Development, validation and uncertainty analysis of quantitative structure and activity relationship models for Log P of disinfection by -products

Hydrophobicity as measured by Log P is an important molecular property related to toxicity and carcinogenicity. With increasing public health concerns for the effects of Disinfection By-Products (DBPs), there are considerable benefits in developing Quantitative Structure and Activity Relationship (QSAR) models capable of accurately predicting Log P. In this research, Log P values of 173 DBP compounds in 6 functional classes were used to develop QSAR models, by applying 3 molecular descriptors, namely, Energy of the Lowest Unoccupied Molecular Orbital (ELUMO), Number of Chlorine (NCl) and Number of Carbon (NC) by Multiple Linear Regression (MLR) analysis. The QSAR models developed were validated based on the Organization for Economic Co-operation and Development (OECD) principles. The model Applicability Domain (AD) and mechanistic interpretation were explored. Considering the very complex nature of DBPs, the established QSAR models performed very well with respect to goodness-of-fit, robustness and predictability. The predicted values of Log P of DBPs by the QSAR models were found to be significant with a correlation coefficient R2 from 81% to 98%. The Leverage Approach by Williams Plot was applied to detect and remove outliers, consequently increasing R 2 by approximately 2% to 13% for different DBP classes. The developed QSAR models were statistically validated for their predictive power by the Leave-One-Out (LOO) and Leave-Many-Out (LMO) cross validation methods. Finally, Monte Carlo simulation was used to assess the variations and inherent uncertainties in the QSAR models of Log P and determine the most influential parameters in connection with Log P prediction. The developed QSAR models in this dissertation will have a broad applicability domain because the research data set covered six out of eight common DBP classes, including halogenated alkane, halogenated alkene, halogenated aromatic, halogenated aldehyde, halogenated ketone, and halogenated carboxylic acid, which have been brought to the attention of regulatory agencies in recent years. Furthermore, the QSAR models are suitable to be used for prediction of similar DBP compounds within the same applicability domain. The selection and integration of various methodologies developed in this research may also benefit future research in similar fields.

Chlorine contribution to quantitative structure and activity relationship models of disinfection by -products' quantum chemical descriptors and toxicities

Quantitative Structure-Activity Relationship (QSAR) has been applied extensively in predicting toxicity of Disinfection By-Products (DBPs) in drinking water. Among many toxicological properties, acute and chronic toxicities of DBPs have been widely used in health risk assessment of DBPs. These toxicities are correlated with molecular properties, which are usually correlated with molecular descriptors. The primary goals of this thesis are: (1) to investigate the effects of molecular descriptors (e.g., chlorine number) on molecular properties such as energy of the lowest unoccupied molecular orbital (E LUMO) via QSAR modelling and analysis; (2) to validate the models by using internal and external cross-validation techniques; (3) to quantify the model uncertainties through Taylor and Monte Carlo Simulation. One of the very important ways to predict molecular properties such as ELUMO is using QSAR analysis. In this study, number of chlorine (NCl ) and number of carbon (NC) as well as energy of the highest occupied molecular orbital (EHOMO) are used as molecular descriptors. There are typically three approaches used in QSAR model development: (1) Linear or Multi-linear Regression (MLR); (2) Partial Least Squares (PLS); and (3) Principle Component Regression (PCR). In QSAR analysis, a very critical step is model validation after QSAR models are established and before applying them to toxicity prediction. The DBPs to be studied include five chemical classes: chlorinated alkanes, alkenes, and aromatics. In addition, validated QSARs are developed to describe the toxicity of selected groups (i.e., chloro-alkane and aromatic compounds with a nitro- or cyano group) of DBP chemicals to three types of organisms (e.g., Fish, T. pyriformis, and P.pyosphoreum) based on experimental toxicity data from the literature. The results show that: (1) QSAR models to predict molecular property built by MLR, PLS or PCR can be used either to select valid data points or to eliminate outliers; (2) The Leave-One-Out Cross-Validation procedure by itself is not enough to give a reliable representation of the predictive ability of the QSAR models, however, Leave-Many-Out/K-fold cross-validation and external validation can be applied together to achieve more reliable results; (3) E LUMO are shown to correlate highly with the NCl for several classes of DBPs; and (4) According to uncertainty analysis using Taylor method, the uncertainty of QSAR models is contributed mostly from NCl for all DBP classes.

Chlorine Contribution to Quantitative Structure and Activity Relationship Models of Disinfection By-Products' Quantum Chemical Descriptors and Toxicities

Quantitative Structure-Activity Relationship (QSAR) has been applied extensively in predicting toxicity of Disinfection By-Products (DBPs) in drinking water. Among many toxicological properties, acute and chronic toxicities of DBPs have been widely used in health risk assessment of DBPs. These toxicities are correlated with molecular properties, which are usually correlated with molecular descriptors. The primary goals of this thesis are: 1) to investigate the effects of molecular descriptors (e.g., chlorine number) on molecular properties such as energy of the lowest unoccupied molecular orbital (ELUMO) via QSAR modelling and analysis; 2) to validate the models by using internal and external cross-validation techniques; 3) to quantify the model uncertainties through Taylor and Monte Carlo Simulation. One of the very important ways to predict molecular properties such as ELUMO is using QSAR analysis. In this study, number of chlorine (NCl) and number of carbon (NC) as well as energy of the highest occupied molecular orbital (EHOMO) are used as molecular descriptors. There are typically three approaches used in QSAR model development: 1) Linear or Multi-linear Regression (MLR); 2) Partial Least Squares (PLS); and 3) Principle Component Regression (PCR). In QSAR analysis, a very critical step is model validation after QSAR models are established and before applying them to toxicity prediction. The DBPs to be studied include five chemical classes: chlorinated alkanes, alkenes, and aromatics. In addition, validated QSARs are developed to describe the toxicity of selected groups (i.e., chloro-alkane and aromatic compounds with a nitro- or cyano group) of DBP chemicals to three types of organisms (e.g., Fish, T. pyriformis, and P.pyosphoreum) based on experimental toxicity data from the literature. The results show that: 1) QSAR models to predict molecular property built by MLR, PLS or PCR can be used either to select valid data points or to eliminate outliers; 2) The Leave-One-Out Cross-Validation procedure by itself is not enough to give a reliable representation of the predictive ability of the QSAR models, however, Leave-Many-Out/K-fold cross-validation and external validation can be applied together to achieve more reliable results; 3) ELUMO are shown to correlate highly with the NCl for several classes of DBPs; and 4) According to uncertainty analysis using Taylor method, the uncertainty of QSAR models is contributed mostly from NCl for all DBP classes.

Effective descriptions of molecular structures and the quantitative structure - Activity relationship studies

In this research. we found CoMFA alone could not obtain sufficiently a strong equation to allow confident prediction for aminobenzenes. When some other parameter. such as heat of molecular formation of the compounds, was introduced into the CoMFA model, the results Were improved greatly. It gives us a hint that a better description for molecular structures will yield a better prediction model, and this hint challenged us to look for another method-the projection areas of molecules in 3D space for 3D-QSAR. It is surprising that much better results than that obtained by using CoMFA Were achieved. Besides the CoMFA analysis. multiregression analysis and neural network methods for building the models were used in this paper.

Synthesis and structure-activity relationship studies of CD4 down-modulating cyclotriazadisulfonamide (CADA) analogues

HIV attachment via the CD4 receptor is an important target for developing novel approaches to HIV chemotherapy. Cyclotriazadisulfonamide (CADA) inhibits HIV at submicromolar levels by specifically down-modulating cell-surface and intracellular CD4. An effective five-step synthesis of CADA in 30% overall yield is reported. This synthesis has also been modified to produce more than 50 analogues. Many tail-group analogues have been made by removing the benzyl tail of CADA and replacing it with various alkyl, acyl, alkoxycarbonyl and aminocarbonyl substituents. A series of sidearm analogues, including two unsymmetrical compounds, have also been prepared by modifying the CADA synthesis, replacing the toluenesulfonyl sidearms with other sulfonyl groups. Testing 30 of these compounds in MT-4 cells shows a wide range of CD4 down-modulation potency, which correlates with ability to inhibit HIV-1. Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were constructed using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) approaches. The X-ray crystal structures of four compounds, including CADA, show the same major conformation of the central 12-membered ring. The solid-state structure of CADA was energy minimized and used to generate the remaining 29 structures, which were similarly minimized and aligned to produce the 3D-QSAR models. Both models indicate that steric bulk of the tail group, and, to a lesser extent, the sidearms mainly determine CD4 down-modulation potency in this series of compounds.

Algorithmic statistical analysis of electrophysiological data for the investigation of structure-activity relationship in single neurons

We are developing computational tools supporting the detailed analysis of the dependence of neural electrophysiological response on dendritic morphology. We approach this problem by combining simulations of faithful models of neurons (experimental real life morphological data with known models of channel kinetics) with algorithmic extraction of morphological and physiological parameters and statistical analysis. In this paper, we present the novel method for an automatic recognition of spike trains in voltage traces, which eliminates the need for human intervention. This enables classification of waveforms with consistent criteria across all the analyzed traces and so it amounts to reduction of the noise in the data. This method allows for an automatic extraction of relevant physiological parameters necessary for further statistical analysis. In order to illustrate the usefulness of this procedure to analyze voltage traces, we characterized the influence of the somatic current injection level on several electrophysiological parameters in a set of modeled neurons. This application suggests that such an algorithmic processing of physiological data extracts parameters in a suitable form for further investigation of structure-activity relationship in single neurons.

Three-Dimensional Quantitative Structure-Activity Relationship Study of Antitumor 2-Formylpyridine Thiosemicarbazones Derivatives as Inhibitors of Ribonucleotide Reductase

In order to extend previous SAR and QSAR studies, 3D-QSAR analysis has been performed using CoMFA and CoMSIA approaches applied to a set of 39 alpha-(N)-heterocyclic carboxaldehydes thiosemicarbazones with their inhibitory activity values (IC(50)) evaluated against ribonucleotide reductase (RNR) of H.Ep.-2 cells (human epidermoid carcinoma), taken from selected literature. Both rigid and field alignment methods, taking the unsubstituted 2-formylpyridine thiosemicarbazone in its syn conformation as template, have been used to generate multiple predictive CoMFA and CoMSIA models derived from training sets and validated with the corresponding test sets. Acceptable predictive correlation coefficients (Q(cv)(2) from 0.360 to 0.609 for CoMFA and Q(cv)(2) from 0.394 to 0.580 for CoMSIA models) with high fitted correlation coefficients (r` from 0.881 to 0.981 for CoMFA and r(2) from 0.938 to 0.993 for CoMSIA models) and low standard errors (s from 0.135 to 0.383 for CoMFA and s from 0.098 to 0.240 for CoMSIA models) were obtained. More precise CoMFA and CoMSIA models have been derived considering the subset of thiosemicarbazones (TSC) substituted only at 5-position of the pyridine ring (n=22). Reasonable predictive correlation coefficients (Q(cv)(2) from 0.486 to 0.683 for CoMFA and Q(cv)(2) from 0.565 to 0.791 for CoMSIA models) with high fitted correlation coefficients (r(2) from 0.896 to 0.997 for CoMFA and r(2) from 0.991 to 0.998 for CoMSIA models) and very low standard errors (s from 0.040 to 0.179 for CoMFA and s from 0.029 to 0.068 for CoMSIA models) were obtained. The stability of each CoMFA and CoMSIA models was further assessed by performing bootstrapping analysis. For the two sets the generated CoMSIA models showed, in general, better statistics than the corresponding CoMFA models. The analysis of CoMFA and CoMSIA contour maps suggest that a hydrogen bond acceptor near the nitrogen of the pyridine ring can enhance inhibitory activity values. This observation agrees with literature data, which suggests that the nitrogen pyridine lone pairs can complex with the iron ion leading to species that inhibits RNR. The derived CoMFA and CoMSIA models contribute to understand the structural features of this class of TSC as antitumor agents in terms of steric, electrostatic, hydrophobic and hydrogen bond donor and hydrogen bond acceptor fields as well as to the rational design of this key enzyme inhibitors.

Anticancer activity of natural cytokinins: a structure-activity relationship study

Cytokinin ribosides (N(6)-substituted adenosine derivatives) have been shown to have anticancer activity both in vitro and in vivo. This study presents the first systematic analysis of the relationship between the chemical structure of cytokinins and their cytotoxic effects against a panel of human cancer cell lines with diverse histopathological origins. The results confirm the cytotoxic activity of N(6)-isopentenyladenosine, kinetin riboside, and N(6)-benzyladenosine and show that the spectrum of cell lines that are sensitive to these compounds and their tissues of origin are wider than previously reported. The first evidence that the hydroxylated aromatic cytokinins (ortho-, meta-, para-topolin riboside) and the isoprenoid cytokinin cis-zeatin riboside have cytotoxic activities is presented. Most cell lines in the panel showed greatest sensitivity to ortho-topolin riboside (IC(50)=0.5-11.6 microM). Cytokinin nucleotides, some synthesized for the first time in this study, were usually active in a similar concentration range to the corresponding ribosides. However, cytokinin free bases, 2-methylthio derivatives and both O- and N-glucosides showed little or no toxicity. Overall the study shows that structural requirements for cytotoxic activity of cytokinins against human cancer cell lines differ from the requirements for their activity in plant bioassays. The potent anticancer activity of ortho-topolin riboside (GI(50)=0.07-84.60 microM, 1st quartile=0.33 microM, median=0.65 microM, 3rd quartile=1.94 microM) was confirmed using NCI(60), a standard panel of 59 cell lines, originating from nine different tissues. Further, the activity pattern of oTR was distinctly different from those of standard anticancer drugs, suggesting that it has a unique mechanism of activity. In comparison with standard drugs, oTR showed exceptional cytotoxic activity against NCI(60) cell lines with a mutated p53 tumour suppressor gene. oTR also exhibited significant anticancer activity against several tumour models in in vivo hollow fibre assays.

Molecular structure-activity relationship study of some non-steroidal antiinflammatory agents using electrostatic potential mapping

A series of 6,11-dihydro-11-oxodibenz[b,e]oxepin-2-acetic acids (DOAA) which are known to be anti-inflammatory agents were studied. The geometries of some of the molecules obtained from X-ray crystallography were used in the calculations as such while the geometries of their derivatives were obtained by local, partial geometry optimization around the Sites of substitution employing the AMI method, keeping the remaining parts of the geometries the same as those in the parent molecules. Molecular electrostatic potential (MEP) mapping was performed for the molecules using optimized hybridization displacement charges (HDC) combined with Lowdin charges, as this charge distribution has been shown earlier to yield near ab initio quality results. A good correlation has been found between the MEP values near the oxygen atoms of the hydroxyl groups of the carboxy groups of the molecules and their anti-inflammatory activities. The result is broadly in agreement with the model proposed earlier by other authors regarding the structure-activity relationship for other similar molecules.

Structure-Activity Relationship of Photocytotoxic Iron(III) Complexes of Modified Dipyridophenazine Ligands

Iron(III) complexes FeL(B)] (1-5) of a tetradentate trianionic phenolate-based ligand (L) and modified dipyridophenazine bases (B), namely, dipyrido-6,7,8,9-tetrahydrophenazine (dpqC in 1), dipyrido3,2-a:2',3'-c]phenazine-2-carboxylic acid (dppzc in 2), dipyrido3,2-a:2',3'-c]phenazine-11-sulfonic acid (dppzs in 3), 7-aminodipyrido3,2-a:2',3'-c]phenazine (dppza in 4) and benzoi]dipyridro3,2-a:2',3'-c]phenazine (dppn in 5), have been synthesized, and their photocytotoxic properties studied along with their dipyridophenazine analogue (6). The complexes have a five. electron paramagnetic iron(III) center, and the Fe(III)/Fe(II) redox couple appears at about 0.69 V versus SCE in DMF-0.1 M TBAP. The physicochemical data also suggest that the complexes possess similar structural features as that of its parent complex FeL(dppz)] with FeO3N3 coordination in a distorted octahedral geometry. The DNA-complex and protein-complex interaction studies have revealed that the complexes interact favorably with the biomolecules, the degree of which depends on the nature of the substituents present on the dipyridophenazine ring. Photocleavage Of pUC19 DNA by the complexes has been studied using visible light of 476, 530, and 647 nm wavelengths. Mechanistic investigations with inhibitors show formation of HO center dot radicals via a photoredox pathway. Photocytotoxicity study of the complexes in HeLa cells has shown that the dppn complex (5) is highly active in causing cell death in visible light with sub micromolar IC50 value. The effect of substitutions and the planarity of the phenazine moiety on the cellular uptake are quantified by determining the total Cellular iron content using the inductively coupled plasma-optical emission spectrometry (ICP-OES) technique. The cellular uptake increases marginally with an increase in the hydrophobicity of the dipyridophenazine ligands whereas complex 3 with dppzs shows very high uptake. Insights into the cell death mechanism by the dppn complex 5, obtained through DAFT nuclear staining in HeLa cells, reveal a rapid programmed cell death mechanism following photoactivation of complex 5 with visible light. The effect of substituent on the DNA photocleavage activity of the complexes has been rationalized from the theoretical studies.

Antimicrobial Peptides with Potential for Biofilm Eradication: Synthesis and Structure Activity Relationship Studies of Battacin Peptides

We report on the first chemical syntheses and structureactivity analyses of the cyclic lipopeptide battacin which revealed that conjugation of a shorter fatty acid, 4-methyl-hexanoic acid, and linearization of the peptide sequence improves antibacterial activity and reduces hemolysis of mouse blood cells. This surprising finding of higher potency in linear lipopeptides than their cyclic counterparts is economically beneficial. This novel lipopeptide was membrane lytic and exhibited antibiofilm activity against Pseudomonas aeruginosa, Staphylococcus aureus, and, for the first time, Pseudomonas syringe pv. actinidiae. The peptide was unstructured in aqueous buffer and dimyristoylphosphatidylcholine-polymerized diacetylene vesicles, with 12% helicity induced in 50% v/v of trifluoroethanol. Our results indicate that a well-defined secondary structure is not essential for the observed antibacterial activity of this novel lipopeptide. A truncated pentapeptide conjugated to 4-methyl hexanoic acid, having similar potency against Gram negative and Gram positive pathogens was identified through alanine scanning.

Experimental solubilities of two lipid derivatives in supercritical carbon dioxide and new correlations based on activity coefficient models

The solubilities of two lipid derivatives, geranyl butyrate and 10-undecen-1-ol, in SCCO2 (supercritical carbon dioxide) were measured at different operating conditions of temperature (308.15 to 333.15 K) and pressure (10 to 18 MPa). The solubilities (in mole fraction) ranged from 2.1 x 10(-3) to 23.2 x 10(-3) for geranyl butyrate and 2.2 x 10(-3) to 25.0 x 10(-3) for 10-undecen-1-ol, respectively. The solubility data showed a retrograde behavior in the pressure and temperature range investigated. Various combinations of association and solution theory along with different activity coefficient models were developed. The experimental data for the solubilities of 21 liquid solutes along with geranyl butyrate and 10-undecen-1-ol were correlated using both the newly derived models and the existing models. The average deviation of the correlation of the new models was below 15%.

Fishery resource surveys: beyond length-weight relationship models

Fisheries resource surveys are regular management tools for rational exploitation of commercial fisheries. In a growing number of cases, the use of these resource surveys has been largely restricted to assessment of the relative well being of fish stocks and the potential yields of such fisheries. This paper seeks to demonstrate that the data from such surveys can also be easily used to evaluate species diversity of such fisheries, both in terms of species richness and equitability of distribution. Using published data on two freshwater and two marine fisheries as case studies, Shannon-Wiener Diversity Function and Simpson's Index were computed for each of these fisheries. These biodiversity indices gave a deeper insight into the environmental status of each of these fisheries, beyond what the length-weight relationship models can reveal. Generally, while the marine fisheries showed more species richness, the freshwater fisheries apparently had more stable and equilibrated fish communities

Structure-activity relationship of surfactant for preparing DMFC anodic catalyst

Three kinds of surfactants as stabilizer were applied to the preparation of electrocatalysts for direct methanol fuel cell (DMFC). The catalysts have been characterized by examining their catalytic activities, morphologies and particle sizes by means of cyclic voltammetry, chronoamperometry, X-ray diffraction and transmission electron microscopy (TEM). It is found that the surfactants with different structures have a significantly influence on the catalyst shape and activity. The catalysts prepared with non-ionic surfactants as the stabilizer show higher activity for direct oxidation of methanol. The structure-activity relationship (SAR) analysis has been explored and the effect of hydrophile-lipophile balance (HLB value) has also been discussed.

Applications of the method of three-dimensional projection of a molecule in quantitative structure-activity relationship of phenol derivatives

Five variables for phenol derivatives were calculated by molecular projection in three-dimensional space which were combined with eight quantum-chemical parameters and three Am indices. These variables were selected by using leaps-and-bounds regression analysis. Multiple linear regression analysis and artificial neural networks' were performed, and the results obtained by using. artificial neural networks are superior than that obtained by using multiple linear regression.