PARTICLE DEVELOPMENT FOR DRUG DELIVERY LEE CHIN CHIAT NATIONAL UNIVERSITY OF SINGAPORE 2004 PARTICLE DEVELOPMENT FOR DRUG DELIVERY LEE CHIN CHIAT B. Sc. (Pharm) (Hons) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT OF PHARMACY NATIONAL UNIVERSITY OF SINGAPORE 2004 To my wife, parents and sister, whom I am greatly indebted to. ACKNOWLEDGMENTS I wish to express my thanks and appreciation to my supervisors, Associate Professor Paul Heng Wan Sia and Associate Professor Chan Lai Wah, for their constant care and guidance throughout the course of my higher degree. I am grateful to the GEANUS Pharmaceutical Processing Research Laboratory, Department of Pharmacy, for the use of the research facilities, as well as to the National University of Singapore, for providing the postgraduate research scholarship. My thanks go to Teresa, Mei Yin, Celine, Tin Wui and Charlene for their technical assistance, and to Professor Lucy Wan and Dr. Anthony Yolande, for their lessons about life. I definitely need to thank Liang Theng, Sze Nam, Kang Teng and Gu Li, for their companionship, encouragement, suggestions and support. Without them, my stay would not have been so memorable. Chin Chiat 1st January 2004 i Table of Contents TABLE OF CONTENTS Page ACKNOWLEDGMENTS i TABLE OF CONTENTS ii SUMMARY x LIST OF FIGURES xiii LIST OF TABLES xix I. INTRODUCTION A. Dissolution A1. Poorly Soluble Drug A2. Strategies in Enhancing Dissolution Rate A3. Particle Shape and Dissolution B. Particle Shape B1. Concept of Particle Shape B2. Shape Factors B2.1 Static Shape Factors B2.1.1 Geometric Shape Factors B2.1.2 Fourier Series in Shape Generation 13 B2.1.3 Fractal Analysis 15 B2.2 Dynamic Shape Factors 18 C. Particle Size 19 C1. Concept of Particle Size 19 C2. Particle Size Measurement 19 C2.1 Image Analysis and Microscopy 25 C2.2 Diffraction 26 ii Table of Contents C3. Size Reduction 27 C3.1 Milling 30 C3.1.1 Theory of Milling 30 C3.1.1.1 Fracture Mechanism 32 C3.1.1.2 Circuit Design 34 C3.1.1.3 Energy Consideration 34 C3.1.2 Milling Equipment 35 C3.1.2.1 Ball Mill 35 C3.1.2.2 Hammer Mill 37 C3.1.2.3 Fluid Energy Mill 37 C3.1.3 Effects of Milling on Crystallinity 38 C3.1.4 Effects of Milling on Particle Shape 40 C4. Size Classification 41 C4.1 Screening 41 C4.2 Fluid Force Classification 42 C4.2.1 Dry Classification 42 C4.2.2 Wet Classification 45 D. Ordered / Interactive Mixtures 48 D1. Definition 48 D2. Factors Affecting Dissolution Rates 50 E. Solid Dispersions 53 E1. Definition 53 E2. Preparation Methods 53 E2.1 Melt / Fusion Method 53 E2.2 Solvent Method 54 iii Table of Contents E2.3 Melt-Solvent Method 55 E3. Types of Physicochemical Structure of Solid Dispersions E3.1 Theoretical Physicochemical Structures 55 55 E3.1.1 Eutectic Systems 56 E3.1.2 Solid Solutions 56 E3.1.2.1 Continuous Solid Solutions 58 E3.1.2.2 Discontinuous Solid Solutions 58 E3.1.2.3 Substitutional Crystalline Solid Solutions 60 E3.1.2.4 Interstitial Crystalline Solid Solutions 60 E3.1.3 Monotectic Systems 60 E3.1.4 Amorphous / Glassy Solid Solutions 62 E3.1.5 Complex Systems 62 E3.2 Actual Physicochemical Structures 63 E4. Factors Affecting Dissolution Rates 64 E4.1 Nature of Drug and Carrier 64 E4.2 Concentration of Carrier 65 E4.3 Molecular Weight of Carrier 68 E4.4 Process Variables 68 E5. Stability of Solid Dispersions 69 E6. Scale Up of Solid Dispersion Production 70 II. OBJECTIVES 73 A. Particle Development 73 B. Dissolution Enhancement of a Practically Insoluble Drugs 73 III. EXPERIMENTAL 74 A. Materials 74 iv Table of Contents A1. Model Crystalline Material 74 A2. Practically Insoluble Model Drug 74 A3. Model Carrier 75 A4. Reagents 75 B. Methods 76 B1. Fluidised Bed (FB) Hammer Mill 76 B1.1 Milling Process 76 B1.1.1 Influence of Beater Rotational Speed 79 B1.1.2 Influence of Classifier Wheel Rotational Speed 79 B1.1.3 Influence of Airflow Rate 79 B1.1.4 Influence of the Length of Grinding Zone 80 B1.1.5 Influence of Starting Material 80 B1.1.6 Combinations of Process Variables 80 B1.2 Particle Characterisation 80 B1.2.1 Size Analysis by Laser Diffraction B1.2.1.1 Rosin-Rammler Distribution (RRD) Function B1.2.1.2 80 84 Size at 99th Percentile of the Cumulative Undersize Distribution (D99) 85 B2. Fluidised Bed Opposed (FBO) Jet Mill B2.1 Micronisation Process 86 86 B2.1.1 Influence of Classifier Wheel Rotational Speed 88 B2.1.2 Influence of Feed Load 88 B2.1.3 Influence of Micronising Air Pressure 89 B2.1.4 Combinations of Process Variables 89 B2.2 Particle Characterisation 91 v Table of Contents B2.2.1 Size Analysis by SEM 91 B2.2.2 Shape Determination by Image Analysis 92 B3. Air Classifying System 96 B3.1 Air Classifying Process 96 B3.1.1 Influence of Classifier Wheel Rotational Speed 98 B3.1.2 Influence of Starting Material 98 B3.1.3 Influence of Airflow Rate 100 B3.1.4 Combinations of Process Variables 100 B3.2 Particle Characterisation 100 B3.2.1 Size Analysis by Laser Diffraction B4. Dissolution Enhancement of Nifedipine B4.1 Nifedipine B4.2. 100 103 103 Preparation of Nifedipine, Interactive Mixtures and Solid Dispersions 103 B4.2.1 Processing of Nifedipine to Obtain Different Size Fractions 103 B4.2.2 Preparation of Interactive Mixtures 103 B4.2.3 Preparation of Solid Dispersions 105 B4.2.4 Codes Employed 106 B4.3 Characterisation of Interactive Mixtures and Solid Dispersions 107 B4.3.1 Dissolution Studies 107 B4.3.2 Determination of Equilibrium Solubility of Nifedipine 108 B4.3.3 Surface Tension Study 108 B4.3.4 Size Analysis by Laser Diffraction 109 B4.3.5 Crystallinity Determination by Powder X-Ray Diffraction (PXRD) 110 vi Table of Contents B4.3.6 Phase Study Using Differential Scanning Calorimetry (DSC) B4.4 Statistical Analysis 110 111 IV. RESULTS AND DISCUSSION 112 A. FB Hammer Mill 112 A1. Rationale for the Choice of Equipment and Method Employed 112 A1.1 Rationale for Choosing FB Hammer Mill 112 A1.2. Rationale for Choosing Laser Diffraction for Particle Sizing 113 A1.3 Rationale for Using RRD Function 114 A2. Characteristic of Starting Materials 114 A3. Factors Affecting the FB Hammer Milling Process 119 A3.1 Influence of Beater Rotational Speed and Starting Materials A3.2 119 Influence of Classifier Wheel Rotational Speed and Starting Materials 126 A3.3 Influence of Airflow Rate and Starting Materials 129 A3.4 Influence of the Length of Grinding Zone and Starting Materials 131 A3.5 Relative Importance of the Process Variables and Starting Materials 134 A4. Complementary Roles of De and D99 135 B. FBO Jet Mill 136 B1. Rationale for the Choice of Equipment and Method Employed 136 B1.1 Rationale for Choosing FBO Jet Mill 136 B1.2 Rationale for Choosing SEM for Particle Sizing 137 B1.3 Choice of Characterising Parameters 138 B2. Factors Affecting the Micronisation Process with Respect to Particle Size 143 vii VI. References 227. Staniforth, J.N., 1981a. Total mixing. Int. J. Pharm. 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APPENDICES (a) ln{ln[100/R (D )]} 2.0 1.0 ln{ln[100/R (D )]} = 1.4673lnD - 2.4422 R = 0.9961 0.0 -1 -1.0 -2.0 -3.0 -4.0 (b) ln{ln[100/R (D )]} 2.0 ln{ln[100/R (D )]} = 1.4824lnD - 2.4772 R = 0.9967 1.0 0.0 -1 -1.0 -2.0 -3.0 -4.0 (c) ln{ln[100/R (D )]} 2.0 1.0 ln{ln[100/R (D )]} = 1.487lnD - 2.4961 R = 0.9972 0.0 -1 -1.0 -2.0 -3.0 -4.0 lnD Appendix 1. Linear regression for the (a) first, (b) second and (c) third determinations of size analysis for batch ZPSB16. 251 VII. Appendices 90 80 Coefficient of Variation 70 60 50 40 30 20 10 0 100 200 300 400 500 600 700 Number of Particles Measured Appendix 2. Correlation between coefficient of variation and number of particles measured. 252 VII. Appendices 40 Coefficient of Variation 35 30 25 20 15 10 0 50 100 150 200 250 300 Number of Particles Measured Appendix 3. Correlation between coefficient of variation of various shape factors and number of particles measured. (Shape factors: Circularity, …; Aspect ratio, {; Modelx, U; Pellips, ‘) 253 VII. Appendices Appendix 4. Fitting of particle size distributions into log-normal, Weibull and gamma models. Batch Numbera Log-normala P(Log-normal)b Weibulla P(Weibull)b Gammaa P(Gamma)b Lactose 100M AFGA1 AFGA2 AFGA3 AFGA4 AFGB1 AFGB2 AFGB3 AFGB4 AFGC1 AFGC2 AFGC3 AFGC4 AFGD1 AFGD2 AFGD3 AFGD4 AFGE1 AFGE2 AFGE3 AFGE4 AFGF1 AFGF2 AFGF3 AFGF4 AFGG1 AFGG2 AFGG3 AFGG4 AFGH1 AFGH2 AFGH3 AFGH4 AFGI1 AFGI2 AFGI3 AFGI4 24.143 62.949 31.073 38.270 8.939 52.251 57.262 18.155 38.429 31.708 22.717 11.005 36.339 59.777 60.154 17.391 14.917 58.473 43.490 23.736 29.051 104.632 63.544 32.594 24.366 47.583 41.058 30.127 44.906 66.020 56.016 38.665 14.943 79.138 27.865 31.361 65.932 0.004 0.000 0.000 0.000 0.348 0.000 0.000 0.052 0.000 0.000 0.007 0.275 0.000 0.000 0.000 0.026 0.061 0.000 0.000 0.008 0.001 0.000 0.000 0.000 0.004 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.060 0.000 0.001 0.001 0.000 15.263 92.763 111.566 73.147 138.913 84.580 103.860 90.489 95.855 54.063 65.302 37.824 93.691 79.243 130.543 98.242 148.853 81.345 133.691 56.347 115.969 117.738 90.653 92.657 66.271 138.527 83.680 73.924 87.544 61.973 75.224 165.457 58.177 79.327 57.528 192.249 147.579 a 0.084 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Calculated Chi-square goodness of fit of the three functions. Calculated probabilities of the three functions. b 254 11.930 98.438 78.016 75.650 51.520 145.750 113.549 63.782 98.476 50.456 49.284 28.590 73.824 98.794 160.597 85.392 70.729 126.231 159.860 52.676 89.089 210.131 109.527 104.665 60.818 142.623 76.862 70.923 94.023 89.020 79.104 125.054 45.787 114.404 57.680 128.553 123.834 0.217 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 VII. Appendices Circularity (a) 1.0 0.9 0.8 0.7 0.6 0.5 0.4 10 15 20 25 30 35 Length (µm) Circularity (b) 1.0 0.9 0.8 0.7 0.6 0.5 0.4 10 15 20 Breadth (µm) Circularity (c) 1.0 0.9 0.8 0.7 0.6 0.5 0.4 10 15 20 25 30 Fmax (µm) Appendix 5. Plots of circularity against (a) length, (b) breadth and (c) Fmax illustrating the lack of relationship between spherical nature and size of particle. 255 35 VII. Appendices Aspect Rati o (a) 4.0 3.5 3.0 2.5 2.0 1.5 1.0 10 15 20 25 30 35 Length (µm) Aspect Ratio (b) 4.0 3.5 3.0 2.5 2.0 1.5 1.0 10 15 20 Breadth (µm) Aspect Ratio (c) 4.0 3.5 3.0 2.5 2.0 1.5 1.0 10 15 20 25 30 Fmax (µm) Appendix 6. Plots of aspect ratio against (a) length, (b) breadth and (c) Fmax illustrating the lack of relationship between elongated nature and size of particle. 256 35 VII. Appendices Modelx (a) 3.5 3.0 2.5 2.0 1.5 1.0 10 15 20 25 30 35 Length (µm) Modelx (b) 3.5 3.0 2.5 2.0 1.5 1.0 10 15 20 Breadth (µm) (c) 3.5 Modelx 3.0 2.5 2.0 1.5 1.0 10 15 20 25 30 Fmax (µm) Appendix 7. Plots of modelx against (a) length, (b) breadth and (c) Fmax illustrating the lack of relationship between elongated nature and size of particle. 257 35 VII. Appendices Pellips (a) 1.0 0.9 0.8 0.7 0.6 10 15 20 25 30 35 Length (µm) Pellips (b) 1.0 0.9 0.8 0.7 0.6 10 15 20 Breadth (µm) (c) 1.0 Pellips 0.9 0.8 0.7 0.6 10 15 20 25 30 Fmax (µm) Appendix 8. Plots of pellips against (a) length, (b) breadth and (c) Fmax illustrating the lack of relationship between eliptical nature and size of particle. 258 35 VIII. Publications VIII. PUBLICATIONS 1. Heng, P.W.S., Chan, L.W., Lee, C.C., 2000. Ultrafine grinding using a fluidized bed opposed jet mill: effects of process parameters on the size distribution of milled particles. STP Pharma Sci., 10(1), 445-451. 2. Chan, L.W., Lee, C.C., Heng, P.W.S., 2002. Ultrafine grinding using a fluidized bed opposed jet mill: effects of feed load and rotational speed of classifier wheel on particle shape. Drug Dev. Ind. Pharm., 28(8), 939-947. 3. Lee, C.C., Heng, P.W.S., Chan, L.W., 2003. Use of a fluidized bed hammer mill for size reduction and classification: effects of process variables and starting materials on the particle size distribution of milled lactose batches. Pharm. Dev. Technol., 8(4), 431-442. 259 [...]... rectangular particle results in the generation of two completely different geometric signature waveforms This system of describing particle shape is also unsuitable for non-analytic particle where multiple R vectors exist for certain θ values giving rise to geometric signature waveform with multiple plots Staniforth and Rees (1981b) came up with the shah shape factor, which is able to quantify non-analytic particles... in particle shape However a high feed load of 450 g brought about a loss in classifier wheel efficiency producing particles bigger in size with broader particle size distribution, and less uniform in shape It was found that particle shape and size were not correlated, thus conditions that caused the start-up loss of classifier wheel efficiency with respect to particle size, were not applicable to particle. .. that the rate of drug appearance in the blood is determined by the absorption rate of the drug whereas for Class II drugs, the rate of dissolution plays a greater role In the case of Class IV drugs, both rates are equally important 1 2 I Introduction I Introduction With the recent advent of high throughput screening for potential therapeutic agents, the number of Class II type of drug candidates has... available for dissolution and this is often achieved by decreasing the particle size of the drug The particle size may be reduced to micrometer or nanometer range and if the particles are in the nanometer range, they are termed nanoparticles (Liversidge and Cundy, 1995; Müller et al., 2001) Size reduction had been also shown to decrease the diffusion boundary layer (h) of sparingly soluble drugs (Anderberg... the particle shape Based on the two-dimensional curve, the particle can be classified as analytic (holomorphic) or non-analytic (non-homorphic) An analytic particle is defined as one where a given vector from its centre of gravity intersects the particle surface only once whereas a non-analytic particle has at least one vector showing multiple intersections with the particle surface (Figure 2) The particle. .. B4.1 Rationale for Studying Particle Shape 157 B4.2 Rationale for the Choice of Method 158 B4.3 Rationale for the Choice of Process Conditions 159 B4.4 Rationale for Employing Nonparametric Statistics 159 B5 Influences of Process Variables on Particle Shape 159 B5.1 Influence of Classifier Wheel Rotational Speed 159 B5.2 Influence of Feed Load 166 B6 Potential Monitoring Indicators for the Micronisation... CONCLUSIONS 215 A Particle Development 215 B Dissolution Enhancement of a Practically Insoluble Drug 218 VI REFERENCES 219 VII APPENDICES 251 VIII PUBLICATIONS 259 ix Summary SUMMARY Enhancing the dissolution of poorly soluble drugs has always been a challenge to researchers It was known that the solubility of this class of drugs was affected by particle size and shape of the drugs, and common industrial... larger particles with broader particle size distribution being collected in the fine fraction This was attributed to the higher vibration experienced when the classifier wheel was rotated at higher speeds and forced entry of large particles due to rebounds off the classifying chamber wall brought about by high centrifugal force of the rotating classifier wheel Starting material with bigger particle. .. deaggregation of the nifedipine particles and the crystallinity of nifedipine and PEG 3350 xii List of Figures LIST OF FIGURES Page Figure 1 Schematic illustration of the dissolution process of oral 2 pharmaceuticals Figure 2 (a) Analytic particle and (b) non-analytic particle, illustrating the 8 vector intersecting with the surface of the particle Figure 3 Geometric signature waveform of a particle (P, Pivot Point;... the rate of drug appearance in the blood circulation They are the dissolution and absorption rates, which are partially dependant on drug solubility and permeability through the GI mucosa respectively On the basis of drug solubility and permeability, Amidon et al (1995) proposed the Biopharmaceutics Classification Scheme (Table 1), consisting of four classes of drugs For Class I and III drugs, the high . PARTICLE DEVELOPMENT FOR DRUG DELIVERY LEE CHIN CHIAT NATIONAL UNIVERSITY OF SINGAPORE 2004 PARTICLE DEVELOPMENT FOR DRUG DELIVERY. Rationale for the Choice of Equipment and Method Employed 112 A1.1 Rationale for Choosing FB Hammer Mill 112 A1.2. Rationale for Choosing Laser Diffraction for Particle Sizing 113 A1.3 Rationale for. Jet Mill 136 B1. Rationale for the Choice of Equipment and Method Employed 136 B1.1 Rationale for Choosing FBO Jet Mill 136 B1.2 Rationale for Choosing SEM for Particle Sizing 137 B1.3 Choice