There are multiple factors contributing to the immune system recognizing cancer and properly launching adaptive immune reaction against it. Ardigen research combines the expertise in Artificial Intelligence, Bioinformatics, Cancer Biology, Immunology and Microbiome to deliver technologies that lead to significantly increased response rates to immunotherapies.
By using the whole metagenomic information available, Ardigen Microbiome Analysis
Platform demonstrates an innovative approach to the discovery and design of microbiome-based therapeutics
Our in silico methods, in particular Artificial Intelligence algorithms, minimize the laboratory effort to identify and evaluate of new probiotic strains. Novel Live Biotherapeutic Products, therapies based on bacterial metabolites and microbiome-related biomarkers can provide significant medical benefit to patients in immuno-oncology and beyond.
BiomeSeer is a high quality tool for analyzing the differences in microbiome between groups of patients, such as the differences between responders and non-responders in the context of a particular therapy.
Ardigen has designed a novel approach to detecting differences between the groups that is aware of the high complexity of metagenomic samples. BiomeScout™ operates on all the genomic features detected in the sample. This approach enables the detection of multiple changes introduced by strains of the same species. Detection of specific genomic features makes an important step towards the full functional analysis of metagenomic samples. It facilitates causative studies of clinically important metagenome functions. Ardigen approach to microbiome analysis provides, among others, the following benefits:
BiomeSeer is a tool for predicting bacterial phenotype, that allows to carry out the analysis in silico, eliminating most of the laboratory procedures. This element of the Microbiome Analysis Platform completes the functionality of BiomeScout, as it analyzes the assembled genomes of novel bacterial strains found in the samples.
BiomeSeer encodes genomic sequences into structures suitable for machine learning algorithms. Then, it performs a set of validation tests using custom functions designed to handle the biological bias. Finally, a number of pre-trained Machine Learning models are applied to the data. Together, they predict the most important aspects of the bacterial phenotypes, significantly reducing the effort necessary to establish the isolation and culturing protocols, including:
Usually only a few of the tumor-related mutations create neoepitopes able to elicit an adaptive immune response. Predicting those neoepitopes is crucial to design effective therapeutic vaccine but this is an extremely challenging task.
Ardigen Neoepitope Prediction Platform can accurately predict cancer neoepitopes, assess their immunogenicity and design personalized cancer vaccines to boost the response to immunotherapy.
Ardigen's Neoepitope Prediction Platform applies Artificial Intelligence algorithms to the assessment of potential immunogenicity of peptides. It recognizes biological features that decide if a given peptide would be presented by the patient's HLA I complex and elicit adaptive immune response.
Recognizing the correct 1% of all the potential peptides that might be produced from mutations in a tumor and prioritizing them for laboratory work increases efficiency of pre-clinical R&D while reducing necessary budget and time. Ardigen's algorithms offer higher accuracy and PPV (Positive Predictive Value) than competing solutions.
ArdImmune Rank offers the functionality of ranking a list or database of short peptides by their potential immunogenicity, taking into account multiple factors, including:
ArdImmune Vax is designed to support the demanding process of personalized cancer vaccine manufacturing. Tumors bear thousands of mutations but usually only a few of them create neoepitopes able to elicit an adaptive immune response. The tumor-related neoepitopes are presented by the HLA I complexes on the tumor cells and recognized by the T-cell receptor. This process can be boosted by the application of a vaccine training CD8+ and CD4+ cells to recognize tumor-specific pHLA complexes.
Cancer, however, is a very heterogeneous disease thus personalized approach for each tumor is poised to be more effective. Ardigen AI-based Neoepitope Prediction Platform uses the patient’s tumor and normal tissue sequencing information to predict the potential neoepitopes and assess their immunogenicity.
ArdImmune Vax can be used in the pre-clinical design and test phase as well in the highly time-constrained post-approval scenario. Our technology is aware of the complexity of neoepitope processing and factors contributing to it's immunogenicity. In particular, it takes into consideration the following:
Ardigen’s holistic approach to immuno-oncology analyzes the properties of tumor and its microenvironment, including composition of immune cells, biomarkers detectable in blood and other fluids, and microbiome composition. The data is used to build a comprehensive picture of a response to an immunotherapy and to select robust biomarkers to be used in the clinical trial assay and companion diagnostics development. Ardigen’s systems biology approach and cutting edge AI form a unique in silico platform for discovery of biomarkers predictive of immunotherapy response and facilitate the design of CTA and CDx.
The Ardigen Biomarker Discovery Platform accepts various data types coming from multi-omics experiments, including raw sequencing of tumor and normal tissue, as well as an array of custom measurements. A range of fine-tuned preprocessing pipelines is applied to prepare high quality data for biomarker discovery modeling. The platform also accepts data that have already been processed and standardized.
Ardigen’s solution includes many ready to use genetic signatures described in peer-reviewed scientific publications to enable wider interpretation of patient stratification and benchmarking with public knowledge, including:
The unique prognostic and predictive abilities of our Biomarker Discovery Platform come from a set of rules that the system has been built around: