Are my (bio)pharmaceutical assay performances reliable? Only probability of success counts!
Gage R&R studies are often conducted in the industry to determine the operating performance of a measurement system and determine if it is capable to monitor a manufacturing process. Several metrics are commonly associated with Gage R&R studies, such as the precision-to-tolerance ratio (P/T), the precision-to-total-variation ratio (%RR), the Signal to noise ratio (SNR), the %Reproducibility and the %Repeatability. While these metrics may suit well the overall industry, they could be problematic once applied in drug manufacturing sector for several reasons, (1) (bio)pharmaceutical assays are often more variable than common physico-chemical measurement systems and the usual criteria are too restrictive for the pharma industry, (2) analytical methods cannot always be improved once qualified, and (3) measurements are usually costly and time consuming, which makes difficult to have enough data to estimate all sources of variance with high precision.
Assisting Continued Process Verification with AI
Patterns of behavior reflected in the data from equipment sensors can give insight into these performance affecting factors. In many cases, these patterns develop before product quality is significantly affected. Putting in place analytics that can detect these patterns gives the plant operations team actionable warning before CPV limits indicate a problem. This warning can be used to limit costly production impacts. Importantly, because the CPV process itself is untouched, these kinds of pattern detection analytics can be implemented without additional filings or regulatory delay. Assisting CPV does not mean replacing or even changing CPV.