SolECOs: A Data-Driven Platform for Sustainable and Comprehensive Solvent Selection in Pharmaceutical Manufacturing

Abstract

Solvent selection in pharmaceutical crystallization plays a pivotal role in determining overall manufacturing efficiency while also significantly impacting environmental performance and regulatory compliance. A data-driven solution for sustainable solvent selection, applicable to both single and binary solvent systems, was developed and integrated into SolECOs (Solution ECOsystems), a modular and user-friendly platform for Sustainable-by-Design solvent selection in pharmaceutical manufacturing. A comprehensive solubility database containing 1,186 active pharmaceutical ingredients (APIs) and 30 solvents was constructed and used in conjunction with thermodynamically informed machine learning models, including the Polynomial Regression Model-based Multi-Task Learning Network (PRMMT), the Point-Adjusted Prediction Network (PAPN), and the Modified Jouyban-Acree-based Neural Network (MJANN), to predict solubility profiles along with associated uncertainties.Sustainability assessment was performed using both midpoint and endpoint life cycle impact indicators (ReCiPe 2016) and industrial benchmarks such as the GSK sustainable solvent framework, enabling a multidimensional ranking of solvent candidates. Experimentally validated case studies involving APIs such as paracetamol, meloxicam, piroxicam, and cytarabine confirmed the approach's robustness, adaptability to various crystallization conditions, and effectiveness in supporting single and binary solvent screening and design.