Jump to content


Photo

NCA analysis of sparse data - how to?


  • Please log in to reply
1 reply to this topic

#1 MojDa01

MojDa01

    Member

  • Members
  • PipPip
  • 13 posts

Posted 27 November 2019 - 10:59 AM

Dear All,

 

I have come across an - may be quite an general - issue, namely, how to (best) derive NCA parameters when I have sparse data available?

For instance I have densely sampled drug concentrations available in adults and sparse data in pediatric patients.

 

1.) My initial idea was to build a POP-PK model using all available data from the adults and pediatric patients and then to use individual simulations of the young to derive the PK parameters via NCA from those simulations.To be consistent with the PK parameter derivation via NCA for the adults and pediatrics, I could also use individual simulations for adults and pediatrics alike.

- Does that procedure makes sense to you?

- How do I get the individual simulations in to the NCA analysis within Phoenix?

 

2.) I have now also come across a procedure for obtaining PK parameters of sparsely sampled PK data by empirical bayes estimates (EBE).

- How does the procedure using EBEs work?

- How do I perform the calcualtions using EBEs in Phoenix?

- Which procedure is better - indvidual simulation + NCA  or  PK parameters based on EBEs?

 

Thank you very much in advance and

best regards,

Daniel


  • Simon Davis likes this

#2 smouksassi1

smouksassi1

    Advanced Member

  • Members
  • PipPipPip
  • 169 posts
  • LocationMontreal

Posted 28 November 2019 - 10:53 AM

Hi Daniel,
Reformulating your problem you have sparse data that does not allow individual NCA but you are interested to determine PK parameters of inerest ( e.g. AUC) 

AUC can be derived using pop pk so dont worry about the method that get you the pk parameter you want.
 

A pop PK approach is usually the go to solution. if you are using simple models you can even derive Cmax and AUC using formulas or of course you can simulate if your model does not allow mathematical solutions.

 

you do not necessarily need to run on NCA there is model based integration that can get you AUC for example.

 

when you request a table simulating at a grid of new timepoints the simulated concentration will be based on the individual EBE
  so you get that automatically when you fit a model your individual parameters output are all based on EBE.

 

you question
Which procedure is better - indvidual simulation + NCA  or  PK parameters based on EBEs?
is not applicable.

 

Samer


  • Simon Davis likes this




0 user(s) are reading this topic

0 members, 0 guests, 0 anonymous users