Main Conference: 29-30 April 2027 | Hamburg, Germany
Cell Line Development & Engineering
Where Host Line Innovation Meets Commercial Manufacturability
Accelerate your journey from sequence to commercial production.
The Cell Line Development & Engineering Track at BioProcess International Europe 2027 brings together leading molecular biologists, cell line engineers, and bioprocess heads to solve expression bottlenecks, control critical quality attributes, and de-risk scale-up from Day Zero.
Go Beyond Theory in Host Cell Engineering
BioProcess International Europe 2027 returns to Hamburg with a dedicated 2-day program focused on transforming cell line engineering from an empirical screening exercise into a predictive, data-driven discipline.
As therapeutic pipelines shift beyond standard monoclonal antibodies toward complex multi-specifics, ADCs, and novel recombinant formats, traditional host lines are pushed to their biological limits. This track delivers actionable case studies and technical breakthroughs across sequence optimization, CRISPR host engineering, epigenetic stability modeling, and predictive machine learning algorithms. Learn how industry leaders are locking in translational fidelity, reducing host cell protein (HCP) burdens at the genetic source, and linking clone selection directly to downstream commercial performance.
Track Themes: A Blueprint for the Cellular Factory
Hear from the senior scientists and academic pioneers who are building the next generation of cell factories.
Translational Fidelity & Sequence Engineering
Maximising Expression Titres: Resolve ribosomal initiation bottlenecks and optimise sequence start-sites to prevent cellular resource waste and eliminate product-related variants.
De-Risking Purification Upstream: Utilise high-resolution mass spectrometry early in clone selection to trace truncated/extended variants and boost downstream polishing step-yields.
Beyond mAbs: Next-Gen Expression Platforms
Adapting Host Lines for Complex Formats: Deploy host engineering strategies to overcome intracellular stress, folding limits, and aggregation in multi-specifics and difficult-to-express molecules.
Platform Retrofitting: Insert sequence-level, platform-agnostic genetic modifications into established host vectors to boost expression while maintaining regulatory acceptance in accordance with ICH Q5E.
Predictive AI/ML Clone Selection
Active Machine Learning in CLD: Move beyond static DoE workflows by leveraging active ML algorithms to evaluate cell-state heterogeneity, phenotypic drift, and growth productivity early.
Predicting Stability from Day Zero to Commercial Scale: Audit genetic and epigenetic markers to forecast long-term phenotypic stability across prolonged bioreactor runs, eliminating late-stage clonal drift.
CQA-Driven Selection & Downstream Integration
Beyond Titre: Shift clone selection criteria from peak titre alone to holistic parameters including secretion competence, homogeneous glycosylation, structural stability, and low HCP burden.
Modular Vector Optimisation: Rapidly screen custom-engineered libraries using automated high-throughput synthesis to compress preclinical timelines into clinical manufacturing.
Explore the full Cell Line Development & Engineering Agenda
Cell Line Development & Engineering: Expert Q&A & Insights
Hear from the senior scientists and academic pioneers who are building the next generation of cell factories.
How does the 2027 CLD track address complex and non-mAb modalities?
The 2027 agenda features dedicated sessions on host cell line engineering for multi-specifics, recombinant conjugates, and non-standard formats. Case studies focus on mitigating intracellular stress, tailoring glycosylation profiles, and deploying platform retrofits that increase secretion rates without compromising structural integrity.
What focus is placed on Machine Learning (ML) and digital clone selection?
Sessions explore applied active machine learning algorithms for early clone screening. Discussions move beyond conceptual DoE to focus on using digital tools to map cell-state heterogeneity, mitigate phenotypic drift, and predict long-term cell health while reducing physical bioreactor runs.
How does this track connect Cell Line Development to Downstream Processing?
A major focus for 2027 is "designing for downstream". Presentations detail how sequence engineering and early clone selection for low Host Cell Protein (HCP) burdens and sequence fidelity directly reduce downstream purification steps and lower chromatography cost of goods (COGs).
How do transposon systems compare to random integration for CHO cell line development?
Unlike random integration, which can lead to unpredictable expression, transposon systems (such as recombinase-mediated integration) target transcriptionally active sites in the host genome. This results in higher clone homogeneity and long-term stability. This technology is a key alternative for accelerating time-to-IND by ensuring predictable high-titre expression early in the development cycle.
What are the benefits of multiplexed CRISPR editing in host cell engineering?
Multiplexed CRISPR strategies allow scientists to simultaneously modify multiple genes, moving beyond simple single-gene knockouts to create "designer" CHO hosts. This approach can engineer complex traits, such as improved cell viability or tailored glycosylation, though developers must carefully navigate technical challenges like off-target effects and delivery efficiency
How does secretome engineering improve downstream processing?
Secretome engineering utilises tools like advanced proteomics and CRISPR to identify and knock out genes coding for problematic Host Cell Proteins (HCPs). By eliminating these impurities at the source (the cell), manufacturers can create a "cleaner" harvest, reducing the number of purification steps required and lowering chromatography resin consumption.
Can Machine Learning (ML) accurately predict clone performance?
Yes, the industry is increasingly using AI/ML models to find top clones from "Day Zero". By analysing early screening data, these models can predict final clone performance and stability, significantly reducing the number of clones that need to be carried forward into downstream development and validating these workflows for GMP environments.