How to think about the different determination methods?

Dear NMusers: I am dealing with the ppf(Propofol) data collected from 3 different centers,in which the drug concentrations ananlysis happens to be 3 different assays.Those are GC,Hplc-UV,HPlc-fluorescence,separaterly.As a item,the assay way is included,labeled as 1,2,3,in order. And as an introduction from the Mannual, the assay way is arranged as the intraindividual variability .The syntax is as follows: IF (ASSY.EQ.1) Y=F*(1+EPS(1)) IF (ASSY.EQ.2) Y=F*(1+EPS(2)) IF (ASSY.EQ.3) Y=F*(1+EPS(3)) And by the way,the pharmacokinetics of ppf were described by a three-compartment model.So the subroutine of advan 11,trans 4 was applied. Of course,the combined Additive and CCV error model were considered at the beginning,but it seems to me that the additive error was so little (0.00001) that even could be ignored.So the CCV model was applied finally,as mentioned above. So there are 6 thetas(Cl,V1,Q2,V2,Q3,V3),6 etas (exp ISV model) and 3 eps in the base model.Then the problem happened. No matter what intial estimates I tried,the results of $EST and $COV steps allways indicate that the model was overparactermized. The hint of R Matrix is either singular or NON-positive semidefinite appeared in the output files.And from the PDx-plotter,the plot of objective function Vs iteration was fairly flat.So I am confirmed that the model was overparactermized.In addtition,I have checked the R matrix in which some values in the line of SG22,SG33,are 0. Here are my questions: Should I take the assay error as an intraindividual variability? How about If I take it as a covariate which would have an influence on any parameter of CL,V,and such and so on? If there is only one eps in the intraindividual model, without the consideration of asssy error.Does it sounds reasonable? Thank you for any comments: This is my last email at this year.Because next several days are the Chines traditional Spring Festival.And I will be far away from the laboratory and stay with my families for celebration.So,taking such a special opportunity,I would say thanks to whom help me before ,now and soon. Also, BEST WISHES TOO ALL THE NMusers.Happy Spring Festival!!! Yours sincerely,Ye hong bo. -- 工作和生活,都要开心的过. 你好,叶红波在此送上真挚的祝福.祝你开心, 叶红波

Dear Ye, Without knowing how well sampled the data are, I would suggest starting with fewer eta terms (start with eta on CL and V1, for example) and then see if you can support additional eta terms. As for the different assays, you might want to start with a single residual variability term and then once you have got a handle on the number of etas that you can include, go back and see if you need to partition out the residual variability by assay. I wouldn't consider assay as a traditional covariate. Regards, Colm Colm Farrell Senior Director, PKPDM&S ICON Development Solutions 2 Globeside Globeside Business Park Marlow Bucks SL7 1TB Phone: + 44 (0)1628 496404 Mobile: +44 (0)771 5750127 Email: [email protected]

Dear Ye hong bo, If I understand you correctly no single sample has been assayed with multiple assay methods? It may be that the assay method only makes a small contribution to the overall residual, but if you have enough information on the three SIGMAs you may keep it as three separate error magnitudes (however, the relative precision of assay methods will be confounded by that one centre may handle their sample collection etc. more accurate than another) As I see it there are two ways to go: Either start out with a simpler model by fixing OMEGAS to zero where you do not have enough information to describe IIV. It is rare that there is enough information to estimate separate etas for inter-compartmental clearance parameters (Q:s), so you may consider using the same eta or fixing one OMEGA to zero there. Also, unless you have good information on the three individual volume parameters you may start out by only having an eta on the total volume (VSS below) and estimate the total volume and the fractions of that volume that represents the central and one of the peripheral volumes (FVC and FVP1 below). You can then proceed by allowing etas on one or both of these fractions according to the code below (estimating OMEGA4 and OMEGA6). An OMEGA BLOCK to estimate the covariance across (etas on) CL and volume parameters may further stabilize the model, if that correlation is important. TVFVC = THETA(4) PHI = LOG(TVFVC/(1-TVFVC)) DENOM = 1 + EXP(PHI + ETA(4)) FVC = EXP(PHI + ETA(4)) / DENOM TFVP1 = THETA(6) PHI2 = LOG(TFVP1/(1-TFVP1)) DENOM2= 1 + EXP(PHI2 + ETA(6)) FVP1 = EXP(PHI2 + ETA(6)) / DENOM2 FVP = 1 - FVC V2 = FVC*VSS VP = FVP*VSS FVP2 = 1 - FVP1 V3 = FVP1 * VP V4 = FVP2 * VP For the above code, FVC and FVP1 are estimated with a logit-transformation which is necessary only when adding etas on these parameters. Also, the logit code used above is a little more complex than needed, with the benefit that THETA(4) and THETA(6) above represent the typical fraction, rather than some value on the logit scale. For alternative 2 below this parameterisation is not suitable as it does not allow MU modelling (I think). The standard way of implementing the logit transformation gives exactly the same fit and allows for MU modelling. Else (alternative 2), estimate your model using the new Monte Carlo methods in NONMEM 7. You can investigate large OMEGA BLOCKs to find out where you have important eta correlations, but for parameters where you have little or no information on the individual level you may have to fix OMEGA to a small value (e.g. 10 or 15% CV, which is biologically more plausible than no variability at all, and still efficient using Monte Carlo methods). However, it is not straight forward to use these estimation methods in nonmem, so allow ample time for getting yourself acquainted with these (settings for the various estimation methods that are appropriate for your data and model + implementing MU modelling in your control stream). I hope this helps and wish you a happy New Year! Jakob

Dear Colm Thank you for your generous advice. Do you mean that in the situation of 2 or 3 compartments, there is no need to include BSV into all the pharmacokineitc parameter? As you have suggested that we could start with eta on the parameter in the centra compartment, are there some literatures or papers published in SCI? I have got few learning about your suggested method. If that step was done,the next step you advised is to check whether I could get support additional eta terms.Do you mean the significant drop in the OFV,such as 3.84,e.g.? Thanks again! Regards!  Dear Ye, Without knowing how well sampled the data are, I would suggest starting with fewer eta terms (start with eta on CL and V1, for example) and then see if you can support additional eta terms. As for the different assays, you might want to start with a single residual variability term and then once you have got a handle on the number of etas that you can include, go back and see if you need to partition out the residual variability by assay. I wouldn't consider assay as a traditional covariate. Regards, Colm Colm Farrell Senior Director, PKPDM&S ICON Development Solutions 2 Globeside Globeside Business Park Marlow Bucks SL7 1TB Phone: + 44 (0)1628 496404 Mobile: +44 (0)771 5750127 Email: Colm.Farrell

Dear Colm Thank you for your generous advice. Do you mean that in the situation of 2 or 3 compartments, there is no need to include BSV into all the pharmacokineitc parameter? As you have suggested that we could start with eta on the parameter in the centra compartment, are there some literatures or papers published in SCI? I have got few learning about your suggested method. If that step was done,the next step you advised is to check whether I could get support additional eta terms.Do you mean the significant drop in the OFV,such as 3.84,e.g.? Thanks again! Regards!  Dear Ye, Without knowing how well sampled the data are, I would suggest starting with fewer eta terms (start with eta on CL and V1, for example) and then see if you can support additional eta terms. As for the different assays, you might want to start with a single residual variability term and then once you have got a handle on the number of etas that you can include, go back and see if you need to partition out the residual variability by assay. I wouldn't consider assay as a traditional covariate. Regards, Colm Colm Farrell Senior Director, PKPDM&S ICON Development Solutions 2 Globeside Globeside Business Park Marlow Bucks SL7 1TB Phone: + 44 (0)1628 496404 Mobile: +44 (0)771 5750127 Email: [email protected]