Development and application of a simple pharmacokinetic model that quantitatively describes the distribution and elimination of the commonly measured proteins

Abstract

Increased plasma concentrations of a variety of cellular enzymes (alanine transaminase, aspartate aminotransferase, alkaline phosphatase, amylase, etc.) are commonly used as routine screening tests for a range of conditions. An increased concentration usually is assumed to result from an increased rate of delivery to the plasma. Factors such as decreased metabolism or excretion or altered extravascular distribution usually are ignored. As a prelude to a detailed analysis of all the factors producing altered plasma enzyme levels, we have reviewed the relevant literature describing the pharmacokinetics (PK) of 13 of the commonly measured plasma proteins and developed a PK model that provides a simple physiological description of all the data. Our model starts with the general 3-compartment, 6-parameter system previously developed for albumin and interprets the fluxes in terms of unidirectional sieved protein convectional volume flows from the plasma to the two tissue compartments and equal lymph flows returning to the plasma. This greatly constrains the model such that each protein is characterized by only two adjustable parameters (plasma clearance and sieving factor). In addition to accurately fitting the plasma kinetics, the model can accurately describe the tissue and lymph protein PK. For example, it can describe the thoracic duct lymph protein concentration following an intravenous infusion or the plasma concentration following a subcutaneous tissue injection. This simple model provides a satisfactory framework for the PK of 12 of the 13 proteins investigated. The glycoprotein intestinal alkaline phosphatase is the exception, requiring the addition of a liver recycling compartment involving the asialoglycoprotein receptor.

Keywords: Protein; alanine transaminase; albumin; alkaline phosphatase; amylase; aspartate aminotransferase; liver; pancreas; pharmacokinetics.

Figure 1.

A) Beeken et al. steady-state kinetic model. B) Interpretation of the model in terms of one-directional convective filtration from plasma to the tissue and return via lymph flows to the plasma.

Figure 2.

Comparison of PK compartmental protein model (line) for bolus IV human I¹³¹ albumin versus experimental data (solid circles) of Takeda and Reeve.

Figure 3.

Comparison of PK physiological protein model prediction (line) following IV bolus monoclonal antibody injection in the human versus experimental data (solid circles). Left panel: mepolizumab; Right panel: Infliximab. Only one parameter (plasma clearance) differs for the two model fits.

Figure 4.

Comparison of physiological protein model prediction (line) of the plasma concentration following a subcutaneous bolus Mepolizumab injection in the human versus experimental data (solid circles) using the identical model parameters used for IV input in Figure 3.

Figure 5.

Comparison of PK physiological protein model prediction (line) following IV bolus of baboon purified pancreatic amylase versus the experimental data in baboon (solid circles). The activity is plotted with the basal background subtracted.

Figure 6.

Comparison of PK physiological protein model (line) for ALT bolus IV injection versus experimental data (solid circles) in dog.

Figure 7.

Comparison of PK physiological protein model lymph thoracic duct ALT concentration following IV bolus injection in dog (line) versus experimental data (solid circles) of Fleisher and Wakim.

Figure 8.

Comparison of PK physiological protein model prediction (line) following IV bolus dose of mitochondrial (left panel) or cytoplasmic (middle panel) AST and creatine kinase (right panel) versus experimental data (solid circles) in dog.

Figure 9.

Comparison of PK physiological protein model prediction (line) following IV bolus injections in the lamb of the sheep purified protein lactate dehydrogenase isoenzymes LD1 (heart) (left) and LD5 (skeletal muscle) (right) versus experimental data (solid circles).

Figure 10.

Comparison of PK physiological protein model prediction (line) following a 60 min IV infusion of recombinant alkaline phosphatase (recAP) in the human versus experimental data (solid circles). The human plasma-tissue exchange parameters (f₁, f₂, L₁, L₂, Figure 1B) were assumed to be identical to those for monoclonals and only the metabolic clearance (Cl_p) was adjusted to try to fit the early time data.

Figure 11.

Modified physiological protein model with the addition of a recycling liver compartment with metabolic clearance Cl_L from the liver compartment.

Figure 12.

Comparison of physiological liver recycling model (Figure 11) prediction (black line) following a 60 min IV infusion of recombinant alkaline phosphatase (recAP) in the human versus experimental data (solid circles). The red line is for the case where the liver exchange rate (k, Figure 11) is set to zero. The green dashed line is for the case where the liver metabolic clearance (Cl_L) is set to zero.

Figure 13.

Comparison of physiological liver recycling model (Figure 11) simulation of the intestinal release of intestinal alkaline phosphatase following a high-fat meal every 24 hours (line) versus the experimental data of Domar et al. (solid circles).

Figure 14.

Comparison of standard physiological protein model (Figure 1B) prediction (line) following IV bolus injection of human purified placental alkaline phosphatase versus experimental data (solid circles).