Optimal neighbor selection in molecular similarity: comparison of arbitrary versus tailored prediction spaces

Three classes of arbitrary quantitative molecular similarity analysis (QMSA) methods have been computed using atom pairs (APs), topological indices (TIs), and principal components (PCs) derived from topological indices. Tailored QMSA models have been developed from TIs selected through ridge regression. K-nearest neighbor (kNN) based estimation has been applied to all of the methods to estimate normal vapor pressure (p(vap)) and water solubility (sol) for a set of 194 chemicals. Results show that the tailored QMSA methods are superior to arbitrary similarity methods in estimating both of these properties for the given set of chemicals.     

Simultaneous determination of two serine proteinases by hydrophobic interaction chromatography

Summary An LC clean-up procedure based upon a complexation between polycyclic aromatic hydrocarbons (PAHs) and silica with chemically bonded 2,4-dinitroaniline has been combined with GC/MS. The LC pre-separation makes it possible to obtain a relatively clean fraction of PAHs free from alkanes, alkylbenzenes and naphthalenes, PCBs, chlorinated pesticides and many other interfering compounds. This fraction has been analyzed using capillary GC and mass selective detector (MSD). Substantial improvement of the MS spectra of PAHs with three or more fused benzene rings is achieved.     

Prediction of the critical temperatures (liquid-vapor) of organic compounds

The ampule method was used to measure the critical temperatures (liquid-vapor) for 1,4-and 1,3-tert-butylbenzenes, 1,3,5-tri-tert-butylbenzene, and 3,5-di-tert-butyltoluene. For alkanes, alkenes, alkylbenzenes, alkylphenols, and alcohols, sets of parameters for the additive components in the Tatevskii bond method and in the method based on the Randic molecular connectivity indices were obtained. The need to create individual correlations for alcohols within the framework of any method in which the normal boiling temperature is used as the basis property was substantiated. The predictive possibilities of various methods were compared. The method based on the Randic molecular connectivity indices was demonstrated to be most effective.     

Optimal characterization of structure for prediction of properties

Different topological and physicochemical parameters have been used to predict hydrophobicity (logP, octanol-water) of chemicals. We calculated a hydrogen bonding parameter (HB₁) and a large number of molecular connectivity and complexity indices for a diverse set of 382 molecules. It is known from earlier studies that topological indices (TIs) predict properties of congeneric sets reasonably well. Since HB₁ is an approximate quantifier of hydrogen bonding and has integral values, we used HB₁ to classify the diverse set into strongly and weakly hydrogen bonding subsets. In an attempt to examine the utility of Us in predicting properties of relatively similar groups of molecules, we carried out a correlation of logP with TIs for a subset (n = 139) of the original diverse set (n = 382) with a weak hydrogen bonding ability (HB₁ = 0). Results show that TIs give a better predictive model for the more homogeneous subset as compared to the diverse set of molecules.     

Comparisons of soxhlet extraction, pressurized liquid extraction, supercritical fluid extraction and subcritical water extraction for environmental solids: recovery, selectivity and effects on sample matrix

Extractions of a polycyclic aromatic hydrocarbon (PAH)-contaminated soil from a former manufactured gas plant site were performed with a Soxhlet apparatus (18 h), by pressurized liquid extraction (PLE) (50 min at 100 degrees C), supercritical fluid extraction (SFE) (1 h at 150 degrees C with pure CO2), and subcritical water (1 h at 250 degrees C, or 30 min at 300 degrees C). Although minor differences in recoveries for some PAHs resulted from the different methods, quantitative agreement between all of the methods was generally good. However, the extract quality differed greatly. The organic solvent extracts (Soxhlet and PLE) were much darker, while the extracts from subcritical water (collected in toluene) were orange, and the extracts from SFE (collected in CH2Cl2) were light yellow. The organic solvent extracts also yielded more artifact peaks in the gas chromatography (GC)-mass spectrometry and GC-flame ionization detection chromatograms, especially compared to supercritical CO2. Based on elemental analysis (carbon and nitrogen) of the soil residues after each extraction, subcritical water, PLE, and Soxhlet extraction had poor selectivity for PAHs versus bulk soil organic matter (approximately 1/4 to 1/3 of the bulk soil organic matter was extracted along with the PAHs), while SFE with pure CO2 removed only 8% of the bulk organic matrix. Selectivities for different compound classes also vary with extraction method. Extraction of urban air particulate matter with organic solvents yields very high concentrations of n- and branched alkanes (approximately C18 to C30) from diesel exhaust as well as lower levels of PAHs, and no selectivity between the bulk alkanes and PAHs is obtained during organic solvent extraction. Some moderate selectivity with supercritical CO2 can be achieved by first extracting the bulk alkanes at mild conditions, followed by stronger conditions to extract the remaining PAHs, i.e., the least polar organics are the easiest organics to extract with pure CO2. In direct contrast, subcritical water prefers the more polar analytes, i.e., PAHs were efficiently extracted from urban air particulates at 250 degrees C, with little or no extraction of the alkanes. Finally, recent work has demonstrated that many pollutant molecules become "sequestered" as they age for decades in the environment (i.e., more tightly bound to soil particles and less available to organisms or transport). Therefore, it may be more important for an extraction method to only recover pollutant molecules that are environmentally-relevant, rather than the conventional attempts to extract all pollutant molecules regardless of how tightly bound they are to the soil or sediment matrix. Initial work comparing SFE extraction behavior using mild to strong conditions with bioremediation behavior of PAHs shows great promise to develop extraction methodology to measure environmentally-relevant concentrations of pollutants in addition to their total concentrations.     

QSPR modeling for solubility of fullerene (C(60)) in organic solvents

Solubility of fullerene C(60) in 75 organic solvents was examined to develop quantitative structure-solubility relationships. Topological indices and polarizability parameter computed from refractive index were used to form the regression models. The models suggested for individual data sets such as alkanes, alkyl halides, alcohols, cycloalkanes, alkylbenzenes, and aryl halides have good predictive ability and are better than the models for the combined groups. Inclusion of an indicator parameter which is a combination of atom contributions and contributions of substituents' position in benzenes improved the predictive ability significantly.     

Emerging chemical patterns: a new methodology for molecular classification and compound selection

A concept termed Emerging Chemical Patterns (ECPs) is introduced as a novel approach to molecular classification. The methodology makes it possible to extract key molecular features from very few known active compounds and classify molecules according to different potency levels. The approach was developed in light of the situation often faced during the early stages of lead optimization efforts: too few active reference molecules are available to build computational models for the prediction of potent compounds. The ECP method generates high-resolution signatures of active compounds. Predictive ECP models can be built based on the information provided by sets of only three molecules with potency in the nanomolar and micromolar range. In addition to individual compound predictions, an iterative ECP scheme has been designed. When applied to different sets of active molecules, iterative ECP classification produced compound selection sets with increases in average potency of up to 3 orders of magnitude.     

Cellulose acetate as stationary phase in microcolumn LC

The retention characteristics of alkylbenzenes and polycyclic aromatic hydrocarbons (PAHs) have been examined in liquid chromatography on a microcapillary column packed with cellulose acetate. Particulate and fibrous cellulose acetate were used as the stationary phase. Fibrous cellulose diacetate was found to be of use as an alternative stationary phase for liquid chromatography, although the separation efficiency was low. The retention characteristics for planar PAHs were linearly correlated with the size of the molecules, but the retention behavior for alkylbenzenes and nonplanar PAHs was affected by exclusion. The retention order was strongly influenced by the addition of dimethylformamide or water to methanol in the mobile phase. The results suggested that the retention behavior is influenced by a slight change in the polymer matrix of cellulose acetate.     

Decision tree SAR models for developmental toxicity based on an FDA/TERIS database

Humans are exposed to thousands of environmental chemicals for which no developmental toxicity information is available. Structure-activity relationships (SARs) are models that could be used to efficiently predict the biological activity of potential developmental toxicants. However, at this time, no adequate SAR models of developmental toxicity are available for risk assessment. In the present study, a new developmental database was compiled by combining toxicity information from the Teratogen Information System (TERIS) and the Food and Drug Administration (FDA) guidelines. We implemented a decision tree modeling procedure, using Classification and Regression Tree software and a model ensemble approach termed bagging. We then assessed the empirical distributions of the prediction accuracy measures of the single and ensemble-based models, achieved by repeating our modeling experiment many times by repeated random partitioning of the working database. The decision tree developmental SAR models exhibited modest prediction accuracy. Bagging tended to enhance the accuracy of prediction. Also, the model ensemble approach reduced the variability of prediction measures compared to the single model approach. Further research with data derived from animal species- and endpoint-specific components of an extended and refined FDA/TERIS database has the potential to derive SAR models that would be useful in the developmental risk assessment of the thousands of untested chemicals.     

Construction of stable multivariate calibration models using unsupervised segmented principal component regression

In multivariate spectral calibration by principal component regression (PCR), the principal components (PCs) are calculated from the response data measured at all employed instrument channels; however some channels are redundant and their responses do not possess useful information. Thus, the extracted PCs possess mixed information from both useful and redundant channels. In this work, we propose a segmentation approach based on unsupervised pattern recognition to identify the most informative spectral region and then to construct a stable multivariate calibration model by PCR. In this method, the instrument channels are clustered into different segments via Kohonen self‐organization map. The spectral data of each segment are then subjected to PCA and the derived PCs are used as input variables for an inverse least square (ILS) regression model employing stepwise selection of the informative PCs. The proposed method was evaluated by the analysis of four simulated and six experimental data sets. It was found that our proposed method can model the above data sets with prediction errors lower than conventional partial least squares (PLS) and PCR methods. In addition, the prediction ability of our method was better than the previously reported models for these data sets. Copyright © 2011 John Wiley & Sons, Ltd.     

Decision tree SAR models for developmental toxicity based on an FDA/TERIS database

Humans are exposed to thousands of environmental chemicals for which no developmental toxicity information is available. Structure-activity relationships (SARs) are models that could be used to efficiently predict the biological activity of potential developmental toxicants. However, at this time, no adequate SAR models of developmental toxicity are available for risk assessment. In the present study, a new developmental database was compiled by combining toxicity information from the Teratogen Information System (TERIS) and the Food and Drug Administration (FDA) guidelines. We implemented a decision tree modeling procedure, using Classification and Regression Tree software and a model ensemble approach termed bagging. We then assessed the empirical distributions of the prediction accuracy measures of the single and ensemble-based models, achieved by repeating our modeling experiment many times by repeated random partitioning of the working database. The decision tree developmental SAR models exhibited modest prediction accuracy. Bagging tended to enhance the accuracy of prediction. Also, the model ensemble approach reduced the variability of prediction measures compared to the single model approach. Further research with data derived from animal species- and endpoint-specific components of an extended and refined FDA/TERIS database has the potential to derive SAR models that would be useful in the developmental risk assessment of the thousands of untested chemicals.     

Quantitative molecular similarity analysis (QMSA) methods for property estimation: a comparison of property-based,arbitrary, and tailored similarity spaces

Three classes of arbitrary quantitative molecular similarity analysis (QMSA) methods have been computed using atom pairs, topological indices, and physicochemical properties. Tailored QMSA models have been developed using a selected number of TIs chosen by ridge regression. The methods have been applied to the K-nearest neighbor based estimation of log P of two sets of chemicals. Results show that the property-based and tailored QMSA methods are superior to the arbitrary similarity methods in estimating log P of both sets of chemicals     

Quantitative molecular similarity analysis (QMSA) methods for property estimation: A comparison of property-based,arbitrary, and tailored similarity spaces

Three classes of arbitrary quantitative molecular similarity analysis (QMSA) methods have been computed using atom pairs, topological indices, and physicochemical properties. Tailored QMSA models have been developed using a selected number of TIs chosen by ridge regression. The methods have been applied to the K -nearest neighbor based estimation of log P of two sets of chemicals. Results show that the property-based and tailored QMSA methods are superior to the arbitrary similarity methods in estimating log P of both sets of chemicals     

Characterization of military fog oil by comprehensive two-dimensional gas chromatography

The most commonly used military fog oil is characterized by comprehensive two-dimensional gas chromatography (GC×GC) coupled to either Flame Ionization Detection (FID) or Time-of-Flight Mass Spectrometric Detection (TOFMS) to advance the knowledge regarding the complete chemical makeup of this complex matrix. Two different GC×GC column sets were investigated, one employing a non-polar column combined with a shape selective column and the other an inverse column set (medium-polar/non-polar). The inverse set maximizes the use of the two-dimensional separation space and segregates aliphatic from aromatic fractions. The shape selective column best separates individual polycyclic aromatic hydrocarbons (PAHs) from the bulk oil. The results reveal that fog oil (FO) is composed mainly of aliphatic compounds ranging from C₁₀ to C₃₀, where naphthenes comprise the major fraction. Although many different species of aromatics are present, they constitute only a minor fraction in this oil, and no conjugated PAHs are found. The composition of chemically similar aliphatic constituents limits the analytical power of silica gel fractionation and GC–MS analysis to characterize FO. Among the aliphatic compounds identified are alkanes, cyclohexanes, hexahydroindanes, decalins, adamantanes, and bicyclohexane. The aromatic fraction is composed of alkylbenzene compounds, indanes, tetrahydronaphthalenes, partially hydrogenated PAHs, biphenyls, dibenzofurans and dibenzothiophenes. This work represents the best characterization of military fog oil to date. As the characterization process shows, information on such complex samples can only be parsed using a combination of sample preprocessing steps, multiple detection schemes, and an intelligent selection of column chemistries.     

Polymer science applied to biological problems: Prediction of cytotoxic drug interactions with DNA

It is taken for granted in the polymer world that the fundamental thermodynamic insights of Hansen solubility parameters (HSP) can be used to predict solvent/chemical interactions with polymers. The same principles should apply equally to bio-molecules interacting with that key polymer, DNA. A necessary (but not sufficient) condition for a drug to be cytotoxic is that it penetrates cell walls and be (thermodynamically) compatible with the core of the DNA polymer. The HSP of 9 cytotoxic drugs conform well to the basic HSP principle that “like (cell walls, DNA) attracts like (all 9 drugs)”. A correlation is not proof. But at the very least, HSP offer a fecund methodology for making predictions not only of how single molecules might interact with biological polymers, but how mixtures of chemicals could be more potent than the individual components. It is predicted, for example, that alcohols would provide synergism with many chemicals with their mixtures having higher affinity for the DNA bases than the individual components.