Σχεδιασμός κλινικών μελετών βιοϊσοδυναμίας (DESIGN OF BIOEQUIVALENCE STUDIES) To demonstrate that two (or more) medicinal products are bioequivalent

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1 Σχεδιασμός κλινικών μελετών βιοϊσοδυναμίας (DESIGN OF BIOEQUIVALENCE STUDIES) AIM OF BIOEQUIVALENCE STUDIES To demonstrate that two (or more) medicinal products are bioequivalent AND Two medicinal products are bioequivalent if they are pharmaceutically equivalent or pharmaceutical alternatives and if their bioavailabilities after administration in the same molar dose are similar to such degree that their effects, with respect to both efficacy and safety, will be essentially the same (NfG CPMP/EWP/QWP/1401/98) pharmaceutical equivalent: medicinal products that contain the same amount of the same active substance in the same dosage form and that meet the same or comparable standards (differ in excipients or manufacturing process) pharmaceutical alternatives: medicinal products which contain the same active moiety but differ in chemical form (salt, ester, etc) of that moiety or in the dosage form or strength foundamental bioequivalence assumption equivalent in bioavailability therapeutically equivalent Bioequivalence studies are surrogates of clinical efficacy and safety studies Regulatory Authorities (EMEA, FDA) accept that BE could be established based on (in order of preference): 1. pharmacokinetics (PK) studies 2. pharmacodynamics (PD) studies 3. clinical studies 4. in vitro studies

2 BASIC DESIGN CONSIDERATIONS Decision on the design of a bioavailability study should be based 1.the scientific questions to be answered are the bioavailabilities of the two products similar to such degree that their effects, with respect to both efficacy and safety, will be essentially the same? Statistical considerations the formulation effect should be distinguished from other effects (EMEA) 2. the nature of the Reference and Test products Reference: Innovator product Test: Generic product 3. the availability of analytical methods analytical method should be of sufficient accuracy, sensitivity and reproducibility to discriminate between inequivalent products (FDA) 4. benefit-risk considerations relative to human testing type and number of subjects BASIC DESIGNS 1) PARALLEL DESIGN

3 Not the design of choice for bioequivalence studies Incapable of identifying and removing the inter-subject variability from the comparison between formulations Provide a less precise statistical inference for the difference in average BA between formulations than that of a crossover design May be considered as an alternative to a cross-over design if i) inter-subject variability small compared to intra-subject variability ii) drug is potentially toxic or has a very long elimination half-life iii) population of interest consists of very ill patients alternative (to cross-over designs) well-established designs could be considered such as parallel design for very-long half life substances (NfG, EMEA) 2) 2x2 CROSSOVER DESIGN Design of Choice for two-products comparisons (EMEA, FDA) It allows for the identification and removal of the inter-subject variability from the comparison between formulations Potential problems Carryover effects Drop-outs HIGHER ORDER CROSSOVER DESIGNS

4 With a 2x2 design: the intra-subject variabilities for the two products cannot be estimated independently carryover effects present Equal Unequal sequence effect is confounded with carryover effect no unbiased estimate of drug effect from both periods To overcome these undesirable properties, a higher order crossover design may be applied Design A (Balaam s design) Sequence Period 1 Period 2 1 T T 2 R R 3 R T 4 T R Design B Sequence Period 1 Period 2 Period 3 1 T R R 2 R T T

5 Design C Sequence Period 1 Period 2 Period 3 Period 4 1 T T R R 2 R R T T 3 T R R T 4 R T T R Variances for Designs A, B,and C (in multiples of σ 2 /n) Design Carryover effects Drug effects (adjusted for Drug effects carryover effects) S 4(2 σ 2 s+σ 2 e)/n - 1 A B C ) CROSSOVER DESIGNS FOR THREE OR MORE PRODUCTS Under the constraint of the number of periods being equal to the number of formulations (t), a balanced design is a set of orthogonal Latin squares Then Number of Sequences = t(t-1) For t 3 the orthogonal Latin squares designs are impractical

6 In these cases a Williams design could be applied Williams design for 3 formulations Sequence Period 1 Period 2 Period 3 1 R T 2 T 1 2 T 1 R T 2 3 T 2 T 1 R 4 T 1 T 2 R 5 T 2 R T 1 6 R T 1 T 2 Williams design for 4 formulations Sequence Period 1 Period 2 Period 3 Period 4 1 R T 3 T 1 T 2 2 T 1 R T 2 T 3 3 T 2 T 1 T 3 R 4 T 3 T 2 R T 1 DESIGN SELECTION Let s read from the NfG (EMEA) If the number of formulations to be compared is two, a two-period, two-sequence crossover design is often considered to be the design of choice.

7 However, under certain circumstances alternative well-established designs could be considered such as parallel design for very-long half life substances and replicate designs for substances with highly variable disposition provided the study design and the statistical analyses are scientifically sound Then how could we choose a scientifically sound design? Factors: 1. number of formulations 2. drug pharmacokinetics 3. study objective 4. subjects availability 5. inter- and intrasubject variabilities 6. duration of study/number of periods 7. cost of adding a subject relative to that of adding one period 8. dropout rate In practice: A crossover design is selected if the number of formulations to be compared is no more than three. A replicate crossover design can be selected for highly variable drugs. A parallel design may be applied if the drug has a very long half-life or it possess potential toxicity.

8 DECISION RULES Two medicinal products are bioequivalent if they are pharmaceutically equivalent or pharmaceutical alternatives and if their bioavailabilities after administration in the same molar dose are similar to such degree that their effects, with respect to both efficacy and safety, will be essentially the same (NfG, EMEA) Yes ok fine But How similar the BA of the two products should be (so that their effects, with respect to both efficacy and safety, to be essentially the same)? (suggestions from the audience welcome!) Fundamental Question in BE: is it a quality control parameter or a surrogate of clinical efficacy and safety Quality Control parameter: a universal tolerable difference in BA can be defined (e.g. 20%) Characteristics: arbitrary, unjustified based on clinical grounds, but very helpful in decision making (and in harmonization of decision making policies and homogenization of decisions) Surrogate of efficacy and safety: a case by case approach should be applied to evaluate the impact of observed differences in BA on the efficacy and safety of the Test product Characteristics: correct approach based on clinical grounds, not-helpful in decision making

9 Logical inconsistency of current NfG approach (hybridic approach): BE is regarded as surrogate of clinical efficacy and safety but specific BE limits are defined The limits are nevertheless negotiable! They can be tightened (e.g. for AUC) In specific cases of a narrow therapeutic range the acceptance interval (for AUC) may need to be tightened They can also be loosened (again for AUC) In rare cases a wider acceptance range may be acceptable if it is based on sound clinical justification The long road of decision policies Or the road from the 75/75 to the 80/20, to the 20% to the 80/125 rule! The interval hypotheses for average BE testing (Schuirmannn, 1981) The confidence interval approach represents today s current thinking of regulatory organizations

10 Basic concept 1: the BA profiles of the two products (should) differ by less than a clinically meaningful limit Basic concept 2 (statistical): BE is demonstrated if the Null hypothesis of bioinequivalence is rejected (with a certain assurance) Ηο: μ Τ - μ R θ L or μ Τ - μ R θ U vs Ha: θ L μ Τ - μ R θ U (θ L = -20% μ R and θ U = 20% μ R ) Two sets of one-sided hypotheses Ηο 1 : μ Τ - μ R θ L vs Ηa 1 : μ Τ - μ R θ L and Ηο 2 : μ Τ - μ R θ U vs Ηa 2 : μ Τ - μ R θ U a Schuirmann s two one-sided tests: BE is concluded (at α level of significance) if: [(μ Τ - μ R ) - θ L ] / σ (1/n 1 + 1/n 2 ) t (α, n 1 + n 2-2) and [(μ Τ - μ R ) θ U ] / σ (1/n 1 + 1/n 2 ) -t (α, n 1 + n 2-2)

11 μ Τ - μ R observed difference of Test and Reference means, σ 2 (1/n 1 + 1/n 2 ) is the error term two one-sided t tests procedure equivalent with classic confidence interval approach i.e. if (1-2α)x100% C.I. for (μ Τ - μ R ) is within (θ L, θ U ) then both Ηο 1 : μ Τ - μ R θ L and Ηο 2 : μ Τ - μ R θ U are rejected at the α level by the two one-sided t tests procedure Bioequivalence Criterion (EMEA, FDA) 90% C.I. for μ Τ /μ R within ( ) Based on pharmacokinetics and statistical considerations, logarithmic transformation of BA metrics (AUC, C max ) is recommended (EMEA, FDA) The 90% C.I. for the ratio of BA metrics is calculated form the ANOVA of the lntransformed data and is given by: antiln [(lnμ Τ - lnμ R ) t (0.05, n -2) σ (1/n 1 + 1/n 2 )] where (lnμ Τ - lnμ R ) is the difference of the least square formulation means from ANOVA analysis and σ 2 is the MSE from the ANOVA table (n = n 1 + n 2 = number of subjects)

12 RECOMMENDED DESIGN CHARACTERISTICS (2 PRODUCTS) i) two-sequence, two-period crossover ii) single dose iii) fasted state iv) healthy subjects v) blood profile of drug (AUC, C max ) vi) validated analytical method (GLP) i) two-sequence, two-period crossover trial Alternatives: replicate crossover design (highly variable drugs) parallel design (long half-life) e.g. amiodarone, etidronate ii) single dose trial Alternative: Steady state trial According to NfG (EMEA) May be required dose- or time-dependent pk, modified release products) Can be considered (inadequate sensitivity in blood level measurement after single dose, high intra-individual variability in plasma concentration or disposition)

13 Ok, in general single dose trials are preferred But Which dose? Problem with the registration of several strengths of the Test product Drug input is linear over the therapeutic dose range? a BE study on one strength acceptable Drug input is not linear over the therapeutic dose range? the strengths where the sensitivity is largest to identify differences in the two products should be used (NfG, EMEA) Interpretation (Q&A document EWP, 40326/2006) When proof of linear absorption or elimination kinetics is lacking, bioequivalence should be established with both the lowest and the highest doses, unless adequately justified If a BE study on only one dose is chosen, dose selection depends on the cause of nonlinearity: If AUC increases more than proportionally with dose: the highest dose If AUC increases less than proportionally with dose: the lowest dose When non-linearity is due to low drug solubility: bioequivalence should be established with both the lowest and the highest dose and should include the highest formulation strength

14 iii) fasted state at least during the night prior to administration of the products. Alternative: Fed state According to NfG (EMEA) If the summary of products characteristics (SPC) of the reference product contains specific recommendations in relation to food intake related to food interaction the study should be designed accordingly Interpretation (Q&A document EWP, 40326/2006) If the SPC recommendation for food intake is based on PK properties BE trial in the fed state Increase tolerability BE trial preferably in the fed state If the SPC leaves a choice (fasting or fed) BE trial under fasting conditions Effect of food should be studied in BE trials of modified / delayed release products (NfG: CPMP/EWP/280/96) iv) healthy subjects both sexes, age years, normal BMI, preferably non-smokers, no history of alcohol/drug abuse inclusion/exclusion criteria clearly stated in the protocol Yes ok fine But How many subjects?

15 Total number of subjects (sample size n) should provide adequate power for BE demonstration Adequate Power: at least 80% power to detect a 20% difference in products BA In practice: Power 80% - 90% Lower power: useless trial Higher Power: forced BE outcome Assuming normal distribution of PK data (AUC, C max ), n can be estimated by: n (CV/D) 2 x 2(Z α/2 + Z β ) 2 CV(%): coefficient of residual variance of PK parameter (literature, pilot study) D(%): minimum difference in BA to be detected (20%) α=0.05, β=0.20 and (Z α/2 + Z β ) 2 = 7.85 i.e. n (CV/20) 2 x Diletti et al. (Int. J. Clin. Pharmacol. Therapy, Toxicol., 8 (1991) 1-8) provided sample sizes for the multiplicative model based on: i) expected μ Τ /μ R ii) intra-subject variance, CV= (expσ 2 1) 1/2 iii) required power Sample sizes to attain power of 80% or 90% with the multiplicative model (from Diletti et al., 1991) μ Τ /μ R CV(%) Power (%)

16 In case of highly variable drugs or drug products (CV from ANOVA 30%) an impractical large number of subjects is required to obtain adequate power The concept of scaled average bioequivalence has been proposed in order to reduce the required sample size (Tothfalusi et al., Pharm. Res. 18 (2001) ) ln(0.80) μ Τ - μ R ln(1.25) when σ σ ο and ln(0.80)/σ ο (μ Τ - μ R )/σ ln(1.25)/σ ο when σ σ ο σ ο : limiting (switching) variability (to be set by regulatory authorities) σ: intra-subject variability (of HVD) For CV=30% σ ο =0.294 v) blood profile of drug (AUC, C max ) Alternatives: According to (NfG, EMEA) Blood profiles of metabolite could be used to assess BE a) if the concentration of the active substance is too low to be accurately measured in the biological matrix Interpretation (Q&A document EWP, 40326/2006) -unsuitability of using parent drug data should convincingly be justified - data showing that parent drug exposure is reflected by metabolite exposure are required b) if metabolites significantly contribute to the net activity of an active substance and the pharmacokinetics system is non-linear, it is necessary to measure both parent drug and active metabolite plasma concentrations and evaluate them separately.

17 Interpretation (Q&A document EWP, 40326/2006) - significance of metabolite contribution should be shown by comparing AUC and non-clinical or clinical PD activity with those of the parent drug - any discrepancy between metabolite and parent compound BE results should be discussed based on relative activities (for C max the results from the parent drug prevail) - Pooling of plasma concentrations or PK parameters is not acceptable Further alternative to blood profile: The use of urinary excretion data may be advantageous (?) in determining the extent of drug input in case of products predominantly excreted renally, but has to be justified when used to estimate the rate of absorption. Interpretation (Q&A document EWP, 40326/2006) The extent of drug input may be determined by the use of urinary excretion data (Ae) provided elimination is dose-linear and is predominantly renal as intact drug Since the rate of absorption cannot be accurately estimated by urinary data (R max ), it is recommended that: If a reliable plasma C max can be determined, this should be combined with urinary data on the extent of absorption for assessing bioequivalence

18 Analysis of Variance for lncmax - Type III Sums of Squares Source Sum of Squares Df Mean Square F-Ratio P-Value MAIN EFFECTS A: seq B: subj(seq) C: per D: drug RESIDUAL TOTAL (CORRECTED) All F-ratios are based on the residual mean square error except for seq where F- ratio is based on subj(seq) Mean Square. Point Estimate (least square mean ratio): 97.35% 90% C.I. (TEST vs. REF.) = 92.65% % What if 90% C.I. is found to be outside (80.00% %) for AUC: Problem! According to (Q&A document EWP, 40326/2006) no widening of BE limits for AUC is permitted For Cmax: It must be justified But, beware, wider acceptance limits should have been prospectively defined in protocol! wider BE limits: in most cases 75%-133.% (Q&A document EWP, 40326/2006)

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