ANALISIS ESTADISTICO DE DATOS PARA ESTUDIOS DE BIOEQUIVALENCIA NISELMAN ADA VIVIANA Cátedra de Matemática Facultad de Farmacia y Bioquímica Universidad de Buenos Aires Número de voluntarios El número de voluntarios de un estudio de bioequivalencia deberá ser calculado teniendo en cuenta la Variabilidad Intraindividual, la Máxima diferencia a ser detectada (20%; 0,20) y los errores de Tipo I (Alfa =0,05) y Tipo II (Beta=0,20). El cálculo del número de voluntarios, deberá figurar en el protocolo, así como la fórmula utilizada para su cálculo y las asunciones estadísticas. el tamaño total de muestra N , se podrá calcular de acuerdo a la siguiente fórmula propuesta por Marzo y Balant (1995): N > 15,68 x CV intraindividual2 / Δ2 Donde: CV es el Coeficiente de Variación Intraindividual Δ2 = 0,202 = 0,04. Análisis estadístico La metodología estadística deberá estar expresada en el protocolo en el informe final estableciendo los “límites de riesgo” de declarar falsamente la bioequivalencia entre dos productos. En la metodología se debe incluir estadística descriptiva estadística inferencial. Análisis estadístico La metodología estadística deberá estar expresada en el protocolo en el informe final estableciendo los “límites de riesgo” de declarar falsamente la bioequivalencia entre dos productos. En la metodología se debe incluir estadística descriptiva estadística inferencial. Estadística descriptiva 1.2. - Para cada individuo: a) Unidad de medida. b) Valores en cada tiempo. c) Secuencia. d) Producto recibido (Test o Ref). - Para cada concentración/tiempo: a) Media aritmética . b) Mediana. c) Desvío estándar. d) Coeficiente de Variación por ciento (CV%). e) Valor mínimo (Mn). f) 1° cuartilo. g) 3° cuartilo h) Valor máximo.(Mx). Gráficos Exigidos 1) concentración/tiempo de cada voluntario con las formulaciones Test y Referencia (dos gráficos por voluntario). 2) Estas figuras se presentarán con los datos no transformados logarítmicamente. 3) una figura resumen con los datos promedio (no transformados logarítmicamente) de cada tiempo (“Curvas resumen”). 4) Se deberán presentar todos los datos, incluso los de aquellos voluntarios que hayan abandonado el estudio o representen valores extremos o atípicos. e) Concentración máxima (Cmáx). f) Tiempo en alcanzar Cmáx. (Tmáx). g) Constante de eliminación (ke). h) Vida media (T½). i) Área bajo la curva a tiempo t (AUCt) j) Área bajo la curva a infinito (AUCinf). . Tabla de la Secuencia para cada voluntario y cada tratamiento con: a) Cmáx. b) Tmáx. c) Ke. d) T½. e) AUC0-t. f) AUCinf. Para cada uno de los parámetros, expresar: • Media aritmética (Md). • Mediana (Mn). • Media geométrica (MG). • Desvío estándar. • Coeficiente de Variación por ciento (CV%). • Valor mínimo (Mn). • 1° cuartilo. • 3° cuartilo • Valor máximo.(Mx). Criterio de BE actual T 0.80 1.25 R Diseño ross-over 2x2 Transformación logaritmo IC al 90% para GMR contenido en (0.80,1.25) Análisis de Variancia (ANOVA) ANOVA de los (ln) de (Cmáx, AUC0-t y AUCInf). Se presentará la tabla del ANOVA de cada uno de los parámetros Especificando las fuentes de variación (Secuencia/arrastre, Período, Tratamiento), grados de libertad, suma de cuadrados, cuadrados medios, valor del estadístico F y los valores correspondientes de p. Análisis de Variancia (ANOVA) La Hipótesis Nula a testear con el ANOVA es: H0: μ T = μ R CMAX IN D Period 1 Period 2 Seq A TR 122 126 B RT 207 102 C RT 123 202 E TR 59 37 F RT 85 66 G TR 54 55 H RT 219 101 I TR 90 182 K RT 60 155 L TR 57 26 M TR 23 57 N RT 47 38 O RT 71 43 P TR 68 97 Q RT 88 28 R TR 99 60 Analysis of variance table: CMAX df SS MS F PValue Carry-over 1 0.5464 0.5464 1.0373 0.3257 Residuals 14 7.3738 0.5267 2.5147 0.0478 Drug 1 0.1821 0.1821 0.8694 0.3669 Period 1 0.1109 0.1109 0.5293 0.4789 Residuals 14 2.9323 0.2095 Total 31 11.145 4 InterSubjects IntraSubjects AUCt I D Period 1 Perio d2 Seq A TR 365 375 B RT 405 595 C RT 703 471 E TR 233 190 F RT 247 257 G TR 178 175 H RT 246 382 I TR 408 361 K RT 315 218 L TR 140 92 M TR 165 269 N RT 88 106 O RT 183 290 P TR 122 230 Q RT 68 144 R TR 275 344 Analysis of variance table: AUCt df SS MS F PValue Carryover 1 0.054 0.054 0.090 0.767 Residuals 14 8.426 0.601 8.226 0.002 Drug 1 0.024 0.024 0.332 0.573 Period 1 0.138 0.138 1.889 0.190 Residuals 14 1.024 0.073 Total 31 9.667 InterSubjects IntraSubjects La Tabla modelo de análisis de variancia: debe especificará el CV Intraindividual %, Con datos log-transformados puede calcularse con la siguiente formula: CV MSE 100 Ejemplo de Cálculo del CV La raiz cuadrada del Cuadrado Medio del Error Residual estima el Coeficiente de Variación Intra –Sujeto. CV CMres 0.0731 0.27 AUCinf Peri od1 Period 2 ID Seq A TR 409 418 B RT 613 432 C RT 492 774 E TR 256 224 F RT 285 265 G TR 205 190 H RT 398 263 I TR 433 406 K RT 236 372 L TR 331 105 M TR 195 327 N RT 125 113 O RT 313 215 P TR 148 266 Q RT 156 113 R TR 292 369 df SS MS F PValue Analysis of variance table: AUCinf Inter-Subjects Seq o Carryover 1 0.0120 0.0120 0.0273 0.8711 Suj dentro de Seq o Residuals 14 6.1520 0.4394 4.2245 0.0054 Drug 1 0.0138 0.0138 0.1328 0.7210 Period 1 0.0195 0.0195 0.1879 0.6713 Residuals 14 1.4563 0.1040 Total 31 7.6537 Intra-Subjects In which cases may a non-parametric statistical model be used? Statistical analysis: “AUC and Cmax should be analysed using ANOVA after log transformation.” The reasons for this request are the following: a) the AUC and Cmax values as biological parameters are usually not normally distributed; c) after log transformation the distribution may allow a parametric analysis. d) due to the small sample size, is not recommended pre-test for normality. e) Parametric testing using ANOVA on log-transformed data should be the rule. f) For tmax, the use of non-parametric methods on the original data set is recommended. TMAX IND Seq Period1 P2 A TR 1,5 1,5 B RT 1,5 1,5 C RT 1,5 0,6 E TR 3 1 F RT 2 1 G TR 1,5 1,5 H RT 1 1 I TR 1,5 0,6 K RT 1,5 1,5 L TR 1 2 M TR 4 1,5 N RT 0,6 0,6 O RT 1,5 1 P TR 0,6 1,5 Q RT 1,5 1,5 R TR 2 2 INTERVALO DE CONFIANZA - Relación T/R (Punto Estimado) y su intervalo de confianza 90%. Se expresará para cada parámetro (Cmáx, AUC0-t y AUCinf), la razón T/R (Punto Estimado) y el intervalo de confianza 90% de la misma. Classical (shortest) IC: CMAX Confidence Bounds Observed Within Equivalence Limits? Lower [10.00]% Conf. limit 0.6918 No Upper [10.00]% Conf. limit 1.0690 Yes Antilogged point estimate =0.86 00 Classical (shortest) Confidence Interval: AUCt Confidence Bounds Observe d Within Equivalence Limits? Lower [10.00]% Conf. limit 0.9291 Yes Upper [10.00]% Conf. limit 1.2017 Yes Antilogged point estimate = 1.0567 Classical (shortest) Confidence Interval: AUCinf Confidence Bounds Observed Within Equivalence Limits? Lower [10.00]% Conf. limit 0.8229 Yes Upper [10.00]% Conf. limit 1.1183 Yes Antilogged point estimate = 0.9593 Parámet ro Geo Mean Media Test Ratio estima do IC Cmax 80.89 69.56 0.86 0.69-1.06 AUCt 276.97 265.70 1.05 0.93-1.20 AUCinf 227.80 240.71 0.96 0.82-1.11 Test de la hipótesis intervalar Anderson Hauck T H0 ) 0.80 R H0 ) o T 1.25 R T 0.80 1.25 R Test de 2 las hipótesis unilaterales Schuirmann H 01 )T / R0.80 H 02 )T / R1.25 H 11 )T / R>0.80 H 12 )T / R<1.25 Schuirmann: CMAX t-Value One-sided p-value to reject non- equivalence Observed Specified Observed Specified Null Hypothesis L t-statistic 1.7613 0.4467 0.0500 0.3310 Null Hypothesis U t-statistic -1.7613 -2.3115 0.0500 0.0183 Schuirmann AUCt t-Value One-sided p-value to reject non-equivalence Observed Specifie d Observe d Specified Null Hypothesis L tstatistic 1.7613 2.9100 0.0500 0.0057 Null Hypothesis U tstatistic -1.7613 -1.7568 0.0500 0.0504 Schuirmann AUCinf t-Value One-sided p-value to reject non-equivalence Observed Specified Observed Specified Null Hypothesis L tstatistic 1.7613 1.5925 0.0500 0.0668 Null Hypothesis U tstatistic -1.7613 -2.3213 0.0500 0.0179 Conclusión Preliminar AUCt y AUCinf satisfacen la cond de BE. Cmax no la cumple con límites 0.80/1.25 Cmax la cumple con límites 0.70/1.33 Country/Region AUC 90% CI Criteria Canada (most drugs) 80 – 125% Cmax 90% CI Criteria none (point estimate only) Europe (some drugs) 80 – 125% 75 – 133% South Africa (most drugs) 80 – 125% 75 – 133% (or broader if justified) Japan (some drugs) 80 – 125% Some drugs wider than 80 – 125% Worldwide (WHO) 80 – 125% “acceptance range for Cmax may be wider than for AUC” Criterios de aceptación de bioequivalencia Wilcoxon-Mann-Whitney TMAX Null Hypothesis L: Mean T- Mean R <= Lower Bound Null Hypothesis U: Mean T- Mean R >= Upper Bound Rank Sums Observed Specified Null Hypothesis L test statistic 48.0000 42.0000 Null Hypothesis U test statistic 16.0000 21.0000 Hodges-Lehmann Interval: TMAX Hodges-Lehmann estimate (median of all possible pairwise differences) = 0.0000 Confidence Bounds Specifie d Observe d Within Equivalence Limits? Lower [5.00]% Conf. limit -0.2853 -0.4200 No Upper [5.00]% Conf. limit 0.2853 0.5000 No Suj Seq Period1 Period2 T/R B RT 207 102 0,49275362 C RT 123 202 1,64227642 F RT 85 66 0,77647059 H RT 219 101 0,46118721 K RT 60 155 2,58333333 N RT 47 38 0,80851064 O RT 71 43 0,6056338 Q RT 88 28 0,31818182 A TR 122 126 0,96825397 E TR 59 37 1,59459459 G TR 54 55 0,98181818 I TR 90 182 0,49450549 L TR 57 26 2,19230769 P TR 68 97 0,70103093 R TR 99 60 1,65 Media 1,08472389 Desvio 0,68714366 2desvios 1,37428732 Med+/-2 desv -0,28956344 2,06143098 Bioavailability is defined as the rate and extent to which the active drug ingredient is absorbed and becomes available at the site of drug action Two drug products are said to be bioequivalent if they are pharmaceutical equivalent or pharmaceutical alternatives, and if their rates and extents of absorption do not show a significant difference. Fundamental Bioequivalence Assumption When a generic drug is claimed bioequivalent to a brandname drug, it is assumed that they are therapeutically equivalent. Bioequivalence is claimed if the ratio of average bioavailabilities between test and reference products is within (80%, 125%) with 90% assurance (logtransformed data). Confidence Interval The classical (shortest) confidence interval Interval Hypotheses Testing Shuirmann’s two one-sided tests procedure FDA guidance on Statistical Approaches to Establishing Bioequivalence (January, 2001) FDA guidance on Bioavailability and Bioequivalence Studies for Orally Administered Drug Products – General Considerations (July, 2002) Most regulatory agencies including the U.S. Food and Drug Administration (FDA) require evidence of bioequivalence in average bioavailabilities between drug products. This type of bioequivalence is referred to as ABE. Based on the 2001 FDA guidance, bioequivalence may be established via population and individual bioequivalence provided that the observed ratio of geometric means is within the bioequivalence limits of 80% and 125%. A generic drug can be used as a substitute for the brand-name drug if it has been shown to be bioequivalent to the brand-name drug. Current regulations do not indicate that two generic copies of the same brandname drug can be used interchangeably, even though they are bioequivalent to the same brand-name drug. Bioequivalence between generic copies of a brand-name drug is not required. Generic Drugs They’re cheaper, but do they work as well? Average Bioequivalence (ABE) Current regulatory requirement Population Bioequivalence (PBE) Prescribability Individual Bioequivalence (IBE) Switchability Aggregate criterion Moment-based approach Scaling method Weighing factors One-sided test Drug Prescribability Brand-name vs. its generic copies Generic copies vs. generic copies Drug Switchability Brand-name vs. its generic copies Generic copies vs. generic copies Current regulation for ABE does not guarantee drug prescribability and drug switchability Population Bioequivalence (PBE) Anderson and Hauck (1990) Chow and Liu (1992) The physician’s choice for prescribing an appropriate drug for his/her patients between the brand-name drug and its generic copies General Approaches for IBE/PBE is a measure of the relative difference between the mean squared errors of yR- yT and yR - yR' E ( yR yR' )2 2 is the within-subject variance of the reference formulation 2 2 ( T R ) 2 TT TR 2 max{ 02 , TR } for PBE 2 2 ( T R ) 2 D2 ( WT WR ) for IBE 2 2 max{ 0 , WR } Individual Bioequivalence (IBE) Anderson and Hauck (1990) Schall and Luus (1993) Holder and Hsuan (1993) Esinhart and Chinchilli (1994) The switch from a drug (e.g., a brandname drug or its generic copies) to another (e.g., a generic copy) within the same patient whose concentration of the drug has been titrated to a steady, efficacious and safe level Notations mT = mean of the test product mR = mean of the reference product sWT2 = within-subject variability for the test product sWR2 = within-subject variability for the reference product sD2 = variability due to the subjectby-formulation interaction IBE Criterion ( T R ) ( 2 max( , W 0 ) 2 Where I 2 D 2 WR 2 WT (ln1.25) 2 2 W0 2 WR ) I General Approaches for IBE/PBE is a measure of the relative difference between the mean squared errors of yR- yT and yR - yR' E ( yR yR' )2 2 is the within-subject variance of the reference formulation 2 2 ( T R ) 2 TT TR 2 max{ 02 , TR } for PBE 2 2 ( T R ) 2 D2 ( WT WR ) for IBE 2 2 max{ 0 , WR } Assessment of IBE Hypotheses Testing H 0 : IBE versus H 0 : IBE IBE is claimed if a 95% confidence upper bound of is less than IBE and the observed ratio of geometric means is within bioequivalence limits of 80% and 125%. References 1. FDA (1999). In Vivo Bioequivalence Studies Based on Population and Individual Bioequivalence Approaches. Food and Drug Administration, Rockville, Maryland, August, 1999. 2. FDA (2001). Guidance for Industry: Statistical Approaches to Establishing Bioequivalence. Food and Drug Administration, Rockville, Maryland, January, 2001. Special Issues Chow, S.C. (Ed.) Special issue on Bioavailability and Bioequivalence of Drug Information Journal, Vol. 29, No. 3, 1995 Chow, S.C. (Ed.) Special issue on Bioavailability and Bioequivalence of Journal of Biopharmaceutical Statistics, Vol. 7, No. 1, 1997 Chow, S.C. and Liu, J.P. (Ed.) Special issue on Individual Bioequivalence of Statistics in Medicine, Vol. 19, No. 20, October, 2000. Review of FDA Guidances Chow, S. C. and Liu, J. P. (1994). Recent statistical development in bioequivalence trials - a review of FDA guidance. Drug Information Journal, 28, 851-864. Liu, J. P. and Chow, S. C. (1996). Statistical issues on FDA conjugated estrogen tablets guideline. Drug Information Journal, 30, 881-889. Chow, S. C. (1999). Individual bioequivalence - a review of FDA draft guidance. Drug Information Journal, 33, 435-444. Wang, H., Shao, J., and Chow, S.C. (2001). On FDA’s statistical approach to establishing population bioequivalence. Unpublished manuscript. Books Chow, S.C. and Liu, J.P. (1998). Design and Analysis of Bioavailability and Bioequivalence Studies, 2nd edition, Marcel Dekker, New York, New York. Chow, S.C. and Shao, J. (2002). Statistics in Drug Research, Marcel Dekker, New York, New York. Chow, S.C., Shao, J., and Wang, H. (2003). Sample Size Calculation in Clinical Research, Marcel Dekker, Inc., New York, New York. Original Articles Shao, J., Chow, S. C., and Wang, B. (2000). Bootstrap methods for individual bioequivalence. Statistics in Medicine, 19, 2741-2754. Chow, S.C., Shao, J., and Wang, H. (2002). Individual bioequivalence testing under 2x3 crossover designs. Statistics in Medicine, 21, 629-648. Chow, S.C. and Shao, J. (2002). In vitro bioequivalence testing. Statistics in Medicine, 22, 55-68 . Chow, S.C., Shao, J., and Wang, H. (2003). Statistical tests for population bioequivalence. Statistica Sinica, 13, 539-554. OBJETIVO a) Discernir entre formulaciones b) Evaluar el efecto producido en la disolución por los cambios en las variables del proceso de manufactura aseguramiento de la calidad uniformidad de lote a lote ¿cómo cuantificar el grado de similitud o diferencia entre dos curvas? FDA. Center for Drug Evaluation and Research, Guidance for Industry: Modified Release Solid Oral Dosage Forms. Scale-up and Post-Approval Changes: Chemistry, Manufacturing and Controls In Vitro, and In Vivo Bioequivalence Documentation [SUPAC- MR ]; 1997 FDA. CDER Guidance for Industry Dissolution Testing of Immediate Release Solid Oral Dosage Forms. [SUPAC-IR]; 1997 METODO MODELO NO DEPENDIENTE, EMPLEANDO LOS FACTORES DE AJUSTE. Jeffrey W. Moore et al (1996) Se compara la diferencia en el % disuelto por unidad de tiempo entre referencia y prueba. Estos factores son f1 (factor de diferenciación) f2 (factor de similitud) : Un valor f2 menor de 50 no indica necesariamente falta de similitud. Si el patrocinador opina que las diferencias en f2 son típicas para el producto se puede presentar la justificación apropiada Como suplemento de aprobación previa. Esta justificación deberá incluir análisis estadísticos de respaldo (p.ej., un análisis de intervalo de confianza del 90%).