There is a current trend in pharmaceutical industry on applying Analytical Quality by Design (AQbD) to method development as a part of analytical method lifecycle. Indeed, there is a considerable interest in enhancing method understanding in order to reduce failures, increase method robustness and reliability through its lifecycle. Reversed Phase-Liquid Chromatography (RP-LC) is the widest technology used in any pharmaceutical analysis so its performance should be widely studied during method development in order to ensure its robustness throughout the entire lifecycle. So, the application of an AQbD approach through a RP-LC method development represents an excellent opportunity to enhance method development activities while turn time into knowledge.

At Hovione, new analytical stepwise methodology based on QbD principles, already used at Hovione for process development, is being developed and has been applied to RP-LC methods development. This involves several steps: from objectives pre-definition; followed by the selection of an appropriate technology; passing through a method understanding process of how potential sources of variability may affect method performance, through structured and scientific-based tools (ex: DoE);  and ends up with a risk mitigation phase, where all sources of variability are reduced and controlled based on method understanding. So, in order for you to understand Hovione's point of view for AQbD application, I invite you to assist this exciting webinar where is presented some case studies in RP-LC, that highlights the application of this new methodology, as well as the newly defined of important AQbD concepts, such as Analytical Target Profile, Method Operable Design Region, Normal Operable Range and Analytical Control Strategy.

Key Learning Objectives:

• Application of QbD principles to reverse-phase liquid chromatography methods

• Development strategy to obtained increased reliability and robustness over method's lifecycle

• Evaluating the relevant QbD elements and establishing their combined performance to reduce failures and costs over method lifecycle.

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