It is remarkable then that much airtime is given to laboratory scale statistical design of experiments as a vehicle for efficient process development, without adequate discussion of how the DOE results can be made scalable. The good news for the drug substance / API synthesis community is that the necessary concepts for scalability are well established and can be put into practice with DynoChem software tools that are easy to use. You can for example achieve equivalent 'mixing' between 100 mL and 1500 L reactors, or match addition times / cooling rates between lab and plant, by investing 15 minutes of your time. Doesn't that beat having to deal with missed deadlines or increased impurity levels on scale? And the associated process rework?
We made a carton animation to raise awareness of the problem and the opportunity. If you see the potential, sign up for DynoChem access.