Modeling and simulation can be used to predict the behavior of a vaccine candidate in the human body, allowing researchers to identify potential issues or limitations before conducting costly and time-consuming clinical trials. This approach can also help optimize the dosage and delivery of the vaccine, as well as identify potential side effects.


Design & Optimize Vaccines In Silico

Our vaccine suite is a powerful tool that integrates immunoinformatics tools and immune system response predictions to aid in the early design of vaccines. With the ability to simulate disease progression and virtual populations, our suite allows for the evaluation of clinical efficacy, making it an invaluable resource for vaccine development. Our suite is designed for professionals in the field looking to streamline their vaccine design process and stay ahead of the curve in disease prevention.

Our company has made significant strides in vaccine development using in silico approaches. We have successfully developed vaccines for Influenza, Covid, and Tuberculosis, and have several more in the pipeline, including Zika, Cholera, Dengue, and HIV etc. Our pipeline offers a wide range of tools that enable us to analyze the efficacy of our vaccines and support their development throughout their life cycle. We are committed to continuing our research and development efforts to provide effective solutions to combat diseases and improve public health.

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 modeling for predictions of efficacy against disease in virtual populations.

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. Our InSilicoVACCINE suite can be used not only for de novo vaccine design, but also to identify and evaluate the effectiveness of existing vaccine components in as additives to novel vaccines.  

Mentioned, an example of selection and evaluation of components for future COVID-19 vaccines. 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

  • 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 modeling 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. 

Mentioned, 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.

  • 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 difference in virtual populations
  • Reduce size and costs of clinical trials while ensuring sufficient power and strengthening evidence of treatment efficacy
  • Support regulatory submissions