Zero-Knowledge Proof-Verified Pharmacogenomic Stratification Enables Privacy-Preserving Adaptive Clinical Trials in Rheumatology via DeSci Infrastructure

Hypothesis

Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) applied to pharmacogenomic genotype data enable privacy-preserving patient stratification in adaptive rheumatology clinical trials, achieving equivalent statistical power to unblinded genomic stratification while maintaining complete genotype confidentiality—thereby unlocking multi-site decentralized science (DeSci) trial coordination without centralized genomic data repositories.

Background

Adaptive clinical trial designs (e.g., Bayesian response-adaptive randomization, MAMS) require real-time covariate adjustment to maintain validity. In rheumatology, pharmacogenomic variants—CYP2C19, NAT2, HLA-DRB1, TPMT, NUDT15—critically modulate drug metabolism and response trajectories for methotrexate, sulfasalazine, azathioprine, and biologics. However, sharing raw genomic data across trial sites introduces re-identification risk even under pseudonymization (Gymrek et al., Science 2013), creating a fundamental tension between adaptive efficiency and genomic privacy.

Proposed Mechanism

We propose a zk-SNARK circuit architecture where each trial site:

1. Locally computes a pharmacogenomic stratum assignment (e.g., CYP2C19 rapid/intermediate/poor metabolizer) from raw genotype data
2. Generates a zero-knowledge proof attesting that the stratum assignment follows the pre-registered algorithm without revealing the underlying alleles
3. Submits the proof + stratum label to a DeSci smart contract (Ethereum L2) that verifies proof validity and executes Bayesian response-adaptive randomization updates

The smart contract maintains a publicly auditable allocation ratio history while the underlying genomic data never leaves the local site. Bayesian posterior updates for treatment arm allocation use only verified stratum labels, not raw genotypes.

Testable Predictions

1. Statistical equivalence: Monte Carlo simulations (≥10,000 trial replications) comparing zk-verified stratification vs. oracle (unblinded) stratification will show ≤5% relative efficiency loss in expected sample size under the global null and ≤3% power loss under plausible effect sizes (DAS28 reduction ≥1.2)
2. Proof generation latency: zk-SNARK proof generation for a 5-variant pharmacogenomic panel will complete in <30 seconds on commodity hardware (Groth16 or PLONK), compatible with real-time adaptive randomization
3. Privacy guarantee: The proof system achieves computational zero-knowledge—no polynomial-time adversary can extract genotype information from the proof transcript beyond the stratum label itself
4. Multi-site coordination: A 5-site simulated trial with heterogeneous HLA-DRB1 allele frequencies across populations will maintain correct Bayesian posterior convergence within 95% credible intervals compared to centralized analysis

Limitations

• zk-SNARK trusted setup (for Groth16) introduces a ceremony requirement; PLONK eliminates this but with larger proof sizes
• Stratum labels themselves carry residual information (metabolizer phenotype)—the privacy guarantee is relative to full genotype disclosure, not absolute
• On-chain gas costs for proof verification on Ethereum L2 may limit update frequency; batched verification (every 5–10 patients) is a practical compromise
• Regulatory acceptance of zk-verified stratification for registrational trials is untested; this framework targets investigator-initiated and DeSci-funded studies initially
• Assumes sites have local genotyping capability—point-of-care pharmacogenomic panels (e.g., Spartan RX) would be prerequisite infrastructure

Clinical Significance

This framework addresses a critical bottleneck in precision rheumatology: the inability to leverage pharmacogenomic stratification in multi-site trials without creating centralized genomic databases vulnerable to breach, re-identification, and jurisdictional conflict (GDPR vs. HIPAA vs. LFPDPPP). By enabling verifiable computation over private genomic inputs, DeSci infrastructure can coordinate adaptive trials across institutions and countries while each site retains full sovereignty over patient genetic data. This is particularly impactful for underrepresented populations (Latin American, African, Southeast Asian) whose pharmacogenomic profiles are poorly represented in existing databases and who face the greatest re-identification risk from data sharing.

RheumaAI Research • rheumai.xyz • DeSci Rheumatology