Biomedical subjects
Publications and source records attributed to David A Winkler.
A series of 2-phenyl-3-(1H-pyrrol-2-yl)acrylonitrile derivatives were synthesized and evaluated for in vitro activity against the endoparasite Haemonchus contortus and the ectoparasite Ctenocephalides felis. Some compounds had significant in vitro activity against these parasites.
The ecdysone receptor is a hormone-dependent transcription factor that plays a central role in regulating the expression of vast networks of genes during development and reproduction in the phylum Arthropoda. The functional receptor is a heterodimer of the two nuclear receptor proteins ecdysone receptor (EcR) and ultraspiracle protein. The receptor is the target of the environmentally friendly bisacylhydrazine insecticides, which are effective against Lepidoptera but not against Hemiptera or several other insect orders. Here we present evidence indicating that much of the selectivity of the bisacylhydrazine insecticides can be studied at the level of their binding to purified ecdysone receptor ligand-binding domain (LBD) heterodimers. We report the crystal structure of the ecdysone receptor LBD heterodimer of the hemipteran Bemisia tabaci (Bt, sweet potato whitefly) in complex with the ecdysone analogue ponasterone A. Although comparison with the corresponding known LBD structure from the lepidopteran Heliothis virescens (Hv) ecdysone receptor revealed the overall mode of ponasterone A binding to be very similar in the two cases, we observed that the BtEcR ecdysteroid-binding pocket is structured differently to that of HvEcR in those parts that are not in contact with ponasterone A. We suggest that these differences in the ligand-binding pocket may provide a molecular basis for the taxonomic order selectivity of bisacylhydrazine insecticides.
We used Bayesian regularized neural networks to model data on the MHC class II-binding affinity of peptides. Training data consisted of sequences and binding data for nonamer (nine amino acid) peptides. Independent test data consisted of sequences and binding data for peptides of length </=25. We assumed that MHC class II-binding activity of peptides depends only on the highest ranked embedded nonamer and that reverse sequences of active nonamers are inactive. We also internally validated the models by using 30% of the training data in an internal test set. We obtained robust models, with near identical statistics for multiple training runs. We determined how predictive our models were using statistical tests and area under the Receiver Operating Characteristic (ROC) graphs (A(ROC)). Most models gave training A(ROC) values close to 1.0 and test set A(ROC) values >0.8. We also used both amino acid indicator variables (bin20) and property-based descriptors to generate models for MHC class II-binding of peptides. The property-based descriptors were more parsimonious than the indicator variable descriptors, making them applicable to larger peptides, and their design makes them able to generalize to unknown peptides outside of the training space. None of the external test data sets contained any of the nonamer sequences in the training sets. Consequently, the models attempted to predict the activity of truly unknown peptides not encountered in the training sets. Our models were well able to tackle the difficult problem of correctly predicting the MHC class II-binding activities of a majority of the test set peptides. Exceptions to the assumption that nonamer motif activities were invariant to the peptide in which they were embedded, together with the limited coverage of the test data, and the fuzziness of the classification procedure, are likely explanations for some misclassifications.
Inhibitors of the enzyme farnesyltransferase show potential as novel anticancer agents. There are many known inhibitors, but efforts to build predictive SAR models have been hampered by the structural diversity and flexibility of inhibitors. We have undertaken for the first time a QSAR study of the potency and selectivity of a large, diverse data set of farnesyltransferase inhibitors. We used novel molecular descriptors based on binned atomic properties and invariants of molecular matrices and a robust, nonlinear QSAR mapping paradigm, the Bayesian regularized neural network. We have built robust QSAR models of farnesyltransferase inhibition, geranylgeranyltransferase inhibition, and in vivo data. We have derived a novel selectivity index that allows us to model potency and selectivity simultaneously and have built robust QSAR models using this index that have the potential to discover new potent and selective inhibitors.
This study aimed to evaluate in silico models based on quantum chemical (QC) descriptors derived using the electronegativity equalization method (EEM) and to assess the use of QC properties to predict chemical metabolism by human UDP-glucuronosyltransferase (UGT) isoforms. Various EEM-derived QC molecular descriptors were calculated for known UGT substrates and nonsubstrates. Classification models were developed using support vector machine and partial least squares discriminant analysis. In general, the most predictive models were generated with the support vector machine. Combining QC and 2D descriptors (from previous work) using a consensus approach resulted in a statistically significant improvement in predictivity (to 84%) over both the QC and 2D models and the other methods of combining the descriptors. EEM-derived QC descriptors were shown to be both highly predictive and computationally efficient. It is likely that EEM-derived QC properties will be generally useful for predicting ADMET and physicochemical properties during drug discovery.
We have employed three families of molecular molecular descriptors, together with Bayesian regularized neural nets, to model the partitioning of a diverse range of drugs and other small molecules across the blood-brain barrier (BBB). The relative efficacy of each descriptors class is compared, and the advantages of flexible, parsimonious, model free mapping methods, like Bayesian neural nets, illustrated. The relative importance of the molecular descriptors for the most predictive BBB model were determined by use of automatic relevance determination (ARD), and compared with the important descriptors from other literature models of BBB partitioning.
Empirical methods for building predictive models of the relationships between molecular structure and useful properties are becoming increasingly important. This has arisen because drug discovery and development have become more complex. A large amount of biological target information is becoming available though molecular biology. Automation of chemical synthesis and pharmacological screening has also provided a vast amount of experimental data. Tools for designing libraries and extracting information from molecular databases and high-throughput screening (HTS) experiments robustly and quickly enable leads to be discovered more effectively. As drug leads progress down the development pipeline, the ability to predict physicochemical, pharmacokinetic, and toxicological properties of these leads is becoming increasingly important in reducing the number of expensive, late-development failures. Neural network methods have much to offer in these areas. This review introduces the concepts behind neural networks applied to quantitative structure-activity relationships (QSARs), points out problems that may be encountered, suggests ways of avoiding the pitfalls, and introduces several exciting new neural network methods discovered during the last decade.
Empirical methods for building predictive models of the relationships between molecular structure and useful properties are becoming increasingly important. This has arisen because drug discovery and development have become more complex. A large amount of biological target information is becoming available through molecular biology. Automation of chemical synthesis and pharmacological screening has also provided a vast amount of experimental data. Tools for designing libraries and extracting information from molecular databases and high-throughput screening experiments robustly and quickly enable leads to be discovered more effectively. As drug leads progress down the development pipeline, the ability to predict physicochemical, pharmacokinetic and toxicological properties of these leads is becoming increasingly important in reducing the number of expensive, late development failures. Quantitative structure-activity relationship (QSAR) methods have much to offer in these areas. However, QSAR analysis has many traps for unwary practitioners. This review introduces the concepts behind QSAR, points out problems that may be encountered, suggests ways of avoiding the pitfalls and introduces several exciting, new QSAR methods discovered during the last decade.
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Partial least squares discriminant analysis (PLSDA), Bayesian regularized artificial neural network (BRANN), and support vector machine (SVM) methodologies were compared by their ability to classify substrates and nonsubstrates of 12 isoforms of human UDP-glucuronosyltransferase (UGT), an enzyme "superfamily" involved in the metabolism of drugs, nondrug xenobiotics, and endogenous compounds. Simple two-dimensional descriptors were used to capture chemical information. For each data set, 70% of the data were used for training, and the remainder were used to assess the generalization performance. In general, the SVM methodology was able to produce models with the best predictive performance, followed by BRANN and then PLSDA. However, a small number of data sets showed either equivalent or better predictability using PLSDA, which may indicate relatively linear relationships in these data sets. All SVM models showed predictive ability (>60% of test set predicted correctly) and five out of the 12 test sets showed excellent prediction (>80% prediction accuracy). These models represent the first use of pattern recognition methods to discriminate between substrates and nonsubstrates of human drug metabolizing enzymes and the first thorough assessment of three classification algorithms using multiple metabolic data sets.