Skip to main content

Advertisement

SpringerLink
Log in

Employing Multivariate Statistics and Latent Variable Models to Identify and Quantify Complex Relationships in Typical Compression Studies

  • Research Article
  • Published:
AAPS PharmSciTech Aims and scope Submit manuscript

A Correction to this article was published on 02 August 2020

This article has been updated

Abstract

The effect of storage condition (% RH) on flufenamic acid:nicotinamide (FFA:NIC) cocrystal compressibility, compactibility, and tabletability profiles was not observed after visual evaluation or linear regression analysis. However, multivariate statistical analysis showed that storage condition had a significant effect on each compressional profile. Shapiro and Heckel equations were used to determine the compression parameters: porosity, Shapiro’s compression parameter (f), densification factor (Da), plastic yield pressure (YPpl), and elastic yield pressure (YPel). Latent variable models such as exploratory factor analysis, principal component analysis, and principal component regression were employed to decode complex hidden main, interaction, and quadratic effects of % RH and the compression parameters on FFA:NIC tablet mechanical strength (TMS). Statistically significant correlations between f and Da, f and YPpl, and Da and YPel supported the idea that both rearrangement and fragmentation, and plastic deformation are important to FFA:NIC TMS. To the authors knowledge, this is the first time that simultaneously operating dual mechanisms of fragmentation and plastic deformation in low and midrange compression, and midrange plastic deformation have been identified and reported. A quantitative PCR model showed that f, Da, and YPel had statistically significant main effects along with a significant antagonist storage condition–porosity “conditional interaction effect”. f exhibited a 2.35 times greater impact on TMS compared to Da. The model root-mean-square error at calibration and prediction stages were 0.04 MPa and 0.08 MPa, respectively. The R2 values at the calibration stage and at the prediction stage were 0.9005 and 0.7539, respectively. This research demonstrated the need for caution when interpreting the results of bivariate compression data because complex latent inter-relationships may be hidden from visual assessment and linear regression analysis, and result in false data interpretation as illustrated in this report.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Change history

  • 02 August 2020

    During the transmission process in publishing the article online, the equal (=) sign was replaced with ���0��� in Equations 1 to 5. The original article has been corrected.

References

  1. Ragnarsson G. Force-displacement and newtwork measurement In: Alderborn G, Nystrom C, editors. Pharmaceutical Powder Compaction Technology. New York: Marcel Dekker, Inc.,; 1996. p. 77–98.

  2. Nystrom C, Karehill P. The importance of intermolecular bonding forces and the concept of bonding area. In: Alderborn G, Nystrom C, editors. Pharmaceutical Powder Compaction Technology. 71. New York: Marcel Dekker, Inc.,; 1996. p. 17–54.

  3. Paronen P, Ikka J. Porosity-pressure functions. In: Nystrom C, Alderborn G, editors. Pharmaceutical Powder Compaction Technology. 71. New York: Marcel Dekker, Inc.; 1996. p. 55–76.

  4. Alderborn G. Particle dimesnions. In: Nystrom C, Alderborn G, editors. Pharmaceutical Powder Compaction Technology. 71. New York: Marcel Dekker, Inc.; 1996. p. 245–82.

  5. Sun C, Grant DJW. Influence of crystal structure on the tableting properties of sulfamerazine polymorphs. Pharm Res. 2001;18(3):274–80.

    Article  CAS  PubMed  Google Scholar 

  6. Tye CK, Sun C, Amidon GE. Evaluation of the effects of tableting speed on the relationships between compaction pressure, tablet tensile strength, and tablet solid fraction. J Pharm Sci. 2005;94(3):465–72.

    Article  CAS  PubMed  Google Scholar 

  7. Haware RV, Kancharla JP, Udupa AK, Staton S, Gupta MR, Al-Achi A, et al. Physico-mechanical properties of coprocessed excipient MicroceLac® 100 by DM3 approach. Pharm Res. 2015;32(11):3618–35.

    Article  CAS  PubMed  Google Scholar 

  8. Gupte A, DeHart M, Stagner WC, Haware RV. Comparative binder efficiency modeling of dry granulation binders using roller compaction. Drug Dev Ind Pharm. 2017;43(4):574–83.

    Article  CAS  PubMed  Google Scholar 

  9. Haware RV, Dave VS, Kakarala B, Delaney S, Staton S, Munson E, et al. Vegetable-derived magnesium stearate functionality evaluation by DM3 approach. Eur J Pharm Sci. 2016;89:115–24.

    Article  CAS  PubMed  Google Scholar 

  10. Huang J, Kaul G, Cai C, Chatlapalli R, Hernandez-Abad P, Ghosh K, et al. Quality by design case study: an integrated multivariate approach to drug product and process development. Int J Pharm. 2009;382(1):23–32.

    Article  CAS  PubMed  Google Scholar 

  11. 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.

    Article  CAS  PubMed  Google Scholar 

  12. 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.

    Article  Google Scholar 

  13. Joshi TV, Singaraju AB, Shah HS, Morris KR, Stevens LL, Haware RV. Structure–mechanics and compressibility profile study of flufenamic acid:nicotinamide cocrystal. Cryst Growth Des. 2018;18(10):5853–65.

    Article  CAS  Google Scholar 

  14. Newton JM, Rowley G, Fell JT, Peacock DG, Ridgway K. Computer analysis of the relation between tablet strength and compaction pressure. J Pharm Pharmacol. 1971;23(S1):195S–201S.

    Article  CAS  PubMed  Google Scholar 

  15. Sonnergaard JM. A critical evaluation of the Heckel equation. Int J Pharm. 1999;193(1):63–71.

    Article  CAS  PubMed  Google Scholar 

  16. Shapiro I. Compaction of powders 11. Application of the general equation to both metal powders and ceramic powders. Adv Powder Metall Particul Mater. 1994;3:41–5.

    Google Scholar 

  17. Klevan I, Nordström J, Bauer-Brandl A, Alderborn G. On the physical interpretation of the initial bending of a Shapiro–Konopicky–Heckel compression profile. Eur J Pharm Biopharm. 2009;71(2):395–401.

    Article  CAS  PubMed  Google Scholar 

  18. Heckel RW. Density–pressure relations in powder compaction. Trans Met Soc AIME. 1961:671–5.

  19. 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.

    Article  CAS  PubMed  Google Scholar 

  20. Khomane KS, More PK, Raghavendra G, Bansal AK. Molecular understanding of the compaction behavior of indomethacin polymorphs. Mol Pharm. 2013;10(2):631–9.

    Article  CAS  PubMed  Google Scholar 

  21. Appendices U. Unscrambler appendices: method references, CAMO software training version, 2012 Oslo, Norway, PP 7,8,16, 547. 2012.

  22. Hill T, Lewicki P, Lewicki P. Statistics: methods and applications : a comprehensive reference for science, industry, and data mining: StatSoft; 2006.

  23. Unscrambler®10.3 CT. Camo, the Unscrambler® 10.3, Version 1.0, Page no. 416–419, 451–453, 606, 651.

  24. Esbensen K, Schoenkopf S, Midtgaard T, Guyot D. Multivariate analysis in practice Trondheim: Camo ASA; 1994.

  25. Martens H, Martens M. Modified Jack-knife estimation of parameter uncertainty in bilinear modelling by partial least squares regression (PLSR). Food Qual Prefer. 2000;11(1–2):5–16.

    Article  Google Scholar 

  26. Martens H, Naes T. Multivariate calibration. Chichester: Wiley and Sons; 1989.

    Google Scholar 

  27. Martens H, Martens M. Modified jack-knife estimation of parameter uncertainty in bilinear modeling by partial least squares regression (PLSR). Food Qual Prefer. 2000;11:5–16.

    Article  Google Scholar 

  28. Shah IG, Ely KJ, Stagner WC. Effect of tapped density, compacted density, and drug concentration on light-induced fluorescence response as a process analytical tool. J Pharm Sci. 2015;104(5):1732–40.

    Article  CAS  PubMed  Google Scholar 

  29. 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.

    Article  CAS  PubMed  Google Scholar 

  30. Sebhatu T, Ahlneck C, Alderborn G. The effect of moisture content on the compression and bond-formation properties of amorphous lactose particles. Int J Pharm. 1997;146(1):101–14.

    Article  CAS  Google Scholar 

  31. Nokhodchi A. Effect of moisture on compaction and compression. Pharm Tech. 2005;6:46–66.

    Google Scholar 

  32. Nokhodchi ALI, Ford JL, Rowe PH, Rubinstein MH. The effect of moisture on the Heckel and energy analysis of hydroxypropylmethylcellulose 2208 (HPMC K4M). J Pharm Pharmacol. 1996;48(11):1122–7.

    Article  CAS  PubMed  Google Scholar 

  33. Haware RV, Tho I, Bauer-Brandl A. Application of multivariate methods to compression behavior evaluation of directly compressible materials. Eur J Pharm Biopharm. 2009;72(1):148–55.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

Authors are grateful to Dr. David Eagerton, Department Chair of Pharmaceutical Sciences, College of Pharmacy & Health Sciences to use PERC facility for this research.

Author information

Author notes

  1. William C. Stagner and Abhay Jain contributed equally to this work.

Authors and Affiliations

  1. Campbell University College of Pharmacy & Health Sciences, Buies Creek, North Carolina, 27506, USA

    William C. Stagner, Abhay Jain, Antoine Al-Achi & Rahul V. Haware

  2. Division of Pharmaceutics Sciences, Arnold & Marie Schwartz College of Pharmacy and Health Sciences, The Long Island University, Brooklyn, New York, 11201-5497, USA

    Rahul V. Haware

Authors
  1. William C. Stagner
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abhay Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Antoine Al-Achi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rahul V. Haware
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

William C. Stagner: conceptualization, formal analysis, writing—original draft, writing—review and editing; Abhay Jain: investigation and review; Antoine Al-Achi: methodology, formal analysis, writing—review and editing, visualization; Rahul V. Haware: conceptualization, methodology, validation, formal analysis, writing—review and editing, visualization, supervision, project administration.

Corresponding author

Correspondence to Rahul V. Haware.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The original version of this article was revised: During the transmission process in publishing the article online, the equal (=) sign was replaced with “0” in Equations 1 to 5.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Stagner, W.C., Jain, A., Al-Achi, A. et al. Employing Multivariate Statistics and Latent Variable Models to Identify and Quantify Complex Relationships in Typical Compression Studies. AAPS PharmSciTech 21, 186 (2020). https://doi.org/10.1208/s12249-020-01712-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1208/s12249-020-01712-1

KEY WORDS

Search

Navigation