Dataset Information

Combined 2D-QSAR, Principal Component Analysis and Sensitivity Analysis Studies on Fluoroquinolones' Genotoxicity.

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 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.

SUBMITTER: Du M

PROVIDER: S-EPMC6862474 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Combined 2D-QSAR, Principal Component Analysis and Sensitivity Analysis Studies on Fluoroquinolones' Genotoxicity.

Du Meijin M Zhang Dan D Hou Yilin Y Zhao Xiaohui X Li Yu Y

International journal of environmental research and public health 20191028 21

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 chemi ...[more]

PMID: 31661905

Similar Datasets

Project description:BackgroundPrincipal component analysis is used to summarize matrix data, such as found in transcriptome, proteome or metabolome and medical examinations, into fewer dimensions by fitting the matrix to orthogonal axes. Although this methodology is frequently used in multivariate analyses, it has disadvantages when applied to experimental data. First, the identified principal components have poor generality; since the size and directions of the components are dependent on the particular data set, the components are valid only within the data set. Second, the method is sensitive to experimental noise and bias between sample groups. It cannot reflect the experimental design that is planned to manage the noise and bias; rather, it estimates the same weight and independence to all the samples in the matrix. Third, the resulting components are often difficult to interpret. To address these issues, several options were introduced to the methodology. First, the principal axes were identified using training data sets and shared across experiments. These training data reflect the design of experiments, and their preparation allows noise to be reduced and group bias to be removed. Second, the center of the rotation was determined in accordance with the experimental design. Third, the resulting components were scaled to unify their size unit.ResultsThe effects of these options were observed in microarray experiments, and showed an improvement in the separation of groups and robustness to noise. The range of scaled scores was unaffected by the number of items. Additionally, unknown samples were appropriately classified using pre-arranged axes. Furthermore, these axes well reflected the characteristics of groups in the experiments. As was observed, the scaling of the components and sharing of axes enabled comparisons of the components beyond experiments. The use of training data reduced the effects of noise and bias in the data, facilitating the physical interpretation of the principal axes.ConclusionsTogether, these introduced options result in improved generality and objectivity of the analytical results. The methodology has thus become more like a set of multiple regression analyses that find independent models that specify each of the axes.

Dataset Information

Combined 2D-QSAR, Principal Component Analysis and Sensitivity Analysis Studies on Fluoroquinolones' Genotoxicity.

Publications

Combined 2D-QSAR, Principal Component Analysis and Sensitivity Analysis Studies on Fluoroquinolones' Genotoxicity.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets