Non-compartmental pharmacokinetics (NCA) is a simple and practical method used to describe how a drug moves through the body without assuming any specific compartment model. Instead of dividing the body into compartments, NCA uses direct observations from plasma concentration–time data to calculate key parameters like AUC, clearance, and mean residence time. It is widely used in clinical studies, bioavailability testing, and regulatory submissions.
What Is Non-Compartmental Analysis?
NCA does not try to fit the drug concentration data into a one- or two-compartment model. Instead, it uses mathematical tools—mainly integrals and statistical moment theory—to estimate pharmacokinetic parameters. This makes it easier, faster, and more flexible for analyzing data from human volunteer studies.
Key Concepts in Non-Compartmental Pharmacokinetics
1. Area Under the Curve (AUC)
AUC represents the total drug exposure over time. It is the most important parameter in NCA.
AUC = ∫ C(t) dt
The AUC is usually calculated using the trapezoidal rule, which connects measured data points with straight lines and calculates the area beneath them.
2. Area Under the First Moment Curve (AUMC)
AUMC is the area under the curve of time × concentration.
It is used to calculate mean residence time (MRT).
3. Mean Residence Time (MRT)
MRT is the average time a drug molecule stays in the body.
MRT = AUMC / AUC
Elimination Rate Constant (kel)
NCA estimates the elimination rate constant by analyzing the terminal (log-linear) portion of the curve. Once k is known, half-life can be calculated using:
t1/2 = 0.693 / k
Clearance (Cl) in NCA
Clearance is calculated without assuming any compartment model:
Cl = Dose / AUC
Clearance indicates how efficiently the body removes the drug.
Volume of Distribution (Vss)
NCA uses MRT to calculate the steady-state volume of distribution:
Vss = Cl × MRT
This value helps understand how widely a drug spreads in the body.
Advantages of NCA
- No assumptions about compartments
- Simple calculations using observed data
- Better for clinical and regulatory studies
- Less risk of model fitting errors
- Useful for estimating bioavailability and bioequivalence
Limitations of NCA
- Cannot describe complex multi-phase drug behavior
- Does not predict future drug levels
- Requires good-quality sampling data
Applications of Non-Compartmental Analysis
1. Bioavailability Studies
AUC and Cmax from NCA help compare the extent and rate of drug absorption.
2. Bioequivalence Studies
FDA and EMA guidelines use NCA parameters to determine if two drug products behave similarly.
3. Clinical Pharmacokinetic Trials
NCA is preferred for Phase 1 studies because it is simple and accurate for real human data.
4. Toxicology Studies
Used to calculate exposure levels in preclinical animal studies.
Important NCA Parameters Summary
| Parameter | Meaning |
|---|---|
| AUC | Total drug exposure |
| AUMC | Time–concentration exposure |
| MRT | Average residence time |
| Cl | Clearance |
| Vss | Volume of distribution at steady state |
| t1/2 | Half-life |
Why Statistical Moment Theory?
NCA uses statistical moment theory to interpret drug behavior similar to how statisticians interpret random variables. AUC is like the “total probability,” and AUMC represents the “expected time” drug molecules stay in the body.
Detailed Notes:
For PDF style full-color notes, open the complete study material below:
PATH: PHARMD/ PHARMD NOTES/ PHARMD FOURTH YEAR NOTES/ BIOPHARMACEUTICS AND PHARMACOKINETICS/ NON COMPARTMENTAL PHARMACOKINETICS.
