Physicochemical parameters, retention importance

Quinones et al. (2000) reported the successful use of neural networks to predict the half-life of a series of 30 antihistamines. The input for the network was derived from the output of CODES, a routine that generates descriptors for a structure based on atom nature, bonding, and connectivity. Attempts to correlate the half-life with the physicochemical parameters log Kow, pKa, molecular weight, molar refractivity, molar volume, parachor, and polarity were unsuccessful. In a subsequent study by Quinones-Torrelo et al. (2001), the authors correlated the half-life of 18 antihistamines with their retention in a biopartitioning micellar chromatography system with a resultant correlation coefficient (R2adj) value of 0.89. The correlation is explained in that the retention in this system is dependent on hydrophobic, electronic, and steric properties, which are also important in determining half-life. 

Structure-based prediction software predicts retention times or important physicochemical processes based on chemical structures. Application databases store chromatographic methods for later retrieval and adaptation to new samples with similar structures and physicochemical parameters. 

The accuracy of this system is dependent on the correlation coefficient of a retention description obtained from studies of QSRR, therefore, the selection of descriptors is the most basic and important task to construct RPS. This selection could be done with statistical framework, even if such description is not clearly derived from theories. The retention description obtained from QSRR studies is more effective for a rapid and accurate prediction of retention than that derived from theoretical models, because the former is simple and does not require introduction of a number of physicochemical parameters (they are often not clearly known and are very difficult and time-consuming to determine) for the latter case. By contrast, the consideration of physical meanings of descriptors derived from QSRR studies gave the overview of retention mechanisms in reversed-phase LC (7-10). That is to say, hydrophobicity, size and shape of alkyl-benzenes and PAHs are dominate factors controlling their retention. 

Physicochemical profiling has gained considerable importance in the last years as most companies realized that inappropriate physicochemical properties could lead to compound withdrawal later in development. The basic physicochemical parameters of interest for drugability prediction are solubility and permeability, the two components of the Biopharmaceutical Classification Scheme. However, these two fundamental parameters are in turn influenced by other physicochemical parameters worth considering, particularly in the lead optimization phase. For example, permeability is influeneed by lipophilicity (induces membrane retention) and pH (ioniz-able compounds), solubility is influenced by pH (ionizable compounds), and dissolution rate is linked to particle size, polymorphism, and wettability. 

The net retention volume and the specific retention volume, defined in Table 1.1, are important parameters for determining physicochemical constants from gas chromatographic data [9,10,32]. The free energy, enthalpy, and. entropy of nixing or solution, and the infinite dilution solute activity coefficients can be determined from retention measurements. Measurements are usually made at infinite dilution (Henry s law region) in which the value of the activity coefficient (also the gas-liquid partition coefficient) can be assumed to have a constant value. At infinite dilution the solute molecules are not sufficiently close to exert any mutual attractions, and the environment of each may be considered to consist entirely of solvent molecules. The activity... 

The accurate determination of the column void time, 0, is of fundamental importance in chromatography [1]. This is explained by the fact that a reliable estimation of this quantity is essential for the correct calculation of the retention factors (some refer to this as the capacity factor), k, which serves as the fundamental parameter for the comparison of retention data and for the interpretation of the physicochemical phenomena taking place within a chromatographic column. However, the determination of this parameter is very sensitive to the estimated value of the column void time, as can be seen from the equation... 

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