ABSTRACT
The measures of bioavailability are based upon measures of the concentration of the drug in the blood and we must assume that there is a direct relationship between the concentration of drug we detect in the blood and the concentration of the drug at the site of action. The criterion involve the evaluation of the peak plasma concentration (Cmax), the time to reach the peak concentration (Tmax), and/or the area under plasma concentration-time curve (AUC). The AUC measures the extent of absorption and the amount of drug that is absorbed by the body, and is the parameter most commonly evaluated in bioequivalence studies. Many excellent text books deal with the issues associated with measuring pharmacokinetic parameters: the extent of bioavailability and bioequivalence (Welling and Tse, 1995; Evans, Schentag, and Jusko, 1992; Winter, 2010). The purpose of this discussion is to focus solely on the statistical manipulation of bioequivalence data. There are three situations requiring bioequivalence testing: a) when a proposed marketed dosage form differs significantly from that used in the major clinical trials for the product; b) when there are major changes in the manufacturing process for a marketed product; and c) when a new generic product is compared to the innovator’s marketed product (Benet and Goyan, 1995). Regulatory agencies allow the assumption of safety and effectiveness if the pharmaceutical manufacturers can demonstrate bioequivalence with their product formulations. Experimental Designs for Bioequivalence Studies Before volunteers are recruited and the actual clinical trial is conducted, an insightful and organized study is developed by the principal investigator. As discussed in Chapter 1, the first two steps in the statistical process are to identify the questions to be answered and the hypotheses to be tested (defined in the study objectives). Then the appropriate research design is selected (to be discussed below) and the appropriate statistical tests are selected. For in vivo bioavailability studies, the FDA requires that the research design identifies the scientific questions to be answered, the drugs(s) and dosage form(s) to be tested, the analytical methods used to assess the outcomes of treatment, and benefit and risk considerations involving human testing (21 Code of Federal Regulations, 320.25(b)). Study protocols should not only include the objectives of the study, the patient inclusion and exclusion criteria, the study design, dosing schedules, and physiological measures, but also a statistics section describing the sample size, power determinations, and the specific analyses that will be performed. These protocols are then reviewed by an institutional review board to evaluate the benefit and risk considerations for the volunteers. Two types of study designs are generally used for comparing the bioavailability parameters for drugs. Each of these designs employs statistics or modifications of statistics presented in previous chapters. The first design is a parallel group design, which is illustrated in Figure 22.1. In this design, volunteers are assigned to one of two similar groups and each group receives only one treatment (either the test drug or the reference standard). In order to establish similar groups, volunteers are randomly assigned to one of the two groups using a random numbers table as discussed in Chapter 2. For example, assume that 30 healthy volunteers (15 per group) are required to compare two formulations of a particular product. Using a random numbers table, the volunteers (numbered 01 to 30)
Figure 22.1 Parallel design involving two groups.
