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Kinetic cell-based morphological screening: prediction of mechanism of compound action and off-target effects.

ABSTRACT: We describe a cell-based kinetic profiling approach using impedance readout for monitoring the effect of small molecule compounds. This noninvasive readout allows continuous sampling of cellular responses to biologically active compounds and the ensuing kinetic profile provides information regarding the temporal interaction of compounds with cells. The utility of this approach was tested by screening a library containing FDA approved drugs, experimental compounds, and nature compounds. Compounds with similar activity produced similar impedance-based time-dependent cell response profiles (TCRPs). The compounds were clustered based on TCRP similarity. We identified novel mechanisms for existing drugs, confirmed previously reported calcium modulating activity for COX-2 inhibitor celecoxib, and identified an additional mechanism for the experimental compound monastrol. We also identified and characterized a new antimitotic agent. Our findings indicate that the TCRP approach provides predictive mechanistic information for small molecule compounds.

SUBMITTER: Abassi YA

PROVIDER: S-EPMC2836189 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature

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Kinetic cell-based morphological screening: prediction of mechanism of compound action and off-target effects.

Abassi Yama A YA Xi Biao B Zhang Wenfu W Ye Peifang P Kirstein Shelli L SL Gaylord Michelle R MR Feinstein Stuart C SC Wang Xiaobo X Xu Xiao X

Chemistry & biology 20090701 7

We describe a cell-based kinetic profiling approach using impedance readout for monitoring the effect of small molecule compounds. This noninvasive readout allows continuous sampling of cellular responses to biologically active compounds and the ensuing kinetic profile provides information regarding the temporal interaction of compounds with cells. The utility of this approach was tested by screening a library containing FDA approved drugs, experimental compounds, and nature compounds. Compounds ...[more]

PMID: 19635408

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Project description:Predictive approaches such as virtual screening have been used in drug discovery with the objective of reducing developmental time and costs. Current machine learning and network-based approaches have issues related to generalization, usability, or model interpretability, especially due to the complexity of target proteins' structure/function, and bias in system training datasets. Here, we propose a new method "DRUIDom" (DRUg Interacting Domain prediction) to identify bio-interactions between drug candidate compounds and targets by utilizing the domain modularity of proteins, to overcome problems associated with current approaches. DRUIDom is composed of two methodological steps. First, ligands/compounds are statistically mapped to structural domains of their target proteins, with the aim of identifying their interactions. As such, other proteins containing the same mapped domain or domain pair become new candidate targets for the corresponding compounds. Next, a million-scale dataset of small molecule compounds, including those mapped to domains in the previous step, are clustered based on their molecular similarities, and their domain associations are propagated to other compounds within the same clusters. Experimentally verified bioactivity data points, obtained from public databases, are meticulously filtered to construct datasets of active/interacting and inactive/non-interacting drug/compound-target pairs (~2.9M data points), and used as training data for calculating parameters of compound-domain mappings, which led to 27,032 high-confidence associations between 250 domains and 8,165 compounds, and a finalized output of ~5 million new compound-protein interactions. DRUIDom is experimentally validated by syntheses and bioactivity analyses of compounds predicted to target LIM-kinase proteins, which play critical roles in the regulation of cell motility, cell cycle progression, and differentiation through actin filament dynamics. We showed that LIMK-inhibitor-2 and its derivatives significantly block the cancer cell migration through inhibition of LIMK phosphorylation and the downstream protein cofilin. One of the derivative compounds (LIMKi-2d) was identified as a promising candidate due to its action on resistant Mahlavu liver cancer cells. The results demonstrated that DRUIDom can be exploited to identify drug candidate compounds for intended targets and to predict new target proteins based on the defined compound-domain relationships. Datasets, results, and the source code of DRUIDom are fully-available at: https://github.com/cansyl/DRUIDom.

Dataset Information

Kinetic cell-based morphological screening: prediction of mechanism of compound action and off-target effects.

Publications

Kinetic cell-based morphological screening: prediction of mechanism of compound action and off-target effects.

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