Coupling of NIR Spectroscopy and Chemometrics for the Quantification of Dexamethasone in Pharmaceutical Formulations
Abstract
:1. Introduction
2. Results
2.1. Strategy I (Individual-Block Modelling)
2.2. Strategy II (Multi-Block Modelling)
3. Discussion
4. Materials and Methods
4.1. Samples
4.2. Collection of FT-NIR Spectra
4.3. Regression Strategies
4.4. Model Calibration and Validation
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Pharmaceutical Security Institute (PSI) Pharma Crime: Geographic Distribution. Available online: https://www.psi-inc.org/geographic-distribution (accessed on 6 May 2021).
- Deepan, T.; Dhanaraju, M.D. Stability indicating HPLC method for the simultaneous determination of dapagliflozin and saxagliptin in bulk and tablet dosage form. Curr. Issues Pharm. Med. Sci. 2018, 31, 39–43. [Google Scholar] [CrossRef] [Green Version]
- Udhayavani, S.; Girija Sastry, V.; Govinda Rajan, R.; Ramya Krishna, V.; Tejaswi, J.K.D. One step quantification analytical method and characterization of valsartan by LC-MS. Int. J. Appl. Pharm. 2018, 10, 108–111. [Google Scholar] [CrossRef]
- Hussain, R.; Iqbal, S.; Shah, M.; Rehman, W.; Khan, S.; Rasheed, L.; Rahim, F.; Dera, A.A.; Kehili, S.; Elkaeed, E.B.; et al. Synthesis of Novel Benzimidazole-Based Thiazole Derivatives as Multipotent Inhibitors of α-Amylase and α-Glucosidase: In Vitro Evaluation along with Molecular Docking Study. Molecules 2022, 27, 6457. [Google Scholar] [CrossRef]
- Hussain, R.; Shah, M.; Iqbal, S.; Rehman, W.; Khan, S.; Rasheed, L.; Naz, H.; Al-Ghulikah, H.A.; Elkaeed, E.B.; Pashameah, R.A.; et al. Molecular iodine-promoted oxidative cyclization for the synthesis of 1,3,4-thiadiazole-fused-[1,2,4]-thiadiazole incorporating 1,4-benzodioxine moiety as potent inhibitors of α-amylase and α-glucosidase: In vitro and in silico study. Front. Chem. 2022, 10, 1023316. [Google Scholar] [CrossRef] [PubMed]
- Sadashivaiah, R.; Rohith, G.; Satheesha Babu, B.K. Quantification of ropinirole hydrochloride in api and tablets by novel stability-indicating rp-hplc method: It’s validation and forced degradation studies. Int. J. Appl. Pharm. 2019, 11, 317–323. [Google Scholar] [CrossRef]
- Habash, I.W.; Al-Shdefat, R.I.; Hailat, M.M.; Dayyih, W.A. A stability indicating rp-hplc method development for simultaneous estimation of alogliptin, pioglitazone, and metformin in pharmaceutical formulations. Acta Pol. Pharm. -Drug Res. 2020, 77, 549–562. [Google Scholar] [CrossRef]
- Bulduk, İ.; Aydın, B.S. Simple high-performance liquid chromatographic method for determination of donepezil hcl in pharmaceutical formulations. J. Chem. Metrol. 2020, 14, 69–76. [Google Scholar] [CrossRef]
- Tomilova, E.; Kurgachev, D.; Kulagina, D.; Sysolyatin, S.; Krylova, S.; Novikov, D. Development of HPLC-Method for Simultaneous Determination of API and Related Components in Thiowurtzine: A New Non-Narcotic Analgesic. Chromatographia 2021, 84, 147–154. [Google Scholar] [CrossRef]
- Zhong, L.; Gao, L.; Li, L.; Nei, L.; Wei, Y.; Zhang, K.; Zhang, H.; Yin, W.; Xu, D.; Zang, H. Method development and validation of a near-infrared spectroscopic method for in-line API quantification during fluidized bed granulation. Spectrochim. Acta -Part A Mol. Biomol. Spectrosc. 2022, 274, 121078. [Google Scholar] [CrossRef]
- Ali, S.L. Counterfeit drugs and analytical tools for their discrimination: European perspectives. Pharm. Chem. J. 2000, 34, 31–32. [Google Scholar] [CrossRef]
- Olsen, B.A.; Kiehl, D.E. Authentication and fingerprinting of suspect counterfeit drugs. Am. Pharm. Rev. 2006, 9, 115–118. [Google Scholar]
- Blackstone, E.A.; Fuhr, J.P., Jr.; Pociask, S. The health and economic effects of counterfeit drugs. Am. Health Drug Benefits 2014, 7, 216–224. [Google Scholar] [PubMed]
- Sarraguça, M.C.; Lopes, J.A. Quality control of pharmaceuticals with NIR: From lab to process line. Vib. Spectrosc. 2009, 49, 204–210. [Google Scholar] [CrossRef]
- Moffat, A.C.; Assi, S.; Watt, R.A. Identifying counterfeit medicines using near infrared spectroscopy. J. Near Infrared Spectrosc. 2010, 18, 1–15. [Google Scholar] [CrossRef]
- Järvinen, K.; Hoehe, W.; Järvinen, M.; Poutiainen, S.; Juuti, M.; Borchert, S. In-line monitoring of the drug content of powder mixtures and tablets by near-infrared spectroscopy during the continuous direct compression tableting process. Eur. J. Pharm. Sci. 2013, 48, 680–688. [Google Scholar] [CrossRef]
- Makraduli, L.; Makreski, P.; Goracinova, K.; Stefov, S.; Anevska, M.; Geskovski, N. A Comparative Approach to Screen the Capability of Raman and Infrared (Mid- and Near-) Spectroscopy for Quantification of Low-Active Pharmaceutical Ingredient Content Solid Dosage Forms: The Case of Alprazolam. Appl. Spectrosc. 2020, 74, 661–673. [Google Scholar] [CrossRef]
- Roggo, Y.; Chalus, P.; Maurer, L.; Lema-Martinez, C.; Edmond, A.; Jent, N. A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies. J. Pharm. Biomed. Anal. 2007, 44, 683–700. [Google Scholar] [CrossRef]
- Gerich, A.; Verhoog, J.; Damen, M.; Verhoeven, W.; Chamarthy, S.P.; Besseling, R. Detection of Lumps in Powder Blends by Inline NIR. Pharm. Technol. 2017, 41, 36–44. [Google Scholar]
- Storme-Paris, I.; Rebiere, H.; Matoga, M.; Civade, C.; Bonnet, P.-A.; Tissier, M.H.; Chaminade, P. Challenging Near InfraRed Spectroscopy discriminating ability for counterfeit pharmaceuticals detection. Anal. Chim. Acta 2010, 658, 163–174. [Google Scholar] [CrossRef]
- Rodionova, O.; Pomerantsev, A.; Houmøller, L.; Shpak, A.; Shpigun, O. Noninvasive detection of counterfeited ampoules of dexamethasone using NIR with confirmation by HPLC-DAD-MS and CE-UV methods. Anal. Bioanal. Chem. 2010, 397, 1927–1935. [Google Scholar] [CrossRef]
- Henrique Frasson Scafi, S.; Pasquini, C. Identification of counterfeit drugs using near-infrared spectroscopy. Analyst 2001, 126, 2218–2224. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Horby, P.; Lim, W.S.; Emberson, J.R.; Mafham, M.; Bell, J.L.; Linsell, L.; Phil, D.; Staplin, N.; Brightling, C.; Med, F.; et al. Dexamethasone in Hospitalized Patients with COVID-19. N. Engl. J. Med. 2021, 8, 693–704. [Google Scholar] [CrossRef]
- Fahriani, M.; Ilmawan, M.; Fajar, J.K.; Maliga, H.A.; Frediansyah, A.; Masyeni, S.; Yusuf, H.; Nainu, F.; Rosiello, F.; Sirinam, S.; et al. Persistence of long COVID symptoms in COVID-19 survivors worldwide and its potential pathogenesis—A systematic review and meta-analysis. Narra J. 2021, 1, e36. [Google Scholar] [CrossRef]
- Fajar, J.K.; Ilmawan, M.; Mamada, S.; Mutiawati, E.; Husnah, M.; Yusuf, H.; Nainu, F.; Sirinam, S.; Keam, S.; Ophinni, Y.; et al. Global prevalence of persistent neuromuscular symptoms and the possible pathomechanisms in COVID-19 recovered individuals: A systematic review and meta-analysis. Narra J. 2021, 1, e48. [Google Scholar] [CrossRef]
- Miyabo, S.; Nakamura, T.; Kuwazima, S.; Kishida, S. A comparison of the bioavailability and potency of dexamethasone phosphate and sulphate in man. Eur. J. Clin. Pharmacol. 1981, 20, 277–282. [Google Scholar] [CrossRef] [PubMed]
- Arthur, K.E.; Wolff, J.-C.; Carrier, D.J. Analysis of betamethasone, dexamethasone and related compounds by liquid chromatography/electrospray mass spectrometry. Rapid Commun. Mass Spectrom. 2004, 18, 678–684. [Google Scholar] [CrossRef] [PubMed]
- Vakili, H.; Wickström, H.; Desai, D.; Preis, M.; Sandler, N. Application of a handheld NIR spectrometer in prediction of drug content in inkjet printed orodispersible formulations containing prednisolone and levothyroxine. Int. J. Pharm. 2017, 524, 414–423. [Google Scholar] [CrossRef]
- Deeley, C.M.; Spragg, R.A.; Threlfall, T.L. A comparison of Fourier transform infrared and near-infrared Fourier transform Raman spectroscopy for quantitative measurements: An application in polymorphism. Spectrochim. Acta Part A Mol. Spectrosc. 1991, 47, 1217–1223. [Google Scholar] [CrossRef]
- Wold, S.; Martens, H.; Wold, H. The Multivariate Calibration Problem in Chemistry Solved by the PLS Method. In Matrix Pencils. Lecture Notes in Mathematics; Kågström, B., Ruhe, A., Eds.; Springer: Berlin, Germany, 1983; pp. 286–293. [Google Scholar]
- Martens, H.; Naes, T. Multivariate Calibration; John Wiley & Sons: New York, NY, USA, 1989. [Google Scholar]
- Roger, J.-M.; Biancolillo, A.; Marini, F. Sequential preprocessing through ORThogonalization (SPORT) and its application to near infrared spectroscopy. Chemom. Intell. Lab. Syst. 2020, 199, 103975. [Google Scholar] [CrossRef]
- Geladi, P.; Kowalski, B.R. Partial least-squares regression: A tutorial. Anal. Chim. Acta 1986, 185, 1–7. [Google Scholar] [CrossRef]
- Biancolillo, A.; Marini, F.; Ruckebusch, C.; Vitale, R. Chemometric strategies for spectroscopy-based food authentication. Appl. Sci. 2020, 10, 6544. [Google Scholar] [CrossRef]
- Maraphum, K.; Saengprachatanarug, K.; Wongpichet, S.; Phuphuphud, A.; Posom, J. Achieving robustness across different ages and cultivars for an NIRS-PLSR model of fresh cassava root starch and dry matter content. Comput. Electron. Agric. 2022, 196, 106872. [Google Scholar] [CrossRef]
- Mishra, P.; Marini, F.; Biancolillo, A.; Roger, J.-M. Improved prediction of fuel properties with near-infrared spectroscopy using a complementary sequential fusion of scatter correction techniques. Talanta 2021, 223, 121693. [Google Scholar] [CrossRef] [PubMed]
- Martínez, L.; Peinado, A.; Liesum, L. In-line quantification of two active ingredients in a batch blending process by near-infrared spectroscopy: Influence of physical presentation of the sample. Int. J. Pharm. 2013, 451, 67–75. [Google Scholar] [CrossRef]
- De Leersnyder, F.; Peeters, E.; Djalabi, H.; Vanhoorne, V.; Van Snick, B.; Hong, K.; Hammond, S.; Liu, A.Y.; Ziemons, E.; Vervaet, C.; et al. Development and validation of an in-line NIR spectroscopic method for continuous blend potency determination in the feed frame of a tablet press. J. Pharm. Biomed. Anal. 2018, 151, 274–283. [Google Scholar] [CrossRef] [Green Version]
- Foschi, M.; Marziale, M.; Biancolillo, A. Advanced Analytical Approach Based on Combination of FT-IR and Chemometrics for Quality Control of Pharmaceutical Preparations. Pharmaceuticals 2022, 15, 763. [Google Scholar] [CrossRef]
- Wright, S. Correlation and Causation. J. Agric. Res. 1921, 20, 557–585. [Google Scholar]
- Biancolillo, A. Method Development in the Area of Multi-Block Analysis Focused on Food Analysis. Ph. D Thesis, University of Copenhagen, Copenhagen, Denmark, 2016. [Google Scholar]
- Biancolillo, A.; Næs, T. The Sequential and Orthogonalized PLS Regression for Multiblock Regression: Theory, Examples, and Extensions. In Data Fusion Methodology and Applications; Cocchi, M., Ed.; Elsevier: Amsterdam, The Netherlands, 2019; Volume 31, pp. 157–177. [Google Scholar] [CrossRef]
- Biancolillo, A.; Marini, F.; Roger, J.-M. SO-CovSel: A novel method for variable selection in a multiblock framework. J. Chemom. 2020, 34, e3120. [Google Scholar] [CrossRef]
- Wold, S.; Johansson, E.; Cocchi, M. PLS—Partial Least-Squares Projections to Latent Structures. In 3D QSAR Drug Design; KLUWER ESCOM Science Publisher: Dordrecht, The Netherlands, 1993; pp. 523–550. [Google Scholar]
- Cocchi, M.; Biancolillo, A.; Marini, F. Chemometric Methods for Classification and Feature Selection. In Data Analysis for Omic Sciences: Methods and Applications, Comprehensive Analytical Chemistry; Jaumot, J., Bedia, C., Tauler, R., Eds.; Elsevier: Amsterdam, The Netherlands, 2018; Volume 82, pp. 265–299. ISBN 9780444640444. [Google Scholar]
- Foo, W.C.; Widjaja, E.; Khong, Y.M.; Gokhale, R.; Chan, S.Y. Application of miniaturized near-infrared spectroscopy for quality control of extemporaneous orodispersible films. J. Pharm. Biomed. Anal. 2018, 150, 191–198. [Google Scholar] [CrossRef]
- Cournoyer, A.; Simard, J.-S.; Cartilier, L.; Abatzoglou, N. Quality control of multi-component, intact pharmaceutical tablets with three different near-infrared apparatuses. Pharm. Dev. Technol. 2008, 13, 333–343. [Google Scholar] [CrossRef] [PubMed]
- Barnes, R.J.; Dhanoa, M.S.; Lister, S.J. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Appl. Spectrosc. 1989, 43, 772–777. [Google Scholar] [CrossRef]
- Savitzky, A.; Golay, M.J.E. Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 1964, 36, 1627–1639. [Google Scholar] [CrossRef]
- Næs, T.; Tomic, O.; Mevik, B.-H.; Martens, H. Path modelling by sequential PLS regression. J. Chemom. 2011, 25, 28–40. [Google Scholar] [CrossRef]
Preprocessing | LVs | R2CV | |
---|---|---|---|
Model I | Raw (+MC) | 5 | 0.1023 |
Model II | SNV (+MC) | 3 | 0.1990 |
Model III | D1 (+MC) | 8 | 0.6275 |
Model IV | D2 (+MC) | 8 | 0.7785 |
Model V | SNV + D1 (+MC) | 9 | 0.6699 |
Model VI | SNV + D2 (+MC) | 8 | 0.7457 |
Sample | Starch (g) | Lactose (g) | Mixture A (g) | Mixture B (g) | Excipient Percentage | Dexamethasone Concentration (mg/kg) | |
---|---|---|---|---|---|---|---|
Starch | Lactose | ||||||
1 | 0.7404 | 2.2155 | 0.0113 | 0.0398 | 25 | 75 | 549 |
2 | 0.3953 | 0.9752 | 0.0494 | 0.0293 | 67 | 33 | 843 |
3 | 2.1843 | 0.7268 | 0.0699 | 0.0239 | 75 | 25 | 1001 |
4 | 0.4833 | 2.4065 | 0.0186 | 0.0968 | 17 | 83 | 1241 |
5 | 1.4298 | 1.4321 | 0.0712 | 0.0700 | 5 | 0.5 | 1513 |
6 | 0.4743 | 2.3706 | 0.0270 | 0.1340 | 17 | 83 | 1731 |
7 | 2.3552 | 0.4706 | 0.1453 | 0.0305 | 83 | 17 | 1876 |
8 | 0.9337 | 1.8688 | 0.0646 | 0.1301 | 33 | 67 | 2095 |
9 | 2.3817 | 0.4660 | 0.1771 | 0.0360 | 83 | 17 | 2230 |
10 | 0.9276 | 1.8312 | 0.0786 | 0.1556 | 33 | 67 | 2515 |
11 | 2.0627 | 0.6863 | 0.1902 | 0.0627 | 75 | 25 | 2693 |
12 | 0.4602 | 2.2760 | 0.0431 | 0.2236 | 17 | 83 | 2871 |
13 | 2.7125 | 0.0000 | 0.2897 | 0.0000 | 1 | 0 | 3084 |
14 | 2.2426 | 0.4459 | 0.2548 | 0.0495 | 83 | 17 | 3257 |
15 | 0.0000 | 2.6738 | 0.0000 | 0.3233 | 0 | 1 | 3495 |
16 | 1.7711 | 0.8855 | 0.2301 | 0.1138 | 67 | 33 | 3680 |
17 | 0.0000 | 2.6397 | 0.0000 | 0.3797 | 0 | 1 | 4074 |
18 | 0.8719 | 1.7464 | 0.1262 | 0.2572 | 33 | 67 | 4120 |
19 | 1.2987 | 1.3007 | 0.2006 | 0.1999 | 5 | 5 | 4296 |
20 | 0.6451 | 1.9355 | 0.1044 | 0.3149 | 25 | 75 | 4513 |
21 | 2.5627 | 0.0000 | 0.4382 | 0.0000 | 1 | 0 | 4668 |
22 | 1.6954 | 0.8476 | 0.3042 | 0.1541 | 67 | 33 | 4903 |
23 | 0.6337 | 1.8945 | 0.1194 | 0.3585 | 25 | 75 | 5133 |
24 | 2.4887 | 0.0000 | 0.5115 | 0.0000 | 1 | 0 | 5450 |
25 | 1.2385 | 1.2340 | 0.2655 | 0.2645 | 5 | 5 | 5680 |
26 | 0.0000 | 2.4418 | 0.0000 | 0.5579 | 0 | 1 | 6025 |
27 | 1.8178 | 0.6053 | 0.4344 | 0.1441 | 75 | 25 | 6181 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Biancolillo, A.; Scappaticci, C.; Foschi, M.; Rossini, C.; Marini, F. Coupling of NIR Spectroscopy and Chemometrics for the Quantification of Dexamethasone in Pharmaceutical Formulations. Pharmaceuticals 2023, 16, 309. https://doi.org/10.3390/ph16020309
Biancolillo A, Scappaticci C, Foschi M, Rossini C, Marini F. Coupling of NIR Spectroscopy and Chemometrics for the Quantification of Dexamethasone in Pharmaceutical Formulations. Pharmaceuticals. 2023; 16(2):309. https://doi.org/10.3390/ph16020309
Chicago/Turabian StyleBiancolillo, Alessandra, Claudia Scappaticci, Martina Foschi, Claudia Rossini, and Federico Marini. 2023. "Coupling of NIR Spectroscopy and Chemometrics for the Quantification of Dexamethasone in Pharmaceutical Formulations" Pharmaceuticals 16, no. 2: 309. https://doi.org/10.3390/ph16020309