The acceptance models used to validate pkident. Each has an expected result fixed from a published table or an analytical derivation before any identifiability code was written (see the package's validation record).
Details
reference_models() lists them; reference_model() builds one as a
pkpd_model() with the expected result attached as attribute expected.
References
Janzén DLI, et al. Parameter identifiability of fundamental pharmacodynamic models. Front Physiol. 2016;7:590. doi:10.3389/fphys.2016.00590
Cheung SYA, Yates JWT, Aarons L. The design and analysis of parallel experiments to produce structurally identifiable models. J Pharmacokinet Pharmacodyn. 2013;40:93-100. doi:10.1007/s10928-012-9291-z
Examples
reference_models()
#> id
#> 1 A1
#> 2 A2
#> 3 A3
#> 4 A4
#> 5 A5
#> 6 A6
#> 7 A7
#> 8 A8
#> 9 A9
#> 10 A10
#> 11 A10_Rtot_known
#> title
#> 1 One-compartment IV bolus
#> 2 One-compartment oral, bioavailability unknown
#> 3 One-compartment oral, bioavailability known
#> 4 Two-compartment IV bolus, central compartment observed
#> 5 One-compartment Michaelis-Menten elimination, IV bolus
#> 6 One-compartment parallel linear and Michaelis-Menten elimination, IV bolus
#> 7 Direct effect, steady-state receptor binding, linear transduction (known Cp)
#> 8 Indirect stimulation of production, steady-state receptor binding (known Cp)
#> 9 Effect compartment, steady-state receptor binding, linear transduction (known Cp)
#> 10 Effect compartment, dynamic receptor binding, linear transduction (known Cp)
#> 11 As A10 with total receptors known (the paper's worked example)
#> source
#> 1 Analytical derivation
#> 2 Janzen et al. 2016 (citing Cheung et al. 2013); analytical derivation
#> 3 Analytical derivation
#> 4 Analytical derivation
#> 5 Analytical derivation
#> 6 Analytical derivation
#> 7 Janzen et al. 2016, Model 1
#> 8 Janzen et al. 2016, Model 3 (baseline kin/kout)
#> 9 Janzen et al. 2016, Model 9
#> 10 Janzen et al. 2016, Model 13
#> 11 Janzen et al. 2016, Model 13, equations 6-8
reference_model("A2")
#> <pkident_model>
#> States: depot, central
#> Outputs: cp
#> Parameters: CL, F, V, ka
#> Known: DOSE
#> d/dt(depot) = -ka*depot; depot(0) = F*DOSE
#> d/dt(central) = ka*depot - CL*central/V; central(0) = 0
#> cp = central/V