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Dear All
Following oral administration of a drug to rabbits (n = 6) I got the following data, I am bit confused that whether it follow multiple compartmental model. Can anyone help me?
Time (h) Plasma Drug Concentration (ug/ml)
0.5 0.214
1 0.426
1.5 0.669
2 0.595
4 0.515
6 0.403
8 0.33
12 0.25
24 0.1
Dr Zafar
[A semi-log plot looks like there may be a second compartment but you should look at data from each animal. Average data can add extra exponentials. Try modeling the data with one or two compartments and see which model is better. - db]
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Dear Zafar,
My proposal consists in:
1. Choose randomly 3 animals as learning (training) group and do 1- and 2- COM PK analysis. 2. Elaborate 1- and 2- COM population PK models.
3. Evaluate the predictive performance (as suggested by Sheiner and Beal, 1981) of each of both PK popmodels (1- and 2-COM models) using the validation group animals (3).
4. You will get a rough insight wich population PK model is better in its predictive abilities.
5. Depending of the aims of your project, you can perform Bayesian forecasting on whole data set of 6 rabbits or on Bayesian priors determined using D-optimal strategy (D'Argenio, 1981).
[for more details you can look at D. Terziivanov et al. Population pharmacokinetics of ciprofloxacin in patients with liver impairments analyzed by NPEM2 algorithm - a retrospective study. Int J Clin Pharmacol Ther. 36: 376-382 (1998)]
Ad hoc drawbacks of the above proposal: 1. Limited number of animals for both Learning (training) group and Validation group; 2. Usage of the same animals when performing Bayesian forecasting on whole data set of 6 rabbits.
Kind regards,
Dimiter
-- Dimiter Terziivanov, MD,PhD,DSc, Professor
Pharmacology and Clinical Pharmacology
SOFIA UNIVERSITY "ST. KLIMENT OHRIDSKI"
FACULTY OF MEDICINE
1 Koziak str.
1407 Sofia, BULGARIA
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Dear Dr. Zafar
Two compartment model best describe the given data. However, it may not be the case with all the 6 animals. If this is the case, non-compartmental approach would be better.
Truly yours Dr. Nadeem Irfan Bukhari
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