Revisiting Principles Behind Drug Clearance and Organ Extraction
The two angles mentioned above are seen as distinct from each other by Rowland and Pang, who distinguish the practical (clinical) applications of clearance from its linking role for connecting the in vitro values and observation in the whole system. They define the first as a mere relationship between the loss of the drug from the system and its link to concentration of the drug entering the eliminating organ(s), as it was defined much earlier on by physiologists working on kidney clearance. Benet et al. argue that “true” clearance cannot be assessed as described above since driving concentrations within the organ of elimination are not known. The latter is essential for IVIVE practice, where a liver model is required to postulate the relationship between the concentrations entering and leaving the organ with reference to what the organ “sees” (i.e., concentration within the organ). It is up to the readers, depending on the dominance of clinical as opposed to translational elements in their practice, to decide whether knowing the liver model will have any impact on what they do or not. In the first instance, it may seem that from the clinical perspective the only thing that matters is to understand the relationship between the loss and the drug concentration (without requiring any model assumptions at all). However, we have to remember that the ability to predict the impact of various factors (such as change in blood flow, drug binding to plasma proteins or red blood cells, change in activity of enzymes and transporters) requires a “model” that links all of these to measured (measurable) entities, i.e., concentration entering and exiting the organ.
In working with models, it is essential to avoid falling in love with them! The ability to detach ourselves from a given model, either to consider necessary pragmatism or question possibilities for more detailed versions, may help with picking the right model for solving the question in hand (the concept of “horses for courses”). One should always refrain from assigning mechanistic meaning or quantitative predictive nature to models beyond their intended use. Otherwise, the models will give false hope in predictive abilities on the one side and stagnate strivings for better understanding of the systems on the other end.