Quantitative Microscopy: Particle Size/Shape Characterization, Addressing Common Errors Using 'Analytics Continuum' Approach.

Particle size/shape characterization of active pharmaceutical ingredient (API) is integral to successful product development. It is more of a correlative property than a decision-making measure. Though microscopy is the only technique that provides a direct measure of particle properties, it is neglected for reasons like non-repeatability and non-reproducibility which is often attributed to a) fundamental error, b) segregation error, c) human error, d) sample randomness, f) sample representativeness etc. Using the "Sucrose" as model sample, we propose "analytics continuum" approach that integrates optical microscope PSD measurements complimented by NIR spectroscopy-based trending analysis as a prescreening tool to demonstrate sample randomness and representativeness. Furthermore, plethora of statistical tests are utilized to infer population statistics. Subsequently, an attribute-based control chart and bootstrap-based confidence interval was developed to monitor product performance. A flowchart to serve as an elementary guideline is developed, which is then extended to handle more complex situations involving API crystallized from two different solvent systems. The results show that the developed methodology can be utilized as a quantitative procedure to assesses the suitability of API/excipients from different batches or from alternate vendors and can significantly help in understanding the differences between material even on a minor scale.

[1]  M. O'Neill,et al.  ON LEVENE'S TESTS OF VARIANCE HOMOGENEITY , 1986 .

[2]  G. Glass Testing Homogeneity of Variances , 1966 .

[3]  N. Dawson,et al.  A proposal for an alternative approach to particle size method development during early stage small molecule pharmaceutical development. , 2019, Journal of pharmaceutical sciences.

[4]  Sekhar R Kanapuram,et al.  Effect of Particle Formation Process on Characteristics and Aerosol Performance of Respirable Protein Powders. , 2019, Molecular pharmaceutics.

[5]  M. C. Bonferoni,et al.  Characteristics of hydroxypropyl methylcellulose influencing compactibility and prediction of particle and tablet properties by infrared spectroscopy. , 2003, Journal of pharmaceutical sciences.

[6]  F. Menegalli,et al.  Image analysis: Statistical study of particle size distribution and shape characterization , 2011 .

[7]  Fernando Rocha,et al.  Using an Online Image Analysis Technique to Characterize Sucrose Crystal Morphology during a Crystallization Run , 2011 .

[8]  P. Luner,et al.  Assessment of crystallinity in processed sucrose by near-infrared spectroscopy and application to lyophiles. , 2014, Journal of pharmaceutical sciences.

[9]  H. Merkus Errors in particle size and concentration analysis of pharmaceuticals , 2019, Pharmaceutical development and technology.

[10]  Y. Pourcelot,et al.  Particle-size distribution of a powder : comparison of three analytical techniques , 1996 .

[11]  Caroline Bouvet de la Maisonneuve,et al.  Furthering the investigation of eruption styles through quantitative shape analyses of volcanic ash particles , 2017 .

[12]  Ivan Marziano,et al.  Integration of active pharmaceutical ingredient solid form selection and particle engineering into drug product design , 2015, The Journal of pharmacy and pharmacology.

[13]  Estimation of uncertainty of percentile values in particle size distribution analysis as a function of number of particles , 2019, Advanced Powder Technology.

[14]  W. Loh,et al.  A comparison of tests of equality of variances , 1996 .

[15]  A. Ghasemi,et al.  Normality Tests for Statistical Analysis: A Guide for Non-Statisticians , 2012, International journal of endocrinology and metabolism.

[16]  Tatsushi Matsuyama An application of bootstrap method for analysis of particle size distribution , 2018, Advanced Powder Technology.

[17]  M. Šimek,et al.  Comparison of Compression and Material Properties of Differently Shaped and Sized Paracetamols , 2017 .

[18]  M. Mazumder,et al.  Electrostatic Effects on Dispersion, Transport, and Deposition of Fine Pharmaceutical Powders: Development of an Experimental Method for Quantitative Analysis , 2002 .

[19]  E. Flöter,et al.  Comparative analysis of dextran-induced sucrose crystal modifications , 2019, Journal of Crystal Growth.

[20]  Prakash Muthudoss,et al.  Micronization and Agglomeration: Understanding the Impact of API Particle Properties on Dissolution and Permeability Using Solid State and Biopharmaceutical “Toolbox” , 2020, Journal of Pharmaceutical Innovation.

[21]  R. Chavan,et al.  Near infra red spectroscopy: a tool for solid state characterization. , 2017, Drug discovery today.

[22]  Theodora Kourti,et al.  Application of latent variable methods to process control and multivariate statistical process control in industry , 2005 .

[23]  A. Davies,et al.  Tentative Assignment of the 1440-nm Absorption Band in the Near-Infrared Spectrum of Crystalline Sucrose , 1988 .

[24]  Manel Bautista,et al.  On-line monitoring of a granulation process by NIR spectroscopy. , 2010, Journal of pharmaceutical sciences.

[25]  Mahmut Camalan The use of non-parametric tests between subsamples and particle population for the assessment of minimum number of particles in microscopic analysis , 2020, Particulate Science and Technology.

[26]  Michael Leane,et al.  A proposal for a drug product Manufacturing Classification System (MCS) for oral solid dosage forms , 2015, Pharmaceutical development and technology.

[27]  N. Kubota,et al.  Effect of Impurities on the Growth Kinetics of Crystals , 2001 .

[28]  D. M. Gottlieb,et al.  Multivariate approaches in plant science. , 2004, Phytochemistry.

[29]  Studies Relating to the Content Uniformity Cif Ethinylo-Estradiol Tablets 10 Ug: Effect of Particle Size of Ethinyloestradiol , 1986 .

[30]  E. Berrezueta,et al.  Representativity of 2D Shape Parameters for Mineral Particles in Quantitative Petrography , 2019, Minerals.

[31]  A. Huvet,et al.  A semi-automated Raman micro-spectroscopy method for morphological and chemical characterizations of microplastic litter. , 2016 .

[32]  S. Pawlowski,et al.  Statistical methodology for scale-up of an anti-solvent crystallization process in the pharmaceutical industry , 2019, Separation and Purification Technology.

[33]  R. Romañach,et al.  Near-infrared spectroscopic applications in pharmaceutical particle technology , 2019, Drug development and industrial pharmacy.

[35]  Josette Bettany-Saltikov,et al.  Selecting the most appropriate inferential statistical test for your quantitative research study. , 2014, Journal of clinical nursing.

[36]  S. Azevedo,et al.  Quantification of the morphology of sucrose crystals by image analysis , 2003 .

[37]  Douglas C. Montgomery,et al.  Percentile‐based control chart design with an application to Shewhart X̅ and S2 control charts , 2018, Qual. Reliab. Eng. Int..

[38]  V. Deniz,et al.  Application of Statistical Process Control for Coal Particle Size , 2013 .

[39]  Dominique Bertrand,et al.  Study of NIR Spectra, Particle Size Distributions and Chemical Parameters of Wheat Flours: A Multi-Way Approach , 2001 .

[40]  Hans Hagen,et al.  Methods for Presenting Statistical Information: The Box Plot , 2006, VLUDS.

[41]  Brian Scarlett,et al.  PQRI recommendations on particle-size analysis of drug substances used in oral dosage forms. , 2007, Journal of pharmaceutical sciences.

[42]  Michael Wood,et al.  Statistical inference using bootstrap confidence intervals , 2004 .

[43]  Vladimir Nikulin,et al.  Prediction of the Shoppers Loyalty with Aggregated Data Streams , 2016, J. Artif. Intell. Soft Comput. Res..

[44]  Laden Husamaldin,et al.  Big Data Analytics Correlation Taxonomy , 2019, Inf..

[45]  B. Shekunov,et al.  Particle Size Analysis in Pharmaceutics: Principles, Methods and Applications , 2007, Pharmaceutical Research.