Ex. 2 L4: Noncompartmental Analysis Flashcards
Noncompartmental analysis
Do not look at compartments; just try to obtain information from the concentration vs time profile
Noncompartmental variables
AUC, AUMC, MRT, CL, Vdss
Clearance and compartmental analysis
Clearance for compartmental and noncompartmental are the same
Mean Residence time (MRT)
Drug X (MW = 300) 1 mg admin as IV bolus
of molecules in 1 mg:
0.001g/(300g/1 mole) * (6.023*10^23)molecules/1 mole =
2x10^18 molecules
MRT of drug X would mean the average time one drug molecule spends in the body from dosing to elimination
t * C is called
the first-moment of the concentration
AUMC
Area under the moment curve
MRT =
AUMC/AUC
ti =
the time ith molecule spends in the body
n =
Total number of molecules
When looking at a curve, always use (early/late) phase to estimate K
Late
After IV admin, AUC and AUMC can be
estimated regardless of peak shapes
MRT and CL can be estimated
Can we simplify two compartment to one compartment?
Major phase concept
What would be average drug concentration at steady state
Superstition principle
Major phase concept, 2 compartment model
A/alpha + B/Beta
Superstition principle
After multiple doses, AUCss,0-t = AUCsingle dose, 0-infinity
The last AUC is the sum of all of the other AUCs, = each other
Cssave
Concentration that gives the same drug exposure as AUCss,0-tau when maintained for tau
MRT approaches for its estimation
After IV admin,
Estimation from elimination rate constant, K
(combination of compartmental and noncompartmental analyses
MRT = 1/Kel
Vdss
Steady state volume of distribution of a drug
The volume of distribution observed after the drug has distributed into the tissues
Vdss= MRT * CL
Noncompartmental summary
Pharmacokinetic analysis performed w/o assuming compartmentalized distribution of drugs in body
Provides descriptive knowledge about drugs
Non compartmental vs compartmental efficacy
Each approach is useful and limited
Noncompartmental characteristics
Minimal number of assumptions (minimizes bias)
Will work for many types of data (descriptive)
Can compare across data sets/drugs
Limited in information about secondary processes
No assumptions about distribution into body or segmented processes in the body (holistic)
Poor relating to specific organ function
Sometimes, favored approach due to lack of assumptions
Compartmental characteristics
Fits data for a specific case
Better accounts for biological processes
Provides better insight into “fate of drug”
Driven by data and by assumptions
Capability to predict concentrations for certain time point
