Built by people who built models when access was internal.
QuantHealth was founded in 2023 by clinical pharmacology researchers who built mechanistic disease models within large development organizations.
simulation-led method
Clinical pharmacology, Tel Aviv, 2023
Phase 2 faces a structural problem. The inputs for better trial design are found in published literature, pharmacological first principles, and population biology. Turning them into a specific design recommendation calls for computational infrastructure that most development teams, particularly smaller biotechs, cannot build in-house. Large organizations with this capability keep it internal for their own pipelines.
We built QuantHealth to make this infrastructure an accessible service: one engagement, one compound, one protocol decision. It matches analysis from a large pharma pre-IND planning exercise, without a full systems pharmacology team.
We are not replacing clinical judgment. We want teams finalizing a Phase 2 protocol to have a probability estimate on the table, rather than relying on an intuitive range.
The model team
Orr builds AI systems for drug development, with a focus on clinical decision support and computational biomedical modeling. He co-founded QuantHealth to make mechanistic simulation available to development teams before the first patient is screened, at the protocol design stage.
Yoav leads the engineering behind large-scale simulation: distributed infrastructure, parallel compute architecture, and data pipelines for running thousands of virtual patients across multiple dose scenarios in hours. His focus is high-performance scientific computing for biological systems.
Noa leads QuantHealth's scientific methodology, applying Bayesian biostatistics to PK/PD modeling, quantitative pharmacology, and population simulation. She oversees model construction, parameter calibration, and the validation framework for each therapeutic area module.
Daniel works in clinical research workflow software, having built tools for trial operations teams at contract research organizations. At QuantHealth, he leads product: turning simulation outputs into decision-ready formats for clinical development teams to use in protocol documentation.
Working with clients
Each engagement opens with a scoping call led by a scientist, never a sales representative. The scientist assessing your compound is the same kind of specialist who runs the simulation. The call tests whether the work can yield a useful result, and we say upfront when available data is insufficient to calibrate a useful model.
Client data is confidential by default. CDAs can be signed before scoping calls on request. Compound data from one client is never used to train or calibrate models for another. Disease module parameters are drawn from published literature, not proprietary engagement data.
We are a small team serving a small number of clients, by design. Simulation quality depends on the scientist-client interaction during intake and review. We are not building a high-volume transaction business. Our aim is to produce analyses that improve protocol decisions.