Dataset Information

Determinants of activity at human Toll-like receptors 7 and 8: quantitative structure-activity relationship (QSAR) of diverse heterocyclic scaffolds.

ABSTRACT: Toll-like receptor (TLR) 7 and 8 agonists are potential vaccine adjuvants, since they directly activate APCs and enhance Th1-driven immune responses. Previous SAR investigations in several scaffolds of small molecule TLR7/8 activators pointed to the strict dependence of the selectivity for TLR7 vis-à-vis TLR8 on the electronic configurations of the heterocyclic systems, which we sought to examine quantitatively with the goal of developing "heuristics" to define structural requisites governing activity at TLR7 and/or TLR8. We undertook a scaffold-hopping approach, entailing the syntheses and biological evaluations of 13 different chemotypes. Crystal structures of TLR8 in complex with the two most active compounds confirmed important binding interactions playing a key role in ligand occupancy and biological activity. Density functional theory based quantum chemical calculations on these compounds followed by linear discriminant analyses permitted the classification of inactive, TLR8-active, and TLR7/8 dual-active compounds, confirming the critical role of partial charges in determining biological activity.

SUBMITTER: Yoo E

PROVIDER: S-EPMC4191598 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Determinants of activity at human Toll-like receptors 7 and 8: quantitative structure-activity relationship (QSAR) of diverse heterocyclic scaffolds.

Yoo Euna E Salunke Deepak B DB Sil Diptesh D Guo Xiaoqiang X Salyer Alex C D AC Hermanson Alec R AR Kumar Manoj M Malladi Subbalakshmi S SS Balakrishna Rajalakshmi R Thompson Ward H WH Tanji Hiromi H Ohto Umeharu U Shimizu Toshiyuki T David Sunil A SA

Journal of medicinal chemistry 20140917 19

Toll-like receptor (TLR) 7 and 8 agonists are potential vaccine adjuvants, since they directly activate APCs and enhance Th1-driven immune responses. Previous SAR investigations in several scaffolds of small molecule TLR7/8 activators pointed to the strict dependence of the selectivity for TLR7 vis-à-vis TLR8 on the electronic configurations of the heterocyclic systems, which we sought to examine quantitatively with the goal of developing "heuristics" to define structural requisites governing ac ...[more]

PMID: 25192394

Similar Datasets

Project description:The International Conference on Harmonization (ICH) M7 guideline allows the use of in silico approaches for predicting Ames mutagenicity for the initial assessment of impurities in pharmaceuticals. This is the first international guideline that addresses the use of quantitative structure-activity relationship (QSAR) models in lieu of actual toxicological studies for human health assessment. Therefore, QSAR models for Ames mutagenicity now require higher predictive power for identifying mutagenic chemicals. To increase the predictive power of QSAR models, larger experimental datasets from reliable sources are required. The Division of Genetics and Mutagenesis, National Institute of Health Sciences (DGM/NIHS) of Japan recently established a unique proprietary Ames mutagenicity database containing 12140 new chemicals that have not been previously used for developing QSAR models. The DGM/NIHS provided this Ames database to QSAR vendors to validate and improve their QSAR tools. The Ames/QSAR International Challenge Project was initiated in 2014 with 12 QSAR vendors testing 17 QSAR tools against these compounds in three phases. We now present the final results. All tools were considerably improved by participation in this project. Most tools achieved >50% sensitivity (positive prediction among all Ames positives) and predictive power (accuracy) was as high as 80%, almost equivalent to the inter-laboratory reproducibility of Ames tests. To further increase the predictive power of QSAR tools, accumulation of additional Ames test data is required as well as re-evaluation of some previous Ames test results. Indeed, some Ames-positive or Ames-negative chemicals may have previously been incorrectly classified because of methodological weakness, resulting in false-positive or false-negative predictions by QSAR tools. These incorrect data hamper prediction and are a source of noise in the development of QSAR models. It is thus essential to establish a large benchmark database consisting only of well-validated Ames test results to build more accurate QSAR models.

Dataset Information

Determinants of activity at human Toll-like receptors 7 and 8: quantitative structure-activity relationship (QSAR) of diverse heterocyclic scaffolds.

Publications

Determinants of activity at human Toll-like receptors 7 and 8: quantitative structure-activity relationship (QSAR) of diverse heterocyclic scaffolds.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets