5 research outputs found
Development of a Computational Tool to Rival Experts in the Prediction of Sites of Metabolism of Xenobiotics by P450s
The metabolism of xenobioticsî¸and more specifically
drugsî¸in
the liver is a critical process controlling their half-life. Although
there exist experimental methods, which measure the metabolic stability
of xenobiotics and identify their metabolites, developing higher throughput
predictive methods is an avenue of research. It is expected that predicting
the chemical nature of the metabolites would be an asset for designing
safer drugs and/or drugs with modulated half-lives. We have developed
IMPACTS (In-silico Metabolism Prediction by Activated Cytochromes
and Transition States), a computational tool combining docking to
metabolic enzymes, transition state modeling, and rule-based substrate
reactivity prediction to predict the site of metabolism (SoM) of xenobiotics.
Its application to sets of CYP1A2, CYP2C9, CYP2D6, and CYP3A4 substrates
and comparison to expertsâ predictions demonstrates its accuracy
and significance. IMPACTS identified an experimentally observed SoM
in the top 2 predicted sites for 77% of the substrates, while the
accuracy of biotransformation expertsâ prediction was 65%.
Application of IMPACTS to external sets and comparison of its accuracy
to those of eleven other methods further validated the method implemented
in IMPACTS
Development of a Computational Tool to Rival Experts in the Prediction of Sites of Metabolism of Xenobiotics by P450s
The metabolism of xenobioticsî¸and more specifically
drugsî¸in
the liver is a critical process controlling their half-life. Although
there exist experimental methods, which measure the metabolic stability
of xenobiotics and identify their metabolites, developing higher throughput
predictive methods is an avenue of research. It is expected that predicting
the chemical nature of the metabolites would be an asset for designing
safer drugs and/or drugs with modulated half-lives. We have developed
IMPACTS (In-silico Metabolism Prediction by Activated Cytochromes
and Transition States), a computational tool combining docking to
metabolic enzymes, transition state modeling, and rule-based substrate
reactivity prediction to predict the site of metabolism (SoM) of xenobiotics.
Its application to sets of CYP1A2, CYP2C9, CYP2D6, and CYP3A4 substrates
and comparison to expertsâ predictions demonstrates its accuracy
and significance. IMPACTS identified an experimentally observed SoM
in the top 2 predicted sites for 77% of the substrates, while the
accuracy of biotransformation expertsâ prediction was 65%.
Application of IMPACTS to external sets and comparison of its accuracy
to those of eleven other methods further validated the method implemented
in IMPACTS
Development of a Computational Tool to Rival Experts in the Prediction of Sites of Metabolism of Xenobiotics by P450s
The metabolism of xenobioticsî¸and more specifically
drugsî¸in
the liver is a critical process controlling their half-life. Although
there exist experimental methods, which measure the metabolic stability
of xenobiotics and identify their metabolites, developing higher throughput
predictive methods is an avenue of research. It is expected that predicting
the chemical nature of the metabolites would be an asset for designing
safer drugs and/or drugs with modulated half-lives. We have developed
IMPACTS (In-silico Metabolism Prediction by Activated Cytochromes
and Transition States), a computational tool combining docking to
metabolic enzymes, transition state modeling, and rule-based substrate
reactivity prediction to predict the site of metabolism (SoM) of xenobiotics.
Its application to sets of CYP1A2, CYP2C9, CYP2D6, and CYP3A4 substrates
and comparison to expertsâ predictions demonstrates its accuracy
and significance. IMPACTS identified an experimentally observed SoM
in the top 2 predicted sites for 77% of the substrates, while the
accuracy of biotransformation expertsâ prediction was 65%.
Application of IMPACTS to external sets and comparison of its accuracy
to those of eleven other methods further validated the method implemented
in IMPACTS
Development of a Computational Tool to Rival Experts in the Prediction of Sites of Metabolism of Xenobiotics by P450s
The metabolism of xenobioticsî¸and more specifically
drugsî¸in
the liver is a critical process controlling their half-life. Although
there exist experimental methods, which measure the metabolic stability
of xenobiotics and identify their metabolites, developing higher throughput
predictive methods is an avenue of research. It is expected that predicting
the chemical nature of the metabolites would be an asset for designing
safer drugs and/or drugs with modulated half-lives. We have developed
IMPACTS (In-silico Metabolism Prediction by Activated Cytochromes
and Transition States), a computational tool combining docking to
metabolic enzymes, transition state modeling, and rule-based substrate
reactivity prediction to predict the site of metabolism (SoM) of xenobiotics.
Its application to sets of CYP1A2, CYP2C9, CYP2D6, and CYP3A4 substrates
and comparison to expertsâ predictions demonstrates its accuracy
and significance. IMPACTS identified an experimentally observed SoM
in the top 2 predicted sites for 77% of the substrates, while the
accuracy of biotransformation expertsâ prediction was 65%.
Application of IMPACTS to external sets and comparison of its accuracy
to those of eleven other methods further validated the method implemented
in IMPACTS
Integrating Medicinal Chemistry, Organic/Combinatorial Chemistry, and Computational Chemistry for the Discovery of Selective Estrogen Receptor Modulators with Forecaster, a Novel Platform for Drug Discovery
As part of a large medicinal chemistry program, we wish to develop novel selective estrogen receptor modulators (SERMs) as potential breast cancer treatments using a combination of experimental and computational approaches. However, one of the remaining difficulties nowadays is to fully integrate computational (i.e., virtual, theoretical) and medicinal (i.e., experimental, intuitive) chemistry to take advantage of the full potential of both. For this purpose, we have developed a Web-based platform, Forecaster, and a number of programs (e.g., Prepare, React, Select) with the aim of combining computational chemistry and medicinal chemistry expertise to facilitate drug discovery and development and more specifically to integrate synthesis into computer-aided drug design. In our quest for potent SERMs, this platform was used to build virtual combinatorial libraries, filter and extract a highly diverse library from the NCI database, and dock them to the estrogen receptor (ER), with all of these steps being fully automated by computational chemists for use by medicinal chemists. As a result, virtual screening of a diverse library seeded with active compounds followed by a search for analogs yielded an enrichment factor of 129, with 98% of the seeded active compounds recovered, while the screening of a designed virtual combinatorial library including known actives yielded an area under the receiver operating characteristic (AU-ROC) of 0.78. The lead optimization proved less successful, further demonstrating the challenge to simulate structure activity relationship studies