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Keywords = hologram quantitative structure-activity relationship (HQSAR)

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19 pages, 15361 KiB  
Article
Chemoinformatics Studies on a Series of Imidazoles as Cruzain Inhibitors
by Alex R. Medeiros, Leonardo L. G. Ferreira, Mariana L. de Souza, Celso de Oliveira Rezende Junior, Rocío Marisol Espinoza-Chávez, Luiz Carlos Dias and Adriano D. Andricopulo
Biomolecules 2021, 11(4), 579; https://doi.org/10.3390/biom11040579 - 15 Apr 2021
Cited by 14 | Viewed by 3684
Abstract
Natural products based on imidazole scaffolds have inspired the discovery of a wide variety of bioactive compounds. Herein, a series of imidazoles that act as competitive and potent cruzain inhibitors was investigated using a combination of ligand- and structure-based drug design strategies. Quantitative [...] Read more.
Natural products based on imidazole scaffolds have inspired the discovery of a wide variety of bioactive compounds. Herein, a series of imidazoles that act as competitive and potent cruzain inhibitors was investigated using a combination of ligand- and structure-based drug design strategies. Quantitative structure–activity relationships (QSARs) were generated along with the investigation of enzyme–inhibitor molecular interactions. Predictive hologram QSAR (HQSAR, r2pred = 0.80) and AutoQSAR (q2 = 0.90) models were built, and key structural properties that underpin cruzain inhibition were identified. Moreover, comparative molecular field analysis (CoMFA, r2pred = 0.81) and comparative molecular similarity indices analysis (CoMSIA, r2pred = 0.73) revealed 3D molecular features that strongly affect the activity of the inhibitors. These findings were examined along with molecular docking studies and were highly compatible with the intermolecular contacts that take place between cruzain and the inhibitors. The results gathered herein revealed the main factors that determine the activity of the imidazoles studied and provide novel knowledge for the design of improved cruzain inhibitors. Full article
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14 pages, 3710 KiB  
Article
Combined 2D-QSAR, Principal Component Analysis and Sensitivity Analysis Studies on Fluoroquinolones’ Genotoxicity
by Meijin Du, Dan Zhang, Yilin Hou, Xiaohui Zhao and Yu Li
Int. J. Environ. Res. Public Health 2019, 16(21), 4156; https://doi.org/10.3390/ijerph16214156 - 28 Oct 2019
Cited by 32 | Viewed by 3693
Abstract
In this paper, two-dimensional quantitative structure–activity relationship (2D-QSAR) and principal component analysis (PCA) methods were employed to screen the main parameters affecting the genotoxicity of fluoroquinolones (FQs), and the rules affecting the genetic toxicity of FQs were investigated by combining 2D-QSAR and PCA [...] Read more.
In this paper, two-dimensional quantitative structure–activity relationship (2D-QSAR) and principal component analysis (PCA) methods were employed to screen the main parameters affecting the genotoxicity of fluoroquinolones (FQs), and the rules affecting the genetic toxicity of FQs were investigated by combining 2D-QSAR and PCA with the sensitivity analysis method. First, four types of parameters were calculated, namely, the geometric parameters (7), electronic parameters (5), physical and chemical parameters (8), and spectral parameters (7), but the physical and chemical parameters heat of formation (HF) and critical volume (CV) were excluded after the establishment of the 2D-QSAR model. Then, after PCA, it was found that the first principal component represented the main driving factors affecting the molecular genetic toxicity of FQs. In addition, after comprehensive analysis of the factor loading of the first, second, and third principal components, seven parameters affecting the genotoxicity of the FQs were screened out, namely, total energy (TE), critical temperature (CT), and molecular weight (Mol Wt) (increased with increasing genotoxicity of the FQs) and steric parameter (MR), quadrupole moment QXX (QXX), quadrupole moment QYY (QYY), and boiling point (BP) (decreased with increasing genotoxicity of the FQs); the above key parameters were also verified by sensitivity analysis. The obtained rules could be used to determine the substitution sites and the substitution groups associated with higher genotoxicity in the process of FQ modification, and these rules agreed well with the hologram quantitative structure–activity relationship (HQSAR) model. Finally, it was also found through SPSS analysis that the parameters screened in this paper were significantly correlated with FQ derivatives’ genetic toxicity. Full article
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17 pages, 2486 KiB  
Review
Coumarins and P450s, Studies Reported to-Date
by Maryam Foroozesh, Jayalakshmi Sridhar, Navneet Goyal and Jiawang Liu
Molecules 2019, 24(8), 1620; https://doi.org/10.3390/molecules24081620 - 24 Apr 2019
Cited by 40 | Viewed by 6874
Abstract
Cytochrome P450 enzymes (CYPs) are important phase I enzymes involved in the metabolism of endogenous and xenobiotic compounds mainly through mono-oxygenation reactions into more polar and easier to excrete species. In addition to their role in detoxification, they play important roles in the [...] Read more.
Cytochrome P450 enzymes (CYPs) are important phase I enzymes involved in the metabolism of endogenous and xenobiotic compounds mainly through mono-oxygenation reactions into more polar and easier to excrete species. In addition to their role in detoxification, they play important roles in the biosynthesis of endogenous compounds and the bioactivation of xenobiotics. Coumarins, phytochemicals abundant in food and commonly used in fragrances and cosmetics, have been shown to interact with P450 enzymes as substrates and/or inhibitors. In this review, these interactions and their significance in pharmacology and toxicology are discussed in detail. Full article
(This article belongs to the Special Issue Coumarins and Coumarin Derivatives: From Chemistry to Drug)
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22 pages, 4055 KiB  
Article
Complementary Quantitative Structure–Activity Relationship Models for the Antitrypanosomal Activity of Sesquiterpene Lactones
by Njogu M. Kimani, Josphat C. Matasyoh, Marcel Kaiser, Mauro S. Nogueira, Gustavo H. G. Trossini and Thomas J. Schmidt
Int. J. Mol. Sci. 2018, 19(12), 3721; https://doi.org/10.3390/ijms19123721 - 22 Nov 2018
Cited by 12 | Viewed by 4019
Abstract
Three complementary quantitative structure–activity relationship (QSAR) methodologies, namely, regression modeling based on (i) “classical” molecular descriptors, (ii) 3D pharmacophore features, and (iii) 2D molecular holograms (HQSAR) were employed on the antitrypanosomal activity of sesquiterpene lactones (STLs) toward Trypanosoma brucei rhodesiense (Tbr), [...] Read more.
Three complementary quantitative structure–activity relationship (QSAR) methodologies, namely, regression modeling based on (i) “classical” molecular descriptors, (ii) 3D pharmacophore features, and (iii) 2D molecular holograms (HQSAR) were employed on the antitrypanosomal activity of sesquiterpene lactones (STLs) toward Trypanosoma brucei rhodesiense (Tbr), the causative agent of the East African form of human African trypanosomiasis. In this study, an extension of a previous QSAR study on 69 STLs, models for a much larger and more diverse set of such natural products, now comprising 130 STLs of various structural subclasses, were established. The extended data set comprises a variety of STLs isolated and tested for antitrypanosomal activity within our group and is furthermore enhanced by 12 compounds obtained from literature, which have been tested in the same laboratory under identical conditions. Detailed QSAR analyses yielded models with comparable and good internal and external predictive ability. For a set of compounds as chemically diverse as the one under study, the models exhibited good coefficients of determination (R2) ranging from 0.71 to 0.85, as well as internal (leave-one-out Q2 values ranging from 0.62 to 0.72) and external validation coefficients (P2 values ranging from 0.54 to 0.73). The contributions of the various tested descriptors to the generated models are in good agreement with the results of previous QSAR studies and corroborate the fact that the antitrypanosomal activity of STLs is very much dependent on the presence and relative position of reactive enone groups within the molecular structure but is influenced by their hydrophilic/hydrophobic properties and molecular shape. Full article
(This article belongs to the Section Molecular Informatics)
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16 pages, 16820 KiB  
Article
Anti-Mycobacterial Evaluation of 7-Chloro-4-Aminoquinolines and Hologram Quantitative Structure–Activity Relationship (HQSAR) Modeling of Amino–Imino Tautomers
by Marcelle L. F. Bispo, Camilo H. S. Lima, Laura N. F. Cardoso, André L. P. Candéa, Flávio A. F. M. Bezerra, Maria C. S. Lourenço, Maria G. M. O. Henriques, Ricardo B. Alencastro, Carlos R. Kaiser, Marcus V. N. Souza and Magaly G. Albuquerque
Pharmaceuticals 2017, 10(2), 52; https://doi.org/10.3390/ph10020052 - 9 Jun 2017
Cited by 4 | Viewed by 6251
Abstract
In an ongoing research program for the development of new anti-tuberculosis drugs, we synthesized three series (A, B, and C) of 7-chloro-4-aminoquinolines, which were evaluated in vitro against Mycobacterium tuberculosis (MTB). Now, we report the anti-MTB and cytotoxicity evaluations [...] Read more.
In an ongoing research program for the development of new anti-tuberculosis drugs, we synthesized three series (A, B, and C) of 7-chloro-4-aminoquinolines, which were evaluated in vitro against Mycobacterium tuberculosis (MTB). Now, we report the anti-MTB and cytotoxicity evaluations of a new series, D (D01D21). Considering the active compounds of series A (A01A13), B (B01B13), C (C01C07), and D (D01D09), we compose a data set of 42 compounds and carried out hologram quantitative structure–activity relationship (HQSAR) analysis. The amino–imino tautomerism of the 4-aminoquinoline moiety was considered using both amino (I) and imino (II) forms as independent datasets. The best HQSAR model from each dataset was internally validated and both models showed significant statistical indexes. Tautomer I model: leave-one-out (LOO) cross-validated correlation coefficient (q2) = 0.80, squared correlation coefficient (r2) = 0.97, standard error (SE) = 0.12, cross-validated standard error (SEcv) = 0.32. Tautomer II model: q2 = 0.77, r2 = 0.98, SE = 0.10, SEcv = 0.35. Both models were externally validated by predicting the activity values of the corresponding test set, and the tautomer II model, which showed the best external prediction performance, was used to predict the biological activity responses of the compounds that were not evaluated in the anti-MTB trials due to poor solubility, pointing out D21 for further solubility studies to attempt to determine its actual biological activity. Full article
(This article belongs to the Special Issue Chemoinformatics and Drug Design)
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19 pages, 1075 KiB  
Article
Hologram QSAR Models of a Series of 6-Arylquinazolin-4-Amine Inhibitors of a New Alzheimer’s Disease Target: Dual Specificity Tyrosine-Phosphorylation-Regulated Kinase-1A Enzyme
by Felipe Dias Leal, Camilo Henrique Da Silva Lima, Ricardo Bicca De Alencastro, Helena Carla Castro, Carlos Rangel Rodrigues and Magaly Girão Albuquerque
Int. J. Mol. Sci. 2015, 16(3), 5235-5253; https://doi.org/10.3390/ijms16035235 - 6 Mar 2015
Cited by 12 | Viewed by 6251
Abstract
Dual specificity tyrosine-phosphorylation-regulated kinase-1A (DYRK1A) is an enzyme directly involved in Alzheimer’s disease, since its increased expression leads to β-amyloidosis, Tau protein aggregation, and subsequent formation of neurofibrillary tangles. Hologram quantitative structure-activity relationship (HQSAR, 2D fragment-based) models were developed for a series of [...] Read more.
Dual specificity tyrosine-phosphorylation-regulated kinase-1A (DYRK1A) is an enzyme directly involved in Alzheimer’s disease, since its increased expression leads to β-amyloidosis, Tau protein aggregation, and subsequent formation of neurofibrillary tangles. Hologram quantitative structure-activity relationship (HQSAR, 2D fragment-based) models were developed for a series of 6-arylquinazolin-4-amine inhibitors (36 training, 10 test) of DYRK1A. The best HQSAR model (q2 = 0.757; SEcv = 0.493; R2 = 0.937; SE = 0.251; R2pred = 0.659) presents high goodness-of-fit (R2 > 0.9), as well as high internal (q2 > 0.7) and external (R2pred > 0.5) predictive power. The fragments that increase and decrease the biological activity values were addressed using the colored atomic contribution maps provided by the method. The HQSAR contribution map of the best model is an important tool to understand the activity profiles of new derivatives and may provide information for further design of novel DYRK1A inhibitors. Full article
(This article belongs to the Section Physical Chemistry, Theoretical and Computational Chemistry)
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