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The classification of anti-inflammatory drugs according to their analgesic, anti- inflammatory and antipyretic activity
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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 m...
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... comparison of particular compounds can be done on the basis of two principal component scores (objects) plots. Principal component scores calculated for all studied compounds and their individual positions on the plane determined by the two factor axes and performed only for structural parameters, only for HPLC retention data, and for all considered above parameters are presented in Table 5 and Table 6. Moreover, it is important to note, that in some previous works [15][16][17] it was established that compounds characterized by identical mechanism of action in the charts of factor analysis form clusters, e.g., classifications of compounds of α- and ß-adrenergic action, antagonists of histamine receptors H 1 and H 2 and psychotropic drugs. ...
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... the case of the first cluster, the most closely were noramidopyrine, piroxicam and sulindac, with further lied nimesulide. All these compounds are characterized by strong (piroxicam and sulindac) or mild (noramidopyrine and nimesulide) analgesic and diverse (low to strong) anti-inflammatory activity, with additional mild antipyretic activity of noramidopyrine and sulindac (Table 6) [31][32][33][34][35][36]. Moreover, all presented compounds possess in their structure sulfur atom. ...
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... scatter diagram (Fig. 3A) a clusters was made by acetylsalicylic acid (ASA), salicylamide (derivative of salicylic acid) with further oriented acetaminophen (derivative of p-aminophenole). Acetaminophen as well as derivatives of salicylic acid are characterized by strong antipyretic and analgesic activity with mild anti- inflammatory properties (Table 6). Additionally, other drugs such as tramadol, aminophenazone, diclofenac and ketorolac as compounds with unsubstituted or chlorine or methoxy-substituted phenyl group with linked some aromatic systems (as pyrazole, o- aminophenylacetic acid residue, pyrrolepyrrolidine or cycloheksanol) form the last cluster characterized by variable analgesic, anti-inflammatory and antipyretic activity (Table 6). ...
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... as well as derivatives of salicylic acid are characterized by strong antipyretic and analgesic activity with mild anti- inflammatory properties (Table 6). Additionally, other drugs such as tramadol, aminophenazone, diclofenac and ketorolac as compounds with unsubstituted or chlorine or methoxy-substituted phenyl group with linked some aromatic systems (as pyrazole, o- aminophenylacetic acid residue, pyrrolepyrrolidine or cycloheksanol) form the last cluster characterized by variable analgesic, anti-inflammatory and antipyretic activity (Table 6). ...
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... the scatter diagram the small cluster containing diclofenac, etodolac and nimesulide was observed, which is rather related to their strong anti-inflammatory and anti-rheumatic activity. Moreover, on that diagram one can also identify two other clusters comprising 1) piroxicam, ketorolac and sulindac, and 2) acetaminophen, noramidopyrine and ASA, what can be connected with mild or strong analgesic activity in the case of compounds from cluster 1), and mild or strong antipyretic activity in the case of compounds from cluster 2) presented above (Table 6). and 2 obtained for all 52 parameters. ...
Citations
... Several techniques have been used for the preconcentration and clean-up of analytes for the detection and quantification of NSAIDs in different matrices for toxicological and therapeutic applications (Baranowska & Kowalski, 2012, Buchberger, 2011, Eric-Jovanovic et al., 1998, Musumarra et al., 1983, Nasal et al., 1997, Sotelo et al., 2012. High-performance liquid chromatography (HPLC) is the more commonly used method for the analysis of NSAIDs in water, biological samples, and pharmaceutical formulations with various detection (Martın et al., 1999, Bhattacharya et al., 2013, Ascar et al., 2013, Cserha´ti & Sz} ogyi, 2012, Fillet et al., 1999, Klimesˇet al., 2001, Koba et al., 2010, Kubala et al., 1993, Mulgund et al., 2009. Additionally, HPLC coupled with various methods such as solid-phase microextraction (SPME)-HPLC (de Oliveira et al., 2005), SPE-liquid chromatography (Sarafraz-Yazdi et al., 2012, Wang et al., 2018, thin-film (TF)-SPME with LC-tandem mass spectrometry (LC-MS/MS) (Krummen et al., 2004, Hirai et al., 1997, SPME with polyethylene glycol-grafted MWCNTs using gas chromatography (GC) with flame ionisation detector (de Oliveira et al., 2005), SPE-GC-MS/MS (Costi et al., 2008), hollow-fibre liquid-phase microextraction (HFLPME)-HPLC for antiretroviral drugs in different waters samples (Paya´n et al., 2009), molecular imprinting solidphase microextraction with HPLC (Togunde et al., 2012, Paya´n et al., 2011, electro-membrane extraction using HPLC with diode array (Debska et al., 2005) and fluorescence detection (Farrington & Regan 2007), and potentiometric (Parham et al., 2009), conductometric (Paya´n et al., 2011), and voltametric methods (Ni & Kokot 2008) have been reported. ...
Non-steroidal anti-inflammatory drugs (NSAIDs) are pharmaceutical compounds with anti-inflammatory, analgesic, and antipyretic effects. Herein, a simple and rapid high-temperature liquid chromatography and superheated water chromatography method was developed and validated for the trace determination of NSAID residues of ketoprofen, naproxen, sodium diclofenac, and ibuprofen in water samples. The NSAIDs were separated in less than five minutes using buffered distilled water as the mobile phase and ODS Zirconia RP-C18 column as the stationary phase. Linearity was observed in the van’t Hoff plots of the tested drugs by employing a low acetonitrile percentage (20% ACN) in the mobile phase, without any significant changes in their retention mechanisms. However, nonlinear van’t Hoff plots were obtained for the superheated water chromatography data of the tested drugs because of significant changes in their retention factors, transition stage of the stationary phase, or the mobile phase properties. The limits of detection for ketoprofen, naproxen, sodium diclofenac, and ibuprofen were 14, 2, 4.2, and 32 µg L⁻¹, respectively, and their limits of quantification were 44, 8, 12, and 98 µg L⁻¹, respectively. The accuracy and precision parameters were determined for selected drugs, where the relative standard deviations were in the range of ±0.2179–2.6741%. In addition, these conditions were employed for the removal of NSAIDs from the water samples using carbon nanotubes. The proposed system was applied for the separation and analyses of drugs in water and pharmaceutical samples, and acceptable recoveries of 90.48–98.15% for the water samples and 99.9–100.08% for the pharmaceutical samples were obtained.
... PCA analysis was proposed to identify the relationship between certain significant structural descriptors derived from chemical structure and pharmacological activity of compounds (6)(7)(8)(9)(10). Numerous reports are available concerning application of chromatographic data to solve multiple analytical problems and evaluate the quantitative structureretention relationships in terms of specific pharmacological activity (8,(10)(11)(12). The approach was adopted to reveal the differences in the drug-stationary phase binding among the analytes and demonstrate them based on hydrophobicity and structural descriptors identified in the molecular modeling. ...
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.
... PCA analysis was proposed to identify the relationship between certain significant structural descriptors derived from chemical structure and pharmacological activity of compounds (6)(7)(8)(9)(10). Numerous reports are available concerning application of chromatographic data to solve multiple analytical problems and evaluate the quantitative structureretention relationships in terms of specific pharmacological activity (8,(10)(11)(12). The approach was adopted to reveal the differences in the drug-stationary phase binding among the analytes and demonstrate them based on hydrophobicity and structural descriptors identified in the molecular modeling. ...
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 18 × 49 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 of a large set of drugs can be predicted on the basis of HPLC retention data using the chemometric method of analysis as the principal component analysis (PCA) (7-10). Therefore, structural descriptors derived by calculation chemistry, or based solely on the structural formula of a given compound in combination with HPLC retention data, can be found as a value of a wide application in QSAR analysis for predicting the pharmacological classification of drugs with the use of PCA (11)(12)(13). ...
... It is also important to note that antibacterial sulphonamides are active against chlamydia and both Gram-positive and Gram-negative bacteria, and inhibit folic acid biosynthesis in prokaryotes by blocking the synthesis of dihydrofolic acid by inhibition of the dihydropteroate synthase (16). The positions of compounds such as sulphacetamide (10), sulphacarbamide (11), and sulphaguanidine (12) with sulphanilamide (9), a little further away form cluster (see cluster IA in Figure 3), comprising un-substituted sulphonamides and N1substituted by non-heterocyclic groups such as acetylcarbonyl-, aminocarbonyl-, and aminoiminometyl-. The points on the diagram of compounds such as sulphamerazine (13), sulphadiazine (14), sulphamoxol (17), sulphamethoxazole (18), sulphathiazole (19), and sulphamethazine (20) form the next cluster (see cluster IB in Figure 3), comprising sulphonamides N1-substituted by five-or six-atoms heterocyclic groups (thiazole or a pyrimidine ring) being un-substituted or methyl-substituted (isoxazole and oxazole, or a pyrimidine ring), compared to the cluster (see cluster IC in Figure 3) including other chemically structural sulphonamides such as sulphadimetxoxine (15) with two methoxyl-group linked to an N1-substituted by sixatoms heterocyclic pyrimidine ring, and sulpaquinoxaline (16) with N1-substituted by two six-atoms heterocyclic quinoxaline group. ...
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.
... The PCA method as so far was used for the pharmacological classification of a large set of drugs based on HPLC retention data1718192021. Recently, PCA of HPLC retention data in combination with molecular modeling structural parameters found a wide application in QSAR analysis for pharmacological classification of drugs [22]. The goal of the present study was to determine the relationships between HPLC retention parameters of a series of compounds differing in chemical structure and characterized by cardiac pharmacological activity and their structural parameters obtained by molecular modeling calculations applying the PCA method. ...
... Moreover, in the set of structural parameters (Figure 2a) the factor 1 depended mostly on total energy (TE), binding energy (BE), atom interaction energy (IAE), electronic energy (EE), core-core interaction energy (ECC), surface area of the molecule available for solvent (SA), volume of molecule (V), refraction (R), polarizability (P), whereas factor 2 depended mostly on heat of formation (HF), lowest unoccupied molecular orbital energy (ELUMO), the " hardness " of molecules (HARD), the value of the highest positive charge of atoms that constitute a molecule (MAX_POS), the difference between the highest positive and negative charges of atoms constituting a molecule (DELTA), and the logarithm of the n-octanol-water partition coefficient (LOG_P). The presented data are in accordance with our previous results [22] obtained for some antipyretic, anti-inflammatory and analgesic drugs, and showed that factor 1 presented mainly properties connected with molecular (size) bulkiness (like SA, V, R, P or TE), whereas factor 2 presented properties related to electronic propertied (like ELUMO, MAX_POS, or DELTA). ...
... On the other hand, factor 2 depended on log k w parameters obtained on a Nucleosil C18 AB column packed with octadecylsilica also at both 2.5 and 7.0 pH. However, these results are contrary to data obtained for some antipyretic, anti-inflammatory and analgesic drugs [22], which showed that factor 1 depended mostly on chromatographically data (log k w ) obtained only on a Nucleosil C18 AB column, whereas factor 2 depended mainly on log k w parameters obtained on columns packed with stationary phases other than octadecylsilica. ...
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).
A simple and precise reversed-phase high-performance liquid chromatography method for the separation and trace determination of selected non-steroidal anti-inflammatory drugs (ketoprofen, naproxen, sodium diclofenac, and ibuprofen) was developed. The proposed method was based on the use of high temperatures with a C18 column and a low volume of acetonitrile as the mobile phase, and the analysis time is less than 8.0 min. The van’t Hoff plots for the tested drugs were linear, suggesting no significant changes in the retention mechanism. The limits of detection for ketoprofen, naproxen, sodium diclofenac, and ibuprofen were found to be 0.00012, 0.00008, 0.00162, and 0.00127 µg L⁻¹, respectively, and their limits of quantification were 0.0004, 0.00025, 0.00541, and 0.00424 µg L⁻¹, respectively. Intraday analysis was performed yielding relative standard deviations (RSD) of less than ±1.5%, whereas the interday RSD was 1.01%. The proposed system was applied for the separation and determination of the drugs in water, urine, and pharmaceutical samples, and acceptable recoveries from 97.20% to 99.6% were obtained.
Biocompatibility, bioactivity, bioconductivity and injectability are the most valuable features of biodegradable polyurethanes for orthopaedic applications. Injectable biomaterials may be used in bone tissue engineering for minimally invasive therapies and other medical applications. Polyurethane composites have been synthesized with concentration of β-tricalcium phosphate in the range of 0–30 wt%. Incorporation of β-tricalcium phosphate leads to an increased glass temperature of soft segments while a decrease of glass temperature was observed for hard segments. Moreover, addition of β-tricalcium phosphate caused an increase in the thermal stability of polyurethane matrix. Porosity has ranged between 27 and 53%. The mechanical and thermal properties of the polyurethane/β-tricalcium phosphate composite samples have been investigated. In vitro degradation tests have been carried out in water, Ringer’s solution and phosphate buffered saline. After 2 weeks incubation in simulated body fluid, scanning electron microscopy observations showed the presence of an inorganic phase deposition which might indicate good bioactivity of the composites. The relationships between their properties and bone regeneration quality were discussed as the composites demonstrate vast potential to be used as injectable scaffolds for bone repair.
In this work three different groups of drugs as 12 analgesic drugs, 11 cardiovascular system drugs and 36 "other" compounds, respectively, were analyzed with cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA) methods. All chemometric analysis were based on the chromatographic parameters (logk and logkw) determined by means high-performance liquid chromatography (HPLC) and also molecular modeling descriptors calculated using various computer programs (HyperChem, Dragon, and the VCCLAB). The clustering of compounds obtained by CA (using various algorithm as e.g. Ward method or unweighted pair-group method using arithmetic averages and also Euclidean or Manhattan distance) allowed to build dendrograms linked drugs with similar physicochemical and pharmacological properties and were discussed. Moreover, the analysis performed with the use of FA or PCA method indicated the almost all information reached in input chromatographic parameters and also molecular modeling descriptors are explained by first two factors for analyzed groups of compounds. Additionally drugs are clustered according to their chemical structure and pharmacological activity. Summarized, the performed classification analysis of studied drugs were focused similarities and differences in used chemometric methods as well as abilities to drugs classification (clustering) on the basis chromatographic experimental and molecular modeling data according to their molecular structures and pharmacological activity. Thus, the most important application of statistically important molecular descriptors from QSRR models to classification analysis allowed detailing biological (pharmacological) classification of analyzed drugs.
Free silanols on the surface of silica are the “villains”, which are responsible for detrimental interactions of those compounds
and the stationary phase (i.e., bad peak shape, low efficiency) as well as low thermal and chemical stability. For these reasons,
we began this review describing new silica and hybrid silica stationary phases, which have reduced and/or shielded silanols.
At present, in liquid chromatography for the majority of analyses, reversed-phase liquid chromatography is the separation
mode of choice. However, the needs for increased selectivity and increased retention of hydrophilic bases have substantially
increased the interest in hydrophilic interaction chromatography (HILIC). Therefore, stationary phases and this mode of separation
are discussed. Then, non-silica stationary phases (i.e., zirconium oxide, titanium oxide, alumina and porous graphitized carbon),
which afford increased thermal and chemical stability and also selectivity different from those obtained with silica and hybrid
silica, are discussed. In addition, the use of these materials in HILIC is also reviewed.
In this paper the quantitative structure-retention relationships (QSRR) studies for three different groups of drugs (cardiovascular system drugs, analgesic drugs and some compounds characterized by divergent pharmacological activity) and for chromatographic parameters (log k’ or log k’w retention factors) determined with the use of the HPLC methods were performed. Molecular descriptors (over 4900 molecular modeling parameters) obtained using the HyperChem and the Dragon computer programs, and the Virtual Computational Chemistry Laboratory (VCCLAB) website were applied to derive the QSRR equations by means multiple linear regression method with stepwise procedure. Several statistically significant QSRR equations as two-parameters for both cardiovascular and analgesic drugs, and seven-parameters for group of “other” drugs were built. Six QSRR equations with correlations R ranged from 0.9822 to 0.9957 were obtained for cardiovascular drugs, twenty-six QSRRs with R equal to 0.9120-0.9776 were derived for analgesics, and ten QSRRs with R from 0.9529 to 0.9924 were calculated for “other“ drugs. Moreover, the proposed QSRR models give important information about physico-chemical properties of analyzed drugs, and indicated that descriptors characterized topology, geometry and lipophilicity of molecular structures of analyzed compounds are crucial for prediction of their retention parameters. Additionally, derived QSRR models can be helpfully to search (to prediction) of HPLC retention factor for the new drug candidates.
