## Running individual simulation and retrieving the results

Once the simulation is loaded (see Loading a simulation and accessing entities), it can be run using runSimulations() to produce an object of the class SimulationResults. Keep in mind that runSimulations() produces a list of SimulationResults objects.

library(ospsuite)
#> Loaded Common Language Runtime version 4.0.30319.42000

simFilePath <- system.file("extdata", "Aciclovir.pkml", package = "ospsuite")

simulationResults <- runSimulations(simulations = sim)
# Extract SimulationResults by simulation id
simulationResults <- simulationResults[[sim$id]] print(simulationResults) #> SimulationResults: #> Number of individuals: 1 The advantage of storing the results in a object is the option to keep different results of the same simulation produced with different settings (e.g., model parameters). Simulated time-value pairs for a specific output from the SimulationResults-object can be accessed with the method getOutputValues. The user can provide either the path(s) of the output (which can be a molecule, a parameter, or an observer), or the object(s) of the type Molecule, Parameter, or Quantity (for observers) with the argument quantitiesOrPaths. If no output is specified, all outputs available in the simulation results are returned. The paths of all available outputs can be accessed via simulationResults$allQuantityPaths
#> [1] "Organism|PeripheralVenousBlood|Aciclovir|Plasma (Peripheral Venous Blood)"

getOutputValues returns a list with two entries: data and metadata:

• data is a dataframe with two predefined columns (IndividualId and Time) as well as one column for each requested output

• IndividualId (not relevant for an individual simulation)
• Time a vector with simulated time values (in minutes, equal for all outputs)
• a vector with simulated entries for each output requested.
• metaData is a list containing one entry for each requested output. Each entry contains information pertinent to the output such as its dimension or the unit.

# Get simulated results by path
resultsPath <- simulationResults$allQuantityPaths[[1]] print(resultsPath) #> [1] "Organism|PeripheralVenousBlood|Aciclovir|Plasma (Peripheral Venous Blood)" resultsData <- getOutputValues(simulationResults, quantitiesOrPaths = resultsPath) resultsTime <- resultsData$data$Time resultsValues <- resultsData$data$Organism|PeripheralVenousBlood|Aciclovir|Plasma (Peripheral Venous Blood) plot(resultsTime, resultsValues, type = "l") The results can be stored in and imported from a *.csv file with the methods exportResultsToCSV and importResultsFromCSV. # Load and run the simulation simFilePath <- system.file("extdata", "Aciclovir.pkml", package = "ospsuite") sim <- loadSimulation(simFilePath) # runSimulations returns a list of SimulationResults. For single simulation, # we directly extract the first element, as only one object is created. simulationResults <- runSimulations(simulations = sim)[[1]] # Export to csv csvResultsPath <- system.file("extdata", "SimResults.csv", package = "ospsuite") exportResultsToCSV(results = simulationResults, filePath = csvResultsPath) # Load from csv resultsLoaded <- importResultsFromCSV(filePaths = csvResultsPath, simulation = sim) print(resultsLoaded) #> SimulationResults: #> Number of individuals: 1 ## Running multiple individual simulations and retrieving the results In some cases, the user might want to run multiple simulations in parallel. This can be achieved easily by simply passing a list of simulations to the runSimulations() function. However, only individual simulations (i.e., the population argument must remain empty) are supported. By default, the simulations will be executed in parallel by using up to all cores available on the machine minus 1. So if there are 8 cores and the user simulates 4 simulations, 4 cores will be used. On the other hand, if the user simulates 10 simulations with only 8 cores available, 7 will be used and then the first 3 available. # Load and run multiple simulations concurrently. simFilePath <- system.file("extdata", "Aciclovir.pkml", package = "ospsuite") # We load 3 times the same simulation for convenience. But in real life scenarios, # they should be different simulations sim1 <- loadSimulation(simFilePath) sim2 <- loadSimulation(simFilePath) sim3 <- loadSimulation(simFilePath) simulationResults <- runSimulations(simulations = list(sim1, sim2, sim3)) The results in this case will be a named list of SimulationResults-object , i.e. one for each simulation. The names of the entries of the list are the ids of the simulation to which the results correspond. This way, it is easy to retrieve the correct results for the specific simulation # Get the id of the second simulation id <- sim2$id
print(id)
#> [1] "3ndqCMIt-kS8c5a6I10Z1w"
# get the corresponding result
sim2Results <- simulationResults[[id]]
print(sim2Results$simulation$id)
#> [1] "3ndqCMIt-kS8c5a6I10Z1w"

By default, only outputs that were selected in PK-Sim or MoBi prior to the export of the simulation to pkml are generated. The user can add new outputs to the simulation with the method addOutputs. The outputs can be provided either as objects of the type(s) Molecule, Parameter, or Quantity, or as path strings. The output list is a property of the simulation. After adding or removing outputs, the corresponding simulation needs to be re-run in order to generate updated results.

# Clear the list of generated outputs
clearOutputs(sim)

# Add new outputs as objects
molecule <- getMolecule("Organism|Kidney|Intracellular|Aciclovir", sim)
observer <- getQuantity("Organism|Lumen|Aciclovir|Fraction dissolved", sim)

# Add new outputs as path strings
"Organism|Lumen|Stomach|Aciclovir",
"Organism|PeripheralVenousBlood|Aciclovir|Whole Blood (Peripheral Venous Blood)"
),
simulation = sim
)

# Run simulation
simulationResults <- runSimulations(simulations = sim)

# Retrieve all generated outputs (e.g. omitting the quantitiesOrPaths property
# will return all available values)
resultsData <- getOutputValues(simulationResults)

# Note that "Organism|PeripheralVenousBlood|Aciclovir|Plasma (Peripheral Venous Blood)"
# is not in the list of generated results any more
names(resultsData$data) #> NULL ## Changing simulation intervals The simulation interval (i.e., the simulation times at which results are stored) are stored as the property$outputSchema of a Simulation object.

print(sim$outputSchema) #> OutputSchema: #> Interval: #> Name: Simulation interval high resolution #> Start time: 0.00 [min] #> End time: 15.00 [min] #> Resolution: 1.00 [pts/min] #> Interval: #> Name: Simulation Interval 1 #> Start time: 15.00 [min] #> End time: 1440.00 [min] #> Resolution: 0.33 [pts/min] #> Interval: #> Name: Simulation Interval 2 #> Start time: 120.00 [min] #> End time: 1440.00 [min] #> Resolution: 0.07 [pts/min] To change the simulation interval, the user can use one of the functions clearOutputIntervals(), addOutputInterval(), and setOutputInterval(). # Remove all output intervals - simulation not possible! clearOutputIntervals(simulation = sim) runSimulations(simulations = sim) #> Error in rClr::clrCall(simulationRunner, "Run", simulationRunArgs): Type: OSPSuite.Utility.Exceptions.OSPSuiteException #> Message: Time points output schema is empty #> Method: Void RunSimulation() #> Stack trace: #> at OSPSuite.SimModel.Simulation.RunSimulation() #> at OSPSuite.Core.Domain.Services.SimModelManager.simulate() #> at OSPSuite.Core.Domain.Services.SimModelManager.doIfNotCanceled(Action actionToExecute) #> at OSPSuite.Core.Domain.Services.SimModelManager.RunSimulation(IModelCoreSimulation simulation, SimulationRunOptions simulationRunOptions) #> at OSPSuite.R.Services.SimulationRunner.run(IModelCoreSimulation simulation, SimulationRunOptions simulationRunOptions) #> at OSPSuite.R.Services.SimulationRunner.Run(SimulationRunArgs simulationRunArgs) # Add an interval addOutputInterval(simulation = sim, startTime = 0, endTime = 20, resolution = 60, intervalName = "highRes") print(sim$outputSchema)
#> OutputSchema:
#> Interval:
#>    Name: highRes
#>    Start time: 0.00 [min]
#>    End time: 20.00 [min]
#>    Resolution: 60.00 [pts/min]

print(sim$outputSchema) #> OutputSchema: #> Interval: #> Name: highRes #> Start time: 0.00 [min] #> End time: 20.00 [min] #> Resolution: 60.00 [pts/min] #> Interval: #> Name: lowRes #> Start time: 30.00 [min] #> End time: 2000.00 [min] #> Resolution: 4.00 [pts/min] # Replace the existing interval(s) with a new one setOutputInterval(simulation = sim, startTime = 0, endTime = 2000, resolution = 4) print(sim$outputSchema)