Detalle de: Good oral absorption prediction on non-nucleoside benzothiadiazine dioxide human cytomegalovirus inhibitors using combined chromatographic and neuronal network techniques
| Title | Good oral absorption prediction on non-nucleoside benzothiadiazine dioxide human cytomegalovirus inhibitors using combined chromatographic and neuronal network techniques |
| Abstract |
The current drugs available against human cytomegalovirus (HCMV) suffer from a number of shortcomings such as toxic side effect, poor bioavailability and/or risk for emergence of drug-resistance virus strains. Due to these limitations, the development of new drugs against HCMV is of great interest. Taking into account the therapeutic potential of benzothiadiazines dioxides (BTD) derivatives, it is most important to know their oral bioavailability because all the current clinical drugs are poorly absorbed. In this work, the utility of CODES neural networks and biopartitioning micellar chromatography (BMC) in predicting pharmacokinetic properties has been used to estimate the oral absorption of BTD derivatives and their efficacy has been verified. The results indicate higher values for BTD derivatives than the currently licensed anti-viral agents. |
| Apa Reference | Gil, C., Dorronsoro, I., Castro, A., & Martinez, A. (2005). Good oral absorption prediction on non-nucleoside benzothiadiazine dioxide human cytomegalovirus inhibitors using combined chromatographic and neuronal network techniques. Bioorganic & Medicinal Chemistry Letters, 15(7), 1919–1921. 10.1016/j.bmcl.2005.01.081 |
| Doi | 10.1016/j.bmcl.2005.01.081 |
| Publication Date | Apr 2005 |
| Publication Type | Article |
| Researcher | Ana Martínez |
| Ontology | Artificial Intelligence, Drug Discovery and design, Deep Learning in Biology |
| Research Groups | Translational Medicinal and Biological Chemistry |
| Tools |