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Wang Y, Jin C, Ma L, Liu X. A Robust TabNet-Based Multi-Classification Algorithm for Infrared Spectral Data of Chinese Herbal Medicine with High-Dimensional Small Samples. J Pharm Biomed Anal 2024; 242:116031. [PMID: 38382317 DOI: 10.1016/j.jpba.2024.116031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 02/08/2024] [Accepted: 02/09/2024] [Indexed: 02/23/2024]
Abstract
Robust classification algorithms for high-dimensional, small-sample datasets are valuable in practical applications. Faced with the infrared spectroscopic dataset with 568 samples and 3448 wavelengths (features) to identify the origins of Chinese medicinal materials, this paper proposed a novel embedded multiclassification algorithm, ITabNet, derived from the framework of TabNet. Firstly, a refined data pre-processing (DP) mechanism was designed to efficiently find the best adaptive one among 50 DP methods with the help of Support Vector Machine (SVM). Following this, an innovative focal loss function was designed and joined with a cross-validation experiment strategy to mitigate the impact of sample imbalance on algorithm. Detailed investigations on ITabNet were conducted, including comparisons of ITabNet with SVM for the conditions of DP and Non-DP, GPU and CPU computer settings, as well as ITabNet against XGBT (Extreme Gradient Boosting). The numerical results demonstrate that ITabNet can significantly improve the effectiveness of prediction. The best accuracy score is 1.0000, and the best Area Under the Curve (AUC) score is 1.0000. Suggestions on how to use models effectively were given. Furthermore, ITabNet shows the potential to apply the analysis of medicinal efficacy and chemical composition of medicinal materials. The paper also provides ideas for multi-classification modeling data with small sample size and high-dimensional feature.
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Affiliation(s)
- Yongjun Wang
- School of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou, 325035, China
| | - Chengliang Jin
- School of Information and Engineering, Wenzhou Business College, Wenzhou, 325035, China.
| | - Li Ma
- College of Information Technology, Shanghai JianQiao University, Shanghai 201306, China
| | - Xiao Liu
- Wenzhou Hospital of Traditional Chinese Medicine, Wenzhou, 325000, China
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2
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Khalikova M, Jireš J, Horáček O, Douša M, Kučera R, Nováková L. What is the role of current mass spectrometry in pharmaceutical analysis? MASS SPECTROMETRY REVIEWS 2024; 43:560-609. [PMID: 37503656 DOI: 10.1002/mas.21858] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 06/02/2023] [Accepted: 06/25/2023] [Indexed: 07/29/2023]
Abstract
The role of mass spectrometry (MS) has become more important in most application domains in recent years. Pharmaceutical analysis is specific due to its stringent regulation procedures, the need for good laboratory/manufacturing practices, and a large number of routine quality control analyses to be carried out. The role of MS is, therefore, very different throughout the whole drug development cycle. While it dominates within the drug discovery and development phase, in routine quality control, the role of MS is minor and indispensable only for selected applications. Moreover, its role is very different in the case of analysis of small molecule pharmaceuticals and biopharmaceuticals. Our review explains the role of current MS in the analysis of both small-molecule chemical drugs and biopharmaceuticals. Important features of MS-based technologies being implemented, method requirements, and related challenges are discussed. The differences in analytical procedures for small molecule pharmaceuticals and biopharmaceuticals are pointed out. While a single method or a small set of methods is usually sufficient for quality control in the case of small molecule pharmaceuticals and MS is often not indispensable, a large panel of methods including extensive use of MS must be used for quality control of biopharmaceuticals. Finally, expected development and future trends are outlined.
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Affiliation(s)
- Maria Khalikova
- Department of Analytical Chemistry, Faculty of Pharmacy in Hradec Králové, Charles University, Hradec Králové, Czech Republic
- Department of Chemistry, Faculty of Science, University of Hradec Králové, Hradec Králové, Czech Republic
| | - Jakub Jireš
- Department of Analytical Chemistry, Faculty of Chemical Engineering, UCT Prague, Prague, Czech Republic
- Department of Development, Zentiva, k. s., Praha, Praha, Czech Republic
| | - Ondřej Horáček
- Department of Pharmaceutical Chemistry and Pharmaceutical Analysis, Faculty of Pharmacy in Hradec Králové, Charles University, Hradec Králové, Czech Republic
| | - Michal Douša
- Department of Development, Zentiva, k. s., Praha, Praha, Czech Republic
| | - Radim Kučera
- Department of Pharmaceutical Chemistry and Pharmaceutical Analysis, Faculty of Pharmacy in Hradec Králové, Charles University, Hradec Králové, Czech Republic
| | - Lucie Nováková
- Department of Analytical Chemistry, Faculty of Pharmacy in Hradec Králové, Charles University, Hradec Králové, Czech Republic
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Krmar J, Stojadinović LT, Đurkić T, Protić A, Otašević B. Predicting liquid chromatography-electrospray ionization/mass spectrometry signal from the structure of model compounds and experimental factors; case study of aripiprazole and its impurities. J Pharm Biomed Anal 2023; 233:115422. [PMID: 37150055 DOI: 10.1016/j.jpba.2023.115422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 04/24/2023] [Accepted: 04/24/2023] [Indexed: 05/09/2023]
Abstract
A priori estimation of analyte response is crucial for the efficient development of liquid chromatography-electrospray ionization/mass spectrometry (LC-ESI/MS) methods, but remains a demanding task given the lack of knowledge about the factors affecting the experimental outcome. In this research, we address the challenge of discovering the interactive relationship between signal response and structural properties, method parameters and solvent-related descriptors throughout an approach featuring quantitative structure-property relationship (QSPR) and design of experiments (DoE). To systematically investigate the experimental domain within which QSPR prediction should be undertaken, we varied LC and instrumental factors according to the Box-Behnken DoE scheme. Seven compounds, including aripiprazole and its impurities, were subjected to 57 different experimental conditions, resulting in 399 LC-ESI/MS data endpoints. To obtain a more standard distribution of the measured response, the peak areas were log-transformed before modeling. QSPR predictions were made using features selected by Genetic Algorithm (GA) and providing Gradient Boosted Trees (GBT) with training data. Proposed model showed satisfactory performance on test data with a RMSEP of 1.57 % and a of 96.48 %. This is the first QSPR study in LC-ESI/MS that provided a holistic overview of the analyte's response behavior across the experimental and chemical space. Since intramolecular electronic effects and molecular size were given great importance, the GA-GBT model improved the understanding of signal response generation of model compounds. It also highlighted the need to fine-tune the parameters affecting desolvation and droplet charging efficiency.
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Affiliation(s)
- Jovana Krmar
- Department of Drug Analysis, University of Belgrade-Faculty of Pharmacy, Vojvode Stepe 450, 11221 Belgrade, Serbia
| | | | - Tatjana Đurkić
- Department of Environmental Engineering, University of Belgrade-Faculty of Technology and Metallurgy, Karnegijeva 4, 11000 Belgrade, Serbia
| | - Ana Protić
- Department of Drug Analysis, University of Belgrade-Faculty of Pharmacy, Vojvode Stepe 450, 11221 Belgrade, Serbia
| | - Biljana Otašević
- Department of Drug Analysis, University of Belgrade-Faculty of Pharmacy, Vojvode Stepe 450, 11221 Belgrade, Serbia.
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Muthudoss P, Tewari I, Chi RLR, Young KJ, Ann EYC, Hui DNS, Khai OY, Allada R, Rao M, Shahane S, Das S, Babla I, Mhetre S, Paudel A. Machine Learning-Enabled NIR Spectroscopy in Assessing Powder Blend Uniformity: Clear-Up Disparities and Biases Induced by Physical Artefacts. AAPS PharmSciTech 2022; 23:277. [PMID: 36229571 DOI: 10.1208/s12249-022-02403-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 08/22/2022] [Indexed: 11/30/2022] Open
Abstract
NIR spectroscopy is a non-destructive characterization tool for the blend uniformity (BU) assessment. However, NIR spectra of powder blends often contain overlapping physical and chemical information of the samples. Deconvoluting the information related to chemical properties from that associated with the physical effects is one of the major objectives of this work. We achieve this aim in two ways. Firstly, we identified various sources of variability that might affect the BU results. Secondly, we leverage the machine learning-based sophisticated data analytics processes. To accomplish the aforementioned objectives, calibration samples of amlodipine as an active pharmaceutical ingredient (API) with the concentrations ranging between 67 and 133% w/w (dose ~ 3.6% w/w), in powder blends containing excipients, were prepared using a gravimetric approach and assessed using NIR spectroscopic analysis, followed by HPLC measurements. The bias in NIR results was investigated by employing data quality metrics (DQM) and bias-variance decomposition (BVD). To overcome the bias, the clustered regression (non-parametric and linear) was applied. We assessed the model's performance by employing the hold-out and k-fold internal cross-validation (CV). NIR-based blend homogeneity with low mean absolute error and an interval estimates of 0.674 (mean) ± 0.218 (standard deviation) w/w was established. Additionally, bootstrapping-based CV was leveraged as part of the NIR method lifecycle management that demonstrated the mean absolute error (MAE) of BU ± 3.5% w/w and BU ± 1.5% w/w for model generalizability and model transferability, respectively. A workflow integrating machine learning to NIR spectral analysis was established and implemented. Impact of various data learning approaches on NIR spectral data.
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Affiliation(s)
- Prakash Muthudoss
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia.,A2Z4.0 Research and Analytics Private Limited, Old No:810, New No:62, CTH Road, Behind Lenskart, Thirumullaivoil, Chennai, Tamilnadu, India
| | - Ishan Tewari
- The Machine Learning Company, Beed, Maharashtra, India.,Institute of Technology, Nirma University, Ahmedabad, Gujarat, India
| | - Rayce Lim Rui Chi
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Kwok Jia Young
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Eddy Yii Chung Ann
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Doreen Ng Sean Hui
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Ooi Yee Khai
- Perkin Elmer Sdn Bhd, L2, 2-01, Wisma Academy, Jalan 19/1, Seksyen 19, 46300, Petaling Jaya, Selangor, Malaysia
| | - Ravikiran Allada
- Novugen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Manohar Rao
- PerkinElmer (India) Private Limited, Vayudooth Chambers, 12th floor, Trinity Circle, Mahatma Gandhi Rd, Bengaluru, Karnataka, 560001, India
| | | | - Samir Das
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Irfan Babla
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Sandeep Mhetre
- Oncogen Pharma (Malaysia), Sdn Bhd, 3, Jalan Jururancang U1/21, Hicom-glenmarie Industrial Park, 40150, Shah Alam, Selangor, Malaysia
| | - Amrit Paudel
- Research Center Pharmaceutical Engineering GmbH (RCPE), Inffeldgasse 13, 8010, Graz, Austria. .,Institute of Process and Particle Engineering, Graz University of Technology, Inffeldgasse 13/3, 8010, Graz, Austria.
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5
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Zhao Y, Li J, Xie H, Li H, Chen X. Covalent organic nanospheres as a fiber coating for solid-phase microextraction of genotoxic impurities followed by analysis using gas chromatography–mass spectrometry. J Pharm Anal 2021; 12:583-589. [PMID: 36105168 PMCID: PMC9463475 DOI: 10.1016/j.jpha.2021.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 12/04/2021] [Accepted: 12/05/2021] [Indexed: 11/16/2022] Open
Affiliation(s)
- Yanfang Zhao
- School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
- Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
| | - Jingkun Li
- Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
| | - Hanyi Xie
- School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
- Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
| | - Huijuan Li
- School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
- Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
| | - Xiangfeng Chen
- School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
- Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
- Corresponding author. School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China.
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