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Biomedical subjects

Joseph V Turner

Publications and source records attributed to Joseph V Turner.

8 recordsLinked to original sources
PubMed2006

Socio-economic distribution of environmental risk factors for childhood injury.

OBJECTIVE: Childhood injury remains the single most important cause of mortality in children aged between 1-14 years in many countries. It has been proposed that lower socio-economic status (SES) and poorer housing contribute to potential hazards in the home environment. This study sought to establish whether the prevalence of observed hazards in and around the home was differentially distributed by SES, in order to identify opportunities for injury prevention. METHODS: This study was a cross-sectional, random sample survey of primary school children from 32 schools in Brisbane. Interviews and house audits were conducted between July 2000 and April 2003 to collect information on SES (income, employment and education) and previously identified household hazards. RESULTS: There was evidence of a relationship between prevalence of household environmental hazards and household SES; however, the magnitude and direction of this relationship appeared to be hazard-specific. Household income was related to play equipment characteristics, with higher SES groups being more likely to be exposed to risk. All three SES indicators were associated with differences in the home safety characteristics, with the lower SES groups more likely to be exposed to risk. CONCLUSION: The differential distribution of environmental risk factors by SES of household may help explain the SES differential in the burden of injury and provides opportunities for focusing efforts to address the problem.

Adolescent↗
PubMed2004

Pharmacokinetic parameter prediction from drug structure using artificial neural networks.

Simple methods for determining the human pharmacokinetics of known and unknown drug-like compounds is a much sought-after goal in the pharmaceutical industry. The current study made use of artificial neural networks (ANNs) for the prediction of clearances, fraction bound to plasma proteins, and volume of distribution of a series of structurally diverse compounds. A number of theoretical descriptors were generated from the drug structures and both automated and manual pruning were used to derive optimal subsets of descriptors for quantitative structure-pharmacokinetic relationship models. Models were trained on one set of compounds and validated with another. Absolute predicted ability was evaluated using a further independent test set of compounds. Correlations for test compounds ranged from 0.855 to 0.992. Predicted values agreed closely with experimental values for total clearance, renal clearance, and volume of distribution, while predictions for protein binding were encouraging. The combination of descriptor generation, ANNs, and the speed and success of this technique compared with conventional methods shows strong potential for use in pharmaceutical product development.

Drug Design↗
PubMed2004

Bioavailability prediction based on molecular structure for a diverse series of drugs.

PURPOSE: Radial basis function artificial neural networks and theoretical descriptors were used to develop a quantitative structure-pharmacokinetic relationship for structurally diverse drug compounds. METHODS: Human bioavailability values were taken from the literature and descriptors were generated from the drug structures. All models were trained with 137 compounds and tested with a further 15, after which they were evaluated for predictive ability with an additional 15 compounds. RESULTS: The final model possessed a 10-31-1 topology and training and testing correlation coefficients were 0.736 and 0.897, respectively. Predictions for independent compounds agreed well with experimental literature values, especially for compounds that were well absorbed and/or had high observed bioavailability. Important theoretical descriptors included solubility parameters, electronic descriptors, and topological indices. CONCLUSIONS: Useful information regarding drug bioavailability was gained from drug structure alone, reducing the need for experimental methods in drug development.

Analysis of Variance↗
PubMed2003

Selective descriptor pruning for QSAR/QSPR studies using artificial neural networks.

Selection of optimal descriptors in quantitative structure-activity-property relationship (QSAR/QSPR) studies has been a perennial problem. Artificial Neural Networks (ANNs) have been used widely in QSAR/QSPR studies but less widely in descriptor selection. The current study used ANNs to select an optimal set of descriptors using large numbers of input variables. The effects of clean, noisy, and random input descriptors with linear, nonlinear, and periodic data on synthetic and real data QSAR/QSPR sets were examined. The optimal set of descriptors could be determined using a signal-to-noise ratio method. The optimal values for the rho parameter, which relates sample size to network architecture, were found to vary with the type of data. ANNs were able to detect meaningful descriptors in the presence of large numbers of random false descriptors.

Journal Article↗
PubMed2003

Multiple pharmacokinetic parameter prediction for a series of cephalosporins.

The goal of quantitative structure-pharmacokinetic relationship analyses is to develop useful models that can predict one or more pharmacokinetic properties of a particular compound. In the present study, a multiple-output artificial neural network model was constructed to predict human half-life, renal and total body clearance, fraction excreted in urine, volume of distribution, and fraction bound to plasma proteins for a series of cephalosporins. Descriptors generated solely from drug structure were used as inputs for the model, and the six pharmacokinetic parameters were simultaneously predicted as outputs. The final 10 descriptor model contained sufficient information for successful predictions using both internal and external test compounds. Descriptors were found to contribute to individual pharmacokinetic parameters to differing extents, such that descriptor importance was independent of the relationships between pharmacokinetic parameters. This technique provides the advantage of simultaneous prediction of multiple parameters using information obtained by nonexperimental means, with the potential for use during the early stages of drug development.

Cephalosporins↗