A cell-based molecular transport simulator for pharmacokinetic prediction and cheminformatic exploration

Abstract

In the body, cell monolayers serve as permeability barriers, determining transport of molecules from one organ or tissue compartment to another. After oral drug administration, for example, transport across the epithelial cell monolayer lining the lumen of the intestine determines the fraction of drug in the gut that is absorbed by the body. By modeling passive transcellular transport properties in the presence of an apical to basolateral concentration gradient, we demonstrate how a computational, cell-based molecular transport simulator can be used to define a physicochemical property space occupied by molecules with desirable permeability and intracellular retention characteristics. Considering extracellular domains of cell surface receptors located on the opposite side of a cell monolayer as a drug's desired site of action, simulation of transcellular transport can be used to define the physicochemical properties of molecules with maximal transcellular permeability but minimal intracellular retention. Arguably, these molecules would possess very desirable features: least likely to exhibit nonspecific toxicity, metabolism, and side effects associated with high (undesirable) intracellular accumulation; and most likely to exhibit favorable bioavailability and efficacy associated with maximal rates of transport across cells and minimal intracellular retention, resulting in (desirable) accumulation at the extracellular site of action. Simulated permeability values showed good correlations with PAMPA, Caco-2, and intestinal permeability measurements, without "training" the model and without resorting to statistical regression techniques to "fit" the data. Therefore, cell-based molecular transport simulators could be useful in silico screening tools for chemical genomics and drug discovery.

Figure 1

Model of an intestinal epithelial cell. A) Cell morphology. B) The path of a hydrophobic weak base across an intestinal epithelial cell. The neutral form of the molecule is indicated as [M] and the protonated, cationic form of the molecule is indicated as [MH⁺].

Figure 2

Measured Caco2 permeability and predicted permeability of seven β-adrenergic receptor blockers are correlated. The X-axis indicates the logarithm values of average measured Caco2 permeability (cm/sec) and the Y-axis indicate the logarithm values of predicted permeability (cm/sec). The dotted line is the linear regression line. The linear regression equation is y = 0.44x − 2.4(R² = 0.76), and the significance F of regression is 0.011 (confidence level is 95%). Numbers 1 through 7 indicate alprenolol, atenolol, metoprolol, oxprenolol, pindolol, practolol, and propranolol respectively. The structures, physicochemical properties, average Caco2 permeability and predictive permeability are summarized in Table 1.

Figure 3

Measured Caco2 permeability and predicted permeability of thirty-seven weakly acid or basic (non-zwitterionic) drugs with a single ionizable functional group at physiological pH. The X-axis indicates the logarithm values of average measured Caco2 permeability (cm/sec) and the Y-axis indicate the logarithm values of predicted permeability (cm/sec). Metoprolol (No.18) was used as a reference drug to define predicted high vs. low permeability categories (dashed line). Compounds in the predicted high permeability category exhibit high Caco2 permeabilities (dashed circle). More detailed information and references relevant to the calculated and average Caco2 permeability measurements are included in the Supporting Information.

Figure 4

Measured human intestinal permeability and predicted permeability are correlated. The X-axis indicates the logarithm values of measured human intestinal permeability (cm/sec) and the Y-axis indicate the logarithm values of predicted permeability (cm/sec), for weakly acidic or basic (non-zwitterionic) drugs with a single ionizable functional group at physiological pH. A simple linear relation was obtained and expressed by the equation: y = 0.95x − 0.57(R² = 0.73), the significance F of regression is 0.0016 (confidence level is 95%). Calculated permeability and human intestinal permeability data is summarized in Table 3.

Figure 5

Varying one physicochemical property at a time of a molecule with metoprolol-like properties (arrows) affects both the intracellular concentration (solid line = cytosolic; dark dotted line = mitochondrial) and permeability (light stippled line) at steady state. A.) Calculations based on varying logP_n and logP_d independently from each other. B.) Calculations based on varying logP_n and logP_d simultaneously, according to the linear relationship expressed as Eqn 27–Eqn 29. Arrows point to the reference liposomal logP_{n, lip}, logP_{d, lip,} and pKa of metoprolol.

Figure 6

The chemical space occupied by molecules with desirable A) permeability (defined as molecules with calculated P_eff equal or larger than P_eff of a virtual molecule with metoprolol-like properties); B) intracellular accumulation (defined as molecules with both calculated C_cyto and C_mito equal or less than that of a virtual molecule with metoprolol-like propeties); and, C) permeability and intracellular accumulation (defined as molecules with calculated P_eff equal or larger than P_eff, and C_cyto and C_mito equal or less than C_cyto and C_mito than that of a virtual molecule with metoprolol-like properties. Each row is a different (rotated) view of the same, 3D physicochemical property space plot. Calculations of physicochemical property space represent simulations obtained by varying logP_n and logP_d independently from each other. Numbers 1 through 7 are alprenolol, propranolol, oxprenolol, metoprolol, pindolol, practolol, and atenolol respectively. The logP_n and logP_d values of each molecule are the liposomal values, listed in Table 1.

Figure 7

The calculated physicochemical property space occupied by molecules with permeant, impermeant, toxic and nontoxic molecules, relative to a metoprolol-like reference molecule. Calculations represent results obtained by varying logP_n and logP_d simultaneously, according to the simple linear relationship expressed in Eqn 27–Eqn 29. Numbers 1 through 7 are alprenolol, propranolol, oxprenolol, metoprolol, pindolol, practolol, and atenolol respectively. The logP_n and logP_d values of each molecule are liposomal values, listed in Table 1.