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Synthaverse AG

Modernising the Onko BCG facility

An oncology and urology GMP manufacturer validating its Onko BCG facility with legacy modernisation and data integration

Industry
Pharmaceuticals (Oncology and Urology GMP Manufacturing)
Headquarters
Heilbronn, Germany
Public information as of
January 2026

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

Strategic priorities

Synthaverse AG is a German pharmaceutical manufacturer operating the Old Plant (1970s vial production) and a new Onko BCG aseptic manufacturing facility. The company is pursuing accelerated validation of the Onko BCG facility to compress the 2027 commercialisation timeline and capture Western European export markets in France, Germany and Switzerland. The immediate priority is surviving the Gap Years (2024-2027) by extracting maximum reliability from legacy infrastructure while completing regulatory validation of the 10x capacity scale-up. Key strategic initiatives include modernising the Old Plant for vial production, achieving GxP validation for the Dara-automated aseptic line, and using the new CBR to develop improved BCG vaccines.

The defining challenge is legacy infrastructure reliability. The Old Plant dates to the 1970s with process monitoring still dependent on physical gauges and manual paper records. Equipment failures during critical production campaigns create schedule disruptions and inventory shortages that push timelines further into the Gap Years. The Dara automated line requires systematic validation across OQ, PQ and CSV phases, with regulatory submissions for EU and WHO prequalification requiring complete validation documentation packages that manual processes cannot produce efficiently.

On the data side, the Old Plant data is captured on paper while newer facilities use digital systems. This creates fragmented data environments that prevent unified operational visibility and slow down the regulatory submissions that are critical to the 2027 commercialisation timeline.

Challenges we see

  • Operations Manufacturing

    Legacy Old Plant reliability creating production campaign failures

    The Old Plant dates to the 1970s with process monitoring dependent on physical gauges and manual paper records. Equipment failures during critical production campaigns create schedule disruptions and inventory shortages that extend the Gap Years timeline.

    Where equipment monitoring is physical gauges, the first sign of an equipment problem is a production deviation or failure. Modern monitoring means equipment health is visible before the failure occurs.

  • Compliance Regulatory

    Dara automated line validation documentation burden exceeding team capacity

    The Dara automated aseptic line requires systematic OQ, PQ and CSV validation across multiple phases. Regulatory submissions for EU and WHO prequalification require complete validation documentation packages that manual processes cannot produce efficiently.

    Where validation documentation is manual, the timeline for EU and WHO submissions is driven by documentation speed rather than by manufacturing capability. Automated documentation means the validation package is assembled from system data.

  • Digital Integration

    Fragmented data environments preventing unified operational visibility

    Old Plant data is captured on paper while newer facilities use digital systems. Fragmented data environments prevent unified operational visibility and slow the regulatory submissions that are critical to the 2027 commercialisation timeline.

    Where data is fragmented by facility and format, the management dashboard is assembled from multiple sources manually. A unified data platform means the operational picture is visible without manual reconciliation.

  • Operations Bioprocess

    Onko BCG scale-up process risk creating batch failure exposure

    Scaling from current production to 10x capacity in the Onko BCG facility creates biological process risks — contamination, process parameter deviations — that are not yet characterised at commercial scale.

    Where scale-up risks are not characterised in simulation, the first full-scale batches carry unknown risk. A digital twin means the scale-up envelope is characterised before the first commercial batch runs.

  • Digital Integration

    R&D data silos preventing cross-functional analytics for vaccine development

    R&D data for improved BCG vaccine development is held in separate systems from manufacturing data. Cross-functional analytics that could accelerate vaccine development are not possible without data integration.

    Where R&D and manufacturing data are in separate systems, the insights that would come from cross-functional analysis are invisible. Unified data environment means the full dataset is available for analytics.

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. Legacy asset monitoring and reliability platform for Old Plant

    The 1970s Old Plant relies on physical gauges and manual paper records for process monitoring. Equipment failures during critical campaigns create schedule disruptions that extend the Gap Years and deplete cash reserves.

    Deploy modern monitoring and reliability platforms for the Old Plant that provide real-time visibility into equipment health, enabling predictive maintenance that reduces unplanned downtime and extends the legacy assets' productive life.

    • Synthaverse operational assessment, 2025

  2. Automated validation documentation for Dara line EU and WHO submissions

    Dara automated line OQ, PQ and CSV validation requires complete documentation packages for EU and WHO prequalification submissions. Manual documentation cannot keep pace with the validation timeline.

    Implement automated validation documentation that generates EU and WHO submission packages from validation test results, accelerating the prequalification submissions that open Western European market access.

    • Synthaverse regulatory affairs assessment, 2025

  3. Cross-facility data integration platform for unified operational visibility

    Old Plant paper records and Onko BCG digital systems create fragmented data environments. Management cannot see unified operational visibility, and regulatory submissions are slowed by data reconciliation.

    Build a cross-facility data integration platform that connects the Old Plant, Onko BCG and Dara line data environments into a unified operational picture, accelerating both management decision-making and regulatory submissions.

    • Synthaverse data infrastructure assessment, 2025

  4. Digital twin for Onko BCG scale-up risk characterisation

    Scaling to 10x Onko BCG capacity creates biological process risks that are not characterised at commercial scale. Failed batches during scale-up would further delay the 2027 commercialisation timeline.

    Develop a digital twin of the Onko BCG process that characterises the scale-up envelope in simulation before full commercial batches run, identifying risk zones and safe operating parameters.

    • Synthaverse scale-up risk assessment, 2025

  5. Unified R&D-manufacturing data environment for vaccine development

    R&D data for improved BCG vaccine development is in separate systems from manufacturing data. Cross-functional analytics that could accelerate vaccine development require manual data exchange.

    Create a unified R&D-manufacturing data environment that connects vaccine development data with manufacturing data, enabling the cross-functional analytics that accelerate the improved BCG vaccine programme.

    • Synthaverse R&D capability assessment, 2025

What we'd propose

  • Digital CDMO

    Legacy asset monitoring and reliability platform for Synthaverse Old Plant

    We deploy a legacy asset monitoring and reliability platform for Synthaverse's Old Plant that provides real-time visibility into critical equipment health, enabling predictive maintenance that reduces unplanned downtime and extends the productive life of 1970s-era manufacturing assets.

    • Modern sensor deployment on legacy equipment

      Critical Old Plant equipment instrumented with modern sensors

      Deploy modern sensors — vibration, temperature, pressure — on critical Old Plant equipment that provides real-time health data for predictive maintenance algorithms, bridging the gap between physical gauges and modern monitoring.

    • Predictive maintenance analytics

      Equipment failures predicted before they cause production disruptions

      Build predictive maintenance analytics that process sensor data to identify equipment health degradation before it causes failures, enabling scheduled maintenance that avoids unplanned production disruptions.

    • Production scheduling optimisation

      Maintenance windows aligned with production campaign schedules

      Integrate predictive maintenance data with production scheduling to align maintenance windows with campaign completion, ensuring equipment is available when needed without unnecessary maintenance downtime.

    • Unplanned downtime reduced by predictive maintenance that identifies equipment health issues before failures occur.
    • Gap Years timeline supported by extending Old Plant productive life without major capital investment.
    • Production campaign reliability improved by systematic equipment health management.

    We've done this before

    See how we do Digital CDMO
  • Digital Lab

    Automated validation documentation for Dara line regulatory submissions

    We implement automated validation documentation for Synthaverse's Dara automated line that generates EU and WHO prequalification submission packages from validation test results, accelerating the regulatory submissions that open Western European market access.

    • Validation test data automation

      OQ, PQ and CSV test results captured and documented automatically

      Build automated validation test data capture that records OQ, PQ and CSV test results directly from test equipment and documentation systems, eliminating manual transcription errors and accelerating documentation assembly.

    • EU and WHO submission package assembly

      Regulatory submissions assembled from validated system data

      Implement automated EU and WHO prequalification submission package assembly that compiles the complete validation documentation — protocols, results, deviations, conclusions — directly from the validation data platform.

    • Validation lifecycle management

      All validation documents versioned and traceable

      Deploy validation lifecycle management that maintains version control, traceability and audit trails for all validation documents, ensuring the validation state is always visible and audit-ready.

    • Regulatory submission timeline accelerated by automating the documentation assembly that manual processes cannot keep pace with.
    • Validation documentation quality improved by eliminating manual transcription errors.
    • Western European market access unlocked by completing EU and WHO prequalification submissions on schedule.

    We've done this before

    See how we do Digital Lab
  • Digital CDMO

    Cross-facility data integration platform for Synthaverse operations

    We build a cross-facility data integration platform for Synthaverse that connects the Old Plant paper records, Onko BCG aseptic facility and Dara automated line into a unified operational data environment, providing management visibility and regulatory submission acceleration.

    • Old Plant paper-to-digital data capture

      Paper production records captured and digitised automatically

      Build automated data capture from Old Plant paper production records, converting manual entries into structured digital data that flows into the integration platform without manual keying.

    • Unified operational dashboard

      All facilities visible on one management dashboard

      Deliver a unified operational dashboard that consolidates production status, quality metrics and capacity data from the Old Plant, Onko BCG and Dara line into a single management view.

    • Regulatory submission data sourcing

      Submission data assembled directly from the integration platform

      Implement regulatory submission data sourcing that pulls the required production and quality data directly from the integration platform, reducing the manual data gathering that precedes regulatory submissions.

    • Management decision-making improved by unified operational visibility across all facilities.
    • Regulatory submission speed improved by eliminating manual data reconciliation between facilities.
    • Cross-facility performance benchmarking enabled by standardised data in one platform.

    We've done this before

    See how we do Digital CDMO
  • Digital CDMO

    Digital twin for Onko BCG scale-up risk characterisation

    We develop a digital twin of Synthaverse's Onko BCG manufacturing process that characterises the 10x scale-up envelope in simulation before full commercial batches run, identifying biological process risk zones and safe operating parameters for the commercial manufacturing campaign.

    • Bioprocess model development at commercial scale

      Scale-up physics captured in a validated simulation model

      Build a bioprocess model of the Onko BCG production that captures the physics and biology of the scale-up — mass transfer, oxygen demand, contamination risk — in a validated simulation model.

    • Scale-up risk zone identification

      Safe operating envelope characterised before first commercial batch

      Run systematic simulation studies across the 10x scale-up design space to identify the operating envelope boundaries where contamination risk and process parameter deviations are most likely, providing empirical guidance for commissioning protocols.

    • Live model updating from commissioning data

      Digital twin refined as actual scale-up data becomes available

      Implement live model updating that incorporates initial commissioning data into the digital twin, refining the scale-up predictions as actual operational data accumulates and improves the model's accuracy.

    • Scale-up risk reduced by characterising the operating envelope in simulation before commercial batches run.
    • Commissioning time shortened by validated operating protocols derived from digital twin studies.
    • 2027 commercialisation timeline protected by preventing scale-up failures that would delay market entry.

    We've done this before

    See how we do Digital CDMO
  • Enterprise AI

    Unified R&D-manufacturing data environment for BCG vaccine development

    We create a unified R&D-manufacturing data environment for Synthaverse that connects vaccine development data with manufacturing data, enabling cross-functional analytics that accelerate the improved BCG vaccine programme.

    • R&D data platform for vaccine development

      All vaccine development data in one accessible environment

      Build an R&D data platform that consolidates all vaccine development data — formulation studies, stability data, preclinical results — into a single accessible environment for the CBR research team.

    • Cross-functional analytics capability

      Manufacturing data accessible to R&D for vaccine improvement

      Implement cross-functional analytics that gives R&D researchers access to relevant manufacturing data and gives manufacturing teams visibility into R&D pipeline status, enabling the collaboration that accelerated vaccine development requires.

    • CBR new product introduction workflow

      R&D pipeline connected to manufacturing scale-up pathway

      Build a CBR new product introduction workflow that connects vaccine development data to the manufacturing scale-up pathway, ensuring the data package required for production transfer is generated during the R&D phase.

    • Vaccine development velocity improved by giving R&D access to the full manufacturing dataset.
    • Manufacturing transfer risk reduced by generating the transfer data package during the R&D phase.
    • Cross-functional collaboration enabled by a shared data environment that both teams trust.

    We've done this before

    See how we do Enterprise AI

Digital maturity: today and target

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

Source: A4BEE analysis of public sources
Legacy Asset Monitoring 15 → 75
Old Plant equipment monitoring depends on physical gauges and manual paper records. No modern sensors, predictive maintenance or digital monitoring are deployed.
Validation Documentation 20 → 80
Dara line validation documentation is manual. No automated validation documentation system is deployed. EU and WHO submissions depend on manual assembly.
Cross-Facility Data Integration 15 → 75
Old Plant paper records and Onko BCG digital systems are not integrated. No cross-facility data platform exists. Unified operational visibility is absent.
Scale-Up Risk Management 20 → 80
Onko BCG 10x scale-up risks are not characterised in simulation. No digital twin exists for the Onko BCG process.
R&D Data Environment 25 → 70
R&D data is held in separate systems from manufacturing. No unified R&D-manufacturing data environment is deployed. Cross-functional analytics are not possible.
Regulatory Affairs Infrastructure 25 → 75
Regulatory submissions depend on manual data gathering and document assembly. No automated submission data sourcing is deployed.

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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 Synthaverse AG, 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].