Study On High-throughput Drug Lipophilicity Determination By P1-modified Microemulsion Liquid Chromatography

PurposeThe present study attempted to combine P1with microemulsion as the mobile phase,which resulted in the addition of P1molecules to the mobile and stationary phases. Thisnew biopartitioning chromatography were used to high-throughput determine druglipophilicity and to mimic biological membrane behavior. The logP prediction modelequations with logk in P1-modified microemulsion liquid chromatography were applied todetermine unionized and ionized drugs lipophilicity. This chromatography were alsoutilized to mimic interaction of drugs with blood-brain barrier membrane and to predictlogBB.Methods1. P1contents and types of surfactantAccording to the result of relative viscosity (25℃) and UV absorbance ofP1-modifiedmicroemulsion solutions with various P1contents, this study chose the system with lowerrelative viscosity and UV absorbance, and more P1as mobile phase.2. Optimization of microemulsion systemThe compositions of microemulsion system was optimized. According to the correlationof logk with logP in different microemulsion system, the system with better correlation wasused for study of the addition of P1.3. Establishment of P1-modified microemulsion liquid chromatographyThe P1-modified microemulsion system was build and was used as the mobile phase forstability and reproducibility studies. The linear solvation energy relationship (LSER) was used to describe the interaction of the solute between the mobile phase and stationary phase.And the P1-modified microemulsion system was compared with other system by distanceparameter (d) and principal component analysis (PCA).4. Establishment of logP prediction model equations with logkThe model equations between logP and logk of the P1-modified microemulsion systemwas established. To improve the logP predictability, stepwise regression was used tointroduce suitable molecular descriptors into the model equation.5. Optimization of the P1-modified microemulsion systemThe compositions of P1-modified microemulsion system was optimized and theP1-modified column system was built. The result was evaluated by the correlation of logkwith logP.6. Application in unionized and ionized drugs with P1-modified microemulsion systemThe P1-modified microemulsion system was applied to the study of unionized andionized drugs. The correlation of logk with logP was compared with that of logk with logDand the logk in P1-modified microemulsion system was compared with other systems byPCA.7. Establishment of logBB prediction model equations with logkThe logBB prediction model equations of the best P1-modified microemulsion systemwas established. To improve the logP predictability, stepwise regression was used tointroduce suitable molecular descriptors into the model equation.Results and conclusionsAccording to the optimization result of P1contents, types of surfactant and thecompositions of SDS microemulsion system, the P1-modified microemulsion system(0.08%P1-3.0%SDS-6.0%n-butanol-0.8%ethyl acetate-90.2%water) was built with goodstability and reproducibility. The LSER model indicated that solute volume and HB basicityhad the most influence on the analyte retention times. PCA and the distance parameterproved that the P1-SDS microemulsion liquid chromatography was similar to then-octanol/water system and had the ability to predict logP. The model equations between logP and logk in P1-modified microemulsion system was established (R2=0.797).Stepwise regression was used to introduce two molecular descriptors, logk and molarvolume (MV), into the model equation, resulting in significantly improved predictability(logP=2.168logkMP12+0.003MV+1.551, R2=0.856). This compositions of P1-modifiedmicroemulsion system was optimized and the best P1-modified microemulsion system(0.08%P1-3.0%SDS-6.0%n-butanol-0.8%ethyl acetate-90.2%water) was obtained. ThelogP predictability of the P1-modified column system was not satisfactory. The study ofunionized and ionized drugs with P1-modified microemulsion system found that thecorrelation of logk with logD (logD7.0=2.030logkMP9+1.796, R2=0.673) was better thanthat of logk with logP. Compared with other systems by P1A, the P1-modifiedmicroemulsion system had the ability to predict logD. It was no relationship between logkand logBB. But the R2of logk with logBB was significantly improved after removed6drugs which molecular weight (MW) is greater than250(R2=0.612). And stepwiseregression was used to introduce two molecular descriptors, logk and molecular weight(MW), into the logBB model equation, resulting in significantly improved predictability(logBB=0.347logk-0.002MW+0.352, R2=0.793).