Dissolution Efficiency and Design Space for an Oral Pharmaceutical

Subscriber access provided by UNIV OF CALIFORNIA SAN DIEGO LIBRARIES

Article

Dissolution and Design Space for an oral pharmaceutical product in tablet form Kalliopi A Chatzizaharia, and Dimitrios T Hatziavramidis Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/ie5050567 • Publication Date (Web): 01 Jun 2015 Downloaded from http://pubs.acs.org on June 8, 2015

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Industrial & Engineering Chemistry Research is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 22

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Industrial & Engineering Chemistry Research

1

Dissolution efficiency and Design Space for an oral

2

pharmaceutical product in tablet form

3

Kalliopi A. Chatzizaharia, Dimitrios T. Hatziavramidis*

4

School of Chemical Engineering, National Technical University of Athens

5

Heroon Polytechniou 9, Zografou 15771, Athens GR

6

KEYWORDS Design Space, Mixture Design, generic oral tablet, dissolution similarity factor,

7

Bayesian approach

8

Abstract

9

The primary drug quality requirements, safety, efficacy and reliability, for oral pharmaceutical

10

products in tablet form, translate into bioavailability, and tablet weight and strength. The

11

bioavailability of an oral drug, i.e., the amount of the drug that can reach the systemic

12

circulation, depends on drug permeation rate through the epithelial membrane or on dissolution

13

rate, in case of bioequivalence. Thus, the critical quality attributes affecting bioavailability can

14

be the dissolution profile, tablet weight and tablet hardness, which are affected by process

15

conditions and drug product composition, i.e., active pharmaceutical ingredients (APIs) and

16

excipients and their mass fractions. A Mixture Design (DOE) experiment has been carried out

17

for a generic oral drug, with input factors the mass fractions of three excipients and response

ACS Paragon Plus Environment

1


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 2 of 22

18

variables the dissolution profile, tablet weight and hardness. While the last two response

19

variables are single-point-value attributes, a dissolution profile is a multi-point-value attribute

20

and is assessed using integral measures, e.g., similarity factor, from pair-wise, model-

21

independent methods. The data from the Mixture Design experiment are used to develop a multi-

22

regression and multi-response optimization model, which, in turn are used to determine the

23

Design Space (DS) for the pharmaceutical product of interest.

24

1. Introduction

25

The majority of oral pharmaceutical products in tablet form are powder mixtures of Active

26

Pharmaceutical Ingredients (API) and excipients. Excipients, in conjunction with process

27

parameters, facilitate processing of the powder mixture and improve quality attributes of the

28

tablet dosage. During the drug development stage, a multi-regression model relating Critical

29

Quality Attributes (CQAs) to critical process and formulation parameters, where the latter

30

consist of the mass fractions, particle size distribution, water content and other properties of

31

excipients and APIs, are constructed and critical process and formulations parameters that

32

optimize the quality attributes are determined 1. Under the Quality by Design (QbD) initiative, it

33

is possible to use knowledge from development studies to create a Design Space (DS) within

34

which changes in formulation and manufacturing processes promoting continuous improvement

35

of process capability and product quality can be implemented without the need for further

36

regulatory approval

37

planning informative experiments. When the composition of the drug mixture (API + excipients)

38

is under investigation to optimize drug quality attributes, a Mixture DOE is utilized. Advances in

39

supporting software, automated synthesis instrumentation, and high-throughput analytical

2–4

. Design of Experiments (DOE) techniques are well-established tools for


2

Page 3 of 22

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


40

techniques have led to the broader adoption of the QbD approach in pharmaceutical discovery

41

and chemical development laboratories 5.

42

Bioavailability of a drug in a solid oral dosage form, i.e., the fraction of drug dose that reaches

43

the systemic circulation, depends on the release of the drug substance from the drug product, the

44

balance among its dissolution, elimination, metabolism and absorption rates, as well as its

45

solubility in the gastrointestinal fluids and permeability across the epithelial membrane. The

46

distinct nature of dissolution and solubility must be emphasized, the former being a quantity of

47

kinetic and the latter of thermodynamic nature. As early as 1995 it has been recognized that drug

48

dissolution and intestinal permeability are the primary factors in determining drug transfer to

49

systemic circulation, and a biopharmaceutics classification system (BCS) was developed to

50

identify classes of drugs for which an in vivo-bioequivalence and in vitro-dissolution (IVIVR)

51

correlation exists. When such a correlation is strong, regulatory testing of in-vivo bioequivalence

52

can be waived in favor of in vitro dissolution testing 6–9. Whenever a waiver can be granted and

53

drug dissolution is tested, the drug under study is compared to a reference drug and both drugs

54

are assumed to be bioequivalent if their dissolution profiles are similar. Both, the European

55

Medicines Agency (EMEA) and the USA Food and Drug Administration (FDA) assure that any

56

methods to prove similarity of dissolution profiles are accepted as long as they are justified 10–12.

57

In a previous paper

13

, the DS for an oral drug granulation was determined from data in the

58

literature by three different methods; response surface, Bayesian approach and neural networks.

59

The effectiveness of a particular method, measured by the composite desirability function,

60

indicated the presence or not of completeness, structure and uncertainty in the data. In this paper,

61

the DS of a generic oral drug for which dissolution is important was determined from data

62

obtained with our involvement, assuming not uncertainty. The aim of pharmaceutical


3


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 4 of 22

63

development is to design a product and a series of processes to manufacture the product and

64

consistently deliver performance to ensure product efficacy, safety and quality. Knowledge

65

gained from pharmaceutical development and manufacturing experience facilitate identification

66

of critical quality attributes (CQA), critical material attributes (CMA), and critical process

67

parameters (CPP) and support the establishment of relations and mechanistic product-process

68

design models between the CQAs, as output variables, and CMAs and CPPs, as input variables

69

and parameters. CMAs and CPPs are identified through an assessment of the impact their

70

variation can have on CQAs. Product and process requirements, attributes performance

71

specifications, along with multivariate models based on chemistry and engineering fundamentals,

72

help to define the feasible region for the subsequently formulated optimization problem. Solution

73

of the multi-objective optimization problem yields an optimal product design14,15.

74

In the combined granulation-compression process of making the tablet dosage form, CQAs

75

include, but are not limited, to granule size, powder and granule flowability, and tablet weight

76

and its variation, crushing strength, friability, disintegration time and dissolution, while CMA

77

and CPP can be type and amount of binders, disintegrants, diluents, lubricants, and inlet air

78

temperature, atomizing air pressure and other process variables, respectively.

79

The regulatory framework regarding the manufacture of pharmaceutical products ensures

80

patient safety through the use of well-defined processes with specified parameter ranges

81

governed by a control plan which is the responsibility of the pharmaceutical company.

82

According to this framework any type of change in formulation or process conditions requires

83

regulatory approval. Under the new Quality by Design (QbD) initiative, however, it is possible to

84

use knowledge from development studies to create a Design Space (DS) within which changes in

85

formulation and manufacturing processes promoting continuous improvement of process


4

Page 5 of 22

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


86

capability and product quality can be implemented without the need for further regulatory

87

approval 16,17.

88

According to ICH Q8, a Design Space is defined as “the multidimensional combination and

89

interaction of input variables (e.g., material attributes) and process parameters that have been

90

demonstrated to provide assurance of quality”. The Design Space is proposed by the applicant

91

and is subject to regulatory assessment and approval. Once approved, it sets the boundaries

92

within which changes in the input variables and process parameters can be made without further

93

regulatory approval. Changes that result in input variable and process parameters values outside

94

the Design Space initiate a regulatory post approval change process2. If the Design Space is

95

intended to span multiple operational scales (lab, pilot plant, plant), normalized (coded) variables

96

in the interval [-1, 1] may be used18.

97

Our literature review has shown that determination of the DS in QbD of pharmaceutical

98

products was done using various methods with little or no regard for the type of experimental

99

data obtained to this end. In a previous work of ours13, a methodology accommodating for the

100

type of experimental data obtained for the sake of determining the DS was presented for

101

pharmaceutical tablet development. This methodology of DS determination is applied in the

102

present work to a generic drug development in which the CQA of the product are tablet weight

103

and hardness, and bioequivalence to the original drug and the CMA the mass fractions of the

104

excipients.

105

In the present work, as in other works19, 20, 21, 22, bioequivalence is reduced to a comparison of

106

dissolution profiles of product (generic) and reference (original) drug at multiple time points. A

107

short review of methods for dissolution profile comparison is given in the Materials and Methods

108

section. To our knowledge, there is no literature on determination of DS when one of the CQAs


5


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 6 of 22

109

is multi-point valued, as is the case with dissolution profiles. The present work proposes the

110

evaluation of previously established integral measures, such as similarity and Dissolution Area

111

Difference factors, at critical and final times, the former defined as the time that separates slow

112

from rapid dissolution, the latter defined as the time for dissolution cessation, for an effective

113

assessment of bioequivalence between product and reference drugs.

114 115

2. Materials and Methods

116

2.1 Materials- Experimental Data

117

A Simplex Centroid mixture design experiment was conducted for a generic tablet

118

formulation, with 2 main components x1 and x2 chosen from the list of main excipients and two

119

replicates of the center point, using Minitab software. A third component, excipient x3, used in a

120

small percentage of 2-5% w/w, is added to facilitate the design analysis and the ternary mixture

121

plots (Table 1). The APIs were mixed with x1, x2, x3 and other excipients, compressed into

122

tablets and then coated. In the following analysis, the three excipients were chosen so that their

123

mass fractions sum up to unity in a mixture of 312 mg, in which the mass remains constant. The

124

response variables for tablet design (CQAs) were tablet weight and hardness, and the dissolution

125

profile.

126 127

2.2 Methods for dissolution profile comparison

128

Regulatory authorities for pharmaceutical products consider as acceptable any approach to

129

establish similarity of dissolution profiles, through comparison of single and multiple time-point

130

dissolution data for reference and test products, by utilizing statistical, model-dependent and


6

Page 7 of 22

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


131

model-independent methods. FDA and EMEA methodologies emphasize the need of providing

132

justification for similarity of dissolution profiles 10,11 .

133

In statistical models, the sources of variation of percent dissolved at each time level can be

134

analyzed by univariate (ANOVA) and multivariate (MANOVA) analysis of variance 19. Model-

135

dependent methods include zero and first order kinetics, Hixson–Crowell, Weibull, Higuchi,

136

Baker–Lonsdale, Korsmeyer–Peppas and Hopfenberg models for the amount of drug released

137

over time

138

about dissolution data. Model-dependent methods utilize expressions for the quantity of released

139

drug as a function of time and drug concentration, thus making the quantitative interpretation of

140

dissolution data easier and becoming more useful in the formulation-development stage of a drug

141

product.

20,21

. Statistical methods are more discriminative and provide detailed information

142

Model-independent methods can be further differentiated as ratio and pair-wise tests. Ratio

143

tests compare the dissolution profiles of two formulations at a particular time point, while pair-

144

wise procedures provide a simple way to describe the comparison of the data but sensitive to the

145

number of dissolution time points.

146

The ratio tests are relations between parameters obtained from the release assay of the

147

reference formulation and test products at the same time and include ratios of percent dissolved

148

drug, area under the release curve or mean dissolution time. The pair-wise procedures of

149

comparing dissolution profiles utilize measures like the difference factor (f1), similarity factor

150

(f2) and Rescigno index (ξi). Like the ratio test, pair-wise procedures compare dissolution

151

profiles of a pair of products and establish 90% confidence intervals 20,22,23.

152

The similarity factor f2 is defined as:

153

f2 =50* log 1+1/N ∑Ni=1x-x ti ri

2 -1/2

*100

(1)


7


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

154

Page 8 of 22

,where N is the number of time points, xti is the mean percent of drug dissolved for the test

155

product and xri is the mean percent drug dissolved for the reference product.

156

When two dissolution profiles are identical, i.e., the difference of averages in Eq.1 is 0%, f2 =

157

100%. If the difference of averages is 10%, f2 ≈ 50% and the two profiles are considered

158

adequately similar. Thus, any value of f2 between 50 and 100% indicates that the two dissolution

159

profiles are similar. The value of f2, as expected, is sensitive to the number of time points and

160

reliable dissolution profile comparison in terms of the similarity factor requires at least three to

161

four more points. Only one time point is needed after 85% dissolution. For products which are

162

rapidly dissolving, i.e., more than 85% of the drug is dissolved in less than 15 min, no

163

dissolution profile comparison is necessary 10,11,22.

164

Other parameters used to characterize the drug release profile are: time to release a determined

165

percentage of the drug, sampling time and dissolution efficiency. The dissolution efficiency (DE)

166

of a pharmaceutical product is the ratio of the area under the dissolution curve up to a testing

167

time point to the area of the rectangle that describes 100% dissolution up to the same time point.

168

It can be calculated by the equation:

169

DE=100*

170

, where d is the function of drug percent dissolved at time t 20.

171

In order to compare dissolution profiles with a combination of the DE criteria and the pair-

172

t d*dt"d100 *t 0

(2)

wise procedure, a Dissolution Area Difference (DAD) factor can be calculated as: t t d *dt" 0 dref *dt $ 0 exp

173

DAD=$1-

174

, where dexp is the dissolution function of drug under investigation at a particular time, dref the

(3)

175

dissolution function of the reference drug dissolved during the same time, and the area

176

calculated using the multiple segment trapezoidal rule

24

t d*dt 0

is

. The division of the time integration


8

Page 9 of 22

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


177

interval into segments is necessitated by steep changes in the dissolution - time curves. A

178

minimum value of the DAD factor implies best similarity of the compared dissolution profiles.

179 180

2.3 Materials- Experimental Data

181

For a generic oral drug considered in this publication, the dissolution rate is a critical quality

182

attributes. The critical formulation attributes are the mass fractions of three excipients. The

183

acceptable ranges of the latter are determined by multivariate models, such as Mixture Design

184

analysis

185

method of choice for data which are complete and lack a correlation structure. The Bayesian

186

approach, on the other hand, takes into account the correlation structure of the data and the

187

uncertainty in determining model parameters. The basis for the Bayesian approach is as follows.

188

If f(x|θ) is the conditional probability distribution and p(θ) the probability of the parameter θ

189

from prior times, the posterior probability p*(θ|x) is:

25–29

, Bayesian method and Neural Networks

13

. The Mixture Design analysis is the

190

p* θ|x= py|θpθ⁄py = py|θpθ"

191

, where p*(y|θ) is the likelihood for fixed (observed) data y 3,30.

192

Both, the Mixture Design Analysis and the Bayesian approach, were used to determine the DS

193

of the generic oral drug of interest. In determining the values of input variables in a multi-

194

response problem that result in optimal product, a popular strategy is to reduce the

195

dimensionality of the problem, by using a single aggregate measure, often defined as a

196

desirability function 31,32. The most popular form of a desirability function is:

θ

py|θpθdθ

(4)


9


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

. / µi µimin 0 , ymin ≤ŷµi ≤Τµi µi Τµi -yµi , , β ŷ -ymin

197

Page 10 of 22

α

dµi = / ŷµi -yµi 0 , Τ ≤ŷ ≤ ymax µi µi µi - Τµi -ymax µi , , 0, ŷ ymax µi µi + µi µi max

(5)

198

, where ŷµi, ymin , ymax , Tµi denote the estimated mean response, minimum and maximum µi µi

199

desired limits and target for ŷµi, respectively, and α, β are input parameters that determine the

200

shape of the reliability function. The aggregate measure, D, called composite desirability, is the

201

geometric mean of p individual desirabilities, dµi:

202

D=dµ1 dµ2 …dµp

1/p

(6).

203 204

3. Results

205

In order to compare the different dissolution profiles of the experimental runs with the

206

reference tablet profile (Fig. 1), the methods proposed are the dissolution similarity factor f2

207

(Eq.1) and the Dissolution Area Difference (DAD) (Eq. 3).

208

Figure 1 shows that time point t = 15 min marks the boundary between two areas of

209

dissolution, one of rapid and one of slow change. Dissolution of the reference drug follows a first

210

order kinetics equation and is simulated with Matlab software as

211

dt=a+b*ek*t

212

, where d is the drug percent dissolved at time t, a = 91.05, b = -91.29, k = -0.57 and the

213

(7)

goodness of fit is R2 = 0.987.

214

In evaluating the integral of DAD according to the trapezoidal rule, the time point t =15 min

215

which marks the boundary between the two rapidly and slowly changing dissolution areas, is

216

selected as the last point in the first trapezoidal segment. The results are shown in Table 2. The


10

Page 11 of 22

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


217

calculated similarity factors f2_15 and f2_60 for times of 15 and 60 min, respectively, are presented

218

in Table 3.

219

The response variables selected for the dissolution profile evaluation in the following DOE

220

analysis are f2_15 and f2_60. The data range and specifications are presented in Table 4, where

221

weight and hardness limits are calculated as ±2.5% of the target value, and the ideal case of

222

100% is selected as the target value for the similarity factor. It should be noted that in Eq. 8, the

223

mass fractions x1, x2 and x3 are expressed in mixture proportions, a fact that enables the scale-up

224

to pilot and production levels.

225

Regression generates the following models:

226

weight = 314.293*x1 +309.056*x2 +315.642*x3 -2.811*x1 *x2

(8a)

227

hardness = 17*x1 +10.73*x2 +359.87*x3 +588.22*x1 *x2

(8b)

228

f2_15 = -296.78*x1 -195.01*x2 +753.42*x3 +1076.85*x1 *x2

(8c)

229

f2_60 = -208.94*x1 -110.04*x2 +646.15*x3 +755.41*x1 *x2

230

The optimization plot for the response variables and desirability is given in Figure 2. An

231

optimum exists for x1 = 38.66 mg (0.4444 coded), x2 = 38.66 mg (0.4444 coded) and x3 = 9.67

232

mg (0.1111 coded).

(8d)

233

In addition to graphical representation of the overlapping common region of successful

234

operating ranges (Fig. 3), the Design Space can be supplemented by a tabular form, where the

235

boundaries (l: lower specification limit-LSL and u: upper specification limit-USL, Table 5) and

236

the composite desirability (Eq. 6, Table 6) are determined by Mixture Design Analysis and

237

Bayesian method. In Figure 3, the white area represents the “external” DS, while the purple area

238

is the DS limited by the restriction of the x3 amount, ranging from 2 to 5%. The effectiveness of


11


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 12 of 22

239

the various methods, in determining the DS, as measured by the composite desirability, is shown

240

in Table 6.

241 242

4. Discussion

243

For the dissolution evaluation, the results of DAD in Table 2 and of the similarity factors f2_15

244

and f2_60 for times of 15 and 60 min in Table 3, both agree that run 2 seems to exhibit the best

245

similarity to the original dissolution profile. Additionally, the optimum component amounts

246

presented in Figure 2 are close to the optimum experimental conditions suggested by the f2 and

247

DAD factors in run 2 of the mixture experiment.

248

Finally, in Table 5, the effectiveness of the three methods is evaluated. The Mixture Design

249

Analysis has a higher composite desirability than the Bayesian method, but the difference is not

250

decisive to preclude the use of the latter when data uncertainty is considered. It should be noted

251

that the values of composite desirability are limited by the setting of f2 target value at the

252

maximum 100%, which is a rather ideal condition. Both methods perform very satisfactory even

253

under this condition.

254 255

5. Conclusions

256

An earlier developed methodology of DS determination from experimental data, with and

257

without uncertainty, obtained for this purpose, was applied to a generic oral drug with CQAs

258

tablet weight and hardness and bioequivalence of product (generic) and reference (original) drug,

259

and CMAs the mass fractions of the excipients. A Mixture Design of experiments was carried

260

out and analyzed to enable development of a multi-regression model with factors the CMAs and

261

responses the CQAs.


12

Page 13 of 22

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


262

Bioequivalence, which involves comparison of dissolution profiles of product and reference

263

drugs on multiple time points, was assessed by evaluating two integral measures, similarity and

264

DAD factors, at two times, the critical time, which marks the boundary between rapid and slow

265

dissolution, and the final time at which dissolution ceases.

266

As in an earlier work of ours, the DS was determined by response optimization and

267

overlapping responses. Multi-response optimization leads to a DS with boundaries in the

268

neighborhood of optimal conditions, while the method of overlapping responses leads to a DS

269

with boundaries corresponding to global lower and upper specification limits of the response

270

variables. The optimal component amounts calculated by these methods are the closest to the

271

optimal experimental conditions suggested by evaluation of the similarity and DAD factors, a

272

fact that validates the multivariate analysis performed and the adequacy of dissolution criteria

273

selected.

274

The effectiveness of the methods used for determination of the DS for the generic oral drug of

275

interest is measured by composite desirability, which is higher for multi-response method.

276

However, if data uncertainty is to be accounted for, the Bayesian method shows a better

277

performance. Finally, it should be noted that the resulting optimal amounts should be replicated

278

at laboratory and pilot scales in order to validate scale-up.

279 280

AUTHOR INFORMATION

281

Corresponding Author

282

* Tel.: +30-210-7723125. Fax: +30-210-7723163. E-mail: [email protected]

283 284

ACKNOWLEDGMENT


13


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 14 of 22

285

The authors acknowledge the financial support to Ms. Kalliopi Chatzizacharia, PhD Candidate

286

in Chemical Engineering, in the form of a scholarship from the National Technical University of

287

Athens.

288

ABBREVIATIONS

289

API Active Pharmaceutical Ingredient; DOE Design of Experiments; DS Design Space; CQA

290

Critical Quality Attributes ; QbD Quality by Design; CMA Critical Material Attributes; CPP

291

Critical Process Parameters; BCS Biopharmaceutics Classification System; EMEA European

292

Medicines Agency; FDA USA Food and Drug Administration; ANOVA Analysis of Variance;

293

DAD Dissolution Area Difference; LSL lower specification limit; USL upper specification limit

294

REFERENCES

295

(1)

296

Dekker Inc, 1999.

297

(2)

ICH (European Medicines Agency). Q8 (R2) Pharmaceutical Development; 2009; Vol. 8.

298

(3)

Hayashi, Y.; Kikuchi, S.; Onuki, Y.; Takayama, K. Reliability Evaluation of Nonlinear

299

Design Space in Pharmaceutical Product Development. J. Pharm. Sci. 2012, 101, 2.

300

(4)

Lepore, J.; Spavins, J. PQLI Design Space. J. Pharm. Innov. 2008, 3, 79.

301

(5)

Martinello, T.; Kaneko, T. M.; Velasco, M. V. R.; Taqueda, M. E. S.; Consiglieri, V. O.

302

Optimization of Poorly Compactable Drug Tablets Manufactured by Direct Compression Using

303

the Mixture Experimental Design. Int. J. Pharm. 2006, 322, 87.

Lewis, G. A.; Mathieu, D.; Phan, R. T. L. Pharmaceutical Experimental Design; Marcel


14

Page 15 of 22

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


304

(6)

Amidon, G. L.; Lennernäs, H.; Shah, V. P.; Crison, J. R. A Theoretical Basis for a

305

Biopharmaceutic Drug Classification: The Correlation of in Vitro Drug Product Dissolution and

306

in Vivo Bioavailability. Pharm. Res. 1995, 12, 413.

307

(7)

308

Drug Discovery and Development: Current Status and Future Extension. J. Pharm. Pharmacol.

309

2005, 57, 273.

310

(8)

311

Dressman, J. B.; Lipper, R. Review of Global Regulations Concerning Biowaivers for Immediate

312

Release Solid Oral Dosage Forms. Eur. J. Pharm. Sci. 2006, 29, 315.

313

(9)

314

Yu, L. X.; Davit, B. M. Statistics on BCS Classification of Generic Drug Products Approved

315

between 2000 and 2011 in the USA. AAPS J. 2012, 14, 664.

316

(10)

317

and Bioequivalence; 2000.

318

(11)

319

Center for Drug Evaluation and Research, C. Guidance for Industry Waiver of In Vivo

320

Bioavailability and Bioequivalence Studies for Immediate-Release Solid Oral Dosage Forms

321

Based on a Biopharmaceutics Classification System; 2000.

322

(12)

323

Guideline on the Investigation of Bioequivalence; 2010; Vol. 1, pp. 1–27.

Lennernäs, H.; Abrahamsson, B. The Use of Biopharmaceutic Classification of Drugs in

Gupta, E.; Barends, D. M.; Yamashita, E.; Lentz, K.; Harmsze, M.; Shah, V. P.;

Nair, A. K.; Anand, O.; Chun, N.; Conner, D. P.; Mehta, M. U.; Nhu, D. T.; Polli, J. E.;

European Medicines Agency. Note for Guidance on the Investigation of Bioavailability

U.S. Department of Health and Human Services; Food and Drug Administration, F.;

Commitee for Medicinal products for Human Use European Medicines Agency.


15


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 16 of 22

324

(13)

Chatzizacharia, K. a.; Hatziavramidis, D. T. Design Space Approach for Pharmaceutical

325

Tablet Development. Ind. Eng. Chem. Res. 2014, 53, 12003.

326

(14)

327

Strategy. J. Pharm. Innov. 2008, 3, 60.

328

(15)

329

Design. Ind. Eng. Chem. Res. 2009, 48, 8566.

330

(16)

331

Pharmaceutical Product Development to Bridge Risk Assessment to Continuous Verification in a

332

Quality by Design Environment. J. Pharm. Innov. 2010, 5, 109.

333

(17)

334

Quality by Design Case Study: An Integrated Multivariate Approach to Drug Product and

335

Process Development. Int. J. Pharm. 2009, 382, 23.

336

(18)

337

Processes Using Data-Driven-Based Methods. J. Pharm. Innov. 2010, 5, 119.

338

(19)

339

ANOVA-Based, Model-Dependent and -Independent Methods. Int. J. Pharm. 2000, 209, 57.

340

(20)

341

Sci. 2001, 13, 123.

342

(21)

343

Formulations. J. Young Pharm. 2010, 2, 21.

Garcia, T.; Cook, G.; Nosal, R. PQLI Key Topics - Criticality, Design Space, and Control

Smith, B. V.; Ierapepritou, M. Framework for Consumer-Integrated Optimal Product

Zomer, S.; Gupta, M.; Scott, A.; Zomer. Application of Multivariate Tools in

Huang, J.; Kaul, G.; Cai, C.; Chatlapalli, R.; Hernandez-Abad, P.; Ghosh, K.; Nagi, A.

Boukouvala, F.; Muzzio, F. J.; Ierapetritou, M. G. Design Space of Pharmaceutical

Yuksel, N.; Kanik, a E.; Baykara, T. Comparison of in Vitro Dissolution Profiles by

Lobo, M. S.; Costa, P. Modeling and Comparison of Dissolution Profiles. Eur. J. Pharm.

Soni, T.; Chotai, N. Assessment of Dissolution Profile of Marketed Aceclofenac


16

Page 17 of 22

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


344

(22)

Shah, V. P.; Tsong, Y.; Sathe, P.; Liu, J. In Vitro Dissolution Profile Comparison-

345

Statistics and Analysis of the Similarity Factor, f2. Pharm. Res. 1998, 15, 889.

346

(23)

Rescigno, A. Bioequivalence. Pharm. Res. 1992, 9, 925.

347

(24)

Burden, R. L.; Faires, J. D. Numerical Analysis; 9th ed.; Brooks/Cole, 2010.

348

(25)

Leesawat, P.; Laopongpaisan, A.; Sirithunyalug, J. Optimization of Direct Compression

349

Aspirin Tablet Using Statistical Mixture Design. C. J. 2004, 3, 97.

350

(26)

351

Roxithromycin Dispersible Tablet Using Experimental Design. Arch. Pharm. Res. 2000, 23, 507.

352

(27)

353

Visual Mixture Design of Experiments Using Property Clustering Techniques. Ind. Eng. Chem.

354

Res. 2009, 48, 2245.

355

(28)

356

Biopharm. Stat. 2008, 18, 959.

357

(29)

Shmidt, S. R.; Launsby, R. G. Understanding Industrial Designed Experiments; 2005.

358

(30)

Peterson, J. J. A Posterior Predictive Approach to Multiple Response Surface

359

Optimization. J. Qual. Technol. 2004, 36.

360

(31)

Weon, K. Y.; Lee, K. T.; Seo, S. H. Optimization Study on the Formulation of

Solvason, C. C.; Chemmangattuvalappil, N. G.; Eljack, F. T.; Eden, M. R. Efficient

Peterson, J. J. A Bayesian Approach to the ICH Q8 Definition of Design Space. J.

Harrington, E. . The Desirability Function. Ind. Qual. Contr. 1965, 121, 494.


17


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 18 of 22

361

(32)

Lee, M. S.; Kim, K. J. Expected Desirability Function: Consideration of Both Location

362

and Dispersion Effects in Desirability Function Approach. Qual. Technol. Quant. Manag. 2007,

363

4, 365.


18

Page 19 of 22

100 90

% drug dissolved

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


80

reference drug

70

run2

60

run4

50

run5

40 30

run1

20

run6

10

run3

0 0

10

20

30

40

50

60

t (min) Figure 1. Experimental dissolution profiles graph


1


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 20 of 22

Figure 2. Mixture Design Optim mization plot


1

Page 21 of 22

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 3. Design Spaace for the Mixture Desiign experimeent


1


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Page 22 of 22

API x1, Excipient 1 x2, Excipient 2 x3, Excipients 3,…,n

Tablets CMAs x1, x2, x3

Design Space

Specification CQAs Space Weight Hardness Dissolution: f2_15 , f2_60


Dissolution Efficiency and Design Space for an Oral Pharmaceutical

Recommend Documents