On the mechanism of human intestinal absorption

Identification of Putative Drug Targets in Highly Resistant Gram-Negative Bacteria; and Drug Discovery Against Glycyl-tRNA Synthetase as a New Target

Background

Gram-negative bacterial infections are on the rise due to the high prevalence of multidrug-resistant bacteria, and efforts must be made to identify novel drug targets and then new antibiotics.

Methods

In the upstream part, we retrieved the genome sequences of 4 highly resistant Gram-negative bacteria (e.g., Acinetobacter baumannii, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Enterobacter cloacae). The core proteins were assessed to find common, cytoplasmic, and essential proteins with no similarity to the human proteome. Novel drug targets were identified using DrugBank, and their sequence conservancy was evaluated. Protein Data Bank files and STRING interaction networks were assessed. Finally, the aminoacylation cavity of glycyl-tRNA synthetase (GlyQ) was virtually screened to identify novel inhibitors using AutoDock Vina and the StreptomeDB library. Ligands with high binding affinity were clustered, and then the pharmacokinetics properties of therapeutic agents were investigated.

Results

A total of 6 common proteins (e.g., RP-L28, RP-L30, RP-S20, RP-S21, Rnt, and GlyQ) were selected as novel and widespread drug targets against highly resistant Gram-negative superbugs based on different criteria. In the downstream analysis, virtual screening revealed that Rimocidin, Flavofungin, Chaxamycin, 11,11'-O-dimethyl-14'-deethyl-14'-methylelaiophylin, and Platensimycin were promising hit compounds against GlyQ protein. Finally, 11,11'-O-dimethyl-14'-deethyl-14'-methylelaiophylin was identified as the best potential inhibitor of GlyQ protein. This compound showed high absorption capacity in the human intestine.

Conclusion

The results of this study provide 6 common putative new drug targets against 4 highly resistant and Gram-negative bacteria. Moreover, we presented 5 different hit compounds against GlyQ protein as a novel therapeutic target. However, further in vitro and in vivo studies are needed to explore the bactericidal effects of proposed hit compounds against these superbugs.

Assessing the bioaccumulation potential of ionizable organic compounds: Current knowledge and research priorities

The objective of the present study was to review the current knowledge regarding the bioaccumulation potential of ionizable organic compounds (IOCs), with a focus on the availability of empirical data for fish. Aspects of the bioaccumulation potential of IOCs in fish that can be characterized relatively well include the pH dependence of gill uptake and elimination, uptake in the gut, and sorption to phospholipids (membrane-water partitioning). Key challenges include the lack of empirical data for biotransformation and binding in plasma. Fish possess a diverse array of proteins that may transport IOCs across cell membranes. Except in a few cases, however, the significance of this transport for uptake and accumulation of environmental contaminants is unknown. Two case studies are presented. The first describes modeled effects of pH and biotransformation on the bioconcentration of organic acids and bases, while the second employs an updated model to investigate factors responsible for accumulation of perfluorinated alkyl acids. The perfluorinated alkyl acid case study is notable insofar as it illustrates the likely importance of membrane transporters in the kidney and highlights the potential value of read-across approaches. Recognizing the current need to perform bioaccumulation hazard assessments and ecological and exposure risk assessment for IOCs, the authors provide a tiered strategy that progresses (as needed) from conservative assumptions (models and associated data) to more sophisticated models requiring chemical-specific information. Environ Toxicol Chem 2017;36:882-897. © 2016 SETAC.

Skin-mimetic chromatography for prediction of human percutaneous absorption of biologically active compounds occurring in medicinal plant extracts

The main aim of this study was to predict quantitatively human percutaneous absorption of chosen compounds commonly occurring in plants which can be used as medicinal extracts in the drug and beauty industries. The most important human percutaneous descriptors, i.e. logK_p (logarithm of the water/skin partition coefficient) and logJ_max (logarithm of the maximum flux of solutes penetrating the skin), of fatty acids and polyphenols were determined using both in vitro and in silico methods. For in vitro determination of human percutaneous absorption, micellar liquid chromatography based on hexadecyltrimethylammonium bromide, sodium dodecyl sulfate and polyoxyethylene (23) lauryl ether (Brij35) was used. Human percutaneous absorption was characterized by entirely new QSAR/QRAR models based on retention, lipophilic, steric and electronic data as well as on the linear free energy relationship parameters. Many different correlations between human skin absorption and different physicochemical parameters were performed, e.g. the in silico estimated logK_p value was correlated with the retention parameter logk_w (logarithm of the retention factor extrapolated to pure water) from the systems imitating a cutaneous environment (R²  = 0.92). Moreover, the influence of lipophilicity on percutaneous absorption was examined. The obtained correlation was excellent (R²  = 0.95).

Applicability of western chemical dietary exposure models to the Chinese population

A range of exposure models, which have been developed in Europe and North America, are playing an increasingly important role in priority setting and the risk assessment of chemicals. However, the applicability of these tools, which are based on Western dietary exposure pathways, to estimate chemical exposure to the Chinese population to support the development of a risk-based environment and exposure assessment, is unclear. Three frequently used modelling tools, EUSES, RAIDAR and ACC-HUMANsteady, have been evaluated in terms of human dietary exposure estimation by application to a range of chemicals with different physicochemical properties under both model default and Chinese dietary scenarios. Hence, the modelling approaches were assessed by considering dietary pattern differences only. The predicted dietary exposure pathways were compared under both scenarios using a range of hypothetical and current emerging contaminants. Although the differences across models are greater than those between dietary scenarios, model predictions indicated that dietary preference can have a significant impact on human exposure, with the relatively high consumption of vegetables and cereals resulting in higher exposure via plants-based foodstuffs under Chinese consumption patterns compared to Western diets. The selected models demonstrated a good ability to identify key dietary exposure pathways which can be used for screening purposes and an evaluative risk assessment. However, some model adaptations will be required to cover a number of important Chinese exposure pathways, such as freshwater farmed-fish, grains and pork.

Applications of polyparameter linear free energy relationships in environmental chemistry

Partitioning behavior of organic chemicals has tremendous influences on their environmental distribution, reaction rates, bioaccumulation, and toxic effects. Polyparameter linear free energy relationships (PP-LFERs) have been proven to be useful to characterize the equilibrium partitioning of organic chemicals in various environmental and technical partitioning systems and predict the respective partition coefficients. Over the past decade, PP-LFER solute descriptors for numerous environmentally relevant organic chemicals and system parameters for environmentally important partitioning systems have been determined, extending substantially the applicability of the PP-LFER approaches. However, the information needed for the use of PP-LFERs including descriptors and parameters is scattered over a large number of publications. In this work, we review the state of the art of the PP-LFER approaches in environmental chemical applications. The solute descriptors and system parameters reported in the literature and the availability of their database are summarized, and their calibration and prediction methods are overviewed. We also describe tips and pitfalls associated with the use of the PP-LFER approaches and identify research needs to improve further the usefulness of PP-LFERs for environmental chemistry.

in vitro and in silico determination of oral, jejunum and Caco-2 human absorption of fatty acids and polyphenols. Micellar liquid chromatography

In this investigation chosen saturated, mono- and polyunsaturated fatty acids as well as polyphenols have been analyzed. The main aim of this study was to determine oral, jejunum and Caco-2 human absorption of chosen fatty acids and polyphenols using in vitro and in silico methods. For in vitro determination of human drug absorption, the usefulness of Micellar Liquid Chromatography (MLC) with mobile phases containing different surfactants (including Brij35-Biopartitioning Micellar Chromatography (BMC)) has been confirmed. On the basis of Foley's equation, 1/k vs. CM correlations for the tested compounds have been done. Satisfactory linearity of the relationships was found over the whole eluents composition range studied with R(2) approximately 0.99 in each case. Moreover, the analyte-micelle association constants (Kma) from Foley's equation have been compared for different micellar environments, containing Brij35, SDS and CTAB as a main component of micellar mobile phases. Completely new models describing human oral as well as Caco-2 and jejunum absorption have been constructed and compared with the cited models. These models are based on the Abraham descriptors and lipophilicity parameters as well as steric descriptors. Furthermore, many different correlations between physicochemical parameters and human intestinal absorption have been done, e.g. the correlation between human jejunum permeability estimated in silico and received using LSER parameters was excellent (R(2) nearly 0.99). Chromatographic parameters have been collated with steric, electronic and physicochemical ones using QRAR (Quantitative Retention - Activity Relationships) and QSAR (Quantitative Structure - Activity Relationships) models. Moreover, retention BMC data have been compared with lipophilicity parameter logPo/w (n-octanol-water partition coefficient). The influence of lipophilicity on oral absorption (%) has been checked. The correlation between predicted oral absorption (%) and logPo/w has been done. Obtained R(2) was 0.82. On the basis of chromatographic, lipophilicity, steric and different physicochemical parameters, the principal components analysis (PCA) has been done.

Development and evaluation of a mechanistic bioconcentration model for ionogenic organic chemicals in fish

A mechanistic mass balance bioconcentration model is developed and parameterized for ionogenic organic chemicals (IOCs) in fish and evaluated against a compilation of empirical bioconcentration factors (BCFs). The model is subsequently applied to a set of perfluoroalkyl acids. Key aspects of model development include revised methods to estimate the chemical absorption efficiency of IOCs at the respiratory surface (E(W) ) and the use of distribution ratios to characterize the overall sorption capacity of the organism. Membrane-water distribution ratios (D(MW) ) are used to characterize sorption to phospholipids instead of only considering the octanol-water distribution ratio (D(OW) ). Modeled BCFs are well correlated with the observations (e.g., r(2)  = 0.68 and 0.75 for organic acids and bases, respectively) and accurate to within a factor of three on average. Model prediction errors appear to be largely the result of uncertainties in the biotransformation rate constant (k(M) ) estimates and the generic approaches for estimating sorption capacity (e.g., D(MW) ). Model performance for the set of perfluoroalkyl acids considered is highly dependent on the input parameters describing hydrophobicity (i.e., log K(OW) of the neutral form). The model applications broadly support the hypothesis that phospholipids contribute substantially to the sorption capacity of fish, particularly for compounds that exhibit a high degree of ionization at biologically relevant pH. Additional empirical data on biotransformation and sorption to phospholipids and subsequent incorporation into property estimation approaches (e.g., k(M) , D(MW) ) are priorities with respect to improving model performance.

Multiple Kernel Learning for Drug Discovery

The support vector machine (SVM) methodology has become a popular and well-used component of present chemometric analysis. We assess a relatively recent development of the algorithm, multiple kernel learning (MKL), on published structure-property relationship (SPR) data. The MKL algorithm learns a weighting across multiple kernel-based representations of the data during supervised classifier creation and, thereby, may be used to describe the influence of distinct groups of structural descriptors upon a single structure-property classifier without explicitly omitting any of them. We observe a statistically significant performance improvement over a conventional, single kernel SVM on all three SPR data sets analysed. Furthermore, MKL output is observed to provide useful information regarding the relative influence of five distinct descriptor subsets present in each data set.

The design of orally bioavailable 2, 5-diketopiperazine oxytocin antagonists: from concept to clinical candidate for premature labor

A short, efficient and highly stereoselective synthesis has been developed for a series of 6-indanyl-3-alkyl-7-aryl/heterocyclic-(3R, 6R, 7R)-2, 5-diketopiperazine amides that are potent and selective oxytocin (OT) antagonists. Property-based design using an estimate of human oral absorption enabled focus to be directed to those templates with the greatest chance of delivering high bioavailability in humans. This led to the 2', 4'-difluorophenyl dimethylamide 40, a highly potent (pK(i) =9.2) and selective OT antagonist (>1,000-fold selectivity vs. the human vasopressin receptors V1a, V2, and V1b) with good oral bioavailability (>50%) in the rat and dog. Increased solubility and an improved Cyp450 profile was achieved with a range of 2'-substituted 7-(1',3'-oxazol-4'-yl)-(3R,6R,7R)-2,5-diketopiperazine amides and branching at the α-carbon of the 3-butyl group led to a superior rat pharmacokinetic profile that resulted in the discovery of the 2'-methyl-1',3'-oxazol-4'-yl morpholine amide derivative 74 GSK221149A (Retosiban), which had the best oral exposure and bioavailability in the rat. Retosiban has sub-nanomolar affinity (K(i) =0.65 nM) for the oxytocin receptor with >1400-fold selectivity over the closely related vasopressin receptors. It has good solubility, low protein binding and has a good Cyp450 profile with no significant inhibition IC(50) >100 µM. Retosiban is >15-fold more potent at the human oxytocin receptor than atosiban (a marketed i.v, peptide OT antagonist) and it has been shown to be an effective tocolytic by i.v. and by oral administration in rats, and was selected for progression as a potential clinical candidate for preterm labor.

A modified uncorrelated linear discriminant analysis model coupled with recursive feature elimination for the prediction of bioactivity

To meet the requirements of providing accurate, robust, and interpretable prediction of bioactivity, a modified uncorrelated linear discriminant analysis (M-ULDA) model was developed. In addition, a feature selection method called recursive feature elimination (RFE), originally used for support vector machine (SVM), was introduced and modified to fit the scheme of ULDA. From the evaluation of six pharmaceutical datasets, the M-UDLA coupled with RFE showed better or comparable classification accuracy with respect to other well-studied methods such as SVM and decision trees. The RFE used for ULDA has the advantage of increasing the computational speed and provides useful insights into biochemical mechanisms related to pharmaceutical activity by significantly reducing the number of variables used for the final model.

Prediction of human intestinal absorption by GA feature selection and support vector machine regression

QSAR (Quantitative Structure Activity Relationships) models for the prediction of human intestinal absorption (HIA) were built with molecular descriptors calculated by ADRIANA.Code, Cerius² and a combination of them. A dataset of 552 compounds covering a wide range of current drugs with experimental HIA values was investigated. A Genetic Algorithm feature selection method was applied to select proper descriptors. A Kohonen's self-organizing Neural Network (KohNN) map was used to split the whole dataset into a training set including 380 compounds and a test set consisting of 172 compounds. First, the six selected descriptors from ADRIANA.Code and the six selected descriptors from Cerius² were used as the input descriptors for building quantitative models using Partial Least Square (PLS) analysis and Support Vector Machine (SVM) Regression. Then, another two models were built based on nine descriptors selected by a combination of ADRIANA.Code and Cerius² descriptors using PLS and SVM, respectively. For the three SVM models, correlation coefficients (r) of 0.87, 0.89 and 0.88 were achieved; and standard deviations (s) of 10.98, 9.72 and 9.14 were obtained for the test set.

Carrier-mediated cellular uptake of pharmaceutical drugs: an exception or the rule?

It is generally thought that many drug molecules are transported across biological membranes via passive diffusion at a rate related to their lipophilicity. However, the types of biophysical forces involved in the interaction of drugs with lipid membranes are no different from those involved in their interaction with proteins, and so arguments based on lipophilicity could also be applied to drug uptake by membrane transporters or carriers. In this article, we discuss the evidence supporting the idea that rather than being an exception, carrier-mediated and active uptake of drugs may be more common than is usually assumed - including a summary of specific cases in which drugs are known to be taken up into cells via defined carriers - and consider the implications for drug discovery and development.

Effects of Cyclodextrins on Drug Delivery Through Biological Membranes

Cyclodextrins have proven themselves to be useful functional excipients. Cyclodextrin derivatives can be hydrophilic or relatively lipophilic based on their substitution and these properties can give insight into their ability to act as permeability enhancers. Lipophilic cyclodextrins such as the methylated derivatives are thought to increase drug flux by altering barrier properties of the membrane through component extraction or fluidization. The hydrophilic cyclodextrin family also modulate drug flux through membranes but via different mechanisms. The current effort seeks to provide various explanations for these observations based on interactions of hydrophilic cyclodextrins with the unstirred water layer that separates the bulk media from biological membranes such as the gastric mucosa, cornea and reproductive tract. Theories on the serial nature of resistances to drug flux are used to explain why hydrophilic cyclodextrins can enhance drug uptake in some situation (i.e., for lipophilic material) but not in others. In addition, the nature of secondary equilibria and competition between cyclodextrins and rheologically important biopolymers such as mucin are assessed to give a complete picture of the effect of these starch derivatives. This information can be useful not only in understanding the actions of cyclodextrin but also in expanding their application and uses.

ADME evaluation in drug discovery. 7. Prediction of oral absorption by correlation and classification

A critically evaluated database of human intestinal absorption for 648 chemical compounds is reported in this study, among which 579 are believed to be transported by passive diffusion. The correlation analysis between the intestinal absorption and several important molecular properties demonstrated that no single molecular property could be used as a good discriminator to efficiently distinguish the poorly absorbed compounds from those that are well absorbed. The theoretical correlation models for a training set of 455 compounds were proposed by using the genetic function approximation technique. The best prediction model contains four molecular descriptors: topological polar surface area, the predicted distribution coefficient at pH = 6.5, the number of violations of the Lipinski's rule-of-five, and the square of the number of hydrogen-bond donors. The model was able to predict the fractional absorption with an r = 0.84 and a prediction error (absolute mean error) of 11.2% for the training set. Moreover, it achieves an r = 0.90 and a prediction error of 7.8% for a 98-compound test set. The recursive partitioning technique was applied to find the simple hierarchical rules to classify the compounds into poor (%FA < or = 30%) and good (%FA > 30%) intestinal absorption classes. The high quality of the classification model was validated by the satisfactory predictions on the training set (correctly identifying 95.9% of the compounds in the poor-absorption class and 96.1% of the compounds in the good-absorption class) and on the test set (correctly identifying 100% of the compounds in the poor-absorption class and 96.8% of the compounds in the good-absorption class). We expect that, in the future, the rules for the prediction of carrier-mediated transporting and first pass metabolism can be integrated into the current hierarchical rules, and the classification model may become more powerful in the prediction of intestinal absorption or even human bioavailability. The databases of human intestinal absorption reported here are available for download from the supporting Web site: http://modem.ucsd.edu/adme.

Acidity, lipophilicity, solubility, absorption, and polar surface area of some ACE inhibitors

Computational chemical methods have been used to correlate the molecular properties of the 10 ACE inhibitors (captopril, enalapril, perindopril, lisinopril, ramipril, trandolapril, quinapril, fosinopril, benazepril, and cilazapril) and some of their active metabolites (enalaprilat, perindoprilat, ramiprilat, trandolaprilat, quinaprilat, fosinoprilat, benazeprilat, and cilazaprilat). The computed pK a values correlate well with the available experimental values. In the dicarboxylic ACE inhibitors, the carboxyalkyl carboxylate group of the ACE inhibitors studied is more acidic than the C-terminal carboxylate. However, at physiological pH = 7.4 both carboxyl groups of ACE inhibitors are completely ionized and the dicarboxyl-containing ACE inhibitors behave as strong acids. The available experimental partition coefficients of these ACE inhibitors investigated are well reproduced by the neural network-based ALOGPs and the fragment-based KoWWiN methods. All parent drugs (and prodrugs), with the exception of fosinopril, are compounds with low lipophilicity. Calculated pK a, lipophilicity, solubility, absorption, and polar surface area of the most effective ACE inhibitors for the prevention of myocardial infarction, perindopril and ramipril, were found similar. Therefore, it is probable that the experimentally observed differences in the survival benefits in the first year after acute myocardial infarction in patients 65 years of age or older correlate closely to the physicochemical and pharmacokinetic characteristics of the specific ACE inhibitor that is used.

Prediction and Mechanistic Interpretation of Human Oral Drug Absorption Using MI-QSAR Analysis

Membrane-interaction [MI]-QSAR analysis, which includes descriptors explicitly derived from simulations of solutes [drugs] interacting with phospholipid membrane models, was used to construct QSAR models for human oral intestinal drug absorption. A data set of 188 compounds, which are mainly drugs, was divided into a parent training set of 164 compounds and a test set of 24 compounds. Stable, but not highly fit [R2 = 0.68] MI-QSAR models could be built for all 188 compounds. However, the relatively large number [47] of drugs having 100% absorption, as well as all zwitterionic compounds [11], had to be eliminated from the training set in order to construct a linear five-term oral absorption diffusion model for 106 compounds which was both stable [R2 = 0.82, Q2 = 0.79] and predictive given the test set compounds were predicted with nearly the same average accuracy as the compounds of the training set. Intermolecular membrane-solute descriptors are essential to building good oral absorption models, and these intermolecular descriptors are displaced in model optimizations and intramolecular solute descriptors found in published oral absorption QSAR models. A general form for all of the oral intestinal absorption MI-QSAR models has three classes of descriptors indicative of three thermodynamic processes: (1) solubility and partitioning, (2) membrane-solute interactions, and (3) flexibility of the solute and/or membrane. The intestinal oral absorption MI-QSAR models were compared to MI-QSAR models previously developed for Caco-2 cell permeation and for blood-brain barrier penetration. The MI-QSAR models for all three of these ADME endpoints share several common descriptors, and suggest a common mechanism of transport across all three barriers. A further analysis of these three types of MI-QSAR models has been done to identify descriptor-term differences across these three models, and the corresponding differences in thermodynamic transport behavior of the three barriers.

Prediction of ADMET Properties

This Review describes some of the approaches and techniques used today to derive in silico models for the prediction of ADMET properties. The article also discusses some of the fundamental requirements for deriving statistically sound and predictive ADMET relationships as well as some of the pitfalls and problems encountered during these investigations. It is the intension of the authors to make the reader aware of some of the challenges involved in deriving useful in silico ADMET models for drug development.

2,5-diketopiperazines as potent, selective, and orally bioavailable oxytocin antagonists. 3. Synthesis, pharmacokinetics, and in vivo potency

A short, efficient, and highly stereoselective synthesis of a series of (3R,6R,7R)-2,5-diketopiperazine oxytocin antagonists and their pharmacokinetics in rat and dog is described. Prediction of the estimated human oral absorption (EHOA) using measured lipophilicity (CHI log D) and calculated size (cMR) has allowed us to rank various 2,5-diketopiperazine templates and enabled us to focus effort on those templates with the greatest chance of high bioavailability in humans. This rapidly led to the 2',4'-difluorophenyl-dimethylamide 25 and the benzofuran 4 with high levels of potency (pK(i)) and good bioavailability in the rat and dog. Dimethylamide 25 is more potent (>20-fold) than 4 in vivo and has a high degree of selectivity toward the vasopressin receptors, >10,000 for hV1a/hV1b and approximately 500 for hV2. It has a good Cyp450 profile with no time dependent inhibition and was negative in the genotoxicity screens with a satisfactory oral safety profile in rats.

Chromatographic Estimation of Drug Disposition Properties by Means of Immobilized Artificial Membranes (IAM) and C18 Columns

Chromatographic retention measurement in immobilized artificial membranes (IAMs) is considered a fast and reliable method to predict biological properties (drug distribution) because of the IAM structure, which consists of phospholipid analogues bonded covalently to silica particles. A new parameter (d) is proposed to estimate the similarity between IAM columns, conventional HPLC columns, and drug distribution systems, and thus the performance of chromatographic systems to predict drug distribution. An IAM.PC.DD2 column has been used for this study, together with two XTerra columns (MSC18 and RP18), at several acetonitrile-water mobile phases. According to the d parameter, good correlations should be obtained between chromatographic systems (both IAM and C18) and octanol-water partition coefficient (log P), and thus both types of columns could be used to obtain log P values. The IAM.PC.DD2 system shows a close similarity to human skin partition, tadpole narcosis, and blood-brain permeability processes, showing that it can be useful as a model for these biological processes. Controversially, it is shown that human skin permeation is more similar to C18 partition than to IAM partition. Other biological processes such as blood-brain distribution and tissue-blood partition show a poor similarity to IAM and C18 systems, demonstrating that estimation of these drug distribution processes by chromatographic measurements may not be adequate.

Theoretical study of structure, pKa, lipophilicity, solubility, absorption, and polar surface area of some centrally acting antihypertensives

The methods of theoretical chemistry have been used to elucidate the molecular properties of the substituted imidazoline and oxazoline structures, a class of potent agonists and antagonists of imidazoline receptors. The geometries of various tautomers and isomers of 2-[2,6-dichlorophenylimino]imidazolidine (clonidine), 1-(N-dicyclopropylmethyl)amino-2-oxazoline (rilmenidine), 4-chloro-N-(4,5-dihydro-1H-imidazol-2yl)-6-methoxy-2-methyl-5-pyrimidinamine (moxonidine), N-(dicyclopropylmethyl)-4,5-dihydro-1H-pyrrol-2-amine (aminopyrroline), N-dicyclopropylmethyl-4,5-dihydrothiazol-2-amine (aminothiazoline), 4,5-dihydro-2-(2-methoxyphenyl)-1H-imidazole (compound_6), 4,5-dihydro-2-(3-methylthiophen-2-yl)-1H-imidazole (compound_7), N-(2-chloro-4-iodophenyl)-4,5-dihydro-5-methyl-3H-pyrrol-2-amine (LNP_911), N-amidino-3,5-diamino-6-chloropyrazine-carboxamide (amiloride), 2-(1,4-benzodioxan-2-yl)-2-imidazoline (idazoxan), (+/-)-2-(2-ethyl-2,3-dihydro-2-benzofuranyl)-2-imidazoline (efaroxan), (4-aminobutyl)guaninine (agmatine), and 1-methyl-9H-pyrido[3,4-b]indole (harmane) have been studied using Becke3LYP/6-31+G(d,p) and BP86/TZ2P DFT methods. The optimized geometries indicate that these molecules show a distinctly nonplanar configuration of the imidazoline and oxazoline moieties. In the gas-phase, rilmenidine and aminothiazoline exist in two forms (amino and imino), the amino tautomers being more stable by about 6 kJ/mol. The calculations showed, in agreement with experiments, that clonidine, moxonidine, and LNP_911 exist in a more stable imino tautomer. The tautomer containing the amino group is by about 30 kJ/mol less stable. Computations that include the effect of solvation indicated that also in water the relative stability order of individual tautomers (amino and imino forms) is preserved. The computed pKa values varied between 6.7 and 9.0, and correlate well with the available experimental pKa's found in the literature. Among the clinically useful antihypertensives moxonidine exhibits the lowest basicity in water. At pH = 7.4 only about 50% of this drug exists in ionized form. The available experimental partition coefficients of compounds investigated are best reproduced by the CLOGP method. The computed partition coefficients varied between -1.80 (agmatine) and 5.35 (LNP_911) (CLOGP). Clonidine, moxonidine, and rilmenidine are moderately lipophilic compounds with lipophilicities between these two extreme values. The computed solubilities (about 0.1-4 g/L) show that the imidazoline and oxazoline derivatives studied have very low water solubility. The analysis of molecular descriptors defined by Lipinski has shown that most of the compounds studied obey 'rule of five'. Amiloride and agmatine 'outlets' exhibit also the lowest absorption. Therefore, in the early stages of the design of ligands acting on imidazoline binding sites, it is becoming more important to determine the pKa, lipophilicity, water solubility, polar surface area, absorption, and other physicochemical properties associated with a drug, before synthetic work is undertaken, with the aim of avoiding the synthesis of compounds that are predicted to have poor biopharmaceutical characteristics.

Effect of molecular descriptor feature selection in support vector machine classification of pharmacokinetic and toxicological properties of chemical agents

Statistical-learning methods have been developed for facilitating the prediction of pharmacokinetic and toxicological properties of chemical agents. These methods employ a variety of molecular descriptors to characterize structural and physicochemical properties of molecules. Some of these descriptors are specifically designed for the study of a particular type of properties or agents, and their use for other properties or agents might generate noise and affect the prediction accuracy of a statistical learning system. This work examines to what extent the reduction of this noise can improve the prediction accuracy of a statistical learning system. A feature selection method, recursive feature elimination (RFE), is used to automatically select molecular descriptors for support vector machines (SVM) prediction of P-glycoprotein substrates (P-gp), human intestinal absorption of molecules (HIA), and agents that cause torsades de pointes (TdP), a rare but serious side effect. RFE significantly reduces the number of descriptors for each of these properties thereby increasing the computational speed for their classification. The SVM prediction accuracies of P-gp and HIA are substantially increased and that of TdP remains unchanged by RFE. These prediction accuracies are comparable to those of earlier studies derived from a selective set of descriptors. Our study suggests that molecular feature selection is useful for improving the speed and, in some cases, the accuracy of statistical learning methods for the prediction of pharmacokinetic and toxicological properties of chemical agents.

Use of Surface Charges from DFT Calculations To Predict Intestinal Absorption

A model for prediction of percent intestinal absorption (%Abs) of neutral molecules was developed based upon surface charges of the molecule calculated by density functional theory (DFT). The surface charges are decomposed into sigma moments which are correlated to a partition coefficient representing transfer of the molecule between water and the epithelial membrane. The model was built and tested using a data set of 241 drugs. It achieved an RMS deviation of 13% on a training set of 38 compounds as well as on a test set of 107 drugs for which the experimental data were classified as high quality. Property maps of the molecule, depicting which atoms contribute to or hinder absorption, are produced to aid drug design.

Biosensor analysis of the interaction between drug compounds and liposomes of different properties; a two-dimensional characterization tool for estimation of membrane absorption

The interactions between 78 drug compounds and immobilised liposomes were investigated using an assay based on surface plasmon resonance technology. The drugs were screened at a single concentration and allowed to interact simultaneously with two different types of liposomes. When the drug-liposome responses are plotted against one another they generally fall into three distinct bands: low response-low percent fraction absorbed in humans (Fa), medium response-medium Fa, and high response-high Fa. For drugs with medium to high Fa values, basic compounds could be resolved from acidic and neutral compounds to a large extent. This technique has the potential to be utilized as a screening tool for binning novel compounds into low, medium, or high Fa based on a simple experimental measurement. The assay was applied to 11 kinase inhibitors, 9 thrombin inhibitors, and 11 carbonic anhydrase inhibitors highlighting a subset that may have incomplete intestinal absorption (low to medium Fa). Assay conditions were optimized making the assay suitable for routine analysis and for compound characterization early in drug discovery where solubility may be an issue.

Permeability through DOPC/dodecane membranes: measurement and LFER modelling

The permeabilities of 43 ionisable compounds through membranes consisting of 2% dioleylphosphatidylcholine (DOPC) in dodecane at pH values between 3 and 10 have been measured. The observed values are corrected for the effects of ionisation and diffusion through the unstirred water layer in order to obtain estimates of intrinsic permeability. The intrinsic permeabilities are modelled using Abraham's linear free energy relation method. This not only provides a predictive model of membrane permeability, but also reveals the factors determining passive permeation through membranes made from 2% DOPC in dodecane. Hydrogen bonding dominates, acting to strongly inhibit permeation, polarity/polarisability effects are less important, and size acts to enhance permeation.

Separation methods for estimating octanol-water partition coefficients

Separation methods for the indirect estimation of the octanol-water partition coefficient (logP) are reviewed with an emphasis on high throughput methods with a wide application range. The solvation parameter model is used to identify suitable separation systems for estimating logP in an efficient manner that negates the need for empirical trial and error experiments. With a few exceptions, systems based on reversed-phase chromatography employing chemically bonded phases are shown to be unsuitable for estimating logP for compounds of diverse structure. This is because the fundamental properties responsible for chromatographic retention tend to be different to those responsible for partition between octanol and water, especially the contribution from hydrogen bonding interactions. On the other hand, retention in several micellar and microemulsion electrokinetic chromatography systems is shown to be highly correlated with the octanol-water partition coefficient. These systems are suitable for the rapid, high throughput determination of logP for neutral, weakly acidic, and weakly basic compounds. For compounds with a permanent charge, electrophoretic migration and electrostatic interactions with the stationary phase results in inaccurate estimation of partition coefficients. The experimental determination of solute descriptors offers an alternative approach for estimating logP, and other biopartitioning properties. A distinct advantage of this approach is that once the solute descriptors are known, solute properties can be estimated for any distribution or transport system for which a solvation parameter model has been established.

Determination of Solvation Descriptors for Ionic Species: Hydrogen Bond Acidity and Basicity

Literature values of Gibbs energies of transfer of ions from water to other solvents have been used in conjunction with our solvation equation to obtain descriptors for univalent ions. It is suggested that descriptors used for nonelectrolytes are not adequate to describe transfers of single ions, and that two specific ionic descriptors (J(+) and J(-)) for cations and anions, respectively, are required. The ions studied include the alkali metal and tetraalkylammonium cations, halide and other anions, and the tetraphenylarsonium, tetraphenylphosphonium, and tetraphenylborate ions. It is shown that simple cations such as Na(+) act as very strong hydrogen bond acids and that the R(4)N(+) ions are only weak hydrogen bond acids. The halide anions are very strong hydrogen bond bases, as is also the acetate anion. Other anions, such as azide, cyanide, and nitrate, are again very strong hydrogen bond bases. The tetraphenylarsonium, tetraphenylphosphonium, and tetraphenylborate ions have no hydrogen bond acidity but are quite strong hydrogen bond bases. It is suggested that this is due to the basic properties of the phenyl groups.