Eurofins CDMO Alphora has introduced an AI‑powered software platform that accelerates high‑throughput screening of salts and co‑crystals for active pharmaceutical ingredients. The tool predicts solid‑form outcomes, cuts trial‑and‑error cycles, shortens development timelines, and lowers material costs, providing a fast, data‑driven solution for drug substance development.
How the AI Platform Works
The platform leverages machine‑learning algorithms trained on extensive solid‑state datasets to forecast the likelihood of salt and co‑crystal formation for a given API or intermediate. Users upload molecular information, and the system returns ranked predictions along with confidence scores, enabling rapid decision‑making before any laboratory work begins.
Predictive Modeling and User Interface
Predictive models analyze structural features, physicochemical properties, and historical screening results. A clean, web‑based interface guides users through data entry, visualizes prediction outcomes, and suggests optimal experimental strategies, making advanced analytics accessible to both chemists and project managers.
Key Benefits for Drug Development
- Accelerated timelines: Reduces solid‑form screening from weeks or months to days.
- Cost savings: Minimizes material consumption and experimental runs.
- Improved solubility and stability: Enables selection of salts or co‑crystals that enhance bioavailability and manufacturability.
- Data‑driven decisions: Provides early insight that informs downstream formulation and manufacturing steps.
Strategic Role Within Eurofins
Eurofins Alphora integrates the AI platform into its broader “one‑roof” service offering, which spans API synthesis, formulation, and sterile fill. The addition strengthens the company’s digital capabilities, positioning it as a comprehensive partner for end‑to‑end drug substance development under a single quality‑managed environment.
Industry Impact and Future Potential
By delivering highly accurate solid‑form predictions, the platform sets a new benchmark for CDMOs seeking digital differentiation. The technology can be extended to related challenges such as polymorph prediction and amorphous solid‑dispersion design, expanding its relevance across the pharmaceutical pipeline and supporting greener, more efficient development practices.
