A variation risk management methodology for an interactive pharmaceutical design and manufacturing environment

Abstract

The pharmaceutical manufacturing field is one of the most challenging fields due to stringent regulations, global harmonization and tough competition. It has been realized that quality, safety and efficacy of pharmaceutical drug products are key elements for success in market. However, improving quality requires better understanding of the product and process critical quality attributes (CQAs). The utmost goal is to build quality upfront during early design stages by implementing guidelines such as quality by design which focuses on targeting design quality into the ingredients, formulation and manufacturing process to deliver the intended performance of the drug product. Achieving such goal is possible by integrating important tools and functions in one interactive usable model. One significant opportunity has been found in the effective utilization of data gathered during different activities such as sampling and testing of pharmaceutical ingredients and products. Extracting patterns of interest of such data can be essential in targeting problems such as variation initiation and variation propagation. Variations are those unwanted deviations from nominal values which greatly impact the final quality and efficacy of products. Manufacturers face some challenges due to lack of quantitative models to prioritize CQAs and quantify their associated risk of variation. Therefore, there is a need for proactive quantitative mechanisms usable during the early design stages to reduce design’s sensitivity to manufacturing variations. The work here introduces a systematic interactive environment that integrates together the design model and the current process capabilities to capture the impact of manufacturing variations on top performance function. A case study on tablet’s crushing strength, expressed by tablet’s tensile strength, has been carried out successfully to prove the effectiveness of the proposed methodology in identifying design’s CQAs and assessing their risk of variation. Moreover, it introduces the mechanism for a quantitative ongoing monitoring of the risk of variation and decision making regarding manufacturers selection.