Characterization of Pharmaceutical Nano- and Microsystems E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: Advances in Pharmaceutical Technology
• Sprache: Englisch

Seitenzahl: 805

Veröffentlichungsjahr: 2020

Beschreibung

Learn about the analytical tools used to characterize particulate drug delivery systems with this comprehensive overview Edited by a leading expert in the field, Characterization of Pharmaceutical Nano- and Microsystems provides a complete description of the analytical techniques used to characterize particulate drug systems on the micro- and nanoscale. The book offers readers a full understanding of the basic physicochemical characteristics, material properties and differences between micro- and nanosystems. It explains how and why greater experience and more reliable measurement techniques are required as particle size shrinks, and the measured phenomena grow weaker. Characterization of Pharmaceutical Nano- and Microsystems deals with a wide variety of topics relevant to chemical and solid-state analysis of drug delivery systems, including drug release, permeation, cell interaction, and safety. It is a complete resource for those interested in the development and manufacture of new medicines, the drug development process, and the translation of those drugs into life-enriching and lifesaving medicines. Characterization of Pharmaceutical Nano- and Microsystems covers all of the following topics: * An introduction to the analytical tools applied to determine particle size, morphology, and shape * Common chemical approaches to drug system characterization * A description of solid-state characterization of drug systems * Drug release and permeation studies * Toxicity and safety issues * The interaction of drug particles with cells Perfect for pharmaceutical chemists and engineers, as well as all other industry professionals and researchers who deal with drug delivery systems on a regular basis, Characterization of Pharmaceutical Nano- and Microsystems also belongs on bookshelves of interested students and faculty who interact with this topic.

Leseprobe

Table of Contents

Cover

Advances in Pharmaceutical Technology

Characterization of Pharmaceutical Nano‐ and Microsystems

Copyright

List of Contributors

Series Preface

List of Abbreviations

1 Selecting a Particle Sizer for the Pharmaceutical Industry

1.1 Introduction

1.2 Particle Size Distribution

1.3 Selecting a Particle Sizer

1.4 Aspects of Some Selected Methods

1.5 Conclusions

Acknowledgements

References

2 Spectroscopic Methods in Solid‐state Characterization

2.1 Solid‐state Structure of Particulates

2.2 Spectroscopy Overview

2.3 Spectroscopic Data Analysis

2.4 Infrared Spectroscopy

2.5 Near‐infrared Spectroscopy

2.6 Terahertz Spectroscopy

2.7 Raman Spectroscopy

2.8 Nonlinear Optics

2.9 Fluorescence Spectroscopy

2.10 Solid‐state Nuclear Magnetic Resonance

2.11 Conclusions

References

3 Microfluidic Analysis Techniques for Safety Assessment of Pharmaceutical Nano‐ and Microsystems

3.1 Microfluidic Bioanalytical Platforms

3.2 Microfabrication Methods and Materials

3.3 Microfluidic Cell Cultures

3.4 Immobilized Enzyme Microreactors for Hepatic Safety Assessment

3.5 Microfluidic Total Analysis Systems

3.6 Epilogue

References

4

In Vitro–In Vivo

Correlation for Pharmaceutical Nano‐ and Microsystems

4.1 Introduction

4.2

In Vitro

Dissolution and

In Vivo

Pharmacokinetics

4.3 Levels of Correlation

4.4 Models of IVIVC

4.5 IVIVC Model Validation: Predictability Evaluation

4.6 IVIVC Development Step‐by‐Step Approach

4.7 Brief Introduction to Micro/Nanosystems and IVIVC Relevance

4.8 Applications of IVIVC for Micro/nanoformulations

4.9 Softwares Used for IVIVC

4.10 Conclusion and Future Prospects

References

5 5Characterization of Bioadhesion, Mucin‐interactions and Mucosal Permeability of Pharmaceutical Nano‐ and Microsystems

5.1 Introduction

5.2. Background and Theory

5.3. Mucosal Membranes

5.4. Use of Mucosal Membranes in Studies of Micro‐ and Nanoparticles

5.5. Selection of Biological Models

5.6. Methods for Testing Biocompatibility

5.7. Methods for Testing Mucoadhesion

5.8. Methods for Testing Mucopenetration

5.9. Methods for Assessing Cell Interactions

5.10. Concluding Remarks

References

6 Cell–Nanoparticle Interactions: Toxicity and Safety Issues

6.1 Introduction

6.2 Mechanisms of NP‐Induced Cellular Toxicity

6.3 In

Vitro

Assays to Evaluate Cell–NP Interactions

6.4 Metal Oxide Nanoparticles

6.5 Non‐metallic Nanoparticles

6.6 Conclusions and Future Perspectives

Acknowledgements

References

7 Intestinal Mucosal Models to Validate Functionalized Nanosystems

7.1 Introduction

7.2 Intestinal Mucosal Characteristics

7.3 In

Vitro

Models

7.4 Ex Vivo Intestinal Models for In Vitro/In Vivo Correlation of Functionalized Nanosystems

7.5 In Situ Models

7.6 In

Vivo

Models

7.7 Conclusion

Acknowledgements

References

8 Biodistribution of Polymeric, Polysaccharide and Metallic Nanoparticles

8.1 Introduction

8.2 Biodistribution and Pharmacokinetics

8.3 Mechanisms Affecting Biodistribution

8.4 Conclusion

References

9 Opportunities and Challenges of Silicon‐based Nanoparticles for Drug Delivery and Imaging

9.1 Synthesis and Characteristics of Silica‐based Nanoparticles

9.2 Solid‐state Characterization

9.3 Medium‐dependent Characterization

9.4 Incorporation of Active Molecules

9.5 Biorelevant Physicochemical Characterization

9.6 Conclusions

References

10 Statistical Analysis and Multidimensional Modeling in Research

10.1 Measurement in Research

10.2 Mean and Sample Mean

10.3 Correlation

10.4 Modeling Relationships Between Series of Observations

10.5 Quality of a Model

10.6 Multivariate Data

10.7 Principal Component Analysis (PCA)

10.8 Conclusions

References

Index

End User License Agreement

List of Tables

Chapter 1

Table 1.1  Definitions of equivalent spherical diameters (ESDs)

Table 1.2  Examples of mean diameters

Table 1.3 Examples of sizing techniques commonly used in the pharmaceutical ...

Chapter 2

Table 2.1 Overview of spectroscopic methods used for solid‐state characteriz...

Table 2.2 The spin properties of commonly used spin‐½ nuclides in the pharma...

Chapter 4

Table 4.1 Various pharmacopoeial and non‐pharmacopoeial in vitro dissolution...

Table 4.2  Various mathematical models used for IVIVC [2,3,18,21–25]

Table 4.3 Regulatory specifications and applications of IVIVC predictability...

Table 4.4 BCS classification and IVIVC assumptions and expectations [3,18,29...

Table 4.5 In vitro dissolution rank order for variable dissolution medium st...

Table 4.6 In vitro dissolution and IVIVC coefficient of correlation results ...

Table 4.7 Factors influencing the selection of optimum in vitro dissolution ...

Table 4.8  Predictability test of the % PE values for AUC and C

max

for FSLNs ...

Table 4.9  IVIVC applications for micro/nanoformulations

Table 4.10 Composition, in vitro dissolution and in vivo pharmacokinetic pro...

Table 4.11 Process variables, in vitro dissolution and in vivo pharmacokinet...

Table 4.12  Softwares used for IVIVC model development

Chapter 5

Table 5.1 Main properties of mucus at different mucosal membranes (mainly bas...

Table 5.2 Overview of cell lines used to study interactions of nano‐ and micr...

Table 5.3 Cellular mechanisms for nano‐ and microparticle uptake in epithelia...

Chapter 6

Table 6.1 Overview of studies assessing the toxicity of different nanosystem...

Chapter 7

Table 7.1 Summary of endocytic mechanisms present in epithelial cells

Table 7.2 Examples of in vitro models that can be used by nanosystems in perm...

Table 7.3 Examples of ex vivo models that can be used by nanosystems in perme...

Table 7.4 Examples of in situ models that can be used by nanosystems in perme...

Chapter 9

Table 9.1 Common hollow silica morphologies and their special performance. Re...

Table 9.2 Interaction forces between nanoparticles

Chapter 10

Table 10.1 Vitamin content of ten randomly selected tablets

Table 10.2 Results of dissolution tests (time to 80% drug release) and C

max

v...

Table 10.3 Results of test where the effect of compression pressure on the cr...

Table 10.4 Number of experiments in full factorial designs in two levels (2

n

)...

Table 10.5 Design of 2

2

experiments

Table 10.6 3

2

study design

Table 10.7 Study design and measured responses

Table 10.8 Numerical coefficients for the fitted model

Table 10.9 Numerical coefficients for the reduced model

Table 10.10 Numerical coefficients for the final model

Table 10.11 The residuals of the fitted final model

Table 10.12 Score values

List of Illustrations

Chapter 1

Figure 1.1 Particle size representations: (A) frequency distribution (non‐sy...

Figure 1.2 Example of number‐, volume‐ and intensity‐weighted particle size ...

Figure 1.3 Measuring principle of dynamic image analysis

Figure 1.4

Interaction of light rays with a particle

.

Figure 1.5 Effect of particle size (d) on the scattering pattern for a given...

Figure 1.6 Influence of the scattering model used in the deconvolution of th...

Figure 1.7 Schematic representation of the static light scattering technique...

Chapter 2

Figure 2.1 Regions of the electromagnetic spectrum and associated quantum st...

Figure 2.2 Transitions associated with photon absorption, emission and scatt...

Figure 2.3 MIR spectra of indomethacin: (a) calculated dimer; (b) calculated...

Figure 2.4 NIR spectra azithromycin upon storage at 60°C and 100% RH of (a) ...

Figure 2.5 (a) Experimental XRPD diffractograms and (b) NIR and (c) Raman s...

Figure 2.6 Quantitative FT‐NIR imaging of two powder blends of three polymor...

Figure 2.7 Terahertz spectra of (a) carbamazepine (forms I and III), (b) ena...

Figure 2.8 (A) Low‐frequency Raman spectra of three piroxicam crystal forms...

Figure 2.9 Time‐resolved spectra of piroxicam solid‐state forms (β and α2 an...

Figure 2.10 (a) Raman spectra of crystalline ESM, amorphous ESM and excipie...

Figure 2.11 Crystal form classification by independent SHG‐guided Raman anal...

Figure 2.12 (a) PCA‐based CARS image and (b) overlaid PCA‐based CARS image a...

Figure 2.13 Absorption and fluorescence spectra for (a) 20 μM ethidium bromi...

Figure 2.14 Sample orientations for collecting fluorescence (a) in solution ...

Figure 2.15 (a) Exciton splitting in a linear molecular polymer with obliqu...

Figure 2.16 FLIM images (80 × 80 μm) of the amorphous indomethacin stored at...

Figure 2.17 Schematic representation of the magic‐angle experiment. The samp...

Figure 2.18 Solid‐state

13

C NMR spectra of crystalline losartan potassium re...

Figure 2.19

13

C CPMAS spectra of form I IBP and silica‐IBP system: (a) full ...

Figure 2.20  (a)

77

Se CPMAS spectrum of the 50%

w/w

dispersion of ebselen in...

Figure 2.21

1

H−

1

H SQ‐DQ Back‐to‐Back (BaBa) experiment of (A) (R/S)‐IBU:NA a...

Figure 2.22

1

H MAS spectra of IBU:NA in MCM‐41 with different weight ratios:...

Chapter 3

Figure 3.1 Schematic representation of the typical PDMS soft lithography pro...

Figure 3.2 Schematic presentation of the fabrication steps of micropillar ar...

Figure 3.3 (a) Illustration of the difference in phenotype of primary hepato...

Figure 3.4 A schematic presentation of the microfluidic organ‐on‐a‐chip used...

Figure 3.5 Left: Representative immunofluorescence of iPSC‐CM cultured on (A...

Figure 3.6 Spheroid formation process in a microwell‐based organ‐on‐a‐chip. ...

Figure 3.7 Biologically inspired design of a human breathing lung‐on‐a‐chip ...

Figure 3.8 Schematic representation of the main different methods of enzyme ...

Figure 3.9 Immobilization of human liver microsomes (HLMs) on streptavidin‐f...

Figure 3.10 Typical designs for increasing the surface‐to‐volume ratio of mi...

Figure 3.11 (a) Scanning electron micrograph of microfabricated frit structu...

Figure 3.12 Representative configuration designs of microfluidic chips that ...

Figure 3.13 Top: A micropillar‐based immobilized enzyme reactor made from OS...

Figure 3.14 (a) Sample loading and dispensing steps on a microchip electroph...

Figure 3.15 Left: Photograph (top) and schematic side‐view (bottom) of a LC ...

Figure 3.16 Schematic diagram of the chip−ESI‐MS system. (a) The system cons...

Chapter 4

Figure 4.1 Schematic representation of (a) the

in vitro

dissolution profile ...

Figure 4.2 Schematic representations of various levels of IVIVC correlation....

Figure 4.3 Schematic representation of factors influencing

in vivo

pharmacok...

Figure 4.4 Schematic representation of the step‐by‐step approach for deconvo...

Figure 4.5 Schematic representations of various advanced micro/nanoformulati...

Chapter 5

Figure 5.1 Schematic illustration of mucoadhesive and mucopenetrating partic...

Figure 5.2 Filtering mechanisms governing permeability of mucus to particles...

Figure 5.3 Illustrations of diffusion chambers: (A) Franz diffusion cell and...

Figure 5.4 Schematic illustration of permeability setup for study of transpo...

Figure 5.5 The triple co‐culture intestine cell model comprising Caco‐2, HT2...

Figure 5.6 Schematic representation of the dual‐chamber model based on the T...

Figure 5.7 Illustration of a cultivated cell from the airway with mucus (rig...

Figure 5.8 Example of SALS 2D patterns of solutions of mucin (1 wt%) at vari...

Figure 5.9 (A) Cross‐section of intestinal epithelia showing distribution of...

Chapter 6

Figure 6.1 Factors influencing the interactions between NPs and cells. The m...

Figure 6.2 Influence of NP shape on the phagocytosis. (A) Internalization ve...

Figure 6.3 In vitro assays for the evaluation of cell–NP interactions. (a) T...

Figure 6.4 Mechanism of ZnO NP immunotoxicity. The release of Zn ions induce...

Figure 6.5 Schematic of the different toxicity mechanisms of ZnO NPs, accord...

Figure 6.6 Cellular uptake of CeO

2

nanomaterials. (a) Flow cytometric analys...

Figure 6.7 Concentration‐dependent uptake of IONPs by microglial cells shown...

Figure 6.8 Schematic structure of a liposome. Hydrophilic drugs, DNA or drug...

Figure 6.9 Cell uptake mechanism of carboxy‐ and amine‐modified PS‐NPs. Carb...

Figure 6.10 (A–F) Endolysosomal escape of the core–shell NPs in the dendriti...

Figure 6.11 The preparation, optimization and nano‐biointeractions of the NP...

Figure 6.12 Influence of lactose‐functionalized dendrimers on normal and gal...

Figure 6.13 TEM images of the A549 cells after exposure to NPs of 20 nm (A a...

Chapter 7

Figure 7.1 Intestinal mucosal surface at the steady state. The intestinal ep...

Figure 7.2 Schematic illustration of the different delivery pathways. Reprin...

Figure 7.3 Illustration of (A) Caco‐2 monoculture model; (B) Caco‐2/HT29‐MTX...

Figure 7.4 Representation of a gut‐on‐a‐chip. Reprinted with permission from...

Figure 7.5 Illustration of a human‐on‐a‐chip. Reprinted with permission from...

Figure 7.6 Representation of diffusion chambers: (A) Ussing chamber with (1)...

Figure 7.7 Schematic of different intestinal mucosal models (examples, advan...

Chapter 8

Figure 8.1 Effects of particle size on biodistribution.

Figure 8.2 Illustration of the different sizes, shapes, and surface properti...

Chapter 9

Figure 9.1 The synthesis of nonporous silica nanoparticles by the Stöber met...

Figure 9.2 Loading of cargos on nonporous silica NPs and the proposed mechan...

Figure 9.3 Schematic illustration of the synthesis of mesoporous silica mate...

Figure 9.4 Schematic illustration for: (a) spray drying, (b) the self‐templa...

Figure 9.5 PSi anodization cell. Reprinted with permission from [49].

Figure 9.6 Anodic divalent dissolution of silicon in HF. Reprinted with perm...

Figure 9.7 Surface treatments of PSi yielding different surface layers.

Figure 9.8 TEM images of porous nanoparticles.

Figure 9.9 Different methods used to determine porosity and pore size distri...

Figure 9.10 TEM in size measurement, and difference between mesoporous silic...

Figure 9.11 Aqueous stability of zeta potential of APSTCPSi‐10 in distilled ...

Figure 9.12 Interaction energy of two approaching surfaces (inset) as a func...

Figure 9.13 Correlation of structure parameter R

g

/R

h

compared with (a) overe...

Figure 9.14 (A) Adsorption of water‐soluble salicylic acid from aqueous sol...

Figure 9.15 Fluorescence spectra of (a) F‐MSN and (b) PEI‐F‐MSN at neutral a...

Figure 9.16 MSNs incubated in (a) HEPES buffer and (b) ethanol; (c) after 10...

Figure 9.17 Design and prediction cycle as proposed by Walkey and Chan. Repr...

Figure 9.18 Model drug (DiI dye) release: (a) in different media plotted as ...

Figure 9.19 (A) Real‐time SPR signal response measured with an SPR waveleng...

Chapter 10

Figure 10.1 Four different scatterplots between two variables. Pearson's cor...

Figure 10.2 Examples of different R

2

values in linear regression.

Figure 10.3 Scatterplot of data from Table 10.3.

Figure 10.4 Scatterplot of the data from Table 10.3 and modeled by a first‐o...

Figure 10.5 Data from Table 10.3 modeled by a second‐order equation (solid l...

Figure 10.6 Data from Table 10.3 modeled by third‐order equation (solid line...

Figure 10.7 Data from Table 10.3 modeled by a 9th‐order equation.

Figure 10.8 The trade‐off between the goodness of fit, R

2

, and goodness of p...

Figure 10.9 Design of experiments and response (bars) for crushing strength ...

Figure 10.10 First‐order model (plane) fitted to the measured results.

Figure 10.11 Second‐order model (curved plane) fitted to the measured result...

Figure 10.12 Pictureof the 3

2

study design.

Figure 10.13 Contour plot of the crushing strength model.

Figure 10.14 Surface plot of the crushing strength model and measured data p...

Figure 10.15 Contour plot of the disintegration time.

Figure 10.16 Contour plot used to optimize tabletting conditions.

Figure 10.17 Fourier transform infrared (FTIR) spectroscopy measurements of ...

Figure 10.18 Score plot.

Figure 10.19 Loadings plot of the first principal component.

Figure 10.20 Loadings plot of the second principal component.

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Advances in Pharmaceutical Technology

A Wiley Book Series

Series Editors:

Dennis Douroumis, University of Greenwich, UK

Alfred Fahr, Friedrich–Schiller University of Jena, Germany

Jurgen Siepmann, University of Lille, France

Martin Snowden, University of Greenwich, UK

Vladimir Torchilin, Northeastern University, USA

Titles in the Series

Hot‐Melt Extrusion: Pharmaceutical Applications

Edited by Dionysios Douroumis

Drug Delivery Strategies for Poorly Water‐Soluble Drugs

Edited by Dionysios Douroumis and Alfred Fahr

Computational Pharmaceutics: Application of Molecular Modeling in Drug Delivery

Edited by Defang Ouyang and Sean C. Smith

Pulmonary Drug Delivery: Advances and Challenges

Edited by Ali Nokhodchi and Gary P. Martin

Novel Delivery Systems for Transdermal and Intradermal Drug Delivery

Edited by Ryan Donnelly and Raj Singh

Drug Delivery Systems for Tuberculosis Prevention and Treatment

Edited by Anthony J. Hickey

Continuous Manufacturing of Pharmaceuticals

Edited by Peter Kleinebudde, Johannes Khinast, and Jukka Rantanen

Pharmaceutical Quality by Design

Edited by Walkiria S Schlindwein and Mark Gibson

In Vitro Drug Release Testing of Special Dosage Forms

Edited by Nikoletta Fotaki and Sandra Klein

Characterization of Pharmaceutical Nano‐ and Microsystems

Edited by Leena Peltonen

Forthcoming Titles:

Process Analytics for Pharmaceuticals

Edited by Jukka Rantanen, Clare Strachan and Thomas De Beer

Mucosal Drug Delivery

Edited by Rene Holm

Basic Biopharmaceutics

Edited by Hannah Batchelor

Characterization of Pharmaceutical Nano‐ and Microsystems

Edited by

LEENA PELTONENUniversity of Helsinki, Finland

This edition first published 2021

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Library of Congress Cataloging‐in‐Publication Data

Names: Peltonen, Leena Johanna, 1970- editor.

Title: Characterization of pharmaceutical nano- and microsystems / Leena

Johanna Peltonen.

Description: First edition. | Hoboken : Wiley, 2020. | Series: Advances in

pharmaceutical technology | Includes bibliographical references and

index.

Identifiers: LCCN 2020027861 (print) | LCCN 2020027862 (ebook) | ISBN

9781119414049 (cloth) | ISBN 9781119414032 (adobe pdf) | ISBN

9781119414025 (epub)

Subjects: LCSH: Pharmaceutical technology. | Nanotechnology. | Drug

development.

Classification: LCC RS192 .C522 2020 (print) | LCC RS192 (ebook) | DDC

615.1/9—dc23

LC record available at https://lccn.loc.gov/2020027861

LC ebook record available at https://lccn.loc.gov/2020027862

Cover Design: Wiley

Cover Image: © Inna Bigun/Shutterstock

List of Contributors

Malgorzata Iwona Adamczak, Department of Pharmacy, Faculty of Mathematics and Natural Sciences, University of Oslo, Norway; and GE Healthcare, Pharmaceutical Diagnostics, Oslo, Norway

Osmo Antikainen, Faculty of Pharmacy, University of Helsinki, Finland

Cláudia Azevedo, INEB – Instituto de Engenharia Biomédica; i3S – Instituto de Investigação e Inovação em Saúde; and Instituto de Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal

Erem Bilensoy, Department of Pharmaceutical Technology, Faculty of Pharmacy, Hacettepe University, Turkey

Preshita P. Desai, Western University of Health Sciences, Pomona, California, USA

Martin Dračínský, Czech Academy of Sciences, Prague, Czech Republic

Nazlı Erdoğar, Department of Pharmaceutical Technology, Faculty of Pharmacy, Hacettepe University, Turkey

Nazanin Zanjanizadeh Ezazi, Drug Research Program, University of Helsinki, Finland

Paulo J. Ferreira, CIEPQPF, Department of Chemical Engineering, University of Coimbra, Portugal

Margarida Figueiredo, CIEPQPF, Department of Chemical Engineering, University of Coimbra, Portugal

Flavia Fontana, Drug Research Program, University of Helsinki, Finland

Ellen Hagesaether, Department of Life Science and Health, Faculty of Health Sciences, Oslo Metropolitan University, Norway

Marianne Hiorth, Department of Pharmacy, Faculty of Mathematics and Natural Sciences, University of Oslo, Norway

Martti Kaasalainen, Laboratory of Industrial Physics, University of Turku, Finland, and Medicortex Finland Oy

Didem Şen Karaman, Pharmaceutical Sciences Laboratory, Faculty of Science and Engineering, Åbo Akademi University, Turku, Finland

Helene Kettiger, Pharmaceutical Sciences Laboratory, Faculty of Science and Engineering, Åbo Akademi University, Turku, Finland

Iiro Kiiski, Drug Research Program, University of Helsinki, Finland

Timo Laaksonen, Tampere University of Technology, Finland

Tiina Lipiäinen, Drug Research Program, University of Helsinki, Finland

M. José Moura, Department of Chemical and Biological Engineering, Polytechnic Institute of Coimbra, Portugal; and CIEPQPF, Department of Chemical Engineering, University of Coimbra, Portugal

Elisa Ollikainen, Drug Research Program, University of Helsinki, Finland

Vandana B. Patravale, Institute of Chemical Technology, Mumbai, Maharashtra, India

Inês Pereira, INEB – Instituto de Engenharia Biomédica; and i3S – Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Portugal

Kaisa Rautaniemi, Tampere University of Technology, Finland

Jessica Rosenholm, Pharmaceutical Sciences Laboratory, Faculty of Science and Engineering, Åbo Akademi University, Turku, Finland

Jukka Saarinen, Drug Research Program, University of Helsinki, Finland

Helder A. Santos, Drug Research Program, University of Helsinki, Finland

Bruno Sarmento, INEB – Instituto de Engenharia Biomédica; and i3S – Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Portugal; and CESPU, Instituto de Investigação e Formação Avançada em Ciências e Tecnologias da Saúde, Gandra, Portugal

Tiina M. Sikanen, Drug Research Program, and Helsinki Institute of Life Science, University of Helsinki, Finland

Marcin Skotnicki, Poznań University of Medical Sciences, Poland

Clare Strachan, Drug Research Program, University of Helsinki, Finland

Nayab Tahir, Drug Research Program, University of Helsinki, Finland; College of Pharmacy, University of Sargodha, Pakistan; and Faculty of Pharmacy and Alternative Medicine, The Islamia University of Bahawalpur, Pakistan

Ingunn Tho, Department of Pharmacy, Faculty of Mathematics and Natural Sciences, University of Oslo, Norway

Cem Varan, Department of Nanotechnology and Nanomedicine, Institute of Graduate Studies in Science, Hacettepe University, Turkey

Gamze Varan, Department of Nanotechnology and Nanomedicine, Institute of Graduate Studies in Science, Hacettepe University, Turkey

Elina Vuorimaa‐Laukkanen, Tampere University of Technology, Finland

Series Preface

The series Advances in Pharmaceutical Technology covers the principles, methods and technologies that the pharmaceutical industry uses to turn a candidate molecule or new chemical entity into a final drug form and hence a new medicine. The series will explore means of optimizing the therapeutic performance of a drug molecule by designing and manufacturing the best and most innovative of new formulations. The processes associated with the testing of new drugs, the key steps involved in the clinical trials process and the most recent approaches utilized in the manufacture of new medicinal products will all be reported. The focus of the series will very much be on new and emerging technologies and the latest methods used in the drug development process.

The topics covered by the series include the following:

Formulation

: The manufacture of tablets in all forms (caplets, dispersible, fast‐melting) will be described, as will capsules, suppositories, solutions, suspensions and emulsions, aerosols and sprays, injections, powders, ointments and creams, sustained release and the latest transdermal products. The developments in engineering associated with fluid, powder and solids handling, solubility enhancement, colloidal systems including the stability of emulsions and suspensions will also be reported within the series. The influence of formulation design on the bioavailability of a drug will be discussed and the importance of formulation with respect to the development of an optimal final new medicinal product will be clearly illustrated.

Drug Delivery

: The use of various excipients and their role in drug delivery will be reviewed. Among the topics to be reported and discussed will be a critical appraisal of the current range of modified‐release dosage forms currently in use and also those under development. The design and mechanism(s) of controlled release systems including macromolecular drug delivery, microparticulate controlled drug delivery, the delivery of biopharmaceuticals, delivery vehicles created for gastrointestinal tract targeted delivery, transdermal delivery and systems designed specifically for drug delivery to the lung will all be reviewed and critically appraised. Further site‐specific systems used for the delivery of drugs across the blood–brain barrier including dendrimers, hydrogels and new innovative biomaterials will be reported.

Manufacturing

: The key elements of the manufacturing steps involved in the production of new medicines will be explored in this series. The importance of crystallization; batch and continuous processing, seeding; and mixing including a description of the key engineering principles relevant to the manufacture of new medicines will all be reviewed and reported. The fundamental processes of quality control including good laboratory practice, good manufacturing practice, Quality by Design, the Deming Cycle, regulatory requirements and the design of appropriate robust statistical sampling procedures for the control of raw materials will all be an integral part of this book series.

An evaluation of the current analytical methods used to determine drug stability, the quantitative identification of impurities, contaminants and adulterants in pharmaceutical materials will be described, as will the production of therapeutic bio‐macromolecules, bacteria, viruses, yeasts, molds, prions and toxins through chemical synthesis and emerging synthetic/molecular biology techniques. The importance of packaging including the compatibility of materials in contact with drug products and their barrier properties will also be explored.

Advances in Pharmaceutical Technology is intended as a comprehensive one‐stop shop for those interested in the development and manufacture of new medicines. The series will appeal to those working in the pharmaceutical and related industries, both large and small, and will also be valuable to those who are studying and learning about the drug development process and the translation of those drugs into new life‐saving and life‐enriching medicines.

Dennis Douroumis

Alfred Fahr

Jürgen Siepmann

Martin Snowden

Vladimir Torchilin

List of Abbreviations

μTAS

micro total analysis systems

AFM

atomic force microscopy

ALP

alkaline phosphatase

AMP

antimicrobial peptide

AOM

acousto‐optic modulator

API

active pharmaceutical ingredient

APS

Aerodynamic Particle Sizer

AR

aspect ratio

ATR

attenuated total reflectance

AUC

area under the curve

BCS

biopharmaceutical classification system

CAD

computer‐aided drawing

CARS

coherent anti‐Stokes Raman scattering

CCD

charge‐coupled device

CDF

continuous density function

CFM

confocal fluorescence microscopy

CLEA

crosslinked enzyme aggregate

CLEC

crosslinked enzyme crystal

CLS

classical least squares

CLSM

confocal laser scanning microscopy

CNS

central nervous system

CP

cross‐polarization

CSA

chemical shift anisotropy

CT

contact time

CYP

cytochrome P450

DC

dendritic cell

DCLS

direct classical least squares

DDS

drug delivery system

DDM

derivative difference minimization

DE

direct excitation

DFT

density functional theory

DLS

dynamic light scattering

DLVO

Derjaguin, Landau, Verwey and Overbeek [theory]

DOE

design of experiments

DRIFTS

diffuse reflectance infrared Fourier transform spectroscopy

DSC

differential scanning calorimetry

DTGS

deuterated triglycine sulfate

ECM

extracellular matrix

EDFM

enhanced dark‐field microscope

EDL

electrical double layer

EFG

electric field gradient

EM

electron microscopy

EPR

enhanced permeability and retention

ER

endoplastic reticulum

ESD

equivalent spherical diameter

FACS

fluorescence‐activated cell sorting

FBRM

focused beam reflectance measurement

FD

Fraunhofer diffraction

FIR

far‐infrared

FITC

fluorescein isothiocyanate

FLIM

fluorescence lifetime imaging microscopy

FRAP

fluorescent recovery after photobleaching

FRET

Förster (or fluorescence) resonance energy transfer

FSLN

furosemide solid lipid nanoparticle

FTIR

Fourier transform infrared

GFP

green fluorescent protein

GIT

gastrointestinal tract

GPC

gel permeation chromatography

HETCOR

heteronuclear correlation

HLM

human liver microsome

HMSN

hollow‐type mesoporous silica nanoparticle

HPLC

high pressure liquid chromatography

HPPD

high‐power proton decoupling

HPV

human papilloma virus

HIS

hyperspectral imaging

IEC

intestinal epithelial cell

IEP

isoelectric point

IgA

immunoglobulin A

IgG

immunoglobulin G

IONP

iron oxide nanoparticle

iPSC

induced pluripotent stem cell

iPSC‐CM

induced pluripotent stem cell‐derived cardiomyocytes

IR

infrared

IUPAC

International Union of Pure and Applied Chemistry

i.v.

intravenous

IVIVC

in vitro–in vivo

correlation

LALLS

low‐angle laser light scattering

LC

liquid chromatographic

LD

laser diffraction

LDA

linear discriminant analysis

LNC

lipid‐core nanocapsule

LOD

limit of detection

LOQ

limit of quantification

MAE

mean absolute error

MAS

magic‐angle spinning

MCR

multivariate curve resolution

MCR‐ALS

multivariate curve resolution‐alternating least squares

MCT

mercury cadmium telluride

MD

molecular dynamic

MDT

mean dissolution time

MIP

multiple image photography

MIR

mid‐infrared

MPS

mononuclear phagocytic system

MRI

magnetic resonance imaging

MRT

mean residence time

MSN

mesoporous silica nanoparticle

MSP

mesoporous silica particle

NA

numerical aperture

NADPH‐CPR

nicotinamide adenine dinucleotide phosphate‐cytochrome P450 reductase

NCE

new chemical entity

NIR

near‐infrared

NLC

nanostructured lipid carrier

NMR

nuclear magnetic resonance

NP

nanoparticle

OFAT

one factor at a time

OI

optical imaging

OSTE

off‐stoichiometric thiol‐ene

PAT

process analytical technology

PBMC

peripheral blood mononuclear cell

PBS

phosphate‐buffered saline

PCA

principal component analysis

PCS

photon correlation spectroscopy

PCR

principal components regression

PDMS

poly(dimethyl siloxane)

PE

predictability evaluation

PEG

polyethylene glycol

PEI

poly(ethylene imine)

PET

positron emission tomography

P‐gp

P‐glycoprotein

pI

isoelectric point

PLGA

poly(lactic‐co‐glycolic) acid

PLS‐DA

partial least‐squares discriminant analysis

PMT

photomultiplier tube

PS

polystyrene

PSi

porous silicon

PTFE

polytetrafluoroethylene

PTX

paclitaxel

PZC

point of zero charge

QbD

Quality by Design

QCM

quartz crystal microbalance

QELS

quasi‐elastic light scattering

RBC

red blood cell

RES

reticular endothelial system

RF

radio frequency

RGD

tripeptide Arg‐Gly‐Asp

RH

relative humidity

RMSE

root mean squared error

ROS

reactive oxygen species

SCXRD

single crystal X‐ray diffraction

SEM

scanning electron microscopy

SEP

standard error of prediction

SFB

segmented filamentous bacteria

SFG

sum frequency generation

SGF

simulated gastric fluid

SHG

second harmonic generation

SIMCA

soft independent modeling of class analogy

SLN

solid lipid nanoparticle

SLS

static light scattering

SORS

spatially offset Raman spectroscopy

SPE

solid‐phase extraction

SPR

surface plasmon resonance

SRG

stimulated Raman gain

SRL

stimulated Raman loss

SRS

stimulated Raman scattering

SSNMR

solid‐state nuclear magnetic resonance

SUPAC

scale‐up and post‐approval change

SVM

support vector machine

TC

thermal carbonization

TEER

transepithelial electrical resistance

TEM

transmission electron microscopy

TERS

tip‐enhanced Raman scattering

TGA

thermogravimetric analysis

THC

thermal hydrocarbonization

THG

third harmonic generation

TOF

time‐of‐flight

TOPSi

thermally oxidized porous silicon

TOSS

total suppression of spinning sidebands

TPEF

two‐photon excited fluorescence

TPS

terahertz pulsed spectroscopy

UGT

uridine 5′‐diphospho‐glucuronosyltransferase

USFDA

United States Food and Drug Administration

UV

ultraviolet

VDOS

vibrational density of states

XPS

X‐ray photoelectron spectroscopy

XRPD

X‐ray powder diffraction

ZP

zeta potential

1Selecting a Particle Sizer for the Pharmaceutical Industry

Margarida Figueiredo1, M. José Moura1,2, and Paulo J. Ferreira1

1CIEPQPF, Department of Chemical Engineering, University of Coimbra, Portugal

2Department of Chemical and Biological Engineering, Polytechnic Institute of Coimbra, Portugal

1.1 Introduction

1.1.1 Relevance of Particle Size in the Pharmaceutical Industry

Knowledge and understanding of particle size data is crucial in a wide range of industries, being vital for the pharmaceutical industry, with applications from drug development to production and quality control. The purpose of particle size analysis is to obtain quantitative data on the mean size, particle size distribution and, sometimes in addition, particle shape of the compounds used in pharmaceutical formulations. It is well known that particle size highly affects not only the final product performance (e.g., dissolution, bioavailability, stability and absorption rates), but also every step of the manufacturing process of both drug substances and excipients (like mixing, flowability, granulation, drying, milling, blending, coating and encapsulation) [1–8]. For example, particle size is often directly related to dissolution/solubility characteristics of solid or suspension delivery systems, which have a direct impact on the bioavailability of pharmaceutical products. Dissolution is directly proportional to particle surface area, which in turn is inversely proportional to particle size (i.e., finer particles promote faster drug dissolution). The same applies to the suspensions where precipitation is highly controlled by particle size (in practice, finer particles generally give more stable suspensions), equally affecting viscosity and flow (Stokes' law relates the settling velocity of particles to the square of particle diameter). Distribution of sizes is another key characteristic that influences, for instance, handling and processing (powder handling characteristics are profoundly affected by changes in flow properties and tendency to segregate, which are both highly dependent on powder size distribution). Ultimately, particle size also has a critical effect on the content uniformity of solid dosage forms. Size analysis also becomes of significant importance with new drug delivery formats such as liposomes and nanoparticles whose characterization requires sophisticated analytical techniques [9–12].

In brief, particle size simultaneously affects safety, efficacy and quality of the drug, and regulatory agencies are becoming increasingly aware of the importance of particle sizing, requiring developers to have greater control and understanding of this aspect of their drug products [3,13–15].

1.1.2 Main Goals

This chapter intends to introduce the problem of particle sizing in the domain of the pharmaceutical industry, especially to those who are not very familiar with this topic. It is by no means an exhaustive description of particle sizing methods, but addresses the basic concepts associated with particle sizing, providing a basis to understand the most important details associated with particle sizing data and its interpretation. It was conceived not only to help the reader to select the most suitable techniques for your particle characterization needs, but also to be a valuable tool in daily work situations.

A considerable effort was made to condense in a single chapter topics that range from the interpretation of sizing data to the working principles, applications and limitations of some selected methods, including their selection criteria, subjects that are normally treated in separate publications/chapters. The idea was to provide the essential information to enable the reader to completely follow all the topics covered here. After discussing the reasons why choosing a particle sizer is not an easy task, some basic definitions of particle size, size distribution and their representations will be given in a concise manner, before addressing some of the most relevant parameters to be taken into consideration when selecting a particle sizing method. Finally, the underlying principles of some selected methods will be presented, together with their strengths and weaknesses. Naturally, the number of addressed methods had to be limited. Hence, this discussion will mainly be directed to sizing techniques normally available for routine analyses in the pharmaceutical field, from nanoparticles to some hundred micrometer particles. In order to encompass one of each class of particle sizing methods, the following techniques were selected: optical microscopy/image analysis and time‐of‐flight, representative of the counting techniques; static and dynamic light scattering, widely used ensemble techniques; and the cascade impactor, a separation technique frequently used for aerosol samples (nasal products). As mentioned, the ultimate goal will be to stimulate the reader's curiosity to consult other sources of information to complement this analysis.

1.1.3 Why it is So Difficult to Select a Particle Sizer

The apparent simplicity of particle size analysis is deceptive as particle sizing is a poorly posed problem. As is well known, only objects of simple geometry, namely spheres, can be unambiguously described by a single linear dimension. Non‐spherical particles, as discussed below, are most conveniently described in terms of derived diameters calculated by measuring a size‐dependent property of the particle and relating it to a linear dimension. As a result, different sizing methods, based on the measurement of different particle properties, might give different sizing data for the same sample. Moreover, the same measuring technique can also generate different sizing results as a consequence of distinct data processing algorithms used by the equipment manufacturers [2,3,16,17]. Complicating this, a wide range of size distributions normally have to be analysed, being not uncommon that the size range of the particles is too wide to be measured with a single device. Besides, particles, namely pharmaceuticals, include dry powders, suspensions, aerosols, emulsions and nanoparticles, which in turn can be presented as primary (individual) particles, aggregates or agglomerates (in aggregates the primary particles are bound strongly by covalent bonds, whereas agglomerates are collections of aggregates loosely held together by weak forces). Also, the recent interest in measuring nanoparticles resulted in a burst of new techniques (or new applications of old techniques) for the nanometer range, being that the smaller the particles, the more difficult it is to characterize them. Accordingly, there has never been so much diversity of sizing equipment (hundreds of commercially available instruments), sample and data treatments.

Additionally, it should be pointed out that formal training in the field of particle technology is not often as widespread as in other fields. Further, the technical information available in particle technology, namely particle sizing vocabulary, is unique and complex, and a clear domain of fine particle technology terminology is indispensable for correct data interpretation.

As a final point, it should be highlighted that the determination of particle size distribution seldom is the ultimate objective: indeed, a particle size measurement is often carried out with the aim of relating particle sizing data to a particular property or behavior of the material, and this relationship should be taken into consideration when choosing a sizing instrument. For example, if we are studying the particles of an airborne aerosol and their deposition in the lungs, a sizing method based on the measurement of the aerodynamic diameter would be more appropriate; furthermore, if a drug product is to be administered as a dry powder, a particle characterization technique capable of measuring the sample as a dry powder dispersion should be used.

Sizing equipment is not often restricted to a specific application, being normally used for more general purposes. Nonetheless, it should be borne in mind that no single technique is superior in all applications. All these reasons render the selection of the most appropriate particle sizing method a challenging process.

1.2 Particle Size Distribution

1.2.1 Equivalent Diameter

It is not possible to rationally discuss the size of a particle without considering the three‐dimensional characteristics of the particle itself (length, breadth, and height). In fact, only the sphere can be fully described by a single dimension, its radius or diameter. However, most real‐world particles are far from round or uniform, and with regard to particle sizing, it is often most convenient to express particle size in terms of derived diameters such as equivalent spherical diameter (ESD). ESD is defined by ISO 9276‐1:1998 [18] as the diameter of a sphere that produces a response by a given particle‐sizing method that is equivalent to the response produced by the particle being measured. In many cases the equivalent sphere is the one with the same volume as the particle, the so‐called volume‐equivalent spherical diameter (a cube of length 1 μm has a volume‐equivalent spherical diameter of 1.24 μm). However, the method of measurement and the property of interest of the particle can lead to the use of other diameters, such as, for instance, the surface‐equivalent spherical diameter, which is the diameter of a sphere having the same surface area as the particle, or the projected area diameter, most used in image analysis, that is the diameter of a circle having the same area as the projected area of the particle. These and other frequently used particle‐equivalent diameters are defined in Table 1.1 [16].

Table 1.1  Definitions of equivalent spherical diameters (ESDs)

ESD

Definition

Volume diameter

Diameter of a sphere having the same volume as the particle

Surface diameter

Diameter of a sphere having the same (external) surface area as the particle

Projected area diameter

Diameter of a circle having the same area as the projected area of the particle resting in a stable position

Surface volume diameter (Sauter diameter)

Diameter of a sphere having the same surface area‐to‐volume ratio as the particle

Sieve diameter

Diameter of a sphere passing through a sieve of defined mesh size (with square or circular apertures)

Stokes diameter

Diameter of a sphere with the same final settling velocity as the particle undergoing laminar flow in a fluid of the same density and viscosity

Hydrodynamic diameter

Diameter of a sphere with the same translational diffusion coefficient as the particle in the same fluid under the same conditions

Mobility diameter

Diameter of a sphere having the same mobility in an electric field as the particle

Fraunhofer diameter

Diameter of a sphere that will scatter light at the same intensity at the same angle as the particle (correspond to the projected area diameter of a particle in random orientation)

Optical diameter

Diameter of a sphere having the same optical cross‐section as the particle

Aerodynamic diameter

Diameter of a unit density sphere that would have the identical settling velocity as the particle

Clearly, non‐spherical particles can lead to very different equivalent diameters depending on the definition chosen, which in turn is related to the measured particle property and ultimately to the sizing instrument/technique used. The further away from spherical the actual particle shape is, the greater the difference in ESD (for non‐spherical compact convex particles, the results will not differ greatly for the various size measurements, but for needles, disks or flakes, with one dimension significantly different from the others, the differences may be quite relevant). Moreover, ESD may not correlate with any single dimension of the particle. On the other hand, identical equivalent diameters may be obtained for different particle shapes. For that, particle size and particle size distribution results are frequently considered as relative measurements, and comparisons of size results from different instruments should be conducted with extreme caution.

Although the ESD approach is simplistic, it is very convenient and it is employed in almost all particle sizing techniques. However, it is absolutely essential to be clear and consistent as to which ESD is being used.

1.2.2 Reporting Particle Size

This section briefly addresses the representation of size distributions, focusing mainly on the types of curves used to express the distribution and some central tendencies. Nowadays, all particle sizers report the data in graphical form (some of which we can select) indicating some statistical parameters. However, a perfect understanding of the distributions and of the associated statistical parameters is absolutely essential for a correct interpretation of the sizing data [16,19–22].

Almost all real‐world samples exist as a distribution of particle sizes, normally expressed as a function of two coordinates: the size (mostly an ESD) plotted on the x‐axis, and the amount of each size, plotted on the y‐axis, as illustrated in Figure 1.1. The size distribution can be represented in the form of either a frequency (differential) distribution curve or a cumulative (normally undersize) distribution curve (typically with a sigmoidal shape), obtained by sequentially adding the percentage frequency values. Both types of plot are useful depending on the information we want from the graphical representation: the frequency distribution presents a clear description of the distribution spread and also shows if the distribution is monomodal or multimodal (i.e., with one or more peaks, respectively) and whether the peak is skewed from the centre; in a cumulative plot, multimodal peaks are not easily observed but the identification of the percentage of particles below a given diameter is much simpler.

Figure 1.1 Particle size representations: (A) frequency distribution (non‐symmetric); (B) cumulative undersize distribution (with most common percentiles).

However, we need to be aware that, depending on the sizing technique, the amount of each size can be weighted in different ways [23]. The more common weighted distributions are: number‐weighted distributions, resulting from counting techniques such as image analysis, where each particle is given equal weighting irrespective of its size; surface‐weighted distribution (normally surface area) where each size is square weighted; volume‐weighted distributions, common in static light‐scattering techniques, in which the contribution of each particle in the distribution relates to its volume (being equivalent to a mass distribution if the density of the particles is uniform); and intensity‐weighted distributions, where the contribution of each particle relates to the intensity of the light scattered by the particle, typical of dynamic light‐scattering‐based instruments. Number, surface and volume weightings vary as size raised to the zero, second and third powers, respectively. The case of intensity‐weighted distributions this is not so simple, and depends on the type of light‐scattering device and also on the size range [16] (for example, the intensity of the light scattered by very small particles (<50 nm) is proportional to [size]6). Figure 1.2 clearly illustrates this point by showing the results of a size distribution of equal numbers of particles with diameters of 5 nm and 50 nm. As expected, the number‐weighted distribution gives equal weighting to both types of particles, whereas the intensity‐weighted distribution corresponds to a much stronger signal for the coarser 50 nm particles (one million times higher). The volume‐weighted distribution is intermediate between the two. This example clearly shows how crucial it is, when reporting particle sizing data, to report not only the size measuring method but also the distribution base. It can then be concluded that different sizing techniques can generate different sizing results for the same sample, not only because different equivalent diameters are being measured, but also because different weighting factors are being used.

Figure 1.2 Example of number‐, volume‐ and intensity‐weighted particle size distributions for the same sample

[reproduced with permission of Malvern Panalytical].

Volume‐weighted (or mass‐weighted) particle size distributions are common for most pharmaceutical materials; however, number‐weighted representations are useful, for instance, for determining the size of primary particles in agglomerated systems or to detect impurities [3]. Although it is mathematically simple to convert from one type of weighting to another, the converted results are often erroneous [3]. In fact, additional information about particle characteristics (such as shape factors or optical properties (refractive index)) is normally required for a more reliable conversion, but in general these elements are not available in practice. Thus, whenever possible, a particle sizing technique that gives the desired weighting without transformation should be used.

1.2.3 Distribution Statistics

While a single number cannot describe the size distribution of the sample, sometimes it is tempting to report an “average size” or a central tendency of the distribution along with one or more values to describe the distribution width. A range of statistical parameters can be used for this purpose [16], as for example:

mean

: “average” size of a population;

median

: size where 50% of the population is below/above – this value is also called D

50

and is one of the most meaningful parameters for particle size distributions;

mode

: size with the highest frequency (highest peak of the distribution), very useful if there is more than one peak in the distribution (multimodal).

For symmetric distributions (also called normal or Gaussian) all these values are numerically equivalent, but for asymmetric distributions with elongated tails, most common in real samples, these parameters correspond to different values, as illustrated in Figure 1.1.

Particular care should be taken with the “mean” values as there are multiple definitions for this parameter related to the basis of the distribution (e.g., number or volume). The various mean calculations are defined in standard documents [23]. Table 1.2 summarizes the most common.

Table 1.2  Examples of mean diameters

Definition

Comment

Number‐weighted mean

(D

1,0

) (also known as arithmetic mean)

Most common in particle counting applications

Surface‐weighted mean

(D

3,2

) (also called Sauter mean)

Most relevant where specific surface area is important e.g., bioavailability, reactivity, dissolution

Volume‐weighted mean

(D

4,3

)

Most common in instruments where the result is displayed as a volume distribution, most sensitive to the presence of large particles

Intensity‐weighted mean

(also called Z‐average diameter, D

Z

, or harmonic mean (D

6,5

))

Most common in DLS for very small particles (Rayleigh scatterers)

The comparison between two or more particle size data is easier when using the cumulative distribution representations, in the same or separate graphs. Furthermore, in order to quantify the width of the size distributions, it is common to use some parameters of the cumulative curve known as percentiles (Dx where x means the percentage of sample with sizes below this value), typically D10, D50 and D90. As mentioned before, D50 (the median) is the middle value of the cumulative distribution where the total frequency of values above and below is equal; D90 describes the diameter where 90% of the distribution has a smaller particle size than this value (and 10% has a larger particle size); and D10 means that 10% of the distribution have diameters lower than this value. These percentiles, easily recognized in a cumulative curve, as previously shown in Figure 1.1, are frequently used to quantify the width (or span) of the size distribution defined as:

Span is normally defined as the distance between two points equally spaced from the median and thus other percentiles can also be used in this definition as, for example, D25 and D75 (also known as quartiles). Finally, it should be pointed out that cumulative distributions can be represented on linear and logarithmic axes for the particle size (the latter is especially suited for widely distributed data) [16,18].

1.3 Selecting a Particle Sizer

1.3.1 Classification

As discussed above, the choice of a particle sizer is not an easy task due to several reasons, one of them being the arsenal of particle‐sizing technologies and instrumentation currently available on the market, that range from the classical sieves to the more modern and sophisticated light‐scattering instruments. As a result of this large variety of methodologies, it is difficult to classify the techniques used for particle size. Nonetheless, some attempts have been made to group them [14,24,25]. One criterion is to divide the sizing techniques into imaging and non‐imaging. Imaging techniques obviously allow the direct visualization of the particles and thus can provide, besides size and size range, additional information on particle characteristics like shape, structure, degree of agglomeration and texture, which the non‐imaging techniques are unable to give. These methods include optical microscopy/image analysis as well as electron microscopy (SEM and TEM), being mandatory whenever particle shape and structure information is required. They are normally slow and labour‐intensive (especially manual microscopy) compared with the non‐imaging methods that, on the other hand, are based on the measurement of a particle property related to its size through an equivalent spherical diameter.

Another type of classification is based on the measurement being made one particle at a time, accumulating counts of particles with similar sizes, as opposed to measuring an ensemble of particles at the same time and subsequently extracting the particle size distribution using an appropriate theory (model). The former are called single particle techniques (also referred to as counting techniques, as particles are individually counted), and typically exhibit high sensitivity and resolution but narrow dynamic size ranges. In contrast, the latter techniques, named ensemble techniques, normally have low resolution and low sensitivity but a broader dynamic size range and high statistical accuracy, being better suited for on‐line and in‐line applications. A high‐resolution instrument can separate two close‐together modes, while a low‐resolution instrument can only detect one broad peak. Sensitivity in particle sizing can be viewed as a measure of the smallest amount of a given size particle that can be detected by the instrument.

Examples of counting methods are not only the microscopy‐based techniques, as image analysis, but the electrozone counters (pioneered by the Coulter company and still known as the Coulter counter technique), the optical counters (optical equivalent to electrozone counters), and the time‐of‐flight counters targeted at aerosols. In these counting techniques, particles pass individually through the sensing zone (an electrical sensing zone in the case of the Coulter counter, or a photozone in the case of an optical counter) and so very low particle concentrations have to be used in order to avoid coincident effects (i.e., multiple particles being counted together). Another common feature of these methods is that they all need prior calibration, accomplished by using uniform particles of known sizes [16,17].

As previously stated, ensemble methods rely on the measurement of a certain property of an ensemble of particles, being the raw detected signal “inverted” mathematically to estimate the particle size distribution of the entire population. For that, the results of these techniques normally depend on the mathematical algorithm used. Examples of ensemble averaging techniques are light‐scattering techniques (static and dynamic) and acoustic spectroscopy [16,17].

The ensemble methods can also involve fractionation of the samples prior to sizing, in which an outside separation force is applied to the particles, physically separating them according to size (fractionating methods). In order to provide a measurement of particle size distribution, the fractionation techniques must be combined with detection techniques, such as optical detection or light attenuation or scattering. Common fractionation techniques are sieves, gravitational sedimentation, differential centrifugal sedimentation, and various forms of particle chromatography [16,17]. Details of some of these techniques, available on the market and commonly used to characterize pharmaceutical products, can be found in Table 1.3 together with the respective size ranges and the corresponding standards.

Table 1.3  Examples of sizing techniques commonly used in the pharmaceutical industry and respective measuring principles, measured equivalent spherical diameter (ESD) and primary distribution weight, size range and related standards

Measurement technique

Method/physical principle

Technique layout

ESD/primary distribution weight

Size range (μm)

Standards

Light microscopy/ image analysis

The basic equipment consists of a microscope, a camera and a computer. The image of a dispersed sample is evaluated to assess the shape and size parameters of each particle. This process can be manual or automated.

Projected area diameter/ number weighted

Static: 1–5000 Dynamic: 30–30000

ISO 13322‐1:2014 [

26

] ISO 13322‐2:2006 [

27

]

Electrical zone sensing (Coulter counter)

Particles homogeneously suspended in an electrolyte solution are forced to flow through a small aperture that separates two electrodes of opposite potential. When a particle passes through the aperture, the resistance of the aperture increases, giving a voltage pulse proportional to the particle volume.

Volume diameter/ number and volume weighted

0.5–1500

ISO 13319:2007 [

28

]

Photo zone sensing (single particle optical sensing: SPOS)

Particles in a liquid suspension are forced between a light source and a detector, producing a shadow or blockage of light on the detector (light obscuration) related to the optical cross‐section of the particle.

[Reproduced with permission of PSS – Particle Sizing Systems]

Optical diameter/ number weighted

0.5–5000

ISO 13099‐2:2012 [

29

]

Time‐of‐flight

An air stream containing the particles is drawn through a fine nozzle into a partial vacuum producing a supersonic flow of air, causing particles to accelerate according to size.

Adapted from [

50

]

Aerodynamic diameter/ number weighted

0.5–20

Laser diffraction(LD)/ Fraunhofer diffraction (FD)/ low angle laser light scattering (LALLS )

A laser beam is passed through a sample of particles and the scattered light intensity is collected at low angles. The light scattering data is converted into a particle size distribution using Fraunhofer theory.

Fraunhofer diameter/ volume weighted

0.020–2000

ISO 13320:2009 [

30

] USP 34 NF 29 [

31

]

Dynamic light scattering (DLS)

The fluctuations of the light scattered by a suspension of submicron particles, due to Brownian motion, are collected over time at a given angle. From the autocorrelation function a diffusion coefficient and an average size is calculated.

Hydrodynamic diameter/light intensity weighted

0.003–3

ISO 22412:2017 [

32

]

Sedimentation (gravitational and centrifugal)

A sample of particles is uniformly suspended in a fluid and allowed to settle due to gravity according to Stokes' law. Centrifugal sedimentation extends the range of analysis to much smaller particles.

Stokes diameter

Gravitational: 1–250 Centrifugal: 0.01–100

ISO 13317‐1:2001 [

33

] ISO 13318‐1:2001 [

34

]

Cascade impactor