Enthalpy Bio
Enthalpy Bio develops causal machine learning models that predict individual immune responses to vaccination using demographic and EHR data, without requiring repeated biomarker testing. The platform enables personalized vaccine scheduling and booster timing for improved durability and reduced healthcare costs. It serves pharmaceutical companies, public health systems, and research institutions across flu, COVID, and HPV immunization programs.
- Artificial Intelligence
- Data & Analytics
- Biotechnology
- Healthcare Technology
- Software Only
Funding
Founders
Product
Problem
Vaccination programs currently follow one-size-fits-all schedules that do not account for individual immune differences, leading to inefficient booster timing, over-vaccination of low-risk populations, and under-protection of high-risk groups. This results in preventable infections among vaccinated individuals and significant avoidable healthcare costs—treating vaccinated people who develop disease costs an estimated £2.68 billion in the UK alone during the COVID pandemic.
Solution
Enthalpy Bio trains proprietary causal machine learning models on multi-omic, immunological, and clinical data from hospital, clinic, and EHR-linked cohorts to predict how individuals will respond to vaccination. Once models are trained, the platform can forecast immune durability, booster timing, and response for new individuals using only demographic and EHR data—no repeated biomarker testing required. The system provides decision-support outputs for healthcare systems, pharmaceutical companies, and researchers, enabling personalized vaccine schedules that align with individual immune profiles. This approach aims to improve population-wide protection, reduce unnecessary doses, and lower healthcare costs across flu, COVID, HPV, and future immunization pathways.
Target Audience
Primary customers are pharmaceutical and biotech companies optimizing clinical trials and vaccine program design, public health systems developing booster policies and resource allocation strategies, and research institutions conducting translational immunology and systems vaccinology studies.
Features
- Proprietary causal AI prediction platform trained on cohort immune, multi-omic, and biomarker datasets to predict durability, booster timing, and response with interpretable inference
- Healthcare system data collection layer that structures patient data, biomarkers, and immune profiling across targeted cohorts for model training and continuous benchmarking
- Decision-support outputs that deliver personalized predictions using biometric EHR data only, eliminating the need for repeated biomarker testing
- Durability forecasting that projects immune waning at scale for influenza, COVID, and other vaccines
- Equity-focused targeting to identify high-risk cohorts early for prioritized outreach and supply allocation
- Program optimization that aligns booster timing with predicted immune thresholds to maximize impact and secondary immune responses