IGC Pharma to Showcase AI Portfolio at AAIC 2026, Including AHA Platform That Reduced Alzheimer's Data Harmonization Time by 90% in Representative Workflow

IGC Pharma (NYSE American: IGC) will showcase its Alzheimer’s-focused AI portfolio in seven presentations at AAIC 2026, July 12-15 in London.

Rhea-AI Summary

IGC Pharma (NYSE American: IGC) will showcase its Alzheimer’s-focused AI portfolio in seven presentations at AAIC 2026, July 12-15 in London.

Highlights include the AHA agentic harmonization platform, which cut a representative 100-variable data workflow from 28 to 2.5 hours, and updates on the Phase 2 CALMA trial of IGC-AD1.

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News Market Reaction – IGC

$29.01M Mkt Cap at Peak Alert

On Jul 9, the day this news came out, IGC closed 1.42% above the previous close. Our momentum scanner recorded 2 alerts for this stock that day.

Data tracked by StockTitan Argus for the Jul 9 session.

Key Figures

AAIC presentations: 7 presentations Conference dates: July 12–15, 2026 Workflow variables: 100 variables +4 more

AAIC presentations

7 presentations

AAIC 2026 scientific program

Conference dates

July 12–15, 2026

AAIC 2026 in London

Workflow variables

100 variables

Representative structured-data harmonization test

Manual harmonization time

28 hours

Baseline internal structured-data workflow

AHA harmonization time

2.5 hours

Same internal workflow including human verification

Workflow time reduction

90% reduction

AHA vs manual harmonization in representative test

CALMA enrollment

100% of baseline randomization target

Phase 2 CALMA trial of IGC-AD1

Previous AI Reports

5 past events · Latest: Jun 23

Same Type 5 events

1. Jun 23

   AI platform update

   24h Move

   -0.6%

   Reported 90% reduction in harmonization time with AHA beta workflow.

2. Mar 18

   AI conference showcase

   24h Move

   -4.7%

   Planned AHA demonstration with ADDI at ADPD 2026 Alzheimer’s meeting.

3. Feb 26

   AI patent filings

   24h Move

   -1.8%

   Filed utility patents for AHA architecture after harmonizing multiple datasets.

4. Nov 03

   AI pipeline expansion

   24h Move

   +1.8%

   Expanded AI in‑silico platform for drug discovery across several Alzheimer’s programs.

5. Oct 22

   AI prize recognition

   24h Move

   -2.7%

   AHA selected as semi‑finalist in Alzheimer’s Insights AI Prize competition.

24h Move is the share-price change in the day after each event; other market factors may also have contributed.

Key Terms

electronic health record, graph neural networks, structural equation modeling, multi-omics, +2 more

6 terms

electronic health record technical

"Electronic Health Record Mining: Applying advanced machine learning to routine medical records"

A digital version of a patient’s medical chart that collects health information — diagnoses, medications, lab results, imaging and doctors’ notes — in one place so authorized clinicians can view and update it. For investors, electronic health records matter because they drive revenue and costs for companies that build, sell or rely on them, influence how quickly care is delivered, and create opportunities (and risks) tied to data access, software updates, regulation and patient privacy. Think of it as an online file cabinet for health that affects how the healthcare system runs and spends money.

graph neural networks technical

"Multi-Omics and Topological Data: Using graph neural networks and structural equation modeling"

Graph neural networks are a type of artificial intelligence that learns from data organized as points and the connections between them — think of it as learning from a map or a social network rather than a spreadsheet. They matter to investors because many real-world problems (supply chains, customer relationships, fraud links, drug-target interactions) are about relationships, and these models can reveal patterns or predict outcomes that traditional methods miss, potentially improving decisions and competitive advantage.

structural equation modeling technical

"Using graph neural networks and structural equation modeling to explore neuropsychiatric pathways"

A statistical technique that models complex relationships among variables, including both directly measured indicators and unobserved “latent” factors, by combining elements of regression and factor analysis. It lets analysts test a proposed blueprint of cause-and-effect links—for example, how underlying customer sentiment, pricing, and marketing together drive sales—so investors can better understand which hidden or observed drivers might explain financial performance or risk. Think of it as a wiring diagram that shows how different pieces of a business or market are connected.

multi-omics technical

"Multi-Omics and Topological Data: Using graph neural networks and structural equation modeling"

Multi-omics is a comprehensive approach that combines different types of biological data—such as genetic information, proteins, and other molecules—to gain a detailed understanding of how living systems function. For investors, this approach can reveal insights into health, disease, or biological processes that may influence the development of new treatments or technologies, potentially impacting market opportunities and innovation in healthcare.

topological data technical

"Multi-Omics and Topological Data: Using graph neural networks and structural equation modeling"

Topological data describes the shape, connections, and layout of elements within a dataset rather than individual values — think of it as a map of how points, clusters, and pathways are linked. Investors care because this kind of information helps reveal networks and patterns (for example in trading flows, supply chains, or patient populations) that traditional numbers can miss, making it easier to spot structural risks, bottlenecks, or hidden relationships.

patient stratification medical

"designed to accelerate data integration, patient stratification, and clinical development workflows"

Patient stratification is the practice of grouping patients into subgroups based on shared characteristics — such as symptoms, test results, or likely response to treatment — so therapies and trials can be targeted more precisely. For investors, it matters because better matching of treatments to the right patients can raise the chances of clinical success, reduce time and costs in development, and increase the likelihood of regulatory approval and profitable market adoption, much like tailoring a product to the right customer segment.

AI-generated analysis. How Rhea-AI works. Not financial advice.

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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.

About IGC Pharma (dba IGC):

IGC Pharma (NYSE American:IGC) is a clinical-stage biotechnology company leveraging AI to develop innovative treatments for Alzheimer's and metabolic disorders. Our lead asset, IGC-AD1, is a therapy currently in a Phase 2 trial (CALMA) for agitation in Alzheimer's dementia. Our pipeline includes TGR-63, targeting amyloid plaques, and early-stage programs focused on neurodegeneration, tau proteins, and metabolic dysfunctions. We integrate AI to accelerate drug discovery, optimize clinical trials, and enhance patient targeting. With a complete patent portfolio and a commitment to innovation, IGC Pharma is advancing breakthrough therapies.

Forward-Looking Statements:

This press release contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. These statements are based on current expectations and involve risks and uncertainties that could cause actual results to differ materially, including risks related to the Company's ability to complete enrollment in its Phase 2 CALMA trial within anticipated timeframes, demonstrate safety and efficacy, the timing of data readouts, regulatory approvals, and other factors discussed in the Company's filings with the U.S. Securities and Exchange Commission (SEC), including its most recent Annual Report on Form 10-KT. The Company undertakes no obligation to update these statements.

Contact Information:

Andres Sanchez
Investor Relations
info@igcpharma.com
+1 301-983-0998 / +1 (202) 569-2566

SOURCE: IGC Pharma, Inc.

View the original press release on ACCESS Newswire

FAQ

AI-generated questions and answers. How Rhea-AI works. Not financial advice.

What is IGC Pharma (IGC) presenting at AAIC 2026?

IGC Pharma is presenting seven Alzheimer’s-focused AI studies at AAIC 2026. According to IGC Pharma, these cover agentic automation, speech-based biomarkers, EHR mining, multi-omics analysis, and predictive analytics to support data integration, patient stratification, and clinical development workflows.

How does IGC Pharma’s AHA platform improve Alzheimer’s data harmonization?

AHA is a multi-agent AI system that accelerates Alzheimer’s data harmonization. According to IGC Pharma, in a representative 100-variable workflow, AHA reduced harmonization time from 28 hours of manual work to 2.5 hours with verification, a 90% time reduction for that test.

What AI-driven biomarker work is IGC Pharma (IGC) showing at AAIC 2026?

IGC Pharma is highlighting speech and audio biomarkers using multilingual deep-learning models. According to IGC Pharma, these models analyze speech patterns linked to cognitive decline, supporting potential scalable, non-invasive Alzheimer’s screening and patient stratification in clinical development settings.

What is the status of IGC Pharma’s IGC-AD1 CALMA Phase 2 trial?

IGC-AD1 is in the Phase 2 CALMA trial for agitation in Alzheimer’s dementia. According to IGC Pharma, CALMA has reached 100% of its baseline randomization target, with limited over-enrollment underway before patient follow-up, database activities, and topline analysis.

How might IGC Pharma’s AI portfolio support future Alzheimer’s trials?

IGC Pharma expects its AI tools to aid future clinical development planning. According to IGC Pharma, the portfolio may improve data integration, cohort generation, cross-study analysis, patient stratification, and clinical trial workflow design across potential Phase 3 and registration-enabling Alzheimer’s programs.

When and where is AAIC 2026, where IGC Pharma (IGC) will present?

AAIC 2026 takes place July 12-15, 2026, in London. According to IGC Pharma, the company will use the conference to showcase its Alzheimer’s AI portfolio, including the AHA platform and multiple machine-learning approaches for patient identification and clinical workflows.