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implications of bioequivalence testing are also described by Patterson and so we do not repeat these here. At the present time average bioequivalence (see Section 7.2) serves as the current international standard for bioequivalence testing using a 2× 2 cross-over design. Alternative designs (e.g., replicate cross-over designs) may be also utilized for drug products to improve power (see Section 7.6). We will consider population and individual bioequivalence testing as these utilize cross-over study designs and were the subject of extensive debate in the 1990s (see Patterson, 2001b, for a summary), but these may not currently be used for access to the marketplace (FDA Guidance, 2002). 7.2 Testing for average bioequivalence The now generally accepted method of testing for ABE is the two-one-sided-tests procedure (TOST) proposed by Schuirmann (1987). It is con-veniently done using a confidence interval calculation. Let µ be the (true) mean values of log(AUC) (or log(Cmax)) when subjects are treated with T and R, respectively. ABE is demonstrated if the 90% two-sided confidence interval for µ falls within the acceptance limits of − ln 1.25 = −0.2231 and + ln 1.25 = 0.2231. These limits are set by the regulator (FDA Guid-ance, 1992, 2001, 2002) and when exponentiated give limits of 0.80 and 1.25. That is, on the natural scale ABE is demonstrated if there is good evidence that: 0.80 ≤ exp(µ ) ≤ 1.25. We note that symmetry of the confidence interval is on the logarithmic scale, not the natural scale. The method gets its name (TOST) because the process of deciding if the 90% confidence interval lies within the acceptance limits is equiva-lent to rejecting both of the following one-sided hypotheses at the 5% significance level: H :µ ≤− ln 1.25 H :µ ≥ ln 1.25. Example 7.1 The derived data given in Tables 7.1 and 7.2are from a pharmacoki-netic study that compared a test drug (T) with a known reference drug (R). The design used was a 2×2 cross-over with 24 healthy volunteers in the RT sequence group and 25 in the TR sequence group. Each volunteer should have provided both an AUC and Cmax value. However, as can
DOI link for implications of bioequivalence testing are also described by Patterson and so we do not repeat these here. At the present time average bioequivalence (see Section 7.2) serves as the current international standard for bioequivalence testing using a 2× 2 cross-over design. Alternative designs (e.g., replicate cross-over designs) may be also utilized for drug products to improve power (see Section 7.6). We will consider population and individual bioequivalence testing as these utilize cross-over study designs and were the subject of extensive debate in the 1990s (see Patterson, 2001b, for a summary), but these may not currently be used for access to the marketplace (FDA Guidance, 2002). 7.2 Testing for average bioequivalence The now generally accepted method of testing for ABE is the two-one-sided-tests procedure (TOST) proposed by Schuirmann (1987). It is con-veniently done using a confidence interval calculation. Let µ be the (true) mean values of log(AUC) (or log(Cmax)) when subjects are treated with T and R, respectively. ABE is demonstrated if the 90% two-sided confidence interval for µ falls within the acceptance limits of − ln 1.25 = −0.2231 and + ln 1.25 = 0.2231. These limits are set by the regulator (FDA Guid-ance, 1992, 2001, 2002) and when exponentiated give limits of 0.80 and 1.25. That is, on the natural scale ABE is demonstrated if there is good evidence that: 0.80 ≤ exp(µ ) ≤ 1.25. We note that symmetry of the confidence interval is on the logarithmic scale, not the natural scale. The method gets its name (TOST) because the process of deciding if the 90% confidence interval lies within the acceptance limits is equiva-lent to rejecting both of the following one-sided hypotheses at the 5% significance level: H :µ ≤− ln 1.25 H :µ ≥ ln 1.25. Example 7.1 The derived data given in Tables 7.1 and 7.2are from a pharmacoki-netic study that compared a test drug (T) with a known reference drug (R). The design used was a 2×2 cross-over with 24 healthy volunteers in the RT sequence group and 25 in the TR sequence group. Each volunteer should have provided both an AUC and Cmax value. However, as can
implications of bioequivalence testing are also described by Patterson and so we do not repeat these here. At the present time average bioequivalence (see Section 7.2) serves as the current international standard for bioequivalence testing using a 2× 2 cross-over design. Alternative designs (e.g., replicate cross-over designs) may be also utilized for drug products to improve power (see Section 7.6). We will consider population and individual bioequivalence testing as these utilize cross-over study designs and were the subject of extensive debate in the 1990s (see Patterson, 2001b, for a summary), but these may not currently be used for access to the marketplace (FDA Guidance, 2002). 7.2 Testing for average bioequivalence The now generally accepted method of testing for ABE is the two-one-sided-tests procedure (TOST) proposed by Schuirmann (1987). It is con-veniently done using a confidence interval calculation. Let µ be the (true) mean values of log(AUC) (or log(Cmax)) when subjects are treated with T and R, respectively. ABE is demonstrated if the 90% two-sided confidence interval for µ falls within the acceptance limits of − ln 1.25 = −0.2231 and + ln 1.25 = 0.2231. These limits are set by the regulator (FDA Guid-ance, 1992, 2001, 2002) and when exponentiated give limits of 0.80 and 1.25. That is, on the natural scale ABE is demonstrated if there is good evidence that: 0.80 ≤ exp(µ ) ≤ 1.25. We note that symmetry of the confidence interval is on the logarithmic scale, not the natural scale. The method gets its name (TOST) because the process of deciding if the 90% confidence interval lies within the acceptance limits is equiva-lent to rejecting both of the following one-sided hypotheses at the 5% significance level: H :µ ≤− ln 1.25 H :µ ≥ ln 1.25. Example 7.1 The derived data given in Tables 7.1 and 7.2are from a pharmacoki-netic study that compared a test drug (T) with a known reference drug (R). The design used was a 2×2 cross-over with 24 healthy volunteers in the RT sequence group and 25 in the TR sequence group. Each volunteer should have provided both an AUC and Cmax value. However, as can
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