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Sylentis, S.L.

Sovereign oligonucleotide manufacturing at digital readiness

Industry
Biotechnology (RNAi and Oligonucleotide Therapeutics)
Headquarters
Getafe, Madrid, Spain
Public information as of
January 2026

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

Strategic priorities

Sylentis, S.L. is a Madrid-based RNAi therapeutics company that experienced a defining strategic inflection in 2023 when its Phase III tivanisiran candidate failed, triggering €43 million in impairments. The company pivoted toward its remaining asset: a sovereign oligonucleotide manufacturing capability funded by €21.1 million in IPCEI NextGenerationEU grants. The centrepiece is a newly inaugurated 10,000 square metre facility in Getafe — Spain's first domestically-owned oligonucleotide production plant — designed to serve both internal pipeline needs and third-party CDMO clients while meeting strict EU sustainability mandates under the SYOLIGO programme.

The Getafe plant is a greenfield site: synthesizers, purification units and QC labs are operational, but they lack unified data connectivity. Batch release for CDMO clients currently depends on data flows that are not yet real-time, and the absence of a unified MES creates latency that the CDMO business model cannot afford. Separately, the SirFinder AI platform — Sylentis's in-house siRNA sequence design tool — generates design insights that are not yet connected back to manufacturing execution or experimental feedback loops.

Energy consumption has increased materially with the new facility's operation, creating a direct tension with the sustainability obligations tied to the IPCEI grant. Manual or spreadsheet-based ESG data collection cannot produce the audit-quality evidence that regulators and grant funders require.

Challenges we see

  • Digital Integration

    Connecting manufacturing islands into a unified data layer across the new Getafe plant

    The newly inaugurated 10,000 sqm Getafe facility houses synthesisers, purification units and QC labs as separate operational islands. Each piece of equipment produces data, but there is no centralised layer connecting them.

    Where MES does not span the full plant, batch release for CDMO clients depends on data being assembled manually from each island. A unified data layer means the batch record is the system of record, not a document assembled from reports.

  • Digital Integration

    Closing the loop between SirFinder AI design outputs and manufacturing execution

    SirFinder generates siRNA sequence design insights that inform synthesis runs. The feedback loop from experimental results back to the design model — what works, what does not — is not automated.

    Where experimental feedback does not retrain the design model automatically, each new molecule follows the same discovery path regardless of what prior runs revealed. Closed-loop data flow means the model learns from every synthesis run.

  • ESG Energy

    Producing audit-quality ESG evidence as energy consumption increases at the new facility

    The Getafe plant's operation has materially increased electricity consumption, directly affecting the sustainability credentials required by the €21.1M IPCEI grant. Manual data collection cannot produce the evidence quality that grant auditors require.

    Where ESG data comes from spreadsheets or manual entry, the audit trail is only as strong as the person who filled it in. Automated IoT-based collection means the evidence is contemporaneous with the consumption it records.

  • Compliance Regulatory

    Accelerating GMP batch release as CDMO client expectations converge with AEMPS, EMA and FDA standards

    Oligonucleotide synthesis involves multi-step chemical processes with hazardous reagents, requiring extensive GMP documentation. Paper-based batch records in a 10,000 sqm facility create bottlenecks and introduce data integrity risk that regulators scrutinise closely.

    Where batch records are paper, any regulatory inspection requires reconstructing what happened from handwritten entries. Electronic batch records with automated compliance reporting mean the documentation is complete before the batch is released.

  • Operations R&D

    Identifying trial failure signals before Phase III costs become irreversible

    The tivanisiran Phase III failure cost €43M in impairments and exposed Sylentis to existential financial risk. No predictive technology currently connects pharmacological response modelling to the clinical trial design process.

    Where clinical outcome prediction relies on historical analogues alone, the first clear signal of a trial risk arrives when the trial is already running. Digital twin models of pharmacological response mean failure modes are explored in simulation before a single patient is enrolled.

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. MES and data integration across the Getafe oligonucleotide plant

    The Getafe synthesizers, purification units and QC labs operate as disconnected data silos. CDMO clients expect real-time batch visibility; without a unified MES, that expectation cannot be met and batch release cycles慢下来.

    A unified MES and LIMS integration with OPC UA connectivity creates a single source of truth across the 10,000 sqm site, enabling real-time batch tracking, automated compliance reports and a secure client portal for CDMO visibility.

    • PharmaMar Non-Financial Statement, 2024
    • IPCEI SYOLIGO project requirements

  2. SirFinder AI integration with manufacturing execution and experimental feedback

    SirFinder generates powerful siRNA sequence insights that are disconnected from manufacturing execution and the experimental feedback loop. The design tool does not learn from what synthesis runs actually produce.

    Lab digitalization infrastructure that connects SirFinder computational outputs to synthesis automation, enabling failed experiments to automatically retrain design algorithms and accelerate time-to-market for new molecules.

    • A4BEE strategic account analysis, 2025

  3. IoT-enabled ESG monitoring and sustainability reporting for grant compliance

    The SYOLIGO programme requires documented progress on Process Mass Intensity and energy reduction. Manual or spreadsheet-based data collection cannot produce the audit-quality evidence that IPCEI grant auditors expect.

    IoT-enabled energy and solvent tracking deployed across synthesis and purification equipment, automating ESG reporting and producing a digital audit trail that protects the €21.1M grant and demonstrates meaningful innovation to stakeholders.

    • IPCEI SYOLIGO project requirements

  4. eQMS deployment for GMP documentation and accelerated batch release

    Paper-based batch records in a 10,000 sqm GMP facility create documentation bottlenecks, slow CDMO client delivery and expose Sylentis to FDA warning letter risk for data integrity violations.

    An electronic Quality Management System with automated compliance reporting accelerates batch release, ensures continuous AEMPS, EMA and FDA readiness, and eliminates the manual transcription errors that create data integrity findings.

    • PharmaMar strategic analysis, 2024

  5. Digital twin of pharmacological response for clinical outcome prediction

    Phase III trial failures have cost €43M in impairments. No predictive technology currently models clinical outcomes from molecular and bioprocess parameters before trials are fully deployed.

    Digital twin models that simulate pharmacological response from molecular design and bioprocess parameters, identifying failure modes in simulation before Phase III trials begin and de-risking R&D investment decisions.

    • PharmaMar strategic analysis, 2024

What we'd propose

  • Digital CDMO

    IT/OT convergence for oligonucleotide manufacturing

    We deploy an OPC UA integration layer across synthesisers, purification units and QC labs at the Getafe facility, connecting each machine to a unified MES/LIMS that serves as the single source of truth for batch records, equipment parameters and client visibility data.

    • OPC UA integration layer

      One protocol, every machine, one data layer

      Deploy OPC UA connectivity across synthesisers, purification units and QC analytical equipment, creating a unified machine-to-system communication layer that normalises data formats and eliminates the manual data aggregation that slows batch release.

    • Unified MES/LIMS deployment

      Batch records and instrument data in one system

      Implement a unified MES and LIMS that manages batch records, equipment parameters, material traceability and QC results in a single platform, enabling automated compliance reports and real-time visibility for CDMO clients via a secure portal.

    • CDMO client digital portal

      Real-time batch progress for external clients

      Build a secure web portal giving CDMO clients live access to batch progress, Certificate of Analysis results and equipment utilisation data, differentiating Sylentis from competitors who still deliver batch records as PDF attachments.

    • Batch release cycle time reduced by eliminating manual data aggregation from each production island.
    • CDMO client satisfaction improved through real-time portal visibility, supporting contract renewals and premium pricing.
    • FDA and AEMPS inspection readiness is a property of the MES, not a preparation exercise before each visit.

    We've done this before

    See how we do Digital CDMO
  • Digital Lab

    Lab digitalization closing the SirFinder feedback loop

    We implement the digital infrastructure needed to connect SirFinder computational outputs to synthesis automation, routing experimental results — success and failure — back into the design model's training set so each new siRNA sequence benefits from everything the factory has already learned.

    • Synthesis-to-design data pipeline

      Every run's result feeds back to the model

      Build a data pipeline that routes synthesis parameters, purity results and yield data automatically into SirFinder's training environment, enabling the model to learn from every completed run without manual data transfer.

    • Automated experiment management

      Design, run, measure, repeat — without manual gaps

      Implement an experiment management layer that connects SirFinder sequence recommendations to synthesis batch definitions, tracking each run's parameters against predicted outcomes and flagging divergences for review.

    • Design-to-batch traceability

      Audit trail from sequence to finished batch

      Establish a complete traceability record linking each SirFinder sequence design to the synthesis batch it originated, the equipment used, and the quality results obtained — creating the lineage record that CDMO clients increasingly require.

    • Time-to-market for new siRNA sequences reduced as the design model learns from every synthesis run automatically.
    • CDMO clients gain confidence that Sylentis's R&D is not siloed from its manufacturing capability.
    • Design-to-batch traceability supports regulatory filings that require provenance chains for sequence design decisions.

    We've done this before

    See how we do Digital Lab
  • Digital CDMO

    IoT sustainability monitoring for the SYOLIGO programme

    We deploy IoT sensors across synthesisers, purification units and utility systems to capture energy consumption, solvent use and Process Mass Intensity in real time, feeding an automated ESG dashboard that produces the audit-quality evidence required by IPCEI grant conditions.

    • Energy and solvent metering

      Every unit of consumption measured automatically

      Install IoT metering on major energy consumers and solvent delivery systems, enabling continuous tracking of electricity, heating and chemical consumption against batch production volumes to calculate true PMI in real time.

    • ESG data automation platform

      From meter to report, without spreadsheets

      Build a data platform that aggregates IoT meter data, calculates ESG KPIs automatically against SYOLIGO targets, and produces formatted sustainability reports ready for IPCEI audit submission — eliminating manual spreadsheet assembly.

    • Sustainability performance dashboard

      Green credential tracking for management and funders

      Deliver a management dashboard showing energy per batch, solvent recovery rates and cumulative PMI reduction against SYOLIGO milestones, giving leadership and grant funders a live view of sustainability performance.

    • Grant compliance risk reduced: evidence is produced automatically, not assembled retrospectively for audits.
    • Sustainability performance visible to management in real time, enabling operational adjustments before quarterly targets are missed.
    • Data quality sufficient to support future EU taxonomy reporting and green bond documentation.

    We've done this before

    See how we do Digital CDMO
  • Digital Lab

    eQMS for GMP documentation and batch release acceleration

    We implement an electronic Quality Management System across the Getafe facility, replacing paper batch records with automated GMP documentation that captures synthesis parameters, QC results and material traceability as data, producing compliance reports for AEMPS, EMA and FDA inspections without manual assembly.

    • Electronic batch record system

      GMP records as data, not paper

      Replace paper batch records with eBR workflows that capture synthesis parameters, operator actions and material lot numbers automatically, enabling QA review and batch release without printing, signing and scanning paper travel sheets.

    • Automated compliance reporting

      Inspection-ready reports produced on demand

      Configure the eQMS to produce automated compliance summaries — deviation reports, change control logs, CAPA records — formatted for AEMPS, EMA and FDA inspections, so the inspection team sees the data they need without waiting for manual document preparation.

    • Data integrity controls

      Audit trail on every record, every field

      Implement electronic signatures, audit logging and version control on all GMP records, satisfying FDA 21 CFR Part 11 requirements and eliminating the data integrity findings that lead to warning letters.

    • Batch release time reduced by eliminating paper-based QA review bottlenecks.
    • FDA and AEMPS inspection readiness supported by automated compliance documentation rather than manual preparation.
    • Data integrity risk eliminated: electronic signatures and audit trails are contemporaneous with the events they record.

    We've done this before

    See how we do Digital Lab
  • Enterprise AI

    Pharmacological response digital twin for clinical de-risking

    We develop a digital twin of pharmacological response that ingests molecular design parameters, bioprocess conditions and historical trial data to simulate clinical outcome distributions, surfacing failure mode probabilities before Phase III investments become irreversible.

    • Molecular-to-clinical data pipeline

      Bringing all relevant data into one model

      Ingest SirFinder sequence design data, synthesis bioprocess parameters and published clinical trial outcomes to build a training dataset that connects molecular properties to clinical response probabilities.

    • Clinical outcome simulation

      Failure modes explored before patients are enrolled

      Deploy statistical and ML models that simulate primary and secondary endpoint distributions under different trial design assumptions, enabling decision-makers to explore go/no-go scenarios with quantified uncertainty before committing to Phase III.

    • R&D investment decision support

      Which molecules, which trials, what investment

      Deliver a decision support interface that ranks the current pipeline by probability of regulatory success, estimated development cost and expected peak revenue, helping Sylentis allocate R&D budget to molecules with the strongest clinical profile.

    • Phase III failure risk quantified before irreversible investment — not discovered when the trial is already running.
    • R&D portfolio decisions informed by predicted clinical success probabilities, not just historical intuition.
    • Grant funders gain confidence that public money is allocated to molecules with the strongest evidence base.

    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 Sylentis, S.L.'s own published ambition implies — not a perfect score.

Source: A4BEE analysis of public sources
Manufacturing connectivity 25 → 80
Greenfield facility with individual instruments operational but no unified MES or cross-equipment data layer. OPC UA integration is planned but not yet deployed.
Lab-to-manufacturing integration 30 → 85
SirFinder AI platform is in use for sequence design but is not yet connected to synthesis execution or experimental feedback loops. Data from lab runs does not automatically feed back into design models.
ESG data automation 20 → 75
Sustainability data collection relies on manual or spreadsheet-based entry. IoT metering is not yet deployed across production equipment, and automated ESG reporting is not in place.
Quality documentation 25 → 90
GMP batch records are paper-based in a facility that must meet AEMPS, EMA and FDA standards. eQMS implementation is identified as a priority but has not yet begun.
Predictive analytics 35 → 80
Clinical outcome prediction is not currently modelled. Digital twin development is at the concept stage. No statistical or ML-based R&D decision support is in production.
CDMO digital infrastructure 15 → 85
Early-stage CDMO business model with basic commercial infrastructure. Client portal, automated CoA delivery and real-time batch visibility are not yet available.

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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 Sylentis, S.L., 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].