Computational tools can play a vital role in streamlining and de-risking vaccine development to achieve a faster process, lower costs, and higher vaccine efficacy.

A revolution in healthcare

Vaccine development is a highly challenging process which requires technically complex and expensive vaccine design and aims to universally weaponize a heterogenous populations’ variable immune systems against continually evolving pathogens. Computational tools can play a vital role in streamlining and de-risking vaccine development to achieve a faster process, lower costs, and higher vaccine efficacy.

Our InSilicoVaccine suite combines immunoinformatic tools and immune system response predictions that can support early vaccine design with disease progression simulations and virtual populations that allow evaluation of clinical efficacy. The heterogeneous virtual populations can be applied to test treatment strategies in silico – both if the vaccine is administered solo and when it is part of a combination therapy – and define target populations. The InSilicoVaccine suite pipeline provides a broad range of tools that enable analysis of the vaccine’s efficacy and support development throughout the vaccine’s life cycle.

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Influenza A

Our InSilicoVaccine pipeline can support vaccine design for Influenza A throughout the development pipeline, through its innovative combination of immunoinformatics tools with disease modeling and virtual populations. In particular, the integration of not only the systemic immune response, but also a specific disease model of influenza A, allows predictions of vaccine efficacy in an infection.

Below, an example of multi-epitope recombinant vector vaccine design supported by the InSilicoVACCINE pipeline with the Influenza A disease model is shown. The pipeline combines a variety of tools and capabilities, and consists of two major steps:

  • immunoinformatics for vaccine design,
  • dynamic modelling for predictions of efficacy against disease in virtual populations.

Influenza A

  • Our unique combination of bioinformatics and virtual patients provides effective support throughout vaccine development, from design to regulatory approval
  • Streamline in silico design of the vaccine via an immunoinformatics pipeline
    • Maximize epitope affinity and vaccine efficacy
    • Account for HLA heterogeneity and achieve large coverage
    • Reduce time and cost of vaccine design
  • Evaluate immunogenicity and efficacy via in silico treatment of virtual patients
    • Predict the immune response to the vaccine
    • Evaluate vaccine efficacy in mono- or combination therapy
    • Run in silico trials on heterogeneous virtual populations to optimize trial design, reduce trial timelines and costs and support regulatory applications


Computational tools show great potential to play a key role in the fast and cost-effective design and evaluation of vaccines and treatments [2]–[4]. Our InSilicoVACCINE suite can be used not only for de novo vaccine design, but also to identify and evaluate the effectivity of existing vaccine components in as additives to novel vaccines.  

Below, an example of selection and evaluation of components for future COVID-19 vaccines is shown [5], [6]. The workflow consists of several distinct steps: (1) identification of elements of BCG and DTP vaccines and selected vaccines adjuvants that show similarity with the SARS-CoV-2 genome, (2) testing of the antigenicity of these identified epitopes by in silico prediction of the T and B cell receptor reactivities, and (3) in silico trials of the vaccines components’ effects on COVID-19 infection through heterogeneous virtual patients of COVID-19 disease course.

Influenza A

  • Quickly identify existing vaccine cross-reactivity to boost treatment effectivity
  • Experiment in in silico trials with different treatment combinations to identify optimal treatment strategy
  • Optimize trial design and reduce costs by evaluating dosing, scheduling, trial size and inclusion criteria with in silico simulations
  • Run in silico trials as evidence for regulatory submission


Our InSilicoVACCINE pipeline can streamline the clinical development of novel tuberculosis treatments by supporting trial design, dosing and scheduling of combination therapies, and regulatory pathways with in silico trials of tuberculosis treatment.

Key components of InSilicoVACCINE are disease progression models and virtual populations. These modelling techniques allow not only a detailed analysis of how treatment may affect active or latent infections, but also allows deeper insight into which immune system components are, or are not, incorporated in the protective mechanism, and how each immune system mechanism can be optimally leveraged. 

Below, an example of in silico trials for the RUTI ® vaccine – a polyantigenic vaccine, consisting of liposomized Mycobacterium Tuberculosis fragments, is shown. The in silico trials show (combination) therapy efficacy, and can support dosing and administration schedule design to optimize vaccine efficacy [4]–[7].

Influenza A

  • Streamline vaccine development by leveraging in silico trials to support trial design
  • Optimize dosing and scheduling with in silico simulations to maximize efficacy
  • De-risk your program by evaluating the effects of heterogeneity and subpopulation differences in virtual populations
  • Reduce size and costs of clinical trials while ensuring sufficient power and strengthening evidence of treatment efficacy
  • Support regulatory submissions
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