Data analytics on raw material properties to accelerate pharmaceutical drug development
Data analytics on raw material properties to accelerate pharmaceutical drug development
复制标题
DOI：
10.1016/j.ijpharm.2019.04.002
复制
发表时间：
2019-05-30
影响因子：
5.8
通讯作者：
Zomer, Simeone
中科院分区：
文献类型：
--
作者：
Benedetti, Antonio;Khoo, Jiyi;Zomer, Simeone
由于材料可用性有限，在开发过程中通常通过经验方法评估活性药物成分（API）的可制造性。这给在高度加速的时间线下设计灵活而稳健的制造工艺带来了挑战。因此，充分利用有限的材料数据集是以最低成本和最大加工能力加速向市场交付高质量最终药品的关键。在这项研究中，我们提出了一种数据驱动的方法来调查原材料数据库，其中多变量分析和机器学习建模的集成有助于根据其可制造性选择新的来料。该程序应用于34种API和7种辅料的工业代表性数据库，其中收集了与41种材料中每种材料的流动特性相关的8个测量值。该模型确定了四组具有不同流动特性的材料。这些模型可作为早期产品开发阶段新API的风险评估工具，基于行为相似的最接近替代材料，以及识别目标和材料对抗实验，以解决二级工艺选择期间的关键风险。
Manufacturability of active pharmaceutical ingredients (APIs) is often evaluated by an empirical approach during development due to limited material availability. This brings challenges in designing flexible yet robust manufacturing processes under highly accelerated timelines. Hence, good utilisation of a limited material dataset is key to accelerate the delivery of high quality final drug product into the market at minimum cost and maximum process capacity. In this study, we present a data-driven method to investigate a raw materials database where the integration of multivariate analysis and machine learning modelling aids the selection of new incoming materials based on their manufacturability. The procedure was applied to an industrial representative database of thirty-four APIs and seven excipients where eight measurements relevant to flow properties for each of those forty-one materials were collected. The models identified four clusters of materials with different flow properties. These models can serve as a risk assessment tool for new API in early product development phases based on the nearest surrogate material which behave similarly, as well as to identify targeted and material sparring experiments to address key risks during secondary process selection.