A Biological Needle in a Haystack

Drug development has single-digit clinical trial success rates despite improved molecular design. Use models like single-cell perturbation datasets to 'pull forward' clinical risk to pre-clinical settings.

The Problem

Despite massive improvements in molecular design and drug discovery, clinical trial success rates remain in the single digits. The problem isn't finding molecules—it's predicting which molecules will actually work in humans.

Most drugs fail not because of molecular properties, but because of:

• •Unexpected toxicity
• •Lack of efficacy in the full biological context
• •Off-target effects that only appear in complex systems

The Solution

Use emerging biological datasets to "pull forward" clinical risk assessment:

• •Single-cell perturbation data: Understand how drugs affect individual cells across tissues
• •Organoid models: Test drugs on complex 3D tissue structures
• •Patient-derived systems: Use actual patient cells to predict individual responses
• •Multi-omics integration: Combine genomic, proteomic, and metabolomic data

The Opportunity

The company that can reliably predict clinical outcomes from pre-clinical data will transform drug development. Every pharma company will need this capability.

Technical Approach

Build models that can find the "biological needle in a haystack"—the specific cellular and molecular signatures that predict clinical success or failure.