IGC Pharma to Showcase AI Portfolio at AAIC 2026, Including AHA Platform That Reduced Alzheimer's Data Harmonization Time by 90% in Representative Workflow
https://www.accessnewswire.com/newsroom/en/bi...ed-1188740
Seven presentations highlight patent-pending agentic automation, speech-based cognitive models, EHR mining, and predictive analytics designed to support Alzheimer's discovery and clinical development
POTOMAC, MD / ACCESS Newswire / July 9, 2026 / IGC Pharma, Inc. (NYSE American:IGC) ("IGC" or the "Company"
, a clinical-stage biotechnology company developing therapeutics for Alzheimer's disease, today announced it will showcase its artificial intelligence portfolio across seven scientific presentations at the Alzheimer's Association International Conference (AAIC) 2026, taking place July 12-15 in London.
IGC Pharma is building computational infrastructure designed to accelerate data integration, patient stratification, and clinical development workflows in Alzheimer's disease. The Company's AAIC presentations map an AI strategy spanning data harmonization, speech-based biomarkers, electronic health record analysis, and predictive machine learning.
"Artificial intelligence is changing how medicines are discovered and developed, but AI models are only as powerful as the data they can learn from," said Ram Mukunda, CEO of IGC Pharma. "Our AAIC presentations highlight a broader AI strategy designed to address major bottlenecks in Alzheimer's development, from harmonizing fragmented datasets to identifying patients faster and supporting more targeted clinical workflows."
Anchoring the AI Portfolio: The AHA Platform
A centerpiece of the Company's AAIC showcase is AHA™, the Agentic Harmonization Assistant. AHA is a proprietary, patent-pending multi-agent architecture designed to accelerate the integration of complex Alzheimer's datasets. In internal testing using a representative Alzheimer's structured-data workflow involving 100 variables, AHA reduced harmonization time from 28 hours of manual processing to 2.5 hours, including human verification of results, a 90% reduction in workflow time for that representative test case. Performance may vary depending on dataset complexity, data quality, and user workflow. External beta testers have also reported similar time-saving potential in comparable data environments.
Predictive Biomarkers and Patient Stratification
Beyond data automation, IGC Pharma's additional AAIC presentations highlight how the Company is applying machine learning and deep learning to support Alzheimer's patient identification, stratification, and clinical development workflows:
Speech and Audio Biomarkers: Evaluating multilingual deep-learning models to identify speech patterns associated with cognitive decline, supporting the potential for scalable, non-invasive screening approaches.
Electronic Health Record Mining: Applying advanced machine learning to routine medical records to support dementia detection, subtyping, and clinical trial recruitment workflows.
Multi-Omics and Topological Data: Using graph neural networks and structural equation modeling to explore neuropsychiatric pathways and support more targeted therapeutic development.
IGC Pharma expects its AAIC presentations to demonstrate how these technologies may support rapid cohort generation, cross-study analysis, patient stratification, and AI-enabled clinical development planning.
In parallel, the Company's lead therapeutic asset, IGC-AD1, is currently being evaluated in the Phase 2 CALMA clinical trial for agitation associated with Alzheimer's dementia. As previously announced, CALMA has reached 100% of its baseline randomization target. The Company is currently completing limited over-enrollment and expects to proceed thereafter with patient follow-up, database activities, and topline analysis. IGC Pharma believes its AI portfolio may help inform future Phase 3 and registration-enabling development planning by improving data integration, cohort identification, patient stratification, and clinical trial workflow design across Alzheimer's programs.
https://www.accessnewswire.com/newsroom/en/bi...ed-1188740
Seven presentations highlight patent-pending agentic automation, speech-based cognitive models, EHR mining, and predictive analytics designed to support Alzheimer's discovery and clinical development
POTOMAC, MD / ACCESS Newswire / July 9, 2026 / IGC Pharma, Inc. (NYSE American:IGC) ("IGC" or the "Company"
, a clinical-stage biotechnology company developing therapeutics for Alzheimer's disease, today announced it will showcase its artificial intelligence portfolio across seven scientific presentations at the Alzheimer's Association International Conference (AAIC) 2026, taking place July 12-15 in London. IGC Pharma is building computational infrastructure designed to accelerate data integration, patient stratification, and clinical development workflows in Alzheimer's disease. The Company's AAIC presentations map an AI strategy spanning data harmonization, speech-based biomarkers, electronic health record analysis, and predictive machine learning.
"Artificial intelligence is changing how medicines are discovered and developed, but AI models are only as powerful as the data they can learn from," said Ram Mukunda, CEO of IGC Pharma. "Our AAIC presentations highlight a broader AI strategy designed to address major bottlenecks in Alzheimer's development, from harmonizing fragmented datasets to identifying patients faster and supporting more targeted clinical workflows."
Anchoring the AI Portfolio: The AHA Platform
A centerpiece of the Company's AAIC showcase is AHA™, the Agentic Harmonization Assistant. AHA is a proprietary, patent-pending multi-agent architecture designed to accelerate the integration of complex Alzheimer's datasets. In internal testing using a representative Alzheimer's structured-data workflow involving 100 variables, AHA reduced harmonization time from 28 hours of manual processing to 2.5 hours, including human verification of results, a 90% reduction in workflow time for that representative test case. Performance may vary depending on dataset complexity, data quality, and user workflow. External beta testers have also reported similar time-saving potential in comparable data environments.
Predictive Biomarkers and Patient Stratification
Beyond data automation, IGC Pharma's additional AAIC presentations highlight how the Company is applying machine learning and deep learning to support Alzheimer's patient identification, stratification, and clinical development workflows:
Speech and Audio Biomarkers: Evaluating multilingual deep-learning models to identify speech patterns associated with cognitive decline, supporting the potential for scalable, non-invasive screening approaches.
Electronic Health Record Mining: Applying advanced machine learning to routine medical records to support dementia detection, subtyping, and clinical trial recruitment workflows.
Multi-Omics and Topological Data: Using graph neural networks and structural equation modeling to explore neuropsychiatric pathways and support more targeted therapeutic development.
IGC Pharma expects its AAIC presentations to demonstrate how these technologies may support rapid cohort generation, cross-study analysis, patient stratification, and AI-enabled clinical development planning.
In parallel, the Company's lead therapeutic asset, IGC-AD1, is currently being evaluated in the Phase 2 CALMA clinical trial for agitation associated with Alzheimer's dementia. As previously announced, CALMA has reached 100% of its baseline randomization target. The Company is currently completing limited over-enrollment and expects to proceed thereafter with patient follow-up, database activities, and topline analysis. IGC Pharma believes its AI portfolio may help inform future Phase 3 and registration-enabling development planning by improving data integration, cohort identification, patient stratification, and clinical trial workflow design across Alzheimer's programs.
