Descriptors Log Research Articles

Development of an a priori computational approach for brain uptake of compounds in an insect model system

Delivery of compounds to the brain is critical for the development of effective treatment therapies of multiple central nervous system diseases. Recently a novel insect-based brain uptake model was published utilizing a locust brain ex vivo system. The goal of our study was to develop a priori, in silico cheminformatic models to describe brain uptake in this insect model, as well as evaluate the predictive ability. The machine learning program Orange® was used to evaluate several machine learning (ML) models on a published data set of 25 known drugs, with in vitro data generated by a single laboratory group to reduce inherent inter-laboratory variability. The ML models included in this study were linear regression (LR), support vector machines (SVN), k-nearest neighbor (kNN) and neural nets (NN). The quantitative structure–property relationship models were able to correlate experimental logCtot (concentration of compound in brain) and predicted brain uptake of r2 > 0.5, with the descriptors log(P*MW−0.5) and hydrogen bond donor used in LR, SVN and KNN, while log(P*MW−0.5) and total polar surface area (TPSA) descriptors used in the NN models. Our results indicate that the locust insect model is amenable to data mining chemoinformatics and in silico model development in CNS drug discovery pipelines.

Comparative assessment of QSTR models based on density functional, hartree–fock, AM1, and PM3 methods for acute toxicity of aliphatic compounds toward Vibrio fischeri

AbstractComparative quantitative‐structure‐toxicity‐relationship models developed using various quantum chemical descriptors calculated at semiempirical (AM1, PM3), Hartree–Fock and density functional theory (DFT) (B3LYP) methods, are compared for predicting the toxicity (pIGC50) of 69 aliphatic compounds toward Vibrio fischeri. The multiple linear regression equations, which can assist in modeling toxicity of these compounds, are developed using molecular descriptor, log P (1‐octanol/water partition coefficient) in conjunction with two quantum chemical descriptors, electrophilicity index (ω) and energy of the lowest unoccupied molecular orbital (ELUMO). The calculated toxicity values (PIGC50) based on the model equations are compared with the experimental data. In general, the stepwise model regression equations based on quantum chemical descriptor ω in combination with molecular descriptor log P, calculated at DFT(B3LYP) with 6‐311G (d,p) and DGDVZP basis sets show better agreement with the experimental data. The electronic origin of the toxicity dependence on descriptors like log P, ω and ELUMO is traced to the cumulative contribution of the lipophilicity and electrophilicity of the chemical moieties to their toxic behavior. © 2012 Wiley Periodicals, Inc.

Comparison of Tetrahymena pyriformis toxicity based on hydrophobicity, polarity, ionization and reactivity of class-based compounds

A toxicity data set containing the toxicities of 970 hydrophobic, polar and ionizable, nitro substituted and α,β-unsaturated compounds to Tetrahymena pyriformis was classified into different groups based on the structure and substituted functional groups. Polar, ionizable and reactive compounds exhibit greater toxicity as compared with the non-polar hydrophobic compounds. Step-by-step analysis was carried out between the toxicity and descriptors representing hydrophobicity, polarity/polarizability, ionization and reactivity of compounds. Significant relationships were developed between the toxicity and these descriptors for the compounds. The models developed are simple, interpretable and transparent, using a small number of descriptors that may reflect the interactions of chemicals with the biological macromolecules at the target sites. Hydrophobic parameter log P reflects bio-uptake process compounds. Polarity/polarizability descriptor S reflects the interaction of hydrophilic residues of polar chemicals with biological macromolecules. The fractions of ionized (F i) and neutral (F 0) forms calculated from pK a reflect the interactions of ionizable compounds with the macromolecules and effect of ionization of ionizable compounds on the bio-uptake process, respectively. A successful single model was developed by using the descriptors log P, S, F i and log F 0 for non-polar, polar and ionizable compounds.

Synthesis of pregnenolone–danazol–ethylendiamine conjugate: relationship between descriptors log P, π, R m, and V m and its antibacterial activity in S. aureus and V. cholerae

In this study, the pregnenolone–danazol–ethylendiamine conjugate (3) was synthesized and its antibacterial activity on Staphylococcus aureus and Vibrio cholerae was evaluated. In order to delineate the structural chemistry of 3 as antibacterial agent, the physicochemical descriptors log P, π, R m, and V m were evaluated. In this sense, the first step was achieved by reacting pregnenolone–danazol (1) with ethylendiamine and formaldehyde to form pregnenolone–danazol–ethylendiamine (3). The structure of 3 was confirmed by spectroscopy and spectrometry data. The 1H NMR spectrum of 3 showed signals at 0.64–1.01 and 0.99–1.00 ppm corresponding to methyls presents in the steroids nucleus. In addition, several signals at 2.62–2.74 ppm for methylene bound to ester group and at 2.68–2.74 ppm for methylenes bound to amine group were found. Finally, a signal at 8.03 ppm for the proton involved in the ring of isoxazole was found. On the other hand, the results of biological activity indicate that bacterial growth of the microorganisms studied was inhibited by 3 in a manner dose-dependent. In addition, the results showed an increase in both, R m and V m and a decrease of log P and π values in the compound 3 in comparison with 1. These data indicate that steric impediment and degree of lipophilicity could affect the antibacterial activity of the pregnenolone–danazol–ethylendiamine conjugate.

Prediction of intrinsic solubility of generic drugs using MLR, ANN and SVM analyses

The machine learning methods artificial neural network (ANN) and support vector machine (SVM) techniques were used to model intrinsic solubility of 74 generic drugs. The models obtained were compared with those obtained using multiple linear regression (MLR) analysis. Cluster analysis was used to split the data into a training set and test set. The appropriate descriptors were selected using a wrapper approach with multiple linear regressions as target learning algorithm. The descriptor selection and model building were performed with 10 fold cross validation using the training data set. The linear model fits the training set ( n = 60) with R 2 = 0.814, while ANN and SVM higher values of R 2 = 0.823 and 0.835, respectively. Though the SVM model shows improvement of training set fitting, the ANN model was slightly superior to SVM and MLR in predicting the test set. The quantitative structure–property relationship study suggests that the theoretically calculated descriptors log P, first-order valence connectivity index ( 1χ v), delta chi (Δ 2 χ ) and information content ( 2IC) have relevant relationships with intrinsic solubility of generic drugs studied.

A Comparative Study of Two Quantum Chemical Descriptors in Predicting Toxicity of Aliphatic Compounds towardsTetrahymena pyriformis

Quantum chemical parameters such as LUMO energy, HOMO energy, ionization energy (I), electron affinity (A), chemical potential (μ), hardness (η) electronegativity (χ), philicity (ωα), and electrophilicity (ω) of a series of aliphatic compounds are calculated at the B3LYP/6-31G(d) level of theory. Quantitative structure-activity relationship (QSAR) models are developed for predicting the toxicity (pIGC50) of 13 classes of aliphatic compounds, including 171 electron acceptors and 81 electron donors, towardsTetrahymena pyriformis. The multiple linear regression modeling of toxicity of these compounds is performed by using the molecular descriptor log P(1-octanol/water partition coefficient) in conjunction with two other quantum chemical descriptors, electrophilicity (ω) and energy of the lowest unoccupied molecular orbital (ELUMO). A comparison is made towards the toxicity predicting the ability of electrophilicity (ω) versusELUMOas a global chemical reactivity descriptor in addition to log P. The former works marginally better in most cases. There is a slight improvement in the quality of regression by changing the unit ofIGC50from mg/L to molarity and by removing the racemates and the diastereoisomers from the data set.

Evaluation of a generalized use of the log Sum(k+1)AA descriptor in a QSRR model to predict peptide retention on RPLC systems

At the current state of knowledge, the rational optimization of the chromatographic separation of peptides, as well as the identification of proteins in proteomics are challenges for analytical chemists. In this paper the generalized applicability of a recently derived descriptor log Sum(k+1)AA in a QSRR equation to model peptide retention in RP-LC systems was evaluated. For that purpose, two sets of peptides analyzed on dissimilar RP-LC systems were considered. A first set of 28 peptides was measured on 17 columns/systems, while a second of 70 peptides was eluted on four. The aim of this work was to confirm the usefulness of the partly experimental log Sum(k+1)AA descriptor for the prediction of peptides retention compared to the initially applied, fully experimental log SumAA descriptor. The verification of the predictive abilities of both QSRR models, applying either the initial or the alternative descriptor, was done by using the leave-one-out and leave-three-out cross-validation procedures. The results seem to demonstrate that the QSRR model with log Sum(k+1)AA, for which the retention measurement of only seven out of 20 existing amino acids is necessary, possesses similar or in some cases even better predictive abilities than that containing log SumAA.

The molecular descriptor log Sum AA and its alternatives in QSRR models to predict the retention of peptides

The use of the experimental molecular descriptor log Sum AA and some possible alternatives were evaluated in the QSRR analysis of peptides. To quantitatively characterize the structure of analytes in a previously proposed QSRR the following three structural descriptors were applied: the logarithm of the sum of gradient retention times of the amino acids composing the individual peptide, log Sum AA; the logarithm of the peptide’s van der Waals volume, log VDW Vol; and the logarithm of its theoretically calculated n-octanol–water partition coefficient, clog P. Taking into consideration that most amino acids were hardly retained in the different RP-HPLC systems on which the peptides retention was measured, the contribution of most amino acids to the log Sum AA descriptor is rather constant. Therefore, to enlarge the variability of the descriptor and the amino acids contributions for a given series of peptides, in a first instance, it was evaluated whether, by changing the chromatographic conditions, the retention differences between the amino acids could be increased, while maintaining their mutual selectivity. It was not evident to find such conditions. Secondly, it was also investigated whether the experimental descriptor log Sum AA can be replaced by a theoretical, either based on a simple or on a weighted counting of the amino acids composing the peptide. The weighting factor for the retained amino acids was determined by their experimental gradient retention times measured on different systems. The predictive abilities of the new QSRR models (applying the alternative descriptors) were assessed using the leave-one-out cross-validation procedure and compared to that of the initial model. Finally, a descriptor was defined for which the retention measurement of only a limited number of amino acids is required. It resulted in QSRR models with similar predictive properties as those with log Sum AA, but with a reduced workload.

Five-membered heterocycles. Part IV: Impact of heteroatom on benzazole aromaticity

To complete a set of 3-D data for phenoxymethyl–benzazoles, molecular structures of respective benzothiazole and benzimidazole were determined using X-ray crystallography. Based on that, the structure and conformation of the benzazole bicycle (6 + 5) with various heteroatoms in position 3 – nitrogen, sulphur or oxygen – are discussed. Subsequently, aromaticity of (6 + 5) bicycles was analyzed on the basis of crystallographic data (our and from CSD) and HOMA index, quantitative estimation of heteroring aromaticity, was calculated. The rings aromaticity is amplified from oxygen to sulphur and nitrogen as the second heteroatom. The dependences between HOMA av indices and respective heteroatom electronegativity were roughly established as second-degree polynomial with maximum in the vicinity of nitrogen. Similar relationship was found between HOMA av and lipophilicity descriptor log D (at pH 7.4) for phenoxymethyl–benzoxazole, –benzothiazole, –benzimidazole. Both relationships explain the categorization of the species with various Y according to the location in periodic table of the elements.

Statistical external validation and consensus modeling: A QSPR case study for Koc prediction

The soil sorption partition coefficient (log K oc) of a heterogeneous set of 643 organic non-ionic compounds, with a range of more than 6 log units, is predicted by a statistically validated QSAR modeling approach. The applied multiple linear regression (ordinary least squares, OLS) is based on a variety of theoretical molecular descriptors selected by the genetic algorithms-variable subset selection (GA-VSS) procedure. The models were validated for predictivity by different internal and external validation approaches. For external validation we applied self organizing maps (SOM) to split the original data set: the best four-dimensional model, developed on a reduced training set of 93 chemicals, has a predictivity of 78% when applied on 550 validation chemicals (prediction set). The selected molecular descriptors, which could be interpreted through their mechanistic meaning, were compared with the more common physico-chemical descriptors log K ow and log S w. The chemical applicability domain of each model was verified by the leverage approach in order to propose only reliable data. The best predicted data were obtained by consensus modeling from 10 different models in the genetic algorithm model population.

QSAR analysis of soil sorption coefficients for polar organic chemicals: Substituted anilines and phenols

Based on descriptors of n-octanol/water partition coefficients (log K ow), molecular connectivity indices, and quantum chemical parameters, several QSAR models were built to estimate the soil sorption coefficients (log K oc) of substituted anilines and phenols. Results showed that descriptor log K ow plus molecular quantum chemical parameters gave poor regression models. Further study was performed to improve the QSAR model by using artificial neural networks (ANNs). It showed that ANN model with suitable network architecture could make a better agreement between predicted and measured values of the soil sorption coefficients. The quality of the QSAR models confirmed the suitability of ANN to predict the soil sorption coefficients for polar organic chemicals of substituted anilines and phenols.

Identification of P-glycoprotein substrates and inhibitors among psychoactive compounds--implications for pharmacokinetics of selected substrates.

The pharmacokinetics of antipsychotic drugs has become an integral part in understanding their pharmacodynamic activity and clinical effects. In addition to metabolism aspects, carrier-mediated transport, particularly secretion by ABC transporters, has been discussed as potentially relevant for this group of therapeutics. In this study, the psychoactive compounds perphenazine, flupentixol, domperidone, desmethyl clozapine, haloperidol, fluphenazine, fluvoxamine, olanzapine, levomepromazine, perazine, desmethyl perazine, clozapine, quetiapine and amisulpride were characterized in terms of P-glycoprotein (P-gp) affinity and transport. Experimental methods involved a radioligand displacement assay with [3H]talinolol as radioligand and transport--as well as transport inhibition--studies of the P-gp substrate [3H]talinolol across Caco-2 cell monolayers. In addition, the physicochemical descriptors log P and deltalog P were determined to test potential correlations between transporter affinity and lipophilicity parameters. All of the tested antipsychotics showed affinity to P-gp albeit their IC50 values (concentration of competitor that displaced 50% of the bound radioligand) differed by a factor exceeding 1000, when compared using the transport inhibition assay. From the group of P-gp substrates, amisulpride and fluphenazine were selected for in-vivo drug-drug interaction studies in rats to demonstrate the in-vivo relevance of the in-vitro findings. Compounds were administered by intraperitoneal injection either alone or in combination with 50 mg kg(-1) ciclosporin. The concentration versus time profiles for both drugs were followed in serum as well as in brain tissues. Significant differences between the treatments with the antipsychotic alone versus the combination of antipsychotic with ciclosporin were found for amisulpride. The distribution of amisulpride to the brain was increased and systemic serum levels were likewise increased indicating decreased systemic clearance for the combination regimen. For fluphenazine, systemic levels with and without co-administration of ciclosporin were comparable while higher brain-to-serum concentration ratios were found after co-administration of ciclosporin. The findings are explained on the basis of the limited contribution of P-gp-mediated transport to the elimination of fluphenazine and to a direct effect with respect to its distribution into the brain.

Drug Absorption from the Isolated Perfused Rat Lung–Correlations with Drug Physicochemical Properties and Epithelial Permeability

The pulmonary absorption of nine low-molecular-weight (225–430 Da) drugs (atenolol, budesonide, enalaprilat, enalapril, formoterol, losartan, metoprolol, propranolol and terbutaline) and one high-molecular-weight membrane permeability marker compound (FITC-dextran 10 000 Da) was investigated using the isolated, perfused and ventilated rat lung (IPL). The relationships between pulmonary transport characteristics, epithelial permeability of Caco-2 cell monolayers and drug physicochemical properties were evaluated using multivariate data analysis. Finally, an in vitro–in vivo correlation was made using in vivo rat lung absorption data.The absorption half-life of the investigated drugs ranged from 2 to 59 min, and the extent of absorption from 21 to 94% in 2 h in the isolated perfused rat lung model. The apparent first-order absorption rate constant in IPL (kalung) was found to correlate to the apparent permeability (Papp) of Caco-2 cell monolayers (r=0.87), cLog D(7.4) (r=0.70), cLog P, and to the molecular polar surface area (%PSA) (r=-0.79) of the drugs. A Partial Least Squares (PLS)-model for prediction of the absorption rate (log kalung) from the descriptors log Papp, %PSA and cLog D(7.4) was found (Q2=0.74, R2=0.78). Furthermore, a strong in vitro–in vivo correlation (r=0.98) was found for the in vitro (IPL) drug absorption half-life and the pulmonary absorption half-life obtained in rats in vivo, based on a sub-set of five compounds.

QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS FOR THE TOXICITY OF NITROBENZENES TO TETRAHYMENA THERMOPHILA

ABSTRACT In this study IGC50 (50% inhibitory growth concentration) values of 26 nitrobenzenes were determined for population growth endpoint of Tetrahymena thermophila. The toxicity order of the observed compounds has been found as follows: dinitro compounds>mononitro compounds; dichloronitrobenzenes>monochloronitrobenzenes; and meta-substituted nitrobenzenes > ortho-/para-substituted nitrobenzenes (NT, NPh, NAnis) except for the dinitrobenzenes and nitroanilines (DNB, NAn). Quantitative structure activity relationships (QSARs) were developed using log of the inverse of the IGC50 in mole liter as the dependent variable and six molecular descriptors—log P, 1 X v, I, 1 Ka, Σσ− and E LUMO as the independent variables. Through multiplicate regression analysis, one best equation was obtained: The equation was used to estimate IGC50 for seven analogues.

Classifying class I and class II compounds by hydrophobicity and hydrogen bonding descriptors.

The successful development of quantitative structure-activity relationships (QSARs) and the prediction of toxicity based on QSARs depend on the correct classification of the mechanism of toxic action of chemical compounds. The toxicity mechanism of a compound can be determined by a few existing methods, such as studying the chemical structure of the compound for certain substructures and assigning a mechanism accordingly. However, these methods are less reliable for compounds with complex structures and are usually complicated. A novel descriptor-based method employing a multivariate statistical technique--discriminant analysis--was developed in this study for the classification of class I and class II compounds. The discriminating variables used in discriminant analysis were the hydrophobicity descriptor log(K(ow)) and the hydrogen bonding descriptors E(LUMO), E(HOMO), Q(+), and Q(-). Because only numerical values of these descriptors are required for this method, which can be calculated, no additional experimental work was required for the toxicity mechanism classification of new compounds. A nonlinear discriminant function was generated that could not be expressed explicitly. Assessing the predicting ability of the discriminant function by the cross-validation method showed that a low total error rate, 4.2% was achieved.

The hydrophobic fragmental constant approach forcalculating log P in octanol/water and aliphatic hydrocarbon/water systems

Lipophilicity is a prime physicochemical parameter in describing both pharmacodynamic and pharmacokinetic aspects of drug action. Its quantitative descriptor (log P) is measured in or calculated for different model solvent systems depending on the respective scientific interest. The first part of this article focuses on the hydrophobic fragmental constant approach (Σf system) for octanol/water partitioning. The original approach and its revisions are presented, followed by recipes for the practical procedure ofΣf calculations and a detailed description of how to apply the correction factor CM.A Σf system for application in the study of aliphatic hydrocarbon/water (ahc/w) partitioning is described in the second part of the article. The current version is based on an optimized coupling of the ahc/w system with f constants for octanol/water partitioning. A total of 70 fahc values are now available.

Classical QSAR and comparative molecular field analyses of the host-guest interaction of organic molecules with cyclodextrins.

The application of classical QSAR and molecular modeling analysis using Comparative Molecular Field Analysis (CoMFA) to the complexation of some natural and modified cyclodextrins (CDs) with guest molecules was examined. For 1:1 complexation systems between natural beta-CD, modified alpha-, beta-, and gamma-CD that bear one p-(dimethylamino)benzoyl (DMAB) moiety (DMAB-alpha-, beta-, and gamma-CDs) and guest molecules of widely varying chemical structures and properties, the binding constants of the complexes were successfully fitted using multiple linear regression (MLR) with hydrophobic descriptor log P (the partition coefficient between 1-octanol and water phases) and molecular connectivity indices. A non-linear dependency of binding constants on the zero-th and/or first order molecular connectivity index as a measure of size becomes apparent. The modeling performance of the CoMFA models with steric/electrostatic fields to DMAB-alpha- and beta-CD systems was comparable to those of MLR models. However, statistically significant CoMFA models for gamma-CD systems which have higher conformational flexibility of the ring could not be obtained. The CoMFA models obtained for DMAB-alpha- and beta-CD systems showed that the predominant effects were steric for the DMAB-alpha-CD system and electrostatic for the DMAB-beta-CD system, respectively.

Prediction of the Remaining Activity of Horse Liver Alcohol Dehydrogenase after Exposure to Various Organic Solvents

Twenty solvents were selected from a principal component (PC) analysis of 103 organic solvents. The solvents were selected so as to obtain a set of test items that spanned a large range of molecular properties of the selected solvents. The activity of free and Celite-immobilized horse liver alcohol dehydrogenase (HLADH) obtained after 20 h exposure to these solvents were used to create a PLS model based on 14 solvent descriptors. A correlation coefficient of 0.997 between the predicted and the observed remaining activity was obtained.A slightly worse PLS model was obtained using only three solvent descriptors (log P, dielectric constant and melting point). A correlation coefficient of 0.957 between the predicted and the observed remaining activity was obtained. The latter model was used to predict the remaining activity of HLADH after its exposure to 93 organic solvents.

Model building for the prediction of initial chromatographic conditions in pharmaceutical analysis using reversed-phase liquid chromatography

The development of a first guess expert system in HPLC requires a rough estimate of retention properties. This paper investigates the extent to which the simplest possible descriptor, namely the total number of carbons in a molecule, can be used. For this purpose, experimental data acquired after investigation of this parameter on the retention behaviour of various acidic, basic and neutral compounds, for a mobile phase composed of methanol-phosphate buffer and a LiChrosorb CN column, were employed. The usefulness of the descriptor log P (calculated according to Rekker's fragment system) was also studied. Similar models were derived for both descriptors. Subsequently these models were used for the selection of initial chromatographic conditions. Both models were compared through a PRESS value. The regression equation including the descriptor log P was found to be more appropriate for the present purpose.

Correlation of various physicochemical parameters of organic amides with their retention data and biological activity

The molecular descriptors (log P, connectivity indices) were derived from the structure of chromatographed amides. The correlations between the chromatographic data and the descriptors were examined. Also the correlations between pharmacological activity and the descriptors were investigated. In the case of weak one-parameter correlation, two parameters were used.