Title
(Quantitative) Structure-Activity Relationships – opportunities for metabolites predicted
Poster
Description
Boryana Mihaylova1, Nadya Agova1, Silvia Stamova2, Svetlana Georgieva2
1Faculty of Pharmacy, Medical University „Prof. D-r Paraskev Stoyanov”, Varna, Bulgaria
2Department of Pharmaceutical chemistry, Faculty of Pharmacy, Medical University „Prof. D-r Paraskev Stoyanov” Varna, Bulgaria
1Faculty of Pharmacy, Medical University „Prof. D-r Paraskev Stoyanov”, Varna, Bulgaria
2Department of Pharmaceutical chemistry, Faculty of Pharmacy, Medical University „Prof. D-r Paraskev Stoyanov” Varna, Bulgaria
Subject
Introduction: (Quantitative) Structure-Activity Relationships [(Q)SARs] are methods for estimating properties of a chemical from its molecular structure and have the potential to provide information on the hazards of chemicals, while reducing time, monetary costs and animal testing currently needed.
The notion of QSAR modeling was evolved from the idea of possible existence of a mathematical correlation between the nature of chemicals and their behavioral manifestation. The development of suitable descriptors (predictor variables) can help in exploring the mechanistic basis involved in a chemical–biological process following mathematical model development. Aim: The aim of this research is to summon and present information about the wide amount opportunities and good sides of quantitative structure–activity relationship (QSAR) model. As well to provide data on predicted metabolites of new synthesized structures. Materials and Methods: In the present work, OECD (Q)SAR Application Toolbox has been used for identifying probable metabolic activity of hydrazone of bexarotene in the liver and its protein and DNA binding. Results: QSAR as a tool is gaining increasing acceptance with medicinal chemists and researchers in the allied fields. This is due to the increased understanding of the underlying science by which one can develop mathematical (QSAR) models. The expansion of knowledge in this area allows us to predict the biological activity or potency of new or untested compounds. Conclusion: QSAR methods are growing faster , they cover a wide range of scientific disciplines and practical application possibilities. The results on the bioavailability, toxicity and effects of QSAR-derived chemicals can be validated and used in the development of new drugs.
Key words: QSAR , clinical trials , probable metabolits
The notion of QSAR modeling was evolved from the idea of possible existence of a mathematical correlation between the nature of chemicals and their behavioral manifestation. The development of suitable descriptors (predictor variables) can help in exploring the mechanistic basis involved in a chemical–biological process following mathematical model development. Aim: The aim of this research is to summon and present information about the wide amount opportunities and good sides of quantitative structure–activity relationship (QSAR) model. As well to provide data on predicted metabolites of new synthesized structures. Materials and Methods: In the present work, OECD (Q)SAR Application Toolbox has been used for identifying probable metabolic activity of hydrazone of bexarotene in the liver and its protein and DNA binding. Results: QSAR as a tool is gaining increasing acceptance with medicinal chemists and researchers in the allied fields. This is due to the increased understanding of the underlying science by which one can develop mathematical (QSAR) models. The expansion of knowledge in this area allows us to predict the biological activity or potency of new or untested compounds. Conclusion: QSAR methods are growing faster , they cover a wide range of scientific disciplines and practical application possibilities. The results on the bioavailability, toxicity and effects of QSAR-derived chemicals can be validated and used in the development of new drugs.
Key words: QSAR , clinical trials , probable metabolits