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Why Beating Alzheimer’s Requires AI And Precision Combo Therapies

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Manage episode 517878285 series 2993661
Content provided by Dung Trinh. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Dung Trinh or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

We dig into why single-target drugs fall short against Alzheimer’s and how explainable AI can design smarter, safer combination therapies that target upstream biology while managing risks like ARIA. We share tools, data sources, and a roadmap to validate predictions with digital twins and organoids.
• global burden and rising projections
• modest gains from new antibodies and unmet need
• failed and ongoing combo trials since 2015
• PK versus PD strategies and examples
• shift to endophenotypes and network medicine
• three design patterns for combinations
• copathologies, CAA, and managing ARIA
• AI toolkits: networks, knowledge graphs, QSP
• validation via real-world data and organoids
• roadblocks in data quality, regulation, and explainability
This podcast is created by Ai for educational and entertainment purposes only and does not constitute professional medical or health advice. Please talk to your healthcare team for medical advice.

Never miss an episode—subscribe on your favorite podcast app!

  continue reading

Chapters

1. Framing The Alzheimer’s Crisis (00:00:00)

2. Limits Of Single-Target Drugs (00:00:29)

3. What Trials Reveal About Combos (00:02:14)

4. PK Vs PD: Getting Mechanisms Right (00:03:28)

5. Targeting Endophenotypes Upstream (00:05:18)

6. Three Patterns For Combo Design (00:06:20)

7. Copathologies And ARIA Risk (00:08:22)

8. Data Foundations For AI (00:10:09)

9. AI Toolkits: Networks, Graphs, QSP (00:12:02)

10. Validation: Digital Twins And Organoids (00:14:07)

11. Roadblocks: Data, Regulation, Trust (00:16:10)

12. Synthesis And Open Questions (00:18:12)

104 episodes

Artwork
iconShare
 
Manage episode 517878285 series 2993661
Content provided by Dung Trinh. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Dung Trinh or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

We dig into why single-target drugs fall short against Alzheimer’s and how explainable AI can design smarter, safer combination therapies that target upstream biology while managing risks like ARIA. We share tools, data sources, and a roadmap to validate predictions with digital twins and organoids.
• global burden and rising projections
• modest gains from new antibodies and unmet need
• failed and ongoing combo trials since 2015
• PK versus PD strategies and examples
• shift to endophenotypes and network medicine
• three design patterns for combinations
• copathologies, CAA, and managing ARIA
• AI toolkits: networks, knowledge graphs, QSP
• validation via real-world data and organoids
• roadblocks in data quality, regulation, and explainability
This podcast is created by Ai for educational and entertainment purposes only and does not constitute professional medical or health advice. Please talk to your healthcare team for medical advice.

Never miss an episode—subscribe on your favorite podcast app!

  continue reading

Chapters

1. Framing The Alzheimer’s Crisis (00:00:00)

2. Limits Of Single-Target Drugs (00:00:29)

3. What Trials Reveal About Combos (00:02:14)

4. PK Vs PD: Getting Mechanisms Right (00:03:28)

5. Targeting Endophenotypes Upstream (00:05:18)

6. Three Patterns For Combo Design (00:06:20)

7. Copathologies And ARIA Risk (00:08:22)

8. Data Foundations For AI (00:10:09)

9. AI Toolkits: Networks, Graphs, QSP (00:12:02)

10. Validation: Digital Twins And Organoids (00:14:07)

11. Roadblocks: Data, Regulation, Trust (00:16:10)

12. Synthesis And Open Questions (00:18:12)

104 episodes

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