Greener Solvent Systems for Pharmaceutical Synthesis: A Case Study of AstraNova Labs

Thesis Abstract

This study addresses the urgent need to reduce ecological and health risks in pharmaceutical synthesis by evaluating greener solvent systems within the operational context of AstraNova Labs. Traditional organic solvents pose environmental, safety, and cost challenges, and there is limited understanding of how alternative solvent architectures perform across diverse pharmaceutical steps at industrial scale. The aim is to optimize solvent selection and process conditions to maintain or improve yield, purity, and regulatory compliance while reducing environmental impact. Specific objectives are (1) to benchmark current solvent usage and associated waste streams against greener alternatives; (2) to identify solvent systems with favorable physicochemical properties (polarity, Hansen solubility parameters, viscosity) that are compatible with existing reactors and catalysts; (3) to quantify process efficiencies, energy consumption, and waste reduction using life cycle assessment (LCA); (4) to develop a decision-support framework integrating process analytical technology (PAT), regression modeling, and multi-criteria decision analysis (MCDA) for solvent selection; and (5) to validate the framework through pilot-scale trials (0.5–2.0 m3) across three representative API synthesis steps. The methodology adopts a mixed-methods, case-study design anchored in industrial chemistry theory and sustainability science. The population comprises ten active pharmaceutical ingredient (API) synthesis workflows at AstraNova Labs, with three representative pilot processes selected for in-depth analysis. A sample of 25 process engineers, chemists, and sustainability officers will be surveyed, complemented by 12 in-depth interviews to capture experiential knowledge and organizational barriers. Data collection instruments include standardized process audit checklists, reactor performance logs, GC-MS and HPLC for impurity profiling, LC-MS for trace solvents, and PAT sensors for real-time monitoring. Validity and reliability will be ensured through triangulation of experimental data with process records, instrument calibration protocols, and inter-laboratory cross-checks. Analytical techniques encompass descriptive statistics, ANOVA to compare solvent performance across processes, multiple linear regression to relate solvent properties to yield and impurity profiles, and response surface methodology (RSM) to optimize solvent–temperature–concentration interactions. Life cycle assessment (cradle-to-gate) will quantify energy use, water footprint, and waste generation, while a multi-criteria decision analysis (PROMETHEE method) will support solvent selection under safety, cost, and regulatory constraints. The study will also apply theoretical lenses from green chemistry (principles and metrics), diffusion of innovation, and the Theory of Planned Behavior to interpret adoption dynamics among staff. Expected findings include that biobased and fluorinated-free solvent blends can achieve comparable or superior yields and purity in two of the three pilot processes, with 20–35% reductions in waste solvent generation and a 12–18% decrease in overall energy consumption. It is anticipated that solvent polarity and task-specific solubility parameters will strongly correlate with reaction rate improvements (p < 0.05), while certain substitutions may increase process mass intensity unless mitigated by solvent recovery and recycling. The framework is expected to produce a practical, scalable solvent-selection tool that integrates PAT data streams, green metrics, and economic viability, enabling rapid, compliant decision-making for new API projects. The study contributes to knowledge by providing robust, plant-scale evidence on the feasibility and impact of greener solvent systems in pharmaceutical synthesis, advancing the empirical basis for solvent selection, process optimization, and sustainability reporting in the industry. It also offers a transferable methodological blueprint combining experimental NLP-style data capture with quantitative and decision-analytic approaches, enhancing reproducibility and cross-site applicability. The main conclusion is that a strategically designed portfolio of greener solvents, coupled with real-time analytics and lifecycle thinking, can deliver substantial environmental and economic benefits without compromising product quality or regulatory compliance. Recommendations include institutionalizing PAT-guided solvent screening in early-stage process development, expanding solvent recovery and distillation integration, investing in supplier collaborations for sustainable solvent sourcing, and extending the framework to additional manufacturing sites to validate generalizability.