Plcebo Corrected PK/PD

From: "MANOJ KHURANA" manoj2570@yahoo.com Subject: [NMusers] Plcebo Corrected PK/PD Date: Wed, March 2, 2005 12:07 pm Dear Group, I am trying to understand the way and need of doing placebo correction for PD response in PK/PD modeling. I went through NONMEM archives but couldn't find much. One reference that I came accross was "P. Mortin et al. Pharmacodynamic and Pharmacokinetic profile of S17092, a new orally active prolyl endopeptidase inhibitor, in elderly healthy volunteers. A phase I study. BJCP, 50, 350-359" and they looked at %inhibition of prolyl endopeptidase for escalating dose cohorts having 9 subjects per cohort (3 placebos + 6 treatment) and placebo correction was done by subtracting the average response at each time point. I need to know if this averaging method is the ideal and acceptable way of doing it when we know that there is intersubject variability in the response in the placebo group other than the assay variability. Is there a way to handle this in the model itself in NONMEM. My next question is vvery general. Can someone explain me under what circumstances do we need a placebo correction and how it is done for different PD responses. What should be our criteria to decide if we need a placebo correction while looking at the placebo response data. I would appreciate if someone could provide me references and guidance for that. Thanks in advance for your time Manoj Khurana

From: "Bachman, William (MYD)" bachmanw@iconus.com Subject: RE: [NMusers] Plcebo Corrected PK/PD Date: Wed, March 2, 2005 1:05 pm Manoj, In a nutshell, averaging and baseline subtraction are probably the worst way to model placebo effect. Particularly when the response in absence of drug is not a "flat line". I'll let the statisticians give all the details but basically, averaging of anything removes information from the data and baseline subtraction has it's own issues. That being said, its often done (and in best case scenarios it may be a good first approximation but not ideal.) There are any number of examples out there of better approaches to accounting for placebo effect - the first two that come to mind are (1) Nick Holford's tacrine model and (2) Bill Jusko's cortisol papers that take into account circadian rhythm. Bill

From: "Gobburu, Jogarao V" GOBBURUJ@cder.fda.gov Subject: RE: [NMusers] Plcebo Corrected PK/PD Date: Wed, March 2, 2005 1:47 pm Dear Manoj, The answer really depends on the experimental design and the modeling objective. In principle, I agree with Bill's statements. Unfortunately, we deal with designs that might not allow anything better than subtracting mean placebo responses. If you are dealing with data from a fixed-dose, parallel trial, then the only way to adjust for placebo effect is by subtracting the mean of the groups (placebo vs. trt). You could use population mean or typical placebo responses for determining the drug effect. Even if the placebo response has a rhythm to it, if you determine the drug effect by time (instead of modeling the placebo response) your analysis would be equivalent to modeling all data simultaneously (provided all you are interested in is the drug effect size+?variance). What you will lose is the ability to simulate the placebo effects for future trials. There are some other advantages such as handling missing data and unbalanced observations. You seem to be concerned about the variability in the placebo group. If you do not have a cross-over design, you simply cannot estimate the true variability in the drug effect. You are stuck to using population means or typical values for placebo effect. No modeling can help you with it. As a general practice you should always account for placebo effects. You should have a reason to exclude placebo data (e.g.: no signal over the trial duration). Most primary endpoints have placebo effects, while several biomarkers might not have a placebo effect. Placebo data allows you to estimate the intercept of the exposure-response model and duration of effect (I am sure you already know this). Hope this is helpful. Regards, Joga

From: "Nick Holford" n.holford@auckland.ac.nz Subject: RE: [NMusers] Plcebo Corrected PK/PD Date: Fri, March 4, 2005 11:10 am Joga Gobburu wrote: I am not sure why you conclude that the variability in drug effect cannot be estimated from a parallel group design. I would include disease progression as part (perhaps the major part) of the response seen in the placebo treatment group. By making assumptions about the time course of the disease/placebo response and how it interacts with the drug effect then one can estimate the population parameters and between subject variability for all components (disease, placebo, drug). Making assumptions is a necessary part of this kind of modelling. The models can be quite helpful for interpretation of clinical trials and have been used successfully for clinical trial simulation and prospective design of trials. Nick Holford NHG, Peace KE. Results and validation of a population pharmacodynamic model for cognitive effects in Alzheimer patients treated with tacrine. Proceedings of the National Academy of Sciences of the United States of America 1992;89(23):11471-11475 Lockwood P. Application of clinical trial simulation in Alzheimers disease. In: Danhof M, Karlsson MO, Powell RJ, editors. 4th International Symposium on Measurement and Kinetics of In Vivo Drug Effects; 2002 April 26; Noordwijkerhout, The Netherlands; 2002. -- 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: "Gobburu, Jogarao V" GOBBURUJ@cder.fda.gov Subject: RE: [NMusers] Plcebo Corrected PK/PD Date: Fri, March 4, 2005 11:38 am Dear Nick, Thanks for your note. I wrote 'cannot estimate the true variability in the drug effect", "true" being the qualifier. Yes, models with appropriate assumptions can be used and are being used to estimate the variability in the drug effect. However, unless we really know how each patient behaves on both placebo and drug (cross-over), we do not have the right data to estimate the bsv and wsv of the drug effects. Our reference for placebo effect, otherwise, would be a typical patient and not that particular patient (in my parallel design example). This ignores the bsv of placebo effect while determining the drug effect in each patient. I thought it is important to note that. Sure, those models will still be useful. Regards, Joga

From: "Nick Holford" n.holford@auckland.ac.nz Subject: Re: [NMusers] Plcebo Corrected PK/PD Date: Fri, March 4, 2005 1:54 pm Joga, I think it is possible to estimate both the disease progress AND the drug effect in an individual in one arm of a parallel group design. Suppose the model is: S(t) = S0 + alpha*t + beta*ce(t) S is disease status ('response'), S0 is the baseline status, alpha is the rate of progression, beta is the slope of a linear PD model, Ce(t) is drug conc at time t. The drug effect (beta*ce(t)) will be superimposed on the disease progress line and with a suitable design and reasonable parameters then the observed status could be used to estimate the parameters of this model with random effects (i.e. BSV) on each parameter. Of course with the over sparse designs that are commonly used in clinical trials or if the time course of disease progression is too closely correlated with the time course of drug effect then there may be an a posteriori identifiability problem but in theory the parameters are a priori identifiable. 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: "Mats Karlsson" Mats.Karlsson@farmbio.uu.se Subject: Re: [NMusers] Plcebo Corrected PK/PD Date: Fri, March 4, 2005 3:13 pm Nick, Would the model not be identifiable only if you assume that the covariance of the random effects for alpha and beta is zero? [At least if Ce is constant. If it is variable, and variable enough, it starts to resemble a cross-over experiment, at least when you have frequent measurement.] Mats

From: "Bhattaram, Atul" BhattaramA@cder.fda.gov Subject: RE: [NMusers] Plcebo Corrected PK/PD Date: Fri, March 4, 2005 4:05 pm Hello Nick I dont think you can estimate the "true variability" unless one does a cross-over design. If you dont have placebo data in the same subject for symptomatic drugs (anti-hypertensives) how would you estimate the "true variability"?. Venkatesh Atul Bhattaram Pharmacometrics, FDA

From: "Bachman, William (MYD)" bachmanw@iconus.com Subject: RE: [NMusers] Plcebo Corrected PK/PD Date: Fri, March 4, 2005 4:23 pm This is done all the time (measure the placebo effect in some subjects and the drug effect in others (- at least where ethical). Look at all those analgesic studies that have been done. It's clear you don't have the variability in the "same" subjects, but, in a well designed study with sufficient power the estimate of variability in placebo effect is sufficiently accurate for the purpose. In many cases, there is no other way to do it. Obviously, the study design is dependent on the indication, severity of disease state and practicality/ethicality (you can't pull teeth from a subject without medication one week and with medication the next).

From: "Nick Holford" n.holford@auckland.ac.nz Subject: Re: [NMusers] Plcebo Corrected PK/PD Date: Fri, March 4, 2005 5:47 pm Atul, It all depends on the model you have in mind. If the time course of the placebo effect is essentially the same as the time course of the drug effect then I agree that the two components of the overall response cannot be distinguished (as I indicated in my previous posting). But if the placebo response (or disease progress) is different from the time course of drug effect then the components should be identifiable. 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: "Nick Holford" n.holford@auckland.ac.nz Subject: Re: [NMusers] Plcebo Corrected PK/PD Date: Fri, March 4, 2005 5:56 pm Mats, As I indicated earlier the ability to distinguish the disease progress (aka placebo) from the drug effect will depend on modelling assumptions. Its not clear to me why one must assume zero covariance of alpha and beta so perhaps you could expand more on why this is a necessary condition? It requires a very special design to ensure that Ce does not change with time! So I would always expect changing Ce. If you wish this is some kind of natural cross over design as effect increases and decreases. The problem is usually being able to design the study to define the time course of effect well enough. But my point is that given a suitable model and reasonable parameters which allow separation of the time course of disease progress and drug effect then it is possible to estimate both components independently. 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: "Gobburu, Jogarao V" GOBBURUJ@cder.fda.gov Subject: RE: [NMusers] Plcebo Corrected PK/PD Date: Fri, March 4, 2005 6:44 pm Dear Nick, 1. Let us first acknowledge what we agree on: For non-progressive responses one cannot estimate the true variability in drug effect reliably. You might be able to estimate the mean effects though. I would also like to make it clear that I am not advocating not-estimating the drug effect variance during modeling or other analysis. I simply want us to note the limitations. 2. Now, where you seem to be having a difference in opinion (along with others?) is when the disease is progressive. In my opinion, the disease progression sub-model is not going to change the consequences of interpreting parallel trial results. May be I am missing something. In any case, I would like to seek your opinion on a specific problem. Let us say, the disease is progressing linearly. All patients start with a zero intercept at randomization (randomized to either placebo or trt). This patient who received the drug had responses of 0, 0, 0 at 1, 3 and 6 months. Basically the drug froze the disease from progressing. Let us even make the analysis simpler by assuming no drop-outs, no missing observations, one dose level only, no effect delay, etc.. So, it does not matter if you used one or two-stage analysis. What reference control effect would you use to determine the drug (ONLY) effect for this patient? NB: Bill, there are cross-over studies for your 'teeth-pulling example' in the literature. Thanks. joga

From: "Mats Karlsson" Mats.Karlsson@farmbio.uu.se Subject: Re: [NMusers] Plcebo Corrected PK/PD Date: Fri, March 4, 2005 11:51 pm Nick, I read your model carelessly. The point I made does not apply to this (intercept/symptomatic) model, but to one where the drug effect slows disease progression, i.e S(t) = S0 + (alpha + beta*ce(t))*t With variations in Ce being unimportant, it is probably clear that with knowing only the variance of alpha from the placebo group, many combinations of variance in beta and covariance(alpha,beta) may exlain the variability in the trt group. If you want to say that the resolution comes from variations in Ce, I won't argue, but it may not always be the case in practise. Mats

From: "Nick Holford" n.holford@auckland.ac.nz Subject: Re: [NMusers] Plcebo Corrected PK/PD Date: Sat, March 5, 2005 4:43 pm Joga, >"Gobburu, Jogarao V" wrote: > > Dear Nick, > > 1. Let us first acknowledge what we agree on: For non-progressive > responses one cannot estimate the true variability in drug effect reliably. > You might be able to estimate the mean effects though. I would also like to > make it clear that I am not advocating not-estimating the drug effect > variance during modeling or other analysis. I simply want us to note the > limitations. ----- I am afraid I dont think I agree with you on this. Non-progressive responses are just a special case of progression where alpha=0. I think it is a simple matter to estimate the variability in drug effect in this case -- under the model of no disease progression then any systematic change in response with time must be attributed to drug effect. The parallel design with a non-progressing placbeo group would let you estimate the residual error and thus be able to distinguish true drug effect from noise. ----- > 2. Now, where you seem to be having a difference in opinion (along with > others?) is when the disease is progressive. In my opinion, the disease > progression sub-model is not going to change the consequences of > interpreting parallel trial results. May be I am missing something. In any > case, I would like to seek your opinion on a specific problem. Let us say, > the disease is progressing linearly. All patients start with a zero > intercept at randomization (randomized to either placebo or trt). This > patient who received the drug had responses of 0, 0, 0 at 1, 3 and 6 months. > Basically the drug froze the disease from progressing. Let us even make the > analysis simpler by assuming no drop-outs, no missing observations, one dose > level only, no effect delay, etc.. So, it does not matter if you used one or > two-stage analysis. What reference control effect would you use to determine > the drug (ONLY) effect for this patient? ----- The situation when the drug affects the rate of progression is harder to identify (this is the case that Mats also thought was problematic). With sufficient variation in Ce within a subject then I think one can identify the protective drug effect on rate of progression. As Mats has pointed out the variation in Ce acts as a form of natural cross over within the treatment period although the basic design does not demand a formal crossover of treatment. In the special case where there is no variation in Ce within a subject then I agree one cannot separate drug effect from disease progression. ---- 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: "Bachman, William (MYD)" bachmanw@iconus.com Subject: RE: [NMusers] Placebo Corrected PK/PD Date: Mon, March 7, 2005 8:07 am >NB: Bill, there are cross-over studies for your 'teeth-pulling example' in >the literature. >Thanks. >joga I doubt they would pass a current IRB. _______________________________________________________