Table 3.
Example of generated population of size 3 in GA for A structural and residual error genes, and B statistical models for random effects
| Model | Absorption | Circulation | Compartments | Elimination | Error |
|---|---|---|---|---|---|
| A | |||||
| 1 | 001 (0 order) | 0 (no) | 10 (3 comp) | 00 (linear) | 01 (proportional) |
| 2 | 011 (1 order) | 0 (no) | 01 (2 comp) | 10 (mixed) | 00 (constant) |
| 3 | 000 (Bolus) | 1 (yes) | 00 (1 comp) | 01 (Michaelis–Menten) | 11 (combined 1) |
| Model | Variability | Correlation | Distribution |
|---|---|---|---|
| B | |||
| 1 | TK0, V, Q2, V3, CL | CL, V, V3 | CL, V, V3, V2: lognormal, TK0: normal |
| 2 | Ka, V, Km and Vm | Ka, V | Ka, Km: lognormal, V, Vm: normal |
| 3 | Not present | NA | NA |
CL clearance, GA genetic algorithm, Ka 1 order absorption, Km and Vm Michaelis–Menten elimination, PK pharmacokinetic Q2 inter-compartmental clearance, TK0 0 order absorption, Tlag lag time, V volume for central compartment, V2 volume for second compartment, V3 volume for third compartment. Parameters depend on the generated structural model