1. Noncompartmental Models

2. Introduction The noncompartmental approach for data analysis does not require any specific compartmental model for the system (body) and can be applied to virtually any pharmacokinetic data. There are various noncompartmental approaches, including statistical moment analysis, system analysis, or the noncompartmental recirculatory model The main purpose of the noncompartmental approach is to estimate various pharmacokinetic parameters, such as systemic clearance, volume of distribution at steady state, mean residence time, and bioavailability without assuming or understanding any structural or mechanistic properties of the pharmacokinetic behavior of a drug in the body

3. Introduction In addition, many noncompartmental methods allow the estimation of those pharmacokinetic parameters from drug concentration profiles without the complicated, and often subjective, nonlinear regression processes required for the compartmental models Owing to this versatility and ruggedness, the noncompartmental approach is a primary pharmacokinetic data analysis method for the pharmaceutical industry

4. Statistical moment theory Suppose one could observe a single molecule, from the time it is administered into the body ( t = 0) until it is eventually eliminated ( t = t el ) Clearly, t el is not predictable This individual molecule could be eliminated during the first minute or could reside in the body for weeks. If, however, one looks at a large number of molecules collectively, their behavior appears much more regular The collective, or mean time of residence, of all the molecules in the dose, is called the mean residence time (MRT).

5. Mean Residence Time (MRT) A mean time interval during which a drug molecule resides in the body before being excreted

6. AUC vs. AUMC

7. Estimating AUC and AUMC Linear trapezoidal method 0 t 1 t 2 t 3 t last

8. Estimating AUC and AUMC Linear trapezoidal method For samples until the last observed concentration (t 2 <= t last )

9. For the last observed sample and infinity (t 2 = ∞) C last is the last observed conc. at time t last,λ is the slope of the terminal phase of the plasma drug concentration-time profile on a semilog scale (i.e. log(Conc) vs. time) Estimating AUC and AUMC Linear trapezoidal method

10. Estimating Pharmacokinetic Parameters with Moment Analysis A.Clearance: The systemic clearance (Cl) of a drug can be estimated as the intravenous dose (D iv ) divided by the AUC after intravenous bolus administration (AUC iv ):

11. Estimating Pharmacokinetic Parameters with Moment Analysis B.Volume of Distribution at Steady State: The volume of drug distribution at steady state (Vdss) can be estimated as the product of MRT after intravenous bolus injection (MRTiv) and CI:

12. Estimating Pharmacokinetic Parameters with Moment Analysis C.Bioavailability. Bioavailability (F) of a drug generally refers to the fraction of a dose administered via a route other than intravenous injection that reaches the systemic circulation:

13. Estimating Pharmacokinetic Parameters with Moment Analysis D.Mean Residence Time. The mean residence time (MRT) is the average time spent by a single drug molecule in the body before being excreted via elimination processes, regardless of the route of administration. The MRT values after administration by routes other than intravenous bolus injection are always greater than MRTiv

14. Estimating Pharmacokinetic Parameters with Moment Analysis Differences in MRT values following administration via these other routes and MRTiv can be viewed as the average time required for drug molecules to reach the systemic circulation from the site of administration

15. mean absorption time (MAT) The difference between MRT after oral administration (MRT oral ) and MRT iv is the mean absorption time (MAT), representing the average time required for the drug to reach the systemic circulation from the gastrointestinal tract after oral administration:

16. intravenous infusion If the dose of drug is administered by intravenous infusion, the MRT iv may be calculated as: