Evidence & Economic Logic

Non-confidential summary of the strategy: improve human causal validation to improve probability of success and reduce expected R&D cost per approval.

Core Economic Framing

Drug development is fundamentally a probability problem. Traditional non-genetic target discovery pathways can produce roughly a 10% probability of approval from Phase I.

Expected cost per approved drug scales as:

Cost per approval = Development cost ÷ Probability of success

When probability of success doubles from ~10% to ~20%, expected cost per approval can decrease by ~40–50%.

Claim Sequence

  1. Problem: ~90% of drug targets fail due to weak human causal validation.
  2. Standard fix: GWAS-supported targets provide ~2× higher approval odds.
  3. Additional insight: Extreme phenotypes and genotype-phenotype mismatch can enrich high-impact causal variants.
  4. Impact: 1.5–2.6× higher successful target identification in enrichment-oriented workflows.
  5. Outcome: Lower attrition, higher PoS, and substantial reduction in expected cost per approved drug.

Figure 1: Probability of Success

Probability comparison chart across traditional non-genetic, standard genetics, and advanced enrichment strategy.
Traditional non-genetic baseline (~10%), standard genetics (~20%), and an illustrative advanced enrichment case (~22–25%).

Figure 2: Cost Impact

Indexed expected cost per approved drug across three strategies.
Indexed view of expected cost per approved drug: higher PoS drives lower expected cost through inverse scaling.

Compared with traditional non-genetic discovery, standard genetics can yield ≥40% lower expected R&D cost per approval. Enrichment-based genetics strategy can approach ~55% reduction, potentially representing over $1B savings per approved drug in some scenarios.