Chapter title |
QSAR Models at the US FDA/NCTR.
|
---|---|
Chapter number | 18 |
Book title |
In Silico Methods for Predicting Drug Toxicity
|
Published in |
Methods in molecular biology, January 2016
|
DOI | 10.1007/978-1-4939-3609-0_18 |
Pubmed ID | |
Book ISBNs |
978-1-4939-3607-6, 978-1-4939-3609-0
|
Authors |
Huixiao Hong, Minjun Chen, Hui Wen Ng, Weida Tong |
Editors |
Emilio Benfenati |
Abstract |
Quantitative structure-activity relationship (QSAR) has been used in the scientific research community for many decades and applied to drug discovery and development in the industry. QSAR technologies are advancing fast and attracting possible applications in regulatory science. To facilitate the development of reliable QSAR models, the FDA had invested a lot of efforts in constructing chemical databases with a variety of efficacy and safety endpoint data, as well as in the development of computational algorithms. In this chapter, we briefly describe some of the often used databases developed at the FDA such as EDKB (Endocrine Disruptor Knowledge Base), EADB (Estrogenic Activity Database), LTKB (Liver Toxicity Knowledge Base), and CERES (Chemical Evaluation and Risk Estimation System) and the technologies adopted by the agency such as Mold(2) program for calculation of a large and diverse set of molecular descriptors and decision forest algorithm for QSAR model development. We also summarize some QSAR models that have been developed for safety evaluation of the FDA-regulated products. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 16 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Doctoral Student | 3 | 19% |
Student > Master | 3 | 19% |
Other | 2 | 13% |
Researcher | 2 | 13% |
Student > Ph. D. Student | 1 | 6% |
Other | 2 | 13% |
Unknown | 3 | 19% |
Readers by discipline | Count | As % |
---|---|---|
Environmental Science | 2 | 13% |
Biochemistry, Genetics and Molecular Biology | 2 | 13% |
Computer Science | 2 | 13% |
Agricultural and Biological Sciences | 2 | 13% |
Medicine and Dentistry | 2 | 13% |
Other | 2 | 13% |
Unknown | 4 | 25% |