{"id":537183,"date":"2026-06-23T15:24:24","date_gmt":"2026-06-23T15:24:24","guid":{"rendered":"https:\/\/www.newjerseyheadlines.com\/news\/story\/537183\/igc-pharmas-nyse-american-igc-ai-platform-cuts-alzheimers-research-by-90-accelerating-drug-discovery.html"},"modified":"2026-06-23T15:24:24","modified_gmt":"2026-06-23T15:24:24","slug":"igc-pharmas-nyse-american-igc-ai-platform-cuts-alzheimers-research-by-90-accelerating-drug-discovery","status":"publish","type":"post","link":"http:\/\/www.northcarolinaheadlines.com\/news\/story\/537183\/igc-pharmas-nyse-american-igc-ai-platform-cuts-alzheimers-research-by-90-accelerating-drug-discovery.html","title":{"rendered":"IGC Pharma’s (NYSE American: IGC) AI Platform Cuts Alzheimer’s Research by 90%, Accelerating Drug Discovery"},"content":{"rendered":"
\"IGC<\/a><\/div>\n

IGC Pharma, Inc. (NYSE: IGC) a clinical-stage Biotechnology<\/strong> company developing treatments for Alzheimer’s Disease<\/strong> and Artificial Intelligence (AI) <\/strong>enabled healthcare technologies, today announced the beta release of its proprietary Agentic Harmonization Assistant (“AHA”), a patent-pending Healthcare AI<\/strong> platform designed to accelerate Alzheimer’s Drug Discovery<\/strong>, Clinical Research<\/strong>, Precision Medicine<\/strong>, and Pharmaceutical Development<\/strong> by helping researchers quickly combine and organize Alzheimer’s disease<\/strong>, aging-related, and other medical data<\/strong> that is often scattered across multiple databases, studies, hospitals, clinical trials, healthcare systems, and research institutions.<\/p>\n

The announcement comes as IGC advances its lead Phase 2 Alzheimer’s Drug<\/strong> candidate, IGC-AD1, for agitation associated with Alzheimer’s Dementia<\/strong>, positioning the Company at the intersection of two rapidly expanding markets: Alzheimer’s Therapeutics<\/strong> and AI-Driven Healthcare Innovation<\/strong>.<\/p>\n

Alongside <\/strong>IGC Pharma, Inc. (NYSE: IGC<\/strong>) watch Atlantic International Corp (NASDAQ: ATLN<\/strong>), Tianci Intl (NASDAQ: CIIT<\/strong>), Peraso Inc. (NASDAQ: PRSO<\/strong>) and Catheter Precision (NYSE: VTAK<\/strong>) all trading activity now.<\/p>\n

“Artificial Intelligence<\/strong>, Machine Learning<\/strong>, and advanced Healthcare AI<\/strong> are transforming how new medicines are discovered and developed,” said Ram Mukunda, CEO of IGC Pharma. “Our vision is for AHA to become a foundational AI Infrastructure Platform<\/strong> that helps pharmaceutical companies, healthcare organizations, academic institutions, and government agencies transform fragmented Biomedical Data<\/strong> into interoperable, reusable, and AI-ready assets capable of supporting faster Drug Discovery<\/strong>, more efficient Clinical Trials<\/strong>, enhanced Precision Medicine<\/strong>, and accelerated Alzheimer’s Research<\/strong>.”<\/p>\n

In internal testing using a representative Alzheimer’s Disease<\/strong> dataset containing 100 variables, AHA reduced estimated data harmonization time from approximately 28 hours to 2.5 hours, including human verification, representing an approximate 90%<\/strong> reduction in processing time. External beta testers have reported similar efficiency gains in comparable research environments.<\/p>\n

IGC Pharma plans to demonstrate AHA in connection with the Alzheimer’s Disease Data Initiative’s AD Workbench ecosystem during the Alzheimer’s Association International Conference (AAIC) 2026<\/strong> in London. The demonstration is expected to showcase AHA’s ability to support rapid cohort generation, cross-study analysis, collaborative research, Biomedical Data Harmonization<\/strong>, and AI-Powered Healthcare Analytics<\/strong>.<\/p>\n

The Company believes Alzheimer’s Disease<\/strong> represents a high-value entry point into a much broader Healthcare Technology<\/strong> opportunity. IGC Pharma is expanding AHA’s architecture to support additional biomedical modalities, including Medical Imaging<\/strong> such as MRI, PET, and CT scans, as well as Genomics<\/strong>, Proteomics<\/strong>, and other Omics-Based Data<\/strong>.<\/p>\n

With Alzheimer’s Disease<\/strong> affecting millions of people worldwide and investment accelerating across Artificial Intelligence<\/strong>, Healthcare AI<\/strong>, Precision Medicine<\/strong>, Machine Learning<\/strong>, Biotechnology<\/strong>, Drug Discovery<\/strong>, Clinical Research<\/strong>, and Neuroscience<\/strong>, IGC Pharma believes AHA positions the Company to participate in the growing convergence of AI-Powered Healthcare Innovation<\/strong> and Alzheimer’s Drug Development<\/strong>.<\/p>\n

About IGC Pharma<\/strong><\/p>\n

IGC Pharma, Inc. (NYSE American: IGC) is a clinical-stage Biotechnology Company<\/strong> developing therapeutics for Alzheimer’s Disease<\/strong> and other indications. The Company’s lead asset, IGC-AD1, is currently being evaluated in the Phase 2 CALMA Clinical Trial<\/strong> for agitation associated with Alzheimer’s Dementia<\/strong>. The Company is also developing AI-Enabled Healthcare Technologies<\/strong> intended to support Biomedical Discovery<\/strong>, Drug Development<\/strong>, Clinical Research<\/strong>, Healthcare Analytics<\/strong>, and Precision Medicine<\/strong> initiatives.<\/p>\n

About AHA<\/strong><\/p>\n

The Agentic Harmonization Assistant (AHA) is IGC Pharma’s proprietary AI-Enabled Platform<\/strong> designed to assist with the harmonization of complex Biomedical Datasets<\/strong>. AHA uses a patent-pending multi-agent, human-in-the-loop workflow to support dataset profiling, mapping, transformation, validation, and review across Healthcare<\/strong>, Pharmaceutical<\/strong>, and Clinical Research<\/strong> applications. AHA was originally developed to support MINT-AD, IGC Pharma’s predictive Alzheimer’s Risk Stratification<\/strong> engine. Training advanced Machine Learning<\/strong>, Artificial Intelligence<\/strong>, and Predictive Analytics<\/strong> models require large volumes of harmonized Biomedical Data<\/strong>; however, preparing those datasets manually remains one of the largest bottlenecks in Healthcare Research<\/strong>, Drug Development<\/strong>, Precision Medicine<\/strong> and Clinical Analytics<\/strong>. Alzheimer’s Research<\/strong> depends on large volumes of patient data collected across Clinical Trials<\/strong>, hospitals, research institutions, and healthcare systems. These datasets are frequently fragmented by inconsistent variable definitions, measurement units, study protocols, and clinical endpoints. Before modern AI Systems<\/strong> can generate reliable insights, the data must be harmonized to become clinically meaningful and interoperable across sources.<\/p>\n

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