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Calculate derived parameters for the 1-, 2-, and 3- compartment linear models.

Source: R/utils.R
rxDerived.Rd

This calculates the derived parameters based on what is provided in a data frame or arguments

Usage

rxDerived(..., verbose = FALSE, digits = 0)

Arguments

...

The input can be:

  • A data frame with PK parameters in it; This should ideally be a data frame with one pk parameter per row since it will output a data frame with one PK parameter per row.

  • PK parameters as either a vector or a scalar

verbose

boolean that when TRUE provides a message about the detected pk parameters and the detected compartmental model. By default this is FALSE.

digits

represents the number of significant digits for the output; If the number is zero or below (default), do not round.

Value

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)

References

Shafer S. L. CONVERT.XLS

Rowland M, Tozer TN. Clinical Pharmacokinetics and Pharmacodynamics: Concepts and Applications (4th). Clipping Williams & Wilkins, Philadelphia, 2010.

Author

Matthew Fidler and documentation from Justin Wilkins, justin.wilkins@occams.com

Examples


## Note that rxode2 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)