Rapid Screening of Drug Absorption Potential Using the Immobilized Artificial Membrane Phosphatidylcholine Column and Molar Volume

The immobilized artificial membrane phosphatidylcholine (IAMPC) chromatography was evaluated for the predictability of oral absorption potential of 40 structurally unrelated drugs. The chromatographic capacity factors (k_IAM) were determined as a function the pH and composition of the mobile phase, and were corrected for the molar volume of the solutes (k_IAM/MWⁿ). The correlation between k_IAM/MWⁿ and the human fraction of intestinal absorption (F_a) was highest when measured at 20% acetonitrile (pH 5.5) with the power function n = 2.5. The best-fit equation for the sigmoid relationship between k_IAM/MWⁿ and F_a was obtained: F_a (%) = 94.3 × {1-exp[-17.9 × (k_IAM/MW^2.5) × 10⁶]}^2.1 (r = 0.925). This in vitro prediction method may be useful in a rapid screening of drug candidates with high oral absorption potential in humans.     

Prediction of drug bioavailability in humans using immobilized artificial membrane phosphatidylcholine column chromatography and in vitro hepatic metabolic clearance

This study reports a rapid screening method for the prediction of oral drug bioavailability in humans based on combined immobilized artificial membrane (IAM) chromatographic capacity factor (k_IAM) and in vitro stability in hepatic microsomes. The fraction of drug absorbed (F_a) in humans was predicted for a set of 15 structurally diverse commercial drugs based on k_IAM values using a mobile phase consisting of acetonitrile: Dulbecco's phosphate‐buffered saline. The hepatic intrinsic clearance (CL ) was calculated from in vitro disappearance half‐life, and the oral bioavailability was predicted using in vitro hepatic clearance (CL_h) and F_a. Significant correlations were observed for the relationships between predicted hepatic extraction ratios (ER_h) and actual presystemic metabolism (r = 0.854) and between predicted and observed oral bioavailabilities (r = 0.805; p < 0.01). The IAM capacity factor together with the hepatic microsomal disappearance half‐life may be useful in identifying compounds with high oral absorption potential in early drug discovery processes. Copyright © 2009 John Wiley & Sons, Ltd.     

Solvent effects on kinetics of an aromatic nucleophilic substitution reaction in mixtures of an ionic liquid with molecular solvents and prediction using artificial neural networks

Kinetics of the reaction between 1‐chloro‐2,4‐dinitrobenzene and aniline was studied in mixtures of 1‐ethyl‐3‐methylimidazolium ethylsulfate ([EMIM][EtSO₄]) with methanol, chloroform, and dimethylsulfoxide at 25°C. Single‐parameter correlations of log k_A versus normalized polarity parameter (E^N_T), hydrogen‐bond acceptor basicity (β), hydrogen‐bond donor acidity (α), and dipolarity/polarizability (π*) of media do not give acceptable results. Multiparameter linear regression (MLR) of log k_A versus the solvatochromic parameters demonstrates that the reaction rate constant increases with E^N_T, π*, and β and decreases with α parameter. To predict accurately solvent effects on the rate constant, optimized artificial neural network with three inputs (including α, π*, and β parameters) was applied for prediction of the log k_A values in the prediction set. It was found that properly selected and trained neural network could fairly represent the dependence of the reaction rate constant on solvatochromic parameters. Mean percent deviation of 5.023 for the prediction set by the MLR model should be compared with the value of 0.343 by the artificial neural network model. These improvements are due to the fact that the reaction rate constant shows nonlinear correlations with the solvatochromic parameters. © 2008 Wiley Periodicals, Inc. Int J Chem Kinet 41: 153–159, 2009     

Retention Behavior of Neutral and Positively and Negatively Charged Solutes on an Immobilized‐Artificial‐Membrane (IAM) Stationary Phase

The retention behavior of neutral, positively charged, and negatively charged solutes on the IAM.PC.DD2 stationary phase was investigated and compared. A set of monofunctional compounds and complex drugs (steroids, nonsteroidal anti‐inflammatory drugs, and β‐blockers) were selected for this study, i.e., neutral solutes and solutes with acidic or basic functionalities which are positively charged or negatively charged at pH 7.0. The correlation between the retention factor log k_w at pH 7.0 on the IAM.PC.DD2 stationary phase and the partition coefficient log P_oct or the distribution coefficient log D_7.0 showed that the retention mechanism depends on the charge state and structural characteristics of the compounds. The neutrals were least retained on the IAM.PC.DD2 stationary phase, and positively charged solutes were more retained than negatively charged ones. This implies that the retention of the charged solutes is controlled not only by lipophilicity but also by the electrostatic interaction with the phospholipid, with which positively charged solutes interact more strongly than negatively charged ones.     

An Efficient and Simple Approach to Predict Kovat's Indexes of Polychlorinated Naphthalenes in Gas Chromatography

Molecular structures of polychlorinated naphthalenes were numerically described with a simple but efficient encoding method. Correspondingly a set of structural parameters were obtained for these compounds and linearly correlated with their gas chromatography retention indexes. A quantitative structure‐retention relationship Model (M1) was developed by using multiple linear regression (MLR) with correlation coefficient R = 0.9880 between the numeric structural codes and the gas chromatography retention indexes of 62 polychlorinated naphthalenes. If the “leave‐one‐out” cross‐validation procedure was employed to construct QSPR model for all samples, the second model M2 with the correlation coefficient being R = 0.9839 was generated. The structural codes of polychlorinated naphthalenes were tested with MLR for estimation and prediction of the GC RI by models M1 and M2, and the results obtained were satisfactory.     

Prediction of Chromatographic Retention of Pyrazine and Alkylpyrazines in RP-LC

Retention prediction models for a group of pyrazines chromatographed under reversed-phase mode were developed using multiple linear regression (MLR) and artificial neural networks (ANNs). Using MLR, the retention of the analytes were satisfactorily described by a two-predictor model based on the logarithm of the partition coefficient of the analytes (log P) and the percentage of the organic modifier in the mobile phase (ACN or MeOH). ANN prediction models were also derived using the predictors derived from MLR as inputs and log k as outputs. The best network architecture was found to be 2-2-1 for both ACN and MeOH data sets. The optimized ANNs showed better predictive properties than the MLR models especially for the ACN data set. In the case of the MeOH data set, the MLR and ANN models have comparable predictive performance.     

A Quantitative Structure–Activity Relationship Study of Calpeptin (Calpain Inhibitor) as an Anticancer Agent

Calpeptin analogs show anticancer properties with inhibition of calpain. In this work, we applied a quantitative structure–activity relationship (QSAR) model on 34 calpeptin derivatives to select the most appropriate compound. QSAR was employed to generate the models and predict the more significant compounds through a series of calpeptin derivatives. The HyperChem, Gaussian 09, and Dragon software programs were used for geometry optimization of the molecules. The 2D and 3D molecular structures were drawn by ChemDraw (Ultra 16.0) and Chem3D (Pro16.0) software. The Unscrambler program was used for the analysis of data. Multiple linear regression (MLR‐MLR), partial least‐squares (MLR‐PLS1), principal component regression (MLR‐PCR), a genetic algorithm‐artificial neural networks (GA‐ANN), and a novel similarity analysis‐artificial neural network (SA‐ANN) method were used to create QSAR models. Among the three MLR models, MLR‐MLR provided better statistical parameters. The R² and RMSE of the prediction were estimated as 0.8248 and 0.26, respectively. Nevertheless, the constructed model using GA‐ANN revealed the best statistical parameters among the studied methods (R² test = 0.9643, RMSE test = 0.0155, R² train = 0.9644, RMSE train = 0.0139). The GA‐ANN model is found to be the most favorable method among the statistical methods and can be employed for designing new calpeptin analogs as potent calpain inhibitors in cancer treatment.