Test Set Compounds Research Articles

CoMFA analysis of the human β 1-adrenoceptor binding affinity of a series of phenoxypropanolamines

A series of 36 phenoxypropanolamines was examined to determine the structure–activity relationships of β-adrenoceptor (β-AR) antagonists for the human β 1-AR. The binding affinities of all the compounds were determined for human β 1-ARs expressed in Chinese hamster ovary cells and the antagonist potency for rat atrial β 1-ARs was determined for 32 of these compounds for comparative purposes. The compounds, based upon a phenoxypropanolamine core structure with various meta-, ortho-, para- and amine-substituents, displayed binding affinities (p K i) for the human β 1-AR ranging from 5.49 to 9.35. Antagonist potencies (p A 2) in the rat ranged from 5.52 to 9.56 and correlated with the human binding affinities ( r 2=0.86). Twenty-six compounds were used as the training set for comparative molecular field analysis (CoMFA) of antagonist binding affinity at the human β 1-AR and also of antagonist potency for rat atrial β 1-ARs. The CoMFA models were derived using both the CoMFA electrostatic and steric field parameters. The initial human β 1-AR model ( n=26, q 2=0.59, ONC=6, SE CV=0.70, r 2=0.98, SE non-CV=0.16, F(6, 19)=148) predicted the binding affinities of seven out of ten test compounds, not included in the training set, with residual p K i values ≤0.50. The final human β 1-AR model ( n=36, q 2=0.66, ONC=5, SE CV=0.61, r 2=0.95, SE non-CV=0.24, F(5, 30)=107), consisting of the training set plus the test set of compounds, may prove useful in the design of new phenoxypropanolamine type β 1-AR antagonists. The initial rat β 1-AR model ( n=26, q 2=0.42, ONC=6, SE CV=0.76, r 2=0.94, SE non-CV=0.25, F(6, 19)=47) predicted the affinities of five out of six test compounds with residual p A 2 values ≤0.50. The final rat β 1-AR model (i.e. training set plus test set of compounds) ( n=32, q 2=0.38, ONC=5, SE CV=0.69, r 2=0.93, SE non-CV=0.24, F(5, 26)=67) in particular has a low q 2 value, indicating that, at least for the rat, the biologically active phenoxypropanolamine conformation may be quite different to the low energy extended conformation chosen for this CoMFA study.

Estimation of aqueous solubility of organic compounds with QSPR approach.

To derive a QSPR model for estimation of aqueous solubility of organic compounds. Solubility data for 930 diverse compounds was investigated with principal component regression analysis. This set of compounds consists of pharmaceuticals, pollutants, nutrients, herbicides, and pesticides. The diversity of this collection was analyzed using MACCS fingerprint and BCUT chemistry space. The training set of the solubility data is as diverse as the Available Chemicals Directory, and more diverse than the MDL Drug Data Report. Forty-six molecular descriptors were screened using a genetic algorithm. A QSPR model with a squared correlation coefficient (r2) of 0.92, a root mean square error of 0.53 log molar solubility (log S(w)), an average absolute estimation error of 0.36 log S(w), and a cross-validated q2 of 0.91 was derived. The QSPR model was validated with a test set of 249 compounds not included in the training set. The absolute estimation error for the test set of compounds was 0.39 log S(w). A highly predictive QSPR model for estimating aqueous solubility was derived and validated. This model can be used to estimate aqueous solubility for virtual screening and combinatorial library design.

Structure-water solubility modeling of aliphatic alcohols using the weighted path numbers

The structure-water solubility modeling of aliphatic alcohols was performed using the weighted path numbers. Aliphatic alcohols were represented by weighted trees. The weight of the edge representing C-O bond was taken to be x , while the weights of C-C bonds were taken to be all equal to one. Four (one-, two-, three- and four-descriptor) models with weighted path numbers were considered. They were compared with models based on surface areas of aliphatic alcohols, models based on the vertex-connectivity indices for the corresponding alkanes, models based on orthogonal valence vertex-connectivity indices, models based on valence vertex- and edge-connectivity indices with optimum exponents and models based on weighted line graphs. The main result of this comparative study is that the models based on two, three, or four weighted path numbers posses the best statistical characteristics of all models considered in this paper. In addition, the predictive performance of these models was also tested using the training/test set partition. Very good and stable predictions for 19 test set compounds were obtained. For this data set we find, in all performed tests of models, that optimum x values are in the range 3.0-4.0. This result supports views about the potential of the weighted path numbers for deriving high quality structure-property models.

Predicting blood-brain barrier partitioning of organic molecules using membrane-interaction QSAR analysis.

Membrane-interaction quantitative structure-activity relationship (OSAR) analysis (MI-QSAR) has been used to develop predictive models of blood-brain barrier partitioning of organic compounds by, in part, simulating the interaction of an organic compound with the phospholipid-rich regions of cellular membranes. A training set of 56 structurally diverse compounds whose blood-brain barrier partition coefficients were measured was used to construct MI-QSAR models. Molecular dynamics simulations were used to determine the explicit interaction of each test compound (solute) with a model DMPC monolayer membrane model. An additional set of intramolecular solute descriptors were computed and considered in the trial pool of descriptors for building MI-QSAR models. The QSAR models were optimized using multidimensional linear regression fitting and a genetic algorithm. A test set of seven compounds was evaluated using the MI-QSAR models as part of a validation process. Significant MI-QSAR models (R2 = 0.845, Q2 = 0.795) of the blood-brain partitioning process were constructed. Blood-brain barrier partitioning is found to depend upon the polar surface area. the octanol/water partition coefficient, and the conformational flexibility of the compounds as well as the strength of their binding" to the model biologic membrane. The blood-brain barrier partitioning measures of the test set compounds were predicted with the same accuracy as the compounds of the training set. The MI-QSAR models indicate that the blood-brain barrier partitioning process can be reliably described for structurally diverse molecules provided interactions of the molecule with the phospholipids-rich regions of cellular membranes are explicitly considered.

Development of Quantitative Structure−Property Relationship Models for Early ADME Evaluation in Drug Discovery. 2. Blood-Brain Barrier Penetration

A new molecular lipoaffinity descriptor was introduced in this paper to account for the effect of molecular hydrophobicity on blood-brain barrier penetration. The descriptor was defined based on Kier and Hall's atom-type electrotopological state indices. Its evaluation requires 2-D molecular bonding information only. A multiple linear regression equation using this descriptor and molecular weight reproduces the experimental logBB values of 55 training set compounds and 11 test set compounds satisfactorily with statistical parameters nearly identical to the best models based on polar surface area and ClogP. The results indicate that the lipoaffinity descriptor defined in this paper may be a significant descriptor for molecular transport properties across lipid bilayers.

High-throughput liquid chromatography/mass spectrometry method for the determination of the chromatographic hydrophobicity index.

A fast gradient reversed-phase liquid chromatography (LC) method, using an acetonitrile gradient was developed to determine the chromatographic hydrophobicity index (CHI), as reported by Valco et al. (Anal. Chem. 1997, 69, 2022-2029). The analytical method provides retention times, based on UV detection at two different wavelengths, which then are converted into CHI values after calibration with a set of test compounds. The CHI of each compound is measured at three different pH values, 2.0, 7.4, and 10.5; so using an 8-min gradient at each pH value one compound can be analyzed in approximately 24 min. The aim of this work is to improve the throughput of the CHI screening using a LC/MS approach, so the application of the LC/MS technique is an extension of the LC/UV approach previously reported by Valco et al. This approach allows contemporary injection of N compounds into the LC/MS system, the retention time of each compound can be then extracted from the selected ion recording chromatograms. The throughput of the existing screening method could be increased by N times, where N is the number of compounds injected, so only three runs are needed to determine the CHI at three different pH values for a set of N compounds. The highest value of N depends on the total number of channels that can be monitored simultaneously; in the present work, 32 channels were used. This LC/MS method has been tested for a number of commercial products analyzed as mixtures, and data obtained were compared with those coming from the classical LC/UV approach. In the same way, the method was tested for a number of compounds associated with two GlaxoWellcome projects in the antibacterial area. Data reported show that the LC/MS method can be successfully applied for analyzing compounds in mixtures and for compounds with poor UW absorption, which cannot be analyzed with the standard LC/UV method.

Three-dimensional quantitative structure–activity relationship analyses of a series of cinnamamides

A 3D-QSAR study was conducted on a series of cinnamamides with anticonvulsant activities using comparative molecular similarity indices analysis (CoMSIA) and comparative molecular field analysis (CoMFA). In the current work, all-space searching procedure was applied to minimize the field sampling process. The comparison of sensitivity of CoMFA and CoMSIA to the different space orientation showed that the CoMSIA q 2 values were less sensitive to the translations and rotations of the aligned molecules with respect to the lattice compared with CoMFA. After all-space searching calculations, we could come up with a good CoMSIA model ( q 2 and r 2 values equal to 0.742 and 0.917, respectively) and the validity of the model was confirmed by additional five compounds in the external test set. Contour surfaces for the molecular fields are presented. These results provide useful information in the characterization and identification of the potential mechanisms of intermolecular interactions between ligand and receptor.

Database mining applied to central nervous system (CNS) activity

A data set of 389 compounds, active in the central nervous system (CNS) and divided into eight classes according to the receptor type, was extracted from the RBI© database and analyzed by Self-Organizing Maps (SOM), also known as Kohonen Artificial Neural Networks. This method gives a 2D representation of the distribution of the compounds in the hyperspace derived from their molecular descriptors. As SOM belongs to the category of unsupervised techniques, it has to be combined with another method in order to generate classification models with predictive ability. The fuzzy clustering (FC) approach seems to be particularly suitable to delineate clusters in a rational way from SOM and to get an automatic objective map interpretation. Maps derived by SOM showed specific regions associated with a unique receptor type and zones in which two or more activity classes are nested. Then, the modeling ability of the proposed SOM/FC Hybrid System tools applied simultaneously to eight activity classes was validated after dividing the 389 compounds into a training set and a test set, including 259 and 130 molecules, respectively. The proper experimental activity class, among the eight possible ones, was predicted simultaneously and correctly for 81% of the test set compounds.

Three-step procedure for infrared spectrum interpretation

A stepwise procedure is proposed that regard computer-supported structure elucidation relying on infrared spectra as leading to hypotheses that have to be verified in the subsequent steps. As test set compounds were selected by a preliminary counter-propagation neural network prediction. As target substructure the phenolic group was investigated, however, structures were also included not containing that group due to false positive predictions of the neural network. For its verification library search procedures, including the partial cross correlation approach, with maximum common substructure analysis were applied. In the majority of instances, erroneous propositions of the neural network could be corrected.

Prediction of odours of aliphatic alcohols and carbonylated compounds using fuzzy partition and self organising maps (SOM)

A set of 114 olfactory molecules divided into fruity, ethereal and camphoraceous compounds, was submitted to an analysis by Kohonen Neural Networks, also known as Self Organising Map (SOM). The compounds are represented in a hyperspace derived from their molecular descriptors and SOM gives a useful projection of this hyperspace onto a 2D map. Owing to the complexity of the olfaction mechanism, evidenced by the fact that one compound can exhibit simultaneously different properties, SOM alone is unable to take into account the olfaction diversity of the original 114 compounds, Then, a Fuzzy Partition method was applied on the Kohonen map previously developed. The obtained results allowed delineating different representative zones for the three odours, expressing more closely the olfactory richness. The ability of the Hybrid System combining SO and Fuzzy Partition to model the three odours was validated by dividing the 114 compounds into a training set and a test set, including 86 and 28 molecules, respectively. The most important olfactory characteristics were reproduced satisfactorily for the entire test set compounds.

Three-Dimension Quantitative Structure-Activity Relationship Analysis of Some Cinnamamides Using Comparative Molecular Similarity Indices Analysis (CoMSIA)

Comparative molecular similarity indices analysis (CoMSIA) has been applied to a data set of cinnamamides. Five different properties: steric, electrostatic, hydrophobic, H-bond donor and H- bond acceptor, assumed to cover the major contributions to ligand binding, were used to generate the 3D-QSAR model. A significant cross-validated correlation coefficient q 2 (0.691) was obtained, indicat- ing the statistical significance of the model for untested compounds of this class. The model was used to predict the anticonvulsant activities of five test-set compounds, and the predicted values were in good agreement with the experimental results. Moreover, from the contour maps, the key features vital to ligand binding have been identified, which are important for us to trace the important properties and gain insight into the potential mechanisms of intermolecular interactions between ligand and receptor.

Prediction of liquid chromatographic retention times of steroids by three-dimensional structure descriptors and partial least squares modeling

Prediction of the retention times of steroids in two liquid chromatographic systems, i.e., straight- and reversed-phase, has been modeled by means of different quantitative structure-retention relationship (QSRR) methods. The 3D-QSRR descriptors were generated by a process including semiempirical structure optimization, structure alignments, and electronic 3D field descriptions by the GRID method or by a novel method based on summation of the electronic charges at grid points ( Q-field). The compound data set was split into a calibration set and a test set using the self-organizing feature map (SOFM) technique. The correlation between the molecular descriptors and the retention time of the compounds in the calibration set was established using the partial least squares (PLS) method. Optimal predictive models were generated by a cross-validation procedure. These models were then used to predict the retention times of the compounds in the independent test set. A further check on the validity of the models was obtained by back-projection of the most important variables of the models onto the molecular structure. In addition, the predictive capability of variable-reduced models was examined. The various 3D-QSRR models generated gave for the reversed-phase system Q prediction 2 values in the range 0.60–0.79 for the test set, the corresponding Q prediction 2 values for the straight-phase system being in the range 0.42–0.75. In the former case, variable-reduced models resulted in considerably better predictions, although these were not as good as for those models obtained by means of classical physical–chemical descriptors.

Comparative molecular field analysis of a series of paclitaxel analogues.

A series of 94 paclitaxel analogues exhibiting antitumor activity by promoting the assembly of microtubules and inhibiting the disassembly process of microtubules to tubulin were investigated using the comparative molecular field analysis (CoMFA) method. These compounds belonging to 10 structural classes were randomly divided into a training set of 80 compounds and a test set of 14 compounds. Since the three-dimension structure of ligand--receptor complex is unknown, from X-ray and NMR data we rationally selected the three-dimension structure of paclitaxel in a polar solution as the active conformation and starting structure for molecule modeling, the other molecules were aligned using this molecule model as the template. The most optimal CoMFA yielded a two-components model, with significant cross-validation r2cv of 0.640 and conventional r2 of 0.868. The predictive ability of training set model was tested on the test set of 14 compounds. The tests not only revealed the robustness of the CoMFA model but demonstrated that for our model r2pred based on the mean activity of test set compounds can accurately estimate external predictivity but r2pred based on the mean activity of training set compounds overestimated the model. The CoMFA model explained why the activity of taxoid is sensitive to the stereochemistry of the atoms at C-2' and C-3' positions and the presence of hydroxyl group at C-2' position. The other factors affecting activity were also elucidated according to standard coefficient contour maps of steric and electrostatic fields derived from the CoMFA model.

Estimation of aqueous solubility of organic molecules by the group contribution approach. Application to the study of biodegradation

A reliable and generally applicable aqueous solubility estimation method for organic compounds based on a group contribution approach has been developed. Two models have been established based on two different sets of parameters. One has a higher accuracy, while the other has a more general applicability. The prediction potentials of these two models have been evaluated through cross-validation experiments. For model I, the mean cross-validated r2 and SD for 10 such cross-validation experiments were 0.946 and 0.503 log units, respectively. While for model II, they were 0.953 and 0.546 log units, respectively. Applying our models to estimate the water solubility values for the compounds in an independent test set, we found that model I can be applied to 13 out of 21 compounds with a SD equal to 0.58 log unit and model II can be applied to all the 21 compounds with a SD equal to 1.25 log units. Our models compare favorably to all the current available water estimation methods. A program based on this approach has been written in FORTRAN77 and is currently running on a VAX/VMS system. The program can be applied to estimate the water solubility of the water solubility of any organic chemical with a good or fairly good accuracy except for except for electrolytes. Applying our aqueous solubility estimation models to biodegradation studies, we found that although the water solubility was not the sole factor controlling the rate of biodegradation, ring compounds with greater solubilities were more likely to biodegrade at a faster rate. The significance of the relationship between water solubility and biodegradation activity has been illustrated by predicting the biodegradation activity of 27 new chemicals based solely on their estimated solubility values.

298. Evaluation of cosmetic irritancy

Measurement of ocular irritancy is a necessary step in the safety evaluation of both industrial and consumer products. Assessment of the acute eye irritation potential is therefore part of the international regulatory requirements for testing of chemicals.The Bovine Corneal Opacity and Permeability (BCOP) assay is generally accepted as a valid in vitro alternative method to the Draize eye irritation test to detect corrosive and severe eye irritants (category 1), but has not proven sensitive enough to discriminate accurately moderate (category 2A/2B) to mild and non-irritating compounds. In the currently accepted BCOP assay, opacity is determined by the amount of light transmission through the cornea, and permeability is determined by the amount of sodium fluorescein dye that passes through all corneal cell layers. Both measurements are used to assign an In Vitro Irritancy Score (IVIS) for prediction of the in vivo ocular irritation potential of a test substance. Nowadays, opacity is measured by an OP-KIT opacitometer providing a center-weighted reading of light transmission by measuring changes in voltage when the transmission of white light passes through the cornea alters. As a consequence, this may underestimate opacity that develops as spots or heterogeneous opaque areas on the periphery of an isolated cornea.A prototype of a laser light-based opacitometer (PLLBO) allowing better measurement of opacities was developed by Van Goethem et al. (2010). This new device showed improved sensitivity to detect subtle changes in corneal transparency. Furthermore, the new opacitometer allowed the analysis of the complete corneal surface and was able to detect more efficiently opaque spots located along the sides of the excised corneas.A further improved prototype of the PLLBO was constructed in combination with a camera and a speckle noise reducer. Treatment conditions of the corneas in the cornea holders were optimized in order to mimic more the real in vivo situation. A set of test compounds with irritancy potencies especially in the mild and moderate range was tested. The improved LLBO showed some promising features which potentially could improve the usefulness of the BCOP test. Adaptation of cornea holders showed to be of limited value and only restricted to concentrations up to 15% which mimics more test conditions in industry.This 3-year research project was sponsored by the Stavros Niarchos Foundation (Greece).