Protein-binding parameters

Combined use of Eqs. (43)—(45) allows free drug concentrations to be predicted for each subcompartment. This approach to modeling free drug concentrations would make use of protein binding parameters (i.e., Bt, Kt) obtained from in vitro experiments. 

Protein binding parameters B, and Kh as depicted in Eq. (44), are obtained from in vitro experiments utilizing filtration, centrifugation, and dynamic dialysis or equilibrium dialysis methods. These techniques have been reviewed elsewhere [54,55],... 

The graphical methods for the estimation of the protein binding parameters are limited to protein systems, which have only 1 class of binding sites. If the protein concentration P is known the product n KA can be calculated by using equation 4. The slope of the regression line is equal to n KA P. 

Pinkerton TC, Koeplinger KA (1990) Determination of warfarin-human serum albumin protein binding parameters by an improved Hummel-Dreyer high performance liquid chromatography method using internal surface reversed-phase columns. Anal Chem 62 2114-2122. 

TABLE 2. Partition Coefficients and Metabolic and Protein-Binding Parameters Used in the PBPK Models for... 

TABLE 8.2. Gold NP-Protein Binding Parameters Obtained from Flnorescence Qnenching Data"... 

B. Sebille, N. Thuaud and J. P. Tillement, Retention data methods for the determination of drug-protein binding parameters by high-performance liquid chromatography,/. Chromatogr., 1981, 204, 285-291. 

In subsequent studies attempting to find a correlation of physicochemical properties and antimicrobial activity, other parameters have been employed, such as Hammett O values, electronic distribution calculated by molecular orbital methods, spectral characteristics, and hydrophobicity constants. No new insight on the role of physiochemical properties of the sulfonamides has resulted. Acid dissociation appears to play a predominant role, since it affects aqueous solubiUty, partition coefficient and transport across membranes, protein binding, tubular secretion, and reabsorption in the kidneys. An exhaustive discussion of these studies has been provided (10). 

Waldmann-Meyer, HK, Protein Ion Equilibria, Total Evaluation of Binding Parameters and Net Charge from the Electrophoretic Mobility as a Function of Ligand Concentration. In Recent Developments in Chromatography and Electrophoresis Frigerio, A McCamish, M, eds. Elsevier Scientific Amsterdam, 1980 Vol. 10, p 125. 

During the characterization process, hits are typically tested for kinetic solubility and permeability in a model of passive diffusion such as PAMPA [22]. As new compounds are synthesized, additional parameters also need to be considered, such as pZa, chemical and plasma stability, and protein binding. Calculated properties such as MW, clogP, and PSA should also be tracked. 

Figure 3.1 shows the appearance of dihydromethysticin in the acceptor well as a function of time [15], The solid curve is a least-squares fit of the data points to Eq. (1), with the parameters Pe = 32 x 10-6 cm s 1, R = 0.42, and t s = 35 min. The membrane retention, R, is often stated as a mole percentage (%R) of the sample (rather than a fraction). Its value can at times be very high - up to 90% for chlor-promazine and 70% for phenazopyridine, when 2% wt/vol DOPC in dodecane is used. Figure 3.2 shows a plot of log %R versus log Ka(7.4), the octanol/water apparent partition coefficient. It appears that retention is due to the lipophilicity of molecules this may be a good predictor of the pharmacokinetic volume of distribution or of protein binding. 

As described above, it will be normal to assume that the dose interval is 24 hours, i.e., once-a-day dosing. Absorption can be estimated with good confidence from absorption in the rat (see Section 6.1). Clearance is the sum of the predicted hepatic, renal, biliary and extrahepatic clearance. Hepatic clearance can be derived from in vitro studies with the appropriate human system, using either microsomes or hepatocytes. We prefer to use an approach based on that described by Houston and Carlile [83], Renal clearance can be predicted allometrically (see section 6.8.1). The other two potential methods of clearance are difficult to predict. To minimize the risks, animal studies can be used to select compounds that show little or no potential for clearance by these routes. As volume can be predicted from that measured in the dog, after correction for human and dog plasma protein binding (see Section 6.2), it is possible to make predictions for all of the important parameters necessary. 

In an early application of in silico approaches to predict human VD, Ritschel and coworkers described an approach using artificial neural networks (ANN), in this case for VDp [34]. However, this was not a truly in silico-only approach as the ANN that yielded accurate predictions of human VD required animal pharmacokinetic data as input parameters, along with in vitro data (protein binding and logP). 

The overall accuracy of the predictions, assessed as the mean-fold error of prediction of the test set was 2.03, making this approach one that would possess suitable accuracy for use in drug design and human pharmacokinetic predictions. Similar methods developed separately for acids and bases showed an improvement in accuracy. This investigation also included a prediction of unbound VD, which should represent a simpler parameter to predict since it would be based only on tissue binding and not plasma protein binding. However, it is interesting to note that this approach was less accurate for this parameter, which would be unexpected. 

Also included in in vivo data is a set of human (90% of drugs) and animal pharmacokinetic (30% of drugs) data. While the in vitro data are generated in-house (Cerep), pharmacokinetic data are gathered from the literature. A variety of different parameters are covered including absolute bioavailability, oral absorption, clearance, volume of distribution, half-life, protein binding and excretion information. 

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