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      Chemometric Analysis of UV-Visible Spectral Fingerprints for the Discrimination and Quantification of Clinical Anthracycline Drug Preparation Used in Oncology

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          Abstract

          In clinical treatment, the analytical quality assessment of the delivery of chemotherapeutic preparations is required to guarantee the patient's safety regarding the dose and most importantly the appropriate anticancer drug. On its own, the development of rapid analytical methods allowing both qualitative and quantitative control of the formulation of prepared solutions could significantly enhance the hospital's workflow, reducing costs, and potentially providing optimal patient care. UV-visible spectroscopy is a nondestructive, fast, and economical technique for molecular characterization of samples. A discrimination and quantification study of three chemotherapeutic drugs doxorubicin, daunorubicin, and epirubicin was conducted, using clinically relevant concentration ranges prepared in 0.9% NaCl solutions. The application of the partial least square discriminant analysis PLS-DA method on the UV-visible spectral data shows a perfect discrimination of the three drugs with a sensitivity and specificity of 100%. The use of partial least square regression PLS shows high quantification performance of these molecules in solution represented by the low value of root mean square error of calibration (RMSEC) and root mean square error of cross validation (RMSCECV) on the one hand and the high value of R-square on the other hand. This study demonstrated the viability of UV-visible fingerprinting (routine approach) coupled with chemometric tools for the classification and quantification of chemotherapeutic drugs during clinical preparation.

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          Most cited references28

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          Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008.

          Estimates of the worldwide incidence and mortality from 27 cancers in 2008 have been prepared for 182 countries as part of the GLOBOCAN series published by the International Agency for Research on Cancer. In this article, we present the results for 20 world regions, summarizing the global patterns for the eight most common cancers. Overall, an estimated 12.7 million new cancer cases and 7.6 million cancer deaths occur in 2008, with 56% of new cancer cases and 63% of the cancer deaths occurring in the less developed regions of the world. The most commonly diagnosed cancers worldwide are lung (1.61 million, 12.7% of the total), breast (1.38 million, 10.9%) and colorectal cancers (1.23 million, 9.7%). The most common causes of cancer death are lung cancer (1.38 million, 18.2% of the total), stomach cancer (738,000 deaths, 9.7%) and liver cancer (696,000 deaths, 9.2%). Cancer is neither rare anywhere in the world, nor mainly confined to high-resource countries. Striking differences in the patterns of cancer from region to region are observed. Copyright © 2010 UICC.
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            SIMPLS: An alternative approach to partial least squares regression

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              Partial least squares-discriminant analysis (PLS-DA) for classification of high-dimensional (HD) data: a review of contemporary practice strategies and knowledge gaps.

              Partial least squares-discriminant analysis (PLS-DA) is a versatile algorithm that can be used for predictive and descriptive modelling as well as for discriminative variable selection. However, versatility is both a blessing and a curse and the user needs to optimize a wealth of parameters before reaching reliable and valid outcomes. Over the past two decades, PLS-DA has demonstrated great success in modelling high-dimensional datasets for diverse purposes, e.g. product authentication in food analysis, diseases classification in medical diagnosis, and evidence analysis in forensic science. Despite that, in practice, many users have yet to grasp the essence of constructing a valid and reliable PLS-DA model. As the technology progresses, across every discipline, datasets are evolving into a more complex form, i.e. multi-class, imbalanced and colossal. Indeed, the community is welcoming a new era called big data. In this context, the aim of the article is two-fold: (a) to review, outline and describe the contemporary PLS-DA modelling practice strategies, and (b) to critically discuss the respective knowledge gaps that have emerged in response to the present big data era. This work could complement other available reviews or tutorials on PLS-DA, to provide a timely and user-friendly guide to researchers, especially those working in applied research.
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                Author and article information

                Contributors
                Journal
                Biomed Res Int
                Biomed Res Int
                BMRI
                BioMed Research International
                Hindawi
                2314-6133
                2314-6141
                2021
                6 May 2021
                : 2021
                : 5580102
                Affiliations
                1Laboratory of Chemical Processes and Applied Materials, Faculty of Science and Technology, Sultan Moulay Slimane University, Beni-Mellal, Morocco
                2Team of Formulation and Quality Control of Health Products, Faculty of Medicine and Pharmacy, Mohammed V University in Rabat, Morocco
                3Rabat Pediatrics Hospital, Ibn Sina University Hospital Center, Rabat, Morocco
                4Departement of Pharmacy, Faculty of Pharmacy, Abulcasis University, Rabat, Morocco
                Author notes

                Academic Editor: Nahide Gulsah Deniz

                Author information
                https://orcid.org/0000-0001-5352-2476
                https://orcid.org/0000-0002-6700-4984
                Article
                10.1155/2021/5580102
                8121585
                9a5b3f72-ef96-4700-898f-b85803b8e8bf
                Copyright © 2021 Aimen El Orche et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 29 January 2021
                : 12 April 2021
                : 29 April 2021
                Categories
                Research Article

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