Atlas Bio provides a cloud‑native AI platform that simulates virtual patient cohorts and predicts clinical trial outcomes using a foundational model trained on billions of biomedical data points. The API‑driven analytics engine integrates with existing R&D workflows, enabling pharma, biotech, and CRO teams to evaluate trial designs and de‑risk programs in minutes.
Funding
Funding not disclosed
RVFounders
Product
Problem
Clinical development programs suffer from high attrition rates, with roughly 90% of trials failing and costs often exceeding $1 billion per program. This uncertainty prolongs time‑to‑market and inflates R&D budgets for biopharma companies. Limited ability to predict patient outcomes early in the trial design further amplifies financial risk.
Solution
Atlas Bio delivers a computational platform that models human biology at scale to inform trial design and execution. By training a foundational model on 6.35 billion data points and clinical records from over 560 k patients, the system can generate virtual patient cohorts and forecast endpoint outcomes for proposed protocols. Predictive analytics are delivered through an API‑driven analytics engine that integrates with existing R&D workflows, enabling researchers to evaluate multiple trial scenarios in minutes rather than months. The platform’s outcome‑prediction module quantifies de‑risking metrics, allowing sponsors to prioritize the most promising candidates and reduce unnecessary patient enrollment. Cloud‑native infrastructure ensures secure, compliant data handling while providing real‑time simulation results to support rapid decision‑making.
Target Audience
The primary customers are pharmaceutical R&D divisions, biotech firms, and contract research organizations that design and manage early‑phase clinical programs and seek data‑driven risk mitigation tools.
Features
- Foundational AI model trained on 6.35 billion biomedical data points, capturing multi‑omics, phenotypic, and longitudinal clinical information
- Virtual patient cohort generation that mirrors target demographics and disease heterogeneity for in‑silico trial simulations
- Outcome prediction engine delivering probabilistic forecasts of primary and secondary endpoints across trial designs
- RESTful and GraphQL APIs for seamless integration with existing clinical data management systems and electronic trial master files (eTMF)
- Cloud‑native analytics pipeline with auto‑scaling compute, ensuring low‑latency simulation runs and secure, HIPAA‑compliant data storage
- Scenario‑comparison dashboard that visualizes risk metrics, power calculations, and cost‑benefit analyses for alternative protocols
- Role‑based access controls and audit logging to meet GxP and regulatory compliance requirements