• Immunology – TCR therapies

Immunology – TCR therapies

Immunity by design

The Immunology Team is rooted in biology and holds deep expertise in bioinformatics, machine learning, and software engineering. Ardigen’s in-house datasets, together with advanced AI platforms, empower the development of effective therapies by eg. TCR discovery.

Our technologies unlock the fast track to therapies driven by engineered TCRs,

TCRm-Abs, immunogenic epitopes, as well as AI-based biomarker discovery.


We are open to partnerships and scientific collaborations. Let’s talk and see how we can help you with your challenges.

TCR therapy

Accelerate discovery and improve safety of TCR therapies using Artificial Intelligence

Following the unique opportunity for curing patients provided by the development of cell therapies (e.g. TCR discovery), Ardigen has set on the path to advance the field with its Artificial Intelligence platform. Many challenges stand in the way of successful therapy discovery and development. Let us know how we can help you!

Are you facing similar challenges?

Target epitopeselection and validationOff-target toxicityTCR optimizationTCR functionalityassessmentTCR promiscuityLimitations of TCRscreening library

Value we deliver

  • Refining selection and validation of the target pMHCs (peptide-Major Histocompatibility Complexes) with AI predictions
  • Avoiding off-target immunotoxicity
  • TCR discovery – accessing the full TCR (T-cell receptor) sequence space for hit identification                         
  • Optimizing time and costs of TCR therapy development

ArdImmune TCRact Platform


Cancer vaccines

Quick solution for effective immune responses in clinical trials

We have created an Artificial Intelligence platform dedicated to being a part of the therapeutic cancer vaccine development. The ArdImmune Vax platform uses sequencing data to generate and prioritize antigen targets for cancer vaccines and T-cell therapies.

Are you facing similar challenges?

Low efficacyof the vaccineHigh toxicityof the vaccineLack of immunogenicityof chosen epitopesTargeting onlyCD8+ T-cellsTumorheterogeneityImmune evasionof the tumorLow specificityof TAA epitopes

Value we deliver

  • AI predictions of functional T-cell responses
  • Evasion of off-target immunotoxicity
  • Providing help of CD4+ T-cells
  • Selecting clonal epitopes
  • Detecting immune escape mechanisms
  • Designing off-the-shelf or personalized cancer vaccines
ArdImmune Vax Platform key functionalities to augment therapy development

The platform consists of bioinformatics and machine learning algorithms that generate putative antigens, determine patients’ MHCs, predict antigen(peptide)-MHC binding affinity, the likelihood of the peptide presentation by the MHC molecules, and the probability of pMHC attracting T-cells to generate functional responses described as immunogenicity.

Vax Platform

ArdImmune Vax incorporates the best-in-class algorithms for each task performed, starting from patient samples and sequencing data, to designed vaccine composition.
  • Sample processing

    We use Whole Exome Sequencing data from the bulk sequencing of a patient’s FFPE*1 sample (tumor tissue), along with the PBMC*2 sample (normal tissue). The data are processed to identify SVs (Structural Variants), somatic SNVs (single nucleotide variants), short indels (insertions/deletions), and other alterations, using multiple variant calling tools. The identified variants are then carefully examined to eliminate false positives. Only mutations present in a sufficient percentage of tumor cells retain.

    *1 Formalin-Fixed Paraffin-Embedded

    *2 Peripheral Blood Mononuclear Cell

    RNA-seq data from the bulk sequencing of a patient’s FFPE sample (tumor tissue) are processed to obtain expression levels for each gene and identify new chimeric genes created by SVs (Structural Variants).

    Variant calling is the process of variants identification based on sequencing data. Different variant callers exist, but they indicate low results correspondence. We perform variant filtering based on our machine learning models to filter out false-positive observations.

  • Diverse sources of epitopes

    Short indels (especially those which affect the open reading frame) may lead to severe distortions of the protein sequences and thus might be a rich source of neoepitopes.



    Tumor-Associated Antigens (TAAs) are non-mutated proteins or peptides with elevated expression levels in tumors but also expressed at lower levels in healthy tissues. TAAs were the first targets used in immunotherapies, giving foundations for successful treatments.

    Somatic SNVs within a tumor lead to local changes in protein sequences. SNVs being the most common type of genetic alterations are rich sources of possibly immunogenic neoepitopes.

    Retrotranspositions are events in which transposons (mobile genomic sequences)  are copied and pasted in new genomic locations. This event may lead to the appearance of new fusion proteins, which might be the source of neoepitopes.

    Alternative splicing events, especially intron retention, lead to the appearance of new proteins. The fragments of such proteins not expressed within healthy tissue are another source of neoepitopes.

    Structural Variants (SVs) are large genomic alterations that may affect a substantial part of the chromosome. Some of them are likely to cause fusion/chimeric genes. Such genes can bring significant alterations in the resulting protein sequences, which might lead to the appearance of new epitopes.

  • Vaccine design modules

    pMHC immunogenicity is acquired only for the minority of presented pMHC molecules. We can accurately assess the immunogenicity potential of the pMHC ligands with our proprietary AI model. We trained the model on the curated set of neoantigens tested in-vitro for immunogenicity.

    Off-target toxicity is one of the biggest challenges in modern cancer vaccine development. It happens when TCR binds antigens presented on a patient’s healthy tissue, thus damaging it. We can identify possible off-targets for all epitopes of interest and eliminate those associated with the high risk of off-target toxicity.

    ArdImmune Tox is verified to pinpoint off-targets from the available clinical data.

    The pMHC molecule presentation on the cancer cell’s surface is an essential process needed for pMHC-TCR interaction to be possible. With the help of our proprietary AI model, we can identify epitopes and neoepitopes with the highest probability of being presented on the cancer cells by a patient’s MHCs. This model is trained on the large set of data obtained from numerous mass spectrometry experiments.

    Immune escape mechanisms, such as HLA LOH*1 or HLA downregulation, are known cancer microevolutionary processes which significantly lower the immunotherapies efficiency. We identify such events and manage the problem by selecting epitopes that are not affected by the immune escape mechanisms.

     *1 Loss of Heterozygosity

    The epitope binding to the patient’s MHC allele is necessary for its’ presentation on the cell surface and the MHC-related immunogenicity. We can accurately identify strong MHC binders via using different tools designed for predicting binding affinity strength.

    Our process accounts for tumor heterogeneity by prioritization of clonal epitopes, driver genes, and hotspot mutations. It maximizes the cleaning potential of an anti-cancer vaccine composition by targeting only genetic alterations vital for tumor development.

    Not all peptide sequences predicted in silico can be synthesized in laboratory conditions. We provide peptide manufacturability predictions to determine which peptides can be difficult or even not possible to manufacture.

    We are relying on the model confidence of our machine learning models to present only meaningful and crucial information.

    The tumor microenvironment (TME) is the environment around a solid tumor composed of surrounding cells and tissues. These can suppress the efficiency of the immune system or even promote tumor growth and survival. Overcoming the highly unfavorable conditions in the tumor microenvironment is the main challenge for effective T cell-based solid cancer therapies.

  • Other applications

    A mouse model is a foremost mammalian model used within laboratories for studying multiple aspects of human health and disease. Due to the manipulation of mice genes, such models are becoming more effective and appropriate for research. It allows for accelerating treatments discovery and drives medical innovations.

    Recent coronavirus pandemic outbreak has proved once more how important a fast response with new vaccine designs is. Our technology allows designing peptide-based vaccines that are effective against infectious diseases and easily redesignable for novel virus variants detection.

    Many therapeutic proteins and peptides had elicited immune system responses that decreased their safety and efficacy. Our technology allows us to suggest amino-acid substitutions that reduce binding strength and prevent the formation of anti-drug antibodies (ADAs). Biotherapeutics prepared in such a way should increase the likelihood of a positive clinical outcome.

    Target identification is one of the initial steps of drug discovery and helps to lead to a suitable disease target. We use bioinformatics tools for data mining and analysis of genetic association or expression profiles to enable finding a target that is efficacious, safe, and druggable *1.

    *1 It means that the target activity can be modulated by a therapeutic.

Our science

We are driven by science and collaborations!

Since 2015, we’ve successfully executed a variety of exciting and complex projects, both for our clients and internally.
High degree of trust and confidentiality is the foundation for our business. That’s why we are always very excited to share results of our scientific projects.
Whenever possible we publish and present materials, such as journal publications, posters, case studies or technical notes, aiming to advance the scientific field and showcase the achievements and experience of our team.


26 August 2020 Community Assessment of the Predictability of Cancer Protein and Phosphoprotein Levels from Genomics and (...)
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    26 August 2020 AI aided design of epitope-based vaccine for the induction of cellular immune responses against SARS-CoV-2
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      30 November 2019 Understanding contribution and independence of multiple biomarkers for predicting response to Atezolizumab
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        Posters and case studies

        11 April 2022 Modeling pHLA:TCR interactions for effective TCR therapies: leveraging AI and Molecular Dynamics See the poster
        10 September 2021 Streamlining design of safe and effective TCR therapies with AI See the poster
        8 June 2020 Accounting for immune escape mechanisms in personalized and shared neoantigen cancer vaccine design See the poster

        On demand webinars

        Immune escape mechanisms in neoantigen driven therapies
        19 March 2021 Immune escape mechanisms in neoantigen driven therapies

          Using AI to design vaccine protecting from COVID-19
          16 September 2020 Using AI to design vaccine protecting from COVID-19

            How AI can help design cancer vaccines and adoptive cell therapies?
            28 May 2020 How AI can help design cancer vaccines and adoptive cell therapies?

              Short movies and interviews

              Limiting TCR promiscuity is key to safe TCR-T therapy
              31 May 2022 Limiting TCR promiscuity is key to safe TCR-T therapy Watch the video
              Off-target toxicity of T-cell receptors
              6 October 2021 Off-target toxicity of T-cell receptors Watch the video
              Untangling the mystery of pMHC TCR binding
              21 September 2021 Untangling the mystery of pMHC TCR binding Watch the video

              Meet us at upcoming events

              • Coming soon
                24 - 25 October 2022
                Wroclaw, Poland
                Central European Technology Forum
              • 24 - 26 October 2022
                Leipzig, Germany
              • 26 -28 June
                PMWC 2022
              • 8-13 April 2022
              • 15-16 February 2022
                Boost your way to successful therapy. AI for Drug Discovery Days”
              • 26 -28 January
                The Festival of Genomics & Biodata
              • 10 - 14 November
                SITC 2021
              • 14 - 18 June
                PMWC 2021 COVID Virtual
              • 10 - 11 & 14 - 18 June
                BIO Digital 2021
              • 18 - 19 May
                Bioinformatics in Cancer virtual conference
              • 19 January
              • 28 September - 1 October
              • 14 - 17 September
                CAR-TCR DIGITAL WEEK

              Contact us

              Are you interested in Ardigen's Immunology Platforms and solutions?

                • https://ardigen.com/wp-content/uploads/2021/05/Mask-Group-115543@2x.jpg Agnieszka Blum, MD, PhD Executive Vice President, General Director of the Immunology
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