Cells on pores: a simulation-driven analysis of transcellular small molecule transport

Abstract

A biophysical, computational model of cell pharmacokinetics (1CellPK) is being developed to enable prediction of the intracellular accumulation and transcellular transport properties of small molecules using their calculated physicochemical properties as input. To test if 1CellPK can generate accurate, quantitative hypotheses and guide experimental analysis of the transcellular transport kinetics of small molecules, epithelial cells were grown on impermeable polyester membranes with cylindrical pores and chloroquine (CQ) was used as a transport probe. The effect of the number of pores and their diameter on transcellular transport of CQ was measured in apical-to-basolateral or basolateral-to-apical directions, at pH 7.4 and 6.5 in the donor compartment. Experimental and simulation results were consistent with a phospholipid bilayer-limited, passive diffusion transport mechanism. In experiments and 1CellPK simulations, intracellular CQ mass and the net rate of mass transport varied <2-fold although total pore area per cell varied >10-fold, so by normalizing the net rate of mass transport by the pore area available for transport, cell permeability on 3 mum pore diameter membranes was more than an order of magnitude less than on 0.4 mum pore diameter membranes. The results of simulations of transcellular transport were accurate for the first four hours of drug exposure, but those of CQ mass accumulation were accurate only for the first five minutes. Upon prolonged incubation, changes in cellular parameters such as lysosome pH rise, lysosome volume expansion, and nuclear shrinkage were associated with excess CQ accumulation. Based on the simulations, lysosome volume expansion alone can partly account for the measured, total intracellular CQ mass increase, while adding the intracellular binding of the protonated, ionized forms of CQ (as reflected in the measured partition coefficient of CQ in detergent-permeabilized cells at physiological pH) can further improve the intracellular CQ mass accumulation prediction.

Figure 1

Microscopic images of polyester membranes and MDCK cells grown on a 0.4µm-membrane. (A) Orthogonal planes of 3D reconstructions reveal cross-sections of MDCK monolayers grown on a polyester membrane with 0.4 µm pores. Cells were stained with LTG, MTR and Hoechst and imaged as detailed in the methods. (B) Confocal microscope images of membranes with 0.4µm pores. C) Confocal microscope images of membranes with 3µm pores. (D) Scanning electron microscope (SEM) images of membranes with 0.4µm pores. E) Scanning electron microscope (SEM) images of membranes with 3µm pores. The table details microscopic measurements of pore geometry, density and cell monolayer characteristics, as analyzed in this study.

Figure 2

The relationship between mass transport rate and the initial concentration of CQ in the donor compartment. (A) AP → BL transport (pH_a = 6.5). (B) BL → AP transport (pH_b = 6.5). (C) AP → BL transport (pH_a = 7.4). (D) BL → AP transport (pH_b = 7.4). The linear regression equations are included in the tables.

Figure 3

The relationship between intracellular CQ mass and the initial concentration of CQ in the donor compartment. (A) AP → BL transport (pH_a = 6.5). (B) BL → AP transport (pH_b = 6.5). (C) AP → BL transport (pH_a = 7.4).(D) BL → AP transport (pH_b = 7.4). The linear regression equations shown in the table (right) were obtained from the four lowest concentrations tested.

Figure 4

Histogram plots of Monte Carlo simulations showing calculated dM/dt (A), P_cell (B), P_app (C), and intracellular CQ mass accumulation at 5 minutes incubation (D), for the indicated experimental conditions analyzed in this study. The solid red lines indicate experimentally-measured mean values and the dashed red lines indicate measured standard deviation.

Figure 5

CQ binding experiments. (A) The bound CQ mass digitonin- and triton-treated cells as a function of CQ concentration in buffer; (B) comparison of passive CQ binding at 4°C (digitonin-treated and triton-treated cells) and CQ uptake by live cells. The values and standard deviations were calculated from the regression lines using CQ concentration equal to 500 or 1000 µM.

Figure 6

Effects of lysosome swelling on CQ intracellular mass accumulation. (A) Comparison of simulated intracellular mass and experimental data at the end of a 5 minute and 4-hour AP→BL transport experiment. (B) Lysotracker Green (LTG) staining of MDCK cells treated with CQ free medium (left) and 50 µM CQ diluted in medium (right) for 4 hours. (C) Histograms of Monte Carlo simulation of lysosome expansion and pH effect on intracellular CQ mass accumulation, excluding (left) or including (right) the measured partitioning component of the ionized CQ species with initial concentration of 50 µM. The left curves in each panel were generated from Monte Carlo simulations without considering lysosome expansion and pH effect. All model parameters were kept the same as in Figure 4 except that the measured lysosome volume and pH values were used as input (as median values of a uniform distribution, see Supporting Information for boundary calculation). The simulations performed to generate the right histogram were described in Supporting Information (Figure S3) except that the initial concentration used for this plot was 50 µM. The red lines show intracellular mass accumulation of CQ with initial concentration of 50 µM extrapolated from regression lines of experimental measurements (Figure 3C).