Chapter title |
In silico methods for toxicity prediction.
|
---|---|
Chapter number | 7 |
Book title |
New Technologies for Toxicity Testing
|
Published in |
Advances in experimental medicine and biology, March 2012
|
DOI | 10.1007/978-1-4614-3055-1_7 |
Pubmed ID | |
Book ISBNs |
978-1-4614-3054-4, 978-1-4614-3055-1
|
Authors |
Combes RD, Robert D. Combes |
Abstract |
The principles and uses of (Q)SAR models and expert systems for predicting toxicity and the biotransformation of foreign chemicals (xenobiotics) are described and illustrated for some key toxicity endpoints, with examples from the published literature. The advantages and disadvantages of the methods and issues concerned with their validation, acceptance and use by regulatory bodies are also discussed. In addition, consideration is given to the potential application of these techniques in regulatory toxicity testing, both individually and as part of a chemically-based read-across approach, particularly for the risk assessment of chemicals within intelligent, integrated decision-tree testing schemes. It is concluded that, while there has been great progress in recent years in the development and application of in silico approaches, there is still much that has to be achieved to enable them to fulfill their potential for regulatory toxicity testing. In particular, there is a need for the wider availability of appropriate biological data and international agreement on how the systems should be validated. In addition, it is important that correlations between activity and physicochemical properties are based on a mechanistic basis to maximize the predictivity of models for novel chemicals. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Bulgaria | 1 | 3% |
Unknown | 38 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 7 | 18% |
Student > Master | 7 | 18% |
Researcher | 5 | 13% |
Student > Bachelor | 4 | 10% |
Other | 3 | 8% |
Other | 6 | 15% |
Unknown | 7 | 18% |
Readers by discipline | Count | As % |
---|---|---|
Chemistry | 6 | 15% |
Pharmacology, Toxicology and Pharmaceutical Science | 5 | 13% |
Agricultural and Biological Sciences | 4 | 10% |
Medicine and Dentistry | 4 | 10% |
Biochemistry, Genetics and Molecular Biology | 3 | 8% |
Other | 7 | 18% |
Unknown | 10 | 26% |