MAD Analysis Results

Although the MAD analysis provides a simple and valuable approach to under- stand and act on solubility and permeability data, much more can be done with regard to modeling dissolution and absorption, and at the same time, incorporat- ing pharmacokinetic concepts, such as metabolism, excretion, and distribution of drug in and out of tissues. By taking a more comprehensive approach to modeling the whole process, commonly referred to absorption, distribution, metabolism, and excretion (ADME), dissolution can be correlated to blood plasma concen- trations and, therefore, Cmax and area under concentration time curve (AUC). In developing an in vitro/in vivo correlation, a mechanistically based approach will be described. This approach is distinct from perhaps the more common and traditional empirical approach. With the empirical approach, dosage forms are made with varying dissolution rates, the resulting dosage forms are dosed in the clinic to determine plasma concentrations, and finally, the plasma concentrations are correlated with the dissolution rates. This approach does not require a mechanistic explanation of the result. Its limitation is that it does not provide a mechanistic framework to predicting outcomes across chemical structures and, therefore, may not be applicable to the development of future drugs. The goal of the mechanistic approach is to predict the outcome before doing the experiment through a fundamental understanding of the dynamics of dissolution, ADME. It is not suggested that the mechanistic approach will eliminate the need to do empirical experiments or eliminate the need to validate predicted outcomes through experimentation. However, as the science progresses, it is certainly a goal of the industry to predict outcomes to increase its success rate by eliminating ill conceived clinical studies, and a fundamental understanding of the ADME processes hold promise to this end. Predicting dissolution falls under the realm of the formulation scientist, whereas methods to predict drug metabolism, toxicity, and efficacy generally do not. However, incorporating key aspects from all disciplines into the decision of what makes a successful drug product is likely to increase the quality of drug candidates. Here again, a mechanistically based approach holds the promise of wider applicability across diverse chemical structures and therapeutic areas. Mathematical models help bring the important parameters from each discipline together in a way so that more rational decisions can be made. As stated before, solubility and permeability are key parameters for the physical scientist working on dosage form development. Scientists involved in drug metabolism typically contribute estimates of drug clearance rates and volumes of distribution. Combining these two disciplines allows the prediction of drug plasma concen- trations and whether or not the dose–exposure relationship will be linear or not. Although it is beyond the scope of this chapter, toxicologists and biological and clinical scientists can then review the predictions to see if projected plasma concentrations meet the needs for toxicological and clinical evaluation. This would ideally occur in project team meetings with representatives present from all disciplines.