Despite a bountiful enumerated list of benefits, implementation of Quality by Design in pharmaceutical development is moving at glacial speed. Needed are cheaper, easier and faster methods for achieving QbD goals. The source of the problem and its potential solution, featuring Inference for QbD, are outlined in an article by Paul Thomas, Senior Editor of pharmaQbD, entitled:

Can Bayesians Get QbD Past Tipping Point?

The article is bases on a persistent refrain heard in pharmaceutical development comprised of the following:

• Clear business benefits of QbD need to be demonstrated before large-scale implementation. However there is a reluctance to initiate demonstration programs because current QbD approaches require a large investment of resources (people, time and materials) to execute the requisite scope of experimental work.
• There is a plethora of prior data from past development studies. However, using classical risk assessment, DOE and multivariate analysis, there is no way to integrate prior data with new studies. Huge benefits would be gained if one could make effective use of the abundance of prior data for QbD planning, execution, decision making and CMC filings.