实验的统计设计(DoE)用于帮助开发和执行制备型液相色谱(LC),以大规模纯化活性药物成分(API)和药物中间体。进行了四个纯化案例研究。在案例研究1中,开发了正相制备二氧化硅方法并进行了建模。经过初始方法筛选,DoE结果用于设置流动相组成,流量,和样品稀释剂。在研究的三种粒径中(10微米,20µm,50µm)仅10µm的二氧化硅树脂能够以项目所需的产率(>96%)和生产率(>1kg/kg-树脂/天)生产纯化的API。第二个案例研究使用DoE研究来确定立柱载荷的关键过程参数,流动相溶剂比和基本改性剂水平为低分辨率,制备性,手性分离。纯度之间的权衡,产量和生产率在严格的分离中量化,这使得对过程结果的妥协成为必要。第三个案例研究解决了在工艺规模的反相LC纯化操作期间经历的产率损失。DoE用于识别流动相中乙腈和磷酸水平之间的关键相互作用。定义并实施了将产率从约85%提高到97%的操作区域。第四个案例研究最初设计为制备型色谱纯化API。使用DoE筛选流动相溶解度。这些实验揭示了API可溶的条件,和杂质不是。在乙腈/水混合物中的溶解度模型通过响应面DoE进一步定义。所得的目标溶剂混合物允许通过溶解API进行批量纯化,同时三种极性较小的杂质保留在固相中并通过过滤除去。这四个案例研究证明了DoE和响应面建模作为流程开发和优化工具的效率。
Statistical design of experiments (DoE) is used to aid in the development and execution of preparative liquid chromatography (LC) for large-scale purification of active pharmaceutical ingredients (API) and pharmaceutical intermediates. Four purification
case studies were undertaken. In
case study 1, a normal phase preparative silica method is developed and modeled. After initial method screening, DoE results were used to set mobile phase composition, flowrate, and sample diluent. Of the three particle sizes studied (10 µm, 20 µm, 50 µm) only 10 µm silica resin was able to produce purified API at the yield (>96%) and productivity (> 1 kg/kg-resin/day) necessitated by the project. The second
case study uses DoE studies to identify critical process parameters of column load, mobile phase solvent ratio and basic modifier level for a low-resolution, preparative, chiral separation. Trade-offs between purity, yield and productivity are quantified in a tight separation which made compromising on process outcomes a necessity. The third case study troubleshoots a loss of yield experienced during operation of a process-scale reverse-phase LC purification. DoE is used to identify a critical interaction between levels of acetonitrile and phosphoric acid in the mobile phase. An operating region which increased yield from around 85% to 97% was defined and implemented. The fourth
case study was initially designed as a preparative chromatography purification of API. DoE was used to screen mobile phase solubility. These experiments uncovered conditions where API is soluble, and impurities are not. The solubility model in acetonitrile/water mixtures is further defined via a response surface DoE. The resulting targeted solvent mixture allows bulk purification via dissolution of API while three less-polar impurities remain in the solid phase and are removed by filtration. These four
case studies demonstrate the efficiency of DoE and response surface modeling as tools for process development and optimization.