Re: RE: Simulation settgin in the precence of Shrinkage in PK when doing PK-PD analysis

On 19/02/2013 7:55 a.m., Leonid Gibiansky wrote: > Hi Matts, > > One method to investigate the problem would be to conduct VPC. If VPC with model-estimated variances provides good (not inflated) range of PK profiles then one can argue that the PK model provides good description of the data and can be used for simulations (including PK-PD). > > Another test could be to do VPCs for the PK-PD model: one with fixed PK parameters (as was used in the sequential PK-PD modeling procedure) and the other one with model-simulated ETAs for both PK and PD parts. Again, if both provide good coverage of observed PK-PD data then combination of PK and PD models can be trusted, and any of the approaches can be applied. If one of the VPCs is inadequate, than it should be noticeable in the too narrow or too wide prediction intervals. > > Leonid On 19/02/2013 5:20 a.m., Ribbing, Jakob wrote: > Resending, since my posting from this morning (below) has not yet appeared on nmusers. > > Apologies for any duplicate postings! > > *From:*Ribbing, Jakob > *Sent:* 18 February 2013 09:59 > *To:* "Kågedal, Matts"; [email protected] > *Cc:* Ribbing, Jakob > > *Subject:* RE: Simulation settgin in the precence of Shrinkage in PK when doing PK-PD analysis > > Hi Matts, > > I think you are correct; the problem you describe has not had much (public) discussion. > > It is also correct like you say that this is mostly a problem in case of all of the below > > ·sequential PK-PD analysis is applied (IPP approach, Zang et al) > > ·non-ignorable degree of shrinkage in PK parameters _of relevance_ > > oOf relevance: the PK parameters effectively driving PD for the mechanism, e.g. CL/F if AUC is driving. In addition, if PD response develops over several weeks/months then shrinkage in IOV may be ignored even for relevant PK parameters > > ·I would also like to add that for this to be an issue individual PK parameters must explain a fair degree of the variability in PD, which is not always the case > > oIf driving PD with typical PK parameters (along with dose and other PD covariates) does not increase PD omegas, compared to IPP, then either PK shrinkage is already massive, or else it is not an issue for the IPP-PD model > > If only the IPP approach is possible/practical a simplistic approach to simulate PD data is as follows: > > ·sample (with replacement) the individual PK parameters along with any potential covariates (maintaining correlation between IIP and covariates, i.e. whole subject vectors for these entities, but generally not for dose since generally should only have only random association with IPP or PK/PD-covariates) > > ·then use the re-sampled datasets for simulating PD according to the PD model (driven by IPP, covariates, dose, etc). The degree of shrinkage is then the same for PD estimation and simulation. > > This approach may for example allow to simulate realistic PD response at multiple dosing, based on only single dose PD. When the MD data becomes available then one may find that variabilities shift between PK and PD due to different PK shrinkage, but I would argue the simulated PD responses still were realistic. This approach is useful for predictions into the same population (especially if sufficient number of subjects available for re-sampling), but may not allow extrapolation into other populations where PK is projected to be different. > > When possible the obvious solution is to apply one of the alternative approaches to simultaneous PK-PD fit; after you have arrived at a final-IPP model. > > If a simultaneous fit is obtainable/practical this is the best option, but notice that e.g. if you have rich PK data in healthy and no PK data in patients (plus PD data in both populations): You can estimate separate omegas for PD parameters in healthy vs. patients, but it may be difficult to tell whether patients higher PD variability is due to PK shrinkage, or due to the actual PD variability being higher in this population (or both). PD variability may be confounded by a number of other factors that are actually variability in PK (fu, active metabolites and bio phase distribution, just to mention a few where information may be absent on the individual level). Depending on the purpose of the modelling this often not an issue, however. > > As you suggest there may be rare situations with IPP where a more complicated approach is needed, with a) simulation and re-estimation of PK model, to obtain Empirical-Bayes Estimates based on simulated data, and then feed these into the subsequent PD model. I would see this as a last resort. There are pitfalls in that if PD parameters have been estimated under one degree of PK shrinkage, then applying these estimates to a simulated example with different PK shrinkage requires adjustment of PD variability. I am not sure anyone has had to go down that route before and if not I hope you do not have to either. Maybe others can advice on this? > > Best regards > > Jakob > > Two methodological references: > > Simultaneous vs. sequential analysis for population PK/PD data II: robustness of methods. > > Zhang L, Beal SL, Sheiner LB. > > J Pharmacokinet Pharmacodyn. 2003 Dec;30(6):405-16. > > Simultaneous vs. sequential analysis for population PK/PD data I: best-case performance. > > Zhang L, Beal SL, Sheiner LB. > > J Pharmacokinet Pharmacodyn. 2003 Dec;30(6):387-404. -- 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 tel:+64(9)923-6730 fax:+64(9)373-7090 mobile:+64(21)46 23 53 email: [email protected] http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford