Case Studies
Real examples of AI-driven drug discovery: from multi-omics data to therapeutic candidates
Overcoming ICI Resistance in Solid Tumors
Immune Checkpoint Inhibitor Resistance
The Challenge
Most patients with solid tumors do not respond to immune checkpoint inhibitors (ICIs), and many initial responders develop resistance. Traditional approaches had failed to identify druggable resistance mechanisms.
Our Approach
Our AI scientists analyzed multi-omics data from thousands of tumor samples, integrating transcriptomic, proteomic, and clinical response data to build digital patient models of ICI resistance.
The Discovery
Identified a novel multi-target mechanism involving compensatory immune evasion pathways. Our AI designed small molecules that simultaneously modulate multiple resistance nodes.
Outcome
Discovered 3 novel druggable targets not previously associated with ICI resistance. Lead compound shows synergy with existing checkpoint inhibitors in digital patient simulations.
Targeting HIV Latent Reservoirs
HIV Functional Cure
The Challenge
HIV persists in latent reservoirs despite antiretroviral therapy, preventing a functional cure. Previous approaches failed to selectively eliminate reservoir cells without broad immune suppression.
Our Approach
Built digital patient models of HIV latency using multi-omics profiles of latently infected cells. AI scientists analyzed reservoir-specific molecular signatures across diverse patient populations.
The Discovery
Identified host factors that distinguish latently infected cells from uninfected immune cells. Designed molecules that selectively induce latent cell death while sparing healthy cells.
Outcome
Novel latency reversal approach with 100x improved selectivity over existing agents in digital patient simulations. Multiple backup compounds with distinct mechanisms.
P-Selectin Inhibition for Sickle Cell Disease
Sickle Cell Vaso-Occlusive Crisis
The Challenge
Current treatments for sickle cell disease have limited efficacy in preventing vaso-occlusive crises. Existing P-selectin inhibitors require IV infusion and have short duration of action.
Our Approach
Digital patient models simulated the complete vaso-occlusive cascade, from RBC sickling through endothelial adhesion to vascular occlusion. AI explored novel intervention points.
The Discovery
Identified an allosteric P-selectin binding site amenable to small molecule inhibition. Designed oral compounds with 24+ hour duration of action.
Outcome
First-in-class oral P-selectin inhibitor with predicted once-daily dosing. Shows superior crisis prevention in virtual patient simulations across diverse genetic backgrounds.
Next-Generation LPA Targeting for Cardiovascular Risk
Elevated Lipoprotein(a)
The Challenge
Lipoprotein(a) is a major cardiovascular risk factor with no approved therapies. RNA-based approaches require frequent injections and have limited durability.
Our Approach
Constructed multi-omics models of LPA synthesis and catabolism. AI scientists explored the complete LPA pathway for novel intervention points beyond hepatic synthesis.
The Discovery
Identified a previously unexploited mechanism for accelerating LPA clearance. Small molecule approach enables oral administration with durable effect.
Outcome
Novel LPA-lowering mechanism with predicted 70-80% reduction from once-daily oral dosing. Digital patient simulations show consistent efficacy across patient subgroups.
How We Achieve These Results
Multi-Omics Integration
Combine genomics, transcriptomics, proteomics, and metabolomics for complete disease understanding
Digital Patient Models
Mechanistic simulations predict drug response before synthesis
Autonomous AI Scientists
24/7 hypothesis generation, testing, and optimization
Frequently Asked Questions
How fast can Omic identify drug targets?
Omic's AI platform identifies validated drug targets in 2-8 weeks, compared to 2-4 years for traditional approaches. Our autonomous AI scientists analyze multi-omics data continuously to discover novel therapeutic opportunities.
What types of diseases has Omic worked on?
Omic has active programs in oncology, infectious disease, hematology, and cardiovascular disease. Our platform is disease-agnostic and can be applied to any therapeutic area with sufficient multi-omics data.
How does Omic validate its AI predictions?
We validate through computational cross-validation, comparison against known biology, and wet lab confirmation. Our digital patient models are continuously refined based on experimental feedback.
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