Development of a Highly Automated Workflow for Investigating Polymorphism and Assessing Risk of Forming Undesired Crystal Forms within a Crystallization Design Space

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• 作者：Joshua A. Selekman ; Daniel Roberts ; Victor Rosso ; Jun Qiu ; Joseph Nolfo ; Qi Gao ; Jacob Janey
• 刊名：Organic Process Research & Development
• 出版年：2016
• 出版时间：January 15, 2016
• 年：2016
• 卷：20
• 期：1
• 页码：70-75
• 全文大小：281K
• ISSN：1520-586X

文摘

A critical component of defining the feasible design space for an active pharmaceutical ingredient (API) crystallization is isolating the preferred polymorph or crystal form of the compound, which defines many of the compound’s performance-defining attributes such as solubility and bioavailability. While automated platforms and workflows exist to support many facets of pharmaceutical process development, few workflows aim to systematically investigate polymorphism and its kinetics as a function of crystallization conditions, thus providing a measure of risk associated with forming an undesired polymorph. Herein, we describe the development and application of a novel automated workflow, designed to interrogate a multidimensional crystallization design space using parallel experimentation to provide resolution around the feasible design space while simultaneously evaluating risk of forming undesired polymorphs. In addition, we describe a case study highlighting how data generated by this highly automated form analysis workflow can be leveraged to advance crystallization development of an API.