Joseph V. Turner

Bioavailability Prediction Based on Molecular Structure for a Diverse Series of Drugs

AbstractPurpose. 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.

Prediction of drug bioavailability based on molecular structure

Oral dosing is the most common method of drug administration, and final plasma concentrations of the drug depend upon its bioavailability. In the current study, a quantitative structure–pharmacokinetic relationship (QSPR) was developed for a diverse range of compounds to allow prediction of drug bioavailability. Bioavailability data for 169 compounds was taken from the literature, and from the molecular structures 94 theoretical descriptors were generated. Stepwise regression was employed to develop a regression equation based on 159 training compounds, and predictive ability was tested on 10 compounds reserved for that purpose. The final regression equation included eight descriptors that represented electronic, steric, hydrophobic and constituent parameters of the drug molecules, all of which could be related to solubility and partitioning properties. Predicted bioavailability for the training set agreed more closely for drugs exhibiting mid-range literature bioavailability values. A correlation of 0.72 was achieved for test set bioavailability predictions when compared with literature values. The structure–pharmacokinetic relationship developed in the current study highlighted solubility and partitioning characteristics that may be useful in designing drugs with appropriate bioavailability.

Molecular structural characteristics as determinants of estrogen receptor selectivity

Recent reports that a wide variety of natural and man-made compounds are capable of competing with natural hormones for estrogen receptors serve as timely examples of the need to advance screening techniques to support human health and ascertain ecological risk. Quantitative structure-activity relationships (QSARs) can potentially serve as screening tools to identify and prioritize untested compounds for further empirical evaluations. Computer-based QSAR molecular models have been used to describe ligand-receptor interactions and to predict chemical structures that possess desired pharmacological characteristics. These have recently included combined and differential relative binding affinities of potential estrogenic compounds at estrogen receptors (ER) alpha and beta. In the present study, artificial neural network (ANN) QSAR models were developed that were able to predict differential relative binding affinities of a series of structurally diverse compounds with estrogenic activity. The models were constructed with a dataset of 93 compounds and tested with an additional dataset of 30 independent compounds. High training correlations (r2=0.83-0.91) were observed while validation results for the external compounds were encouraging (r2=0.62-0.86). The models were used to identify structural features of phytoestrogens that are responsible for selective ligand binding to ERalpha and ERbeta. Numerous structural characteristics are required for complexation with receptors. In particular, size, shape and polarity of ligands, heterocyclic rings, lipophilicity, hydrogen bonding, presence of quaternary carbon atom, presence, position, length and configuration of a bulky side chain, were identified as the most significant structural features responsible for selective binding to ERalpha and ERbeta.

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.

Molecular Structural Characteristics of Estrogen Receptor Modulators as Determinants of Estrogen Receptor Selectivity

This review will discuss the structural determinants and requirements necessary for estrogen receptors alpha and beta selectivity and ligand-receptor binding affinity. In addition, strategies likely to result in the development of a pharmacophore model that account for the differences in estrogenic effects between different ligands will be discussed.

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.

Artificial neural network modeling of phytoestrogen binding to estrogen receptors

Differential pathophysiological roles of estrogen receptors alpha (ERα) and beta (ERβ) are of particular interest for phytochemical screening. A QSAR incorporating theoretical descriptors was developed in the present study utilizing sequential multiple-output artificial neural networks. Significant steric, constitutional, topological and electronic descriptors were identified enabling ER affinity differentiation.

Structure-activity relationships for serotonin transporter and dopamine receptor selectivity

Antipsychotic medications have a diverse pharmacology with affinity for serotonergic, dopaminergic, adrenergic, histaminergic and cholinergic receptors. Their clinical use now also includes the treatment of mood disorders, thought to be mediated by serotonergic receptor activity. The aim of our study was to characterise the molecular properties of antipsychotic agents, and to develop a model that would indicate molecular specificity for the dopamine (D(2)) receptor and the serotonin (5-HT) transporter. Back-propagation artificial neural networks (ANNs) were trained on a dataset of 47 ligands categorically assigned antidepressant or antipsychotic utility. The structure of each compound was encoded with 63 calculated molecular descriptors. ANN parameters including hidden neurons and input descriptors were optimised based on sensitivity analyses, with optimum models containing between four and 14 descriptors. Predicted binding preferences were in excellent agreement with clinical antipsychotic or antidepressant utility. Validated models were further tested by use of an external prediction set of five drugs with unknown mechanism of action. The SAR models developed revealed the importance of simple molecular characteristics for differential binding to the D(2) receptor and the 5-HT transporter. These included molecular size and shape, solubility parameters, hydrogen donating potential, electrostatic parameters, stereochemistry and presence of nitrogen. The developed models and techniques employed are expected to be useful in the rational design of future therapeutic agents.

Progesterone for preventing pregnancy termination after initiation of medical abortion with mifepristone

Abstract Introduction: Abortion is often a difficult and traumatic decision for a woman to make. Perhaps greater distress occurs when a woman commences a medical abortion but then changes her mind and wishes to keep the now-threatened pregnancy. One published case series detailed a potential method to counter/reverse the abortifacient effect of mifepristone by administering parenteral progesterone in such situations. Objectives: The present report details cases of women in similar circumstances who have been treated with progesterone. The aims were to document occurrences of where women have changed their mind after commencing medical abortion, as well as to explore some of the controversies and clinical issues surrounding their circumstances. Methods: Women who had commenced medical abortion by ingesting mifepristone but who had not taken misoprostol independently contacted a national pregnancy support service the same day. Those meeting criteria for treatment received progesterone pessaries per vaginum for two weeks. Results: Cases: 28-year-old woman, 6 weeks plus 1 day gestation; 35-year-old woman, 8 weeks plus 5 days gestation; and 27-year-old woman, 7 weeks plus 3 days gestation. Outcomes respectively were: healthy male baby delivered at 39 weeks gestation; healthy male baby delivered at term; and completed medical abortion. Conclusions: Women have changed their mind after commencing medical abortion. Progesterone use in early pregnancy is low risk and its application to counter the effects of mifepristone in such circumstances may be clinically beneficial in preserving her threatened pregnancy. Further research is required, however, to provide definitive evidence.

Pesticides as estrogen disruptors: QSAR for selective ERα and ERβ binding of pesticides.

Evidence suggests that environmental exposure to estrogen-like compounds can cause adverse effects in humans and wildlife. The Endocrine Disruptor Screening and Testing Advisory Committee (EDSTAC) has advised screening of 87,000 compounds in the interest of human safety. This may best be accomplished by pre-screening using quantitative structure-activity relationship (QSAR) modelling. The present study aimed to develop in silico QSARs based on natural, semi-synthetic, synthetic, and phytoestrogens, to predict the potential estrogenic toxicity of pesticides. A diverse set of 170 compounds including steroidal-, synthetic- and phytoestrogens, as well as pesticides was used to construct the QSAR models using artificial neural networks (ANNs). Mean correlation coefficients between experimentally measured and predicted binding affinities were all greater than 0.7 and models had few false negative results, an important consideration for screening tools. This study demonstrated the utility of ANNs as QSAR models for pre-screening of potential endocrine disruptors.