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Mabqi

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 Mabqi's published strategy and is not endorsed by, or produced in cooperation with, Mabqi. Company website

Strategic priorities

Mabqi operates across 4 stated priorities, with the most concrete near-term plan anchored on platform-driven discovery excellence.

use the LiteMab Antibody Discovery Studio with AI-augmented phage and yeast display to deliver fully human antibody leads for challenging targets including GPCRs, ion channels, and pH-sensitive tumor microenvironment binders within accelerated four-month timelines.

Scale the strategic alliance with Abzena to provide smooth discovery-through-manufacturing services, combining LiteMab discovery with Abzena's ThioBridge ADC conjugation and GMP manufacturing to de-risk the development lifecycle for pharma clients.

Progress internal therapeutic assets, led by MQI-201 (anti-TRPV6) for solid tumors and a first-in-class sarcoma antibody, toward clinical readiness to drive enterprise value and attract strategic investment.

Challenges we see

  • Digital Integration

    Joining records across systems

    Mabqi's LiteMab platform generates massive datasets from phage and yeast display campaigns screening libraries of approximately 10 billion clones. Without unified data infrastructure, screening results, binding characterization data, and biophysical property assessments remain fragmented across separate instruments and manual records.

    Fragmented screening data drives delayed lead selection, duplicated experiments, and inability to use AI models effectively for predictive binding and developability analysis.

  • Compliance Regulatory

    Scaling Quality Systems for Clinical-Stage Operations

    As Mabqi transitions from CRO services to advancing proprietary assets like MQI-201 toward Phase I, the company must upgrade from research-grade quality systems to full GMP-compliant frameworks aligned with ALCOA+ principles and EMA/FDA filing requirements.

    Immature quality management infrastructure could delay IND submissions, jeopardize regulatory interactions, and undermine the credibility of clinical data packages needed to attract investors and partners.

  • Operations Manufacturing

    Manual Workflows in Antibody Characterization

    Antibody characterization involving SPR, mass spectrometry, and functional assays requires significant manual data entry and cross-referencing between instruments. With only 17-20 employees handling both CRO projects and internal pipeline work, manual bottlenecks limit throughput capacity.

    Manual characterization workflows increase the risk of transcription errors, inconsistent data formatting, and reduced capacity to handle parallel discovery campaigns for multiple clients and internal programs simultaneously.

  • Operations Integration

    Technology Transfer Reproducibility with Abzena

    The Abzena partnership requires smooth transfer of antibody leads from Mabqi's discovery platform in Grabels to Abzena's development and manufacturing facilities. Ensuring the molecule discovered is the molecule manufactured demands aligned analytical platforms and standardized data handoff protocols.

    Without digitized technology transfer workflows and unified data standards, process deviations during handoff could result in failed developability assessments, delayed timelines, and erosion of client confidence in the integrated service offering.

  • Digital Operations

    Cybersecurity for Proprietary IP and Client Data

    Mabqi's intellectual property, including proprietary library designs, AI selection algorithms, and client-specific discovery data, represents the core of its competitive advantage. As the company expands partnerships and attends investor conferences, the attack surface for IP theft grows.

    A data breach where proprietary library sequences, client screening results, or internal pipeline data could catastrophically undermine Mabqi's competitive position and partnership trust, particularly given its unfunded status and reliance on IP-driven revenue.

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. Fragmented Discovery Data Management

    Mabqi's high-throughput screening campaigns generate diverse datasets from phage display, yeast display, SPR, and AI prediction tools that are stored across disconnected systems, preventing real-time cross-analysis and delaying lead candidate selection decisions.

    Deploy an integrated data platform with automated pipelines connecting all screening instruments and AI models into a unified analytics environment, enabling real-time comparison of binding affinity, developability scores, and biophysical properties across the entire LiteMab discovery workflow.

  2. Paper-Based Quality and Compliance Gaps

    As a lean organization transitioning to clinical-stage operations, Mabqi likely relies on manual documentation and paper-based quality records that do not meet the ALCOA+ data integrity standards required for GMP compliance and regulatory filings supporting MQI-201's clinical advancement.

    Implement a digital Laboratory Execution System (LES) that enforces GMP-compliant workflows, automates data capture from analytical instruments, and provides audit-ready electronic records aligned with EMA and FDA submission requirements.

  3. Manual Antibody Characterization Bottleneck

    With a team of only 17-20 specialists managing both CRO client projects and internal pipeline work, manual data entry and cross-referencing between SPR, mass spectrometry, and functional assay instruments creates throughput limitations that constrain growth capacity.

    Automate characterization data flows from analytical instruments into a centralized platform with real-time KPI dashboards showing binding kinetics, aggregation profiles, and stability metrics, enabling scientists to focus on interpretation rather than data wrangling.

  4. Undigitized Technology Transfer to Abzena

    The strategic Abzena partnership requires transferring complex discovery data packages between organizations, but without standardized digital handoff protocols, technology transfer risks process deviations and delays that undermine the promised gene-to-clinic acceleration.

    Build a digital technology transfer framework with standardized data formats, automated report generation, and shared dashboards that ensure analytical platform alignment between Mabqi's discovery operations and Abzena's development and manufacturing workflows.

  5. IP and Client Data Security Exposure

    Mabqi's proprietary LiteMab library designs, AI algorithms, and confidential client discovery data lack enterprise-grade cybersecurity protections, creating vulnerability as the company expands its partnership network and investor engagement activities.

    Implement a zero-trust security architecture with network segmentation isolating OT laboratory systems from IT networks, encrypted data transmission for partner data exchanges, and identity-based access controls protecting proprietary IP assets.

What we'd propose

  • Enterprise AI

    Integrated Discovery Data Platform

    Deploy a unified data architecture connecting all LiteMab screening instruments, AI prediction models, and characterization tools into a single analytics environment with automated data pipelines and real-time visualization dashboards.

    • 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

    GMP-Ready Digital Laboratory Execution System

    Implement a Laboratory Execution System (LES) that digitizes quality workflows, automates instrument data capture, and enforces ALCOA+ compliance to prepare Mabqi's operations for clinical-stage regulatory requirements.

    • 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

    Automated Characterization Workflow Platform

    Automate the flow of antibody characterization data from analytical instruments to centralized dashboards, freeing scientists from manual data wrangling and increasing throughput capacity for parallel CRO and pipeline programs.

    • 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

    Digital Technology Transfer Framework

    Build a standardized digital data exchange platform between Mabqi and Abzena that ensures smooth transfer of discovery data packages, analytical protocols, and quality documentation across the gene-to-clinic development lifecycle.

    • 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

    Laboratory Cybersecurity and IP Protection Program

    Implement a zero-trust cybersecurity architecture protecting Mabqi's proprietary library designs, AI algorithms, and confidential client data through network segmentation, encrypted communications, and identity-based access 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 Mabqi's own published ambition implies — not a perfect score.

Source: A4BEE analysis of public sources
Data Integration & Interoperability 30 → 80
Screening instruments and AI tools operate in silos; no unified data platform connects phage display, yeast display, and characterization data streams
Laboratory Digitalization 25 → 75
Small team likely relies on manual workflows and paper records for quality documentation; no LES or automated instrument data capture in place
Process Automation 35 → 80
AI-augmented discovery represents advanced capability but characterization and reporting workflows remain largely manual with significant human intervention
Cybersecurity & Data Protection 20 → 70
Lean startup operations likely lack enterprise-grade security architecture; growing partnership and investor exposure increases risk profile
Partner Integration & Collaboration 30 → 85
Abzena partnership announced but digital infrastructure for seamless cross-organization data exchange and technology transfer not yet established
Regulatory & Compliance Readiness 35 → 80
QbD principles and ALCOA+ awareness demonstrated but digital compliance systems needed for clinical-stage regulatory submissions supporting MQI-201

Check this yourself

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Related reading

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 Mabqi, 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].