pressure sensors, and routing the values through an edge layer, lets the equipment flag drift and the team schedule maintenance before a batch is lost.

    • Core Biogenesis operational friction analysis, 2024
    • Core Biogenesis equipment retrofitting brief

  4. Reconfiguring purification lines between protein products

    The Strasbourg facility must switch between Peauvita, Peauforia and future protein products to meet high-frequency, low-volume commercial orders. Current production infrastructure does not support rapid retooling.

    Implementing Module Type Package (MTP) standards around the purification modules lets the line recognize a step from a declared interface rather than a re-engineered integration, so changeover work is moved from the project to the recipe.

    • Core Biogenesis commercial commitments, 2024
    • Module Type Package (MTP) standard reference

  5. Comparing current runs against historical best-run profiles

    Without a centralized data platform, Core Biogenesis cannot compare current production runs against historical best-run profiles, which makes it harder to optimize the production of newer actives entering high-volume commercial production.

    A digital twin of the biological and mechanical process, with reference profiles built from the highest-yielding runs, lets the team see when a current batch is drifting from the best-run envelope and decide where to intervene.

    • Core Biogenesis digital friction analysis, 2024
    • Digital twin maturity model for biotech, 2024

What we'd propose

  • Enterprise AI

    Industrial data platform for greenhouse-to-factory integration

    An ontology-based data platform that unifies greenhouse environmental monitoring, lab proteomics and production line systems into a single data model, with pipelines that make upstream conditions and downstream outcomes queryable in one place.

    • Shared bioproduction ontology

      One agreed set of entities

      Define seed lot, cultivation batch, harvest, purification run, batch and instrument as explicit entities with agreed relationships, so greenhouse, lab and line data load against the same model instead of three separate tables.

    • Sensor and instrument pipelines

      Data from greenhouse to line

      Connect greenhouse climate sensors (temperature, humidity, CO2, light spectrum) and production-line equipment through OPC UA (Open Platform Communications Unified Architecture) or MQTT, with schema validation at the boundary so a bad reading fails loudly rather than silently.

    • Predictive yield analytics

      Correlating upstream and downstream

      Run correlation and prediction models that relate greenhouse conditions and harvest composition to downstream purification yields, and surface the result against the batch that is currently on the line.

    • Greenhouse, lab and line data load against one model instead of three.
    • Yield deviations are linked to the upstream conditions that produced them.
    • Future products and acquisitions attach to the model rather than triggering another migration.

    We've done this before

    See how we do Enterprise AI
  • Digital Lab

    Validated paperless lab for GMP operations

    A validated digital lab environment that captures critical process parameters as data with electronic signatures and audit trails, replacing the manual logs and isolated spreadsheets that currently sit between the process and the batch release record.

    • Electronic batch and process records

      Process data captured at the source

      Capture process parameters, timestamps and operator actions against the batch record as they happen, with each entry carrying its own signature and audit trail so the release evidence is generated by the process rather than compiled for it.

    • Lab-to-batch record data flow

      Sample to batch record

      Map lab sample and result records onto the manufacturing batch record so the analytical result that justifies a release decision is traceable to the specific run that produced it.

    • 21 CFR Part 11 and GAMP5 compliance

      Evidence that holds up

      Implement electronic signature, versioning, access control and audit-trail handling to 21 CFR Part 11 and GAMP5, so the documentary evidence stands on its own during an inspection.

    • Release waits on the analytical result, not on the paperwork that follows it.
    • Inspection questions are answered from the record itself rather than from a reconstruction.
    • The same validated platform supports both FDA and EMA evidence expectations.

    We've done this before

    See how we do Digital Lab
  • Digital CDMO

    Retrofitting extraction lines with live condition sensing

    An instrumentation and edge-computing package for the grinding and centrifugation stages that surfaces vibration, temperature and pressure as live signals, with predictive maintenance indicators and real-time alerts delivered to the operators on the line.

    • Vibration and thermal monitoring

      Equipment health as a live signal

      Deploy vibration, temperature and pressure sensors on grinding and centrifugation equipment, with the values streamed to a local edge layer so the signal is available at the machine in milliseconds rather than minutes.

    • Predictive maintenance indicators

      Drift flagged before failure

      Build a baseline of equipment behaviour from historical runs and current sensors, then flag deviation against that baseline so engineering knows when a bearing or a calibration is drifting before it costs a batch.

    • Process-aware recipe control

      Recipes that read the line

      Make the recipe for grinding and centrifugation read the live equipment state, so a step pauses or adjusts when a sensor signal crosses a defined threshold rather than continuing on a fixed profile.

    • Thermal spikes and calibration drift are caught before the batch is lost.
    • Unplanned downtime is reduced because maintenance is scheduled against a signal, not a calendar.
    • Process knowledge from the equipment is captured in the data platform rather than in the operator's head.

    We've done this before

    See how we do Digital CDMO
  • Digital CDMO

    Modular production architecture for multi-product manufacturing

    An MTP (Module Type Package) standard-based architecture for the purification lines that lets individual modules be reconfigured or replaced without re-engineering the rest of the line, with an orchestration layer that recognizes each module from its declared interface.

    • MTP module packages

      Standardized purification modules

      Wrap each purification stage as an MTP-compliant module with a declared interface, so a chromatography step, a buffer step or a filtration step can be exchanged or upgraded without rewriting the line orchestration.

    • Plug and produce orchestration

      Modules recognized on connection

      Deploy an orchestration layer that detects a module on the network, reads its declared interface, and integrates it into the active line, so changeover between Peauvita and Peauforia runs becomes a recipe change rather than an integration project.

    • Order-aware batch scheduling

      Schedules driven by incoming orders

      Plan production sequences against incoming orders, available feedstock quality and equipment availability, so the line sequence that best meets the public committed lead times is the one suggested by the scheduling system.

    • Changeover between products is measured in hours rather than days.
    • 1 kg minimum order quantities are achievable without breaking the production cadence.
    • New protein products plug into the same architecture instead of triggering a new line build.

    We've done this before

    See how we do Digital CDMO
  • Digital Lab

    Digital twin for bioprocess simulation and golden batch profiling

    A digital twin of Core Biogenesis's end-to-end production process — from greenhouse cultivation through extraction to purification — built from historical runs and used for virtual experimentation, golden batch profiling and continuous process optimization.

    • Process simulation engine

      Virtual experimentation before the lab

      Build physics-informed and data-driven models of the cultivation, extraction and purification steps so scientists can run scenarios for greenhouse conditions and process parameters against a simulated batch before committing a real one.

    • Golden batch profiler

      Best-run reference, applied to current runs

      Establish the reference profile from the highest-yielding production runs and compare each current batch against that envelope, with the deviation surfaced to the operator as the batch progresses.

    • Carbon and energy impact model

      Sustainability as a measurable number

      Integrate energy, water and material consumption into the digital twin so the carbon-negative production claim is supported by a measured number per batch, not a single annual figure.

    • Process development is partly moved from the wet lab to the simulation.
    • Best-run knowledge is encoded in a profile rather than held by a single person.
    • The carbon-negative claim is backed by per-batch numbers a partner can verify.

    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 Core Biogenesis's own published ambition implies — not a perfect score.

Source: A4BEE analysis of public sources
Data integration 20 → 75
Greenhouse OT data is held separately from lab IT and production line data, with critical process parameters in manual logs and isolated spreadsheets. The work ahead is to name the entities each stage shares and load against one model.
Process automation 30 → 80
Mechanical extraction relies on manual calibration and lacks real-time sensing on grinding and centrifugation equipment. The available path is to instrument the lines and route the values through an edge layer.
Regulatory readiness 25 → 85
Dual FDA and EMA evidence chains for cell therapy clinical partners, plus USDA and FDA biosafety rules for genetically engineered crops, call for GAMP5 and 21 CFR Part 11-aligned digital systems across the production record.
Advanced analytics 15 → 70
There is no predictive analytics correlating greenhouse conditions to yields, no golden batch profiling, and no digital twin of the production process. The data platform and the simulation layer are both ahead of the company.
Flexible manufacturing 25 → 80
Production lines cannot rapidly switch between protein products. Implementing MTP and the surrounding orchestration is the available route to sub-two-week lead times on a 1 kg minimum order quantity.
Cybersecurity 30 → 70
Startup-era digital tools with a growing attack surface as OT systems connect to enterprise IT during scale-up. The available path is a segmented architecture for the Strasbourg factory and a documented OT cybersecurity baseline.

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This is an independent analysis prepared by A4BEE from publicly available information as of July 2026. It reflects A4BEE's own interpretation and opinion, is not affiliated with, endorsed by, or verified with Core Biogenesis, 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].