[ARDD 2024 Recap] Dual-Purpose Target Discovery and Aging Research with Precious GPT Models

Summary

Frank presents the advancements in AI-driven drug discovery, focusing on dual-purpose target discovery for both aging and cancer treatments. He discusses the integration of multiomic data and AI models to identify potential therapeutic targets. The research highlights the discovery of the target TEIC for IPF (idiopathic pulmonary fibrosis), which progressed to clinical trials within 30 months. He also showcases the identification of the KD M1A target, validated through experiments, demonstrating how AI facilitates the discovery of targets that impact both cancer and aging simultaneously.

Takeaways

• 📈 Frank discusses the application of AI in drug discovery, specifically in target discovery, indication expansion, and drug repurposing.
• 🏥 The paper published in Translational Pharmacological Sciences highlights AI's role in drug discovery, with AI-identified targets advancing to clinical trials.
• 🧬 The AI and bioinformatics models utilize multiomic data to calculate disease-gene interactions in real-time, aiding in target discovery and indication expansion.
• 📚 A paper published in Nature about 'teic' as a target for IPF (Idiopathic Pulmonary Fibrosis) showcases AI's comprehensive impact on drug discovery.
• 🔍 The AI platform 'Pandemics' identified 'teic' as a promising target for IPF, emphasizing the importance of targeting age-associated diseases.
• 🧪 Generative AI, specifically 'Chemistry 42', was used to design novel molecules to target 'teic', demonstrating AI's role in molecular design.
• ⏱️ The development from target identification to clinical trials was achieved in a record 30 months, showcasing the efficiency of AI-assisted drug discovery.
• 🔬 The study integrates panomics to identify targets implicated in multiple aging diseases, considering aging as a primary driver of diseases.
• 🌐 Collaboration with the University of Oso focuses on identifying dual-purpose targets effective for both cancer and aging treatments.
• 🧪 KDM1A is highlighted as a potential dual-purpose target, being upregulated in multiple solid tumors and negatively associated with aging hallmarks.

Q & A

What is the main focus of the presentation?

The presentation focuses on the use of AI, specifically GPT models, in drug discovery, target discovery, indication expansion, and drug repurposing.

What success has been achieved since the use of AI in target discovery?

Since 2020, several AI-identified targets have been validated in the lab, and one of the identified targets has progressed to human clinical phase 2 trials.

What approach is used for target discovery in the research?

The approach uses multiomic data and text data, combined with AI and bioinformatics models, to calculate interaction scores between diseases, genes, and biological processes in real time. This helps identify target candidates for diseases or match the best indications for specific targets.

What specific program is highlighted as a success story in the use of AI for drug discovery?

The program highlighted is TEIC for idiopathic pulmonary fibrosis (IPF), which was identified as the best target using the AI platform Panomics in 2019. It successfully moved through preclinical trials and into clinical phase 1 and 2 within 30 months.

How does the research relate aging to drug discovery?

The research identifies therapeutic targets implicated in aging, as aging is seen as a driving factor for many diseases. The platform assesses how these targets are integrated into the hallmarks of aging to develop more effective treatments.

What is the significance of dual-purpose targets in this research?

Dual-purpose targets refer to targets that can be used to treat both cancer and aging simultaneously. The research aims to identify such targets by examining shared biological pathways in cancer and aging.

What is the significance of KDM1A in the study?

KDM1A is highlighted as a dual-purpose target, upregulated in 10 out of 11 solid tumors, and negatively associated with aging. Experiments show that knocking down KDM1A may increase lifespan, making it a promising target for both cancer and aging treatment.

What AI tool is used to design molecules targeting specific diseases?

The AI tool 'Chemistry 42' is used to design novel molecules that target specific diseases, such as TEIC for IPF, after the targets are identified.

How does the AI platform facilitate drug repurposing?

The AI platform facilitates drug repurposing by providing a list of the best indications for specific targets, allowing researchers to find new uses for existing drugs.

What collaboration is mentioned in the research regarding dual-purpose targets?

The research mentions a collaboration with Evandro from the University of Oslo, focusing on identifying targets integrated in both cancer and aging, particularly across 11 common solid tumors.