Utilization of Near-Infrared Spectroscopy Combined with PLS-2 Regression Learner to Predict Metformin HCL Tablet Dissolution Profile

Authors

  • Mohamad Rahmatullah Zakaria Faculty of Pharmacy, Universitas Indonesia, West Java, Indonesia
  • Sutriyo Faculty of Pharmacy, Universitas Indonesia, West Java, Indonesia
  • Hayun Faculty of Pharmacy, Universitas Indonesia, West Java, Indonesia
  • Taufiq Indra Rukmana Faculty of Pharmacy, Universitas Indonesia, West Java, Indonesia

DOI:

https://doi.org/10.59188/eduvest.v5i1.1566

Keywords:

Dissolution Rate, Metformin, NIR, SVR

Abstract

One of the assurances of pharmaceutical tablet's quality, effectivity, and safety is the dissolution test, which is commonly known by pharmaceutical manufacturers. Conventionally, this test is performed by simulating the release rate of a drug using a Dissolution Tester, which mimics the human gastrointestinal condition. As stated by the current compendial for tablet dosage form, the dissolution rate is mandatory, with no exception for Metformin HCl tablets. This laboratory method is often time-consuming, unsafe for organic reagent exposure, and produces waste. This problem requires rapid, simple, and nondestructive technologies, hence having powerful analytical performance. One of the technologies that is widely used is Near Infrared (NIR) spectroscopy. This study utilized the NIR spectrum as a predictor to generate a mathematical model using Partial Least Square Regression (PLS-2) to build a dissolution rate model for the Metformin HCl tablet, which uses the Farmakope Indonesia IV <1231> (FI-IV) dissolution method as the compendial reference method. The PLS-2 model was built, which shows the low difference between SEC and SECV in each sampling point and a good correlation in the coefficient of determination (R2) of each point's time of dissolution within 0.900 to 0.953. The challenge test was performed to prove the predictability of the PLS-2 model with NIR against the actual reference FI-IV method using differential and similarity Factors (f2 & f1), enabling real-time release testing (RTRT).

References

Cobbinah, E., Generalao, O., Lageshetty, S. K., Adrianto, I., Singh, S., & Dumancas, G. G. (2022). Using Near-Infrared Spectroscopy and Stacked Regression for the Simultaneous Determination of Fresh Cattle and Poultry Manure Chemical Properties. Chemosensors, 10(10), 410. https://doi.org/10.3390/chemosensors10100410

Devianti, D., Sufardi, S., Zulfahrizal, Z., & Munawar, A. A. (2019). Near Infrared Reflectance Spectroscopy: Prediksi Cepat dan Simultan Kadar Unsur Hara Makro pada Tanah Pertanian. AgriTECH, 39(1), 12. https://doi.org/10.22146/agritech.42430

Endokrinologi Indonesia PEDOMAN PENGELOLAAN DAN PENCEGAHAN DIABETES MELITUS TIPE, P. (2021). PEDOMAN PENGELOLAAN DAN PENCEGAHAN DIABETES MELITUS TIPE 2 DEWASA DI INDONESIA-2021 PERKENI i Penerbit PB. PERKENI.

Ferreira, A. P., & Tobyn, M. (2015). Multivariate analysis in the pharmaceutical industry: enabling process understanding and improvement in the PAT and QbD era. Pharmaceutical Development and Technology, 20(5), 513–527. https://doi.org/10.3109/10837450.2014.898656

Fonseca, F. G., Funke, A., Saechua, W., & Sirisomboon, P. (2019). Precision test for the spectral characteristic of FT-NIR for the measurement of water content of wheat straw. IOP Conference Series: Earth and Environmental Science, 301(1), 012034. https://doi.org/10.1088/1755-1315/301/1/012034

Fonteyne, M., Vercruysse, J., De Leersnyder, F., Besseling, R., Gerich, A., Oostra, W., Remon, J. P., Vervaet, C., & De Beer, T. (2016). Blend uniformity evaluation during continuous mixing in a twin screw granulator by in-line NIR using a moving F-test. Analytica Chimica Acta, 935, 213–223. https://doi.org/10.1016/j.aca.2016.07.020

Gosselin, R., Durão, P., Abatzoglou, N., & Guay, J. M. (2017). Monitoring the concentration of flowing pharmaceutical powders in a tableting feed frame. Pharmaceutical Development and Technology, 22(6), 699–705. https://doi.org/10.3109/10837450.2015.1102278

Jiménez-Romero, C., Simithy, J., Severdia, A., Álvarez, D., Grosso, M., Spivey, N., Arias, A., Solís, P. N., Li, J., & Hidalgo, I. J. (2020). Near infrared (NIR)-spectroscopy and in-vitro dissolution absorption system 2 (IDAS2) can help detect changes in the quality of generic drugs. Drug Development and Industrial Pharmacy, 46(1), 80–90. https://doi.org/10.1080/03639045.2019.1701004

Márquez, C., López, M. I., Ruisánchez, I., & Callao, M. P. (2016). FT-Raman and NIR spectroscopy data fusion strategy for multivariate qualitative analysis of food fraud. Talanta, 161, 80–86. https://doi.org/10.1016/j.talanta.2016.08.003

Martelo-Vidal, M. J., & Vázquez, M. (2014). Determination of polyphenolic compounds of red wines by UV-VIS-NIR spectroscopy and chemometrics tools. Food Chemistry, 158, 28–34. https://doi.org/10.1016/j.foodchem.2014.02.080

Mateo-Ortiz, D., Colon, Y., Romañach, R. J., & Méndez, R. (2014). Analysis of powder phenomena inside a Fette 3090 feed frame using in-line NIR spectroscopy. Journal of Pharmaceutical and Biomedical Analysis, 100, 40–49. https://doi.org/10.1016/j.jpba.2014.07.014

Molano, M. L., Cortés, M. L., Ávila, P., Martens, S. D., & Muñoz, L. S. (2016). Ecuaciones de calibración en espectroscopía de reflectancia en el infrarrojo cercano (NIRS) para predicción de parámetros nutritivos en forrajes tropicales. Tropical Grasslands-Forrajes Tropicales, 4(3), 139. https://doi.org/10.17138/TGFT(4)139-145

Mrad, M. A., Csorba, K., Galata, D. L., Nagy, Z. K., & Nagy, B. (2022). Spectroscopy-Based Partial Prediction of In Vitro Dissolution Profile Using Artificial Neural Networks. Periodica Polytechnica Electrical Engineering and Computer Science, 66(2), 122–131. https://doi.org/10.3311/PPee.18552

Murphy, D. J., O’ Brien, B., O’ Donovan, M., Condon, T., & Murphy, M. D. (2022). A near infrared spectroscopy calibration for the prediction of fresh grass quality on Irish pastures. Information Processing in Agriculture, 9(2), 243–253. https://doi.org/10.1016/j.inpa.2021.04.012

Ojala, K., Myrskyranta, M., Liimatainen, A., Kortejärvi, H., & Juppo, A. (2020). Prediction of drug dissolution from Toremifene 80 mg tablets by NIR spectroscopy. International Journal of Pharmaceutics, 577. https://doi.org/10.1016/j.ijpharm.2020.119028

Porep, J. U., Kammerer, D. R., & Carle, R. (2015). On-line application of near infrared (NIR) spectroscopy in food production. In Trends in Food Science and Technology (Vol. 46, Issue 2, pp. 211–230). Elsevier Ltd. https://doi.org/10.1016/j.tifs.2015.10.002

Pyzowski, J., Lenartowicz, M., Sobańska, A. W., & Brzezińska, E. (2017). Fast and Convenient NIR Spectroscopy Procedure for Determination of Metformin Hydrochloride in Tablets. Journal of Applied Spectroscopy, 84(4), 710–715. https://doi.org/10.1007/s10812-017-0534-z

Shi, Z., Rao, K. S., Thool, P., Kuhn, R., Thomas, R., Rich, S., & Mao, C. (2022). Development of a Near-Infrared Spectroscopy (NIRS)-Based Characterization Approach for Inherent Powder Blend Heterogeneity in Direct Compression Formulations. The AAPS Journal, 25(1), 9. https://doi.org/10.1208/s12248-022-00775-1

Sutari, W., Sauman, J., Mubarok, S., Bhernike Sitepu, R., & Risti Oktavia, A. (2018). Non-Destructive Measurement of Green Bitter Gourd Quality Component Using Near Infrared Spectroscopy (NIRS). In Science and Technology Indonesia (Vol. 3, Issue 2). http://doi.org/11.26554/sti.2218.3.2.59-65

Tsanaktsidou, E., Karavasili, C., Zacharis, C. K., Fatouros, D. G., & Markopoulou, C. K. (2020). Partial least square model (PLS) as a tool to predict the diffusion of steroids across artificial membranes. Molecules, 25(6). https://doi.org/10.3390/molecules25061387

Wening, O. P., & Kuswurjanto, R. (2023). Prediksi Unsur Hara Sampel Tanah Menggunakan Near Infrared Spectroscopy. Indonesian Sugar Research Journal, 3(1), 1–11. https://doi.org/10.54256/isrj.v3i1.88

Zaborenko, N., Shi, Z., Corredor, C. C., Smith-Goettler, B. M., Zhang, L., Hermans, A., Neu, C. M., Alam, M. A., Cohen, M. J., Lu, X., Xiong, L., & Zacour, B. M. (2019). First-Principles and Empirical Approaches to Predicting In Vitro Dissolution for Pharmaceutical Formulation and Process Development and for Product Release Testing. AAPS Journal, 21(3). https://doi.org/10.1208/s12248-019-0297-y

Zeng, Q., Wang, L., Wu, S., Fang, G., Liu, H., Li, Z., Hu, Y., & Li, W. (2022). Dissolution profiles prediction of sinomenine hydrochloride sustained-release tablets using Raman mapping technique. International Journal of Pharmaceutics, 620, 121743. https://doi.org/10.1016/j.ijpharm.2022.121743

Zhang, Z., Ding, J., Zhu, C., Wang, J., Ma, G., Ge, X., Li, Z., & Han, L. (2021). Strategies for the efficient estimation of soil organic matter in salt-affected soils through Vis-NIR spectroscopy: Optimal band combination algorithm and spectral degradation. Geoderma, 382. https://doi.org/10.1016/j.geoderma.2020.114729

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Published

2025-01-20

How to Cite

Zakaria, M. R., Sutriyo, S., Hayun, H., & Rukmana, T. I. (2025). Utilization of Near-Infrared Spectroscopy Combined with PLS-2 Regression Learner to Predict Metformin HCL Tablet Dissolution Profile. Eduvest - Journal of Universal Studies, 5(1), 881–892. https://doi.org/10.59188/eduvest.v5i1.1566