|Abstract Title:||Use of different computer-aided method development software in late stages across global sites in pharmaceutical industry|
|Presenter Name:||Kai Chen|
|Co-authors:|| Jean-Paul Boon|
Peter Van Broeck
|Company/Organisation:||Janssen Pharmaceutical Companies of Johnson & Johnson|
|Session Choice:||High Throughput versus High Efficiency Separations (CS)|
Abstract Information :
In contrast to early development, method development in late development (LD) in pharmaceutical industries is aimed to provide a robust method for right-first-time method transfer and lifecycle management. Systematic, scientific, risk-based, multivariate and proactive approaches are therefore needed to be already applied in the method development stage.
By considering the current practice of liquid chromatographic method development, certain gaps are identified towards these goals. Many methods from earlier phases are developed on the screening platform and optimized from time to time mostly dependent on analyst's personal experience and preference. The trial-and-error and one-factor-at-a-time (OFAT) methodology enhances chance of missing the optimal conditions and brings larger variance in method output. Furthermore, although most global sites are equipped with computer-assisted LC method development software, the use of different stationary and mobile phases in screening, along with the application of different algorithms/strategies in optimization often results in different optimal method outputs from these softwares. In addition, robustness estimation is lacking so that the performance of the developed method cannot be foreseen for follow-up method validation, transfer and lifecycle management.
In this presentation, the current practices of using computer-assisted LC method development software in different sites are compared. The advantages and disadvantages of each practice with software like AutoChrom, ChromSword, DryLab and Fusion are concluded. A harmonized work flow is proposed aiming to obtain same/similar method developed from different sites, by minimizing the impact of the native differences in instrumentation and software and avoiding overcomplicated experiment design or method development. A pilot testing is performed to evaluate the proposed work flow with a real project sample. By this, sources of variability that may lead to development deviation or poor method robustness are identified for further improvement.