In the 20th century, toxicology developed slowly and relied on live animal experiments for a long time several decades. With the increasing interest in, which was far from the three R principles of animal experiment ethics alternatives to in vivo testing has become more and more important. As an additional aspect, it is to be mentioned that, traditional in vivo experiments in animals sometimes fail to have many uncertainties, making it difficult to accurately predict the toxicological effects of chemicals on humans. Therefore, in vitro toxicity experiments have rapidly developed, using biological macromolecules, cell lines, biological tissues, and organs as targets instead of live animals. Additionally, computational toxicological research began in the early 1980s due to the high cost and social opposition to animal experiments. Data mining is carried out through computer science and artificial intelligence technology to find out certain rules with the aim to use existing toxicological data, find out rules between structure and toxic effects, define a model, and use the model to predict the expected toxicity of a new compound. Then a toxicity prediction model is established, and the possible toxicity of the compounds is predicted according to the model.
Predictive toxicology uses chemical approaches, molecular biological models, chemical approaches, and mathematical and computer models to explore the relationship between environmental exposures towards xenobiotics, including those in the environment and adverse effects of chemicals, providing solutions for supporting the risk assessment of chemicals, food, drugs, cosmetics, pesticides, and other their related products. It has become an important direction path for the development and application of modern toxicology.
This research topic aims to achieve describe efficient prediction and evaluation of drug toxicity by, improving the understanding of the mechanism of toxicity, and carrying out high-throughput risk predictive management of drugs. This research topic focuses on the discovery and application of knowledge bases such as toxic ingredient information, dose-time-toxicity relationship information, structure-toxicity relationship information, and clinical toxicity characteristic information of toxic drugs by using information technology such as computers, artificial intelligence technologies, and biotechnology such as in vitro experiments. This provides a data basis for risk prediction of toxic drug candidates and a regular model evaluation offers clinical toxicity risk prediction.
Possible research contributions under this topic include the development of in vitro prediction models of drug toxicity and the use of these models to explore the toxic effects and mechanisms of drug toxicity on multiple cells, organs, and systems. Using computational methods and experimental techniques, experimental data on drug exposure can be mined to form a knowledge base of drugs. Data analysis and mathematical modeling techniques can be used to establish predictive toxicological models to understand drug toxicity, the relationship between toxicity and structure, and the mechanism of intermediate toxicity. This provides a comprehensive and quantitative evaluation of the drug risk.
Potential topics contributions may include dealing with, but are not limited to:
• Advances in predictive toxicology for the discovery of toxicological knowledge.
• Research methods or techniques of predictive toxicology in drug toxicity.
• Construction of the toxicology data repository.
• Research on toxicity discovery, toxicity mechanism, and toxicity prediction by information technology.
• Research on toxicity discovery, toxicity mechanism, and toxicity prediction of in vitro biotechnology.
• Establishment of computer models for predicting drug toxicity and risks.
• Establishment of models of drug toxicology in vitro (cell, tissue, system, or others).
• In vitro to in vivo model extrapolation.
In the 20th century, toxicology developed slowly and relied on live animal experiments for a long time several decades. With the increasing interest in, which was far from the three R principles of animal experiment ethics alternatives to in vivo testing has become more and more important. As an additional aspect, it is to be mentioned that, traditional in vivo experiments in animals sometimes fail to have many uncertainties, making it difficult to accurately predict the toxicological effects of chemicals on humans. Therefore, in vitro toxicity experiments have rapidly developed, using biological macromolecules, cell lines, biological tissues, and organs as targets instead of live animals. Additionally, computational toxicological research began in the early 1980s due to the high cost and social opposition to animal experiments. Data mining is carried out through computer science and artificial intelligence technology to find out certain rules with the aim to use existing toxicological data, find out rules between structure and toxic effects, define a model, and use the model to predict the expected toxicity of a new compound. Then a toxicity prediction model is established, and the possible toxicity of the compounds is predicted according to the model.
Predictive toxicology uses chemical approaches, molecular biological models, chemical approaches, and mathematical and computer models to explore the relationship between environmental exposures towards xenobiotics, including those in the environment and adverse effects of chemicals, providing solutions for supporting the risk assessment of chemicals, food, drugs, cosmetics, pesticides, and other their related products. It has become an important direction path for the development and application of modern toxicology.
This research topic aims to achieve describe efficient prediction and evaluation of drug toxicity by, improving the understanding of the mechanism of toxicity, and carrying out high-throughput risk predictive management of drugs. This research topic focuses on the discovery and application of knowledge bases such as toxic ingredient information, dose-time-toxicity relationship information, structure-toxicity relationship information, and clinical toxicity characteristic information of toxic drugs by using information technology such as computers, artificial intelligence technologies, and biotechnology such as in vitro experiments. This provides a data basis for risk prediction of toxic drug candidates and a regular model evaluation offers clinical toxicity risk prediction.
Possible research contributions under this topic include the development of in vitro prediction models of drug toxicity and the use of these models to explore the toxic effects and mechanisms of drug toxicity on multiple cells, organs, and systems. Using computational methods and experimental techniques, experimental data on drug exposure can be mined to form a knowledge base of drugs. Data analysis and mathematical modeling techniques can be used to establish predictive toxicological models to understand drug toxicity, the relationship between toxicity and structure, and the mechanism of intermediate toxicity. This provides a comprehensive and quantitative evaluation of the drug risk.
Potential topics contributions may include dealing with, but are not limited to:
• Advances in predictive toxicology for the discovery of toxicological knowledge.
• Research methods or techniques of predictive toxicology in drug toxicity.
• Construction of the toxicology data repository.
• Research on toxicity discovery, toxicity mechanism, and toxicity prediction by information technology.
• Research on toxicity discovery, toxicity mechanism, and toxicity prediction of in vitro biotechnology.
• Establishment of computer models for predicting drug toxicity and risks.
• Establishment of models of drug toxicology in vitro (cell, tissue, system, or others).
• In vitro to in vivo model extrapolation.