1
|
Awotunde O, Lu J, Cai J, Roseboom N, Honegger S, Joseph O, Wicks A, Hayes K, Lieberman M. Mitigating the impact of gelatin capsule variability on detection of substandard and falsified pharmaceuticals with near-IR spectroscopy. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2024; 16:1611-1622. [PMID: 38406859 DOI: 10.1039/d4ay00001c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Portable NIR spectrometers are effective in detecting authentic pharmaceutical products in intact capsule formulations, which can be used to screen for substandard or falsified versions of those authentic products. However, the chemometric models are trained on libraries of authentic products, and are generally unreliable for detection of quality problems in products from outside their training set, even for products that are nominally the same active pharmaceutical ingredient and same dosage as products in the training set. As part of our research directed at developing better non-brand-specific strategies for pharmaceutical screening, we investigated the impact of capsule composition on NIR modeling. We found that capsule features like gelatin type, color, or thickness, give rise to a similar amount of variance in the NIR spectra as the type of API stored within the capsules. Our results highlight the efficacy of orthogonal projection to latent structures in mitigating the impacts of different types of capsules on the accuracy of NIR chemometric models for classification and regression analysis of lab-made samples. The models showed good performance for classification of field-collected doxycycline capsules as good or bad quality when an NIR-based % w/w metric was used, identifying five samples that were adulterated with talc. However, the % w/w was systematically underestimated, so when evaluating the capsules based on their absolute API content according to the monograph standard, the classification accuracy decreased from 100% to 70%. The underestimation was attributed to an unforeseen variability in the quantities and types of excipients present in the capsules.
Collapse
Affiliation(s)
- Olatunde Awotunde
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA.
| | - Jiaqi Lu
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA.
| | - Jin Cai
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA.
| | - Nicholas Roseboom
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA.
| | - Sarah Honegger
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA.
| | - Ornella Joseph
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA.
| | - Alyssa Wicks
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA.
| | - Kathleen Hayes
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA.
| | - Marya Lieberman
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA.
| |
Collapse
|
2
|
Awotunde O, Roseboom N, Cai J, Hayes K, Rajane R, Chen R, Yusuf A, Lieberman M. Discrimination of Substandard and Falsified Formulations from Genuine Pharmaceuticals Using NIR Spectra and Machine Learning. Anal Chem 2022; 94:12586-12594. [PMID: 36067409 DOI: 10.1021/acs.analchem.2c00998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Near-infrared (NIR) spectroscopy is a promising technique for field identification of substandard and falsified drugs because it is portable, rapid, nondestructive, and can differentiate many formulated pharmaceutical products. Portable NIR spectrometers rely heavily on chemometric analyses based on libraries of NIR spectra from authentic pharmaceutical samples. However, it is difficult to build comprehensive product libraries in many low- and middle-income countries due to the large numbers of manufacturers who supply these markets, frequent unreported changes in materials sourcing and product formulation by the manufacturers, and general lack of cooperation in providing authentic samples. In this work, we show that a simple library of lab-formulated binary mixtures of an active pharmaceutical ingredient (API) with two diluents gave good performance on field screening tasks, such as discriminating substandard and falsified formulations of the API. Six data analysis models, including principal component analysis and support-vector machine classification and regression methods and convolutional neural networks, were trained on binary mixtures of acetaminophen with either lactose or ascorbic acid. While the models all performed strongly in cross-validation (on formulations similar to their training set), they individually showed poor robustness for formulations outside the training set. However, a predictive algorithm based on the six models, trained only on binary samples, accurately predicts whether the correct amount of acetaminophen is present in ternary mixtures, genuine acetaminophen formulations, adulterated acetaminophen formulations, and falsified formulations containing substitute APIs. This data analytics approach may extend the utility of NIR spectrometers for analysis of pharmaceuticals in low-resource settings.
Collapse
Affiliation(s)
- Olatunde Awotunde
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Nicholas Roseboom
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Jin Cai
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Kathleen Hayes
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Revati Rajane
- Precise Software Solutions Inc, Rockville, Maryland 20850, United States
| | - Ruoyan Chen
- Precise Software Solutions Inc, Rockville, Maryland 20850, United States
| | - Abdullah Yusuf
- Precise Software Solutions Inc, Rockville, Maryland 20850, United States
| | - Marya Lieberman
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States
| |
Collapse
|
3
|
Khan A, Munir MT, Yu W, Young BR. Near‐infrared spectroscopy and data analysis for predicting milk powder quality attributes. INT J DAIRY TECHNOL 2020. [DOI: 10.1111/1471-0307.12734] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Asma Khan
- Department of Chemical & Materials Engineering Faculty of Engineering The University of Auckland Symond Street Auckland1010New Zealand
| | - Muhammad Tajammal Munir
- College of Engineering and Technology American University of the Middle East Kuwait1010Kuwait
| | - Wei Yu
- Department of Chemical & Materials Engineering Faculty of Engineering The University of Auckland Symond Street Auckland1010New Zealand
| | - Brent R. Young
- Department of Chemical & Materials Engineering Faculty of Engineering The University of Auckland Symond Street Auckland1010New Zealand
| |
Collapse
|
4
|
Postelmans A, Aernouts B, Saeys W. Estimation of Particle Size Distribution from Bulk Scattering Spectra: Validation on Monomodal Suspensions. Anal Chem 2019; 91:10040-10048. [PMID: 31318541 DOI: 10.1021/acs.analchem.9b01913] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
A particle size distribution (PSD) estimation method based on light-scattering properties was validated on experimental visible/near-infrared scattering spectra of polystyrene suspensions, with a nominal particle size ranging from 0.1 to 12 μm in diameter. On the basis of μs and g spectra extracted from double integrating sphere measurements, good PSD estimates were obtained for particles ≥1 μm. The particle volume fraction estimates in the case of μs were close to the target concentrations, although influenced by small baseline fluctuations on the spectra. For submicrometer particles, on the other hand, the non-oscillating μs spectra lack discriminating power, resulting in erroneous PSD estimates. The reduced scattering coefficient spectra (μs') were found less useful for particle size estimation as they lack a characteristic shape, causing an over- or underestimation of the distribution width. In summary, the estimation routine proved to deliver PSD estimates in line with the reference measurements for micrometer-sized or larger particles based on their μs and g scattering spectra. Additional validation on more polydisperse samples forms the next step before going to bimodal PSD estimates.
Collapse
Affiliation(s)
- Annelies Postelmans
- Department of Biosystems , MeBioS, KU Leuven , Kasteelpark Arenberg 30 , 3001 Leuven , Belgium
| | - Ben Aernouts
- Department of Biosystems , MeBioS, KU Leuven , Kasteelpark Arenberg 30 , 3001 Leuven , Belgium.,Department of Biosystems, Biosystems Technology Cluster , KU Leuven Campus Geel , Kleinhoefstraat 4 , 2440 Geel , Belgium
| | - Wouter Saeys
- Department of Biosystems , MeBioS, KU Leuven , Kasteelpark Arenberg 30 , 3001 Leuven , Belgium
| |
Collapse
|
5
|
Dai S, Pan X, Ma L, Huang X, Du C, Qiao Y, Wu Z. Discovery of the Linear Region of Near Infrared Diffuse Reflectance Spectra Using the Kubelka-Munk Theory. Front Chem 2018; 6:154. [PMID: 29869631 PMCID: PMC5949317 DOI: 10.3389/fchem.2018.00154] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Accepted: 04/19/2018] [Indexed: 11/16/2022] Open
Abstract
Particle size is of great importance for the quantitative model of the NIR diffuse reflectance. In this paper, the effect of sample particle size on the measurement of harpagoside in Radix Scrophulariae powder by near infrared diffuse (NIR) reflectance spectroscopy was explored. High-performance liquid chromatography (HPLC) was employed as a reference method to construct the quantitative particle size model. Several spectral preprocessing methods were compared, and particle size models obtained by different preprocessing methods for establishing the partial least-squares (PLS) models of harpagoside. Data showed that the particle size distribution of 125–150 μm for Radix Scrophulariae exhibited the best prediction ability with Rpre2 = 0.9513, RMSEP = 0.1029 mg·g−1, and RPD = 4.78. For the hybrid granularity calibration model, the particle size distribution of 90–180 μm exhibited the best prediction ability with Rpre2 = 0.8919, RMSEP = 0.1632 mg·g−1, and RPD = 3.09. Furthermore, the Kubelka-Munk theory was used to relate the absorption coefficient k (concentration-dependent) and scatter coefficient s (particle size-dependent). The scatter coefficient s was calculated based on the Kubelka-Munk theory to study the changes of s after being mathematically preprocessed. A linear relationship was observed between k/s and absorption A within a certain range and the value for k/s was >4. According to this relationship, the model was more accurately constructed with the particle size distribution of 90–180 μm when s was kept constant or in a small linear region. This region provided a good reference for the linear modeling of diffuse reflectance spectroscopy. To establish a diffuse reflectance NIR model, further accurate assessment should be obtained in advance for a precise linear model.
Collapse
Affiliation(s)
- Shengyun Dai
- Key Laboratory of TCM-Information Engineering of State Administration of TCM, Pharmaceutical Engineering and New Drug Development of Traditional Chinese, Medicine of Ministry of Education, Beijing University of Chinese Medicine, Beijing, China
| | - Xiaoning Pan
- Key Laboratory of TCM-Information Engineering of State Administration of TCM, Pharmaceutical Engineering and New Drug Development of Traditional Chinese, Medicine of Ministry of Education, Beijing University of Chinese Medicine, Beijing, China
| | - Lijuan Ma
- Key Laboratory of TCM-Information Engineering of State Administration of TCM, Pharmaceutical Engineering and New Drug Development of Traditional Chinese, Medicine of Ministry of Education, Beijing University of Chinese Medicine, Beijing, China
| | - Xingguo Huang
- Key Laboratory of TCM-Information Engineering of State Administration of TCM, Pharmaceutical Engineering and New Drug Development of Traditional Chinese, Medicine of Ministry of Education, Beijing University of Chinese Medicine, Beijing, China
| | - Chenzhao Du
- Key Laboratory of TCM-Information Engineering of State Administration of TCM, Pharmaceutical Engineering and New Drug Development of Traditional Chinese, Medicine of Ministry of Education, Beijing University of Chinese Medicine, Beijing, China
| | - Yanjiang Qiao
- Key Laboratory of TCM-Information Engineering of State Administration of TCM, Pharmaceutical Engineering and New Drug Development of Traditional Chinese, Medicine of Ministry of Education, Beijing University of Chinese Medicine, Beijing, China
| | - Zhisheng Wu
- Key Laboratory of TCM-Information Engineering of State Administration of TCM, Pharmaceutical Engineering and New Drug Development of Traditional Chinese, Medicine of Ministry of Education, Beijing University of Chinese Medicine, Beijing, China
| |
Collapse
|
6
|
Calvo NL, Maggio RM, Kaufman TS. Characterization of pharmaceutically relevant materials at the solid state employing chemometrics methods. J Pharm Biomed Anal 2018; 147:538-564. [DOI: 10.1016/j.jpba.2017.06.017] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2017] [Revised: 06/08/2017] [Accepted: 06/12/2017] [Indexed: 11/28/2022]
|
7
|
Rapid characterization of tanshinone extract powder by near infrared spectroscopy. Int J Anal Chem 2015; 2015:704940. [PMID: 25866511 PMCID: PMC4381857 DOI: 10.1155/2015/704940] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2014] [Revised: 03/04/2015] [Accepted: 03/08/2015] [Indexed: 11/18/2022] Open
Abstract
Chemical and physical quality attributes of herbal extract powders play an important role in the research and development of Chinese medicine preparations. The active pharmaceutical ingredients have a direct impact on the herbal extract's efficacy, while the physical properties of raw material affect the pharmaceutical manufacturing process and the final products' quality. In this study, tanshinone extract powders from Salvia miltiorrhiza which are widely used for the treatment of cardiovascular diseases in the clinic are taken as the research object. Both the chemical information and physical information of tanshinone extract powders are analyzed by near infrared (NIR) spectroscopy. The partial least squares (PLS) and least square support vector machine (LS-SVM) models are investigated to build the relationship between NIR spectra and reference values. PLS models performed well for the content of crytotanshinone, tanshinone IIA, the moisture, and average median particle size, while, for specific surface area and tapped density, the LS-SVM models performed better than the PLS models. Results demonstrated NIR to be a valid and fast process analytical technology tool to simultaneously determine multiple quality attributes of herbal extract powders and indicated that there existed some nonlinear relationship between NIR spectra and physical quality attributes.
Collapse
|
8
|
Wu Z, Du M, Shi X, Xu B, Qiao Y. Robust PLS Prediction Model for Saikosaponin A in Bupleurum chinense DC. Coupled with Granularity-Hybrid Calibration Set. JOURNAL OF ANALYTICAL METHODS IN CHEMISTRY 2015; 2015:583841. [PMID: 25821634 PMCID: PMC4363675 DOI: 10.1155/2015/583841] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/25/2014] [Accepted: 09/04/2014] [Indexed: 06/04/2023]
Abstract
This study demonstrated particle size effect on the measurement of saikosaponin A in Bupleurum chinense DC. by near infrared reflectance (NIR) spectroscopy. Four types of granularity were prepared including powder samples passed through 40-mesh, 65-mesh, 80-mesh, and 100-mesh sieve. Effects of granularity on NIR spectra were investigated, which showed to be wavelength dependent. NIR intensity was proportional to particle size in the first combination-overtone and combination region. Local partial least squares model was constructed separately for every kind of samples, and data-preprocessing techniques were performed to optimize calibration model. The 65-mesh model exhibited the best prediction ability with root mean of square error of prediction (RMSEP) = 0.492 mg·g(-1), correlation coefficient (R P ) = 0.9221, and relative predictive determinant (RPD) = 2.58. Furthermore, a granularity-hybrid calibration model was developed by incorporating granularity variation. Granularity-hybrid model showed better performance than local model. The model performance with 65-mesh samples was still the most accurate with RMSEP = 0.481 mg·g(-1), R P = 0.9279, and RPD = 2.64. All the results presented the guidance for construction of a robust model coupled with granularity-hybrid calibration set.
Collapse
Affiliation(s)
- Zhisheng Wu
- Beijing University of Chinese Medicine, Beijing 100102, China
- Key Laboratory of TCM-Information Engineering of State Administration of TCM, Beijing 100102, China
- Beijing Key Laboratory for Basic and Development Research on Chinese Medicine, Beijing 100102, China
| | - Min Du
- World Federation of Chinese Medicine Societies, Beijing 100101, China
| | - Xinyuan Shi
- Beijing University of Chinese Medicine, Beijing 100102, China
- Key Laboratory of TCM-Information Engineering of State Administration of TCM, Beijing 100102, China
- Beijing Key Laboratory for Basic and Development Research on Chinese Medicine, Beijing 100102, China
| | - Bing Xu
- Beijing University of Chinese Medicine, Beijing 100102, China
- Key Laboratory of TCM-Information Engineering of State Administration of TCM, Beijing 100102, China
- Beijing Key Laboratory for Basic and Development Research on Chinese Medicine, Beijing 100102, China
| | - Yanjiang Qiao
- Beijing University of Chinese Medicine, Beijing 100102, China
- Key Laboratory of TCM-Information Engineering of State Administration of TCM, Beijing 100102, China
- Beijing Key Laboratory for Basic and Development Research on Chinese Medicine, Beijing 100102, China
| |
Collapse
|
9
|
Mehl F, Marti G, Merle P, Delort E, Baroux L, Sommer H, Wolfender JL, Rudaz S, Boccard J. Integrating metabolomic data from multiple analytical platforms for a comprehensive characterisation of lemon essential oils. FLAVOUR FRAG J 2014. [DOI: 10.1002/ffj.3230] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Florence Mehl
- School of Pharmaceutical Sciences, EPGL; University of Geneva, University of Lausanne; 20, Bd d'Yvoy 1211 Geneva 4 Switzerland
| | - Guillaume Marti
- School of Pharmaceutical Sciences, EPGL; University of Geneva, University of Lausanne; 20, Bd d'Yvoy 1211 Geneva 4 Switzerland
| | | | | | - Lucie Baroux
- Firmenich, Corporate Research; Geneva Switzerland
| | - Horst Sommer
- Firmenich, Corporate Research; Geneva Switzerland
| | - Jean-Luc Wolfender
- School of Pharmaceutical Sciences, EPGL; University of Geneva, University of Lausanne; 20, Bd d'Yvoy 1211 Geneva 4 Switzerland
| | - Serge Rudaz
- School of Pharmaceutical Sciences, EPGL; University of Geneva, University of Lausanne; 20, Bd d'Yvoy 1211 Geneva 4 Switzerland
| | - Julien Boccard
- School of Pharmaceutical Sciences, EPGL; University of Geneva, University of Lausanne; 20, Bd d'Yvoy 1211 Geneva 4 Switzerland
| |
Collapse
|
10
|
Porfire A, Rus L, Vonica AL, Tomuta I. High-throughput NIR-chemometric methods for determination of drug content and pharmaceutical properties of indapamide powder blends for tabletting. J Pharm Biomed Anal 2012; 70:301-9. [DOI: 10.1016/j.jpba.2012.07.026] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2012] [Revised: 07/18/2012] [Accepted: 07/22/2012] [Indexed: 10/28/2022]
|
11
|
Galvis-Sánchez AC, Lopes JA, Delgadillo I, Rangel AOSS. Fourier transform near-infrared spectroscopy application for sea salt quality evaluation. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2011; 59:11109-11116. [PMID: 21905642 DOI: 10.1021/jf202204d] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Near-infrared (NIR) spectroscopy in diffuse reflectance mode was explored with the objective of discriminating sea salts according to their quality type (traditional salt vs "flower of salt") and geographical origin (Atlantic vs Mediterranean). Sea salts were also analyzed in terms of Ca(2+), Mg(2+), K(+), alkalinity, and sulfate concentrations to support spectroscopic results. High concentrations of Mg(2+) and K(+) characterized Atlantic samples, while a high Ca(2+) content was observed in traditional sea salts. A partial least-squares discriminant analysis model considering the 8500-7500 cm(-1) region permitted the discrimination of salts by quality types. The regions 4650-4350 and 5900-5500 cm(-1) allowed salts classification according to their geographical origin. It was possible to classify correctly 85.3 and 94.8% of the analyzed samples according to the salt type and to the geographical origin, respectively. These results demonstrated that NIR spectroscopy is a suitable and very efficient tool for sea salt quality evaluation.
Collapse
Affiliation(s)
- Andrea C Galvis-Sánchez
- CBQF/Escola Superior de Biotecnologia, Universidade Católica Portuguesa, R. Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal
| | | | | | | |
Collapse
|