U.S. flag

An official website of the United States government

Display Settings:

Items per page

PMC Full-Text Search Results

Items: 10

1.
Fig. 2

Fig. 2. From: Application of PBPK modeling to predict monoclonal antibody disposition in plasma and tissues in mouse models of human colorectal cancer.

Dose normalized plasma concentration–time profiles for T84.66 and 8C2. Mean concentrations of T84.66 were normalized to the injected dose, and plotted vs. time for: (a) T84.66 in control, non-tumor bearing SCID mice, (b) T84.66 in LS174T-tumor bearing SCID mice, (c) T84.66 in HT29 tumor-bearing SCID mice, and (d) 8C2 PK in LS174T-tumor bearing SCID mice. Symbols for all panels: open triangle (0.025 mg/kg), filled square (0.1 mg/kg), filled circle (1 mg/kg), open circle (10 mg/kg), filled triangle (25 mg/kg)

Lubna Abuqayyas, et al. J Pharmacokinet Pharmacodyn. ;39(6):683-710.
2.

Fig. 4. From: Application of PBPK modeling to predict monoclonal antibody disposition in plasma and tissues in mouse models of human colorectal cancer.

Comparison of PBPK model-predicted and observed T84.66 concentration data in mice bearing LS174T xenografts. Shaded areas represents the PBPK model-predicted 90 % confidence intervals (n = 1,000 subjects); dashed lines represent the predicted 5th, 50th and 95th percentiles. Filled circles depict the observed concentrations from individual mice. Shown curves represent T84.66 PK after intravenous injection at a 1 mg/kg, b 10 mg/kg, and c 25 mg/kg to LS174T-xenograft bearing SCID mice

Lubna Abuqayyas, et al. J Pharmacokinet Pharmacodyn. ;39(6):683-710.
3.
Fig. 10

Fig. 10. From: Application of PBPK modeling to predict monoclonal antibody disposition in plasma and tissues in mouse models of human colorectal cancer.

Comparison of PBPK model-predicted and observed T84.66 plasma and tissues concentration versus time profiles in anti-VEGF treated LS174T xenograft-bearing SCID mice The shaded area represents the PBPK model-predicted 90 % confidence interval (n = 1,000 subjects), and dashed lines depict the predicted 5th, 50th and 95th percentiles. Filled circles represent the observed concentrations from individual mice. Plots show T84.66 concentration versus time data following intravenous injection (10 mg/kg) into anti-VEGF treated LS174T xenograft-bearing SCID mice

Lubna Abuqayyas, et al. J Pharmacokinet Pharmacodyn. ;39(6):683-710.
4.
Fig. 5

Fig. 5. From: Application of PBPK modeling to predict monoclonal antibody disposition in plasma and tissues in mouse models of human colorectal cancer.

Comparison of PBPK model-predicted and observed T84.66 population mean AUCs in HT29 xenograft-bearing SCID mice. Black bars represent the mean population predicted T84.66 AUC0–10 days in plasma, HT29 tumors, and other tissues. Error bars represent the standard deviation around the mean (n = 1,000 subjects). Gray bars represent the mean T84.66 AUC0–10 days in plasma, tumor and other tissues. Sub groups of 1–3 mice were sacrificed at several time points (t = 1 h–10 days). Error bars represent the standard deviations around the mean calculated using a modified Bailer method

Lubna Abuqayyas, et al. J Pharmacokinet Pharmacodyn. ;39(6):683-710.
5.

Fig. 8. From: Application of PBPK modeling to predict monoclonal antibody disposition in plasma and tissues in mouse models of human colorectal cancer.

Comparison of PBPK model-predicted and observed 8C2 plasma and tissue concentration versus time profiles in LS174T xenograft-bearing SCID mice. The shaded area represents the PBPK model-predicted confidence interval (n = 1,000 subjects), and dashed lines represent the predicted 5th, 50th and 95th percentiles. Filled circles represent the observed concentrations from individual mice. Curves depict 8C2 concentration versus time data after intravenous injection of a 1 mg/kg, and b 25 mg/kg into LS174T-xenograft bearing SCID mice

Lubna Abuqayyas, et al. J Pharmacokinet Pharmacodyn. ;39(6):683-710.
6.
Fig. 7

Fig. 7. From: Application of PBPK modeling to predict monoclonal antibody disposition in plasma and tissues in mouse models of human colorectal cancer.

Comparison of PBPK model-predicted and observed 8C2 AUC values in LS174T xenograft-bearing SCID mice. Black bars depict the mean population predicted 8C2 AUC0–10 days in plasma, LS174T tumors, and in other tissues. Error bars represent the standard deviation around the mean (n = 1,000 subjects). Gray bars represent the mean 8C2 AUC0–10 days in plasma, tumor, and other tissues. Sub groups of three mice were sacrificed at several time points (t = 1 h–10 days). Error bars represent the standard deviations around the mean, calculated using a modified Bailer method

Lubna Abuqayyas, et al. J Pharmacokinet Pharmacodyn. ;39(6):683-710.
7.

Fig. 6. From: Application of PBPK modeling to predict monoclonal antibody disposition in plasma and tissues in mouse models of human colorectal cancer.

Comparison of PBPK model-predicted and observed T84.66 plasma and tissue concentration versus time profiles in HT29 xenograft-bearing SCID mice. The shaded area represents the PBK model-predicted population confidence interval (n = 1,000 subjects). Dashed lines represent the predicted 5th, 50th and 95th percentiles. Filled circles represent the observed concentrations from individual mice. The curves represent T84.66 concentration versus time profiles after intravenous injection of a 0.025 mg/kg, b 0.1 mg/kg, c 1.0 mg/kg, d 10 mg/kg, e 25 mg/kg to HT29-xenograft bearing SCID mice

Lubna Abuqayyas, et al. J Pharmacokinet Pharmacodyn. ;39(6):683-710.
8.
Fig. 9

Fig. 9. From: Application of PBPK modeling to predict monoclonal antibody disposition in plasma and tissues in mouse models of human colorectal cancer.

Comparison of PBPK model-predicted and observed AUCs in plasma and tissues in anti-VEGF treated, LS174T xenograft-bearing mice. Black bars represent the mean population predicted T84.66 AUC0–10 days in plasma, LS174T tumors, and in other tissues. Error bars represent the standard deviation around the mean (n = 1,000 subjects). Gray bars represent the mean T84.66 AUC0–10 days in plasma, tumor, and other tissues. Sub groups of three mice were sacrificed at several time points (t = 1 h–10 days), and AUCs were calculated using NCA with WinNonlin 6.1. Error bars represent the standard deviations around the mean, as calculated with a modified Bailer method

Lubna Abuqayyas, et al. J Pharmacokinet Pharmacodyn. ;39(6):683-710.
9.
Fig. 3

Fig. 3. From: Application of PBPK modeling to predict monoclonal antibody disposition in plasma and tissues in mouse models of human colorectal cancer.

Comparison of PBPK model-predicted and observed T84.66 population mean AUCs in LS174T xenograft-bearing SCID mice. Black bars represent the predicted T84.66 AUC0–10 days in plasma, LS174T tumors, and other tissues. Error bars represent the standard deviation around the mean (n = 1,000 subjects). Gray Bars represent the observed mean T84.66 AUC0–10 days in plasma, tumor and other tissues. Sub groups of three mice were sacrificed at several time points (t = 1 h–10 days), AUCs were calculated using NCA for sparse sampling module (WinNonlin 6.1). Error bars represent the standard deviations around the mean calculated using a modified Bailer method

Lubna Abuqayyas, et al. J Pharmacokinet Pharmacodyn. ;39(6):683-710.
10.
Fig. 1

Fig. 1. From: Application of PBPK modeling to predict monoclonal antibody disposition in plasma and tissues in mouse models of human colorectal cancer.

Schematic diagram of the PBPK model of mAb disposition. a System Model: The plasma and lymphatic flow to and from each organ are presented by solid and dashed arrows. Each tissue within this model is divided into vascular, endosomal and interstitial sub-compartments. b Organ Model: Qi and Li are the plasma and lymph flow rates, R1 is the endosomal uptake and recycling rate constant for IgG, FR is the fraction of recycled antibody that is transported to the vascular compartment. The vascular and lymph reflection coefficients are and is IgG-FcRn equilibrium dissociation constant. CLi represents the rate of organ specific clearance of unbound IgG, and fui is the unbound fraction of IgG within the endosomal compartment. To allow for anti-cancer antibody interaction with cellular antigens, an additional cellular sub-compartment was assumed for tumor. c Tumor model: The model assumes equilibrium binding kinetics between T84.66 in the interstitial space and cellular CEA. The fraction of IgG not bound to CEA is , and the T84.66-CEA complex degrades irreversibly by the internalization rate constant, kint. The total T84.66 concentration in each tissue was based on the sum of the total antibody amount in each sub-compartment and divided by the weight of each organ. For the tumor compartment, the total tumor weight increased as a function of time, based on the growth rate of the tumor

Lubna Abuqayyas, et al. J Pharmacokinet Pharmacodyn. ;39(6):683-710.

Display Settings:

Items per page

Supplemental Content

Recent activity

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

See more...
Support Center