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Calculates volume of distribution from concentration data

Source: R/run_single_point_base.R
calculate_vd.Rd

Calculates the volume of distribution (Vd) using an adaptive single-point approach

Usage

calculate_vd(
  dat,
  half_life = NULL,
  dose_type = NULL,
  pooled_ctrl = pooled_control(),
  route = c("bolus", "oral", "infusion")
)

Arguments

dat

A data frame containing raw time–concentration data in the standard nlmixr2 format.

half_life

Optional numeric value for the drug's half-life. If not provided, it will be estimated using get_hf() from pooled observations.

dose_type

Specifies the dosing context of the pharmacokinetic observations. Required when half_life is not provided. Classified as first_dose, repeated_doses, or combined_doses based on whether observed concentrations occur following the first administration, during repeated dosing, or across both contexts.

pooled_ctrl

Optional list of control parameters used by get_pooled_data() for pooling observations. Defaults to output from pooled_control().

route

Character string specifying the route of administration. Must be one of bolus, oral, or infusion. Currently, oral is not implemented.

Value

A list with two elements:

  • vd_df: individual volume of distribution estimates

  • trimmed_mean_vd: population volume of distribution estimated as a trimmed geometric mean using a 5 percent trimming level

Details

The function uses a concentration observed within the first 20% of the elimination half-life after dosing as the early point for estimating the volume of distribution.

$$Vd = \frac{\text{Dose}}{C_0}$$ For infusion: $$Vd = \frac{\text{Rate} \times \min(\text{TIME}, \text{durationobs})}{C_0}$$

Here, \(C_0\) represents the early concentration observed within the first 20% of the elimination half-life after dosing, which is used as an approximation of the initial concentration for estimating volume of distribution (Vd). TIME refers to time after dose; durationobs is the actual infusion duration.

When half_life is not provided, it is estimated from pooled data using the functions get_pooled_data() and get_hf().

See also

get_pooled_data, get_hf, trimmed_geom_mean

Author

Zhonghui Huang

Examples


dat <- Bolus_1CPT
out <- processData(dat)
#> 
#> 
#> Infometrics                               Value          
#> ----------------------------------------  ---------------
#> Dose Route                                bolus          
#> Dose Type                                 combined_doses 
#> Number of Subjects                        120            
#> Number of Observations                    6951           
#> Subjects with First-Dose Interval Data    120            
#> Observations in the First-Dose Interval   2276           
#> Subjects with Multiple-Dose Data          120            
#> Observations after Multiple Doses         4675           
#> ----------------------------------------  ------
fdat<- out$dat
froute <-out$Datainfo$Value[out$Datainfo$Infometrics == "Dose Route"]
half_life <- get_hf(dat = fdat)$half_life_median
#> Estimating half-life....................
#> Half-life estimation complete: Estimated t1/2 = 11 h
calculate_vd(dat = fdat, half_life = half_life,route=froute)$trimmed_mean_vd
#> [1] 67.57943