Bernard,

An alternative approach would be consider hair "concentrations" accumulating in an output compartment. Each time the hair is cut you "empty" the compartment and reset it to accumulate until the next hair cut. This is similar to using an output compartment to predict amounts in urine.

NONMEM has a convenient way to obtain a prediction of the amount output from a compartment (such as plasma or the end of transit chain). You just need to estimate an output fraction to explain the actual amount you measure.

Best wishes,

Nick

 +--------------------------------------------------------------------+
 |                                                                    |
 |                     OUTPUT FRACTION PARAMETER                      |
 |                                                                    |
 +--------------------------------------------------------------------+

 MEANING: Output fraction (F0) parameter for PREDPP
 CONTEXT: Additional PK Parameters

 USAGE:
 $PK
  F0= ....

 DISCUSSION:

 The  output fraction parameter is used with PREDPP.  It is an optional
 additional PK parameter.  With NM-TRAN, it are symbolized in  the  $PK
 block  by  any one of the reserved variables FO, F0, or Fn, where n is
 the compartment number of the output compartment.

 With any of the kinetic models a (peripheral)  output  compartment  is
 always  present.  Associated  with this compartment is a PK parameter,
 the output fraction , denoted here by Fo. Of the entire amount, Ao, of
 drug  introduced  into  the system by various dosage patterns and then
 eliminated from the system during a given time interval, a fraction Fo
 of
  Ao goes into this output compartment.

 If  the output compartment is never turned on, the output fraction can
 be ignored. If the value of the output fraction is not computed in PK,
 it is always understood to be 1

 The  use  of  Fo  depends on the assumption that the rate of change of
 drug amount in the output compartment is linear in the other  compart-
 ment amounts. Other than this linearity restriction, the system can be
 nonlinear.


On 12/06/2015 3:54 p.m., Leonid Gibiansky wrote:
Dear Bernard,

This looks like really interesting problem. Based on the idea that it should be a long delay, I would start with the transit compartment model (you can google for the references on this type of models) with the input from the plasma compartment. The last compartment will represent a barber shop. The number of transit compartment can be increased until you get a sufficiently long delay. Observation compartment can be either the last one, or the sum of several, depending on how measurements are done (at a particular hair length, or by grinding the hair together before measurement). Depending on whether hair can eliminate the drug (or it happens only in the barber shop), hair clearance can be assigned to all or only to the last of those transit compartments.

It could be that a simple effect compartment model with a very slow ke0 could describe it as well but you should be able to see it by increasing or decreasing the number of transit compartments.

Regards,
Leonid



--------------------------------------
Leonid Gibiansky, Ph.D.
President, QuantPharm LLC
web:    www.quantpharm.com
e-mail: LGibiansky at quantpharm.com
tel:    (301) 767 5566



On 6/12/2015 8:56 AM, Bernard Ngara wrote:
Dear all

I am a working on a study that measures both short and long term
exposure to drug using plasma and hair drug concentration. What
methods can I use to model hair drug concentration. You can give
references so that I can read.

Thanks once again.

Regards


--
Nick Holford, Professor Clinical Pharmacology
Dept Pharmacology & Clinical Pharmacology, Bldg 503 Room 302A
University of Auckland,85 Park Rd,Private Bag 92019,Auckland,New Zealand
office:+64(9)923-6730 mobile:NZ+64(21)46 23 53 FR+33(7)80 48 55 50
email: [email protected]
http://holford.fmhs.auckland.ac.nz/

Holford SD, Allegaert K, Anderson BJ, Kukanich B, Sousa AB, Steinman A, Pypendop, 
B., Mehvar, R., Giorgi, M., Holford,N.H.G. Parent-metabolite pharmacokinetic models 
- tests of assumptions and predictions. Journal of Pharmacology & Clinical 
Toxicology. 2014;2(2):1023-34.
Holford N. Clinical pharmacology = disease progression + drug action. Br J Clin 
Pharmacol. 2015;79(1):18-27.

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