Substructure versus Whole-molecule Approaches for Calculating Log P

Assessment of Lipophilicity Descriptors of Selected NSAIDs Obtained at Different TLC Stationary Phases

Lipophilicity study of selected NSAIDs, the group of the bioactive compounds usually used in humans and animals medicine, with the use of experimental and calculation methods was evaluated. LogP values are proposed and compared as descriptors of the lipophilicity of eleven compounds (from oxicams and coxibs). Obtained data were designated by thin-layer chromatography (TLC) in various chromatographic conditions, with stationary phases with different properties. The mobile phase systems were prepared by mixing the respective amounts of water and organic modifier, methanol and acetone, in the range of 30 to 80% (v/v) in 5% increments. Retention parameters (RF, RM and RM0) were calculated and statistically evaluated to establish correlations. All experimentally determined RM0 values were compared with partition coefficients obtained by computational methods using linear regression analysis. Moreover, in order to extract information about the lipophilicity of compounds from large retention datasets, two chemometric approaches, namely principal component analysis (PCA) and cluster analysis (CA) were carried out. Established models of lipophilicity may have the potential to predict the biological activity of a number of drugs. The presented knowledge may also be of use during drug discovery processes, broadening the knowledge of potential ways to modify the physicochemical properties of chemical compounds.

Simultaneous determination of the lipophilicity and dissociation constants of dialkyl phosphinic acids by negligible depletion hollow fiber membrane-protected liquid-phase microextraction

Determination of the physicochemical properties, especially the lipophilicity (expressed as the logarithm of distribution coefficient, log D) and dissociation constant (pK_a), is of great importance in the early stage of environmental risk assessment for an ionizable compound without these data. Currently, the log D and pK_a values of dialkyl phosphinic acids (DPAs), the environmental hydrolysates of aluminum dialkyl phosphinates (ADPs) that is one class of emerging phosphorus-containing flame retardants, are not available. In this study, the log D and pK_a values of three DPAs including methylethylphosphinic acid (MEPA), diethylphosphinic acid (DEPA) and methylcyclohexyl phosphinic acid (MHPA), were simultaneously determined by negligible depletion hollow fiber supported liquid phase microextraction (nd-HF-LPME) followed by ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS). The pK_a and log D of DPAs were determined by curve-fitting the experimental data with equations derived on the basis of the Henderson-Hasselbalch equation and compared with model calculated data. For MEPA, DEPA and MHPA, the pK_a values were close and around 3, but the log Ds were strongly pH-dependent with values from -5.01 to 1.01. The log K_OW of the neutral form (logK_OW,HA) and ionic form (logK_OW,A) were in the range of -0.67-1.02 and -3.86--1.33, respectively. The experimentally determined pK_a values were highly in good agreement with ACD/pK_a predicted values and the measured log K_OW,HA values were closely related to KOWWIN calculated ones, suggesting ACD/pK_a and KOWWIN are good alternative methods to estimate pK_a and log K_OW of DPAs, respectively. As far as we know, this is the first report on the pK_a and log D data for DPAs, which are fundamental for the product design and evaluating the environmental behavior and effects of DPAs and ADPs.

The Structure-Antimicrobial Activity Relationships of a Promising Class of the Compounds Containing the N-Arylpiperazine Scaffold

This research was focused on in silico characterization and in vitro biological testing of the series of the compounds carrying a N-arylpiperazine moiety. The in silico investigation was based on the prediction of electronic, steric and lipohydrophilic features. The molecules were screened against Mycobacterium avium subsp. paratuberculosis CIT03, M. smegmatis ATCC 700084, M. kansasii DSM 44162, M. marinum CAMP 5644, Staphylococcus aureus ATCC 29213, methicillin-resistant S. aureus 63718, Escherichia coli ATCC 25922, Enterococcus faecalis ATCC 29212, Candida albicans CCM 8261, C. parapsilosis CCM 8260 and C. krusei CCM 8271, respectively, by standardized microdilution methods. The eventual antiproliferative (cytotoxic) impact of those compounds was examined on a human monocytic leukemia THP-1 cell line, as a part of the biological study. Promising potential against M. kansasii was found for 1-[3-(3-ethoxyphenylcarbamoyl)oxy-2-hydroxypropyl]-4-(3-trifluoromethylphenyl)piperazin-1-ium chloride (MIC = 31.75 μM), which was comparable to the activity of isoniazid (INH; MIC = 29.17 μM). Moreover, 1-{2-hydroxy-3-(3-methoxyphenylcarbamoyl)oxy)propyl}-4-(4-fluorophenyl)piperazin-1-ium chloride was even more effective (MIC = 17.62 μM) against given mycobacterium. Among the tested N-arylpiperazines, 1-{2-hydroxy-3-(4-methoxyphenylcarbamoyl)oxy)propyl}-4-(3-trifluoromethylphenyl)piperazin-1-ium chloride was the most efficient against M. marinum (MIC = 65.32 μM). One of the common features of all investigated substances was their insignificant antiproliferative (i.e., non-cytotoxic) effect. The study discussed structure–antimicrobial activity relationships considering electronic, steric and lipophilic properties.

Diglycerol-based polyesters: melt polymerization with hydrophobic anhydrides

The melt polymerization of diglycerol with bicyclic anhydride monomers derived from a naturally occurring monoterpene provides an avenue for polyesters with a high degree of sustainability. The hydrophobic anhydrides are synthesized at ambient temperature via a solvent-free Diels-Alder reaction of α-phellandrene with maleic anhydride. Subsequent melt polymerizations with tetra-functional diglycerol are effective under a range of [diglycerol]/[anhydride] ratios. The hydrophobicity of α-phellandrene directly impacts the swelling behavior of the resulting polyesters. The low E factors (<2), large amount of bio-based content (>75%), ambient temperature monomer synthesis, and polymer degradability represent key factors in the design of these sustainable polyesters.

Chemical Entity Semantic Specification: Knowledge representation for efficient semantic cheminformatics and facile data integration

BACKGROUND

Over the past several centuries, chemistry has permeated virtually every facet of human lifestyle, enriching fields as diverse as medicine, agriculture, manufacturing, warfare, and electronics, among numerous others. Unfortunately, application-specific, incompatible chemical information formats and representation strategies have emerged as a result of such diverse adoption of chemistry. Although a number of efforts have been dedicated to unifying the computational representation of chemical information, disparities between the various chemical databases still persist and stand in the way of cross-domain, interdisciplinary investigations. Through a common syntax and formal semantics, Semantic Web technology offers the ability to accurately represent, integrate, reason about and query across diverse chemical information.

RESULTS

Here we specify and implement the Chemical Entity Semantic Specification (CHESS) for the representation of polyatomic chemical entities, their substructures, bonds, atoms, and reactions using Semantic Web technologies. CHESS provides means to capture aspects of their corresponding chemical descriptors, connectivity, functional composition, and geometric structure while specifying mechanisms for data provenance. We demonstrate that using our readily extensible specification, it is possible to efficiently integrate multiple disparate chemical data sources, while retaining appropriate correspondence of chemical descriptors, with very little additional effort. We demonstrate the impact of some of our representational decisions on the performance of chemically-aware knowledgebase searching and rudimentary reaction candidate selection. Finally, we provide access to the tools necessary to carry out chemical entity encoding in CHESS, along with a sample knowledgebase.

CONCLUSIONS

By harnessing the power of Semantic Web technologies with CHESS, it is possible to provide a means of facile cross-domain chemical knowledge integration with full preservation of data correspondence and provenance. Our representation builds on existing cheminformatics technologies and, by the virtue of RDF specification, remains flexible and amenable to application- and domain-specific annotations without compromising chemical data integration. We conclude that the adoption of a consistent and semantically-enabled chemical specification is imperative for surviving the coming chemical data deluge and supporting systems science research.

A framework for evaluating the contribution of transformation products to chemical persistence in the environment

The REACH legislation of the EU requires that transformation products be included in chemicals assessment for chemicals produced or imported in amounts exceeding 100 tones/year. However, including transformation products in assessments could be considered an intractable problem, particularly given the paucity of available data and the difficulty of predicting the most likely transformation route from the many possible products of a complex parent chemical (the so-called "combinatorial explosion" problem). Here, we present a scheme for identifying transformation products that substantially contribute to the joint persistence of a parent chemical and its substance family. Our scheme integrates methods for the prediction of biodegradation products, the estimation of physicochemical properties and degradation half-lives, and the calculation of a persistence metric, the joint persistence. We compare results from our scheme to 22 test cases with known transformation products. Our results highlight that the "combinatorial explosion" problem can be managed but that there is a serious need for better data for environmental half-lives of chemicals.

Predicting physical properties of emerging compounds with limited physical and chemical data: QSAR model uncertainty and applicability to military munitions

Reliable, up-front information on physical and biological properties of emerging materials is essential before making a decision and investment to formulate, synthesize, scale-up, test, and manufacture a new material for use in both military and civilian applications. Multiple quantitative structure-activity relationships (QSARs) software tools are available for predicting a material's physical/chemical properties and environmental effects. Even though information on emerging materials is often limited, QSAR software output is treated without sufficient uncertainty analysis. We hypothesize that uncertainty and variability in material properties and uncertainty in model prediction can be too large to provide meaningful results. To test this hypothesis, we predicted octanol water partitioning coefficients (logP) for multiple, similar compounds with limited physical-chemical properties using six different commercial logP calculators (KOWWIN, MarvinSketch, ACD/Labs, ALogP, CLogP, SPARC). Analysis was done for materials with largely uncertain properties that were similar, based on molecular formula, to military compounds (RDX, BTTN, TNT) and pharmaceuticals (Carbamazepine, Gemfibrizol). We have also compared QSAR modeling results for a well-studied pesticide and pesticide breakdown product (Atrazine, DDE). Our analysis shows variability due to structural variations of the emerging chemicals may be several orders of magnitude. The model uncertainty across six software packages was very high (10 orders of magnitude) for emerging materials while it was low for traditional chemicals (e.g. Atrazine). Thus the use of QSAR models for emerging materials screening requires extensive model validation and coupling QSAR output with available empirical data and other relevant information.

QSAR studies on peptide alpha-ketoamides and alpha-ketohydroxamate derivatives as calpain I inhibitors

Quantitative Structure Activity Relationship (QSAR) studies were conducted on 34 peptide alpha-ketoamide and alpha-ketohydroxamate derivatives of Calpain I using multiple linear regression (MLR) procedure. The activity contributions of these compounds were determined from regression equation and the validation procedures that analyze the predictive ability of QSAR models were described. Among forty six descriptors that were considered in generating the QSAR model, three descriptors such as LogP, Heat of formation and HOMO resulted in a statistically significant model with 0.877 r(2) and 0.937 q(2) respectively. The inter-correlation between descriptors was 0.42. The proposed QSAR model indicates an increase in logP value increases hydrophobicity in order to achieve cellular permeability and an increase in heat of formation as well as decrease in HOMO energy favors better binding and activity towards development of potent calpain I inhibitors.

Relationship between physicochemical properties, lipophilicity parameters, and local anesthetic activity of dibasic esters of phenylcarbamic acid

The basic physicochemical properties, lipophilicity parameters of dibasic alkyloxy-substituted phenylcarbamic acids were estimated. For the prepared set of compounds the experimentally obtained solubility, acidity, and lipophilicity parameters were correlated with those computed using various computer programs based on the associative artificial neural network and fragmental methods. The results of pharmacological evaluation were used as entry data for the complex correlations.

Predicting the partitioning behavior of various highly fluorinated compounds

Due to their high degree of fluorination, highly fluorinated compounds (HFCs) have unique substance properties that differ from many other organic contaminants. To predict the environmental behavior of HFCs, models that predict both absorptive and adsorptive partitioning are needed; however, the accuracy of existing models has not heretofore been thoroughly investigated for these compounds. This report has two parts: first we show that a well-established polyparameter linear free energy relationship used to predict experimental adsorption constants underestimates values for HFCs by several orders of magnitude. We found a mechanistic explanation for the model's inaccuracy and adjusted it accordingly. In the second part of this report, we evaluate various models that predict saturated subcooled liquid vapor pressure (pL*), air-water partition constant (Kaw), and the octanol-water partition constant (Kow) based on molecular structure. These parameters are typically required for general environmental fate and transport models. Here, we found that SPARC and COSMOtherm make predictions usually within 1 order of magnitude of the experimental value, while the commonly used EPI SUITE and ClogP perform more inaccurately. The least accurate predictions occurred with ClogP for the fluorotelomer alcohols, where the estimated values were off by 2 to almost 5 orders of magnitude.

SVM approach for predicting LogP

The logarithm of the partition coefficient between n-octanol and water (logP) is an important parameter for drug discovery. Based upon the comparison of several prediction logP models, i.e. Support Vector Machines (SVM), Partial Least Squares (PLS) and Multiple Linear Regression (MLR), the authors reported SVM model is the best one in this paper.

Whole-molecule calculation of log p based on molar volume, hydrogen bonds, and simulated 13C NMR spectra

The prediction of Log P is usually accomplished using either substructure or whole-molecule approaches. However, these methods are complicated, and previous whole-molecule approaches have not been successful for the prediction of Log P in very complex molecules. The observed chemical shifts in nuclear magnetic resonance (NMR) spectroscopy are related to the electrostatics at the nucleus, which are influenced by solute-solvent interactions. The different solvation effects on a molecule by either water or methanol have a strong effect on the NMR chemical shift value. Therefore, the chemical shift values observed in an aqueous and organic solvent should correlate to Log P. This paper develops a rapid, objective model of Log P based on molar volume, hydrogen bonds, and differences in calculated 13C NMR chemical shifts for a diverse set of compounds. A partial least squares (PLS) model of Log P built on the sum of carbon chemical shift differences in water and methanol, molar volume, number of hydrogen bond donors and acceptors in 162 diverse compounds gave an r2 value of 0.88. The average r2 for 10 training models of Log P made from 90% of the data was 0.87+/-0.01. The average q2 for 10 leave-10%-out cross-validation test sets was 0.87+/-0.05.

Different retention behavior of structurally diverse basic and neutral drugs in immobilized artificial membrane and reversed-phase high performance liquid chromatography: comparison with octanol-water partitioning

The retention behavior of 43 structurally diverse neutral and basic drugs in immobilized artificial membrane chromatography was investigated and compared to the reversed-phase retention and octanol-water partitioning. IAM chromatography was performed using morpholinepropanesulfonic acid (MOPS) or phosphate buffer saline (PBS) at pH 7.4 as the aqueous component of the mobile phase. The differences in the retention factors were attributed to increased electrostatic interactions in the MOPS environment, dependent on the fraction of charged species. Electrostatic interactions were found to play a key role in the relationships with reversed-phase retention factors determined under two different mobile phase conditions as well as in the relationships with lipophilicity data. IAM retention factors correlated better with octanol-water partition coefficients log P than with log D(7.4), as a result of the contribution of electrostatic forces in IAM retention. With log D(7.4) the relationships were improved when the fraction of charged species was taken into consideration. In any case the regression coefficient of log P or log D(7.4) was considerably lower than 1 reflecting the reduced hydrophobic environment of the IAM stationary phase. The different data sets were submitted to principal component analysis for further exploration of their similarities/dissimilarities.

Statistically validated QSARs, based on theoretical descriptors, for modeling aquatic toxicity of organic chemicals in Pimephales promelas (fathead minnow)

The use of Quantitative Structure-Activity Relationships in assessing the potential negative effects of chemicals plays an important role in ecotoxicology. (LC50)(96h) in Pimephales promelas (Duluth database) is widely modeled as an aquatic toxicity end-point. The object of this study was to compare different molecular descriptors in the development of new statistically validated QSAR models to predict the aquatic toxicity of chemicals classified according to their MOA and in a unique general model. The applied multiple linear regression approach (ordinary least squares) is based on theoretical molecular descriptor variety (1D, 2D, and 3D, from DRAGON package, and some calculated logP). The best combination of modeling descriptors was selected by the Genetic Algorithm-Variable Subset Selection procedure. The robustness and the predictive performance of the proposed models was verified using both internal (cross-validation by LOO, bootstrap, Y-scrambling) and external statistical validations (by splitting the original data set into training and validation sets by Kohonen-artificial neural networks (K-ANN)). The model applicability domain (AD) was checked by the leverage approach to verify prediction reliability.