1. Regulatory requirements for BE and Existing Guidelines
Alfredo García – Arieta, PhD
WHO Workshop on Assessment of Bioequivalence Data, 31 August – 3 September, 2010, Addis Ababa

2. Subjects: Drop-outs and withdrawals
Healthy volunteers: inclusion / exclusion criteria
Sufficient number to compensate for drop-outs or withdrawals
Replacement of subjects complicates the statistical model and analysis
Reason for withdrawal (e.g. AE or personal reason) must be reported
Protocol must state replacement policy or add-on designs

3. Subjects: Drop-outs and withdrawals
Recruit a few subject more to compensate for drop-outs or withdrawals
Protocol should state whether samples from this extra subjects will be assayed if not required for stat. analysis
EU EMA, on the contrary:
The data from all treated subjects should be treated equally
It is not acceptable to have a protocol which specifies that ‘spare’ subjects will be included in the analysis only if needed as replacements for other subjects who have been excluded
It should be planned that all treated subjects should be included in the analysis, even if there are no drop-outs.

4. EU Subject Accountability
Ideally, all treated subjects should be included in the statistical analysis.
However, subjects in a crossover trial who do not provide evaluable data for both of the test and reference products (or who fail to provide evaluable data for the single period in a parallel group trial) should not be included.
The analysis for each comparison should be conducted excluding the data from the treatments that are not relevant for the comparison in question (e.g. a three period study including two references, US and EU).

5. Exclusion of subjects in EU
Unbiased assessment of results from randomised studies requires that all subjects are observed and treated according to the same rules
These rules should be independent from treatment or outcome
In consequence, the decision to exclude a subject from the statistical analysis must be made before bioanalysis
In principle any reason for exclusion is valid provided it is specified in the protocol and the decision to exclude is made before bioanalysis
However the exclusion of data should be avoided, as the power of the study will be reduced and a minimum of 12 evaluable subjects is required

6. Exclusion of subjects in EU
Examples
Vomiting
Diarrhoea
the use of concomitant medication, in exceptional cases,.
It should be noted in the CRF as the study is being conducted
Clearly described and listed in the study report
Exclusion of data cannot be accepted on the basis of statistical analysis or for pharmacokinetic reasons alone, because it is impossible to distinguish the formulation effects from other effects influencing the pharmacokinetics.

7. Exception where subject can be excluded
A subject with lack of any measurable concentrations or only very low plasma concentrations for reference
Very low plasma concentrations: if its AUC is less than 5% of reference medicinal product geometric mean AUC (which should be calculated without inclusion of data from the outlying subject)
The exclusion of data due to this reason will only be accepted in exceptional cases and may question the validity of the trial.

8. Exceptions where subject must be excluded
Subjects with non-zero baseline concentrations > 5% of Cmax
Excluded from bioequivalence calculation to avoid carry- over effects
In case of immediate release products it can be caused by:
subject non-compliance (mouth check)
insufficient wash-out period (sufficient wash-out)
The samples from subjects excluded from the statistical analysis should still be assayed and the results listed

9. Not to exclude
Subjects whose AUC(0-t) covers less than 80% of AUC(0- ∞)
But if the percentage is less than 80% in more than 20% of the observations then the validity of the study may need to be discussed
This does not apply if the sampling period is 72 h or more and AUC(0-72h) is used instead of AUC(0-t)

10. Add-on designs If bioequivalence cannot be demonstrated because:
Larger variability than expected
Larger difference than expected but still within 20%
An add-on design can be performed using not less than half the number of subjects in the initial study
Provided this was anticipated in the protocol
Combining data only if:
Same protocol
Same batches

11. Add-on designs
Add-on designs must be given appropriate statistical treatment
Canadian add-on design requires:
No interaction formulation effect x study phase
No statistical difference in variability of both phases
But the consumer’s risk is increased
Statistically inadequate
It must be a sequential design with control of the alpha expenditure (EMA)

12. Two-stage design
An initial group of subjects can be treated and their data analysed.
If bioequivalence has not been demonstrated an additional group can be recruited and the results from both groups combined in a final analysis.
If this approach is adopted appropriate steps must be taken to preserve the overall type I error of the experiment and the stopping criteria should be clearly defined prior to the study.
The analysis of the first stage data should be treated as an interim analysis and both analyses conducted at adjusted significance levels (with the confidence intervals accordingly using an adjusted coverage probability which will be higher than 90%)

13. Two-stage design
For example, using 94.12% confidence intervals for both the analysis of stage 1 and the combined data from stage 1 and stage 2 would be acceptable, but there are many acceptable alternatives and the choice of how much alpha to spend at the interim analysis is at the company’s discretion.
The plan to use a two-stage approach must be pre- specified in the protocol along with the adjusted significance levels to be used for each of the analyses.
When analysing the combined data from the two stages, a term for stage should be included in the ANOVA model.

14. Genetic Phenotyping
Phenotyping for metabolizing activity for high-clearance drugs that are metabolized by enzymes that are subject to genetic polymorphism, e.g. propranolol
Phenotyping of subjects can be considered for studies of drugs that show phenotype-linked metabolism and for which a parallel group design is to be used, because it allows fast and slow metabolizers to be evenly distributed in the two groups of subjects
Phenotyping could also be important for safety reasons, determination of sampling times and wash-out periods in cross-over design studies

15. Study standardization
Standardization of study conditions is important to minimize the magnitude of variability other than in the pharmaceutical products
Standardization should cover:
Exercise
Diet
fluid intake
Posture
Restriction of the intake of alcohol, caffeine, certain fruit juices and concomitant medicines for a specified time period before and during the study
Volunteers should not take any other medicine, alcoholic beverages or over the-counter (OTC) medicines and supplements for an appropriate interval either before or during the study

16. Study standardization
In the event of emergency, the use of any non-study medicine must be reported (dose and time of administration).
Physical activity and posture should be standardized as far as possible to limit their effects on gastrointestinal blood flow and motility
The same pattern of posture and activity should be maintained for each day of the study
The time of day at which the study drug is to be administered should be specified
All meals should be standardized and the composition stated in the study protocol and report.

17. Selection of dose
In bioequivalence studies the molar equivalent dose of multisource and comparator product must be used
If possible
If not, potency correction (see Canadian Guideline)
Applicable if PK is dose-proportional
Generally the marketed strength with the greatest sensitivity to bioequivalence assessment should be administered as a single unit

18. Selection of dose This will usually be the highest marketed strength
Drug with low solubility and dose-proportional PK
Drug with more than proportional increase in PK (AUC) R T

19. Selection of dose
A lower strength in the linear part if PK shows a less than proportional increase with dose due to saturation of absorption process
Both a lower strength in the linear part and the highest strength if PK shows a less than proportional increase with dose due to solubility limitations

20. Selection of dose
A higher dose (i.e. more than one dosage unit) may be employed when analytical difficulties exist
In this case the total single dose should not exceed the maximal daily dose of the dosage regimen
Alternatively, the application of area under the curve (AUC) truncated to 3 × median tmax of the comparator formulation would avoid problems of lack of assay sensitivity in many cases
In certain cases a study performed with a lower strength can be considered acceptable if this lower strength is chosen for reasons of safety.

21. Sample fluids and their collection
Usually blood should be the biological fluid sampled
In most cases the analyte is measured in serum or plasma
Urine only if the analyte cannot be measured in plasma, etc.
Not necessary that “the API is excreted predominantly unchanged in the urine”
The volume of each sample must be measured at the study centre, where possible immediately after collection, and included in the report
In most cases the exclusive use of urine excretion data should be avoided as this does not allow estimation of the tmax and Cmax
Samples should be processed and stored under conditions that have been shown not to cause degradation of the analytes
The sample collection methodology must be specified in the study protocol.

22. Parameters to be assessed
The shape of the plasma concentration versus time curves is assessed by:
Cmax is peak exposure
AUC is extent of exposure
Tmax is the time of peak exposure
Focus now on exposure instead of absorption
AUC is extent of absorption if PK is linear /not saturated
Tmax and Cmax gives some information on rate of absorption

23. Primary parameters to be assessed
For single-dose studies, the following parameters should be measured or calculated:
AUC0–t, where t is the last sampling time point with a measurable concentration in the individual formulation tested.
The method of calculating AUC-values should be specified.
In general AUC should be calculated using the linear/log trapezoidal integration method.
In practice linear/linear trapezoidal rule is more frequent
The use of compartmental-based parameters is not recommended.
Cmax is the maximum or peak concentration observed representing peak exposure of API (or metabolite) in plasma, serum or whole blood.

24. Secondary parameters to be assessed
AUC0-inf representing total exposure,
where AUC0- = AUC0–t + Clast/;
Clast is the last measurable drug concentration
 is the terminal or elimination rate constant
tmax is the time after administration of the drug at which Cmax is observed.
Additional information:
T1/2 is the plasma (serum, whole blood) half-life

25. Application of truncated AUC in BE
In US-FDA and EU: AUC0-72 h for drugs with long half-life
There are two important advantages to the use of truncated areas:
more blood samples can be clustered around tmax to give greater precision in the estimation of both tmax and Cmax;
high assay sensitivity to define the disposition phase is not required.
The applicability of the truncated AUC approach merits particular consideration in the following cases:
where low concentrations occur in the terminal portion of the plasma concentration versus time curve, which may not be quantifiable by means of an adequately validated, sensitive analytical method; and
for products of APIs with long half-lives.

26. Application of truncated AUC in BE
When considering BE of immediate-release formulations for systemic delivery, the most important portion of the plasma concentration versus time curve is until the absorption phase is complete
On the other hand, the disposition phase does not illustrate formulation differences between the multisource product and comparator product in the bioequivalence decision-making process
Gaureault examined the use of partial (truncated) AUC using Monte Carlo simulations and found a high degree of concordance between the bioequivalence decision based on the partial area truncated to four times tmax and the area extrapolated to infinity
The evidence suggests that for immediate-release formulations it is unnecessary to take blood samples beyond four times tmax.

27. Parameters to be assessed in SS and Urine
For steady-state studies (prolonged release):
AUC is AUC over one dosing interval () at steady-state;
Cmax;
Cmin is concentration at the end of a dosing interval;
Peak trough fluctuation is percentage difference between Cmax and Cmin
Immediate release: only AUC and Cmax.
When urine samples are used
Cumulative urinary recovery (Ae) instead of AUC
Maximum urinary excretion rate instead of Cmax.

28. Parameters to be assessed in SS
For steady-state studies (prolonged release):
AUC
Cmax
Cmin
PTF

29. Analyte: parent or metabolite
Generally, evaluation of pharmacokinetic bioequivalence will be based upon the measured concentrations of the parent drug released from the dosage form rather than the metabolite
The concentration–time profile of the parent drug is more sensitive to changes in formulation performance than a metabolite, which is more reflective of metabolite formation, distribution and elimination.

30. Analyte: parent or metabolite
In some situations it may be necessary to measure metabolite concentrations rather than those of the parent drug:
The measurement of concentrations of therapeutically active metabolite is acceptable if the substance studied is a pro-drug.
Measurement of a metabolite may be preferred when concentrations of the parent drug are too low to allow reliable analytical measurement in blood, plasma or serum for an adequate length of time, or when the parent compound is unstable in the biological matrix.

31. Analyte: parent or metabolite
It is important to state a priori in the study protocol which chemical entities (pro-drug, drug (API) or metabolite) will be analysed in the samples.
It is important to note that measurement of one analyte, API or metabolite, carries the risk of making a type-I error (the consumer risk) to remain at the 5% level
However, if more than one of several analytes is selected retrospectively as the bioequivalence determinant, then both the consumer and producer risks change.
When measuring the active metabolites wash-out period and sampling times may need to be adjusted to enable adequate characterization of the pharmacokinetic profile of the metabolite.

32. Enantiomers
A non-stereoselective assay is currently acceptable for most pharmacokinetic bioequivalence studies
When the enantiomers have very different pharmacological or metabolic profiles, assays that distinguish between the enantiomers of a chiral API may be appropriate
Stereoselective assay is also preferred when systemic availability of different enantiomers is demonstrated to be non-linear

33. Enantiomers in US FDA
For BE studies, the racemate using an achiral assay.
Measurement of individual enantiomers in BE studies is recommended only when all of the following conditions are met:
the enantiomers exhibit different PD characteristics,
the enantiomers exhibit different PK characteristics,
primary efficacy and safety activity resides with the minor enantiomer, and
Nonlinear absorption is present (as expressed by a change in the enantiomer concentration ratio with change in the input rate of the drug) for at least one of the enantiomers

34. Enantiomers in EU
The use of achiral bioanalytical methods is generally acceptable.
However, the individual enantiomers should be measured when all the following conditions are met:
the enantiomers exhibit different pharmacokinetics
the enantiomers exhibit pronounced difference in pharmacodynamics
the exposure (AUC) ratio of enantiomers is modified by a difference in the rate of absorption.

35. Enantiomers in EU
The individual enantiomers should also be measured if the above conditions are fulfilled or are unknown
If one enantiomer is pharmacologically active and the other is inactive or has a low contribution to activity, it is sufficient to demonstrate bioequivalence for the active enantiomer

36. Statistical Analysis: Concern
The primary concern in BE assessment is to limit the risk of a false declaration of equivalence
Statistical analysis of the BE trial should demonstrate that a clinically significant difference in bioavailability between the multisource product and the comparator product is unlikely

37. Statistical Analysis: Method
The statistical procedures should be specified in the protocol before the data collection starts
The statistical method for testing PK BE is based upon the determination of the 90% confidence interval around the ratio of the log-transformed population means (multisource/comparator) for the pharmacokinetic parameters under consideration and by carrying out two one-sided tests at the 5% level of significance

38. Statistical Analysis: Conclusion
To establish PK BE, the calculated confidence interval should fall within a preset bioequivalence limit
80-125% for the 90% CI is the conventional acceptance range
80-125% for the 90% CI for AUC and Cmax always in FDA
Except scaling for HVD based on intra-subject variability
80-125% for 90 CI% of AUC and for PE of Cmax in Canada
90-111% for 90% CI of AUC and % for CI of Cmax in Canada
80-125% for 90% CI of AUC and Cmax normally in EU
Except scaling for Cmax in HVD based on intra-subject variability
Narrowing to in AUC and/or Cmax in NTI drugs

39. Statistical Analysis: Why 0.80-1.25?
The procedures should lead to a decision scheme which is symmetrical with respect to the two formulations (i.e. leading to the same decision whether the multisource formulation is compared to the comparator product or the comparator product to the multisource formulation)

40. Statistical Analysis: log-transformation
All concentration-dependent pharmacokinetic parameters (e.g. AUC and Cmax) should be log-transformed using either common logarithms to the base 10 or natural logarithms
The choice of common or natural logs should be consistent and should be stated in the study report.

41. Statistical Analysis: ANOVA
Logarithmically transformed, concentration-dependent pharmacokinetic parameters should be analysed using analysis of variance (ANOVA)
Usually the ANOVA model includes the formulation, period, sequence or carry-over and subject factors
Parametric methods, i.e. those based on normal distribution theory, are recommended for the analysis of log-transformed bioequivalence measures

42. Statistical Analysis: in log scale
The general approach is to construct a 90% confidence interval for the quantity μT−μR and to reach a conclusion of pharmacokinetic equivalence if this confidence interval is within the stated limits
The nature of parametric confidence intervals means that this is equivalent to carrying out two one-sided tests of the hypothesis at the 5% level of significance
The antilogs of the confidence limits obtained constitute the 90% confidence interval for the ratio of the geometric means between the multisource and comparator products

43. Statistical Analysis
The same procedure should be used for analysing parameters from steady state trials or cumulative urinary recovery, if required:
For steady-state studies:
AUC is AUC over one dosing interval () at steady-state;
Cmax;
Cmin is concentration at the end of a dosing interval;
Peak trough fluctuation is percentage difference between Cmax and Cmin.
When urine samples are used
Cumulative urinary recovery (Ae) instead of AUC
Maximum urinary excretion rate instead of Cmax.

44. Statistical Analysis: Tmax
For tmax descriptive statistics should be given
If tmax is to be subjected to a statistical analysis this should be based on non-parametric methods and should be applied to untransformed data
For drugs where onset of action is important
EU before 90% CI, but now not required 90% but PE
A sufficient number of samples around predicted maximal concentrations should have been taken to improve the accuracy of the tmax estimate

45. Statistical Analysis: other parameters
For parameters describing the elimination phase (T1/2) only descriptive statistics should be given

46. Statistical Analysis: outliers
Methods for identifying and handling of possible outlier data should be specified in the protocol.
Medical or pharmacokinetic explanations for such observations should be sought and discussed
As outliers may be indicative of product failure, post hoc deletion of outlier values is generally discouraged.
An approach to dealing with data containing outliers is to apply distribution-free (non-parametric), statistical methods
No longer valid or acceptable
No parametric methods ignore outlier values and it is equivalent to remove them

47. Statistical Analysis: non-parametric
If the distribution of log-transformed data is not normal, non-parametric statistical methods can be considered
No longer valid
Outliers make the distribution non-normal
Only tmax is analyzed with non-parametric methods
ANOVA is robust despite deviations from Normality
The justification of the intent to use nonparametric statistical methods should be included a priori in the protocol.

48. Acceptance ranges: AUC-ratio
The 90% confidence interval for this measure of relative bioavailability should lie within a bioequivalence range of 0.80–1.25
If the therapeutic range is particularly narrow, the acceptance range may need to be reduced based on clinical justification
Canada: %
EU: %
A larger acceptance range may be acceptable in exceptional cases if justified clinically
HVD in US FDA (not in Guidance for Industry)

49. Acceptance ranges: Cmax-ratio
In general the acceptance limit 0.80–1.25 should be applied
However, this measure of relative bioavailability is inherently more variable, and in certain cases a wider acceptance range (e.g. 0.75– 1.33) may be acceptable
EU for HVD only widening in Cmax based on intra-subject variability
The range used must be defined prospectively and should be justified, taking into account safety and efficacy considerations.
In exceptional cases, a simple requirement for the point estimate to fall within bioequivalence limits of 0.80–1.25 may be acceptable with appropriate justification in terms of safety and efficacy
Canada
Not statistically correct.

50. Acceptance ranges: tmax-difference
Statistical evaluation of tmax makes sense only if there is a clinically relevant claim for rapid onset of action or concerns about adverse effects
The nonparametric 90% confidence interval for this measure of relative bioavailability should lie within a clinically relevant range.
EU presently only based on Point Estimate
For other pharmacokinetic parameters the same considerations as outlined above apply

51. Fixed-dose combination products
The study design should follow the same general principles as described in previous sections
The multisource FDC product should be compared with the pharmaceutically equivalent comparator FDC product
In certain cases (e.g. when no comparator FDC product is available on the market) separate products administered in free combination can be used as a comparator

52. Fixed-dose combination products
Sampling times should be chosen to enable the pharmacokinetic parameters of all APIs to be adequately assessed.
The bioanalytical method should be validated on respect to all compounds measured
Statistical analyses should be performed with pharmacokinetic data collected on all active ingredients; the 90% confidence intervals of test/comparator ratio of all active ingredients should be within acceptance limits.

53. Highly variable drugs: The problem
A “highly variable drug” has been defined as an API with a within- subject variability of > 30% in terms of the ANOVA-CV
Moreover “highly variable drugs” are generally safe drugs with shallow dose–response curves
Proving the bioequivalence of medicinal products containing “highly variable drugs” is problematic because the higher the ANOVA-CV, the wider the 90% confidence interval
Thus large numbers of subjects must be enrolled in studies involving highly variable drugs to achieve adequate statistical power

54. Highly variable drugs: The solutions
Some regulatory authorities permit the use of broadened bioequivalence limits provided there is adequate justification
For example, the regulatory agency could broaden the bioequivalence limits from 0.8–1.25 to 0.75–1.33 taking into consideration the therapeutic category of the drug
In EU previously.
Not presently
Now acceptance range is widen based on intra-subject variability if clinically justified for Cmax only

55. Highly variable drugs: The solutions
Some regulatory authorities permit the use of scaling to broaden the bioequivalence limits
South-Africa
In a two-period design, the limits are scaled to the residual standard deviation, or in a replicate design, to the within-subject standard deviation of the comparator formulation
In US-FDA and EU a replicate design in necessary to estimate the real intra-subject variability of the comparator

56. Highly variable drugs: Japan’s solution
Some regulatory authorities allow the following acceptance criteria:
“Products are considered to be bioequivalent, if the 90% confidence interval of average ratios of AUC and Cmax between test and reference products is within the acceptable range of 0.8–1.25
If the confidence interval is not in the above range, test products are accepted as bioequivalent, if the following three conditions are satisfied:
the total sample size of the initial bioequivalence study is not less than 20 (n = 10/group) or pooled sample size of the initial and add-on subject studies is not less than 30
the ratio of geometric least squares means of AUC and Cmax between the multisource and comparator product are between 0.9 and 1.11; and
dissolution rates of test and reference products are evaluated to be equivalent under all dissolution testing conditions
This rule cannot be applied to slowly dissolving products from which less than 80% of a drug dissolves within the final testing time (2 hr in pH 1.2 medium and 6 hr in others) under any conditions of the dissolution tests described

57. Highly variable drugs: Canada’s solution
Some regulatory authorities do not allow for any adjustments
In Canada Cmax variability is not a problem because it is usually assessed with Point Estimate
In US-FDA no widening if the drug is not HVD
Now in EU no widening for AUC
Now in EU no widening for Cmax if the drug is not HVD or if it is not clinically justified

58. Highly variable drugs: Proposal
AUC is usually critical: no widening despite variability
Like Canada and EU
Cmax can be widening if the comparator Cmax is HV
A clinical justification is necessary (EU)
A replicate design is necessary (EU and US-FDA)
Predefined in the protocol
Proportionality constant of EU is statistically more adequate than the one of US-FDA with respect to consumer’s risk
Endrenyi L, Tothfalusi L. Regulatory conditions for the determination of bioequivalence of highly variable drugs. J Pharm Pharm Sci 2009; 12(1):

59. Limits at CV = 30 or 25%

60. Highly variable drugs: EU
Highly variable drug products (HVDP) are those whose intra-subject variability for a parameter is larger than 30%
If an applicant suspects that a drug product can be considered as highly variable in its rate and/or extent of absorption, a replicate cross-over design study can be carried out
Any replicate design is acceptable
The FDA recommends a specific one (RTR/TRR)

61. Highly variable drugs: EU
Those HVDP for which a wider difference in Cmax is considered clinically irrelevant based on a sound clinical justification can be assessed with a widened acceptance range
If this is the case the acceptance criteria for Cmax can be widened to a maximum of – %
For the acceptance interval to be widened the bioequivalence study must be of a replicate design where it has been demonstrated that the within-subject variability for Cmax of the reference compound in the study is >30%
The applicant should justify that the calculated intra-subject variability is a reliable estimate and that it is not the result of outliers

62. Highly variable drugs: EU
If this is the case the acceptance criteria for Cmax can be widened to a maximum of – %
The extent of the widening is defined based upon the within-subject variability seen in the bioequivalence study using scaled-average-bioequivalence according to [U, L] = exp [±k·sWR],
Where U is the upper limit of the acceptance range,
L is the lower limit of the acceptance range,
k is the regulatory constant set to and
sWR is the within-subject standard deviation of the log-transformed values of Cmax of the reference product

63. Highly variable drugs: EU
The table below gives examples of how different levels of variability lead to different acceptance limits using this methodology

64. Highly variable drugs: EU
The geometric mean ratio (GMR) should lie within the conventional acceptance range %.
The possibility to widen the acceptance criteria based on high intra-subject variability does not apply to AUC where the acceptance range should remain at – % regardless of variability
It is acceptable to apply either a 3-period or a 4-period crossover scheme in the replicate design study

65. Narrow Therapeutic Index Drugs
It is difficult to define NTI drugs
US-FDA list in SUPAC guideline
JAPAN list is also available in BE guideline
Canada Guideline on Critical Dose Drugs
“Critical dose drugs” are defined as those drugs where comparatively small differences in dose or concentration lead to dose-and concentration-dependent, serious therapeutic failures and/or serious adverse drug reactions which may be persistent, irreversible, slowly reversible, or life threatening, which could result in inpatient hospitalization or prolongation of existing hospitalization, persistent or significant disability or incapacity, or death. Adverse reactions that require significant medical intervention to prevent one of these outcomes are also considered to be serious
Cyclosporine , Digoxin, Flecainide, Lithium, Phenytoin, Sirolimus, Tacrolimus, Theophylline, Warfarin
EU: case by case
Ciclosporine: AUC and Cmax should be narrowed in both fed and fasting state studies
Tacrolimus: AUC should be narrowed

66. Narrow Therapeutic Index Drugs
FDA
80.00 – 125%
Canada
90% CI AUC T/R should be within 90.0 to 112.0%
90% CI Cmax T/R should be between 80.0 and 125.0%
Both the fasted and fed states EU
In specific cases, the acceptance interval for AUC should be tightened to %
Where Cmax is of particular importance for safety, efficacy or drug level monitoring the % acceptance interval should also be applied for this parameter

67. Thank you very much for your attention!