Using Ethereum blockchain to store and query pharmacogenomics data via smart contracts
Related Experiment Video
Updated: Dec 19, 2025

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
Published on: December 11, 2016
Gamze Gürsoy1, Charlotte M Brannon2, Mark Gerstein3,4,5
1Program in Computational Biology and Bioinformatics, Yale University, Whitney Avenue, New Haven, 06520, CT, USA.
Background:
As pharmacogenomics data becomes increasingly integral to clinical treatment decisions, appropriate data storage and sharing protocols need to be adopted. One promising option for secure, high-integrity storage and sharing is Ethereum smart contracts. Ethereum is a blockchain platform, and smart contracts are immutable pieces of code running on virtual machines in this platform that can be invoked by a user or another contract (in the blockchain network). The 2019 iDASH (Integrating Data for Analysis, Anonymization, and Sharing) competition for Secure Genome Analysis challenged participants to develop time- and space-efficient Ethereum smart contracts for gene-drug relationship data.
Methods:
Here we design a specific smart contract to store and query gene-drug interactions in Ethereum using an index-based, multi-mapping approach. Our contract stores each pharmacogenomics observation, a gene-variant-drug triplet with outcome, in a mapping searchable by a unique identifier, allowing for time and space efficient storage and query. This solution ranked in the top three at the 2019 IDASH competition. We further improve our "challenge solution" and develop an alternate "fastQuery" smart contract, which combines together identical gene-variant-drug combinations into a single storage entry, leading to significantly better scalability and query efficiency.
Results:
On a private, proof-of-authority network, both our challenge and fastQuery solutions exhibit approximately linear memory and time usage for inserting into and querying small databases (<1,000 entries). For larger databases (1000 to 10,000 entries), fastQuery maintains this scaling. Furthermore, both solutions can query by a single field ("0-AND") or a combination of fields ("1- or 2-AND"). Specifically, the challenge solution can complete a 2-AND query from a small database (100 entries) in 35ms using 0.1 MB of memory. For the same query, fastQuery has a 2-fold improvement in time and a 10-fold improvement in memory.
Conclusion:
We show that pharmacogenomics data can be stored and queried efficiently using Ethereum blockchain. Our solutions could potentially be used to store a range of clinical data and extended to other fields requiring high-integrity data storage and efficient access.
Related Concept Videos
Issues And Trends In Healthcare Delivery System
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Analysis of Population Pharmacokinetic Data
Pharmacokinetics in Obese Patients: Drug Metabolism and Excretion
Genomics
Genome-wide Association Studies-GWAS
GWAS does not require the identification of the target gene involved in...
Pharmacokinetic Models: Overview
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...

