Population physiologically-based pharmacokinetic model incorporating lymphatic uptake for a subcutaneously administered pegylated peptide

Abstract

Purpose: Physiologically-based pharmacokinetic (PBPK) models provide a rational mechanistic approach for predicting the time course of macromolecules in plasma. Population PBPK models for large molecules necessitate incorporation of lymphatic circulation to mechanistically account for biodistribution. Moreover, characterization of subcutaneous absorption requires consideration of the microvascular transit from the injection site to the systemic circulation. A PBPK model for a pegylated peptide conjugate, previously developed for primates, was modified to describe the lymphatic uptake in a population of humans by incorporation of interindividual variability in the lymphatic circulation and a unique lymphatic drainage compartment (LDC). The model was then used to simulate the time course of the drug in a population of humans and compared to the same drug administered to a group of human subjects participating in a first-in-human study.

Methods: Organ, blood and lymph masses for the population were sampled from either normal or log-normal distributions. Blood flows were calculated for each organ based on mean organ perfusion per gram of organ tissue and lymphatic flow was set as a fixed fraction of blood flow. Interindividual variability in lymphatic volume was assumed to be similar to that of blood volume. The volume of the LDC was parameterzed as a fraction of the injection volume. Sensitivity analysis was performed to study uncertain parameters and distribution assumptions.

Results: The population generator was capable of simulating a virtual population incorporating the lymphatic circulation. Incorporation of a LDC resulted in similar line shape relative to the observed data and incorporation of anthropometric variability accounted for individual differences in the absorption and elimination phases across all dose cohorts. Line shape was sensitive to the inclusion of LDC while peak and elimination portions of the time course were influenced by the magnitude of variance assumed for blood volume and renal clearance, respectively.

Conclusion: Lymphatic circulation can be incorporated into a population PBPK model assuming similar interindividual variability as observed for blood volume. Incorporation of an LDC, where the volume of this transit compartment is proportional to the SC injection volume may be an important mechanistic means of predicting the transit from the SC depot to the systemic circulation.

Keywords: Lymph; Pegylated; Peptide; Physiologically-based pharmacokinetic model; Subcutaneous.

Fig. 1

a Structure of a whole-body PBPK platform (adapted from Shah and Betts 2012). Solid black arrows indicate plasma flow. Dark grey dashed arrows indicate lymphatic transport. S. Int. and L. Int. represent small and large intestines. Each organ compartment includes lymphatic flow emptying from the organ into the lymph nodes. For IV administration, drug is administered into the “Venous Supply”. For SC administration, drug is administered into the “Skin Compartment” interstitium. b Sub-compartment model for all organs other than skin (top) and skin (bottom)

Fig. 2

Model output anthropometric distribution of weight (upper left panel), height (upper right panel), body mass index (lower left panel) and derived glomerular filtration rate (lower right panel) for 1000 simulated individuals

Fig. 3

Model simulated and observed plasma concentrations of a linear PEG-40 conjugated peptide versus time in humans on linear scale (left panel) and log scale (right panel). Within each panel, the left set of profiles represents the model without a lymph transit compartment and right side, with a lymph transit compartment incorporated. Closed geometric symbols represent unique individual subjects across 5 dose levels. Solid lines and grey shaded ribbon represents the median and 5^th–95^th percentile simulated concentrations from respective models

Fig. 4

Mean parameter sensitivity perturbations vs. percent change in AUC (top left), Cmax (top right) and Tmax (bottom left) for the 50^th percentile following simulation of 100 subjects. Closed geometric symbols represent perturbations as indicated in the legend

Fig. 5

Median (dashed line) and 5^th–95^th percentile (shaded ribbon) following simulation of 1000 virtual individuals on linear scale (left panel) and log scale (right panel). For sub-panels 1–11 in each panel, the following unique scenarios are presented: (1) Final model (2) 0.5-fold final model CV % for Vfrac (3) 2-fold final model CV % for Vfrac (4): Addition of 10 % CV % on LS (5) Addition of 50 % CV % on LS (6) Addition of 10 % CV % on σ_i (7) Addition of 50 % CV % on σ_i (8) Removing distribution on blood mass (9) Removing distribution on lymph mass (10) Removing distribution on skin mass (11) Addition of a 20 % CV % on FGFR