Biopartitioning micellar chromatography(BMC)is a potentially high throughput and low cost alternative for in vitro prediction of drug absorption,which can mimic the drug partitioning process in biological systems.In t...Biopartitioning micellar chromatography(BMC)is a potentially high throughput and low cost alternative for in vitro prediction of drug absorption,which can mimic the drug partitioning process in biological systems.In this paper,a data set of 56 compounds representing acidic,basic,neutral and amphoteric drugs from various structure classes with human oral absorption(HOA)data available were employed to show the effect of acidity of drugs in oral absorption prediction.HOA was reciprocally correlated to the negative value of the capacity factor(kBMC)determined by BMC at pH 7.4 and 6.5.The relationships between kBMC and the corresponding HOA values of all compounds were rather poor,but the correlations were improved when the acidity of drugs was taken into consideration.Moreover,the proposed models allowed obtaining of good predictive values for both highly and poorly absorbed compounds.It is demonstrated that the constructed models derived from compounds with the same kind of charge property are of more practically meaningful and rigorous.展开更多
The usefulness of biopartitioning micellar chromatography （BMC） for predicting oral drug acute toxicity and apparent bioavailability was demonstrated. A logarithmic model （an LD50 model） and the second order polyn...The usefulness of biopartitioning micellar chromatography （BMC） for predicting oral drug acute toxicity and apparent bioavailability was demonstrated. A logarithmic model （an LD50 model） and the second order polynomial models （apparent bioavailability model） have been obtained using the retention data of the selective calcium channel blockers to predict pharmacological properties of compounds. The use of BMC is simple, reproducible and can provide key information about the acute toxicity and transport properties of new compounds during the drug discovery process.展开更多
文摘Biopartitioning micellar chromatography(BMC)is a potentially high throughput and low cost alternative for in vitro prediction of drug absorption,which can mimic the drug partitioning process in biological systems.In this paper,a data set of 56 compounds representing acidic,basic,neutral and amphoteric drugs from various structure classes with human oral absorption(HOA)data available were employed to show the effect of acidity of drugs in oral absorption prediction.HOA was reciprocally correlated to the negative value of the capacity factor(kBMC)determined by BMC at pH 7.4 and 6.5.The relationships between kBMC and the corresponding HOA values of all compounds were rather poor,but the correlations were improved when the acidity of drugs was taken into consideration.Moreover,the proposed models allowed obtaining of good predictive values for both highly and poorly absorbed compounds.It is demonstrated that the constructed models derived from compounds with the same kind of charge property are of more practically meaningful and rigorous.
基金Project supported by the National Natural Science Foundation of China （No. 20375010）, Natural Science Foundation of Hebei Province （No. 202096）, Bairen Project of Chinese Academy of Sciences, and the Excellent Teacher Program and the Specialized Research Fund for the Doctoral Program of Higher Education.Acknowledgements The authors wish to thank professor Yang LIANG and Yongjian ZHANG for a generous gift of m-nisoldipine, nisoldipine and nicardipine.
文摘The usefulness of biopartitioning micellar chromatography （BMC） for predicting oral drug acute toxicity and apparent bioavailability was demonstrated. A logarithmic model （an LD50 model） and the second order polynomial models （apparent bioavailability model） have been obtained using the retention data of the selective calcium channel blockers to predict pharmacological properties of compounds. The use of BMC is simple, reproducible and can provide key information about the acute toxicity and transport properties of new compounds during the drug discovery process.
