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.
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
RelatedItem (type = host)
TitleInfo
Title
Camden Graduate School Electronic Theses and Dissertations
Identifier (type = local)
rucore10005600001
Identifier
ETD_5822
Identifier (type = doi)
doi:10.7282/T30000J8
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (x, 59 p.: ill.)
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Abena Boison
Subject (authority = RUETD)
Topic
Chemistry
Subject (authority = ETD-LCSH)
Topic
GABA--Receptors
Subject (authority = ETD-LCSH)
Topic
Anxiety--Effect of drugs on
Subject (authority = ETD-LCSH)
Topic
Ligands (Biochemistry)
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.