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Cancer and virus leads by HTS, chemical design and SEA data mining.

ABSTRACT: A variety of medicinal chemistry approaches can be used for the identification of hits, generation of leads and to accelerate the development of drug candidates. The Emory Chemical and Biology Discovery Center (ECBDC) has been an active participant in the NIH's high-throughput screening (HTS) endeavor to identify potent small molecule probes for poorly studied proteins. Several of Emory's projects relate to cancer or virus infection. We have chosen three successful examples including discovery of potent measles virus RNA-dependent RNA polymerase inhibitors, development of Heat Shock Protein 90 (Hsp90) blockers and identification of angiogenesis inhibitors using transgenic Zebrafish as a HTS model. In parallel with HTS, a unique component of the Emory virtual screening (VS) effort, namely, substructure enrichment analysis (SEA) program has been utilized in several cases.

SUBMITTER: Thepchatri P

PROVIDER: S-EPMC4442615 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

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Cancer and virus leads by HTS, chemical design and SEA data mining.

Thepchatri Pahk P Min Jaeki J Ganesh Thota T Du Yuhong Y Lewis Iestyn I Kurtkaya Serdar S Prussia Andrew A Li Lian L Sneed Blossom B Plemper Richard K RK Fu Haian H Liotta Dennis C DC Snyder James P JP Dingledine Raymond R Sun Aiming A

Current topics in medicinal chemistry 20090101 13

A variety of medicinal chemistry approaches can be used for the identification of hits, generation of leads and to accelerate the development of drug candidates. The Emory Chemical and Biology Discovery Center (ECBDC) has been an active participant in the NIH's high-throughput screening (HTS) endeavor to identify potent small molecule probes for poorly studied proteins. Several of Emory's projects relate to cancer or virus infection. We have chosen three successful examples including discovery o ...[more]

PMID: 19807668

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Project description:Quantitative high throughput screening (qHTS) assays use cells or tissues to screen thousands of compounds in a short period of time. Data generated from qHTS assays are then evaluated using nonlinear regression models, such as the Hill model, and decisions regarding toxicity are made using the estimates of the parameters of the model. For any given compound, the variability in the observed response may either be constant across dose groups (homoscedasticity) or vary with dose (heteroscedasticity). Since thousands of compounds are simultaneously evaluated in a qHTS assay, it is not practically feasible for an investigator to perform residual analysis to determine the variance structure before performing statistical inferences on each compound. Since it is well-known that the variance structure plays an important role in the analysis of linear and nonlinear regression models it is therefore important to have practically useful and easy to interpret methodology which is robust to the variance structure. Furthermore, given the number of chemicals that are investigated in the qHTS assay, outliers and influential observations are not uncommon. In this article we describe preliminary test estimation (PTE) based methodology which is robust to the variance structure as well as any potential outliers and influential observations. Performance of the proposed methodology is evaluated in terms of false discovery rate (FDR) and power using a simulation study mimicking a real qHTS data. Of the two methods currently in use, our simulations studies suggest that one is extremely conservative with very small power in comparison to the proposed PTE based method whereas the other method is very liberal. In contrast, the proposed PTE based methodology achieves a better control of FDR while maintaining good power. The proposed methodology is illustrated using a data set obtained from the National Toxicology Program (NTP). Additional information, simulation results, data and computer code are available online as supplementary materials.

Dataset Information

Cancer and virus leads by HTS, chemical design and SEA data mining.

Publications

Cancer and virus leads by HTS, chemical design and SEA data mining.

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