Periodic Reporting for period 3 - Inno4Vac (Innovations to accelerate vaccine development and manufacture)
Reporting period: 2023-09-01 to 2024-08-31
Vaccines are a huge public health achievement, saving an estimated 2.5 million lives every year and protecting millions more from illness and disability. However, developing new vaccines is extremely time-consuming and financially risky; on average it takes over 10 years and costs more than EUR 800 million to bring a vaccine to the market.
However, in recent years, researchers in academia and biotech companies have made huge strides in fields such as immunology, big data, and artificial intelligence. These advances could potentially speed up the development of new vaccines and make the entire process more efficient.
The aim of Inno4Vac is to harness these advances and incorporate them into the vaccine industry. The project brings together experts in clinical research, immunology, microbiology, systems biology, mathematical models, and regulatory issues.
This diverse team focusses on four key areas, so-called subtopics (STs). Two of these areas focus on in silico (i.e. computer-based) tools. One builds in silico models for immune formation based on the application of machine learning algorithms to lab-based data. The models and predictive tools developed are then combined into an open-access, cloud-based platform for predicting immune formation following vaccination. The second in silico area focuses on developing a modular computational platform for modelling the manufacture and stability testing of vaccines.
The other two areas focus on lab-based and clinical tools. One is developing new and improved controlled human infection models for flu, RSV and clostridioides that can be used to study vaccine efficacy early in the clinical development process. The other aims to deliver in vitro tissue models based on human cells that will offer a reliable view of the level of immune protection a vaccine could offer.
Throughout the project, the partners will develop strategies and roadmaps to ensure that their models meet the needs of medicines regulators and integrate them into vaccine development processes.
Ultimately, the models developed by the project should help to make vaccine development both faster and more efficient.
However, in recent years, researchers in academia and biotech companies have made huge strides in fields such as immunology, big data, and artificial intelligence. These advances could potentially speed up the development of new vaccines and make the entire process more efficient.
The aim of Inno4Vac is to harness these advances and incorporate them into the vaccine industry. The project brings together experts in clinical research, immunology, microbiology, systems biology, mathematical models, and regulatory issues.
This diverse team focusses on four key areas, so-called subtopics (STs). Two of these areas focus on in silico (i.e. computer-based) tools. One builds in silico models for immune formation based on the application of machine learning algorithms to lab-based data. The models and predictive tools developed are then combined into an open-access, cloud-based platform for predicting immune formation following vaccination. The second in silico area focuses on developing a modular computational platform for modelling the manufacture and stability testing of vaccines.
The other two areas focus on lab-based and clinical tools. One is developing new and improved controlled human infection models for flu, RSV and clostridioides that can be used to study vaccine efficacy early in the clinical development process. The other aims to deliver in vitro tissue models based on human cells that will offer a reliable view of the level of immune protection a vaccine could offer.
Throughout the project, the partners will develop strategies and roadmaps to ensure that their models meet the needs of medicines regulators and integrate them into vaccine development processes.
Ultimately, the models developed by the project should help to make vaccine development both faster and more efficient.
After 3 years, the subtopics of Inno4Vac have made the following progress.
ST1 have developed pipelines for bulk and antigen specific sequencing of BCR/TCR repertoires before and after vaccination of healthy volunteers. The data will be fed into development and refinement of in silico tools modelling and predicting immune formation following vaccination. At this stage, we have developed in silico strategies for quantifying the heterogeneous baseline of human adaptive immunity, integrated specific antigens into in silico germinal center modelling, and developed predictive algorithms based on machine learning for identification of B- and T-cell epitopes. Data from the BCR/TCR in vivo experiment will be needed for development of a model predicting TCR-pMHCI interactions, as well as refining predictions of B cell epitopes. Further, we have added the developed models and tools to a first blueprint model of the in silico predictive platform, integrating different predictive tools and simulations.
ST2 advanced in identification, characterisation and production of three challenge agents (Influenza, RSV and Clostridioides difficile) necessary to establish 3 separate Controlled Human Infection Models (CHIM). The selection of Influenza and RSV strains suitable for human challenges was completed, with RSV strains now being characterised. Influenza strains of interest have been chosen and access to suitable viruses is being solved with external collaborators. Animal experiments are ongoing to select the influenza strain. Virus-specific immune assays that may be used to define immune responses elicited in challenge study participants have been prioritized. Draft clinical trial protocols for RSV and Influenza have been finalised, along with the protocol document for infection control and Faecal Microbial Transplantation (FMT) rescue treatment in C. difficile challenged volunteers. The facility for challenge production has been identified for C. difficile and RSV, meanwhile for Influenza the selection is ongoing. The first clinical activities for RSV and C. difficile are planned to start in reporting period 4. .
ST3 developed next-generation human in vitro 3D models for gastro-intestinal, respiratory and urovaginal mucosae that include relevant immune system components. Multiple models were designed and tested to optimise a variety of conditions in combination with selected pathogens (or toxins produced by pathogens). Phase 1 (‘Development”) was completed, with most models passing Stage Gate 1. Phase 2 (“Exploration”) has begun, aiming to incorporate relevant immune-system components into the prioritized models, focusing on final use and “validation” scope of the models.
ST4 collected biomanufacturing data from industry partners and began development of in silico models for fermentation, clarification, and purification processes as well as stability prediction for protein subunit vaccines. Advances were made in developing models for in silico process evaluation of all unit operations in scope for ST4. For upstream process modelling, a compartment model of bioreactor mixing and a preliminary metabolic model were developed. In downstream process modelling, both capture chromatography and ultra scale-down of the centrifugation was achieved. In the area of vaccine stability, design of experiments and Bayesian models were deployed and validated with industry data. Further to the single unit operation modelling activities, case studies to exemplify the power of the models in an industry setting integrated across operations were agreed upon. Progress was also made in the presentation of the approach to regulatory agencies via workshops and presentation to the EMA Quality Innovation Group.
The subtopics initiated and continued discussions with regulatory bodies about the project stage at which regulatory considerations were relevant (ST1), a strategy for the integration of CHIMs into pharmaceutical vaccine development (ST2), acceptance of next-generation human in vitro 3D models in de-risking vaccine development (ST3) and acceptance of in silico models into CMC dossier (ST4).
ST1 have developed pipelines for bulk and antigen specific sequencing of BCR/TCR repertoires before and after vaccination of healthy volunteers. The data will be fed into development and refinement of in silico tools modelling and predicting immune formation following vaccination. At this stage, we have developed in silico strategies for quantifying the heterogeneous baseline of human adaptive immunity, integrated specific antigens into in silico germinal center modelling, and developed predictive algorithms based on machine learning for identification of B- and T-cell epitopes. Data from the BCR/TCR in vivo experiment will be needed for development of a model predicting TCR-pMHCI interactions, as well as refining predictions of B cell epitopes. Further, we have added the developed models and tools to a first blueprint model of the in silico predictive platform, integrating different predictive tools and simulations.
ST2 advanced in identification, characterisation and production of three challenge agents (Influenza, RSV and Clostridioides difficile) necessary to establish 3 separate Controlled Human Infection Models (CHIM). The selection of Influenza and RSV strains suitable for human challenges was completed, with RSV strains now being characterised. Influenza strains of interest have been chosen and access to suitable viruses is being solved with external collaborators. Animal experiments are ongoing to select the influenza strain. Virus-specific immune assays that may be used to define immune responses elicited in challenge study participants have been prioritized. Draft clinical trial protocols for RSV and Influenza have been finalised, along with the protocol document for infection control and Faecal Microbial Transplantation (FMT) rescue treatment in C. difficile challenged volunteers. The facility for challenge production has been identified for C. difficile and RSV, meanwhile for Influenza the selection is ongoing. The first clinical activities for RSV and C. difficile are planned to start in reporting period 4. .
ST3 developed next-generation human in vitro 3D models for gastro-intestinal, respiratory and urovaginal mucosae that include relevant immune system components. Multiple models were designed and tested to optimise a variety of conditions in combination with selected pathogens (or toxins produced by pathogens). Phase 1 (‘Development”) was completed, with most models passing Stage Gate 1. Phase 2 (“Exploration”) has begun, aiming to incorporate relevant immune-system components into the prioritized models, focusing on final use and “validation” scope of the models.
ST4 collected biomanufacturing data from industry partners and began development of in silico models for fermentation, clarification, and purification processes as well as stability prediction for protein subunit vaccines. Advances were made in developing models for in silico process evaluation of all unit operations in scope for ST4. For upstream process modelling, a compartment model of bioreactor mixing and a preliminary metabolic model were developed. In downstream process modelling, both capture chromatography and ultra scale-down of the centrifugation was achieved. In the area of vaccine stability, design of experiments and Bayesian models were deployed and validated with industry data. Further to the single unit operation modelling activities, case studies to exemplify the power of the models in an industry setting integrated across operations were agreed upon. Progress was also made in the presentation of the approach to regulatory agencies via workshops and presentation to the EMA Quality Innovation Group.
The subtopics initiated and continued discussions with regulatory bodies about the project stage at which regulatory considerations were relevant (ST1), a strategy for the integration of CHIMs into pharmaceutical vaccine development (ST2), acceptance of next-generation human in vitro 3D models in de-risking vaccine development (ST3) and acceptance of in silico models into CMC dossier (ST4).
At project end, we expect to have developed several innovative methods to de-risk vaccine development and shorten vaccine development timelines.
• Open-access, cloud-based platform to predict immune formation following vaccination, to enable early selection of vaccine candidates more likely to produce efficient immune responses and succeed through later stages of vaccine development.
• New and improved controlled human infection models (CHIM) for influenza (i.e. flu), Respiratory Syncytial Virus (RSV, i.e. a common respiratory virus that can be serious for infants and older adults) and Clostridium difficile will enable early vaccine efficacy evaluation.
• Cell-based human in vitro 3D models simulating an infection at the mucosa to more reliably predict immune protection. These models will be combined with development of related functional immune assays for clinically relevant (surrogate) endpoints. 3D models will help reduce and replace animal experiments and de-risk pre-clinical development.
• Modular one-stop computational platform for in silico (i.e. computer-based) modelling of vaccine bio-manufacturing and stability testing will help optimise vaccine production.
• Open-access, cloud-based platform to predict immune formation following vaccination, to enable early selection of vaccine candidates more likely to produce efficient immune responses and succeed through later stages of vaccine development.
• New and improved controlled human infection models (CHIM) for influenza (i.e. flu), Respiratory Syncytial Virus (RSV, i.e. a common respiratory virus that can be serious for infants and older adults) and Clostridium difficile will enable early vaccine efficacy evaluation.
• Cell-based human in vitro 3D models simulating an infection at the mucosa to more reliably predict immune protection. These models will be combined with development of related functional immune assays for clinically relevant (surrogate) endpoints. 3D models will help reduce and replace animal experiments and de-risk pre-clinical development.
• Modular one-stop computational platform for in silico (i.e. computer-based) modelling of vaccine bio-manufacturing and stability testing will help optimise vaccine production.