Enhanced Understanding of Pharmaceutical Materials Through Advanced Characterisation and Analysis


The impact of pharmaceutical materials properties on drug product quality and manufacturability is well recognised by the industry. An ongoing effort across industry and academia, the Manufacturing Classification System consortium, aims to gather the existing body of knowledge in a common framework to provide guidance on selection of appropriate manufacturing technologies for a given drug and/or guide optimization of the physical properties of the drug to facilitate manufacturing requirements for a given processing route. Simultaneously, material scientists endeavour to develop characterisation methods such as size, shape, surface area, density, flow and compactibility that enable a stronger understanding of materials powder properties. These properties are routinely tested drug product development and advances in instrumentation and computing power have enabled novel characterisation methods which generate larger, more complex data sets leading to a better understanding of the materials. These methods have specific requirements in terms of data management and analysis. An appropriate data management strategy eliminates time-consuming data collation steps and enables access to data collected for multiple methods and materials simultaneously. Methods ideally suited to extract information from large, complex data sets such as multivariate projection methods allow simpler representation of the variability contained within the data and easier interpretation of the key information it contains. In this review, an overview of the current knowledge and challenges introduced by modern pharmaceutical material characterisation methods is provided. Two case studies illustrate how the incorporation of multivariate analysis into the material sciences workflow facilitates a better understanding of materials.

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  1. 1.

    Sacks LV, Shamsuddin HH, Yasinskaya YI, Bouri K, Lanthier ML, Sherman RE. Scientific and regulatory reasons for delay and denial of FDA approval of initial applications for new drugs, 2000-2012. JAMA. 2014;311(4):378–84.

    CAS  PubMed  Google Scholar 

  2. 2.

    Leane M, Pitt K, Reynolds G, Anwar J, Charlton S, Crean A, et al. A proposal for a drug product manufacturing classification system (MCS) for oral solid dosage forms. Pharm Dev Technol. 2015;20(1):12–21.

    CAS  PubMed  Google Scholar 

  3. 3.

    Leane M, Pitt K, Reynolds GK, Dawson N, Ziegler I, Szepes A, et al. Manufacturing Classification System in the real world: factors influencing manufacturing process choices for filed commercial oral solid dosage formulations, case studies from industry and considerations for continuous processing. Pharm Dev Technol. 2018:1–59.

  4. 4.

    Docherty R., Back K. (2017) Design of Physical Properties and Solid Form Design. In: Roberts K., Docherty R., Tamura R. (eds) Engineering Crystallography: From Molecule to Crystal to Functional Form. NATO Science for Peace and Security Series A: Chemistry and Biology. Springer, Dordrecht 

  5. 5.

    Docherty R., O’Connor G., Penchev R.Y., Pickering J., Ramachandran V. (2017) From Molecules to Crystals to Functional Form: Science of Scale. In: Roberts K., Docherty R., Tamura R. (eds) Engineering Crystallography: From Molecule to Crystal to Functional Form. NATO Science for Peace and Security Series A: Chemistry and Biology. Springer, Dordrecht

    Google Scholar 

  6. 6.

    Feeder N, Pidcock E, Reilly AM, Sadiq G, Doherty CL, Back KR, et al. The integration of solid-form informatics into solid-form selection. J Pharm Pharmacol. 2015;67(6):857–68.

    CAS  PubMed  Google Scholar 

  7. 7.

    Docherty R, Kougoulos T, Horspool K. Materials science and crystallization: the interface of drug substance and drug product. Am Pharmaceut Rev. 2009;12(6):34–43.

  8. 8.

    Heng JYY, Bismarck A, Lee AF, Wilson K, Williams DR. Anisotropic Surface Energetics and Wettability of Macroscopic Form I Paracetamol Crystals. Langmuir. 2006;22(6):2760–9.

    CAS  PubMed  Google Scholar 

  9. 9.

    Spingler B, Schnidrig S, Todorova T, Wild F. Some thoughts about the single crystal growth of small molecules. CrystEngComm. 2012;14(3):751–7.

    CAS  Google Scholar 

  10. 10.

    Jones HP, Davey RJ, Cox BG. Crystallization of a salt of a weak organic acid and base: solubility relations, supersaturation control and polymorphic behavior. J Phys Chem B. 2005;109(11):5273–8.

    CAS  PubMed  Google Scholar 

  11. 11.

    Goh HP, Heng PWS, Liew CV. Comparative evaluation of powder flow parameters with reference to particle size and shape. Int J Pharm. 2018;547(1–2):133–41.

    CAS  PubMed  Google Scholar 

  12. 12.

    MacLeod CS, Muller FL. On the fracture of pharmaceutical needle-shaped crystals during pressure filtration: case studies and mechanistic understanding. Org Process Res Dev. 2012;16(3):425–34.

    CAS  Google Scholar 

  13. 13.

    Gamble JF, Leane M, Olusanmi D, Tobyn M, Šupuk E, Khoo J, et al. Surface energy analysis as a tool to probe the surface energy characteristics of micronized materials—a comparison with inverse gas chromatography. Int J Pharm. 2012;422(1–2):238–44.

    CAS  PubMed  Google Scholar 

  14. 14.

    Olusanmi D, Jayawickrama D, Bu D, McGeorge G, Sailes H, Kelleher J, et al. A control strategy for bioavailability enhancement by size reduction: effect of micronization conditions on the bulk, surface and blending characteristics of an active pharmaceutical ingredient. Powder Technol. 2014;258:222–33.

    CAS  Google Scholar 

  15. 15.

    Olusanmi D, Roberts KJ, Ghadiri M, Ding Y. The breakage behaviour of aspirin under quasi-static indentation and single particle impact loading: effect of crystallographic anisotropy. Int J Pharm. 2011;411(1–2):49–63.

    CAS  PubMed  Google Scholar 

  16. 16.

    Olusanmi D, Wang C, Ghadiri M, Ding Y, Roberts KJ. Effect of temperature and humidity on the breakage behaviour of aspirin and sucrose particles. Powder Technol. 2010;201(3):248–52.

    CAS  Google Scholar 

  17. 17.

    Gamble JF, Terada M, Holzner C, Lavery L, Nicholson SJ, Timmins P, et al. Application of X-ray microtomography for the characterisation of hollow polymer-stabilised spray dried amorphous dispersion particles. Int J Pharm. 2016;510(1):1–8.

    CAS  PubMed  Google Scholar 

  18. 18.

    Shekunov BY, Chattopadhyay P, Tong HHY, Chow AHL. Particle size analysis in pharmaceutics: principles, methods and applications. Pharm Res. 2007;24(2):203–27.

    CAS  PubMed  Google Scholar 

  19. 19.

    Gamble JF, Tobyn M, Hamey R. Application of image-based particle size and shape characterization systems in the development of small molecule pharmaceuticals. J Pharm Sci. 2015;104(5):1563–74.

    CAS  PubMed  Google Scholar 

  20. 20.

    Gamble JF, Chiu WS, Tobyn M. Investigation into the impact of sub-populations of agglomerates on the particle size distribution and flow properties of conventional microcrystalline cellulose grades. Pharm Dev Technol. 2011;16(5):542–8.

    CAS  PubMed  Google Scholar 

  21. 21.

    Califice A, Michel F, Dislaire G, Pirard E. Influence of particle shape on size distribution measurements by 3D and 2D image analyses and laser diffraction. Powder Technol. 2013;237:67–75.

    CAS  Google Scholar 

  22. 22.

    Li RF, Penchev R, Ramachandran V, Roberts KJ, Wang XZ, Tweedie RJ, et al. Particle shape characterisation via image analysis: from laboratory studies to in-process measurements using an in situ particle viewer system. Org Process Res Dev. 2008;12(5):837–49.

    CAS  Google Scholar 

  23. 23.

    Borchert C, Temmel E, Eisenschmidt H, Lorenz H, Seidel-Morgenstern A, Sundmacher K. Image-based in situ identification of face specific crystal growth rates from crystal populations. Crystal Growth and Design. 2014;14(3):952–71.

    CAS  Google Scholar 

  24. 24.

    Shah UV, Olusanmi D, Narang AS, Hussain MA, Gamble JF, Tobyn MJ, et al. Effect of crystal habits on the surface energy and cohesion of crystalline powders. Int J Pharm. 2014;472(1–2):140–7.

    CAS  PubMed  Google Scholar 

  25. 25.

    Hamilton P, Littlejohn D, Nordon A, Sefcik J, Slavin P, Andrews J, et al. Investigation of factors affecting isolation of needle-shaped particles in a vacuum-agitated filter drier through non-invasive measurements by Raman spectrometry. Chem Eng Sci. 2013;101:878–85.

    CAS  Google Scholar 

  26. 26.

    Hare CL, Ghadiri M, Dennehy R, Collier A. Particle Breakage in Agitated Dryers, AIP Conference Proceedings 1145, 851 (2009)

  27. 27.

    Lekhal A, Girard KP, Brown MA, Kiang S, Khinast JG, Glasser BJ. The effect of agitated drying on the morphology of L-threonine (needle-like) crystals. Int J Pharm. 2004;270(1–2):263–77.

    CAS  PubMed  Google Scholar 

  28. 28.

    Kougoulos E, Chadwick CE, Ticehurst MD. Impact of agitated drying on the powder properties of an active pharmaceutical ingredient. Powder Technol. 2011;210(3):308–14.

    CAS  Google Scholar 

  29. 29.

    Kom PK, Cook W, Kougoulos E. Impact of laboratory vacuum contact drying on material drying rates and physical properties. Org Process Res Dev. 2011;15(2):360–6.

    CAS  Google Scholar 

  30. 30.

    Gamble JF, Dennis AB, Hutchins P, Jones JW, Musembi P, Tobyn M. Determination of process variables affecting drug particle attrition withinmulti-component blends during powder feed transmission. Pharm Dev Technol. 2017;22(7):904–9.

    CAS  PubMed  Google Scholar 

  31. 31.

    Gamble JF, Hoffmann M, Hughes H, Hutchins P, Tobyn M. Monitoring process induced attrition of drug substance particles within formulated blends. Int J Pharm. 2014;470(1–2):77–87.

    CAS  PubMed  Google Scholar 

  32. 32.

    Hoffmann M, Wray PS, Gamble JF, Tobyn M. Investigation into process-induced de-aggregation of cohesive micronised API particles. Int J Pharm. 2015;493(1–2):341–6.

    CAS  PubMed  Google Scholar 

  33. 33.

    Huck D. Image analysis coupled with classification—a powerful combination for the study of agglomeration. Powder Handl Process. 2007;19(1):42–4.

  34. 34.

    Gamble J, Jones J, Tobyn M. Understanding the effect of API changes in pharmaceutical processing. Eur Pharm Rev. 2017;22(1):20–2.

  35. 35.

    Dobry DE, Settell DM, Baumann JM, Ray RJ, Graham LJ, Beyerinck RA. A model-based methodology for spray-drying process development. J Pharm Innov. 2009;4(3):133–42.

    PubMed  PubMed Central  Google Scholar 

  36. 36.

    Tobyn M, Brown J, Dennis AB, Fakes M, Gao Q, Gamble J, et al. Amorphous drug-PVP dispersions: application of theoretical, thermal and spectroscopic analytical techniques to the study of a molecule with intermolecular bonds in both the crystalline and pure amorphous state. J Pharm Sci. 2009;98(9):3456–68.

    CAS  PubMed  Google Scholar 

  37. 37.

    Gamble JF, Ferreira AP, Tobyn M, DiMemmo L, Martin K, Mathias N, et al. Application of imaging based tools for the characterisation of hollow spray dried amorphous dispersion particles. Int J Pharm. 2014;465(1–2):210–7.

    CAS  PubMed  Google Scholar 

  38. 38.

    Wong J, D’Sa D, Foley M, Chan J, Chan H-K. NanoXCT: a novel technique to probe the internal architecture of pharmaceutical particles. Pharm Res. 2014;31(11):3085–94.

    CAS  PubMed  Google Scholar 

  39. 39.

    Wang XZ, Calderon De Anda J, Roberts KJ. Real-time measurement of the growth rates of individual crystal facets using imaging and image analysis: a feasibility study on needle-shaped crystals of L-glutamic acid. Chem Eng Res Des. 2007;85(7 A):921–7.

    CAS  Google Scholar 

  40. 40.

    Wray PS, Sinclair WE, Jones JW, Clarke GS, Both D. The use of in situ near infrared imaging and Raman mapping to study the disproportionation of a drug HCl salt during dissolution. Int J Pharm. 2015;493(1–2):198–207.

    CAS  PubMed  Google Scholar 

  41. 41.

    Polizzi MA, García-Muñoz S. A framework for in-silico formulation design using multivariate latent variable regression methods. Int J Pharm. 2011;418(2):235–42.

    CAS  PubMed  Google Scholar 

  42. 42.

    Mullarney MP, Leyva N. Modeling pharmaceutical powder-flow performance using particle-size distribution data. Pharm Technol. 2009;33(3):126–34.

    CAS  Google Scholar 

  43. 43.

    Ferreira AP, Tobyn M. Multivariate analysis in the pharmaceutical industry: enabling process understanding and improvement in the PAT and QbD era. Pharm Dev Technol. 2015;20(5):513–27.

    CAS  PubMed  Google Scholar 

  44. 44.

    Banerjee S, Wasser D. Does the data lake have to be validated? Paper presented at: 2018 PDA Manufacturing Intelligence Workshop; 2018 Mar 21–22; Orlando, Fl.

  45. 45.

    Brereton RG. Chemometrics: Data analysis for the laboratory and chemical plant. Chichester: John Wiley & Sons Ltd; 2003. p. 489.

    Google Scholar 

  46. 46.

    Næs T, Isaksson T, Fearn T, Davies T. A user-friendly guide to multivariate calibration and classification 2nd ed. Chichester: NIR Publications; 2017. 344 p

    Google Scholar 

  47. 47.

    Otto M. Chemometrics: statistics and computer application in analytical chemistry. 3rd ed. Wiley-VCH: Weinheim; 2016.

    Google Scholar 

  48. 48.

    Geladi P, Grahn H. Chapter 2—the philosophy and fundamentals of handling, modeling, and interpreting large data sets—the multivariate chemometrics approach In: Ferreira AP, Menezes JC, Tobyn M, editors. Multivariate analysis in the pharmaceutical industry. London: Academic Press; 2018. p. 13–34.

    Google Scholar 

  49. 49.

    Geladi P, Kowalski BR. Partial least-squares regression: a tutorial. Anal Chim Acta. 1986;185:1–17.

    CAS  Google Scholar 

  50. 50.

    Esbensen KH, Geladi P. 2.13 - principal component analysis: concept, geometrical interpretation, mathematical background, algorithms, history, practice. In: Brown SD, Tauler R, Walczak B, editors. Comprehensive Chemometrics. Oxford: Elsevier; 2009. p. 211–26.

    Google Scholar 

  51. 51.

    U.S. Food and Drug Administration - Center for Drug Evaluation and Research, Center for Veterinary Medicine and Office of Regulatory Affairs. Guidance for Industry PAT -A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance. Available from: https://www.fda.gov/downloads/drugs/guidances/ucm070305.pdf. Accessed 23 October 2018.

  52. 52.

    ICH. Q8(R2)—pharmaceutical development. International conference on harmonisation of technical requirements for registration of Pharmaceuticals for Human use; 2009.

  53. 53.

    ICH. Q10—pharmaceutical quality system. International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use; 2008, 2008.

  54. 54.

    Ferreira AP, Menezes JC, Tobyn M, editors. Multivariate analysis in the pharmaceutical industry. 1st ed. London: Academic Press; 2018.

    Google Scholar 

  55. 55.

    Tobyn M, Ferreira AP, Morris C, Menezes JC. Chapter 1 - The preeminence of multivariate data analysis as a statistical data analysis technique in pharmaceutical R&D and Manufacturing. In: Ferreira AP, Menezes JC, Tobyn M, editors. Multivariate analysis in the pharmaceutical industry. London: Academic Press; 2018. p. 3–12.

  56. 56.

    Esbensen KH, Swarbrick B. Multivariate date analysis—an introduction to multivariate data analysis, Process analytical technology and quality by design. 6th ed. Oslo: CAMO Software AS; 2018.

    Google Scholar 

  57. 57.

    Wold S, Sjöström M, Eriksson L. PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst. 2001;58(2):109–30.

    CAS  Google Scholar 

  58. 58.

    Workman J Jr. The state of multivariate thinking for scientists in industry: 1980-2000. Chemom Intell Lab Syst. 2002;60(1–2):13–23.

    CAS  Google Scholar 

  59. 59.

    Doherty SJ, Lange AJ. Avoiding pitfalls with chemometrics and PAT in the pharmaceutical and biotech industries. TrAC Trends Anal Chem. 2006;25(11):1097–102.

    CAS  Google Scholar 

  60. 60.

    Kjeldahl K, Bro R. Some common misunderstandings in chemometrics. J Chemom. 2010;24(7–8):558–64.

    CAS  Google Scholar 

  61. 61.

    Badertscher M, Pretsch E. Bad results from good data. TrAC—Trends in Analytical Chemistry. 2006;25(11):1131–8.

    CAS  Google Scholar 

  62. 62.

    Esbensen KH, Geladi P. Principles of proper validation: use and abuse of re-sampling for validation. J Chemom. 2010;24(3–4):168–87.

    CAS  Google Scholar 

  63. 63.

    Westad F, Marini F. Validation of chemometric models—a tutorial. Anal Chim Acta. 2015;893:14–24.

    CAS  PubMed  Google Scholar 

  64. 64.

    Jørgensen AC, Miroshnyk I, Karjalainen M, Jouppila K, Siiriä S, Antikainen O, et al. Multivariate data analysis as a fast tool in evaluation of solid state phenomena. J Pharm Sci. 2006;95(4):906–16.

    PubMed  Google Scholar 

  65. 65.

    Stauffer F, Vanhoorne V, Pilcer G, Chavez PF, Rome S, Schubert MA, et al. Raw material variability of an active pharmaceutical ingredient and its relevance for processability in secondary continuous pharmaceutical manufacturing. Eur J Pharm Biopharm. 2018;127:92–103.

    CAS  PubMed  Google Scholar 

  66. 66.

    Sandler N, Wilson D. Prediction of granule packing and flow behavior based on particle size and shape analysis. J Pharm Sci. 2010;99(2):958–68.

    CAS  PubMed  Google Scholar 

  67. 67.

    Yu W, Muteki K, Zhang L, Kim G. Prediction of bulk powder flow performance using comprehensive particle size and particle shape distributions. J Pharm Sci. 2011;100(1):284–93.

    CAS  PubMed  Google Scholar 

  68. 68.

    Roopwani R, Buckner IS. Understanding deformation mechanisms during powder compaction using principal component analysis of compression data. Int J Pharm. 2011;418(2):227–34.

    CAS  PubMed  Google Scholar 

  69. 69.

    Roopwani R, Shi Z, Buckner IS. Application of principal component analysis (PCA) to evaluating the deformation behaviors of pharmaceutical powders. J Pharm Innov. 2013;8(2):121–30.

    Google Scholar 

  70. 70.

    Faulhammer E, Fink M, Llusa M, Lawrence SM, Biserni S, Calzolari V, et al. Low-dose capsule filling of inhalation products: critical material attributes and process parameters. Int J Pharm. 2014;473(1):617–26.

    CAS  PubMed  Google Scholar 

  71. 71.

    Soh JLP, Wang F, Boersen N, Pinal R, Peck GE, Carvajal MT, et al. Utility of multivariate analysis in modeling the effects of raw material properties and operating parameters on granule and ribbon properties prepared in roller compaction. Drug Dev Ind Pharm. 2008;34(10):1022–35.

    CAS  PubMed  Google Scholar 

  72. 72.

    Boersen N, Carvajal MT, Morris KR, Peck GE, Pinal R. The influence of API concentration on the roller compaction process: modeling and prediction of the post compacted ribbon, granule and tablet properties using multivariate data analysis. Drug Dev Ind Pharm. 2015;41(9):1470–8.

    CAS  PubMed  Google Scholar 

  73. 73.

    Calvo NL, Maggio RM, Kaufman TS. Characterization of pharmaceutically relevant materials at the solid state employing chemometrics methods. J Pharm Biomed Anal. 2018;147:538–64.

    CAS  PubMed  Google Scholar 

  74. 74.

    Ferreira AP, Rawlinson-Malone CF, Gamble J, Nicholson S, Tobyn M. Chapter 10—Applications of multivariate analysis to monitor and predict pharmaceutical materials properties. Multivariate analysis in the pharmaceutical industry. London: Academic Press; 2018. p. 235–67.

    Google Scholar 

  75. 75.

    Ferreira AP, Olusanmi D, Sprockel O, Abebe A, Nikfar F, Tobyn M. Use of similarity scoring in the development of oral solid dosage forms. Int J Pharm. 2016;514(2):335–40.

    CAS  PubMed  Google Scholar 

  76. 76.

    Kushner J IV. Utilizing quantitative certificate of analysis data to assess the amount of excipient lot-to-lot variability sampled during drug product development. Pharm Dev Technol. 2013;18(2):333–42.

    CAS  PubMed  Google Scholar 

  77. 77.

    Hertrampf A, Müller H, Menezes JC, Herdling T. Advanced qualification of pharmaceutical excipient suppliers by multiple analytics and multivariate analysis combined. Int J Pharm. 2015;495(1):447–58.

    CAS  PubMed  Google Scholar 

  78. 78.

    Haware RV, Tho I, Bauer-Brandl A. Multivariate analysis of relationships between material properties, process parameters and tablet tensile strength for α-lactose monohydrates. Eur J Pharm Biopharm. 2009;73(3):424–31.

    CAS  PubMed  Google Scholar 

  79. 79.

    Haware RV, Bauer-Brandl A, Tho I. Comparative evaluation of the powder and compression properties of various grades and brands of microcrystalline cellulose by multivariate methods. Pharm Dev Technol. 2010;15(4):394–404.

    CAS  PubMed  Google Scholar 

  80. 80.

    Haware RV, Shivagari R, Johnson PR, Staton S, Stagner WC, Gupta MR. Application of multivariate methods to evaluate the functionality of bovine- and vegetable-derived magnesium stearate. J Pharm Sci. 2014;103(5):1466–77.

    CAS  PubMed  Google Scholar 

  81. 81.

    Thoorens G, Krier F, Rozet E, Carlin B, Evrard B. Understanding the impact of microcrystalline cellulose physicochemical properties on tabletability. Int J Pharm. 2015;490(1):47–54.

    CAS  PubMed  Google Scholar 

  82. 82.

    Paul S, Chang SY, Dun J, Sun WJ, Wang K, Tajarobi P, et al. Comparative analyses of flow and compaction properties of diverse mannitol and lactose grades. Int J Pharm. 2018;546(1–2):39–49.

    CAS  PubMed  Google Scholar 

  83. 83.

    Willecke N, Szepes A, Wunderlich M, Remon JP, Vervaet C, De Beer T. Identifying overarching excipient properties towards an in-depth understanding of process and product performance for continuous twin-screw wet granulation. Int J Pharm. 2017;522(1):234–47.

    CAS  PubMed  Google Scholar 

  84. 84.

    Willecke N, Szepes A, Wunderlich M, Remon JP, Vervaet C, De Beer T. A novel approach to support formulation design on twin screw wet granulation technology: understanding the impact of overarching excipient properties on drug product quality attributes. Int J Pharm. 2018;545(1):128–43.

    CAS  PubMed  Google Scholar 

  85. 85.

    García-Muñoz S, Mercado J. Optimal selection of raw materials for pharmaceutical drug product design and manufacture using mixed integer nonlinear programming and multivariate latent variable regression models. Ind Eng Chem Res. 2013;52(17):5934–42.

    Google Scholar 

  86. 86.

    Zhang Y, Xu B, Wang X, Dai S, Sun F, Ma Q, et al. Setting up multivariate specifications on critical raw material attributes to ensure consistent drug dissolution from high drug load sustained-release matrix tablet. Drug Dev Ind Pharm. 2018:1–41.

  87. 87.

    Copelli D, Cavecchi A, Merusi C, Leardi R. Multivariate evaluation of the effect of the particle size distribution of an active pharmaceutical ingredient on the performance of a pharmaceutical drug product: a real-case study. Chemom Intell Lab Syst. 2018;178:1–10.

    CAS  Google Scholar 

  88. 88.

    Stepney K, Martin E, Montague G. Multivariate analysis of API particle size distribution variation in a manufacturing environment. In: Karimi IA, Srinivasan R, editors. Computer aided chemical engineering vol. 31. Amsterdam: Elsevier; 2012 p 1140–4.

    Google Scholar 

  89. 89.

    Eriksson L, Byrne T, Johansson E, Trygg J, Vikstrom C. Multi- and megavariate data analysis: basic principles and applications. Umetrics Academy: Malmo; 2013.

    Google Scholar 

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Correspondence to Ana Patricia Ferreira.

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Guest Editors: William C. Stagner and Rahul V. Haware

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Ferreira, A.P., Gamble, J.F., Leane, M.M. et al. Enhanced Understanding of Pharmaceutical Materials Through Advanced Characterisation and Analysis. AAPS PharmSciTech 19, 3462–3480 (2018). https://doi.org/10.1208/s12249-018-1198-6

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  • pharmaceutical materials characterisation
  • manufacturing classification system
  • multivariate analysis
  • data management