# PK model with transit compartments for absorption

I am new to Pumas and modeling - killer combo! I have seen some recent posts on LinkedIn about you, so I thought I could ask for your advice. Not sure if this is the right way to approach this forum. I guess other experts can weigh in as well. I wanted to check if you can confirm is the following model syntax looks right. My model has a one compartment disposition, a slow absorption. Tlag was deemed to inappropriate so I was trying a transit compartment absorption model.

#### My code begins here

``````ka_transit2_1cmt = @model begin
@param begin
tvmtt          ∈ RealDomain(lower=5, upper=100.0)
tvcl              ∈ RealDomain(lower=0.001)
tvvc             ∈ RealDomain(lower=0.001)
end

@pre begin
CL            = tvcl
Vc            = tvvc
ktr           = 1/tvmtt
end

@dynamics begin
Depot'         = -ktr * Depot_slow
Transit1'     = ktr * (Depot_slow - Transit1)
Transit2'     = ktr * (Transit1 - Transit2)
Central'      = ktr * Transit2 - CL/Vc * Central
end

@derived begin
cp            = @. (Central/Vc)
end
end
``````

#### My code ends here

hi Bob,

Welcome to the community and we are glad to have you on board!

You can take a look at some of the absorption model examples here Absorption models

Just looking at the code (on my phone), it seems correct what you have coded up there.

Best,
Vijay

Dr Vijay - Thank you very much. May be you or some other expert can help me with understanding the statistical implications of my parameterization - ktr vs mtt. Would there be any preference in adding a between-subject variability on one over the other? Typically kinetic models are specified from first principles in rates and rate-constants; and not in terms of time (except for tlag). Would the community recommend specifying the BSV on ktr; and derive mtt from the reciprocal individual estimate of ktr? Or directly specify the variance on mtt? Hope I am not asking a very silly question. I do not want to take undue advantage of you; so if you think I should write to another expert, please suggest.
Bob

Feel free to ask away. This is an open inclusive community for everyone to learn and contribute

Not sure if there is a preference one way or the other. `MTT` seems like a parameter that has a better interpretation than `ktr` in my opinion. I would add BSV there. However, these are just a play of numerical arrangements at the end of the day. We should use something that helps us articulate our results better.

I absolutely agree with @vijay. One thing you could do is try it both ways (BSV on ktr and BSV on MTT) and see if one of the 2 models seems better (e.g. lower OFV), or the distribution of the random effects looks more normal or one or the other parameter (maybe look at QQ-plots). Just a suggestion.