Molecules

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15 pages, 1248 KiB

Open AccessArticle

Evaluation of the Usefulness of Topological Indices for Predicting Selected Physicochemical Properties of Bioactive Substances with Anti-Androgenic and Hypouricemic Activity

by Dawid Wardecki, Małgorzata Dołowy and Katarzyna Bober-Majnusz

Molecules 2023, 28(15), 5822; https://doi.org/10.3390/molecules28155822 - 2 Aug 2023

Cited by 4 | Viewed by 1502

Abstract

Due to the observed increase in the importance of computational methods in determining selected physicochemical parameters of biologically active compounds that are key to understanding their ADME/T profile, such as lipophilicity, there is a great need to work on accurate and precise in [...] Read more.

Due to the observed increase in the importance of computational methods in determining selected physicochemical parameters of biologically active compounds that are key to understanding their ADME/T profile, such as lipophilicity, there is a great need to work on accurate and precise in silico models based on some structural descriptors, such as topological indices for predicting lipophilicity of certain anti-androgenic and hypouricemic agents and their derivatives, for which the experimental lipophilicity parameter is not accurately described in the available literature, e.g., febuxostat, oxypurinol, ailanthone, abiraterone and teriflunomide. Therefore, the following topological indices were accurately calculated in this paper: Gutman (M, M^ν), Randić (⁰χ, ¹χ, ⁰χ^ν, ¹χ^ν), Wiener (W), Rouvray–Crafford (R) and Pyka (A, ⁰B, ¹B) for the selected anti-androgenic drugs (abiraterone, bicalutamide, flutamide, nilutamide, leflunomide, teriflunomide, ailanthone) and some hypouricemic compounds (allopurinol, oxypurinol, febuxostat). Linear regression analysis was used to create simple linear correlations between the newly calculated topological indices and some physicochemical parameters, including lipophilicity descriptors of the tested compounds (previously obtained by TLC and theoretical methods). Our studies confirmed the usefulness of the obtained linear regression equations based on topological indices to predict ADME/T important parameters, such as lipophilicity descriptors of tested compounds with anti-androgenic and hypouricemic effects. The proposed calculation method based on topological indices is fast, easy to use and avoids valuable and lengthy laboratory experiments required in the case of experimental ADME/T studies. Full article

(This article belongs to the Special Issue New Winds in Chemometrics: Theory and Application)

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12 pages, 2038 KiB

Open AccessArticle

Multivariate Curve Resolution Methodology Applied to the ATR-FTIR Data for Adulteration Assessment of Virgin Coconut Oil

by Michele De Luca, Giuseppina Ioele, Fedora Grande, Maria Antonietta Occhiuzzi, Martina Chieffallo, Antonio Garofalo and Gaetano Ragno

Molecules 2023, 28(12), 4661; https://doi.org/10.3390/molecules28124661 - 9 Jun 2023

Cited by 3 | Viewed by 1660

Abstract

Virgin coconut oil (VCO) is a functional food with important health benefits. Its economic interest encourages fraudsters to deliberately adulterate VCO with cheap and low-quality vegetable oils for financial gain, causing health and safety problems for consumers. In this context, there is an [...] Read more.

Virgin coconut oil (VCO) is a functional food with important health benefits. Its economic interest encourages fraudsters to deliberately adulterate VCO with cheap and low-quality vegetable oils for financial gain, causing health and safety problems for consumers. In this context, there is an urgent need for rapid, accurate, and precise analytical techniques to detect VCO adulteration. In this study, the use of Fourier transform infrared (FTIR) spectroscopy combined with multivariate curve resolution–alternating least squares (MCR-ALS) methodology was evaluated to verify the purity or adulteration of VCO with reference to low-cost commercial oils such as sunflower (SO), maize (MO) and peanut (PO) oils. A two-step analytical procedure was developed, where an initial control chart approach was designed to assess the purity of oil samples using the MCR-ALS score values calculated on a data set of pure and adulterated oils. The pre-treatment of the spectral data by derivatization with the Savitzky–Golay algorithm allowed to obtain the classification limits able to distinguish the pure samples with 100% of correct classifications in the external validation. In the next step, three calibration models were developed using MCR-ALS with correlation constraints for analysis of adulterated coconut oil samples in order to assess the blend composition. Different data pre-treatment strategies were tested to best extract the information contained in the sample fingerprints. The best results were achieved by derivative and standard normal variate procedures obtaining RMSEP and RE% values in the ranges of 1.79–2.66 and 6.48–8.35%, respectively. The models were optimized using a genetic algorithm (GA) to select the most important variables and the final models in the external validations gave satisfactory results in quantifying adulterants, with absolute errors and RMSEP of less than 4.6% and 1.470, respectively. Full article

(This article belongs to the Special Issue New Winds in Chemometrics: Theory and Application)

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16 pages, 4847 KiB

Open AccessArticle

Green Chemometric Determination of Cefotaxime Sodium in the Presence of Its Degradation Impurities Using Different Multivariate Data Processing Tools; GAPI and AGREE Greenness Evaluation

by Yasmine Ahmed Sharaf, Adel Ehab Ibrahim, Sami El Deeb and Rania Adel Sayed

Molecules 2023, 28(5), 2187; https://doi.org/10.3390/molecules28052187 - 26 Feb 2023

Cited by 13 | Viewed by 2045

Abstract

Four eco-friendly, cost-effective, and fast stability-indicating UV-VIS spectrophotometric methods were validated for cefotaxime sodium (CFX) determination either in the presence of its acidic or alkaline degradation products. The applied methods used multivariate chemometry, namely, classical least square (CLS), principal component regression (PCR), partial [...] Read more.

Four eco-friendly, cost-effective, and fast stability-indicating UV-VIS spectrophotometric methods were validated for cefotaxime sodium (CFX) determination either in the presence of its acidic or alkaline degradation products. The applied methods used multivariate chemometry, namely, classical least square (CLS), principal component regression (PCR), partial least square (PLS), and genetic algorithm-partial least square (GA-PLS), to resolve the analytes’ spectral overlap. The spectral zone for the studied mixtures was within the range from 220 to 320 nm at a 1 nm interval. The selected region showed severe overlap in the UV spectra of cefotaxime sodium and its acidic or alkaline degradation products. Seventeen mixtures were used for the models’ construction, and eight were used as an external validation set. For the PLS and GA-PLS models, a number of latent factors were determined as a pre-step before the modelsʹ construction and found to be three for the (CFX/acidic degradants) mixture and two for the (CFX/alkaline degradants) mixture. For GA-PLS, spectral points were minimized to around 45% of the PLS models. The root mean square errors of prediction were found to be (0.19, 0.29, 0.47, and 0.20) for the (CFX/acidic degradants) mixture and (0.21, 0.21, 0.21, and 0.22) for the (CFX/alkaline degradants) mixture for CLS, PCR, PLS, and GA-PLS, respectively, indicating the excellent accuracy and precision of the developed models. The linear concentration range was studied within 12–20 μg mL^–1 for CFX in both mixtures. The validity of the developed models was also judged using other different calculated tools such as root mean square error of cross validation, percentage recoveries, standard deviations, and correlation coefficients, which indicated excellent results. The developed methods were also applied to the determination of cefotaxime sodium in marketed vials, with satisfactory results. The results were statistically compared to the reported method, revealing no significant differences. Furthermore, the greenness profiles of the proposed methods were assessed using the GAPI and AGREE metrics. Full article

(This article belongs to the Special Issue New Winds in Chemometrics: Theory and Application)

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19 pages, 1538 KiB

Open AccessArticle

Comparison of Multivariate ANOVA-Based Approaches for the Determination of Relevant Variables in Experimentally Designed Metabolomic Studies

by Miriam Pérez-Cova, Stefan Platikanov, Dwight R. Stoll, Romà Tauler and Joaquim Jaumot

Molecules 2022, 27(10), 3304; https://doi.org/10.3390/molecules27103304 - 20 May 2022

Cited by 6 | Viewed by 2857

Abstract

The use of chemometric methods based on the analysis of variances (ANOVA) allows evaluation of the statistical significance of the experimental factors used in a study. However, classical multivariate ANOVA (MANOVA) has a number of requirements that make it impractical for dealing with [...] Read more.

The use of chemometric methods based on the analysis of variances (ANOVA) allows evaluation of the statistical significance of the experimental factors used in a study. However, classical multivariate ANOVA (MANOVA) has a number of requirements that make it impractical for dealing with metabolomics data. For this reason, in recent years, different options have appeared that overcome these limitations. In this work, we evaluate the performance of three of these multivariate ANOVA-based methods (ANOVA simultaneous component analysis—ASCA, regularized MANOVA–rMANOVA, and Group-wise ANOVA-simultaneous component analysis—GASCA) in the framework of metabolomics studies. Our main goals are to compare these various ANOVA-based approaches and evaluate their performance on experimentally designed metabolomic studies to find the significant factors and identify the most relevant variables (potential markers) from the obtained results. Two experimental data sets were generated employing liquid chromatography coupled to mass spectrometry (LC-MS) with different complexity in the design to evaluate the performance of the statistical approaches. Results show that the three considered ANOVA-based methods have a similar performance in detecting statistically significant factors. However, relevant variables pointed by GASCA seem to be more reliable as there is a strong similarity with those variables detected by the widely used partial least squares discriminant analysis (PLS-DA) method. Full article

(This article belongs to the Special Issue New Winds in Chemometrics: Theory and Application)

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14 pages, 966 KiB

Open AccessArticle

Composition of Fatty Acids in Bone Marrow of Red Deer from Various Ecosystems and Different Categories

by Żaneta Steiner-Bogdaszewska, Katarzyna Tajchman, Piotr Domaradzki and Mariusz Florek

Molecules 2022, 27(8), 2511; https://doi.org/10.3390/molecules27082511 - 13 Apr 2022

Cited by 2 | Viewed by 2166

Abstract

In this study, the influence of the living conditions of red deer (Cervus elaphus) fawns (wild vs. farmed) and effect of the category of free-living animals (fawns vs. does) on the fatty acid (FA) profile of the leg bone marrow was [...] Read more.

In this study, the influence of the living conditions of red deer (Cervus elaphus) fawns (wild vs. farmed) and effect of the category of free-living animals (fawns vs. does) on the fatty acid (FA) profile of the leg bone marrow was assessed. The composition of FAs in the deer bone marrow was determined by the gas chromatography method. In all groups, oleic acid (18:1 c9) was the most abundant in deer bone marrow and comprised of approximately 37% of total FAs. The bone marrow of young wild deer was characterized by a significantly (p < 0.001) higher fat content and saturated FAs proportion, while farmed fawns contained more moisture (p < 0.005) and fat-free dry matter (p < 0.001), as well as more monounsaturated FAs cis branched-chain FAs and monounsaturated FAs trans (p < 0.001). Although no significant (p > 0.05) differences were found between fawns, in terms of partial sums of PUFA, a significantly (p < 0.001) higher level of the sum of n-3 and n-6 FAs and more favorable n-6/n-3 ratio in the bone marrow of wild fawns were determined. In general, the legs of wild fawns were better prepared for wintering than farmed ones. In turn, comparing the category-related FAs composition in the bone marrow of free-living animals, a more favorable profile was observed in the adult (does) than in the young (fawns) animals, as the bone marrow of the wild does was characterized by significantly (p < 0.001) lower percentages of saturated FAs and a higher percentage of monounsaturated FAs cis. Full article

(This article belongs to the Special Issue New Winds in Chemometrics: Theory and Application)

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13 pages, 1779 KiB

Open AccessArticle

Preclassification of Broadband and Sparse Infrared Data by Multiplicative Signal Correction Approach

by Hafeez Ur Rehman, Valeria Tafintseva, Boris Zimmermann, Johanne Heitmann Solheim, Vesa Virtanen, Rubina Shaikh, Ervin Nippolainen, Isaac Afara, Simo Saarakkala, Lassi Rieppo, Patrick Krebs, Polina Fomina, Boris Mizaikoff and Achim Kohler

Molecules 2022, 27(7), 2298; https://doi.org/10.3390/molecules27072298 - 1 Apr 2022

Cited by 4 | Viewed by 2668

Abstract

Preclassification of raw infrared spectra has often been neglected in scientific literature. Separating spectra of low spectral quality, due to low signal-to-noise ratio, presence of artifacts, and low analyte presence, is crucial for accurate model development. Furthermore, it is very important for sparse [...] Read more.

Preclassification of raw infrared spectra has often been neglected in scientific literature. Separating spectra of low spectral quality, due to low signal-to-noise ratio, presence of artifacts, and low analyte presence, is crucial for accurate model development. Furthermore, it is very important for sparse data, where it becomes challenging to visually inspect spectra of different natures. Hence, a preclassification approach to separate infrared spectra for sparse data is needed. In this study, we propose a preclassification approach based on Multiplicative Signal Correction (MSC). The MSC approach was applied on human and the bovine knee cartilage broadband Fourier Transform Infrared (FTIR) spectra and on a sparse data subset comprising of only seven wavelengths. The goal of the preclassification was to separate spectra with analyte-rich signals (i.e., cartilage) from spectra with analyte-poor (and high-matrix) signals (i.e., water). The human datasets 1 and 2 contained 814 and 815 spectra, while the bovine dataset contained 396 spectra. A pure water spectrum was used as a reference spectrum in the MSC approach. A threshold for the root mean square error (

R M S E

) was used to separate cartilage from water spectra for broadband and the sparse spectral data. Additionally, standard noise-to-ratio and principle component analysis were applied on broadband spectra. The fully automated MSC preclassification approach, using water as reference spectrum, performed as well as the manual visual inspection. Moreover, it enabled not only separation of cartilage from water spectra in broadband spectral datasets, but also in sparse datasets where manual visual inspection cannot be applied. Full article

(This article belongs to the Special Issue New Winds in Chemometrics: Theory and Application)

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18 pages, 12046 KiB

Open AccessArticle

The Use of Constituent Spectra and Weighting in Extended Multiplicative Signal Correction in Infrared Spectroscopy

by Johanne Heitmann Solheim, Boris Zimmermann, Valeria Tafintseva, Simona Dzurendová, Volha Shapaval and Achim Kohler

Molecules 2022, 27(6), 1900; https://doi.org/10.3390/molecules27061900 - 15 Mar 2022

Cited by 9 | Viewed by 3067

Abstract

Extended multiplicative signal correction (EMSC) is a widely used preprocessing technique in infrared spectroscopy. EMSC is a model-based method favored for its flexibility and versatility. The model can be extended by adding constituent spectra to explicitly model-known analytes or interferents. This paper addresses [...] Read more.

Extended multiplicative signal correction (EMSC) is a widely used preprocessing technique in infrared spectroscopy. EMSC is a model-based method favored for its flexibility and versatility. The model can be extended by adding constituent spectra to explicitly model-known analytes or interferents. This paper addresses the use of constituent spectra and demonstrates common pitfalls. It clarifies the difference between analyte and interferent spectra, and the importance of orthogonality between model spectra. Different normalization approaches are discussed, and the importance of weighting in the EMSC is demonstrated. The paper illustrates how constituent analyte spectra can be estimated, and how they can be used to extract additional information from spectral features. It is shown that the EMSC parameters can be used in both regression tasks and segmentation tasks. Full article

(This article belongs to the Special Issue New Winds in Chemometrics: Theory and Application)

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15 pages, 18588 KiB

Open AccessArticle

Preprocessing Strategies for Sparse Infrared Spectroscopy: A Case Study on Cartilage Diagnostics

by Valeria Tafintseva, Tiril Aurora Lintvedt, Johanne Heitmann Solheim, Boris Zimmermann, Hafeez Ur Rehman, Vesa Virtanen, Rubina Shaikh, Ervin Nippolainen, Isaac Afara, Simo Saarakkala, Lassi Rieppo, Patrick Krebs, Polina Fomina, Boris Mizaikoff and Achim Kohler

Molecules 2022, 27(3), 873; https://doi.org/10.3390/molecules27030873 - 27 Jan 2022

Cited by 10 | Viewed by 3878

Abstract

The aim of the study was to optimize preprocessing of sparse infrared spectral data. The sparse data were obtained by reducing broadband Fourier transform infrared attenuated total reflectance spectra of bovine and human cartilage, as well as of simulated spectral data, comprising several [...] Read more.

The aim of the study was to optimize preprocessing of sparse infrared spectral data. The sparse data were obtained by reducing broadband Fourier transform infrared attenuated total reflectance spectra of bovine and human cartilage, as well as of simulated spectral data, comprising several thousand spectral variables into datasets comprising only seven spectral variables. Different preprocessing approaches were compared, including simple baseline correction and normalization procedures, and model-based preprocessing, such as multiplicative signal correction (MSC). The optimal preprocessing was selected based on the quality of classification models established by partial least squares discriminant analysis for discriminating healthy and damaged cartilage samples. The best results for the sparse data were obtained by preprocessing using a baseline offset correction at 1800 cm⁻¹, followed by peak normalization at 850 cm⁻¹ and preprocessing by MSC. Full article

(This article belongs to the Special Issue New Winds in Chemometrics: Theory and Application)

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