Periodic Reporting for period 1 - ALADDIN (Accelerated Discovery Nanobody Platform)
Reporting period: 2024-01-01 to 2024-12-31
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, +160 mAbs are in clinical use for different diseases, out of which nearly 100 target cancer. Global market of mAbs was +220 USD billion in 2023 and it is expected to reach 500 USD by 2030. 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 and preclinical validation. All these are not integrated, costly, require large equipments 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 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: 1) integrating selection and in vivo affinity maturation of Abs in bacterial cells holding an universal library of single domain Abs (nanobodies, Nbs) that fully eliminates animal inmunization; 2) using in silico Artificial Intelligent (AI) tools for structure-based epitope mapping, AI-guided affinity maturation, and Nb humanization; 3) developing cost-effective miniaturized microfluidic-based devices for in vitro Ab selection from bacterial cultures; 4) accelerating Ab validation with a fast non-mammalian in vivo model for preclinical testing based on patient-derived tumor xenografts in zebrafish larvae; 5) impacting target and Ab validation with dynamic mathematical models to extract clinical and efficacy data of the Ab candidates. These ambitious goals are possible through the multidisciplinary ALADDIN consortium, formed by eight partners with complementary skills, including microbiology, synthetic biology, nanobody engineering, artificial intelligence, protein folding and dynamics, physics, microfluidics, proteomic and transcriptomic analysis, preclinical validation, mouse and zebrafish tumor xenograft models, oncology, mathematics, communication and exploitation.
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: 1) integrating selection and in vivo affinity maturation of Abs in bacterial cells holding an universal library of single domain Abs (nanobodies, Nbs) that fully eliminates animal inmunization; 2) using in silico Artificial Intelligent (AI) tools for structure-based epitope mapping, AI-guided affinity maturation, and Nb humanization; 3) developing cost-effective miniaturized microfluidic-based devices for in vitro Ab selection from bacterial cultures; 4) accelerating Ab validation with a fast non-mammalian in vivo model for preclinical testing based on patient-derived tumor xenografts in zebrafish larvae; 5) impacting target and Ab validation with dynamic mathematical models to extract clinical and efficacy data of the Ab candidates. These ambitious goals are possible through the multidisciplinary ALADDIN consortium, formed by eight partners with complementary skills, including microbiology, synthetic biology, nanobody engineering, artificial intelligence, protein folding and dynamics, physics, microfluidics, proteomic and transcriptomic analysis, preclinical validation, mouse and zebrafish tumor xenograft models, oncology, mathematics, communication and exploitation.
Here is a summary of the work performed during the first reporting period across the different work packages (WPs) of the ALADDIN project:
WP1: Discovery and Optimization of Nanobodies (Nbs) using Biology and AI
• Selection of Nbs with low-to-medium affinity against FGFR4 and LGR5 from a naïve E. coli library.
• Adaptation of the T7-DIVA mutagenesis system to improve Nb affinity in E. coli.
• Development of AI models for Nb-antigen complexes to guide affinity maturation using ssDNA recombineering.
WP2: Development of Microfluidic Sorting Devices for Nb Selection
• Design of fluorescence-based and magnetic sorting modules for selecting Nb-expressing bacteria.
• Evaluation of optical and piezoelectric detection strategies to optimize sorting speed and accuracy.
WP4: Identification of Novel Therapeutic Targets in Colorectal and Pancreatic Cancer
• Proteomic studies on patient samples identified dysregulated proteins linked to recurrence and drug resistance in CRC and PDAC.
• Validation of MANF as a potential plasma biomarker for CRC recurrence.
• Analysis of circulating tumor cells to determine key factors in cancer progression.
• Development of computational models for selecting therapeutic targets based on clinical and gene expression data.
WP6: Communication and Exploitation
• Establishment of the project’s visual identity, including a logo and website.
• Creation of social media profiles and execution of science dissemination activities through conferences and public engagement.
• Implementation of a communication and dissemination strategy, including publications, webinars, and meetings with hospitals and pharmaceutical companies.
WP7: Project Coordination and Management
• Kick-off and regular coordination meetings were held to align efforts among partners.
• Administrative, financial, and legal management, including a Grant Agreement amendment to adjust WP3 timelines.
• Quality assurance and compliance monitoring ensured timely submission of project deliverables.
Overall Progress
The project is advancing as planned, though some WPs have faced technical and personnel recruitment delays. However, contingency plans have been implemented to mitigate any impact on the overall objectives.
WP1: Discovery and Optimization of Nanobodies (Nbs) using Biology and AI
• Selection of Nbs with low-to-medium affinity against FGFR4 and LGR5 from a naïve E. coli library.
• Adaptation of the T7-DIVA mutagenesis system to improve Nb affinity in E. coli.
• Development of AI models for Nb-antigen complexes to guide affinity maturation using ssDNA recombineering.
WP2: Development of Microfluidic Sorting Devices for Nb Selection
• Design of fluorescence-based and magnetic sorting modules for selecting Nb-expressing bacteria.
• Evaluation of optical and piezoelectric detection strategies to optimize sorting speed and accuracy.
WP4: Identification of Novel Therapeutic Targets in Colorectal and Pancreatic Cancer
• Proteomic studies on patient samples identified dysregulated proteins linked to recurrence and drug resistance in CRC and PDAC.
• Validation of MANF as a potential plasma biomarker for CRC recurrence.
• Analysis of circulating tumor cells to determine key factors in cancer progression.
• Development of computational models for selecting therapeutic targets based on clinical and gene expression data.
WP6: Communication and Exploitation
• Establishment of the project’s visual identity, including a logo and website.
• Creation of social media profiles and execution of science dissemination activities through conferences and public engagement.
• Implementation of a communication and dissemination strategy, including publications, webinars, and meetings with hospitals and pharmaceutical companies.
WP7: Project Coordination and Management
• Kick-off and regular coordination meetings were held to align efforts among partners.
• Administrative, financial, and legal management, including a Grant Agreement amendment to adjust WP3 timelines.
• Quality assurance and compliance monitoring ensured timely submission of project deliverables.
Overall Progress
The project is advancing as planned, though some WPs have faced technical and personnel recruitment delays. However, contingency plans have been implemented to mitigate any impact on the overall objectives.
The ALADDIN project has successfully initiated the development of an advanced nanobody discovery platform. Key achievements include the selection of Nbs against FGFR4 and LGR5 using the nanobody naïve library expressed in E. coli, optimization of affinity maturation tools for nanobodies based on T7-DIVA in vivo mutagenesis and AI combined with ssDNA recombineering, and design of microfluidic sorting devices for bacteria. Proteomic analyses identified novel therapeutic targets in CRC and PDAC, with MANF validated as a potential biomarker. Computational models are also being developed to enhance target selection. Potential impacts include faster nanobody discovery, improved precision oncology tools, and reduced reliance on animal models. Dissemination efforts have increased awareness among scientific and industrial stakeholders, setting the stage for future clinical translation and commercialization.