Process knowledge discovery tools in compliance with Quality by Design concept. - Joanna Ronowicz (Polish Pharmaceutical Society, 2019)
The significant change in the pharmaceutical quality approach (QbD concept, PAT strategy) and focusing on improving the quality of drug products by reducing variability in the manufacturing process force the use of effective tools to discover knowledge about the process and product. The Quality by Design concept is an essential part of the modern approach to pharmaceutical quality. Due to the current trend of being QbD compliant, the use of chemometrics in pharmaceutical sciences is increasing. This strategy can shorten a drug development phase and reduce the overall experimental work and costs. Moreover, continuous process monitoring requires the application of advanced, often non-invasive instrumental analytical techniques that generate a huge amount of process data. Their effective analysis and interpretation is possible thanks to chemometrics. The appropriate chemometric algorithms allow to detect cause-effect relations between the raw materials properties, process parameters and the drug product quality. The detected relations can be easily translated into accurate and rationally justified technological decisions, leading to manufacturing process optimization and consequently significant improvement of drug product quality. The effective building of process and product knowledge within the QbD concept is possible by means of non-invasive analytical methods generating process data sets which in-depth analysis and interpretation is possible with the use of the appropriate chemometric algorithms. The use of chemometric techniques enables better understanding of the effect of material attributes and process factors on the final drug product quality. Prognostic mathematical models can be generated to describe in detail the interrelations between excipients and the critical quality attribute of final formulation. This approach should be taken into consideration by industrial formulation scientists to support rational decision making at the drug product development stage.