Biomedical subjects

Weida Tong

Publications and source records attributed to Weida Tong.

40 records · Page 3Linked to original sources

PubMed2002

Prediction of estrogen receptor binding for 58,000 chemicals using an integrated system of a tree-based model with structural alerts.

A number of environmental chemicals, by mimicking natural hormones, can disrupt endocrine function in experimental animals, wildlife, and humans. These chemicals, called "endocrine-disrupting chemicals" (EDCs), are such a scientific and public concern that screening and testing 58,000 chemicals for EDC activities is now statutorily mandated. Computational chemistry tools are important to biologists because they identify chemicals most important for in vitro and in vivo studies. Here we used a computational approach with integration of two rejection filters, a tree-based model, and three structural alerts to predict and prioritize estrogen receptor (ER) ligands. The models were developed using data for 232 structurally diverse chemicals (training set) with a 10(6) range of relative binding affinities (RBAs); we then validated the models by predicting ER RBAs for 463 chemicals that had ER activity data (testing set). The integrated model gave a lower false negative rate than any single component for both training and testing sets. When the integrated model was applied to approximately 58,000 potential EDCs, 80% (approximately 46,000 chemicals) were predicted to have negligible potential (log RBA < -4.5, with log RBA = 2.0 for estradiol) to bind ER. The ability to process large numbers of chemicals to predict inactivity for ER binding and to categorically prioritize the remainder provides one biologic measure to prioritize chemicals for entry into more expensive assays (most chemicals have no biologic data of any kind). The general approach for predicting ER binding reported here may be applied to other receptors and/or reversible binding mechanisms involved in endocrine disruption.

Animals↗

PubMed1996

Self-Inclusion Complexes Derived from Cyclodextrins: Synthesis and Characterization of 6(A),6(B)-Bis-O-[p-(allyloxy)phenyl]-Substituted beta-Cyclodextrins.

The syntheses, structures, and spectroscopic properties of 6(A),6(B)-bis-O-[p-(allyloxy)phenyl]-substituted beta-cyclodextrins have been investigated. Selective activation of the 6(A),6(B)-hydroxy groups was carried out by treating heptakis(2,3-di-O-methyl)-beta-cyclodextrin (1) with 2,4-dimethoxybenzene-1,5-disulfonyl chloride to give 6(A),6(B)-bissulfonate ester 2 in a yield of only 3%. This material was treated with sodium p-(allyloxy)phenoxide in DMF to form 6(A),6(B)-bis-O-[p-(allyloxy)phenyl]-heptakis(2,3-di-O-methyl)-beta-cyclodextrin (3), which had two isomers. One (3A) has the two p-(allyloxy)phenyl arms directed away from the cyclodextrin cavity, and the other (3B) has one of the p-(allyloxy)phenyl groups through the cavity to form a self-inclusion complex. When either 3A or 3B was treated with methyl iodide and sodium hydride, the resulting permethylated 6(A),6(B)-bis-O-[p-(allyloxy)phenyl]heptakis(2,3-di-O-methyl)-6(C),6(D),6(E),6(F),6(G)-penta-O-methyl-beta-cyclodextrin (4) was composed of two isomers, in which 4B is a self-inclusion complex. 3A and 3B also can be converted into a mixture of 3A and 3B in strong base but not when melted in the absence of base. 4A and 4B do not isomerize. Detailed 1D and 2D NMR spectroscopic studies were carried out to characterize the structures of these new compounds, and molecular mechanics techniques were used to explain the experimental facts.

Journal Article↗

PubMedDate not supplied

Decision forest: combining the predictions of multiple independent decision tree models.

The techniques of combining the results of multiple classification models to produce a single prediction have been investigated for many years. In earlier applications, the multiple models to be combined were developed by altering the training set. The use of these so-called resampling techniques, however, poses the risk of reducing predictivity of the individual models to be combined and/or over fitting the noise in the data, which might result in poorer prediction of the composite model than the individual models. In this paper, we suggest a novel approach, named Decision Forest, that combines multiple Decision Tree models. Each Decision Tree model is developed using a unique set of descriptors. When models of similar predictive quality are combined using the Decision Forest method, quality compared to the individual models is consistently and significantly improved in both training and testing steps. An example will be presented for prediction of binding affinity of 232 chemicals to the estrogen receptor.

Algorithms↗

PubMedDate not supplied

An approach to comparative analysis of chromatographic fingerprints for assuring the quality of botanical drugs.

The present study was focused on developing the chemometric methods for analysis of the chromatographic fingerprint to control the quality of botanical drugs, which has gained attention in Asia and other countries. We developed a novel approach to generate a set of fingerprint features, called Fisher components (FCs) that were extracted from the chromatographic fingerprint. The method greatly reduces the dimensionality of the fingerprint vector, and the resulting FCs still retain most discriminatory information of the original fingerprint. Choosing an example of relevance to contemporary botanical drugs, we applied the FCs to a set of Shenmai injection samples. We successfully identified the manufacturers of the samples using two classifiers, linear discriminant analysis (LDA) and k-Nearest Neighbor (k-NN) based on the FCs. We also applied a similarity assessment together with the visual analysis using the FCs to exam the products from different manufacturers. We found that the lot-to-lot consistency of products can be accurately determined using the FCs. Finally, we demonstrated that the application of chemometric methods for chromatographic fingerprinting offers reliability to detect suspected fraud samples. In summary, we demonstrated that the presented approaches could be useful to determine the identity, consistency, and authenticity of Shenmai injection through chromatographic fingerprinting. The methods are equally applicable to other botanical drugs.

Chromatography↗