RE: PK data from individuals who vomited after drug ingestion

From: Martin Bergstrand Date: August 27, 2009 technical Source: mail-archive.com
Dear Kok-Yong Seng, With my naive thinking what happens if you vomit is that a part or all of what is remaining to be absorbed will disappear. If you know the time of vomiting I can see a nice and mechanistic way of handling this. >From your depot compartment you should in this case have an elimination pathway instead of only an absorption pathway. As long as the subject doesn't vomit the elimination pathway is turned off (elimination rate constant=0). If and when a vomiting event is recorded the elimination pathway is turned on for a short and fix amount of time (with the fix amount of time a first order rate constant can conveniently be used to describe this). I have provided a short coding example for this in the end of the email. If you for some reason doesn't have the exact time of the vomiting events I would suggest a covariate for the number of vomiting events within the relevant timeframe (while there is significant absorption). This covariate should then be included for the bioavailability so that the relative bioavailability is 1 for zero vomiting events and relatively different for 1, 2 and 3 vomiting events. This should however in my mind be a far cruder way of handling this than my firs suggestion. ;; Mechanistic vomiting effect on absorption ------------------------- ID AMT TIME DV EVID CMT VOM(vomiting) Comments 1 100 0 . 1 1 0 Dosing 1 . 0.25 0.5 0 2 0 Observation before vomiting 1 . 0.4 . 4 0 0 Vomiting event (obs.VOM=0) 1 . 0.5 . 4 0 1 End of vomiting event (obs.VOM=1) 1 . 0.5 1.25 0 1 0 Observation after vomiting ; In the above described case the vomiting covariate was turned on between TIME 0.4-0.5. $SUBROUTINE ADVAN5 $MODEL COMP = (1_DEPOT) ; Depot compartment COMP = (2_CENT) ; Central observation compartment K1T2 = THETA(1) ; Absorption rate constant (KA) IF(VOM.EQ.1) THEN K1T0 = THETA(2) ; Elimination from depot compartment turned on at vomiting event ELSE K1T0 = 0 ; No elimination from depot compartment without vomiting ENDIF ;; ------------------------------------------------------------------- >From the estimated K1T0 and the fixed time that VOM is set to 1 you can calculate the average fraction of the remaining drug substance in the depot compartment that is lost. I hope that this is of some use to you. Kind regards, Martin Bergstrand, MSc, PhD student ----------------------------------------------- Department of Pharmaceutical Biosciences, Uppsala University ----------------------------------------------- P.O. Box 591 SE-751 24 Uppsala Sweden ----------------------------------------------- [email protected] ----------------------------------------------- Work: +46 18 471 4639 Mobile: +46 709 994 396 Fax: +46 18 471 4003
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-----Original Message----- From: [email protected] [mailto:[email protected]] On Behalf Of Seng Kok Yong Sent: den 27 augusti 2009 07:53 To: Nick Holford; nmusers Subject: RE: [NMusers] PK data from individuals who vomited after drug ingestion Dear Nick, Thank you for your reply. Yes, a single compartment model with Michaelis-Menten elimination fits the data significantly better compared to a single compartment model with linear elimination. GAM analysis, using AIC, and covariate analysis within NONMEM do not suggest that vomiting incidence is a significant covariate. Could this be attributed to the fact that the systemic alcohol levels at the time of vomiting (~1 h post-dose) is only about 25% of what they consumed (assuming a elimination half-life of 30 min)? Or, could it be because the number of vomiting cases makes up only 15% of the total number of subjects? Thank you and best wishes, Kok-Yong Seng -----Original Message----- From: [email protected] [mailto:[email protected]] On Behalf Of Nick Holford Sent: Thursday, 27 August 2009 1:15 PM To: nmusers Subject: Re: [NMusers] PK data from individuals who vomited after drug ingestion Kok-Seng, I would not use a mixture model because you already have the information about which group vomited. I am surprised you describe the elimination of ethanol with a half-life. If you gave an ethanol dose big enough to induce vomiting I would expect you to describe elimination with a mixed order (saturable) elimination process. Best wishes, Nick Seng Kok Yong wrote: > > Dear NM-users, > > I have a question regarding NONMEM modeling of PK data from > individuals who vomited after drug ingestion. Basically, I have a set > of data from about 150 subjects (about 5 measurements per subject) who > orally ingested alcohol at time = 0 h. However, about 15% of this > group vomited more than 1 h post-dose. Based on NONMEM modeling of PK > data from subjects who did not vomit during the study, the mean > elimination half-life is about 30 min. > > May I ask if I should model vomiting incidence (a "yes" or a "no") as > a categorical covariate for analysis of all the data? I have > demographic covariate information at hand. Or, should I introduce a > mixture model during NONMEM modeling? I would expect the vomiting > subgroup to display different values for Vd, Ka etc as compared to the > non-vomiting subgroup. > > Thank you very much for your time and kind advice. > > Best wishes, > > Kok-Yong Seng, PhD > > DSO National Laboratories > > Republic of Singapore > -- Nick Holford, Professor Clinical Pharmacology Dept Pharmacology & Clinical Pharmacology University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand [email protected] tel:+64(9)923-6730 fax:+64(9)373-7090 mobile: +64 21 46 23 53 http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford
Aug 26, 2009 Seng Kok Yong PK data from individuals who vomited after drug ingestion
Aug 27, 2009 Seng Kok Yong PK data from individuals who vomited after drug ingestion
Aug 27, 2009 Nick Holford Re: PK data from individuals who vomited after drug ingestion
Aug 27, 2009 Martin Bergstrand RE: PK data from individuals who vomited after drug ingestion
Aug 28, 2009 Martin Bergstrand RE: PK data from individuals who vomited after drug ingestion