The determination of corrective and preventive actions (CAPA) in biopharmaceutical manufacturing is fundamentally a subjective, experiential activity with a high potential of failure for resolving process issues, which carries far-reaching implications for our industry. CAPA determination can be more successful by understanding the key pitfalls and the application of good science.
As I write this, two Pioneer satellites (10 and 11, launched in 1972 and 1973, respectively), which are hurtling away from Earth in opposite directions, are not where they should be — by about 248,500 miles according to calculations of gravitation. It's back to the drawing board for those involved to determine a cause for the error in the distance calculation. The scientist tracking the two satellites and their distance error had not released his findings for 15 years because the data did not match the theoretical calculations. And maybe we shouldn't blame him because the offset was, after all, just one part in 300,000 (incidentally, this is running just beyond a Six Sigma level of 3.4 parts-per-million). But working with licensed biological products, we don't have the luxury of not submitting aberrant data, and I'd like to think that any of us would feel ethically bound to reveal all the data, for better or for worse. But there is a strong tendency to believe what we perceive to be true. Our perception of the world shapes the things we know, and conversely, what we know skews how we view the world.
As observers, we are also inherently and inextricably active participants in the world around us. Twentieth century physicist Niels Bohr noted that "the experimenter chooses what to observe in a given experiment." This statement has serious implications for solving issues that occur in our biopharmaceutical industry, where empirical data are considered essential for understanding and resolving process deviations and assigning CAPA. But aren't these data susceptible to being assigned different, subjective, levels of importance by us? When faced with process deviations, our understanding of these issues is necessarily tied to how we choose to observe them.
We live in a world of uncertainty, one in which the "path of an object first comes into existence when we observe it" (Heisenberg), and one in which the picture we get is determined by how we choose to observe it (cf. The Double Slit Experiment by Thomas Young). Though this sounds like metaphysics, it is patent truth.
It is essential, then, to consciously strive for objectivity whenever possible. CAPAs are intended to resolve a problem or prevent future recurrences of the same problem. But they are, of course, determined by an analysis of the problem, and that analysis is performed by someone who chooses to observe certain data (which simultaneously disregards other data). Many times when we try to solve a problem we form cause-and-effect hypotheses predicated on ideas that are familiar to us, and this biases our perception of the data we collect (for example, we may select an experiment to test for 'x' preferentially over an experiment to test for 'y'. Though this biased screening of variables may in some cases make sense, it introduces a subjectivity that affects the results of our experiments).