GlaxoSmithKline (GSK) has identified casopitant as a potent NK₁ antagonist. It was selected for its potential activities on a number of therapeutic targets such as inflammatory bowel disease, overactive bladder, CNS disorders and others. The mesylate salt of casopitant was selected for full development using a quality by design (QbD) approach in which a control strategy was developed from a design space, underpinned by process understanding and risk analysis for an enhanced level of quality assurance. As the concepts of design space and control strategy in the QbD framework move on from their inception to implementation, the ambiguity of definition has led to considerable discussion, diversity of opinions and uncertainty around how best to define these ideas. This contribution provides an outline of the sequential experimentation and analyses that led to the construction of a Bayesian approach to the ICH Q8 definition of Design Space. This uses a predictive approach for multiple response surface optimisation to identify a region of process operating conditions where all quality attributes of the active pharmaceutical ingredient (API) are likely to meet specifications with a high degree of confidence. Boundaries of assurance for design space and control strategy are introduced that provide a basis for defining dynamic operating ranges that should enhance the scope of operation and manufacturing flexibility that the regulatory agencies are encouraging. This new paradigm provides an opportunity to move away from the traditional concept of documenting static normal operating ranges (NOR) and proven acceptable ranges (PAR) univariately in a table or providing them as a list to regulators as part of regulatory submissions. While the latter approach is practical for documentation and serves as a simple instruction to manufacturing as to where a process should be allowed to run, it is not ideally suitable for describing a multidimensional design space. Model/parameter-based control is discussed through a worked example on the casopitant crystallisation step of a particle-forming routine.

中文翻译：

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设计原理对定义甲磺酸卡苏吡坦的稳健结晶过程的设计原理的应用

葛兰素史克（GSK）已确定casopitant是有效的NK ₁拮抗剂。选择它是因为它在许多治疗目标上具有潜在的活性，例如炎症性肠病，膀胱过度活动症，中枢神经系统疾病等。选择了卡索吡坦的甲磺酸盐，使用“按质量设计”（QbD）方法进行全面开发，该方法从设计空间开发了控制策略，并以过程理解和风险分析为基础，以提高质量保证水平。随着QbD框架中设计空间和控制策略的概念从开始到实施的不断发展，定义的歧义已引起了广泛的讨论，观点的多样性以及围绕如何最好地定义这些思想的不确定性。该贡献提供了顺序实验和分析的概述，从而导致了针对设计空间的ICH Q8定义的贝叶斯方法的构建。这使用了一种预测方法来进行多重响应表面优化，以识别过程操作条件的区域，在该区域中，活性药物成分（API）的所有质量属性都可能高度可信地满足规格要求。引入了设计空间和控制策略的保证边界，为定义动态运行范围提供了基础，动态范围应扩大监管机构鼓励的运行范围和制造灵活性。这种新的范式提供了一个机会，可以摆脱在表格中单变量记录静态正常工作范围（NOR）和经证明的可接受范围（PAR）的传统概念，也可以将它们作为监管机构提交清单的一部分提供给监管机构。尽管后一种方法对于文档编制很实用，并且可以作为制造过程中允许在何处运行的简单说明，但它并不理想地适合于描述多维设计空间。通过一个有关颗粒形成程序中casopitant结晶步骤的实例，讨论了基于模型/参数的控制。尽管后一种方法对于文档编制很实用，并且可以作为制造过程中允许在何处运行的简单说明，但它并不理想地适合于描述多维设计空间。通过一个有关颗粒形成程序中casopitant结晶步骤的实例，讨论了基于模型/参数的控制。虽然后一种方法对于文档编制很实用，并且可以作为制造过程中允许在何处运行的简单说明，但它并不理想地适合于描述多维设计空间。通过一个有关颗粒形成程序中casopitant结晶步骤的实例，讨论了基于模型/参数的控制。