PD modeling of dataset with opposite values in the measurement effect

From: "Li-Pin Kung" lpkung@aol.com Subject: [NMusers] PD modeling of dataset with opposite values in the measurement effect Date: Wed, 25 Jan 2006 14:19:35 -0500 Dear friends, When the population data of a pharmacological effect is distributed across zero, is it a variability or a dual effect or...? We don't accept the PK data when it is negative. However, we can not reject a PD data when it indicates a negative value of the desired effect. I believe the Hills eq holds when (E-E0 )/Emax is positive. Am I correct? I am working on a -blocker. I want to build a model for PKPD correlation. All the PK data are though concentrations. 20% of the corresponding PD data showed unfavorable to the pharmacological effect, i.e. the individual blood pressure at the though level was higher than its baseline. Is it reasonable to fit all the data to one Hills equation? What would be the base model do you suggest? Thanks, Liping

From: Gibiansky, Katya Subject: RE: [NMusers] PD modeling of dataset with opposite values in the measurement effect Date: Wednesday, January 25, 2006 3:28 PM Liping, A couple of notes. First, increase in blood pressure compared to baseline is likely due to variability of baseline and not due to the drug. In the absence of any drug effect and some systematic change (e.g. circadian variation), one would expect 50% of PD data points to go up and 50% to go down compared to baseline. To cope with the negative effect, you need to account for baseline variability (i.e. estimate it and use estimated population+individual values in the model rather than the observed value). Second, if it is not a variability or some systematic difference in the measurements issue, and you want to correlate it with the drug concentrations, there is no rational why the correlation should be described by the same relationship for those who respond negatively and positively to the drug. Katya _______________________________________________________

From: Paul Hutson prhutson@pharmacy.wisc.edu Subject: RE: [NMusers] PD modeling of dataset with opposite values in the measurement effect Date: Fri, 27 Jan 2006 14:13:14 -0600 Li-Pin: I think that: a) your "base" model (Eo) is going to need to be dynamic, either as a function of drug concentration and/or duration of exposure. The change in Eo may possibly even be related to the magnitude of beta blockade achieved, and b) it doesn't sound like a very good beta-blocker Good luck. Paul

From: "Pereira, Luis" Luis.Pereira@bos.mcphs.edu Subject: RE: [NMusers] PD modeling of dataset with opposite values in the measurement effect Date: Fri, 27 Jan 2006 16:56:23 -0500 Dear LiPing Some effect data can cross the baseline value over time giving the impression of a negative effect not translatable into a 'negative' concentration. I suggest you look at the E vs. C plot since you may have a case of short term tolerance (or rebound effect) depicted by a clockwise hysteresis loop (or proteresis). In that case you may minimize the hysteresis defining your PK in terms of the concentration at the biophase, and then use an Emax, Sigmoid Emax or any other appropriate PD model. Best luck Luis --------------------------------------------------------------- Luis M. Pereira, Ph.D. Assistant Professor, Biopharmaceutics and Pharmacokinetics Massachusetts College of Pharmacy and Health Sciences 179 Longwood Ave, Boston, MA 02115 Phone: (617) 732-2905 Fax: (617) 732-2228 Luis.Pereira@bos.mcphs.edu

From: Scott VanWart Scott.VanWart@cognigencorp.com Subject: RE: [NMusers] PD modeling of dataset with opposite values in the measurement effect Date: Fri, 27 Jan 2006 18:01:56 -0500 Dear Li-Pin, If blood pressure is the PD response you are modeling, there is already a wide body of evidence in the literature that suggests that there is a circadian pattern to this type of process. Therefore, you may need to model your baseline response as a function of clock-time prior to trying to study the pharmocology of your drug. A good example of one approach that has been done to do this has been published by Hempel et al. (1998), Clinical Pharmacology and Therapeutics; 64: 622-635. I would also consider looking at other factors that may confound your ability to detect a concentration-response relationship, such as use of other non-study related medications or increases in the production of other endogenous modulators (if measured). A second point to consider is that there may be patients, who for any number of reasons, simply do not respond regardless of what drug concentration they are exposed to. There are several different approaches that you could consider here as well, one of which is the use of a mixture model within NONMEM to identify "responders" versus "non-responders". Scott Van Wart -- ----------------------------------- Scott Van Wart Assistant Director, Population PK/PD Cognigen Corporation 395 Youngs Road Buffalo, NY 14221-5831 (716) 633-3463 ext 241