R/rxLinCmt.R
rxDerived.Rd
This calculates the derived parameters based on what is provided in a data frame or arguments
rxDerived(..., verbose = FALSE, digits = 0)
... | The input can be:
|
---|---|
verbose | boolean that when TRUE provides a message about the detected pk parameters
and the detected compartmental model. By default this is |
digits | represents the number of significant digits for the output; If the number is zero or below (default), do not round. |
Return a data.frame of derived PK parameters for a 1-, 2-, or 3-compartment linear model given provided clearances and volumes based on the inferred model type.
The model parameters that will be provided in the data frame are:
vc
: Central Volume (for 1-, 2- and 3-
compartment models)
kel
: First-order elimination rate (for 1-, 2-, and
3-compartment models)
k12
: First-order rate of transfer from central to
first peripheral compartment; (for 2- and 3-compartment models)
k21
: First-order rate of transfer from first
peripheral to central compartment, (for 2- and 3-compartment
models)
k13
: First-order rate of transfer from central to
second peripheral compartment; (3-compartment model)
k31
: First-order rate of transfer from second
peripheral to central compartment (3-compartment model)
vp
: Peripheral Volume (for 2- and 3- compartment models)
vp2
: Peripheral Volume for 3rd compartment (3- compartment model)
vss
: Volume of distribution at steady state; (1-, 2-, and 3-compartment models)
t12alpha
: \(t_{1/2,\alpha}\); (1-, 2-, and 3-compartment models)
t12beta
: \(t_{1/2,\beta}\); (2- and 3-compartment models)
t12gamma
: \(t_{1/2,\gamma}\); (3-compartment model)
alpha
: \(\alpha\); (1-, 2-, and 3-compartment models)
beta
: \(\beta\); (2- and 3-compartment models)
gamma
: \(\beta\); (3-compartment model)
A
: true A
; (1-, 2-, and 3-compartment models)
B
: true B
; (2- and 3-compartment models)
C
: true C
; (3-compartment model)
fracA
: fractional A; (1-, 2-, and 3-compartment models)
fracB
: fractional B; (2- and 3-compartment models)
fracC
: fractional C; (3-compartment model)
Shafer S. L. CONVERT.XLS
Rowland M, Tozer TN. Clinical Pharmacokinetics and Pharmacodynamics: Concepts and Applications (4th). Clipping Williams & Wilkins, Philadelphia, 2010.
Matthew Fidler and documentation from Justin Wilkins, justin.wilkins@occams.com
## Note that RxODE parses the names to figure out the best PK parameter
params <- rxDerived(cl = 29.4, v = 23.4, Vp = 114, vp2 = 4614, q = 270, q2 = 73)
## That is why this gives the same results as the value before
params <- rxDerived(CL = 29.4, V1 = 23.4, V2 = 114, V3 = 4614, Q2 = 270, Q3 = 73)
## You may also use micro-constants alpha/beta etc.
params <- rxDerived(k12 = 0.1, k21 = 0.2, k13 = 0.3, k31 = 0.4, kel = 10, v = 10)
## or you can mix vectors and scalars
params <- rxDerived(CL = 29.4, V = 1:3)
## If you want, you can round to a number of significant digits
## with the `digits` argument:
params <- rxDerived(CL = 29.4, V = 1:3, digits = 2)