PRE-FORMULATION STUDIES ON MICROCRYSTALLINE CELLULOSE FOR GROUPING AND PREDICTING THEIR PERFORMANCE IN EXTRUSION-SPHERONIZATION SOH LAY PENG, JOSEPHINE (B.Sc.(Pharm.)(Hons.), NUS) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT OF PHARMACY NATIONAL UNIVERSITY OF SINGAPORE 2006 ACKNOWLEDGEMENTS I am very grateful to my supervisor, Dr Celine Liew for her infinite patience, meticulousness and guidance. Her passion for research as well as her generosity in imparting knowledge and offering advice has been instrumental in enriching my learning experience. I am deeply indebted to my co-supervisor, Associate Professor Paul Heng for his guidance and supervision. Through his impeccable knowledge and resourcefulness, he has challenged and inspired me to achieve greater heights in life. I am also grateful for the opportunities he provided to acquire new skills and knowledge. His wise words and encouragement have tided me through the toughest of times to make this possible. The Faculty of Science, National University of Singapore is acknowledged for providing the Research Scholarship and the facilities for carrying out my research. I wish to thank Professor Lucy Wan and Associate Professor Chan Lai Wah for their invaluable advice on life and research. My friends in GEA-NUS Pharmaceutical Processing and Research Laboratory are fondly remembered for their friendship and all the good times we have shared. In particular, Ms Ooi Shing Ming, is gratefully acknowledged for her unwavering support and faith in our friendship. I also thank Mr Chee Sze Nam and Ms Cheong Wai See for their selflessness in sharing research experiences and ideas. My appreciation to the laboratory officers for their assistance, especially Mrs Teresa Ang, who has been a great help and a good friend. Last but not least, I am grateful to my entire family, especially my parents, Uncle Eng Tai and my three aunts (Jenny, Jesmine and Cyndy) for their constant care and support throughout my education. Without them, I would not have made it this far in my academic endeavors. Josephine September 2006 i Dedicated to my late grandparents whom I know have been watching over me all these years… ii TABLE OF CONTENTS TABLE OF CONTENTS Page ACKNOWLEDGEMENTS i TABLE OF CONTENTS iii SUMMARY ix LIST OF TABLES xi LIST OF FIGURES xiv PART I. INTRODUCTION PHARMACEUTICAL EXCIPIENTS: THEIR ROLES AND CHARACTERIZATION 1.1 Significance of Excipient Characterization and Classification 1.2 Recent Trends in Excipient Characterization and Classification MICROCRYSTALLINE CELLULOSE 2.1 Structure and Manufacture 2.2 Available Forms of MCC 2.2.1 Powdered MCC 10 2.2.2 Colloidal MCC 13 2.3 Source Variation in MCC PHARMACEUTICAL USES OF MCC 14 17 3.1 Pharmaceutical Granulation–Overview 18 3.2 Wet Granulation–Spheronization 19 3.2.1 Mechanisms of Agglomerate Formation in Wet Granulation 20 iii TABLE OF CONTENTS 3.2.2 Extrusion-Spheronization 21 3.2.3 Role of MCC in Extrusion-Spheronization 24 3.2.3.1 Function 24 3.2.3.2 Mechanism of action 29 PRE-FORMULATION STUDIES ON MCC 4.1 Solid State Characterization 4.1.1 Flow Properties 32 32 32 4.1.1.1 Flow patterns and common problems associated with flow 32 4.1.1.2 Impact on pharmaceutical processes 33 4.1.1.3 Flow assessment methods 35 4.1.1.3.1 Conventional methods 35 4.1.1.3.2 Avalanche flow method 37 4.1.2 MCC-Water Interaction 39 4.1.2.1 Significance and theoretical basis 39 4.1.2.2 Dynamic vapor sorption (DVS) 41 4.2 Characterization of Moistened Masses Containing MCC 4.2.1 Rheological Properties 43 44 4.2.1.1 Mixer torque rheometry 44 4.2.1.2 Other characterization methods 48 4.2.2 Extrusion Properties 49 4.2.2.1 Characterization methods 49 4.2.2.2 Instrumentation of extruders 51 PART II. OBJECTIVES 57 PART III. MATERIALS AND METHODS 61 iv TABLE OF CONTENTS MATERIALS 61 METHODS 62 2.1 Physical Characterization of MCC Powders 62 2.1.1 Particle Size and Size Distribution 62 2.1.2 Crystallinity 63 2.1.3 Micromeritic Properties 63 2.1.4 Moisture Sorption Isotherms 65 2.1.5 True Density Determination 66 2.1.6 Compressibility 66 2.1.7 Repose Angle and Angle of Fall Determinations 67 2.1.8 Avalanche Flow Properties 68 2.2 Characterization of Moistened Masses Containing MCC 2.2.1 Rheological Properties 2.2.1.1 Degree of liquid saturation 2.2.1.2 Theoretical water content to achieve capillary stage of liquid 2.2.1.3 2.2.2 69 69 69 saturation 72 Rheological profiles 73 Thermo-Gravimetry (Drying Profiles) 73 2.2.2.1 Percent bound water of the MCC grades, % H2O(s) 74 2.2.2.2 Thickness of bound water layer 75 2.3 Preparation and Characterization of Spheroids 75 2.3.1 Spheroid Preparation 75 2.3.2 Characterization of spheroids 76 2.3.2.1 Size analysis 76 v TABLE OF CONTENTS 2.3.2.2 Crushing strength 77 2.3.2.3 Friability 77 2.3.2.4 Bulk and tapped densities 77 2.4 Data and Statistical Analysis 2.5 Grouping of MCC Grades Using Artificial Neural Network (ANN) and Data Clustering 78 2.5.1 Data Modeling Using Artificial Neural Network 79 2.5.2 Grouping of MCC Grades 80 2.5.2.1 Multi-dimensional scaling (MDS) 80 2.5.2.2 Discrete incremental clustering (DIC) 82 RESULTS AND DISCUSSION 85 PART IV. 78 PHYSICAL CHARACTERIZATION OF MCC GRADES 85 1.1 Particle Size Analysis 86 1.2 Crystallinity 86 1.3 Micromeritic Properties 89 1.4 True Density Determination 92 1.5 Powder Compressibility and Flow Properties 92 1.5.1 Repose Angles and Angles of Fall 92 1.5.2 Compressibility 96 1.5.3 Avalanche Flow Properties 103 1.5.3.1 Proposed avalanche flow indices 1.5.3.2 Comparison of Avalanche Flow Properties with Conventional Flow Assessment Methods 108 115 vi TABLE OF CONTENTS 1.6 Moisture Sorption 116 1.6.1 Adsorption-Desorption Isotherms 116 1.6.2 Effects of MCC Physical Properties on Sorption Parameters 120 CHARACTERIZATION OF MOISTENED MASSES CONTAINING MCC 2.1 123 Drying Behaviors 124 2.1.1 Differential Thermo-Gravimetric Analysis 124 2.1.2 Percentage of Bound water 129 2.1.3 Thickness of Bound water 132 2.1.4 Effects of MCC Physical Properties 135 2.1.4.1 On ease of water loss (T50% and Temp50%) 135 2.1.4.2 On thickness of bound water layer 138 2.2 Rheological Properties 2.2.1 Effect of Water Content on Torque Profiles 139 139 2.2.1.1 MCC powders 139 2.2.1.2 MCC-lactose binary mixtures 142 2.2.2 Theoretical Water Requirements at Capillary State of Liquid Saturation 143 2.2.3 Effects of Mixing Time on Torque Profiles 146 2.2.3.1 MCC powders 147 2.2.3.2 MCC-lactose binary mixtures 151 2.2.4 Effects of MCC Physical Properties 156 2.2.4.1 On CEM(MCC) values 156 2.2.4.2 On CEM(blend) values 163 vii TABLE OF CONTENTS CHARACTERIZATION OF SPHEROIDS 166 3.1 Extrusion-Spheronization Parameters 166 3.2 Spheroid Properties 171 DEVELOPMENT OF A PRE-FORMULATION TOOL TO GROUP MCCS AND PREDICT THEIR PERFORMANCE IN EXTRUSIONSPHERONIZATION 176 4.1 Relationship between Torque Parameters and Spheroid Quality 176 4.2 Incorporation of Modern Computational Techniques 182 4.2.1 Data Modeling Using Artificial Neural Network (ANN) 183 4.2.2 Data Clustering 186 4.2.2.1 Multi-dimensional scaling (MDS) 186 4.2.2.2 Discrete incremental clustering (DIC) 188 4.3 Effects of MCC Physical Properties on Grouping Results 189 4.4 Predicting Spheroid Quality Using the Developed Pre-Formulation Tool 193 PART V. CONCLUSIONS 196 PART VI. REFERENCES 204 LIST OF PUBLICATIONS AND POSTER PRESENTATIONS 224 THE END 229 viii SUMMARY SUMMARY Microcrystalline cellulose (MCC) is an excipient with wide-ranging applications in the pharmaceutical industry. Particularly in the process of extrusion-spheronization, MCC has shown unsurpassed efficiency in terms of process control and end product quality. Attempts to prepare spheroids by extrusion-spheronization with very little or no MCC have not been encouraging. It has thus been regarded as essential for a wellcontrolled system of spheroid production. Inherent variabilities in the physical properties of MCC grades sourced from different suppliers have been shown to affect MCC’s function and performance. In this work, physical characterization of fourteen MCC grades in the solid (powder) and moistened state were performed. 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Soh, J. L. P., C. V. Liew, and P. W. S. Heng. "New indices to characterize powder flow based on their avalanching behavior." Pharmaceutical Development and Technology 11(2006): 93-102. 4. Liew, C. V., J. L. P. Soh, F. Chen, D. M. Shi, and P. W. S. Heng. "Application of multidimensional scaling to preformulation sciences: a discriminatory tool to group microcrystalline celluloses." Chemical and Pharmaceutical Bulletin 53 (2005): 1227-1231. 5. Liew, C.V., L. Gu, J. L. P. Soh, and P. W. S. Heng. "Functionality of crosslinked polyvinylpyrrolidone as a spheronisation aid: A promising alternative to microcrystalline cellulose." Pharmaceutical Research 22 (2005): 1387-1398. 6. Heng, P. W. S., and J. L. P. Soh. "Artificial intelligence as a pre-formulation tool in pharmaceutical excipient selection." Business Briefings: Pharmagenerics. (2004): 67-69. 225 7. Soh, J. L. P., F. Chen, C. V. Liew, D. M. Shi, and P. W. S. Heng. "A novel pre-formulation tool to group microcrystalline celluloses using artificial neural network and data clustering." Pharmaceutical Research 21 (2004): 2360-2368. 8. Heng, P. W. S., C. V. Liew, and J. L. P. Soh. "Pre-formulation studies on moisture absorption in microcrystalline cellulose using differential thermogravimetric analysis." Chemical and Pharmaceutical Bulletin 52 (2004): 384390. 226 LIST OF POSTER PRESENTATIONS 1. Soh, J. L. P., C. V. Liew, and P. W. S. Heng. "Comparing the sensitivities of various shape factors in differentiating the shape of extrusion-spheronized pellets." American Association of Pharmaceutical Scientists Annual Meeting and Poster Exposition. Tennessee, USA, November 6-11, 2005. 2. Soh, J. L. P., C. V. Liew, and P. W. S. Heng. "Characterizing the extrudability of moistened powders containing microcrystalline celluloses." American Association of Pharmaceutical Scientists Annual Meeting and Poster Exposition. Tennessee, USA, November 6-11, 2005. 3. Soh, J. L. P., C. V. Liew, and P. W. S. Heng. "Use of dynamic vapor sorption to determine the thickness of bound (structured) water layer in pharmaceutical powders." Science Faculty Graduate Congress. National University of Singapore, Singapore, September 21, 2005. 4. Soh, J. L. P., C. V. Liew, and P. W. S. Heng. "Torque rheometry as a preformulation tool to predict the physical properties of spheroids containing microcrystalline cellulose (MCC) and lactose." American Association of Pharmaceutical Scientists Annual Meeting and Poster Exposition. Washington, USA, November 7-11, 2004. 227 5. Soh, J. L. P., C. V. Liew, and P. W. S. Heng. "Comparison between various methods of flow determination in lactose powders." Inaugural AASP Conference. Pharmacy Education, Research and Practice in Asia. Beijing, PRC, June 4-6, 2004. 6. Soh, J. L. P., C. V. Liew, and P. W. S. Heng. "Determination of rheological properties of microcrystalline cellulose/water mixtures using mixer torque rheometry (MTR)." ROC-Japan Joint Symposium on Particle Design, Nanotechnology and Drug Delivery. Hsinchu, Taiwan, ROC, May 9-11, 2004. 228 THE END 229 [...]... for MCCs in the same group This grouping tool provided a basis for interchangeability between MCCs and to predict their performance in extrusion- spheronization Certainly, it also presents considerable advantages in expediting pre- formulation studies in MCC With more MCC grades added into the existing pool, it is possible to identify other critical properties that could have an effect on the functionality... M101 and N: M102) 187 xviii PART I INTRODUCTION 1 INTRODUCTION PART I 1 INTRODUCTION PHARMACEUTICAL EXCIPIENTS: THEIR ROLES AND CHARACTERIZATION Excipients are vital, functional constituents in a drug formulation and there is a strong emphasis for their consistency in quality Thus, quality and source identification are one of the most basic requisites for an excipient to be acceptable for use in pharmaceutical... the human brain (Agatonovic-Kustrin and Beresford, 2000) ANNs collate knowledge by recognizing patterns and relationships in data and learn through experience or continual training It consists of many single units, known as neurons, connected with coefficients which constitute the neural structure The ANN learns an approximate nonlinear relationship through a training process Training is defined as a... pharmaceutical industry amidst the quest for better process understanding 3 INTRODUCTION There has been a rising popularity in the use of modern computational techniques, especially in the form of artificial intelligence, to factor the effects and influences of individual components in pharmaceutical formulation, development and production Artificial intelligence can be divided into 2 main types (Agatonovic-Kustrin... polymers and showed good correlation between water uptake and glass transition temperatures This study highlighted the usefulness of ANN as a pre- formulation tool for the characterization of amorphous polymers More significantly, it heralded the introduction and incorporation of artificial intelligence in the pharmaceutical industry especially in pre- formulation studies where physical characterization and. .. process for the optimized set of weight values which can minimize the squared error between the estimated and experimental data of units in the output layer During the training process, the interunit connections are optimized until the error in prediction is minimized and a desired level of accuracy is attained Once the network is trained, it can be continuously fed with new input information to predict... (dotted lines) isotherms for PH 101 and PH 102 117 Figure 23 Moisture adsorption (solid lines) and desorption (dotted lines) isotherms for PH 301, PH 302 and Ceolus 801 118 Figure 24 Moisture adsorption (solid lines) and desorption (dotted lines) isotherms for Emcocel and Prosolv 119 Figure 25 Monolayer capacities for MCC grades (a) Adsorption and (b) desorption Error bars represent the standard deviations... representations (i.e finding the inner hidden mappings among data) for modeling data and to forecast future values of the time series variable Both of these goals require the identification and description of the pattern of observed time series data Once the pattern is established, it can be interpreted and integrated with other data Regardless of the depth of understanding and validity of the interpretation... responses Although this is a relative new field, it has found wide applications in pharmaceutical research Dissolution profiles of matrix controlled release theophylline spheroids have been predicted using ANN and yielded promising results in terms of the accuracy of 4 INTRODUCTION prediction (Peh et al., 2000) Prediction methods for pharmacokinetic parameters and their variability in bioequivalence studies. .. needed to form the multi-molecular layers of free water on their surfaces (Parker and Rowe, 1991) A more extensive range of MCC grades was characterized and correlated to their performance in extrusion- spheronization (Heng and Koo, 2001) Some critical properties, namely packing densities and pore volumes, were identified as having strong influences on spheroid quality Effects of the type and origin of . PRE- FORMULATION STUDIES ON MICROCRYSTALLINE CELLULOSE FOR GROUPING AND PREDICTING THEIR PERFORMANCE IN EXTRUSION- SPHERONIZATION SOH LAY PENG, JOSEPHINE (B.Sc.(Pharm.)(Hons.),. and to predict their performance in extrusion- spheronization. Certainly, it also presents considerable advantages in expediting pre- formulation studies in MCC. With more MCC grades added into. 3.1 Extrusion- Spheronization Parameters 166 3.2 Spheroid Properties 171 4 DEVELOPMENT OF A PRE- FORMULATION TOOL TO GROUP MCCS AND PREDICT THEIR PERFORMANCE IN EXTRUSION- SPHERONIZATION 176