Subcellular Pharmacokinetics and Drug Properties: Numerical Simulations in Multicompartment Systems

Abstract

The methodology and results of numerical simulations of drug distribution in multicompartment systems, represented by a catenary set of alternating aqueous and lipid compartments, are reviewed. Transport, membrane accumulation, protein binding, first order elimination (biotransformation or excretion), but not ionisation have been considered as the processes determining the drug distribution. The simulations provide the time courses of the drug concentrations in individual compartments as dependent on hydrophobicity and elimination rate parameters of drugs because the model parameters are substituted by their relationships to the drug properties. The simulation results are fitted with empirical functions, mostly in the form of the hydrophobicity‐concentration profiles for a pre‐defined time of exposure. The shape of the profiles is different for open and closed biological systems (with and without elimination, respectively) as well as for individual periods of the drug distribution in closed systems (non‐equilibrium, mixed, equilibrium). Fitting the empirical functions to experimental data enables one to verify quantitatively the simulation results. The use of the approach for formulation of mechanistic conclusions is exemplified.

Cellular Quantitative Structure–Activity Relationship (Cell-QSAR): Conceptual Dissection of Receptor Binding and Intracellular Disposition in Antifilarial Activities of Selwood Antimycins

“…The red electronegative-favoring region in the midsection of the binding site improves the affinity (log K) of 15 of 31 compounds with an electronegative chlorine atom in the second ring, even though their overall activities differ significantly because of varying disposition. This interaction also causes the compounds with the electronegative oxygen (2−11, 13−15, 27, 29) or carbonyl (12) linkers between the second and third rings to have a higher binding affinity than compounds with other linkers (16,26,30).…”

“…The K term in eq 3 needs to be replaced by a suitable conceptual 3D-QSAR correlation equation, and the disposition function can be selected from the available repertoire. 30 , 32 This cell-QSAR concept represents the recipe for the formulation of conceptual QSARs for cell-based assays and other complex systems.…”

“…Equation and equivalent PK/PL/PD relationships provide the blueprint for a conceptual combination of the disposition function with any ligand-based or receptor-based 3D-QSAR method for prediction of binding affinities. The K term in eq needs to be replaced by a suitable conceptual 3D-QSAR correlation equation, and the disposition function can be selected from the available repertoire. , This cell-QSAR concept represents the recipe for the formulation of conceptual QSARs for cell-based assays and other complex systems.…”

“…Subsequently, other parameters, including the ionization constant, 17 the exposure time, the metabolic rate parameters, 18 the ratio of the partition coefficients in alkane/water and 1-octanol/water systems, 19 − 22 and the polar surface area 23 , 24 as descriptors of H-bonding ability, the partition coefficient between phosphatidylcholine headgroups and hexadecane as a predictor of bilayer localization and partitioning, 25 the membrane-interaction QSAR parameters, 26 , 27 the polarizability, 28 the cross-sectional area of the molecule, 29 and numerous others, were added. The DF functional forms developed from the parabolic, 13 bilinear, 15 nonequilibrium, 30 equilibrium, 17 and mixed models to the pseudoequilibrium DF. 31 , 32 Some of these DFs predicted reliably outside the used property ranges, in contrast to empirical models.…”