This pharmaceutical company uses digital simulation and AI to accelerate drug manufacturing. Their platform replaces traditional experiments with artificial simulations, significantly reducing the cost and time involved in pharmaceutical production development.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional pharmaceutical process development relies on extensive physical experiments to determine optimal manufacturing parameters, which is a time-consuming and expensive process. The need to test numerous variables, such as temperature, mixing speed, and ingredient amounts, significantly delays the time to market for new drugs and increases development costs.
Solution
Auxilart provides a digital simulation platform that accelerates pharmaceutical process development by replacing physical experiments with mechanistic models. Their proprietary technology, developed at the University of Tokyo, uses differential equations to simulate phenomena, requiring significantly less data than traditional AI approaches. By simulating synthetic pathways, cultivation behavior, and purification processes, Auxilart's platform enables the identification of critical material attributes (CMA) and critical process parameters (CPP) to predict critical quality attributes (CQA). This approach reduces the number of experiments needed, shortens development timelines, and lowers costs associated with pharmaceutical manufacturing. The platform supports various modalities, including biopharmaceuticals, regenerative medicine, and small molecules.
Target Audience
The primary target audience includes pharmaceutical companies, biopharmaceutical companies, and research institutions involved in drug development and manufacturing process optimization.
Features
- Mechanistic models simulate phenomena with differential equations, requiring only a few time-series datasets.
- Identification of CMA and CPP, and prediction of CQA for biopharmaceuticals.
- Simulation models for synthetic pathways to derive optimal synthetic conditions for small molecules.
- Simulation models for fill-freeze-thaw processes in regenerative medicine.
- Prediction of cultivation behavior and passage timing for regenerative medicine.
- Optimization of purification processes.
- Transitioning from batch to flow processes.