This is an outdated version published on 2024-04-22. Read the most recent version.

Мultivariate analysis for rapid screening and prediction of solid-state compatibility in pharmaceutical preformulation studies-paving the road for machine learning

Authors

DOI:

https://doi.org/10.20450/mjcce.2024.2838

Keywords:

interaction, binary mixtures, principal component analysis, partial least squares-discriminant analysis, machine learning

Abstract

Multivariate analysis models were developed to evaluate the results obtained from a compatibility study designed for ibuprofen with a large group of different types of excipients, as a possible approach for rapid screening of the incompatibility between the active pharmaceutical ingredient (API) and excipients. The solid-state characterization of the binary mixtures was performed by Fourier transform infrared spectroscopy (FTIR) and differential scanning calorimetry (DSC). Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) using SIMCA® software were applied for evaluation of the experimentally obtained results. The optimal PCA model for the FTIR spectra explains 96.2 % of the variations in the dataset with good statistical indicators (R2X = 0.960, Q2 = 0.900), which was also the case for the PCA model for the DSC curves (R2X = 0.981, Q2 = 0.866). The applied PLS-DA models have shown similar behaviour to the PCA. Moreover, the main spectral variations in the FTIR spectra and the thermal events in the DSC data were attributed the highest variable importance for the projection (VIP) scores in the corresponding VIP plots, confirming the model capability for predicting ibuprofen interactions. Furthermore, the prediction power of the optimal models for FTIR and DSC experimental data was evaluated by the root mean square error of prediction (RMSEP) of 0.10 and 0.16, respectively. The obtained results demonstrated the potential of multivariate statistical analysis as a machine learning-based technique for screening and prediction of ibuprofen-excipients solid-state compatibility in the preformulation phase of the pharmaceutical development of dosage forms.

Author Biographies

Elena Cvetkovska Bogatinovska, Institute of Research & Development, Alkaloid AD, Blvd. Aleksandar Makedonski 12, 1000 Skopje

Fields of interest: Molecular spectroscopy, Pharmaceutical analysis, Multivariate analysis

Nikola Geshkovski, Institute of Pharmaceutical Technology, Faculty of Pharmacy, Ss. Cyril and Methodius University, Mother Teresa 47, 1000 Skopje

Fields of interest: Chemometrics (classification, pattern recognition, cluster analysis), Pharmaceutical technology and development, Quality by Design (QbD), Process Analytical Technology, Multivariate analysis, Statistical Design of experiments

Gjorgji Petrushevski, Quality Control Department, Alkaloid AD, Blvd. Aleksandar Makedonski 12, 1000 Skopje

Fields of interest: Spectroscopy, Solid-state chemistry, Pharmaceutical development

Viktor Stefov, Institute of Chemistry, Faculty of Natural Sciences and Mathematics, Ss. Cyril and Methodius University, Arhimedova 5, 1000 Skopje

Fields of itnerest: Molecular spectroscopy and Structural chemistry, Spectra-structural correlations of minerals, inorganic and organic compounds

References

(1) Gupta, K. R.; Pounikar, A. R.; Umekar, M. J., Drug ex-cipient compatibility testing protocols and characteriza-tion: a review. Asian J. Chem. Sci. 2019, 6 (3), 1‒22. https://doi.org/10.9734/AJOCS/2019/v6i319000

(2) Rebiere, H.; Martin, M.; Ghyselinck, C.; Bonnet, P. A.; Brenier, C., Raman chemical imaging for spectroscopic screening and direct quantification of falsified drugs. J. Pharm. Biomed. Anal. 2018, 148, 316–323.

https://doi.org/10.1016/j.jpba.2017.10.005

(3) Hossain, M. N.; Igne, B.; Anderson, C. A.; Drennen III, J. K., Influence of moisture variation on the performance of Raman spectroscopy in quantitative pharmaceutical analyses. J. Pharm. Biomed. Anal. 2019, 164, 528–535. https://doi.org/10.1016/j.jpba.2018.10.022

(4) Čapková, T.; Pekárek, T.; Hanulíková, B.; Matějka, P., Application of reverse engineering in the field of pharma-ceutical tablets using Raman mapping and chemometrics. J. Pharm. Biomed. Anal. 2022, 209, 114496. https://doi.org/10.1016/j.jpba.2021.114496

(5) Antovska, P.; Petruševski, G.; Makreski, P., Solid‒state compatibility screening of excipients suitable for devel-opment of indapamide sustained release solid‒dosage formulation. Pharm. Dev. Technol. 2013, 18 (2), 481–489. https://doi.org/10.3109/10837450.2012.717948

(6) Chadha, R.; Bhandari, S., Drug-excipient compatibility screening ‒ Role of thermoanalytical and spectroscopic techniques. J. Pharm. Biomed. Anal. 2014, 87, 82‒97. https://doi.org/10.1016/j.jpba.2013.06.016

(7) Selvaraj, C.; Chandra, I.; Singh, S. K., Artificial intelli-gence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical indus-tries. Mol. Diversity 2021, 23, 1‒21.

https://doi.org/10.1007/s11030-021-10326-z

(8) International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use. ICH Harmonised Tripartite Guideline on Pharmaceutical Development Q8 (R2), European Medi-cines Agency, London, 2009.

(9) Mansuri, N.; Patel, K.; Mehta, M.; Vyas, G.; Reddy, J. P.; Shah, T.; Steinbach, D.; Desai, D., Quality by design (QbD) approach to match tablet glossiness. Pharm. Dev. Technol. 2020, 25 (8), 1010–1017.

https://doi.org/10.1080/10837450.2020.1772291

(10) Feng, H.; Mohan, S., Application of process analytical technology for pharmaceutical coating: challenges, pitfalls and trends. AAPS PharmSciTech. 2020, 21, 1–17. https://doi.org/10.1208/s12249-020-01727-8

(11) Guidance for industry: PAT – A framework for innova-tive pharmaceutical development, manufacturing and quality assurance, U.S. Department of Health and Hu-man Services, Food and Drug Administration, USA, 2004.

(12) Lou, H.; Lian, B.; Hageman, M. J., Applications of ma-chine learning in solid oral dosage form development. J. Pharm. Sci. 2021, 110 (9), 3150‒3165.

https://doi.org/10.1016/j.xphs.2021.04.013

(13) Patel, S.; Patel, M.; Kulkarni, M.; Patel, M. S., DE-INTERACT: A machine-learning-based predictive tool for the drug-excipient interaction study during product development – Validation through paracetamol and vanil-lin as a case study. Int. J. Pharm. 2023, 637, 122839. https://doi.org/10.1016/j.ijpharm.2023.122839

(14) Wesolowski, M.; Rojek, B., Thermogravimetric detection of incompatibilities between atenolol and excipients using multivariate techniques. J. Therm. Anal. Calorim. 2013, 113, 169–177.

https://doi.org/10.1007/s10973-013-3070-y

(15) Rojek, B.; Wesolowski, M., A combined differential scanning calorimetry and thermogravimetry approach for the effective assessment of drug substance-excipient compatibility. J. Therm. Anal. Calorim. 2023, 148 (3), 845–858. https://doi.org/10.1007/s10973-022-11849-9

(16) Siozou, E.; Sakkas, V.; Kourkoumelis, N., Quantification and classification of diclofenac sodium content in dis-persed commercially available tablets by attenuated total reflection infrared spectroscopy and multivariate data analysis. Pharmaceuticals 2021, 14 (5), 440.

https://doi.org/10.3390/ph14050440

(17) Stojanovska Pecova, M.; Geskovski, N.; Petrushevski, G.; Chachorovska, M.; Krsteska, L.; Ugarkovic, S.; Makreski, P., Solid‒state interaction of ibuprofen with magnesium stearate and product characterization thereof. Drug Dev. Ind. Pharm. 2020, 46 (8), 1308–1317. https://doi.org/10.1080/03639045.2020.1788067

(18) Talwar, S.; Pawar, P.; Wu, H.; Sowrirajan, K.; Wu, S.; Igne, B.; Friedman, R.; Muzzio, F. J.; Drennen III, J. K., NIR spectroscopy as an online PAT tool for a narrow therapeutic index drug: Toward a platform approach across lab and pilot scales for development of a powder blending monitoring method and endpoint determination. AAPS PharmSciTech. 2022, 24 (6), 103.

https://doi.org/10.1208/s12248-022-00748-4

(19) Stojanovska Pecova, M.; Geskovski, N.; Petrushevski, G.; Makreski, P., A novel method for rapid particle size analysis of ibuprofen using near-infrared spectroscopy. AAPS PharmSciTech. 2021, 22, 1–13.

https://doi.org/10.1208/s12249-021-02156-x

(20) Company, A. D.; Simonetti, S., DFT study of the chemi-cal reaction and physical properties of ibuprofen sodium. Tetrahedron 2022, 120, 132899.

https://doi.org/10.1016/j.tet.2022.132899

(21) Censi, R.; Martena, V.; Hoti, E.; Malaj, L.; Di Martino, P., Sodium ibuprofen dihydrate and anhydrous: study of the dehydration and hydration mechanisms. J. Therm. Anal. Calorim. 2013, 111, 2009–2018.

https://doi.org/10.1007/s10973-012-2194-9

(22) Rossi, P.; Macedi, E.; Paoli, P.; Bernazzani, L.; Carignani, E.; Borsacchi, S.; Geppi, M., Solid–solid tran-sition between hydrated racemic compound and anhy-drous conglomerate in Na-Ibuprofen: A combined X-ray diffraction, solid-state NMR, calorimetric, and computa-tional study. Cryst. Growth Des. 2014, 14 (5), 2441‒2452. https://doi.org/10.1021/cg500161e

(23) Chaiya, P. O.; Phaechamud, T. H., Differential scanning calorimetric analysis for incompatibility: sodium stea-rate/magnesium stearate and acidic compounds. Key Eng. Mater. 2020, 859, 307‒312.

https://doi.org/10.4028/www/scientific.net/KEM.859.307.

(24) Kararli, T. T; Needham, T. E.; Seul, C. J.; Finnegan, P. M., Solid‒state interaction of magnesium oxide and ibu-profen to form a salt. Pharm. Res. 1989, 6, 804–808. https://doi.org/10.1023/A:1015983732667.

(25) Ravichandran, V.; Lee, M.; Nguyen Cao, T.G.; Shim, M. S., Polysorbate-based drug formulations for brain-targeted drug delivery and anticancer therapy. Appl. Sci. 2021, 11 (19), 9336.

https://doi.org/10.3390/app11199336

(26) Lombardo, R.; Musumeci, T.; Carbone, C.; Pignatello, R., Nanotechnologies for intranasal drug delivery: an update of literature. Pharm. Dev. Technol. 2021, 26 (8), 824–845. https://doi.org/10.1080/10837450.2021.1950186

(27) Kriegel, C.; Festag, M.; Kishore, R. S.; Roethilsberger, D.; Schmitt, G., Pediatric safety of polysorbates in drug formulations. Children 2019, 7 (1), 1.

https://doi.org/10.3390/children7010001

(28) Garbacz, P.; Wesolowski, M., DSC, FTIR and Raman spectroscopy coupled with multivariate analysis in a study of co-crystals of pharmaceutical interest. Molecules 2018, 23 (9), 2136.

https://doi.org/10.3390/molecules23092136

(29) Gao, Y.; Zhang, Y.; Hong, Y.; Wu, F.; Shen, L.; Wang, Y.; Lin, X., Multifunctional role of silica in pharmaceuti-cal formulations. AAPS PharmSciTech. 2022, 23 (4), 90. https://doi.org/10.1208/s12249-022-02237-5

(30) Rojek, B.; Gazda, M.; Wesolowski, M., Quantification of compatibility between polymeric excipients and atenolol using principal component analysis and hierarchical clus-ter analysis. AAPS PharmSciTech. 2022, 23, 1‒16. https://doi.org/10.1208/s12249-021-02143-2

(31) Lasalvia, M.; Capozzi, V.; Perna, G., A comparison of PCA-LDA and PLS-DA techniques for classification of vibrational spectra. Appl. Sci. 2022, 12 (11), 5345. https://doi.org/10.3390/app12115345

Downloads

Additional Files

Published

2024-04-22

Versions

How to Cite

Cvetkovska Bogatinovska, E., Geshkovski, N., Petrushevski, G., & Stefov, V. (2024). Мultivariate analysis for rapid screening and prediction of solid-state compatibility in pharmaceutical preformulation studies-paving the road for machine learning. Macedonian Journal of Chemistry and Chemical Engineering, 43(1). https://doi.org/10.20450/mjcce.2024.2838

Issue

Vol. 43 No. 1 (2024): Golden Jubilee

Section

Pharmaceutical Engineering

License

Copyright (c) 2024 Elena Cvetkovska Bogatinovska, Nikola Geshkovski, Gjorgji Petrushevski, Viktor Stefov

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

The authors agree to the following licence: Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)

  • Adapt — remix, transform, and build upon the material
  • for any purpose, even commercially.
The licensor cannot revoke these freedoms as long as you follow the license terms.

 Under the following terms:

  • Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

  • NonCommercial — You may not use the material for commercial purposes.

Most read articles by the same author(s)