Complying and curating public bioassay data for chemical toxicity and anxiety drug discovery studies
Creators: name: Boison, Abena
Publisher: name: No Publisher Supplied
Year: 2014

description: Recent investigations suggest that ligands such as steroids inhibit the binding of [35S] t-butylbicyclophosphorothionate ([35S] TBPS) to the convulsant site in the aminobutyric acid type A (GABAA) receptor complex. Currently, most interest is centered on ligands with [35S] TBPS displacement properties. Ligands binding to the GABAA receptor, block GABA-gated chloride ion flux in a non-competitive manner,  resulting in convulsions. Traditionally, [35S] TBPS inhibition studies are measured using animal tests. Testing compounds, using rat tests, for potentially new ligands are costly and time-consuming. Therefore, developing computational models to predict potential [35S] TBPS displacement could provide many opportunities for the discovery and development of new ligands acting on the GABAA receptor convulsant site, resulting in the preventions of convulsions.  In this study, Quantitative Structure Activity Relationship (QSAR) approaches were used to develop several computational models for a series of novel and diverse types of compounds (steroids derivatives, Arylsulfonyl derivatives and Propofol analogues). The specific inhibition of [35S] TBPS binding to the GABAA convulsant site by these compounds was modeled. A database of 266 GABAA receptor compounds was    compiled. Duplicates, mixtures and salts were removed to prepare the dataset for modeling. The remaining 210 compounds were used for modeling and chemical descriptors for each compound were generated. After calculating descriptors for each compound, computational tools such as k-Nearest-Neighbor (kNN), Support Vector Machine (SVM) and Random Forest (RF) were used to develop QSAR models. The generated models were validated using five-fold cross validation. Furthermore, predicting the activities of the external set, compounds not used in the modeling set, validated the developed models. The correct classification rates (CCR) for all the models were between 66% and 83%. Prediction values were relatively lower than accepted. However, applying an applicability domain (AD) increased the predictivity (CCR= 77% to 86%) and reduced the coverage (45%). The QSAR models developed in this study could be used to screen chemical libraries and identify potentially new GABAA receptor convulsant site compounds.  High Throughput Screening (HTS) assays that measure the in vitro toxicity of environmental compounds have been widely used as an alternative to in vivo animal tests. Current HTS studies provide the community with rich toxicology information that has the potential to be integrated into toxicity research. The available in vitro toxicity data is updated daily in structured formats (e.g., deposited into PubChem and other data sharing web portals) or in unstructured ways (papers, laboratory reports, toxicity website updates, etc.) The information derived from the current toxicity data is so large and complex that it becomes difficult to process using available database management tools or traditional data processing applications. For this reason, it is necessary to develop a “Big Data” approach when conducting modern chemical toxicity research.    In-vitro data for a compound, obtained from meaningful bioassays, can be viewed as a response profile that gives detailed information about the compound’s ability to affect relevant biological protein/receptors. This information is critical for the evaluation of complex bio-activities (e.g., animal toxicities) and grows rapidly as “big data” in toxicology communities. This review focuses mainly on the existing structured in vitro data (e.g., PubChem datasets) as response profiles for compounds of environmental interest (e.g., potential human/animal toxicants). Potential modeling and mining tools used to process big data in chemical toxicity research are also described.