Mechanism-Based QSAR Modeling of Skin Sensitization.

Many chemicals can induce skin sensitization, and there is a pressing need for non-animal methods to give a quantitative indication of potency. Using two large published data sets of skin sensitizers, we have allocated each sensitizing chemical to one of 10 mechanistic categories and then developed good QSAR models for the seven categories that have a sufficient number of chemicals to allow modeling. Both internal and external validation checks showed that each model had good predictivity.

Extended duration therapy with pegylated interferon and ribavirin for patients with genotype 3 hepatitis C and advanced fibrosis: final results from the STEPS trial.

BACKGROUND & AIMS: Patients with genotype 3 hepatitis C virus (HCV) infection and cirrhosis have poor response rates after 24 weeks treatment with pegylated interferon and ribavirin. Treatment for 48 weeks is therefore recommended, although the benefits of this are untested. We examined extended therapy in patients with genotype 3 HCV and advanced fibrosis. METHODS: Multicentre, open labelled randomized trial comparing therapy with 24 weeks pegylated interferon and ribavirin to 48 weeks of the same therapy. RESULTS: 136 patients completed the study. 67 received 24 weeks therapy and the SVR rate (48%) did not differ from that seen in the 69 patients who received 48 weeks therapy (42%). The response rates in patients with biopsy proven cirrhosis (13 patients treated for 24 weeks, 18 patients treated for 48 weeks) or cirrhosis proven on imaging (28 patients treated for 24 weeks and 25 patients treated for 48 weeks) were 46% in those treated for 24 weeks and 40% in those treated for 48 weeks. The differences were not significantly different. Treatment failure was due to relapse in the majority of patients. CONCLUSIONS: Patients with genotype 3 HCV and advanced fibrosis do not benefit from extended therapy with pegylated interferon and ribavirin.

Effect of the structural factors of organic compounds on the acute toxicity toward Daphnia magna.

The acute toxicity of organic compounds towards Daphina magna was subjected to QSAR analysis. The two-dimensional simplex representation of molecular structure (2D SiRMS) and the support vector machine (SVM), gradient boosting (GBM) methods were used to develop QSAR models. Adequate regression QSAR models were developed for incubation of 24 h. Their interpretation allowed us to quantitatively describe and rank the well-known toxicophores, to refine their molecular surroundings, and to distinguish the structural derivatives of the fragments that significantly contribute to the acute toxicity (LC50) of organic compounds towards D. magna. Based on the results of the interpretation of the regression models, a molecular design (modification) of highly toxic compounds was performed in order to reduce their hazard. In addition, acceptable classification QSAR models were developed to reliably predict the following mode of action (MOA): specific and non-specific toxicity of organic compounds towards D. magna. When interpreting these models, we were able to determine the structural fragments and the physicochemical characteristics of molecules that are responsible for the manifestation of one of the modes of action. The on-line version of the OCHEM expert system (https://ochem.eu), HYBOT descriptors, and the random forest and SVM methods were used for a comparative QSAR investigation.

In silico prediction of aqueous solubility: the solubility challenge.

The dissolution of a chemical into water is a process fundamental to both chemistry and biology. The persistence of a chemical within the environment and the effects of a chemical within the body are dependent primarily upon aqueous solubility. With the well-documented limitations hindering the accurate experimental determination of aqueous solubility, the utilization of predictive methods have been widely investigated and employed. The setting of a solubility challenge by this journal proved an excellent opportunity to explore several different modeling methods, utilizing a supplied dataset of high-quality aqueous solubility measurements. Four contrasting approaches (simple linear regression, artificial neural networks, category formation, and available in silico models) were utilized within our laboratory and the quality of these predictions was assessed. These were chosen to span the multitude of modeling methods now in use, while also allowing for the evaluation of existing commercial solubility models. The conclusions of this study were surprising, in that a simple linear regression approach proved to be superior over more complex modeling methods. Possible explanations for this observation are discussed and also recommendations are made for future solubility prediction.

Contribution assessment of multiparameter optimization descriptors in CNS penetration.

Assessment of the influence of six physicochemical properties used in the multiparameter optimization (MPO) approach for chemical penetration of the blood-brain barrier was carried out by means of application of logistic regression and multiple linear regression, using a data set of 578 diverse chemicals. It was found that use of an aggregation MPO-score descriptor did not give satisfactory results with central nervous system (CNS)/non-CNS classification. Thus an application of the MPO approach for CNS penetration is ambiguous. An alternative to the MPO approach in this work contains detailed (quantitative) structure-activity relationship analysis using a number of methods (linear discriminant analysis, random forest, support vector machine, Gaussian process). Three properties (molecular weight, number of H-bond donors and octanol-water partition coefficient) yielded optimal categorical models with modest statistical parameters (accuracy 0.730-0.765 for CNS/non-CNS classification). The poor statistics of regression models for the common data set suggested the presence of subsets with different mechanisms of penetrations. Based on graphic comparison of experimental and calculated Cu,b values, subset clusters have satisfactory statistics. The regression models obtained allowed the estimation of descriptor contributions in log Cu,b. This means that medicinal chemists now have a simple additive scheme for at least preliminary quantitative assessment of this important pharmacokinetic parameter.

Linear QSAR regression models for the prediction of bioconcentration factors by physicochemical properties and structural theoretical molecular descriptors.

The development of QSAR models useful for the prediction of fish bioconcentration factor (BCF) for a wide range of different chemical classes is crucial for the assessment and prioritisation of potentially persistent bioaccumulative and toxic substances. In this study we present QSAR models for BCF developed on a wide range of chemical structural classes of environmental and toxicological interest (such as dyes and various chlorinated and brominated compounds). The aim is to provide valid QSAR models, statistically validated for predictivity, for the prediction of BCF in general, but also for problematical chemical classes such as highly hydrophobic chemicals. Several descriptors, calculated by different commercially available software packages, have been employed in order to take into account relevant information provided by physicochemical properties (octanol/water partition coefficient and water solubility) and molecular features (structural and quantum-chemical molecular descriptors). The best descriptor subsets for the models were selected using the Genetic Algorithm-Variable Subset Selection strategy (GA-VSS) and calculations were performed by ordinary least squares regression. Starting from a data set of 640 compounds (logK(ow) range from -2.34 to 12.66), we developed linear QSARs, firstly for a data set of 620 compounds (logK(ow) range from -2.34 to 10.35) and secondly specifically for 87 highly hydrophobic chemicals (logK(ow) range from 6.00 to 10.35). All these models have been statistically validated (both internally by cross-validation and bootstrap and externally, by "a priori" splitting of available data by Kohonen Map-ANN in training and prediction sets) and their structural chemical domain has been verified by the leverage approach.

Improved correlation between animal and human potency of non-steroidal anti-inflammatory drugs using quantitative structure-activity relationships (QSARs).

Animal models are known not to predict human responses well, in general. However, we have been able to demonstrate that, for a series of non-steroidal anti-inflammatory drugs that are or were in clinical use, the incorporation of two simple physicochemical properties results in excellent correlations between human and rodent potencies for anti-inflammatory, analgesic and anti-pyretic activities. This has the potential to allow the use of historical data to improve drug development.

Six global and local QSPR models of aqueous solubility at pH = 7.4 based on structural similarity and physicochemical descriptors.

Aqueous solubility at pH = 7.4 is a very important property for medicinal chemists because this is the pH value of physiological media. The present work describes the application of three different methods (support vector machine (SVM), random forest (RF) and multiple linear regression (MLR)) and three local quantitative structure-property relationship (QSPR) models (regression corrected by nearest neighbours (RCNN), arithmetic mean property (AMP) and local regression property (LoReP)) to construct stable QSPRs with clear mechanistic interpretation. Our data set contained experimental values of aqueous solubility at pH = 7.4 of 387 chemicals (349 in the training set and 38 in the test set including 16 own measurements). The initial descriptor pool contained 210 physicochemical descriptors, calculated from the HYBOT, DRAGON, SYBYL and VolSurf+ programs. Six QSPRs with good statistics based on fundamentals of aqueous solubility and optimization of descriptor space were obtained. Those models have an RMSE close to experimental error (0.70), and are amenable to physical interpretation. The QSPR models developed in this study may be useful for medicinal chemists. Global MLR, RF and SVM models may be valuable for consideration of common factors that influence solubility. The RCNN, AMP and LoReP local models may be helpful for the optimization of aqueous solubility in small sets of related chemicals.

Henry's law constant of hydrocarbons in air-water system: the cavity ovality effect on the non-electrostatic contribution term of solvation free energy.

In this study, a quantitative structure-property relationship (QSPR) model for the prediction of Henry's law constants of aliphatic hydrocarbons in air-water system has been developed, based on a data-set of 189 compounds. The well-known linear thermodynamic relation between the logarithm of Henry's law constant and solvation free energy has been used for developing the model. It is emphasised that the solvent-accessible surface area (SASA) descriptor is not adequate for predicting the solvation free energy of a wide range of aliphatic hydrocarbons; there are many compounds that have the same solvent-accessible surface area with different solvation free energy. Therefore, we have introduced cavity ovality as a good descriptor of molecular cavity shape factor. The root mean square error (RMSE) of the QSPR regression model based on SASA improves from 0.40 to 0.22 by introducing the cavity ovality descriptor. The QSPR linear ovality model has good statistical parameters (r(2) = 0.90). To emphasise the significant effect of the new descriptor, a non-linear neural network model with only two nodes in the hidden layer was developed, and also yielded a RMSE of 0.22.

QSAR study of some anti-hyperglycaemic sulphonylurea drugs.

Sulphonylureas are widely used anti-hyperglycaemic drugs for the treatment of type 2 diabetes. The only published quantitative structure-activity relationship (QSAR) models for sulphonylurea drugs have been found to be questionable, for a number of reasons. We have re-analysed the human anti-hyperglycaemic potencies, acute mouse intraperitoneal toxicities (LD50) and plasma protein-binding abilities of the 15 drugs using multiple linear regression and obtained good QSAR models for each endpoint. The obtained QSARs all comply well with the Organisation for Economic Co-operation and Development (OECD) Guidelines for the Validation of (Q)SARs. We could not carry out external validation of our models for acute toxicity and plasma protein-binding because of the very small datasets available.

Partitioning and lipophilicity in quantitative structure-activity relationships.

The history of the relationship of biological activity to partition coefficient and related properties is briefly reviewed. The dominance of partition coefficient in quantitation of structure-activity relationships is emphasized, although the importance of other factors is also demonstrated. Various mathematical models of in vivo transport and binding are discussed; most of these involve partitioning as the primary mechanism of transport. The models describe observed quantitative structure-activity relationships (QSARs) well on the whole, confirming that partitioning is of key importance in in vivo behavior of a xenobiotic. The partition coefficient is shown to correlate with numerous other parameters representing bulk, such as molecular weight, volume and surface area, parachor and calculated indices such as molecular connectivity; this is especially so for apolar molecules, because for polar molecules lipophilicity factors into both bulk and polar or hydrogen bonding components. The relationship of partition coefficient to chromatographic parameters is discussed, and it is shown that such parameters, which are often readily obtainable experimentally, can successfully supplant partition coefficient in QSARs. The relationship of aqueous solubility with partition coefficient is examined in detail. Correlations are observed, even with solid compounds, and these can be used to predict solubility. The additive/constitutive nature of partition coefficient is discussed extensively, as are the available schemes for the calculation of partition coefficient. Finally the use of partition coefficient to provide structural information is considered. It is shown that partition coefficient can be a valuable structural tool, especially if the enthalpy and entropy of partitioning are available.

Structural basis for the haemotoxicity of dapsone: the importance of the sulphonyl group.

The structural basis of dapsone (4,4'-diaminodiphenyl sulphone) haemotoxicity has been determined by investigation of the in vitro bioactivation of a series of 4-substituted arylamines. In the presence of rat liver microsomes, dapsone (100 microM) was the most potent former of methaemoglobin in human erythrocytes (44.8 +/- 6.7%). Substitution of the sulphone group with sulphur (11.6 +/- 1.4% methaemoglobin), oxygen (4.5 +/- 1.1%), nitrogen (0.0 +/- 3.2%), carbon (13.6 +/- 0.8%) or a keto group (34.0 +/- 6.1%) resulted in a decrease in methaemoglobin formation. Only one compound, 4,4'-diaminodiphenylamine, generated significant (P < 0.001) amounts of methaemoglobin (25.6 +/- 2.5%) in the absence of NADPH. To assess further the role of the 4-substituent in methaemoglobinaemia, the toxicity of a series of 4-substituted aniline derivatives was also studied. Of the anilines studied, 4-nitroaniline caused the most methaemoglobin (36.5 +/- 8.0%), whilst aniline caused the least (0.3 +/- 0.5%). Overall, there was a significant correlation (r2 = 0.83) between the haemotoxicity and the Hammett constant, sigma(p), suggesting that it is the electron-withdrawing properties of the substituent that influence the methaemoglobin formation. In the presence of microsomes prepared from two human livers, dapsone was the most haemotoxic bis arylamine, whereas 4-iodoaniline was the most potent methaemoglobin former (60.6 and 73.6%) and aniline the least potent (1.1 and 2.4%). As a whole, these results indicate that the sulphonyl group, which is essential for the pharmacological activity of dapsone, is also largely responsible for the haemotoxicity seen with this drug.

In vitro quantification of cross-reactivity between beta-lactam antibiotics and anti-benzylpenicillin antibodies.

The phenomenon of cross-reactivity between allergens and anti-antibodies is believed to be a major factor underlying the incidence of immunologically mediated adverse reactions. A sensitive reproducible competitive enzyme-linked immunosorbent assay (ELISA) has been developed for the quantitative determination of the extent of cross-reactivity occurring between native beta-lactam antibiotics and anti-benzylpenicillin serum antibodies (anti-BPAbs) raised in presensitized rabbit serum in which free native antibiotic at a concentration of 3 mm, and benzylpenicillin cytochrome c (BPCC) adsorbed onto a solid surface, compete to bind free anti-BPAbs. The extent of BPPC-anti-BPAb binding is determined spectrophotometrically, following the addition of enzyme-labelled antispecies antiglobulin and a suitable substrate. The assay has been used to determine the levels of cross-reactivity exhibited by 29 commercially available beta-lactam antibiotics (14 penicillins and 15 cephalosporins), with results attained ranging for the penicillins between 9.1 and 100% and for the cephalosporins from 0.0 to 19.9%. The results thus indicate that cross-reactivity between different beta-lactams and anti-BPAbs does occur in vitro and that different beta-lactams cross-react to different extents, with cephalosporins cross-reacting less than the penicillins.

Quantitative structure-permeability relationships (QSPRs) for percutaneous absorption.

Quantitative structure-permeability relationships (QSPRs) have been derived by many researchers to model the passive, diffusion-controlled, percutaneous penetration of exogenous chemicals. Most of these relationships are based on experimental data from the published literature. They indicate that molecular size (as molecular weight) and hydrophobicity (as the logarithm of the octanol-water partition coefficient; log k(ow)) are the main determinants of transdermal penetration. This article reviews the current state of the art in QSPRs for absorption of chemicals through the skin, and where this technology can be exploited in future research. The main shortfalls in QSPR models result from inconsistency and error of the experimental values used to derive them. This is probably caused by the manner in which they employ data from a variety of sources and, in some cases, slightly different experimental protocols. Further, most current models are based on data generated from either aqueous or ethanolic solution, where each penetrant is present at its saturated solubility or a fraction of its saturated solubility. No models currently account for the influences of formulation upon percutaneous penetration. Current QSPR models provide a significant tool for assessing the percutaneous penetration of chemicals. They may be important in determining the bioavailability of a range of topically applied exogenous chemicals, and in issues of dermal toxicology and risk assessment. However, their current use may be limited by their lack of applicability across different formulation types. As a consequence, their true value may be to make predictions within specific formulation types, as opposed to a general model based on a range of formulation types. In addition, the endpoint of models may be inappropriate for specific applications other than the systemic delivery of topically applied chemicals.

Investigation of the mechanism of flux across human skin in vitro by quantitative structure-permeability relationships.

Permeability coefficients for 114 compounds across excised human skin in vitro were taken from Kirchner et al. Forty-seven descriptors were calculated encompassing the relevant physicochemical parameters of the compounds. Quantitative structure-permeability relationships (QSPRs) were developed using least-squares regression analysis. A two-parameter QSPR, describing the permeability coefficients (Kp) across excised skin, was obtained: log Kp=0.772 log P -0.0103 Mr - 2.33 where log P is the logarithm of the octanol-water partition coefficient and Mr is molecular mass. This equation indicates that percutaneous absorption is mediated by the hydrophobicity and the molecular size of the penetrant. Comparison with a QSPR based on penetration across a synthetic (polydimethylsiloxane) membrane suggests that the mechanisms of drug flux across polydimethylsiloxane membranes and excised human skin are significantly different.

Correlation between gastric irritancy and anti-inflammatory activity of non-steroidal anti-inflammatory drugs.

Gastric irritancies and anti-inflammatory potencies of 25 commercially available non-steroidal anti-inflammatory drugs (NSAIDs) have been measured in the rat. When irritancy is measured as the dose required to produce a specified level of gastric mucosal damage, it is found that irritancy increases with anti-inflammatory potency. However, when irritancy is measured as the level of gastric mucosal damage at the anti-inflammatory ED50 (which is a clinically realistic measure) then irritancy decreases as anti-inflammatory potency increases. Hence it should be possible to design high-potency, low-irritancy NSAIDs.

The use of atomic charges and orbital energies as hydrogen-bonding-donor parameters for QSAR studies: comparison of MNDO, AM1 and PM3 methods.

Hydrogen-bonding, important in drug-receptor interactions, also determines the solubility and partitioning of drugs between phases. It is, therefore, important to incorporate the effects of hydrogen-bonding in studies of quantitative structure-activity relationships (QSAR). In this study the atomic charge on the most positively charged hydrogen atom in a molecule and the energy of the lowest unoccupied molecular orbital (LUMO) have been used as a measure of hydrogen-bond-donor capacity. For several hydrogen-bonding acids the Mulliken atomic charges and the energy of the LUMO produced by use of three semiempirical methods, AM1, PM3 and MNDO, and MNDO electrostatic-potential-derived atomic charges, have been compared in correlations with solvatochromic hydrogen-bonding acidity (sigmaalpha(H)2). Atomic charges and LUMO energies, particularly those calculated by use of the AM1 and MNDO methods, were found to correlate well with sigmaalpha(H)2. They were also found to be good models of hydrogen-bonding in QSAR correlations.

A QSAR study of anti-inflammatory N-arylanthranilic acids.

A detailed quantitative structure-activity relationship (QSAR) analysis of a series of 112 anti-inflammatory N-arylanthranilic acids has been performed to determine which physicochemical properties of these compounds are responsible for their anti-inflammatory activity. The results indicate that activity is modelled best by molecular shape parameters. The angle between the planes of the two benzene rings, dictated by the substitution pattern of the compounds, also appears relevant to activity. Dipole moments show some significance, but log P and other physiochemical parameters correlate poorly with activity. The best QSAR obtained was: [equation: see text] where B1 and B3 are Verloop substituent width parameters and mu(bond) is bond dipole (position in parentheses).

Design, synthesis and biological testing of a novel series of anti-inflammatory drugs.

Many of the non-steroidal anti-inflammatory drugs (NSAIDs) currently marketed produce severe gastro-toxic side effects. The benefits of producing NSAIDs without these side effects are obvious, particularly for patients requiring long-term therapy. The aim of this investigation was to produce novel NSAIDs, based on paracetamol, that exhibit little or no gastro-toxicity. The work covers design, synthesis and testing of 13 drug candidates. The analgesic and anti-inflammatory potencies of the drug candidates were measured using the mouse abdominal constriction assay and the carrageenan-induced rat paw oedema assay, respectively. The stomachs of the rats were examined post-mortem, to assess the gastro-toxicity of the drugs. Of the 13 compounds described herein, 11 were shown to possess analgesic activity at 2-10 times the potency of aspirin, while 8 demonstrated anti-inflammatory activity at 3-10 times the potency of aspirin. Significantly, all of the compounds showed very low gastro-toxicity when compared with aspirin. The results of this study indicate that it is possible to develop novel, potent NSAIDs based on the structure of paracetamol. These compounds have the advantage of demonstrating much lower gastro-toxicity than NSAIDs currently available. Drugs of this type may, in future, provide effective treatments for inflammatory disorders.