Smart Formulation: AI-Driven Web Platform for Optimization and Stability Prediction of Compounded Pharmaceuticals
Artur Grigoryan1, Stefan Helfrich2, Valentin Lequeux1
1Fripharm®, Pharmacy Department, Groupe Hospitalier Centre Edouard Herriot, Hospices Civils de Lyon, 5, Place d'Arsonval, F-69437 Lyon, France.
Abstract:
Background/Objectives: Smart Formulation is an artificial intelligence-based platform designed to predict the Beyond Use Dates (BUDs) of compounded oral solid dosage forms. The study aims to develop a decision-support tool for pharmacists by integrating molecular, formulation, and environmental parameters to assist in optimizing the stability of extemporaneous preparations. Methods: A tree ensemble regression model was trained using a curated dataset of 55 experimental BUD values collected from the Stabilis database. Each formulation was encoded with molecular descriptors, excipient composition, packaging type, and storage conditions. The model was implemented using the KNIME platform, allowing the integration of cheminformatics and machine learning workflows. After training, the model was used to predict BUDs for 3166 APIs under various formulation and storage scenarios. Results: The analysis revealed a significant impact of excipient type, number, and environmental conditions on API stability. APIs with lower LogP values generally exhibited greater stability, particularly when formulated with a single excipient. Excipients such as cellulose, silica, sucrose, and mannitol were associated with improved stability, whereas HPMC and lactose contributed to faster degradation. The use of two excipients instead of one frequently resulted in reduced BUDs, possibly due to moisture redistribution or phase separation effects. Conclusions: Smart Formulation represents a valuable contribution to computational pharmaceutics, bridging theoretical formulation design with practical compounding needs. The platform offers a scalable, cost-effective alternative to traditional stability testing and is already available for use by healthcare professionals. Its implementation in hospital and community pharmacies may help mitigate drug shortages, support formulation standardization, and improve patient care. Future developments will focus on real-time stability monitoring and adaptive learning for enhanced precision.
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