Biomedical subjects

John C Dearden

Publications and source records attributed to John C Dearden.

11 recordsLinked to original sources

PubMed2004

Design of new cognition enhancers: from computer prediction to synthesis and biological evaluation.

To discover new cognition enhancers, a set of virtually designed synthesizable compounds from different chemical series was investigated using two computer-aided approaches. One of the approaches is prediction of biological activity spectra for substances (PASS) and the second is prediction of toxicity, mutagenicity, and carcinogenicity (DEREK). To increase the probability of finding new chemical entities, we investigated a heterogeneous set of highly diverse chemicals including different types of heterocycles: five-membered (thiophenes, thiazoles, imidazoles, oxazoles, pyrroles), six-membered (pyridines, pyrimidines), seven-membered (diazepines, triazepines), fused five+six-membered heterocycles (indoles, benzothiazoles, purines, indolizines, neutral, mesoionic, and cationic azolopyridines). A database including 5494 structures of compounds was created. On the basis of the PASS and DEREK prediction results, eight compounds with the highest probability of cognition-enhancing effect were selected. The cognition-enhancing activity testing showed that all of the selected compounds had a pronounced antiamnesic effect and were found to reduce significantly scopolamine-induced amnesia of passive avoidance reflex (PAR). The action of compounds at doses of 1 and 10 mg/kg caused a statistically significant increase in latent time of reflex and in the number of animals, which did not enter the dark chamber when testing the PAR. Therefore, on the basis of computer prediction, new cognition-enhancing agents were discovered within the chemical series, in which this activity was not known previously.

Amnesia↗

PubMed2004

The use of molecular electrostatic potentials as hydrogen-bonding-donor parameters for QSAR studies.

Hydrogen bonding is an important interaction, which controls solubility, partitioning and transport of drugs and is an important force in drug-receptor interactions. Unfortunately it is difficult to quantify, and so inclusion of hydrogen bonding descriptors into quantitative structure-activity relationships (QSARs) is often restricted to indicator variables. The prime objective of this work was to devise readily accessible hydrogen bonding descriptors by means of theoretical chemistry to use in QSAR studies. Because of the dominantly electrostatic nature of this bond, molecular-electrostatic potential was considered and the highest electrostatic potential on the solvent accessible surface (ESP+) was used as the hydrogen-bonding-donor ability of the molecule. The ability of this descriptor to predict the measured hydrogen bonding parameter of sigmalphaH2 was compared with that of the empirically derived atomic charges. The efficiency of ESP+ in a QSAR was also examined. The results suggested that ESP+ was superior to the atomic charge descriptor and that the use of this parameter as the hydrogen bonding parameter in QSAR studies was successful.

Air Pollutants↗

PubMed2004

Assessment and modeling of the toxicity of organic chemicals to Chlorella vulgaris: development of a novel database.

This study reports a database of toxicity values for 91 compounds assessed in a novel, rapid, and economical 15 min algal toxicity test. The toxicity data were measured using the unicellular green alga Chlorella vulgaris in an assay that determined the disappearance of fluorescein diacetate. The chemicals tested covered a wide range of physicochemical properties and mechanisms of action. Quantitative activity-activity relationships with the toxicity of the chemicals to other species (Tetrahymena pyriformis, Vibrio fischeri, and Pimephales promelas) showed strong relationships, although some differences resulting from different protocols were established. Quantitative structure-activity relationships (QSARs) were determined using linear [multiple linear regression (MLR)] and nonlinear [k-nearest neighbors (KNN)] methods. Three descriptors, accounting for hydrophobicity, electrophilicity, and a function of molecular size corrected for the presence of heteroatoms, were found to be important to model toxicity. The predictivity of MLR was compared to KNN using leave-one-out cross-validation and the simulation of an external test set. MLR demonstrated greater stability in validation. The results of this study showed that method selection in QSAR is task-dependent and it is inappropriate to resort to more complicated but less transparent methods, unless there are clear indications (e.g., inability of MLR to deal with the data set) for the need of such methods.

Chemical Phenomena↗

PubMed2003

Quantitative structure-activity relationships for human health effects: commonalities with other endpoints.

This article describes the use of quantitative structure-activity relationships (QSARs) to predict toxicity endpoints for ecologically relevant and human-surrogate species. The interrelationships between the endpoints, and the possibilities of exploring the commonalities of chemical action from one species to another as well as from one endpoint to another, are evaluated. A number of toxic endpoints are discussed including mutagenicity and carcinogenicity; developmental toxicity (teratogenicity); acute toxicity; skin sensitization; skin, eye, and sensory irritation; and the modeling of membrane permeability. A number of electrophilic molecular substructures have been identified that are common to a number of toxicities. It is postulated that if such a substructure is observed in a molecule, it may exhibit a range of toxicities. Further, there appear to be relationships between the toxicity to ecologically relevant and human-surrogate species, which may allow for appreciation and possible extrapolation in both directions. Overall, however, QSARs are limited by the paucity of available toxicological data and information.

Animals↗

PubMed2003

Quantitative structure-property relationships for prediction of boiling point, vapor pressure, and melting point.

Boiling point, vapor pressure, and melting point are important physicochemical properties in the modeling of the distribution and fate of chemicals in the environment. However, such data often are not available, and therefore must be estimated. Over the years, many attempts have been made to calculate boiling points, vapor pressures, and melting points by using quantitative structure-property relationships, and this review examines and discusses the work published in this area, and concentrates particularly on recent studies. A number of software programs are commercially available for the calculation of boiling point, vapor pressure, and melting point, and these have been tested for their predictive ability with a test set of 100 organic chemicals.

Chemical Phenomena↗

PubMed2003

Quantitative structure-property relationships for predicting Henry's law constant from molecular structure.

Various models are available for the prediction of Henry's law constant (H) or the air-water partition coefficient (Kaw), its dimensionless counterpart. Incremental methods are based on structural features such as atom types, bond types, and local structural environments; other regression models employ physicochemical properties, structural descriptors such as connectivity indices, and descriptors reflecting the electronic structure. There are also methods to calculate H from the ratio of vapor pressure (p(v)) and water solubility (S(w)) that in turn can be estimated from molecular structure, and quantum chemical continuum-solvation models to predict H via the solvation-free energy (deltaG(s)). This review is confined to methods that calculate H from molecular structure without experimental information and covers more than 40 methods published in the last 26 years. For a subset of eight incremental methods and four continuum-solvation models, a comparative analysis of their prediction performance is made using a test set of 700 compounds that includes a significant number of more complex and drug-like chemical structures. The results reveal substantial differences in the application range as well as in the prediction capability, a general decrease in prediction performance with decreasing H, and surprisingly large individual prediction errors, which are particularly striking for some quantum chemical schemes. The overall best-performing method appears to be the bond contribution method as implemented in the HENRYWIN software package, yielding a predictive squared correlation coefficient (q2) of 0.87 and a standard error of 1.03 log units for the test set.

Chemical Phenomena↗

PubMed2003

Guidelines for developing and using quantitative structure-activity relationships.

Numerous quantitative structure-activity relationships (QSARs) have been developed to predict properties, fate, and effects of mostly discrete organic chemicals. As the demand for different types of regulatory testing increases and the cost of experimental testing escalates, there is a need to evaluate the use of QSARs and provide some guidance to avoid their misuse, especially as QSARs are being considered for regulatory purposes. This paper provides some guidelines that will promote the proper development and use of QSARs. While this paper uses examples of QSARs to predict toxicity, the proposed guidelines are applicable to QSARs used to predict physical or chemical properties, environmental fate, ecological effects and health effects.

Animals↗

PubMed2003

Quantitative cationic-activity relationships for predicting toxicity of metals.

Developing and validating quantitative cationic-activity relationships or (Q)CARs to predict the toxicity metals is challenging because of issues associated with metal speciation, complexation and interactions within biological systems and the media used to study these interactions. However, a number of simplifying assumptions can be used to develop and validate (Q)CARs to predict the toxicity of metals: The ionic form is the most active form of a metal; the bioactivity of a dissolved metal is correlated with its free ion concentration or activity; most metals exist in biological systems as cations, and differences in metal toxicity result from differences in metal ion binding to biological molecules (ligand-binding). In summary, it appears that certain useful correlations can be made between several physical and chemical properties of ions (mostly cations) and toxicity of metals. This review provides a historical perspective of studies that have reported correlations between physical and chemical properties of cations and toxicity to mammalian and nonmammalian species using in vitro and in vivo assays. To prepare this review, approximately 100 contributions dating from 1839 to 2003 were evaluated and the relationships between about 20 physical and chemical properties of cations and their potential to produce toxic effects were examined.

Animals↗

PubMedDate not supplied

Structure-based classification of antibacterial activity.

The aim of this study was to develop a simple quantitative structure-activity relationship (QSAR) for the classification and prediction of antibacterial activity, so as to enable in silico screening. To this end a database of 661 compounds, classified according to whether they had antibacterial activity, and for which a total of 167 physicochemical and structural descriptors were calculated, was analyzed. To identify descriptors that allowed separation of the two classes (i.e. those compounds with and without antibacterial activity), analysis of variance was utilized and models were developed using linear discriminant and binary logistic regression analyses. Model predictivity was assessed and validated by the random removal of 30% of the compounds to form a test set, for which predictions were made from the model. The results of the analyses indicated that six descriptors, accounting for hydrophobicity and inter- and intramolecular hydrogen bonding, provided excellent separation of the data. Logistic regression analysis was shown to model the data slightly more accurately than discriminant analysis.

Anti-Bacterial Agents↗

PubMedDate not supplied

QSAR analysis of the toxicity of aromatic compounds to Chlorella vulgaris in a novel short-term assay.

The use of alternative toxicity tests and computational prediction models is widely accepted to fill experimental data gaps and to prioritize chemicals for more expensive and time-consuming assessment. A novel short-term toxicity test using the alga Chlorella vulgaris was utilized in this study to produce acute aquatic toxicity data for 65 aromatic compounds. The compounds tested included phenols, anilines, nitrobenzenes, benzaldehydes and other poly-substituted benzenes. The toxicity data were employed in the development of quantitative structure-activity relationships (QSARs). Using multiple regression (MLR) and partial least squares (PLS) analyses, statistically significant, transparent and interpretable QSARs were developed using a small number of physicochemical descriptors. A two-descriptor model was developed using MLR (log(1/EC50)=0.73 log Kow-0.59 Elumo-1.91; n=65, r2=0.84, r2CV=0.82, s=0.43) and a four-descriptor model using PLS (log(1/EC50)=0.40 log Kow-0.23 Elumo+9.84 Amax+0.20 0chiv-5.40; n=65, r2=0.86, q2=0.84, RMSEE=0.40). The latter model was obtained by stepwise elimination of variables from a set of 102 calculated descriptors. Both models were validated successfully by simulating external prediction through the use of complementary subsets. The two factors, which were identified as being critical for the acute algal toxicity of this set of compounds were hydrophobicity and electrophilicity.

Chlorella↗

PubMedDate not supplied

In silico prediction of drug toxicity.

It is essential, in order to minimise expensive drug failures due to toxicity being found in late development or even in clinical trials, to determine potential toxicity problems as early as possible. In view of the large libraries of compounds now being handled by combinatorial chemistry and high-throughput screening, identification of putative toxicity is advisable even before synthesis. Thus the use of predictive toxicology is called for. A number of in silico approaches to toxicity prediction are discussed. Quantitative structure-activity relationships (QSARs), relating mostly to specific chemical classes, have long been used for this purpose, and exist for a wide range of toxicity endpoints. However, QSARs also exist for the prediction of toxicity of very diverse libraries, although often such QSARs are of the classification type; that is, they predict simply whether or not a compound is toxic, and do not give an indication of the level of toxicity. Examples are given of all of these. A number of expert systems are available for toxicity prediction, most of them covering a range of toxicity endpoints. Those discussed include TOPKAT, CASE, DEREK, HazardExpert, OncoLogic and COMPACT. Comparative tests of the ability of these systems to predict carcinogenicity show that improvement is still needed. The consensus approach is recommended, whereby the results from several prediction systems are pooled.

Carcinogenicity Tests↗