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AAX

Trustworthy data for antibody screening

A high-throughput antibody epitope-mapping platform handling massive parallel screening data for pharma partners

Industry
Antibody Characterisation
Headquarters
Stockholm, Sweden
Public information as of
January 2026

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

Strategic priorities

AAX is a Swedish biotech operating the Seqitope platform for high-throughput antibody epitope mapping — characterising up to 100 antibodies in parallel using mutagenic scanning. The Opti-mAb platform stabilises scFv formats for CAR-T and bispecific antibody applications. AAX is located within the Karolinska Institutet ecosystem in Stockholm and collaborates with Daiichi Sankyo, Vascurie, and other pharmaceutical partners on antibody validation programmes.

The core production asset is an advanced automation robot that runs parallel mutagenic scanning experiments. The platform generates large data volumes per run, but the data flow from instrument to scientist currently involves manual export steps, fragmented file formats, and spreadsheet-based analysis — scientists wait days for insights that could be available in hours if the pipeline were automated.

The strategic priority is to demonstrate to pharmaceutical partners that the Seqitope data meets the evidentiary standard required for clinical antibody development and regulatory submissions. This requires both a validated digital workflow (GAMP5, ALCOA+) and a data pipeline that produces a complete, defensible audit trail from the robotic platform to the regulatory submission.

Challenges we see

  • Digital Integration

    Data pipeline gaps between the robotic platform and the scientist

    The advanced automation robot generates massive parallel mutagenic scanning data, but the data flows through fragmented exports and manual spreadsheet analysis rather than a unified pipeline. Scientists receive results days after the run completes rather than having real-time visibility into what the platform is producing.

    Where robotic platform data requires manual extraction before analysis can begin, the time from run completion to insight delivery is measured in days. For a pharma partner with an active antibody validation programme, that delay in the feedback loop slows down the iterative optimisation cycle that AAX's platform is meant to accelerate.

  • Digital Integration

    Manual hand-offs breaking the process orchestration chain

    Between sample preparation, robotic scanning, and data retrieval, manual interventions create bottlenecks that prevent the platform from operating at its designed throughput. The gap between the robotic platform's mechanical speed and the human speed of the surrounding workflow determines overall cycle time.

    When the robotic platform can process 100 antibodies per run but the surrounding workflow requires manual step-by-step oversight, the practical throughput is determined by the slowest step — which is the human one, not the robotic one.

  • Compliance Regulatory

    Paper-based or unvalidated digital records for regulatory submissions

    For the Seqitope epitope mapping data to be used in clinical antibody development and regulatory submissions, the data must meet FDA/EMA evidentiary standards with a complete digital audit trail. Current practices do not include validated electronic records for all steps of the characterisation workflow.

    Where paper notebooks or unvalidated digital systems record how binding sites were determined, the epitope mapping data cannot be used as the primary evidence in a regulatory submission or patent defence — it can only support it, which limits the IP value of the platform.

  • Operations Manufacturing

    Manufacturing instability of scFv formats at clinical scale

    Single-chain variable fragments are inherently prone to aggregation at clinical-scale production. While the Opti-mAb platform provides a molecular stabilisation approach, the physical manufacturing process requires rigorous monitoring of environmental parameters — temperature, pH, shear forces — that affect stability during scale-up.

    When the monitoring data from a manufacturing run at clinical scale is not connected to the platform's characterisation data, the correlation between the molecular profile of an scFv candidate and its real-world stability behaviour remains a knowledge gap rather than a predictive capability.

  • Digital Operations

    Data sharing across organisational boundaries without Zero Trust Architecture

    AAX shares high-resolution molecular data with partners including Vascurie, ToxoTech, and Daiichi Sankyo. The current data sharing mechanism does not implement a Zero Trust Architecture — each collaboration uses whatever transmission method is most convenient rather than a governed, auditable pipeline.

    When high-resolution epitope mapping data moves between organisations without a governed pipeline, the integrity of the received data cannot be independently verified by the receiving partner. This creates a trust gap that limits the depth of collaborative validation programmes.

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. Automated data pipeline from robotic platform to scientist

    The advanced automation robot generates massive parallel mutagenic scanning data, but this information flows through fragmented exports and manual spreadsheets rather than a unified pipeline. Scientists wait days for insights that should be available in real-time.

    Implement an Industrial Data Platform with automated data pipelines that contextualise scanning data as it is generated, enabling immediate visualisation and AI-ready analytics for pharmaceutical partners — collapsing the insight delivery time from days to hours.

    • AAX Seqitope platform description and automation architecture
    • Daiichi Sankyo collaboration scope documentation

  2. Process orchestration for the robotic platform

    The robotic platform operates with manual hand-offs between sample preparation, scanning, and data retrieval, creating bottlenecks that prevent achieving the true high throughput that the platform's hardware promises.

    Deploy a Process Orchestration Layer that coordinates all steps of the antibody characterisation workflow, from sample input to data output — eliminating manual interventions and ensuring the robotic platform's throughput advantage is realised in practice.

    • AAX high-throughput characterisation workflow documentation
    • Seqitope platform operational throughput analysis

  3. GxP-compliant digital workflows for regulatory submissions

    Current data management practices lack the validated digital audit trails required for FDA/EMA clinical antibody development submissions. Epitope mapping data cannot establish a defensible IP position without proper documentation of how binding sites were determined.

    Implement GAMP5-compliant digital workflows and paperless systems that ensure 100 percent data traceability from the sensor to the regulatory submission, enabling audit-ready documentation for clinical antibody development and IP defence.

    • FDA/EMA evidentiary standards for antibody characterisation data
    • Seqitope IP defence requirements, regulatory submission roadmap

  4. MTP-based modular lab equipment integration

    AAX's location within the Karolinska ecosystem means shared lab equipment alongside company-specific IP, creating data silo challenges. Multiple device types and protocols require custom integration for each new piece of equipment.

    Implement Module Type Package standards enabling Plug and Produce modularity, so that laboratory equipment can be added or reconfigured without custom engineering for each device — accelerating the integration of new characterisation tools into the platform.

    • Karolinska Institutet ecosystem equipment sharing framework
    • AAX laboratory equipment inventory and integration status

  5. Digital sustainability tracking for Nordic ESG requirements

    As part of the Nordic life sciences sector, AAX faces increasing pressure to meet ESG goals around energy efficiency and water usage. Without digital tracking of lab processes, there is no granularity to optimise the carbon footprint of high-throughput screening runs.

    Deploy process monitoring capabilities that track energy consumption, water usage, and reagent consumption per characterisation run — building a sustainability evidence base that supports ESG reporting to Nordic investors and meaningful innovation partners.

    • Nordic ESG reporting requirements for life sciences
    • High-throughput screening energy intensity benchmarks

What we'd propose

  • Enterprise AI

    Industrial Data Platform for High-Throughput Antibody Characterisation

    We build an ontology-based data lakehouse that automatically ingests, contextualises, and processes massive parallel mutagenic scanning data from AAX's robotic platform — transforming raw sensor readings into AI-ready datasets that scientists can query in real time rather than waiting for manual data exports.

    • Automated robotic platform data ingestion

      Scanner data loaded in real time, not by hand

      Deploy a validated middleware layer between the robotic platform's data outputs and the centralised data lake that automatically extracts, normalises, and ingests scanning data in near-real-time — eliminating the manual export step that currently delays analysis by days.

    • Ontology-driven data contextualisation

      Scanning data labelled and linked automatically

      Build an ontology layer that maps each data point from the mutagenic scan to its experimental context — antibody identity, scan parameters, reagent batch, operator — so that any downstream analysis query can retrieve the complete provenance chain without manual data assembly.

    • Real-time dashboard for pharma partners

      Partners see run progress as it happens

      Deliver a partner-facing portal that provides real-time visibility into run status, preliminary results, and data quality metrics as the robotic platform executes — enabling pharma partners to begin their review process immediately upon run completion rather than waiting for a report.

    • The insight delivery time collapses from days to hours, enabling pharma partners to begin their validation analysis immediately after a run completes rather than waiting for a manually compiled data package.
    • The complete provenance chain for each data point is captured automatically, providing the evidentiary basis for IP claims without requiring retrospective documentation effort.
    • The data platform scales to handle the volume of 100-antibody parallel runs without proportional increases in data management headcount.

    We've done this before

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  • Digital Lab

    Laboratory Process Orchestration System

    We design and implement a Process Orchestration Layer that coordinates the entire antibody characterisation workflow — from sample preparation through robotic scanning to data delivery — eliminating manual hand-offs and ensuring the robotic platform's hardware throughput advantage is fully realised in practice.

    • End-to-end workflow sequencing

      The whole workflow orchestrated, not just the robot

      Configure a workflow engine that manages the sequence, dependencies, and status tracking for every step of the characterisation run — sample registration, reagent allocation, robotic execution, data acquisition, QC checks — so that the entire workflow runs without manual step-by-step oversight.

    • Sample preparation automation

      Sample prep keeps pace with the robot

      Integrate the upstream sample preparation stage with the robotic platform's queue management, ensuring that the next sample batch is prepared and queued while the current run is executing — eliminating the idle time between runs that manual preparation currently introduces.

    • Data retrieval and filing automation

      Results filed automatically at run completion

      Automate the data retrieval step that follows each robotic scan, so that results are automatically filed into the correct experimental record and LIMS entry rather than waiting for a manual export and upload step.

    • The practical throughput of the characterisation platform increases because the bottleneck shifts from the human workflow to the robotic platform — where it should be.
    • The per-antibody cost of characterisation decreases as idle time between runs is eliminated and the platform runs closer to its designed continuous throughput.
    • The workflow orchestration system provides an auditable record of what happened at each step, creating the execution record that regulatory inspectors require.

    We've done this before

    See how we do Digital Lab
  • Digital Lab

    GxP-Compliant Digital Workflow Implementation

    We deploy validated paperless workflows meeting GAMP5 and ALCOA+ requirements for clinical antibody development — covering electronic signatures, audit trails, and complete data traceability from the characterisation sensor to the regulatory submission, enabling AAX to defend its Seqitope IP position in regulatory proceedings.

    • GAMP5-compliant LIMS deployment

      Electronic records replacing paper notebooks

      Implement a validated LIMS with 21 CFR Part 11 compliance covering the complete Seqitope characterisation workflow — electronic sample registration, instrument integration, result recording, and electronic batch records — so that every characterisation run produces an audit-ready record by default.

    • Electronic signature and approval workflows

      IP defence package assembled automatically

      Configure electronic signature workflows for scientific review and QA release of characterisation data, so that the complete IP defence package — run record, QC review, sign-off — is assembled automatically as each run concludes rather than compiled retrospectively.

    • Regulatory submission data package generation

      Submission packages built from validated records

      Build a submission data package generator that pulls validated characterisation records from the LIMS and formats them into the structure required by FDA/EMA technical sections — reducing the manual assembly effort that currently makes submission timelines unpredictable.

    • The Seqitope epitope mapping data meets the evidentiary standard required for regulatory submissions and patent defence, establishing the full IP value of the platform for partnership negotiations.
    • Regulatory submission packages are generated from validated records rather than assembled by hand, making submission timelines predictable and reducing the quality risk that comes from retrospective data compilation.
    • The GAMP5-validated LIMS satisfies the audit trail requirements that pharma partners require before they will rely on AAX data in their own regulatory submissions.

    We've done this before

    See how we do Digital Lab
  • Digital CDMO

    MTP-Based Modular Laboratory Integration

    We implement Module Type Package standards across AAX's characterisation laboratory equipment, enabling vendor-agnostic Plug and Produce integration that allows new analytical instruments to be added to the platform without custom engineering per device — accelerating the integration of new capabilities into the Seqitope workflow.

    • MTP equipment package library

      Existing equipment described in standard format

      Create MTP-compliant equipment descriptions for the existing characterisation instruments — covering parameter interfaces, alarm definitions, and material connections — so that each instrument can be recognised and interfaced by the orchestration layer without custom code.

    • New instrument rapid onboarding

      New devices operational within days of installation

      Build a fast-track MTP onboarding workflow that allows a new analytical instrument to be integrated into the Seqitope characterisation workflow within days rather than weeks — reducing the engineering cost of platform capability expansion.

    • Cross-site equipment sharing interface

      Karolinska ecosystem equipment connected to platform

      Implement MTP-based integration for shared-use equipment within the Karolinska ecosystem, enabling AAX to access and schedule characterisation instruments across organisational boundaries through a governed data interface rather than manual file transfer.

    • Platform capability expansion is accelerated because new instruments can be integrated in days rather than weeks — the bottleneck becomes the instrument's arrival, not the engineering to connect it.
    • The Karolinska ecosystem equipment sharing arrangement becomes operationally manageable rather than requiring bespoke bilateral agreements per shared instrument.
    • The MTP equipment library reduces the maintenance burden of custom integration code as the platform evolves.

    We've done this before

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  • Digital Lab

    Laboratory Digital Twin for Process and Sustainability Optimisation

    We build a digital twin of AAX's characterisation laboratory that simulates process behaviour, tracks environmental parameters in real time, and enables predictive optimisation of both scientific outcomes and sustainability metrics — providing the granular data foundation for ESG reporting to Nordic partners and investors.

    • Environmental parameter tracking per run

      Temperature, energy, and water per characterisation run

      Deploy IoT sensors on the characterisation instruments that record environmental parameters — energy consumption, cooling demand, reagent usage — at the per-run level, providing the granularity needed to identify optimisation opportunities and generate per-run sustainability metrics.

    • Process simulation for scFv stability

      Stability predictions before clinical-scale runs

      Build a process model of the scFv stability behaviour under different environmental conditions, calibrated against the Opti-mAb platform characterisation data — enabling prediction of stability behaviour at clinical scale before a manufacturing run is commissioned.

    • ESG reporting dashboard

      Nordic ESG reports populated from instrument data

      Deliver a sustainability dashboard that aggregates per-run environmental metrics into the format required by Nordic ESG frameworks, providing evidence for meaningful innovation claims and reducing the manual effort of sustainability reporting.

    • Per-run sustainability metrics are tracked automatically, enabling AAX to demonstrate concrete ESG progress rather than estimated facility-level figures.
    • The scFv stability model provides predictive guidance for clinical-scale manufacturing runs, reducing the empirical testing required before committing to a production campaign.
    • ESG reporting effort is reduced because the dashboard auto-populates from instrument data rather than requiring manual data collection.

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

Source: A4BEE analysis of public sources
Data Integration 25 → 80
The robotic platform generates large data volumes per run, but no ontology-driven data pipeline exists to process this information automatically. Scientists currently extract and assemble data manually, creating a multi-day lag between run completion and insight availability.
Process Automation 35 → 85
The robotic platform hardware is installed and operational, but the process orchestration layer that would coordinate sample preparation, robotic execution, and data retrieval is absent. Manual hand-offs between workflow stages prevent the platform from achieving its designed continuous throughput.
Regulatory Compliance 30 → 90
Current data management practices do not include validated electronic records for the complete Seqitope characterisation workflow. Paper notebooks or unvalidated digital records cannot serve as primary evidence in FDA/EMA submissions or IP defence proceedings.
Equipment Interoperability 20 → 75
Each new instrument added to the characterisation platform requires custom engineering to connect to the data environment. The Karolinska ecosystem equipment sharing arrangement has no standardised integration mechanism, limiting the practical scope of cross-organisational collaboration.
Cybersecurity 25 → 80
Data sharing with pharmaceutical partners uses transmission methods that have not been security-hardened under a Zero Trust Architecture. High-resolution epitope mapping data shared with Daiichi Sankyo and other partners lacks integrity verification on receipt.
Sustainability Tracking 15 → 70
No granular energy or water tracking exists at the per-run level. Sustainability reporting relies on facility-level utility data rather than process-level consumption, providing insufficient granularity to demonstrate ESG progress or identify efficiency opportunities.

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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 AAX, 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].