InSilicoVACCINE Suite

InSilicoVACCINE Suite is a comprehensive computational platform designed to streamline and de-risk vaccine development across the full development lifecycle. By combining immunoinformatic tools, immune system response predictions, disease progression simulations, and heterogeneous virtual populations, the suite enables faster, more cost-effective vaccine design and evaluation. It is intended for vaccine developers, clinical researchers, and regulatory teams working on infectious disease programs who need to reduce trial timelines, lower costs, and improve vaccine efficacy outcomes.

The InSilicoVACCINE pipeline integrates two major methodological pillars: immunoinformatics for in silico vaccine design, and dynamic modelling with virtual patients for predictions of clinical efficacy. This combination supports vaccine programs from early antigen selection through to regulatory submission, and can be applied to both de novo vaccine design and the evaluation of existing vaccine components. The suite has demonstrated applicability across multiple disease areas, including Influenza A, COVID-19, and Tuberculosis.

Core Platform Capabilities

• Immunoinformatics pipeline to streamline in silico vaccine design, maximising epitope affinity and population coverage
• Accounting for HLA heterogeneity to achieve broad population coverage and higher vaccine efficacy
• Prediction of immune responses to candidate vaccines using mechanistic immune system models
• Disease progression models and virtual populations enabling detailed analysis of treatment effects on active or latent infections
• In silico trials on heterogeneous virtual populations to optimise trial design, dosing, scheduling, and inclusion criteria
• Evaluation of vaccine efficacy in monotherapy and combination therapy settings
• Support for regulatory submissions through in silico trial evidence

Influenza A Application

• Supports multi-epitope recombinant vector vaccine design through the full development pipeline
• Integrates systemic immune response modelling with a specific Influenza A disease model to predict vaccine efficacy during infection
• Maximises epitope affinity and accounts for HLA heterogeneity to achieve large population coverage
• Enables in silico treatment of virtual patients to predict immunogenicity and evaluate efficacy in mono- or combination therapy
• Reduces time and cost of vaccine design by replacing or supplementing early experimental work with computational predictions
• Supports optimisation of clinical trial design and regulatory applications through virtual population simulations

COVID-19 Application

• Supports both de novo vaccine design and identification of existing vaccine components with cross-reactivity to novel pathogens
• Enables identification of elements from existing vaccines (e.g. BCG, DTP) and adjuvants showing genomic similarity to SARS-CoV-2
• Tests antigenicity of identified epitopes through in silico prediction of T and B cell receptor reactivities
• Runs in silico trials of vaccine component effects on COVID-19 infection using heterogeneous virtual patient populations
• Allows experimentation with different treatment combinations to identify optimal treatment strategies
• Optimises trial design and reduces costs by evaluating dosing, scheduling, trial size, and inclusion criteria through simulation
• Generates in silico trial data suitable as evidence for regulatory submission

Tuberculosis Application

• Streamlines clinical development of novel tuberculosis treatments by supporting trial design and regulatory pathways with in silico trials
• Disease progression models provide detailed analysis of treatment effects on both active and latent tuberculosis infections
• Offers deeper insight into which immune system components are involved in protective mechanisms and how they can be optimally leveraged
• Demonstrated application with the RUTI® polyantigenic vaccine, showing combination therapy efficacy and supporting dosing and administration schedule design
• De-risks development programs by evaluating the effects of population heterogeneity and subpopulation differences in virtual populations
• Reduces the size and cost of clinical trials while maintaining sufficient statistical power and strengthening evidence of treatment efficacy
• Supports regulatory submissions through in silico trial outputs

InSilicoVACCINE Suite is offered with a demo booking option, allowing teams to explore its capabilities directly. The platform is applicable across the vaccine development lifecycle — from early antigen and epitope selection through clinical trial optimisation and regulatory support — making it a versatile tool for organisations seeking to accelerate and strengthen their vaccine programs.