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  • [NMusers] covariates
    Date Fri September 17 2004 7 29 am Nick I do not question allometric scaling law described in the papers that you cited I question an assertion that this law implies CL Q i WT 3 4 V i WT dependences for each and every drug This is a good guess that should be checked and corrected if needed Any compartmental model is just a crude approximation of the very complicated biological processes and it is hard to expect that coefficients of that approximation behave exactly as CL WT 3 4 V WT for every drug If data contradict these dependencies one should follow the data rather than impose artificial restriction on the model In particular if the model without WT describes the data better than the one with WT better in terms of model diagnostic e g observed vs predicted plots stratified by WT I do not see why should one ignore it For covariate search I advocate common sense rather that OF search you should look on diagnostic random effect versus covariate plots in addition to OF There are also statistical approaches to multiple testing roughly if you conduct a lot of tests you should be more stringent in terms of OF drop in order to claim significance But OF drop is a good measure for a quick screening of multiple covariates Leonid From Nick Holford n holford auckland ac nz Subject RE NMusers covariates Date Fri September 17 2004 7 51 pm Leonid Thanks for making it clear that we agree that allometric scaling is a plausible model It seems we disagree on whether or not one can assume that CL V1 Q V2 parameters of the two compartment disposition model are correlated with physiological anatomical properties I accept that it is an assumption that I make when I use allometric models to scale these parameters Given my understanding of the physiological anatomical processes that I expect to govern pharmacokinetic disposition I do not see any need to seriously question this assumption I agree that common sense should be used to guide a covariate search in particular biological mechanistically guided common sense I accept that one might use OFV changes as a screening procedure but of course not for formal hypothesis testing given the well known failure of the chi square assumption for the null OFV change distribution using NONMEM I do not consider covariate effects are of practical importance unless one can also show that the random effect variance e g as estimated by OMEGA for a parameter of interest e g clearance is reduced by some relevant amount It is not uncommon to see drops in OFV suggesting covariate effects but with a negligible change in OMEGA e g See Matthews et al for an example where the contribution of several covariates in explaining the variability in clearance was estimated and the predictive performance of each covariate model was tested There are examples where the OFV fell but there was no improvement in clearance variance nor improvemement in predictive performance Matthews I Kirkpatrick C Holford NHG Quantitative justification for target concentration intervention Parameter variability and predictive performance using population pharmacokinetic models for aminoglycosides British Journal of Clinical Pharmacology 2004 58 1 8 19 Nick Nick Holford Dept Pharmacology Clinical Pharmacology University of Auckland 85 Park Rd Private Bag 92019 Auckland New Zealand email n holford auckland ac nz tel 64 9 373 7599x86730 fax 373 7556 http www health auckland ac nz pharmacology staff nholford From Leonid Gibiansky leonidg metrumrg com Subject RE NMusers covariates Date Fri September 17 2004 10 50 pm Nick I think the range of disagreement even more narrow the only point that I d like to add to your allometric scale CL and V representations is that we need to check whether random effects of the resulting model are independent of WT If they are this is great it would be another confirmation of the general law If for some particular drug random effects depend on WT even after the allometric scaling it needs to be corrected either by changing the power x of the dependencies CL WT x V WT x or by addition of some other covariates that correlate with WT and can remove observed dependence of random effects on WT Thanks for the references Leonid From Ying Hong yinghong pharm usyd edu au Subject RE NMusers covariates Date Fri September 17 2004 10 50 pm Hello Nick Leonid I did try the allometric scaling law for each PK parameter CL V1 Q V2 one by one in the basic model Unfortunately the results are either Minimization terminated or insignificant change of OFV It seems that allometric scaling model doesn t fit to my study data Perhaps the formulation of this study drug liposome may modify the disposition of encapsulated drug which can not be explained by allometric scaling model I also tried the covariate equation like that TVP THETA 1 1 THETA 2 COV median COV P is the PK parameter COV is the covariate However error message shows that P is negative don t know why So I am back to the allometric modelling again and this time I replace 0 75 or 1 with THETA 5 like that TVP THETA 1 WT 21 THETA 5 21 is the median of WT in the study patient cohort the minimum and maximum limit of THETA 5 was given in the THETA block The results seems to be OK since OFV decreased significantly and CV of CL and V1 also decreased Is it proper to do in this way by letting NONMEM to find the THETA 5 estimate If yes Can I fix this THETA 5 to the final estimate when running the next level by incorporating more covariates Kind Regards Renee From Nick Holford n holford auckland ac nz Subject RE NMusers covariates Date Fri September 17 2004 11 37 pm Renee Hello Nick Leonid I did try the allometric scaling law for each PK parameter CL

    Original URL path: http://nonmem.org/nonmem/nm/99sep152004.html (2016-04-25)
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  • [NMusers] Interoccassion variability and Auto-induction model
    Partha Nandy Clinical Discovery BMS From Gordi Toufigh Toufigh Gordi cvt com Subject RE NMusers Interoccassion variability and Auto induction model Date Fri September 10 2004 1 54 pm Dear Partha I guess the answer depends on how you model the auto induction and what data you have We have applied a semi physiological model to data on the auto induction of artemisinin given orally The model includes estimations of IOV on ka and time lag The induction is modeled as increase in the enzyme amounts which increase the extraction ratio of the compound thereby affecting its F and CL The manuscript is sent to BJCP and is under review I would be more than happy to provide you with the control stream Best wishes Toufigh Gordi From Gastonguay Marc marcg metrumrg com Subject RE NMusers Interoccassion variability and Auto induction model Date Mon September 13 2004 9 57 am Hello Partha Given an adequate experimental design and an appropriate auto induction model you should be able to separate the systematic time dependent increases in clearance due to auto induction from the non systematic random fluctuations in clearance across sampling occasions You can check the precision reliability of the estimates

    Original URL path: http://nonmem.org/nonmem/nm/99sep102004.html (2016-04-25)
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  • [NMusers] Log transformation
    am Dear All I have been advised that to improve the fit of my model I should log transform the data and I have some basic questions that I hope someone could help me with Do I need to log

    Original URL path: http://nonmem.org/nonmem/nm/99sep082004.html (2016-04-25)
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  • [NMusers] NONMEM dataset development
    this limit is that it refers to the number of observation records not all records for an individual You can increase the number of observations per subject by changing the PARAMETER NO in NSIZES and TSIZES then recompiling NONMEM e g to allow 500 obs subject C NO MAX NO OF OBSERVATION RECORDS INDIVIDUAL RECORD PARAMETER NO 500 You may also want to change these parameters in NSIZES as well This will increase other array sizes that depend on NO LADD 250500 LIM1 50000 LIM2 50000 2 Is it correct that AMT and DV observed at the time when dose is given cannot be recorded in the same data record If you are using PREDPP e g ADVAN1 you must have AMT and DV values on different records There are two ways that having AMT and DV on the same record can be coded Case 1 If you have a DV value on the same record as an AMT value and do not define a MDV data item then NM TRAN will add MDV 1 for the AMT record and the DV value will be ignored NONMEM will run but NM TRAN will give you q warning like this DATA WARNING 5 RECORD 1 DATA ITEM 4 11 9 THE DV DATA ITEM IS POSITIVE BUT THE MDV DATA ITEM IS 1 Case 2 If you define an MDV 0 item for a record with AMT 0 then NONMEM considers this a data error 3 Some covariables are not available for some patients Is it correct that dot or 0 should be used to indicate the missing values You may use any value you want to indicate a missing covariate value NONMEM treats a dot and 0 as the same thing It is probably a good idea to use a negative value e g 99 to signal a missing value This makes it clearer to a human reader of the data file that this is a strange value You must of course write your own code to handle the case of a missing covariate NONMEM does not do anything sensible by default with missing covariates 3 Some patients have the study drug for more than one occasion NONMEM is employed to work out the BOV Therefore data item OCC is added in the data set to indicate the sequence of the occasion Apart from that what else should be included in the data set Does the ID for each occasion of the same patient keep the same Estimation of BSV and BOV is a good idea It requires that you have a data item e g called OCC to signal different occasions An occasion can be defined in any way you like but often each dose is considered a different occasion The ID data item should be the same for any given subject who has one or more different occasions of data I think of the ID data item as the covariate for BSV while the OCC data item is

    Original URL path: http://nonmem.org/nonmem/nm/99sep042004.html (2016-04-25)
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  • [NMusers] WRES vs. time
    Subject RE NMusers WRES vs time Date Wed September 1 2004 8 01 pm I wonder if you have many BLQ observations at the end of the profiles These BLQs might explain the apparent bias since they are usually excluded in the analysis Just a thought From bvatul bvatul verizon net Subject RE NMusers WRES vs time Date Wed September 1 2004 8 36 pm Hello Toufigh It is likely that your model is not able to describe the data well at later time points You can include more complexity to your model One vs Two compartment etc and would probably help you Venkatesh Atul Bhattaram CDER FDA From Mats Karlsson mats karlsson farmbio uu se Subject RE NMusers WRES vs time Date Thu September 2 2004 1 18 am Toufigh I agree with what has been suggested before Just one additional point There may be nothing wrong with your model but with you diagnostic plot WRES is the perfect residual to inspect when we use the first order FO method as there is a direct correspondence between the estimation method and the residual However not so when we use conditional estimation methods FOCE FOCE INTER but we still use

    Original URL path: http://nonmem.org/nonmem/nm/98sep012004.html (2016-04-25)
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  • [NMusers] Drug initially inhibits one enzyme and then the second enzyme isinduced
    acts on 2 enzymes During the first few days it inhibits one enzyme which is reflected in reduced clearance of the drug but over the next few days a second set of enzyme s gets induced resulting in steady increase of clearance of the drug and from that point onwards it takes over Here are some of the caveats I do not have the metabolite data nor the enzyme characteristics

    Original URL path: http://nonmem.org/nonmem/nm/99sep012004.html (2016-04-25)
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  • [NMusers] Does EVID=4 reset the baseline too?
    endogenous compound steady state which is defined before Thanks in advance Kai Wu Department of Pharmaceutics University of Florida Gainesville Fl Office phone 352 846 2730 From Bachman William MYD bachmanw iconus com Subject RE NMusers Does EVID 4 reset the baseline too Date Fri August 27 2004 7 21 am The very best way to answer questions like this is to set up a simple test problem and determine

    Original URL path: http://nonmem.org/nonmem/nm/99aug262004.html (2016-04-25)
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  • [NMusers] Indirect response model in NONMEM
    be modified in case I want to estimate a baseline value I have found some previous comments on initializing the effect compartment but have not been able to make it work Thank you Toufigh Gordi From Luann Phillips luann phillips cognigencorp com Subject RE NMusers Indirect response model in NONMEM Date Wed August 25 2004 6 10 pm Toufigh Question Can you actually measure cmt 5 If yes Add a record to your dataset at time 0 very first record with cmt 5 amt first concentration value from cmt 5 and dv missing In the control stream add F5 1 exp eta n to allow for estimation of measurement error in the first concentration of cmt 5 You can keep the first concentration from cmt 5 as an observation record in addition to using it as a dose record but I usually delete it If no Add a record to your dataset at time 0 very first record with cmt 5 set amt 1 All later predictions of A 5 will be relative concentrations ie fractional changes If you can not measure A 5 you will never be able to estimate a baseline value for the compartment You assume a beginning concentration amt 1 and then your model estimates the relative impact that the drug has on CMT 5 Note In both cases you should treat it as a bolus dose If whatever A 5 represents is at steady state prior to introducing the drug then also use SS 1 Review the bug about ADVAN9 and the SS6 routine Also make sure that IEFF is restricted between 0 and 1 to prevent your input rate for cmt 5 from becoming negative Regards Luann From mathangi mathangi msn com Subject RE NMusers Indirect response model in NONMEM Date Wed August 25

    Original URL path: http://nonmem.org/nonmem/nm/99aug252004.html (2016-04-25)
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