Impacts of USP Modernization Initiatives on Analytical Development for Drug Products

Description:
This presentation will describe key elements within the USP <1210> and USP <1220> guidances which provide a new framework and workflow for analytical procedure development. The workflow discussion will include 1) addressing the Analytical Target Profile (ATP) as a negotiated specification which includes production considerations, 2) executing an enhanced approach which fully integrates robustness to develop a multi-dimensional, robust Method Operable Design Region (MODR), 3) optimization of the Replication Strategy to identify the most efficient “preparation*injection” strategy for generating Reportable Results which meet the analytical method precision requirements defined in the ATP, and 4) the risk-based business case for AQbD-based analytical procedure development – dealing with perception of time and cost versus the reality of the dramatic savings in both which AQbD-based development can provide. The AQbD analytics and workflow will be described within the context of successful LC and LC-MS method modernization case studies.

Speaker: Richard Verseput - S-Matrix Corporation
Richard Verseput has over 30 years of experience applying Quality by Design tools and methods in various industries, including Aerospace, Electronics, Chemical, and Pharmaceutical. For the past 18 years Mr. Verseput has focused on adapting QbD tools and methods to Analytical R&D with a specific emphasis on liquid chromatography (LC) analytical methods. During that time, he has worked with LC instrument manufacturers, regulators, and global pharma customers to develop and advance QbD-aligned experiment automation and chromatography data modeling, and he has taught Analytical QbD training courses to pharma companies worldwide. Richard Verseput is currently president of S-Matrix Corporation. S-Matrix is the developer of Fusion QbD® – a regulatory compliance supporting software platform which automates QbD experimentation, peak tracking, and chromatography data modeling for successful development, optimization, validation, and transfer of LC methods.