Accelerated Discovery Nanobody Platform
The ALADDIN project aims to revolutionize therapeutic antibody discovery for cancer by integrating nanobody technology, AI tools, and innovative models to enhance efficiency and reduce reliance on animal testing.
Projectdetails
Introduction
Since the approval of the first monoclonal antibody (mAb) 30 years ago, therapeutic mAbs and Ab-derived molecules have come to dominate the biologics market. Currently, over 100 Abs are in clinical use for different diseases, out of which more than 30 mAb target cancer.
Challenges in Therapeutic Antibody Discovery
Despite the huge market potential, multiple and complex steps make the therapeutic Ab discovery process long, expensive, laborious, and inefficient. These steps include:
- Target identification
- Animal immunization
- Ab selection and engineering
- Humanization
- Preclinical validation
All these steps are not integrated, costly, require large equipment and facilities, and are highly dependent on experimental animals (mostly mammals, including genetically modified mice), both for immunization and for preclinical validation of the therapeutic Ab candidates.
ALADDIN Project Overview
The ALADDIN project emerges to bring to the market a novel AcceLerAteD DIscovery Nanobody platform that will increase the efficiency of therapeutic Ab discovery and preclinical validation for human cancer by:
- Integrating selection and in vivo affinity maturation of Abs in bacterial cells holding a universal library of single domain Abs (nanobodies, Nbs) that fully eliminates animal immunization.
- Using in silico Artificial Intelligence (AI) tools for structure-based epitope mapping, AI-guided affinity maturation, and Nb humanization.
- Developing cost-effective miniaturized microfluidic-based devices for in vitro Ab selection from bacterial cultures.
- Accelerating Ab validation with a fast non-mammalian in vivo model for preclinical testing based on patient-derived tumor xenografts in zebrafish larvae.
- Impacting target and Ab validation with dynamic mathematical models to extract clinical and efficacy data of the Ab candidates.
Conclusion
These ambitious goals will be possible through the multidisciplinary ALADDIN consortium, formed by eight partners with complementary skills.
Financiële details & Tijdlijn
Financiële details
Subsidiebedrag | € 3.315.441 |
Totale projectbegroting | € 3.315.441 |
Tijdlijn
Startdatum | 1-1-2024 |
Einddatum | 31-12-2027 |
Subsidiejaar | 2024 |
Partners & Locaties
Projectpartners
- AGENCIA ESTATAL CONSEJO SUPERIOR DE INVESTIGACIONES CIENTIFICASpenvoerder
- INTERNATIONAL IBERIAN NANOTECHNOLOGY LABORATORY
- THE HEBREW UNIVERSITY OF JERUSALEM
- LINKOPINGS UNIVERSITET
- INSTITUTO INVESTIGACION SANITARIA FUNDACION JIMENEZ DIAZ
- INSTITUTO DE SALUD CARLOS III
- INCELLIA IKE
- ZIRKA INNOTECH SL
Land(en)
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Vergelijkbare projecten uit andere regelingen
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---|---|---|---|---|
Computer aided de novo design of nanobodiesThe project aims to automate the design of fully de novo nanobodies with nanomolar affinity using AI-driven methods, eliminating animal use and enhancing efficiency in antibody development. | ERC Proof of... | € 150.000 | 2024 | Details |
Computational design of synthetic antibody repertoires for accelerated therapeutic discoveryCADABRE aims to design and optimize diverse human antibody repertoires with enhanced stability and developability for therapeutic discovery using advanced protein design and AI-driven screening methods. | ERC Advanced... | € 2.741.000 | 2024 | Details |
Learning the interaction rules of antibody-antigen bindingThis project aims to enhance antibody-antigen binding prediction by generating large-scale sequence and structural data through high-throughput screening and machine learning techniques. | ERC Consolid... | € 2.000.000 | 2024 | Details |
Haalbaarheid van het Formula Y-platform voor versnelde antilichaam ontwikkelingHet project richt zich op het versnellen van de ontwikkeling van nieuwe antilichaamtherapieën door generatieve machine-learning algoritmes te gebruiken voor het ontwerpen van specifieke antilichamen. | Mkb-innovati... | € 20.000 | 2023 | Details |
Allosteric modulation of immune checkpoint complexes as a new mode of therapeutic intervention in immunotherapyThe project aims to develop novel Nanobodies as safe and effective modulators of immune checkpoint complexes for cancer and autoimmune diseases, potentially outperforming current therapies. | ERC Advanced... | € 2.499.674 | 2024 | Details |
Computer aided de novo design of nanobodies
The project aims to automate the design of fully de novo nanobodies with nanomolar affinity using AI-driven methods, eliminating animal use and enhancing efficiency in antibody development.
Computational design of synthetic antibody repertoires for accelerated therapeutic discovery
CADABRE aims to design and optimize diverse human antibody repertoires with enhanced stability and developability for therapeutic discovery using advanced protein design and AI-driven screening methods.
Learning the interaction rules of antibody-antigen binding
This project aims to enhance antibody-antigen binding prediction by generating large-scale sequence and structural data through high-throughput screening and machine learning techniques.
Haalbaarheid van het Formula Y-platform voor versnelde antilichaam ontwikkeling
Het project richt zich op het versnellen van de ontwikkeling van nieuwe antilichaamtherapieën door generatieve machine-learning algoritmes te gebruiken voor het ontwerpen van specifieke antilichamen.
Allosteric modulation of immune checkpoint complexes as a new mode of therapeutic intervention in immunotherapy
The project aims to develop novel Nanobodies as safe and effective modulators of immune checkpoint complexes for cancer and autoimmune diseases, potentially outperforming current therapies.