Create a PK/Indirect response model — pkindirectmodel • Certara.RsNLME

Use to create a PK/Indirect response model.

Usage

pkindirectmodel(
  isPopulation = TRUE,
  parameterization = "Clearance",
  absorption = "Intravenous",
  numCompartments = 1,
  isClosedForm = TRUE,
  isTlag = FALSE,
  hasEliminationComp = FALSE,
  isFractionExcreted = FALSE,
  isSaturating = FALSE,
  infusionAllowed = FALSE,
  isDuration = FALSE,
  isSequential = FALSE,
  isPkFrozen = FALSE,
  hasEffectsCompartment = FALSE,
  indirectType = "LimitedStimulation",
  isBuildup = TRUE,
  isExponent = FALSE,
  indirectFrozen = FALSE,
  data = NULL,
  columnMap = TRUE,
  modelName = "",
  workingDir = "",
  ...
)

Arguments

isPopulation

Is this a population model TRUE or individual model FALSE?

parameterization

Type of parameterization. Options are "Clearance", "Micro", "Macro", or "Macro1".

absorption

Type of absorption. Options are "Intravenous", "FirstOrder", "Gamma", "InverseGaussian", "Weibull" .

numCompartments

Value of either 1, 2, or 3.

isClosedForm

Set to TRUE to convert model from a differential equation to close form.

isTlag

Set to TRUE to add a lag time parameter to the model.

hasEliminationComp

Set to TRUE to add an elimination compartment to the model.

isFractionExcreted

Set to TRUE if elimination compartment (hasEliminationComp = TRUE) contains a fraction excreted parameter.

isSaturating

Set to TRUE to use Michaelis-Menten kinetics for elimination. Only applicable to models with paramteterization = "Clearance"

infusionAllowed

Set to TRUE if infusions allowed.

isDuration

Set to TRUE if infusions use duration instead of rate (must also set infusionAllowed = TRUE).

isSequential

Set to TRUE to freeze PK fixed effects and convert the corresponding random effects into covariates as well as remove the PK observed variable from the model.

isPkFrozen

Set to TRUE to freeze PK fixed effects and remove the corresponding random effects as well as the PK observed variable from the model.

hasEffectsCompartment

Set to TRUE to include an effect compartment into the model.

indirectType

Type of drug actions for the indirect response model. Options are "LimitedStimulation", "InfiniteStimulation", "LimitedInhibition", "InverseInhibition", "LinearStimulation", or "LogLinearStimulation".

isBuildup

Set to FALSE to have the drug actions affect the loss/degradation instead of the production.

isExponent

Set to TRUE to add an exponent parameter to the drug action term.

indirectFrozen

Set to TRUE to freeze PD fixed effects and remove the corresponding random effects as well as the PD observed variable from the model.

data

Input dataset

columnMap

If TRUE (default) column mapping arguments are required. Set to FALSE to manually map columns after defining model using colMapping.

modelName

Model name for subdirectory created for model output in current working directory.

workingDir

Working directory to run the model. Current working directory will be used if workingDir not specified.

...

Arguments passed on to pkindirectmodel_MappingParameters

ID

Column mapping argument for input dataset column(s) that identify individual data profiles. Only applicable to population models isPopulation = TRUE.

Time

Column mapping argument that represents the input dataset column for the relative time used in a study and only applicable to time-based models.

A1

Column mapping argument that represents the input dataset column for the amount of drug administered. Only applicable to the following types of models:

Models with absorption = "Intravenous" and parameterization set to either "Clearance","Micro", or "Macro"
Models with absorption set to either "Gamma", "InverseGaussian", or "Weibull"

Aa

Column mapping argument that represents the input dataset column for the amount of drug administered and only applicable to models with absorption = "FirstOrder".

A

Column mapping argument that represents the input dataset column for the amount of drug administered and only applicable to models with absorption = "Intravenous" and parameterization = "Macro1".

A1_Rate

Column mapping argument that represents the input dataset column for the rate of drug administered. Only applicable to the following types of models:

Models with absorption = "Intravenous", infusionAllowed = TRUE and parameterization set to either "Clearance","Micro" or "Macro"
Models with absorption set to either "Gamma", "InverseGaussian", or "Weibull" and infusionAllowed = TRUE

A1_Duration

Column mapping argument that represents the input dataset column for the duration of drug administered. Only applicable to the following types of models:

Models with absorption = "Intravenous", infusionAllowed = TRUE with isDuration = TRUE and parameterization set to either "Clearance","Micro" or "Macro"
Models with absorption set to either "Gamma", "InverseGaussian", or "Weibull" and infusionAllowed = TRUE with isDuration = TRUE

Aa_Rate

Column mapping argument that represents the input dataset column for the rate of drug administered and only applicable to models with absorption = "FirstOrder", infusionAllowed = TRUE.

Aa_Duration

Column mapping argument that represents the input dataset column for the duration of drug administered and only applicable to models with absorption = "FirstOrder", infusionAllowed = TRUE, and isDuration = TRUE.

A_Rate

Column mapping argument that represents the input dataset column for the rate of drug administered and only applicable to models with absorption = "Intravenous", infusionAllowed = TRUE, and parameterization = "Macro1".

A_Duration

Column mapping argument that represents the input dataset column for the duration of drug administered and only applicable to models with absorption = "Intravenous", infusionAllowed = TRUE, isDuration = TRUE, and parameterization = "Macro1".

A1Strip

Column mapping argument that represents the input dataset column for the stripping dose and only applicable to models with parameterization = "Macro".

CObs

Column mapping argument that represents the input dataset column for the observations of drug concentration in the central compartment and only applicable to models with parameterization being either set to either "Clearance" or "Micro".

C1Obs

A0Obs

Column mapping argument that represents the input dataset column for the observed amount of drug in the elimination compartment. (hasEliminationComp = TRUE).

EObs

Column mapping argument that represents the input dataset column for the observed drug effect.

nV

If isSequential = TRUE, mapped to the input dataset column that lists the values for random effect nV.

nV2

If isSequential = TRUE, mapped to the input dataset column that lists the values for random effect nV2.

nV3

If isSequential = TRUE, mapped to the input dataset column that lists the values for random effect nV3.

nCl

If isSequential = TRUE, mapped to the input dataset column that lists the values for random effect nCl.

nCl2

If isSequential = TRUE, mapped to the input dataset column that lists the values for random effect nCl2.

nCl3

If isSequential = TRUE, mapped to the input dataset column that lists the values for random effect nCl3.

nKa

If isSequential = TRUE, mapped to the input dataset column that lists the values for random effect nKa.

nA

If isSequential = TRUE, mapped to the input dataset column that lists the values for random effect nA.

nAlpha

If isSequential = TRUE, mapped to the input dataset column that lists the values for random effect nAlpha.

nB

If isSequential = TRUE, mapped to the input dataset column that lists the values for random effect nB.

nBeta

If isSequential = TRUE, mapped to the input dataset column that lists the values for random effect nBeta.

nC

If isSequential = TRUE, mapped to the input dataset column that lists the values for random effect nC.

nGamma

If isSequential = TRUE, mapped to the input dataset column that lists the values for random effect nGamma.

nKe

If isSequential = TRUE, mapped to the input dataset column that lists the values for random effect nKe.

nK12

If isSequential = TRUE, mapped to the input dataset column that lists the values for random effect nK12.

nK21

If isSequential = TRUE, mapped to the input dataset column that lists the values for random effect nK21.

nK13

If isSequential = TRUE, mapped to the input dataset column that lists the values for random effect nK13.

nK31

If isSequential = TRUE, mapped to the input dataset column that lists the values for random effect nK31.

nTlag

If isSequential = TRUE, mapped to the input dataset column that lists the values for random effect nTlag.

nKm

If isSequential = TRUE, mapped to the input dataset column that lists the values for random effect nKm.

nVmax

If isSequential = TRUE, mapped to the input dataset column that lists the values for random effect nVmax.

nFe

If isSequential = TRUE and isFractionExcreted = TRUE, mapped to the input dataset column that lists the values for random effect nFe.

nMeanDelayTime

If isSequential = TRUE, mapped to the input dataset column that lists the values for random effect nMeanDelayTime.

nShapeParam

If isSequential = TRUE, mapped to the input dataset column that lists the values for random effect nShapeParam.

nShapeParamMinusOne

If isSequential = TRUE, mapped to the input dataset column that lists the values for random effect nShapeParamMinusOne.

Value

NlmePmlModel object

Column mapping

Note that quoted and unquoted column names are supported. Please see colMapping.

Examples

model <- pkindirectmodel(
  parameterization = "Micro",
  data = pkpdData,
  ID = "ID",
  Time = "Time",
  A1 = "Dose",
  CObs = "CObs",
  EObs = "EObs"
)

# View PML Code
print(model)
#> 
#>  Model Overview 
#>  ------------------------------------------- 
#> Model Name        :  Model_25_04_07_20_40
#> Working Directory :  C:/Users/jcraig/Documents/GitHub/R-RsNLME/docs/reference/Model_25_04_07_20_40
#> Is population     :  TRUE
#> Model Type        :  PK_INDIRECT
#> 
#>  PK 
#>  ------------------------------------------- 
#> Parameterization  :  Micro
#> Absorption        :  Intravenous
#> Num Compartments  :  1
#> Dose Tlag?        :  FALSE
#> Elimination Comp ?:  FALSE
#> Infusion Allowed ?:  FALSE
#> Sequential        :  FALSE
#> Freeze PK         :  FALSE
#> 
#>  Indirect 
#>  ------------------------------------------- 
#> Indirect Type     :  LimitedStimulation
#> Effect Compartment:  FALSE
#> Buildup           :  TRUE
#> Exponent          :  FALSE
#> Indirect Frozen   :  FALSE
#> 
#>  PML 
#>  ------------------------------------------- 
#> test(){
#>     cfMicro(A1, Ke)
#>     dosepoint(A1)
#>     C = A1 / V
#>     deriv(E = Kin * (1 + Emax * C / (C + EC50)) - Kout * E)
#>     sequence{ E= Kin / Kout}
#>     error(EEps=1)
#>     observe(EObs=E + EEps)
#>     error(CEps=0.1)
#>     observe(CObs=C * ( 1 + CEps))
#>     stparm(V = tvV * exp(nV))
#>     stparm(Ke = tvKe * exp(nKe))
#>     stparm(Kin = tvKin * exp(nKin))
#>     stparm(Kout = tvKout * exp(nKout))
#>     stparm(Emax = tvEmax * exp(nEmax))
#>     stparm(EC50 = tvEC50 * exp(nEC50))
#>     fixef( tvV = c(,1,))
#>     fixef( tvKe = c(,1,))
#>     fixef( tvKin = c(,1,))
#>     fixef( tvKout = c(,1,))
#>     fixef( tvEmax = c(,1,))
#>     fixef( tvEC50 = c(,1,))
#>     ranef(diag(nV,nKe,nKin,nKout,nEmax,nEC50) =  c(1,1,1,1,1,1))
#> }
#> 
#>  Structural Parameters 
#>  ------------------------------------------- 
#>  V Ke Kin Kout Emax EC50
#>  ------------------------------------------- 
#> Observations:
#> Observation Name :  EObs
#> Effect Name      :  E
#> Epsilon Name     :  EEps
#> Epsilon Type     :  Additive
#> Epsilon frozen   :  FALSE
#> is BQL           :  FALSE
#>  ------------------------------------------- 
#> Observation Name :  CObs
#> Effect Name      :  C
#> Epsilon Name     :  CEps
#> Epsilon Type     :  Multiplicative
#> Epsilon frozen   :  FALSE
#> is BQL           :  FALSE
#>  ------------------------------------------- 
#>  Column Mappings 
#>  ------------------------------------------- 
#> Model Variable Name : Data Column name
#> id                  : ID
#> time                : Time
#> A1                  : Dose
#> EObs                : EObs
#> CObs                : CObs
#>