- Introduction
- Intravenous bolus injection
- Intravenous infusion
- Oral administration
- Using different parametrizations

**Objectives:** learn how to define and use a PK model for single route of administration.

**Projects:** bolusLinear_project, bolusMM_project, bolusMixed_project, infusion_project, oral1_project, oral0_project, sequentialOral0Oral1_project, simultaneousOral0Oral1_project, oralAlpha_project, oralTransitComp_project

## Introduction

Once a drug is administered, we usually describe subsequent processes within the organism by the pharmacokinetics (PK) process known as *ADME*: absorption, distribution, metabolism, excretion. A PK model is a dynamical system mathematically represented by a system of *ordinary differential equations* (ODEs) which describes transfers between compartments and elimination from the central compartment.

`Mlxtran`

is remarkably efficient for implementing simple and complex PK models:

- The function
`pkmodel`

can be used for standard PK models. The model is defined according to the provided set of named arguments. The`pkmodel`

function enables different parametrizations, different models of absorption, distribution and elimination, defined here and summarized in the following.. - PK macros define the different components of a compartmental model. Combining such PK components provide a high degree of flexibility for complex PK models. They can also extend a custom ODE system.
- A system of ordinary differential equations (ODEs) can be implemented very easily.

It is also important to highlight the fact that the data file used by `Monolix`

for PK modelling only contains information about dosing, i.e. how and when the drug is administrated. There is no need to integrate in the data file any information related to the PK model. This is an important remark since it means that any (complex) PK model can be used with the same data file. In particular, we make a clear distinction between administration (related to the data) and absorption (related to the model).

### The pkmodel function

The PK model is defined by the names of the input parameters of the `pkmodel`

function. These names are **reserved keywords**.

Absorption

`p`: Fraction of dose which is absorbed`ka`: absorption constant rate (first order absorption)- or,
`Tk0`: absorption duration (zero order absorption) `Tlag`: lag time before absorption- or,
`Mtt, Ktr`: mean transit time & transit rate constant

Distribution

`V`: Volume of distribution of the central compartment`k12, k21`: Transfer rate constants between compartments 1 (central) & 2 (peripheral)- or
`V2, Q2`: Volume of compartment 2 (peripheral) & inter compartment clearance, between compartments 1 and 2, `k13, k31`: Transfer rate constants between compartments 1 (central) & 3 (peripheral)- or
`V3, Q3`: Volume of compartment 3 (peripheral) & inter compartment clearance, between compartments 1 and 3.

Elimination

`k`: Elimination rate constant- or
`Cl`: Clearance `Vm, Km`: Michaelis Menten elimination parameters

Effect compartment

`ke0`: Effect compartment transfer rate constant

## Intravenous bolus injection

### Linear elimination

**bolusLinear_project**

A single iv bolus is administered at time 0 to each patient. The data file `bolus1_data.txt` contains 4 columns: id, time, amt (the amount of drug in mg) and y (the measured concentration). The names of these columns are recognized as keywords by `Monolix`

:

It is important to note that, in this data file, a row contains either some information about the dose (in which case `y = "."`) or a measurement (in which case `amt = "."`). We could equivalently use the data file `bolus2_data.txt` which contains 2 additional columns: `EVID` (in the green frame) and `IGNORED OBSERVATION (in the blue frame):`

Here, the EVENT ID column allows the identification of an event. It is an integer between 0 and 4. It helps to define the type of line. `EVID=1` means that this record describes a dose while `EVID=0` means that this record contains an observed value.

On the other hand, the IGNORED OBSERVATION column enables to tag lines for which the information in the OBSERVATION column-type is missing. `MDV=1` means that the observed value of this record should be ignored while `MDV=0` means that this record contains an observed value. The two data files `bolus1_data.txt` and `bolus2_data.txt` contain exactly the same information and provide exactly the same results. A one compartment model with linear elimination is used with this project:

$$\begin{array}{ccl} \frac{dA_c}{dt} &=& – k A_c(t) \\ A_c(t) &= &0 ~~\text{for}~~ t<0 \end{array} $$

Here, \(A_c(t)\) and \(C_c(t)=A_c(t)/V\) are, respectively, the amount and the concentration of drug in the central compartment at time t. When a dose **D** arrives in the central compartment at time \(\tau\), an iv bolus administration assumes that

$$A_c(\tau^+) = A_c(\tau^-) + D$$

where \(A_c(\tau^-)\) (resp. \(A_c(\tau^+)\)) is the amount of drug in the central compartment just before (resp. after) \(\tau\) Parameters of this model are V and k. We therefore use the model `bolus_1cpt_Vk` from the `Monolix`

PK library:

[LONGITUDINAL] input = {V, k} EQUATION: Cc = pkmodel(V, k) OUTPUT: output = Cc

We could equivalently use the model `bolusLinearMacro.txt` (click on the button `Model`

and select the new PK model in the library `6.PK_models/model`)

[LONGITUDINAL] input = {V, k} PK: compartment(cmt=1, amount=Ac) iv(cmt=1) elimination(cmt=1, k) Cc = Ac/V OUTPUT: output = Cc

These two implementations generate exactly the same C++ code and then provide exactly the same results. Here, the ODE system is linear and `Monolix`

uses its analytical solution. Of course, it is also possible (but not recommended with this model) to use the ODE based PK model `bolusLinearODE.txt` :

[LONGITUDINAL] input = {V, k} PK: depot(target = Ac) EQUATION: ddt_Ac = - k*Ac Cc = Ac/V OUTPUT: output = Cc

Results obtained with this model are slightly different from the ones obtained with the previous implementations since a numeric scheme is used here for solving the ODE. Moreover, the computation time is longer (between 3 and 4 time longer in that case) when using the ODE compared to the analytical solution.

Individual fits obtained with this model look nice

but the VPC show some misspecification in the elimination process:

### Michaelis Menten elimination

**bolusMM_project**

A non linear elimination is used with this project:

$$\frac{dA_c}{dt} = – \frac{ V_m \, A_c(t)}{V\, K_m + A_c(t) }$$

This model is available in the `Monolix`

PK library as `bolus_1cpt_VVmKm`:

[LONGITUDINAL] input = {V, Vm, Km} PK: Cc = pkmodel(V, Vm, Km) OUTPUT: output = Cc

Instead of this model, we could equivalently use PK macros with `bolusNonLinearMacro.txt` from the library `6.PK_models/model`:

[LONGITUDINAL] input = {V, Vm, Km} PK: compartment(cmt=1, amount=Ac, volume=V) iv(cmt=1) elimination(cmt=1, Vm, Km) Cc = Ac/V OUTPUT: output = Cc

or an ODE with `bolusNonLinearODE`:

[LONGITUDINAL] input = {V, Vm, Km} PK: depot(target = Ac) EQUATION: ddt_Ac = -Vm*Ac/(V*Km+Ac) Cc=Ac/V OUTPUT: output = Cc

Results obtained with these three implementations are identical since no analytical solution is available for this non linear ODE. We can then check that this PK model seems to describe much better the elimination process of the data:

### Mixed elimination

**bolusMixed_project**

THe `Monolix`

PK library contains “standard” PK models. More complex models should be implemented by the user in a model file. For instance, we assume in this project that the elimination process is a combination of linear and nonlinear elimination processes:

$$ \frac{dA_c}{dt} = -\frac{ V_m A_c(t)}{V K_m + A_c(t) } – k A_c(t) $$

This model is not available in the `Monolix`

PK library. It is implemented in `bolusMixed.txt`:

[LONGITUDINAL] input = {V, k, Vm, Km} PK: depot(target = Ac) EQUATION: ddt_Ac = -Vm*Ac/(V*Km+Ac) - k*Ac Cc=Ac/V OUTPUT: output = Cc

This model, with a combined error model, seems to describe very well the data:

## Intravenous infusion

**infusion_project**

Intravenous infusion assumes that the drug is administrated intravenously with a constant rate (*infusion rate*), during a given time (*infusion time*). Since the amount is the product of infusion rate and infusion time, an additional column `INFUSION RATE` or INFUSION DURATION is required in the data file: `Monolix`

can use both indifferently. Data file `infusion_rate_data.txt` has an additional column rate:

It can be replaced by `infusion_tinf_data.txt` which contains exactly the same information:

We use with this project a 2 compartment model with non linear elimination and parameters , , , , :

$$\begin{aligned} k_{12} &= Q/V_1 \\ k_{21} &= Q/V_2 \\\frac{dA_c}{dt} & = k_{21} \, Ap(t) – k_{12} \, Ac(t)- \frac{ V_m \, A_c(t)}{V_1\, K_m + A_c(t) } \\ \frac{dA_p}{dt} & = – k_{21} \, Ap(t) + k_{12} \, Ac(t) \\ Cc(t) &= \frac{Ac(t)}{V_1} \end{aligned}$$

This model is available in the `Monolix`

PK library as `infusion_2cpt_V1QV2VmKm`:

[LONGITUDINAL] input = {V1, Q, V2, Vm, Km} PK: V = V1 k12 = Q/V1 k21 = Q/V2 Cc = pkmodel(V, k12, k21, Vm, Km) OUTPUT: output = Cc

## Oral administration

### first-order absorption

**oral1_project**

This project uses the data file `oral_data.txt`. For each patient, information about dosing is the time of administration and the amount. A one compartment model with first order absorption and linear elimination is used with this project. Parameters of the model are ka, V and Cl. we will then use model `oral1_kaVCl.txt` from the `Monolix`

PK library

[LONGITUDINAL] input = {ka, V, Cl} EQUATION: Cc = pkmodel(ka, V, Cl) OUTPUT: output = Cc

Both the individual fits and the VPCs show that this model doesn’t describe the absorption process properly.

Many options for implementing this PK model with `Mlxtran`

exists:

– using PK macros: `oralMacro.txt`:

[LONGITUDINAL] input = {ka, V, Cl} PK: compartment(cmt=1, amount=Ac) oral(cmt=1, ka) elimination(cmt=1, k=Cl/V) Cc=Ac/V OUTPUT: output = Cc

– using a system of two ODEs as in `oralODEb.txt`:

[LONGITUDINAL] input = {ka, V, Cl} PK: depot(target=Ad) EQUATION: k = Cl/V ddt_Ad = -ka*Ad ddt_Ac = ka*Ad - k*Ac Cc = Ac/V OUTPUT: output = Cc

– combining PK macros and ODE as in `oralMacroODE.txt` (macros are used for the absorption and ODE for the elimination):

[LONGITUDINAL] input = {ka, V, Cl} PK: compartment(cmt=1, amount=Ac) oral(cmt=1, ka) EQUATION: k = Cl/V ddt_Ac = - k*Ac Cc = Ac/V OUTPUT: output = Cc

– or equivalently, as in `oralODEa.txt`:

[LONGITUDINAL] input = {ka, V, Cl} PK: depot(target=Ac, ka) EQUATION: k = Cl/V ddt_Ac = - k*Ac Cc = Ac/V< OUTPUT: output = Cc

**Remark:** Models using the pkmodel function or PK macros only use an analytical solution of the ODE system.

### zero-order absorption

**oral0_project**

A one compartment model with zero order absorption and linear elimination is used to fit the same PK data with this project. Parameters of the model are Tk0, V and Cl. We will then use model `oral0_1cpt_Tk0Vk.txt` from the `Monolix`

PK library

[LONGITUDINAL] input = {Tk0, V, Cl} EQUATION: Cc = pkmodel(Tk0, V, Cl) OUTPUT: output = Cc

**Remark 1:** implementing a zero-order absorption process using ODEs is not easy… on the other hand, it becomes extremely easy to implement using either the `pkmodel `function or the PK macro `oral(Tk0)`.

**Remark 2:** The duration of a zero-order absorption has nothing to do with an infusion time: it is a parameter of the PK model (exactly as the absorption rate constant ka for instance), it is not part of the data.

### sequential zero-order first-order absorption

**sequentialOral0Oral1_project**

More complex PK models can be implemented using `Mlxtran`

. A sequential zero-order first-order absorption process assumes that a fraction `Fr` of the dose is first absorbed during a time `Tk0` with a zero-order process, then, the remaining fraction is absorbed with a first-order process. This model is implemented in `sequentialOral0Oral1.txt` using PK macros:

[LONGITUDINAL] input = {Fr, Tk0, ka, V, Cl} PK: compartment(amount=Ac) absorption(Tk0, p=Fr) absorption(ka, Tlag=Tk0, p=1-Fr) elimination(k=Cl/V) Cc=Ac/V OUTPUT: output = Cc

Both the individual fits and the VPCs show that this PK model describes very well the whole ADME process for the same PK data:

### simultaneous zero-order first-order absorption

**simultaneousOral0Oral1_project**

A simultaneous zero-order first-order absorption process assumes that a fraction `Fr` of the dose is absorbed with a zero-order process while the remaining fraction is absorbed simultaneously with a first-order process. This model is implemented in `simultaneousOral0Oral1.txt` using PK macros:

[LONGITUDINAL] input = {Fr, Tk0, ka, V, Cl} PK: compartment(amount=Ac) absorption(Tk0, p=Fr) absorption(ka, p=1-Fr) elimination(k=Cl/V) Cc=Ac/V OUTPUT: output = Cc

### alpha-order absorption

**oralAlpha_project**

An -order absorption process assumes that the rate of absorption is proportional to some power of the amount of drug in the depot compartment:

This model is implemented in `oralAlpha.txt` using ODEs:

[LONGITUDINAL] input = {r, alpha, V, Cl} PK: depot(target = Ad) EQUATION: dAd = Ad^alpha ddt_Ad = -r*dAd ddt_Ac = r*Ad - (Cl/V)*Ac Cc = Ac/V OUTPUT: output = Cc

### transit compartment model

**oralTransitComp_project**

A PK model with a transit compartment of transit rate Ktr and mean transit time Mtt can be implemented using the PK macro `oral(ka, Mtt, Ktr)`, or using the `pkmodel` function, as in `oralTransitComp.txt`:

[LONGITUDINAL] input = {Mtt, Ktr, ka, V, Cl} EQUATION: Cc = pkmodel(Mtt, Ktr, ka, V, Cl) OUTPUT: output = Cc

## Using different parametrizations

The PK macros and the function `pkmodel` use some preferred parametrizations and some reserved names as input arguments: `Tlag, ka, Tk0, V, Cl, k12, k21`. It is however possible to use another parametrization and/or other parameter names. As an example, consider a 2-compartment model for oral administration with a lag, a first order absorption and a linear elimination. We can use the `pkmodel` function with, for instance, parameters ka, V, k, k12 and k21:

[LONGITUDINAL] input = {ka, V, k, k12, k21} PK: Cc = pkmodel(ka, V, k, k12, k21) OUTPUT: output = Cc

Imagine now that we want *i)* to use the clearance instead of the elimination rate constant , *ii)* to use capital letters for the parameter names. We can still use the `pkmodel` function as follows:

[LONGITUDINAL] input = {KA, V, CL, K12, K21} PK: Cc = pkmodel(ka=KA, V, k=CL/V, k12=K12, k21=K21) OUTPUT: output = Cc