Innovations in Immunology

Advancing Targeted Treatments for Immune-Related Ailments

Navigating Challenges in Immune-Related Disorders

Autoimmune disorders impact 5% – 8% percent of the U.S. population. Immune related disorders encompasses autoimmune, allergic, infectious, cancerous, metabolic, neurodegenerative like Alzheimer’s, and cardiovascular disorders.

The immune system’s dual role, protecting and sometimes harming the body as in autoimmune disorders, poses a complex challenge in achieving the right modulation for therapeutic purposes. Immunology research holds promise for effective treatments, personalized medicine, and seeks to enhance the lives of those affected by immune-related disorders.

In advancing drug discovery and development, Aganitha utilizes Generative AI and ML to:

Comprehend disease heterogeneity

Identify therapeutic targets

Design and optimize therapeutic modalities

In silico evaluate drug efficacy

AI and ML Solutions: Navigating Challenges in Immune-related Disorders

Unleashing the power of AI and ML to tackle immunological disorders with targeted diagnosis and treatment strategies.

Complexity of Immunological Processes

Owing to the complex interactions between various cell types, signaling pathways, and molecular components, Computational models can simulate and analyze complex immunological processes, helping understand the dynamics of immune responses in health and disease.

Biomarker discovery

Due to the severity, organ involvement, and systemic manifestations of immune disorders, biomarker discovery is challenging. AI can be used to analyze large datasets to identify potential biomarkers, aiding diagnostic and prognostic efforts.

Drug discovery and Optimization

Current immune-modulatory drugs are broad-acting, non-disease-specific, and linked to side effects like infection and malignancy. In silico tools facilitate virtual screening, enable precision drug design, predict side effects, personalize treatment, and expedite drug development.

Varied Immunotherapy Responses

Due to varied immunotherapy responses across all patients, predicting and optimizing responses is a significant challenge. ML models analyze patient data to identify factors influencing outcomes, guiding the development of more effective strategies.

Managing Big Data

Due to the generation of diverse data types, integrating them becomes challenging. AI and ML tools can integrate multi-omics data, providing a holistic view of immune system function and dysfunction.

Personalized Medicine

Due to significant inter-individual variability, requiring personalized treatment, AI and ML models analyze patient data to identify patterns and predict responses, facilitating the development of tailored personalized medicine.

Understanding the MoA of immune-related disorders using single-cell transcriptomics

Explore key features of Aganitha’s pipeline to get deep insights into disease mechanism

Clustering analysis and cell annotation

We perform clustering analysis on single cell transcriptomics data. Cells are assigned to different clusters based on cell types or cell states using UMAP plots. This is followed by cell annotation.

 

 

Differential gene expression

We then compare cells of different clusters and generate a list of differentially expressed genes. DGE provides input for gene set enrichment analysis or pathway analysis. 

 

 

Pathway enrichment analysis

We perform pathway enrichment analysis for biological functions,  gene ontologies or regulatory pathways that are overrepresented in a condition group of genes on the basis of differentially expressed (DE) genes. 

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