Periodic Reporting for period 2 - Inno4Vac (Innovations to accelerate vaccine development and manufacture)
Berichtszeitraum: 2022-09-01 bis 2023-08-31
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, costly and 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 whole 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 will focus on four key areas. Two areas focus on in silico (i.e. computer-based) tools. One will see the project use artificial intelligence, big data analysis and computational modelling to build an open-access, cloud-based platform for developing vaccines and assessing their efficacy in silico. 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 tools. One will develop new and improved models of certain diseases such as flu that can be used to study vaccine efficacy early in the development process. The other aims to deliver models based on human cells that will offer a more 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 whole 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 will focus on four key areas. Two areas focus on in silico (i.e. computer-based) tools. One will see the project use artificial intelligence, big data analysis and computational modelling to build an open-access, cloud-based platform for developing vaccines and assessing their efficacy in silico. 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 tools. One will develop new and improved models of certain diseases such as flu that can be used to study vaccine efficacy early in the development process. The other aims to deliver models based on human cells that will offer a more 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.
During the second year, the four subtopics of Inno4Vac advanced towards achieving the main project goals.
ST1 developed methods for modelling and quantifying the heterogeneous baseline of human adaptive immunity, and for the prediction of antigen & pathogen features, and then developed pipelines for bulk and antigen specific sequencing of BCR/TCR repertoires before and after vaccination of healthy volunteers. In addition, a model to predict TCR-pMHC interactions at an improved resolution was established. The first blueprint model of the in silico predictive platform, integrating different predictive tools and simulations, was developed.
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 is progressing, with RSV strains now being characterised. Influenza strains of interest have been chosen and access to suitable viruses is being solved with external collaborators. 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, meanwhile for Influenza and RSV this exercise is ongoing. Clostridium difficile will be the first challenge agent to be tested in a CHIM trial, planned to start in reporting period 3. The strain for the challenge has been carefully selected based on the defined criteria.
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 started development of in silico models for fermentation, clarification, and purification processes as well as stability prediction for protein subunit vaccines. In reporting period 2, advances were made in developing models for in silico process evaluation of all unit operations in scope for ST4 (VAXinS). For upstream process modelling, a compartment model of bioreactor mixing and a preliminary metabolic model (WP17) were developed. In downstream process modelling, both capture chromatography and ultra scale-down of the centrifugation (WP18) was achieved. In the area of vaccine stability (WP16) DoE 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 (WP19). Progress was also made in the presentation of the approach to Regulatory agencies (WP20) via workshops and presentation to the EMA Quality Innovation Group.
ST2, ST3 and ST4 initiated and continued discussions with regulatory bodies about strategy for the integration of CHIMs into pharmaceutical vaccine development, acceptance of next-generation human in vitro 3D models in de-risking vaccine development and acceptance of in silico models into CMC dossier.
ST1 developed methods for modelling and quantifying the heterogeneous baseline of human adaptive immunity, and for the prediction of antigen & pathogen features, and then developed pipelines for bulk and antigen specific sequencing of BCR/TCR repertoires before and after vaccination of healthy volunteers. In addition, a model to predict TCR-pMHC interactions at an improved resolution was established. The first blueprint model of the in silico predictive platform, integrating different predictive tools and simulations, was developed.
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 is progressing, with RSV strains now being characterised. Influenza strains of interest have been chosen and access to suitable viruses is being solved with external collaborators. 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, meanwhile for Influenza and RSV this exercise is ongoing. Clostridium difficile will be the first challenge agent to be tested in a CHIM trial, planned to start in reporting period 3. The strain for the challenge has been carefully selected based on the defined criteria.
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 started development of in silico models for fermentation, clarification, and purification processes as well as stability prediction for protein subunit vaccines. In reporting period 2, advances were made in developing models for in silico process evaluation of all unit operations in scope for ST4 (VAXinS). For upstream process modelling, a compartment model of bioreactor mixing and a preliminary metabolic model (WP17) were developed. In downstream process modelling, both capture chromatography and ultra scale-down of the centrifugation (WP18) was achieved. In the area of vaccine stability (WP16) DoE 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 (WP19). Progress was also made in the presentation of the approach to Regulatory agencies (WP20) via workshops and presentation to the EMA Quality Innovation Group.
ST2, ST3 and ST4 initiated and continued discussions with regulatory bodies about strategy for the integration of CHIMs into pharmaceutical vaccine development, acceptance of next-generation human in vitro 3D models in de-risking vaccine development and acceptance of in silico models into CMC dossier.
At the end of the project, we expect to have developed several innovative methods which will de-risk vaccine development and shorten vaccine development timelines.
• An open-access and cloud-based platform to predict if a vaccine candidate is effective will allow evaluation of which candidate is more likely to succeed in 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 that simulate an infection at the mucosa and more reliably predict immune protection. These models will be combined with the development of related functional immune assays for clinically relevant (surrogate) endpoints. 3D models will help to reduce and replace animal experiments and de-risk pre-clinical development.
• A 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.
• An open-access and cloud-based platform to predict if a vaccine candidate is effective will allow evaluation of which candidate is more likely to succeed in 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 that simulate an infection at the mucosa and more reliably predict immune protection. These models will be combined with the development of related functional immune assays for clinically relevant (surrogate) endpoints. 3D models will help to reduce and replace animal experiments and de-risk pre-clinical development.
• A 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.