Pharmacokinetics analysis series: Compartmental analysis
- Datapharm Australia
- May 19, 2023
Compartmental analysis (CA) is a type of pharmacokinetics (PK) analysis and is less common than non-compartmental analysis. It uses individual PK data to fit a statistical model across all subjects, usually with exponential equations. Commonly, PK parameters such as drug clearance, volume of distribution and half-life are included as fixed effects, and the subject is included as a random effect. Contributing factors can be added as additional predictor variables such as the drug dose, patient age, and whether the patient has liver disease or not. The final model can be used to estimate the drug’s PK properties, such as the drug concentration over time based on the dose(s), and the impact of included predictors.
As an inferential model with flexibility in the choice of parameters, more estimations are possible with CA than are with NCA; however, this is at the cost of increased data requirements. The model choice and assumptions should be discussed with a clinician and selected based on the drug of interest, number of doses, study design and expected contributing factors.
CA requires assumptions on a predefined number of body compartments based on non-linear regression analysis. The complexity and data requirements increase with the number of compartments.
- 1 compartment: This assumes that the drug is equally distributed throughout the body. With only one compartment, no distribution parameter is calculated; thus linear clearance is assumed. This may be suitable for drugs that do not distribute throughout the whole body.
- 2 compartments: This assumes a central compartment (for plasma and highly perfused tissues) and a peripheral compartment (for poorly perfused tissues).
- 3 compartments: This assumes a central compartment (for plasma), a first peripheral compartment (for highly perfused tissues) and a second peripheral compartment (for poorly perfused tissues).
- Whole-body model: Each body tissue has its own compartment. This model is highly dependent on physiological and biological knowledge. Due to the large number of compartments, many variables can be included to represent physiological parameters such as blood flow between specific organs, organ size and tissue partitioning coefficients. However, this analysis is very complex and consequently requires a very considerable amount of data.
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