```
using Dates
using Random
using Pumas
using PumasUtilities
Random.seed!(1234)
```

# Generating and Simulating Populations

## 1 Introduction

In this tutorial, we will cover the fundamentals of generating populations to simulate with Pumas. We will demonstrate how to specify dosage regimens and covariates, and then how to piece these together to form a population to simulate.

## 2 The model

Below is a Pumas model that specifies a 1-compartment oral absorption system with between-subject variability on all the parameters. Details of the model specification are provided in the documentation.

```
= @model begin
model @param begin
∈ RealDomain(; lower = 0)
tvcl ∈ RealDomain(; lower = 0)
tvvc ∈ RealDomain(; lower = 0)
tvka ∈ PSDDomain(3)
Ω ∈ RealDomain(; lower = 0)
σ_prop end
@random begin
~ MvNormal(Ω)
η end
@covariates Wt
@pre begin
= tvcl * (Wt / 70)^0.75 * exp(η[1])
CL = tvvc * (Wt / 70) * exp(η[2])
Vc = tvka * exp(η[3])
Ka end
@dynamics begin
' = -Ka * Depot
Depot' = Ka * Depot - Central * CL / Vc
Centralend
@derived begin
= @. Central / Vc
conc ~ @. Normal(conc, conc * σ_prop)
dv end
end
```

```
PumasModel
Parameters: tvcl, tvvc, tvka, Ω, σ_prop
Random effects: η
Covariates: Wt
Dynamical variables: Depot, Central
Derived: conc, dv
Observed: conc, dv
```

## 3 Setting up parameters

Next we provide the initial estimates of the parameters to simulate from. The fixed effects are provided in the typical values `tv`

parameters (`tvcl`

, `tvvc`

, `tvka`

) and the between-subject variability parameters are provided in the Ω matrix as a covariance matrix. So, 0.04 variance on `Ω₁,₁`

suggests a 20% coefficient of variation (\(\sqrt{0.04} = 0.2\)). Similarly, `σ_prop`

has a 20% proportional residual error.

```
=
fixeffs = 0.4, tvvc = 20, tvka = 1.1, Ω = Diagonal([0.04, 0.04, 0.04]), σ_prop = 0.2) (; tvcl
```

```
(tvcl = 0.4,
tvvc = 20,
tvka = 1.1,
Ω = [0.04 0.0 0.0; 0.0 0.04 0.0; 0.0 0.0 0.04],
σ_prop = 0.2,)
```

## 4 Single dose example

`DosageRegimen()`

is the function that lets you construct a dosing regimen. The first argument of the `DosageRegimen`

is `amt`

and is a *positional* argument and not a *keyword* argument. All subsequent arguments need to be named, since they are keyword arguments. Let’s try a simple example where you provide a 100 mg dose at `time = 0`

.

`= DosageRegimen(100; time = 0) ev `

Row | time | cmt | amt | evid | ii | addl | rate | duration | ss | route |
---|---|---|---|---|---|---|---|---|---|---|

Float64 | Int64 | Float64 | Int8 | Float64 | Int64 | Float64 | Float64 | Int8 | NCA.Route | |

1 | 0.0 | 1 | 100.0 | 1 | 0.0 | 0 | 0.0 | 0.0 | 0 | NullRoute |

As you can see above, we provided a single 100 mg dose. `DosageRegimen`

provides some defaults when it creates the dosage regimen dataset, `time = 0`

, `evid = 1`

, `cmt = 1`

, `rate = 0`

, `ii = 0`

and `addl = 0`

.

Note that `ev`

is of type `DosageRegimen`

. Specified like above, `DosageRegimen`

is one of the four fundamental building block of a `Subject`

(more on `Subject`

below).

### 4.1 Building Subjects

Let’s create a single subject

```
= Subject(;
s1 = 1,
id = ev,
events = (; Wt = 70),
covariates = (; dv = nothing),
observations )
```

```
Subject
ID: 1
Events: 1
Observations: dv: (n=0)
Covariates: Wt
```

Note that the `s1`

`Subject`

created above is composed of:

`id`

: an unique identifier`observations`

: observations, represented by a`NamedTuple`

`covariates`

: covariates, represented by a`NamedTuple`

`events`

: events, represented by a`DosageRegimen`

`DataFrame(s1.events)`

Row | time | amt | evid | cmt | rate | duration | ss | ii | route |
---|---|---|---|---|---|---|---|---|---|

Float64 | Float64 | Int8 | Int64 | Float64 | Float64 | Int8 | Float64 | NCA.Route | |

1 | 0.0 | 100.0 | 1 | 1 | 0.0 | 0.0 | 0 | 0.0 | NullRoute |

The events are presented by basic information such as the dose of drug, the time of dose administration, the compartment number for administration, and whether the dose is an instantaneous input or an infusion.

Below is how the covariates are represented, as a `NamedTuple`

.

` s1.covariates`

`Pumas.ConstantCovar{NamedTuple{(:Wt,), Tuple{Int64}}}((Wt = 70,))`

(Note: defining distributions for covariates will be discussed in detail later.)

Using this one subject, `s1`

, let us simulate a simple concentration time profile using the model above, and plot the result:

```
= simobs(model, s1, fixeffs; obstimes = 0:0.1:120)
sims1
sim_plot(
model,
sims1;= [:conc],
observations = (; fontsize = 18),
figure = (; xlabel = "Time (hr)", ylabel = "Predicted Concentration (ng/mL)"),
axis )
```

### 4.2 Building Populations

Now, lets create one more subject, `s2`

.

```
= Subject(;
s2 = 2,
id = ev,
events = (; Wt = 70),
covariates = (; dv = nothing),
observations )
```

```
Subject
ID: 2
Events: 1
Observations: dv: (n=0)
Covariates: Wt
```

If we want to simulate both `s1`

and `s2`

together, we need to bring these subjects together to form a `Population`

. A `Population`

is essentially a collection (vector) of subjects.

`= [s1, s2] twosubjs `

```
Population
Subjects: 2
Covariates: Wt
Observations: dv
```

Let’s see the details of the first and the second subject:

`1] twosubjs[`

```
Subject
ID: 1
Events: 1
Observations: dv: (n=0)
Covariates: Wt
```

`2] twosubjs[`

```
Subject
ID: 2
Events: 1
Observations: dv: (n=0)
Covariates: Wt
```

Now, we can simulate this `Population`

of 2 subjects, and plot the result:

```
= simobs(model, twosubjs, fixeffs; obstimes = 0:0.1:120)
sims2
sim_plot(
model,
sims2;= [:conc],
observations = (; fontsize = 18),
figure = (; xlabel = "Time (hr)", ylabel = "Predicted Concentration (ng/mL)"),
axis )
```

Similarly, we can build a population of any number of subjects. But before we do that, let’s dive into covariate generation.

### 4.3 Covariates

As was discussed earlier, a `Subject`

can also be provided details regarding covariates. In the model above, a continuous covariate body weight `Wt`

impacts both `CL`

and `Vc`

. Let us now specify covariates to a population of 10 subjects:

`choose_covariates() = (; Wt = rand(55:80))`

`choose_covariates (generic function with 1 method)`

`choose_covariates`

will randomly choose a `Wt`

between 55-80 kgs

We can make a vector with covariates for ten subjects through an array comprehension:

`= [choose_covariates() for i = 1:10] cvs `

Now, we add these covariates to the population as below. The `map(f, xs)`

will return the result of `f`

on each element of the collection `xs`

. Let’s map a function that build’s a subject with the randomly chosen covariates in order to build a `Population`

:

```
= map(
pop_with_covariates -> Subject(;
i = i,
id = ev,
events = choose_covariates(),
covariates = (; dv = nothing),
observations
),1:10,
)
```

```
Population
Subjects: 10
Covariates: Wt
Observations: dv
```

Simulate into the population, and visualize the output:

```
= simobs(model, pop_with_covariates, fixeffs; obstimes = 0:0.1:120)
sims3
sim_plot(
model,
sims3;= [:conc],
observations = (; fontsize = 18),
figure = (; xlabel = "Time (hr)", ylabel = "Predicted Concentration (ng/mL)"),
axis )
```

## 5 Multiple dose example

`DosageRegimen`

provides additional controls over the specification of the dosage regimen. For example, `ii`

defines the “interdose interval”, or the time difference between two doses, while `addl`

defines how many additional times to repeat a dose. Thus, let’s define a dose of `100`

that’s repeated `7`

times at `24`

hour intervals:

`= DosageRegimen(100; ii = 24, addl = 6) md `

Row | time | cmt | amt | evid | ii | addl | rate | duration | ss | route |
---|---|---|---|---|---|---|---|---|---|---|

Float64 | Int64 | Float64 | Int8 | Float64 | Int64 | Float64 | Float64 | Int8 | NCA.Route | |

1 | 0.0 | 1 | 100.0 | 1 | 24.0 | 6 | 0.0 | 0.0 | 0 | NullRoute |

Let’s create a new subject, `s3`

with this dosage regimen, and see the results:

```
= Subject(;
s3 = 3,
id = md,
events = (; Wt = 70),
covariates = (; dv = nothing),
observations
)
= simobs(model, s3, fixeffs; obstimes = 0:0.1:240)
sims4
sim_plot(
model,
[sims4];= [:conc],
observations = (; fontsize = 18),
figure = (; xlabel = "Time (hr)", ylabel = "Predicted Concentration (ng/mL)"),
axis )
```

## 6 Combining dosage regimens

We can also combine dosage regimens to build a more complex regimen. Recall from the introduction that using arrays will build the element-wise combinations. Thus, let’s build a dose of `500`

into compartment `1`

at time `0`

, and `7`

doses into compartment `1`

of `100`

spaced by `24`

hours:

`= DosageRegimen([500, 100]; time = [0, 24], cmt = 1, addl = [0, 6], ii = [0, 24]) ldmd `

Row | time | cmt | amt | evid | ii | addl | rate | duration | ss | route |
---|---|---|---|---|---|---|---|---|---|---|

Float64 | Int64 | Float64 | Int8 | Float64 | Int64 | Float64 | Float64 | Int8 | NCA.Route | |

1 | 0.0 | 1 | 500.0 | 1 | 0.0 | 0 | 0.0 | 0.0 | 0 | NullRoute |

2 | 24.0 | 1 | 100.0 | 1 | 24.0 | 6 | 0.0 | 0.0 | 0 | NullRoute |

Let’s see if this result matches our intuition:

```
= Subject(;
s4 = 4,
id = ldmd,
events = (; Wt = 70),
covariates = (; dv = nothing),
observations
)
= simobs(model, s4, fixeffs; obstimes = 0:0.1:120)
sims5
sim_plot(
model,
[sims5];= [:conc],
observations = (; fontsize = 18),
figure = (; xlabel = "Time (hr)", ylabel = "Predicted Concentration (ng/mL)"),
axis )
```

Another way to build complex dosage regiments is to combine previously constructed regimens into a single regimen. For example:

```
= DosageRegimen(500; cmt = 1, time = 0, addl = 0, ii = 0)
e1 = DosageRegimen(100; cmt = 1, time = 24, addl = 6, ii = 24)
e2 = DosageRegimen(e1, e2) evs
```

Row | time | cmt | amt | evid | ii | addl | rate | duration | ss | route |
---|---|---|---|---|---|---|---|---|---|---|

Float64 | Int64 | Float64 | Int8 | Float64 | Int64 | Float64 | Float64 | Int8 | NCA.Route | |

1 | 0.0 | 1 | 500.0 | 1 | 0.0 | 0 | 0.0 | 0.0 | 0 | NullRoute |

2 | 24.0 | 1 | 100.0 | 1 | 24.0 | 6 | 0.0 | 0.0 | 0 | NullRoute |

```
= Subject(;
s5 = 5,
id = evs,
events = (; Wt = 70),
covariates = (; dv = nothing),
observations
)
= simobs(model, s5, fixeffs; obstimes = 0:0.1:120)
sims6
sim_plot(
model,
[sims6];= [:conc],
observations = (; fontsize = 18),
figure = (; xlabel = "Time (hr)", ylabel = "Predicted Concentration (ng/mL)"),
axis )
```

is the same regimen as before.

Putting these ideas together, we can define a `Population`

where individuals with different covariates undergo different regimens, and simulate them all together:

```
= DosageRegimen(100; ii = 24, addl = 6)
e3 = DosageRegimen(50; ii = 12, addl = 13)
e4 = DosageRegimen(200; ii = 24, addl = 2)
e5
= map(
pop1 -> Subject(;
i = i,
id = e3,
events = choose_covariates(),
covariates = (; dv = nothing),
observations
),1:5,
)
= map(
pop2 -> Subject(;
i = i,
id = e4,
events = choose_covariates(),
covariates = (; dv = nothing),
observations
),6:8,
)
= map(
pop3 -> Subject(;
i = i,
id = e5,
events = choose_covariates(),
covariates = (; dv = nothing),
observations
),9:10,
)
= vcat(pop1, pop2, pop3)
pop
= simobs(model, pop, fixeffs; obstimes = 0:0.1:120)
sims7
sim_plot(
model,
sims7;= [:conc],
observations = (; fontsize = 18),
figure = (; xlabel = "Time (hr)", ylabel = "Predicted Concentration (ng/mL)"),
axis )
```

## 7 Defining Infusions

An infusion is a dosage which is defined as having a non-zero positive rate at which the drug enters the system. Let’s define a single infusion dose of total amount `100`

with a rate of `3`

unit amount per time into the compartment `2`

:

`= DosageRegimen(100; rate = 3, cmt = 2) inf `

Row | time | cmt | amt | evid | ii | addl | rate | duration | ss | route |
---|---|---|---|---|---|---|---|---|---|---|

Float64 | Int64 | Float64 | Int8 | Float64 | Int64 | Float64 | Float64 | Int8 | NCA.Route | |

1 | 0.0 | 2 | 100.0 | 1 | 0.0 | 0 | 3.0 | 33.3333 | 0 | NullRoute |

Now let’s simulate a subject undergoing this treatment strategy:

```
= Subject(;
s6 = 5,
id = inf,
events = (; Wt = 70),
covariates = (; dv = nothing),
observations
)
= simobs(model, s6, fixeffs; obstimes = 0:0.1:120)
sims8
sim_plot(
model,
[sims8];= [:conc],
observations = (; fontsize = 18),
figure = (; xlabel = "Time (hr)", ylabel = "Predicted Concentration (ng/mL)"),
axis )
```

## 8 Final Note on Julia Programming

Note that all of these functions are standard Julia functions, and thus standard Julia programming constructions can be utilized to simplify the construction of large populations. We already demonstrated the use of `map`

and array comprehension, but we can also make use of constructs like `for`

loops and `if-else`

statements.

## 9 Conclusion

This tutorial shows the tools for generating populations of infinite complexity, defining covariates and dosage regimens on the fly and simulating the results of the model.