**2.11 Methods of assessment of bioavailability**

Pharmacokinetic methods are used for the assessment of bioavailability of drug products that exists as a linear relation between the drug level in the biological fluid and therapeutic response. Therefore, these methods are also known indirect methods. Because therapeutically active drug can be accurately measured in biological fluids, plasma and urine data give the most objective information on bioavailability [23].

### *2.11.1 Indirect methods or pharmacokinetic methods*

Plasma data are most widely used and accepted method for the assessment of bioavailability of the drug product. The basic assumption in this method is that drug products that are bioequivalent product super imposable plasma level time curve. The parameters Tmax and Cmax are the measures of the rate of absorption of the drug, while the parameters AUC is a measure of the extent of absorption.

Urinary excretion method is based on the general observation that the rate of urinary excretion of a drug is directly proportional to the concentration of the drug in the blood. Therefore, the bioavailability can be calculated as the ratio of the total amount of the unchanged drug recovered in urine following the administration of test and standard formulations. Urinary metabolite excretion data are not used for the estimation of bioavailability since the drug can undergo metabolism at different sites including the gut and liver, and the rates of metabolism may vary because of various reasons.

The relative bioavailability should lie within an acceptance range of 0.80–1.25 if 90% confidence interval is considered. In case of an especially narrow therapeutic range, the acceptance range may need to be tighter. In rare cases such as highly variable drugs, a wider acceptance range may be acceptable if it has right clinical justification. Cmax ratio is the measure of relative bioavailability that may be more variable than the AUC ratio, and a wider acceptance range may be acceptable. The range used in the protocol should be justified taking into account safety and efficacy consideration. Tmax is a measure of release or action or signs for a relation to adverse effects.

#### *2.11.2 Direct methods or pharmacodynamic methods*

The pharmacodynamic methods are used when assessment of bioavailability by pharmacokinetic methods is not possible due to nonavailability of a sensitive analytical method for the measurement of the drug or the analytical methods lacks sufficient accuracy and/or reproducibility. The two pharmacodynamic methods used for the estimation of bioavailability are based on the measurement of acute pharmacological effect and clinical response. In order to estimate the bioavailability of a drug product accurately by measurement of acute pharmacological effect, the following criteria should meet. These are an easily measurable response such as heart rate, ECG, blood pressure, pupil diameter, etc. and an established doserelated response curve.

#### **2.12 Statistical analysis of the data and analysis of variance (ANOVA)**

Due to biological and experimental variations, some differences always exist, and it is necessary to ascertain whether these differences are simply chance occurrences or are due to actual differences in treatment administered to the subjects. Statistical methods are used to evaluate the pharmacokinetic data in order to identify the different sources of variation and if possible to measure the contribution of each identified variable and isolate the specific observation of primary interest. The analysis of variance (ANOVA), a statistical procedure that used for a crossover design is widely used method in bioavilability testing [24].

The pharmacokinetic parameters derived from blood drug concentration and time from bioavailability studies are subjected to ANOVA. In ANOVA, the variance is due to subjects, periods, and treatment. The classical null hypothesis test is considered where H0: μT = μR if the pharmaceutical products are bioequivalent and alternate hypothesis therefore is H1: μT ≠ μR where products are bioinequivalent

**75**

**Table 3.**

Total Tn-1

*and N is the number of subjects.*

*Bioavailability and Bioequivalence Studies DOI: http://dx.doi.org/10.5772/intechopen.85145*

pared to the crossover design (**Tables 3** and **4**).

standard drug, respectively.

where μT and μR are the expected mean bioavailability of the test and reference or

Bioavailability studies are designed in two ways, and these are design 1 and design 2. Design 1 is parallel design in which the subjects divide into two treatment groups and assign one treatment to each group. Design 2 is crossover design in which each subject has one block and applies both the treatments to each subject with washout period in between them. In a parallel design, variability due to the treatment is considered, and in the crossover design, variability due to treatment, subject, and period are considered to minimize variability. Error sum of squares in design 1 (SSE1) and sum of error sum of squares in design 2 (SSE2) are equal. The error mean sum of square for design 1 (MSE1) will be greater than the error mean sum of square for design 2 (MSE2) if the degrees of freedom for SSE are the same in the both designs then error variability is greater in the parallel group design com-

The mean sum of squares is compared with the mean sum of squares due to error (F = MST/MSE), and if these are comparable, no difference between the levels of a factor is concluded, otherwise a difference is achieved. The treatment mean sum of squares is larger than the error mean sum of squares if difference is achieved between the treatments. Then the chances of getting treatment mean sum of squares being bigger than the error mean sum of squares are more in design 2 compared to design 1. Therefore, chances of showing a statistically significant difference are higher in design 2 compared to design 1. This is equivalent to saying that design 2 is more competent than design 1. Null hypothesis H0 μT = μR provides an assessment amount of drug absorbed from the test product is identical or equal or similar to the amount of drug absorbed from the reference. They may be different or nearly equal but not identical in most of the cases. If the trial is run under tightly controlled conditions and the number of subjects is large enough, no matter how small the difference between the formulations and it will be detected as significant. The difference may give rise to following anomalies due to a large difference

between two formulations, sample size not large enough (**Table 5**).

Error (T-1)(N-2) SSE MSE

*Analysis of variance (ANOVA) table for t-period, t-treatment crossover design.*

In some cases, simple null hypothesis was inappropriate and alternative approach to ANOVA for bioequivalence studies is considered as Type I and II error. Type I error is a manufacturer's risk that is explained by probability of rejecting a formulation which is in fact bioequivalent. Manufacturer's risk is the probability (α = 0.05) of rejecting H0 when H0 is true. Similarly, type II error is the consumer's risk that is explained as the probability (β) of accepting a formulation which is bioinequivalent that is accepting H0 when H0 is false. FDA restricts the power of the test which should be 80% and the consumer's risk β to 20%, but this may not a satisfactory solution for either the consumer or the regulatory agencies. It makes

**Sources of variance Degree of freedom (D F) Sum of squares (SS) Mean of squares (MS) F statistic** Treatment T-1 SST MST MST/MSE Subjects N-1 SSS MSS MSS/MSE Period T-1 SSP MSP MSP/MSE

*T is the number of treatments, SST-sum of squares due to treatments, SSP-sum of squares due to period, MSS-mean sum of squares due to subjects, MST-mean sum of squares due to treatments, MSP-mean sum of squares due to period,* 

### *Bioavailability and Bioequivalence Studies DOI: http://dx.doi.org/10.5772/intechopen.85145*

*Pharmaceutical Formulation Design - Recent Practices*

various reasons.

to adverse effects.

related response curve.

*2.11.1 Indirect methods or pharmacokinetic methods*

*2.11.2 Direct methods or pharmacodynamic methods*

Plasma data are most widely used and accepted method for the assessment of bioavailability of the drug product. The basic assumption in this method is that drug products that are bioequivalent product super imposable plasma level time curve. The parameters Tmax and Cmax are the measures of the rate of absorption of the drug, while the parameters AUC is a measure of the extent of absorption.

Urinary excretion method is based on the general observation that the rate of urinary excretion of a drug is directly proportional to the concentration of the drug in the blood. Therefore, the bioavailability can be calculated as the ratio of the total amount of the unchanged drug recovered in urine following the administration of test and standard formulations. Urinary metabolite excretion data are not used for the estimation of bioavailability since the drug can undergo metabolism at different sites including the gut and liver, and the rates of metabolism may vary because of

The relative bioavailability should lie within an acceptance range of 0.80–1.25 if 90% confidence interval is considered. In case of an especially narrow therapeutic range, the acceptance range may need to be tighter. In rare cases such as highly variable drugs, a wider acceptance range may be acceptable if it has right clinical justification. Cmax ratio is the measure of relative bioavailability that may be more variable than the AUC ratio, and a wider acceptance range may be acceptable. The range used in the protocol should be justified taking into account safety and efficacy consideration. Tmax is a measure of release or action or signs for a relation

The pharmacodynamic methods are used when assessment of bioavailability by pharmacokinetic methods is not possible due to nonavailability of a sensitive analytical method for the measurement of the drug or the analytical methods lacks sufficient accuracy and/or reproducibility. The two pharmacodynamic methods used for the estimation of bioavailability are based on the measurement of acute pharmacological effect and clinical response. In order to estimate the bioavailability of a drug product accurately by measurement of acute pharmacological effect, the following criteria should meet. These are an easily measurable response such as heart rate, ECG, blood pressure, pupil diameter, etc. and an established dose-

**2.12 Statistical analysis of the data and analysis of variance (ANOVA)**

design is widely used method in bioavilability testing [24].

Due to biological and experimental variations, some differences always exist, and it is necessary to ascertain whether these differences are simply chance occurrences or are due to actual differences in treatment administered to the subjects. Statistical methods are used to evaluate the pharmacokinetic data in order to identify the different sources of variation and if possible to measure the contribution of each identified variable and isolate the specific observation of primary interest. The analysis of variance (ANOVA), a statistical procedure that used for a crossover

The pharmacokinetic parameters derived from blood drug concentration and time from bioavailability studies are subjected to ANOVA. In ANOVA, the variance is due to subjects, periods, and treatment. The classical null hypothesis test is considered where H0: μT = μR if the pharmaceutical products are bioequivalent and alternate hypothesis therefore is H1: μT ≠ μR where products are bioinequivalent

**74**

where μT and μR are the expected mean bioavailability of the test and reference or standard drug, respectively.

Bioavailability studies are designed in two ways, and these are design 1 and design 2. Design 1 is parallel design in which the subjects divide into two treatment groups and assign one treatment to each group. Design 2 is crossover design in which each subject has one block and applies both the treatments to each subject with washout period in between them. In a parallel design, variability due to the treatment is considered, and in the crossover design, variability due to treatment, subject, and period are considered to minimize variability. Error sum of squares in design 1 (SSE1) and sum of error sum of squares in design 2 (SSE2) are equal. The error mean sum of square for design 1 (MSE1) will be greater than the error mean sum of square for design 2 (MSE2) if the degrees of freedom for SSE are the same in the both designs then error variability is greater in the parallel group design compared to the crossover design (**Tables 3** and **4**).

The mean sum of squares is compared with the mean sum of squares due to error (F = MST/MSE), and if these are comparable, no difference between the levels of a factor is concluded, otherwise a difference is achieved. The treatment mean sum of squares is larger than the error mean sum of squares if difference is achieved between the treatments. Then the chances of getting treatment mean sum of squares being bigger than the error mean sum of squares are more in design 2 compared to design 1. Therefore, chances of showing a statistically significant difference are higher in design 2 compared to design 1. This is equivalent to saying that design 2 is more competent than design 1. Null hypothesis H0 μT = μR provides an assessment amount of drug absorbed from the test product is identical or equal or similar to the amount of drug absorbed from the reference. They may be different or nearly equal but not identical in most of the cases. If the trial is run under tightly controlled conditions and the number of subjects is large enough, no matter how small the difference between the formulations and it will be detected as significant. The difference may give rise to following anomalies due to a large difference between two formulations, sample size not large enough (**Table 5**).

In some cases, simple null hypothesis was inappropriate and alternative approach to ANOVA for bioequivalence studies is considered as Type I and II error. Type I error is a manufacturer's risk that is explained by probability of rejecting a formulation which is in fact bioequivalent. Manufacturer's risk is the probability (α = 0.05) of rejecting H0 when H0 is true. Similarly, type II error is the consumer's risk that is explained as the probability (β) of accepting a formulation which is bioinequivalent that is accepting H0 when H0 is false. FDA restricts the power of the test which should be 80% and the consumer's risk β to 20%, but this may not a satisfactory solution for either the consumer or the regulatory agencies. It makes


*T is the number of treatments, SST-sum of squares due to treatments, SSP-sum of squares due to period, MSS-mean sum of squares due to subjects, MST-mean sum of squares due to treatments, MSP-mean sum of squares due to period, and N is the number of subjects.*
