Despite significant advances in green chemistry, the environmental performance of active pharmaceutical ingredient (API) manufacturing remains difficult to assess during early-stage route development because experimental data are typically limited to laboratory scale, making it challenging to project future industrial performance and associated impacts. To address this gap, this study combines cradle-to-gate life cycle assessment (LCA) with quantitative green chemistry indicators to evaluate two routes to (−)-chloramphenicol: the historical eight-step kinetic-resolution synthesis (Path A) and a modern asymmetric organocatalytic route (Path B). Both pathways are assessed at laboratory scale and under simulated industrial scale-up using established process design and energy estimation frameworks. To address data gaps, machine-learning (ML) models were employed to predict key physicochemical properties, e.g., temperature-dependent solubility and heat capacity, to parametrize solvent usage and energy demand. Environmental impacts are quantified using ReCiPe 2016 at midpoint and endpoint levels, while Monte Carlo simulation is applied to assess uncertainty and ensure robustness. Results show that Path B consistently outperforms Path A across most impact categories, driven by a shorter synthesis and reduced solvent and reagent demand. Industrial-scale modeling provides clearer differentiation than laboratory-scale comparisons, and LCA identifies environmental hotspots, such as solvent use, catalyst production, and energy-intensive unit operations, that are not captured by process mass intensity (PMI) and other green chemistry metrics. This work delivers the first cradle-to-gate LCA of (−)-chloramphenicol and demonstrates how ML-assisted property prediction, combined with uncertainty analysis, can improve the reliability of early-stage environmental decision-making in pharmaceutical synthesis.
Ungureanu et al. (Wed,) studied this question.