Theoretical Basis for the EDA Concept

Abstract

Efficient Data Analysis (EDA) was designed specifically to address quality control (QC) decisions with respect to the CI-measured APSD from an OIP. The general goal of QC testing is to confirm that the batch in question is of suitable quality. In the case of EDA, this testing is intended to confirm that the OIP in question generates an aerosol with expected particle size characteristics to deliver drug to the human respiratory tract. Note that this process necessarily takes the form of sampling a relatively small number of units, measuring properties of the aerosols generated by these samples, and making a decision concerning the quality of the sampled batch. This practice leads to three primary considerations:

• 1. The properties measured should be relevant to detecting significant abnormalities from the expected APSD.

• 2. The measurements should possess sufficient precision and accuracy over the range of interest.

• 3. The decision process based on the measurements should reliably make correct inference about the quality of the batch by appropriately minimizing and balancing the risk of decision errors, i.e., judging a batch suitable when it is not suitable and conversely judging a batch unsuitable when it is suitable.

This chapter will briefly introduce the latter two considerations, but will primarily focus on the first. A detailed discussion of the evaluation of measurements and the decision-making process is the topic of Chap. 8.