Transcript
0:00Classical models and PBPK models
Lesson 7.4 of our prediction module: PBPK models. A pharmacokinetic model is a set of equations for the drug concentration over time, with fixed numbers called parameters. A compartment is a space in the model with one drug concentration throughout. In a classical model, a central compartment represents the plasma, the liquid part of the blood, and the parameters usually lack physiological meaning. A physiologically based pharmacokinetic model, or PBPK model, makes each compartment a real organ or tissue, with the volume and blood flow of that organ.
0:47System parameters and drug parameters
The parameters of a PBPK model belong to two groups. System parameters describe the body: organ volumes, blood flows, tissue composition and enzyme amounts. They change with the species and the population, and they do not depend on the drug that the model describes. Drug parameters describe the drug, for example its solubility in water and fat, its charge, its binding to plasma proteins and its rate of metabolism.
1:22The tissue-to-plasma partition coefficient
The tissue-to-plasma partition coefficient, Kp, is the total drug concentration in a tissue divided by the total concentration in plasma, when both are constant. Binding to tissue proteins, dissolution in tissue lipids and trapping in lysosomes, small acidic compartments in cells, raise Kp. Kp depends on the drug and on the tissue, so it has one value for each tissue.
1:55The structure of a PBPK model
Three points describe a PBPK model: its structure, its inputs and its uses. The first is the structure. Arterial blood carries the drug into each organ, and venous blood carries it out. Blood from the gut flows to the liver through the portal vein. For each organ, one equation sets the rate of change of the drug amount equal to the rate in minus the rate out. In the usual model, each tissue comes to equilibrium with the venous blood that leaves it, at the ratio that Kp sets. The liver and kidney equations usually also subtract the rate of metabolism or excretion, and the gut wall equation can do the same.
2:45The inputs
The second is the inputs. The system parameters come from published measurements of human anatomy and physiology. For a population, the PBPK software creates virtual subjects whose organ sizes and enzyme amounts vary as in real people. The rate of metabolism is measured in liver cells and then scaled up to the whole liver. Kp values come from equations that combine the drug properties with the lipid and water content of each tissue. In two studies of 67 drugs, these equations predicted 84 to 89 percent of measured values in most rat tissues within a factor of 3.
3:32The uses and FDA submissions
The third is the uses. Before the first dose in humans, the model predicts the plasma concentration-time curve from laboratory data, after a check in animals. Exposure is often measured as the AUC, the area under this curve. The model also predicts drug interactions, the changes in exposure to one drug that a second drug causes. An inhibitor lowers the activity of an enzyme, and an inducer raises the amount of the enzyme. For special populations, such as children or patients with liver or kidney disease, the model predicts exposure with changed system parameters. The FDA decides case by case whether a PBPK result can replace clinical pharmacokinetic data. From 2020 to 2024, 65 of the 245 new drugs that the FDA approved, or 26.5 percent, included a PBPK model in the submission. Drug interactions were 95 of the 116 PBPK uses, about 82 percent.
4:56Ibrutinib
The example of ibrutinib shows a PBPK model that predicted a drug interaction before the study and then supported dose advice. Ibrutinib is an oral cancer drug. Almost all of its metabolism is by the enzyme CYP3A, in the gut wall and the liver.
5:19Ketoconazole and rifampicin
In 18 healthy men, ketoconazole, a strong CYP3A inhibitor, raised the ibrutinib AUC 24-fold, after correction for the dose. The authors checked the PBPK model against this result. The model then predicted that rifampicin, a strong CYP3A inducer, would lower the AUC 10-fold. In the study of 17 healthy adults, rifampicin lowered the AUC 10-fold.
5:53Dose advice with moderate inhibitors
The model then predicted the effect of moderate CYP3A inhibitors, such as erythromycin. From these predictions, the authors advised 140 milligrams with a moderate inhibitor, one quarter of 560 milligrams, the highest dose at that time. The drug label at that time adopted this advice. A later study in 26 patients found that 140 milligrams with erythromycin gave about three quarters of the exposure of 560 milligrams alone. The US label of October 2025 gives 280 milligrams with a moderate inhibitor.
6:42Summary
In summary, a PBPK model connects organ compartments, each with its real volume and blood flow, as the circulation connects the organs. System parameters describe the body, drug parameters describe the drug, and Kp depends on both. The model predicts exposure before the first human dose, in drug interactions and in special populations. About one quarter of the new drugs that the FDA approved from 2020 to 2024 included a PBPK model in the submission. For ibrutinib, the model predicted the 10-fold effect of rifampicin before the study and supported the first dose advice with moderate CYP3A inhibitors. This concludes lesson 7.4 on PBPK models.
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Illustrations adapted from Servier Medical Art (smart.servier.com), CC BY 4.0. Structures drawn with RDKit.
