Evaluation of Chemotherapeutic Activity of the Selected Bases' Analogues of Nucleic Acids Supported by ab initio Various Quantum Chemical Calculations.

BACKGROUND: Pharmacological and physicochemical classification of bases' selected analogues of nucleic acids is proposed in the study. OBJECTIVE: Structural parameters received by the PCM (Polarizable Continuum Model) with several types of calculation methods for the structures in vacuo and in the aquatic environment together with the huge set of extra molecular descriptors obtained by the professional software and literature values of biological activity were used to search the relationships. METHODS: Principal Component Analysis (PCA) together with Factor Analysis (FA) and Multiple Linear Regressions (MLR) as the types of the chemometric approach based on semi-empirical ab initio molecular modeling studies were performed. RESULTS: The equations with statistically significant descriptors were proposed to demonstrate both the common and differentiating characteristics of the bases' analogues of nucleic acids based on the quantum chemical calculations and biological activity data. CONCLUSION: The obtained QSAR models can be used for predicting and explaining the activity of studied molecules.

Activity Evaluation and Selection of Some Classes of Antibiotics with the use of Semi-Empirical Quantum Mechanics and Quantitative Structure- Activity Relationships Approach.

BACKGROUND: A set of ß-lactam antibiotics, aminoglycoside antibiotics, and tetracycline antibiotics were proposed and analyzed with the use of Quantitative Structure-Activity Relationships (QSAR) method. OBJECTIVE: The characterization of selected antimicrobial compounds in terms of both physicochemical and pharmacological on the basis of calculations of quantum mechanics and possessed biological activity data. METHODS: During the study, Multiple Linear Regression (MLR) supported with Factor Analysis (FA) and Principal Component Analysis (PCA) was made, as the types of proposed chemometric approach; the semi-empirical level of in silico molecular modeling was used for calculations and comparison of molecular descriptors both in a vacuum and in the aquatic environment. RESULTS: The relationships between structure and microbiological activity enabled the characterization and description of the analyzed molecules using statistically significant descriptors belonging in most cases to different structural, geometric and electronic elements defining at the same time the properties of the studied three different classes of examined antibiotics. CONCLUSION: The chemometric methods used revealed the influence of some of the elements of structures examined molecules belonging to main antibiotics classes and responsible for the antimicrobial activity.

Activity evaluation of some psychoactive drugs with the application of QSAR/QSPR modeling methods.

A set of psychoactive drugs has been analyzed with the use of quantitative structure-activity/property relationships methods. The purpose of this study was to demonstrate both the common and differentiating characteristics of the above-mentioned chemical compounds, physicochemical as well as pharmacological based on the quantum chemical calculations and selected biological activity data and chromatographic retention parameters. During the study, the ab initio model of molecular modeling was performed and PCA, FA, and MLR as the types of chemometric approach. QSAR/QSPR models were proposed based on chosen statistically significant descriptors. The relationship between the structure and biological activity data was able to class and describe the psychoactive properties of the molecules studied. The applied chemometric approaches revealed the influential features of tested structures responsible for their pharmacological activity together with some additional physicochemical properties.

Application of QSAR Analysis and Different Quantum Chemical Calculation Methods in Activity Evaluation of Selected Fluoroquinolones.

BACKGROUND: A set of antibiotic fluoroquinolones with confirmed antimicrobial activity were analyzed with the use of two types of quantum chemical calculation methods and quantitative structure-activity relationships (QSAR). OBJECTIVE: The purpose of this study was to demonstrate the common and differentiating characteristics of the above-mentioned chemical compounds alike physicochemically as well as pharmacologically based on the quantum chemical calculations and microbiological activity data. METHODS: During the study PCA and MLR analysis were performed, as the types of proposed chemometric approach. The semi-empirical level of in silico molecular modeling was performed for calculations of molecular descriptors. RESULTS: QSAR models were proposed based on chosen descriptors. The relationship between the structure and microbiological activity and physicochemical parameters data was able to class and describe them with the use of statistically significant molecular descriptors. CONCLUSION: The applied chemometric approaches revealed the influential features of tested structures responsible for the antimicrobial activity of analyzed compounds.

Chemometric approach to correlations between retention parameters of non-polar HPLC columns and physicochemical characteristics for ampholytic substances of biological and pharmaceutical relevance.

The correlations between the retention parameters of forty ampholytic, biologically active and/or pharmaceutically relevant substances (obtained for three non-polar HPLC columns at various compositions of mobile phases and pH conditions: 2.5, 7.0, 11.5) and their thirty-two physicochemical (calculated/spectral) characteristics were investigated by applying chemometric methods of analysis. In three cases (among seven cases considered), Quantitative Property-Retention Relation (QPRR) models meeting the predictive capability criteria were developed (the values of R2, Q2CV, Q2Ext were higher than 0.76, 0.66 and 0.67, respectively, while values of RMSEC, RMSECV and RMSEExt were lower than 0.51, 0.65 and 0.65 in each developed model). These models create a useful platform for predicting retention parameters of untested chemicals and, to some extent, gaining pharmaceutically valuable information on the biologically active ampholytic substances based on their properties and the conditions of chromatographic separation.

QSPR analysis of some agonists and antagonists of α-adrenergic receptors.

Thirty-three compounds belonging to the sympatholytics and sympathomimetics were analyzed during the study. The biological activity data for the parameters of binding affinity to the α1- and α2-adrenergic receptors together with parameters of the logarithm of the partition coefficient n-octanol/water (log P) were performed using a semi-empirical calculations methods for isolated molecules (in vacuo) and for the molecules placed in an aqueous environment. Additionally, the chromatographic retention data were used as extra dependent variables of the structural parameters for a part of the considered compounds. Finally, all those groups of parameters were analyzed using MLR, PCA, and FA methods for the classification of studied compounds according to their chemical structures and pharmacological activity to the adrenoceptors.

The comparison between the calculated and HPLC-predicted lipophilicity parameters for selected groups of drugs.

The parameters of lipophilicity for three different groups of drugs (twelve analgesics drugs, eleven cardiovascular system drugs, and thirty six compounds characterized by divergent pharmacological activity) were experimentally determined by HPLC methods as well as calculated using various computer programs (HyperChem, ACD/Labs, ChemAxon, Dragon and VCCLab). The relationships between experimental (chromatographic) parameters of lipophilicity (log k and log kw) and the chemical structure of the studied compounds, and their comparison due to their lipophilic and hydrophilic character were presented. Moreover, the experimental and calculated values of parameters of lipophilicity were correlated and compared. Finally, both these groups of parameters of lipophilicity were analyzed using PCA or FA methods for the classification of studied compounds according to their chemical structures and pharmacological activity.

Chemometric exploration of the dependencies between molecular modeling descriptors and analytical chemistry data of antihistaminic drugs.

The relationships between experimental and computational descriptors of antihistamine drugs were studied using principal component analysis (PCA). Empirical data came from UV and IR spectroscopic measurements. Nonempirical data, such as structural molecular descriptors and chromatographic data, were obtained from HyperChem software. Another objective was to test whether the parameters used as independent variables (nonempirical and empirical-spectroscopic) could lead to attaining classification similar to that developed on the basis of the chromatographic parameters. To arrive at the answer to the question, a matrix of 18x49 data, including HPLC and UV and IR spectroscopic data, together with molecular modeling studies, was evaluated by the PCA method. The obtained clusters of drugs were consistent with the drugs' chemical structure classification. Moreover, the PCA method applied to the HPLC retention data and structural descriptors allowed for classification of the drugs according to their pharmacological properties; hence it may potentially help limit the number of biological assays in the search for new drugs.

Pharmacological classification and activity evaluation of furan and thiophene amide derivatives applying semi-empirical ab initio molecular modeling methods.

Pharmacological and physicochemical classification of the furan and thiophene amide derivatives by multiple regression analysis and partial least square (PLS) based on semi-empirical ab initio molecular modeling studies and high-performance liquid chromatography (HPLC) retention data is proposed. Structural parameters obtained from the PCM (Polarizable Continuum Model) method and the literature values of biological activity (antiproliferative for the A431 cells) expressed as LD(50) of the examined furan and thiophene derivatives was used to search for relationships. It was tested how variable molecular modeling conditions considered together, with or without HPLC retention data, allow evaluation of the structural recognition of furan and thiophene derivatives with respect to their pharmacological properties.

Pharmacological classification of drugs by principal component analysis applying molecular modeling descriptors and HPLC retention data.

Pharmacological classification of drugs by principal component analysis (PCA) based on molecular modeling and high-performance liquid chromatography (HPLC) retention data is proposed. First, a group of 20 drugs of recognized pharmacological classification are chromatographed in eight diversified HPLC systems, applying columns with octadecylsilanes, phosphatidylcholine, as well as α1-glycoprotein and albumin. Additionally, molecular modeling studies, based on the structural formula of the drugs considered, are performed. Sixteen structural descriptors are derived. A matrix of 20 × 24 HPLC data together with molecular parameters are subjected to principal component analysis, and this revealed five main factors with eigenvalues higher than 1. The first principal component (factor 1) accounted for 47.8% of the variance in the data, and the second principal component (factor 2) explained 21.0% of data variance. The total data variance was 82.6% and is explained by the first three factors. The clustering of drugs is in accordance with their pharmacological classification, which proved that the PCA of the HPLC retention data, together with their structural descriptors, allowed the drugs to be segregated accurately to their pharmacological properties. This may be of help in reducing the number of biological assays needed in the development of a new drug.

Principal component analysis of HPLC retention data and molecular modeling structural parameters of cardiovascular system drugs in view of their pharmacological activity.

Evaluation of relationships between molecular modeling structural parameters and high-performance liquid chromatography (HPLC) retention data of 11 cardiovascular system drugs by principal component analysis (PCA) in relation to their pharmacological activity was performed. The six retention data parameters were determined on three different HPLC columns (Nucleosil C18 AB with octadecylsilica stationary phase, IAM PC C10/C3 with chemically bounded phosphatidylcholine, and Nucleosil 100-5 OH with chemically bounded propanodiole), and using isocratically acetonitrile: Britton-Robinson buffer as the mobile phase. Additionally, molecular modeling studies were performed with the use of HyperChem software and MM+ molecular mechanics with the semi-empirical AM1 method deriving 20 structural descriptors. Factor analysis obtained with the use of various sets of parameters: structural parameters, HPLC retention data, and all 26 considered parameters, led to the extraction of two main factors. The first principal component (factor 1) accounted for 44-57% of the variance in the data. The second principal component (factor 2) explained 29-33% of data variance. Moreover, the total data variance explained by the first two factors was at the level of 73-90%. More importantly, the PCA analysis of the HPLC retention data and structural parameters allows the segregation of circulatory system drugs according to their pharmacological (cardiovascular) properties as shown by the distribution of the individual drugs on the plane determined by the two principal components (factors 1 and 2).

Influence of HPLC retention data and molecular modeling descriptors on prediction of pharmacological classification of drugs using principal component analysis method.

The usage of principal component analysis (PCA) method in prediction of pharmacological classification of the drugs based on high-performance liquid chromatography (HPLC) retention data and on non-empirical structural parameters was studied. A group of 36 drugs of established pharmacological classification were chromatographed in ten carefully designed HPLC systems. Additionally, twelve structural descriptors were derived by molecular modeling studies based on the structural formula of considered drugs. A matrix of 36 x 22 HPLC data together with molecular properties parameters was subjected to chemometric analysis by PCA. Although that size of the training set could be sometimes disputable, the work remains as a demonstration of the basic methodology without the straight focus primarily intended asa report on a comprehensive predictive model. Nevertheless, the obtained clustering of drugs was in accordance with their pharmacological classification as well as chemical structures classification. The PCA method of the HPLC retention data and structural descriptors allowed to segregate drugs and drug candidates according to their pharmacological properties,and may be of potential help to limit the number of biological assays in the search for new drugs.

Evaluation of molecular descriptors and HPLC retention data of analgesic and anti-inflammatory drugs by factor analysis in relation to their pharmacological activity.

Factor analysis (FA) was performed for some analgesic, anti-inflammatory and antipyretic drugs to model relationships between molecular descriptors and HPLC retention parameters. FA performed using 26 sets of structural parameters, 26 sets of HPLC retention data, and considering all parameters together (52 parameters) led to the extraction of two main factors. The first principal component (factor 1) accounted for 65-73% of the variance in the data. The second principal component (factor 2) explained 27-35% of data variance. Moreover, of the 52 parameters tested, the highest influence on factor value was found with chromatographic parameters and selected structural parameters (i.e., energy quantum-chemical parameters and electron affinity specifying parameters). Additionally, the pattern of distribution of individual drugs within the plane determined by the two principal components (factors 1 and 2) was in good agreement with their pharmacological (analgesic, anti-inflammatory and antipyretic) properties. The findings are discussed from the point of view of structure-activity relationships.

Factor analysis of microbiological activity data and structural parameters of antibacterial quinolones.

Factor analysis (FA) was performed on quinolone derivatives with antibacterial activity to model relationships between molecular descriptors and microbiological activities determined on five bacterial cell lines (Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus and Streptococcus pneumoniae). Molecular modeling studies were performed with the use of HyperChem software and MM+ molecular mechanics with the semi-empirical AM1 method. Factor analysis led to the extraction of two main factors, with the share of factor 1 amounting to about 76% and factor 2 to about 24% for all the parameters used in the statistical analysis. Moreover, FA results indicated that energy of orbitals lowest unoccupied molecular orbital, energy of ionization, electron affinity, electronegativity, maximum electron density, refraction and polarizability appeared to be descriptors important for the antibacterial activity of quinolones.