CNS Disease Research

Enhancing CNS Disorder Management through Deep Science and Deep Tech Solutions

Overcoming Challenges in the Diagnosis and Treatment of CNS Disorders

According to the World Health Organization, Central Nervous System disorders stand as the leading cause of disability and the second leading cause of death for people under 60. 

From neurodegenerative conditions like Alzheimer’s and Parkinson’s to neuroimmunological disorders such as Multiple Sclerosis, and psychiatric conditions like Depression and Schizophrenia, they pose a substantial global health burden. Understanding these disorders alleviates individual suffering and also carries broader societal implications, including reduced healthcare costs, improved quality of life, and enhanced insights into cognition, behavior, and consciousness.

Aganitha leverages AI and ML to improve the detection, management, and treatment of neurological disorders to aid:

Early diagnosis & risk prediction

Predicting disease progression

Drug discovery and optimization

Personalized medicine

Challenges in CNS disorders Management

Managing CNS disorders presents several significant challenges from a scientific perspective in the areas of Diagnosis, Prognosis and Drug Discovery & Development

Complexity of Brain Structure-Function Connections

Due to the bidirectional nature of the brain’s structure-function connections, Generative AI modeling aids in improving the accuracy of identifying Alzheimer’s Disease by learning the mapping function between structural and functional domains.

Mechanobiology of the Brain

Due to our limited understanding of how neuronal and glial cell mechanics affect age-related cognitive decline and diseases like Alzheimer’s, AI and ML analyze data to comprehend changes in the brain’s mechanical properties.

Therapeutic Target Identification

Due to heterogeneity in diseases like ALS, it is a challenge to identify specific cryptic splicing events relevant to its different subtypes. AI and ML can aid in deciphering these cryptic splicing events as novel therapeutic targets and biomarkers.

Delivery of drugs into the CNS

Due to the blood-brain barrier’s selectivity, drug passage is limited. Generative AI aids in designing small-molecule compounds, Antibodies and gene therapies to potentially bypass the BBB and target neurological disorders effectively.

Complexity of seizure patterns

Due to our limited understanding of seizure patterns including variability in seizure onset and duration in conditions like epilepsy, deep learning models can be trained to recognize and predict these complex patterns, aiding early detection and prevention.

Data Scarcity in neurodegenerative disorders

Due to the scarcity of data in neurodegenerative disorders, Generative Adversarial Networks are being employed to generate realistic freezing of gait data in disorders like Parkinson’s Disease to train AI models.

Advancing Research, Therapeutics, and BioPharma

The use of in silico computational methods has been instrumental in advancing CNS research, therapeutic interventions, and pharmaceutical development. These methods leverage the computational power of generative AI, modeling, machine learning, simulations, and data analytics to accelerate progress in this scientific field.

Analyzing NGS data:

We screen and analyze NGS data for Quality Checks, multi-level error corrections and bias removal for denoising, etc. and plot the analysis.

Tools: High throughput sequencing data analysis, pattern matching based screening, and sequence alignment statistics.

Characterizing Variants for improved features:

We seek to perform tissue-wise analysis of enrichment profiles of our viral vector variants. We Identify and cluster enriched variants into families based on their identity, physicochemical properties & cell tropism.

Tools: Statistical analysis, correlation analysis, probabilistic approaches, and clustering analysis.

In silico modeling for viral vector design :

We seek to do fitness modeling and optimization for hit expansion with MD and Protein Language Models. We leverage insights from in vivo analysis and MD to optimize BBB penetration.

Tools: Molecular docking, PLM, and Transformers.

Developing an end to end pipeline:

We seek to develop a single point user interface (GUI) pipeline for screening our viral vector for desired features such as tissue tropism. This makes handling high-throughput data easier and cheaper.

Tools: User Interface, data fetching from API endpoints, customized user experience, high performance computing cluster.

Robust storage and data retrieval:

We seek to provide better accessibility and storage features to the pipeline. We expanded metadata capture at higher granularity and added functionality to analyze data at different levels such as serotype, tissue, species, delivery routes.

Tools: Database modeling, and data retrieval from various sources.

Visualization and data analysis:

We seek to visualize the enrichment profile of variants across cell types, identify statistically significant relative enrichment or depletion of amino acids at each position, and characterize similar variants into families. Visualization of large tissue datasets from multiple experiments.

Tools: Clustering analysis, and integrated platform for data visualization.

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