Periodic Reporting for period 1 - GET-IN (The GEne Therapy INnovation Training Network)
Reporting period: 2023-09-01 to 2025-08-31
Gene therapy has evolved from concept to clinical reality, with several rAAV-based therapies approved and many phase III trials ongoing. CRISPR-based editing is also progressing, offering hope for previously untreatable disorders. Yet, widespread adoption remains constrained by inefficient, costly, and largely manual vector manufacturing, and by limitations in delivery specificity and cross-species predictivity. Addressing these bottlenecks requires scalable, safe, and precise platforms.
The MSCA GET-IN (Gene Therapy Innovation Network) unites seven academic and eight non-academic partners with expertise in viral vector technology, genome editing, process engineering, biomanufacturing, and humanized organ-on-chip (OoC) models. Ten Doctoral Candidates (DCs) are trained as the next generation of innovators.
Objectives
GET-IN aims to drive disruptive innovation across the gene therapy value chain by:
Optimizing bioprocesses for scalable, modular, and cost-effective vector manufacturing.
Developing improved vectors and genome editing tools that are safer and more widely applicable.
Creating targeted delivery systems for higher tissue specificity and efficacy.
Establishing human OoC models for predictive, ethical safety and efficacy testing.
These goals aim to lower production costs, enhance outcomes, and accelerate translation from bench to clinic.
The MSCA GET-IN (Gene Therapy Innovation Network) unites seven academic and eight non-academic partners with expertise in viral vector technology, genome editing, process engineering, biomanufacturing, and humanized organ-on-chip (OoC) models. Ten Doctoral Candidates (DCs) are trained as the next generation of innovators.
Objectives
GET-IN aims to drive disruptive innovation across the gene therapy value chain by:
Optimizing bioprocesses for scalable, modular, and cost-effective vector manufacturing.
Developing improved vectors and genome editing tools that are safer and more widely applicable.
Creating targeted delivery systems for higher tissue specificity and efficacy.
Establishing human OoC models for predictive, ethical safety and efficacy testing.
These goals aim to lower production costs, enhance outcomes, and accelerate translation from bench to clinic.
During RP1, GET-IN moved from DC recruitment to coordinated delivery across scientific work packages:
WP1 – From data harmonization to actionable production designs (MS4, MS7)
Experimental descriptors and QC criteria were standardized across sites to generate a harmonized rAAV dataset with reference materials and cross-lab checkpoints. This enabled the first mechanistic/hybrid digital twin for upstream modelling and calibration sensitivity analyses (MS4). Between M10–M24, model-guided design dossiers were created for producer-cell selection, transfection schemes, and high-density culture compatible with closed processing. A decision-support prototype and production improvement tools (MS7) now enable faster, data-driven DoE cycles and robust scale-up.
WP2 – Targeting discovery, compact editors, and validation (MS6)
WP2 established a discovery pipeline integrating diverse datasets to identify transient, tissue-specific targeting moieties. Validated in silico candidates (MS6, M18) advanced into binding and transduction assays. Compact nucleases progressed to cellular testing, and CFTR-focused editing strategies entered organoid and airway models, in alignment with WP3. The WP now transitions to experimental winnowing and human-relevant validation of top candidates.
WP3 – Immune-competent Lung-on-Chip Platform
WP3 established a stable lung-on-chip model with epithelial, stromal, vascular, and immune compartments, supporting vascular and topical delivery routes for rAAV, VLPs, and EVs. With model stability achieved and immune components (macrophages, PBMCs) integrated, the platform is ready to test WP2’s top candidates and WP1-optimized vectors. It provides early insight into delivery efficacy and immune response, feeding results back to improve vector design and targeting.
Cross-WP integration and milestones
MS4, MS6, and MS7 connected the program’s core pillars: a curated dataset and calibrated digital twin (MS4), ranked targeting moieties (MS6), and documented production improvements plus a decision-support tool (MS7), aligning manufacturing, delivery, and efficacy strategies.
Publications involving GET-IN DCs (RP1)
Krug, Saidane, Martinello et al. J. Exp. Clin. Cancer Res. (Sept 2024): in vivo CD8-targeted lentiviral delivery of an anti-CD4 CAR for T-cell lymphoma, showing high CAR expression, selective clearance of malignant CD4 cells, and survival benefit after one dose—proof of GET-IN’s receptor-targeted transient delivery concept.
Dipalo, Mikkelsen, Gijsbers & Carlon. Hum. Gene Ther. (Aug 2025): review on engineered EVs/VLPs for CRISPR delivery in cystic fibrosis, emphasizing transient RNP delivery, design for cargo loading and retargeting, and the need for standardized production and QC—directly aligning with GET-IN’s WP1–WP3 integration.
PubMed ID 40850492: review on clinical ATMP trial trends (2008–2023) mapping maturation of the field and informing policy discussions on the evolving ATMP landscape.
Main achievements and impact
• WP1: The digital twin and design dossiers enable data-driven iteration with reduced experimental waste, translating lab workflows into scalable processes.
• WP2: Ranked targeting moieties and compact editors turn delivery into a systematic engineering challenge; the JECCR paper validates receptor-targeted in vivo gene transfer.
• WP3: The lung-on-chip model generates human-relevant biodistribution, function, and immune-response data, guiding dose selection and reducing attrition.
• Integration: Knowledge flow ensured that modelling, targeting, and validation informed each other—wet-lab feedback refined the model, manufacturability shaped targeting, and OoC readouts improved design.
With MS4 (modelling), MS6 (targeting), and MS7 (production design) achieved, the network is poised to pivot from tool building to tool integration:
– WP1’s twin can propose high-value parameter sets for yield optimization.
– WP2 inserts top-ranked moieties and editors into those designs.
– WP3 validates outcomes on the human lung-on-chip before animal or clinical testing.
The linked publications reinforce GET-IN’s central message: receptor-mediated, transient delivery is increasingly feasible, and the EV/VLP toolbox for CRISPR editing is mature enough to require standardized manufacturing and QC—an effort GET-IN’s integrated WPs are built to deliver.
WP1 – From data harmonization to actionable production designs (MS4, MS7)
Experimental descriptors and QC criteria were standardized across sites to generate a harmonized rAAV dataset with reference materials and cross-lab checkpoints. This enabled the first mechanistic/hybrid digital twin for upstream modelling and calibration sensitivity analyses (MS4). Between M10–M24, model-guided design dossiers were created for producer-cell selection, transfection schemes, and high-density culture compatible with closed processing. A decision-support prototype and production improvement tools (MS7) now enable faster, data-driven DoE cycles and robust scale-up.
WP2 – Targeting discovery, compact editors, and validation (MS6)
WP2 established a discovery pipeline integrating diverse datasets to identify transient, tissue-specific targeting moieties. Validated in silico candidates (MS6, M18) advanced into binding and transduction assays. Compact nucleases progressed to cellular testing, and CFTR-focused editing strategies entered organoid and airway models, in alignment with WP3. The WP now transitions to experimental winnowing and human-relevant validation of top candidates.
WP3 – Immune-competent Lung-on-Chip Platform
WP3 established a stable lung-on-chip model with epithelial, stromal, vascular, and immune compartments, supporting vascular and topical delivery routes for rAAV, VLPs, and EVs. With model stability achieved and immune components (macrophages, PBMCs) integrated, the platform is ready to test WP2’s top candidates and WP1-optimized vectors. It provides early insight into delivery efficacy and immune response, feeding results back to improve vector design and targeting.
Cross-WP integration and milestones
MS4, MS6, and MS7 connected the program’s core pillars: a curated dataset and calibrated digital twin (MS4), ranked targeting moieties (MS6), and documented production improvements plus a decision-support tool (MS7), aligning manufacturing, delivery, and efficacy strategies.
Publications involving GET-IN DCs (RP1)
Krug, Saidane, Martinello et al. J. Exp. Clin. Cancer Res. (Sept 2024): in vivo CD8-targeted lentiviral delivery of an anti-CD4 CAR for T-cell lymphoma, showing high CAR expression, selective clearance of malignant CD4 cells, and survival benefit after one dose—proof of GET-IN’s receptor-targeted transient delivery concept.
Dipalo, Mikkelsen, Gijsbers & Carlon. Hum. Gene Ther. (Aug 2025): review on engineered EVs/VLPs for CRISPR delivery in cystic fibrosis, emphasizing transient RNP delivery, design for cargo loading and retargeting, and the need for standardized production and QC—directly aligning with GET-IN’s WP1–WP3 integration.
PubMed ID 40850492: review on clinical ATMP trial trends (2008–2023) mapping maturation of the field and informing policy discussions on the evolving ATMP landscape.
Main achievements and impact
• WP1: The digital twin and design dossiers enable data-driven iteration with reduced experimental waste, translating lab workflows into scalable processes.
• WP2: Ranked targeting moieties and compact editors turn delivery into a systematic engineering challenge; the JECCR paper validates receptor-targeted in vivo gene transfer.
• WP3: The lung-on-chip model generates human-relevant biodistribution, function, and immune-response data, guiding dose selection and reducing attrition.
• Integration: Knowledge flow ensured that modelling, targeting, and validation informed each other—wet-lab feedback refined the model, manufacturability shaped targeting, and OoC readouts improved design.
With MS4 (modelling), MS6 (targeting), and MS7 (production design) achieved, the network is poised to pivot from tool building to tool integration:
– WP1’s twin can propose high-value parameter sets for yield optimization.
– WP2 inserts top-ranked moieties and editors into those designs.
– WP3 validates outcomes on the human lung-on-chip before animal or clinical testing.
The linked publications reinforce GET-IN’s central message: receptor-mediated, transient delivery is increasingly feasible, and the EV/VLP toolbox for CRISPR editing is mature enough to require standardized manufacturing and QC—an effort GET-IN’s integrated WPs are built to deliver.
GET-IN delivers the first mechanistic digital twin for rAAV production—enabling predictive, data-driven process design beyond traditional trial-and-error. Integration of receptor-targeted delivery and compact genome editors, validated in human organ-on-chip systems and immune-competent lung-on-chip models, sets a new benchmark for precision, transient gene-therapy interventions.