Covariate Models Using Weight

From: "Piotrovskij, Vladimir [JanBe]" <VPIOTROV@janbe.jnj.com> Subject:Covariate Models Using Weight Date: Tue, 16 Nov 1999 11:24:31 +0100 Dear Rebecca, You should be more specific when posting your questions. The convergence behaviour of NONMEM depends very much on the METHOD you select for $EST. It can be totally different for METHOD=0 (the default first order linearization method) and for METHOD=1 (first-order conditional method). It would be better if you attach the entire NM-TRAN control. The way you implement the fixed effect of WT is not optimal. Firstly, it is preferable to center it using median WT (say, 70) as offset. Then, it worth to include an intercept in the fixed effect model, e.g.: TVCL=THETA(1)+THETA(2)*(WT-70). THETA(1) corresponds to the typical clearance at median WT. Lastly, I would strongly recommend to avoid using TRANS3. You will have much less troubles with TRANS4. Hope this helps, Vladimir ---------------------------------------------------------------------- Vladimir Piotrovsky, Ph.D. Janssen Research Foundation Clinical Pharmacokinetics B-2340 Beerse Belgium Email: vpiotrov@janbe.jnj.com

Date: Wed, 17 Nov 1999 14:09:55 +1300 From: Nick Holford <n.holford@auckland.ac.nz> Subject: Covariate Models Using Weight I agree with your comments about the value of centering to improve the estimation of parameters in the covariance model. There is no need to be obsessional about using the median. Any convenient value that is approximately in the middle of your data is fine. Remember that the final parameter estimate you obtain will be defined in terms of this centering value. I prefer to refer to this centering value as the standard covariate value e.g. a weight of 70 kg is a widely recognized standard for human adult weight. However, I have argued (Holford 1996) on data driven and biological grounds that models for body size should be based on the allometric model: CLi = CLstd * (Wi/Wstd)**3/4 Vi = Vstd * (Wi/Wstd)**1 where CLi, Vi are CL and V in an individual with weight Wi and Wstd is a standard weight e.g. 70 kg. CLstd and Vstd are the population parameters standardized to the size of an individual with weight Wstd. The exponent value of 3/4 for CL can be justified on theoretical grounds (West et al. 1997) and is supported by allometric studies of a wide variety of body functions with an estimate of this exponent indistinguishable from 0.75 (Peters 1983). Justification for V and other body volumes having an allometric exponent of 1 has been reviewed by Anderson et al. 1997. Note these models do not have an intercept parameter. I believe it is an a priori more reasonable model to expect that CL or V will be zero when WT is zero. I prefer to put my faith in biology and mechanism when choosing a model. I resort to statistical heuristics (e.g. change in log-likelihood) when the biology or mechanism is not obvious. I suspect that empirical estimates of allometric exponents reported in the literature for PK parameters are most likely indistinguishable from the a priori value of 3/4 for CL and 1 for V. If the null hypothesis that the exponents are 3/4 and 1 is rejected then careful thought should be given to other confounding factors in the data rather than rejecting a priori well established biological principles. Anderson BJ, McKee D, Holford NHG. Size, myths and the clinical pharmacokinetics of analgesia in paediatric patients. Clinical Pharmacokinetics 1997;33:313-327 Holford NHG. A size standard for pharmacokinetics. Clin. Pharmacokin. 1996: 30:329-332 Peters RH. The ecological implications of body size. Cambridge University Press.1983 West GB. Brown JH. Enquist BJ. A general model for the origin of allometric scaling laws in biology. Science. 1997; 276:122-6 -- Nick Holford, Dept Pharmacology & Clinical Pharmacology University of Auckland, Private Bag 92019, Auckland, New Zealand email:n.holford@auckland.ac.nz tel:+64(9)373-7599x6730 fax:373-7556 http://www.phm.auckland.ac.nz/Staff/NHolford/nholford.htm

From: "Piotrovskij, Vladimir [JanBe]" <VPIOTROV@janbe.jnj.com> Subject: RE: Covariate Models Using Weight Date: Wed, 17 Nov 1999 09:08:04 +0100 I totally agree with you, Nick, when you highlight the importance of "biology and mechanism when choosing a model". And I also agree with the value of allometry in general. But only in general. And only in the context of interspecies correlations. I don't believe in allometry when it is applied to a single species like humans. Specifically, the dependence of CL on body size is very complex and cannot be modelled in terms of a simple and universal function like CLi = CLstd * (Wi/Wstd)**3/4. First of all, we cannot apply the same function for drugs eliminated mainly via hepatic metabolism and via renal excretion controlled either by glomerular filtration, or by enzymes that govern active secretion and reabsorption, or both. Then, if a drug is extensively metabolised and the hepatic blood flow does not play any significant role (most typical case), the dependence of CL on body size is minor, and it is usually better correlated with LBM or IBW than with WT itself. Of course, TVCL=THETA(1)+THETA(2)*(WT-70) is not ideal, but most suitable as the first approximation. TVCL=THETA(1)*(WT/70)**THETA(2) might be preferable if the range of WT is wide enough, however, THETA(2) certainly should not be fixed to 0.75. The situation with V is not less complicated than with CL. Firstly, there is no V, but VC, VP, etc., and each volume may depend on body size differenly. Again, allometry is not something to be taken into account when building a model for one species. Best regards, Vladimir ---------------------------------------------------------------------- Vladimir Piotrovsky, Ph.D. Janssen Research Foundation Clinical Pharmacokinetics B-2340 Beerse Belgium Email: vpiotrov@janbe.jnj.com

From: "Piotrovskij, Vladimir [JanBe]" <VPIOTROV@janbe.jnj.com> Subject: RE: Covariate Models Using Weight Date: Wed, 17 Nov 1999 11:54:41 +0100 Sorry, Nick, I have no time to read your reviews. I've read a lot about allometry when I did PBPK modelling myself (more than 10 years ago). You say CL is not the same in 10 kg child and in 200 kg rugby player. Sure, but it is another story. I this case the difference should better be described in terms of the age effect which may be different from the body size effect. Or do you really think WT is the only reason why CL in those two individuals differs? Even for adults age and body size can independently affect CL. I give you just one example of the analysis I have done recently. The drug was eliminated via renal and hepatic routes in parallel, and the CL submodel was: TVCL = 9.4+0.072*CLCR+0.034*(AGE-75)+0.050*(WT-67) Note, this was mainly the elderly population (median age is 75), however, there were also a lot of young subjects (but no children) in the data set. Note also the model was developed using formal PK study data and sparse data, but the covariate model was based mainly on formal studies. I attach the scatter plot (MS Word Document) of CL random effects vs covariates. I don't know if you can open this attachment, but believe me, no trend can be seen even using a microscope. *****JENNIFER, PLEASE MAKE THE WORD "MS Word Document" THE LINK TO THE FILE 44.DOC ************** I have a lot of similar examples (unfortunately, not published) where the linear model for CL vs. WT works perfectly. If you work with the industry you do not have opportunity to publish a lot. Best regards, Vladimir ---------------------------------------------------------------------- Vladimir Piotrovsky, Ph.D. Janssen Research Foundation Clinical Pharmacokinetics B-2340 Beerse Belgium Email: vpiotrov@janbe.jnj.com

Date: Thu, 18 Nov 1999 09:28:55 +0000 From: James <J.G.Wright@ncl.ac.uk> Subject: Re: Covariate Models Using Weight >Dear Nick & Vladimir, > >I too agree that using prior information is essential in building a sensible model but I draw the line at extrapolating from points on a log-log plot representing different species to a clinical population of patients. Do we really believe that a human beings clearance changes in a predictable manner if they put on a few kilos? At least half of the western world is overweight these days. Comparisons using children and adults are necessarily confounded by many factors. I think in this day and age we should be looking for useful predictive covariates rather than claiming we can apply WT^3/4 to all possible drugs and patient populations on very limited evidence. In my personal experience, WT^3/4 and WT may not actually differ very much once other covariates have been used but I would need far more evidence before I could support Nicks view that WT^3/4 is the default choice. > >James

Date: Thu, 18 Nov 1999 22:55:39 +1300 From: Nick Holford <n.holford@auckland.ac.nz> Subject: Re: Covariate Models Using Weight James Wright wrote: > I too agree that using prior information is essential in building a > sensible model but I draw the line at extrapolating from points on a > log-log plot representing different species to a clinical population of > patients. Please read Anderson et al. (1997) for data using human clinical populations. > Do we really believe that a human beings clearance changes in a > predictable manner if they put on a few kilos? If it is a matter of *belief* then YES I do believe that between subject differences in weight (even by a few kilos) are reflected in the typical individual by an increase in clearance. If I did not believe that then how would I explain in any meaningful biological way the extensive within and across species data showing that clearance and weight are correlated? > At least half of the western world is overweight these days. ... > Comparisons using children and adults are necessarily confounded ... Body composition and stage of maturation are separate covariates that can be helpful in describing between subject variability in clearance. These factors are correlated with weight and therefore a systematic approach to separating them can be based initially on the use of an allometric model using weight (Holford 1996). > I think in this day and > age we should be looking for useful predictive covariates rather than > claiming we can apply WT^3/4 to all possible drugs and patient populations > on very limited evidence. I am perfectly willing to listen to your suggestions for how to disentangle the separate influences of weight, body composition and maturation. My proposal is firstly to account for the influence of size using allometric scaling (perhaps using lean body weight instead of total body weight for obese patients), then introduce other covariates such as body mass index and age. How would *you* suggest it be done "in this day and age"? The allometric scaling exponent of 3/4 for functional properties (e.g. metabolic rate) and 1 for structural properties (e.g. blood volume) is based on extensive evidence (see Peters 1983 for a book on the subject and West et al. 1997 Table 1). I *believe* it is reasonable to extend the principle that if an exponent of 3/4 is reasonable for metabolic rate then it should also work for clearance, similarly an exponent of 1 for blood volume is reasonable for apparent volume of distribution. Of course, it has not been evaluated for "all possible drugs and patient populations". That would be absurd. But the model does have a theoretical and biological basis (West et al. 1997). It is not a black box empirical model. If a drug is eliminated renally then I feel comfortable using a marker of renal function e.g. predicted creatinine clearance, as a covariate for the renal component of drug clearance. This is the basic application of biology to modelling. The allometric relationship is being applied in the same way. Anderson BJ, McKee D, Holford NHG. Size, myths and the clinical pharmacokinetics of analgesia in paediatric patients. Clin. Pharmacokin. 1997;33:313-327 Holford NHG. A size standard for pharmacokinetics. Clin. Pharmacokin. 1996; 30:329-332 Peters RH. The ecological implications of body size. Cambridge University Press. 1983 West GB. Brown JH. Enquist BJ. A general model for the origin of allometric scaling laws in biology. Science. 1997; 276:122-6 -- Nick Holford, Dept Pharmacology & Clinical Pharmacology University of Auckland, Private Bag 92019, Auckland, New Zealand email:n.holford@auckland.ac.nz tel:+64(9)373-7599x6730 fax:373-7556 http://www.phm.auckland.ac.nz/Staff/NHolford/nholford.htm

Date: Thu, 18 Nov 1999 10:04:51 +0000 From: James <J.G.Wright@ncl.ac.uk> Subject: Re: Covariate Models Using Weight Dear Nick, Just to make this clear - if I put on weight ie fat tissue, my clearance increases? How? Does my liver get bigger? I shall read your selected references (again, as I have read two of them in the past, although asking me to read a book before considering my views seems a little demanding), but given that only two of them appear to be about clinical populations, I can hardly consider this an overwhelming body of evidence. Can I suggest that you take a look at? Nawaratne S. Brien JE. Seeman E. Fabiny R. Zalcberg J. Cosolo W. Angus P. Morgan DJ. Relationships among liver and kidney volumes, lean body mass and drug clearance. British Journal of Clinical Pharmacology. 46(5):447-52, 1998 Nov James

Date: Fri, 19 Nov 1999 12:01:50 +1300 From: Nick Holford <n.holford@auckland.ac.nz> Subject: Re: Covariate Models Using Weight James Wright wrote: > Just to make this clear - if I put on weight ie fat tissue, my clearance > increases? How? Does my liver get bigger? I do not expect an increase in weight due entirely to an increase in fat to increase clearance as predicted by the allometric model using weight. However, it is possible that there are extra metabolic demands put on the body by the extra fat that could lead to increased liver size and perhaps enhanced drug clearance. The allometric scaling model has to be underststood as a model for ONE covariate (weight) among many that might be associated with changes in clearance. Some of these covariates (age, obesity) will be correlated with weight so it will require some thought about a model and appropriate data with sufficient variability in the covariates to test the model. > I shall read your selected references... but given that only two > of them appear to be about clinical populations, I can hardly > consider this an overwhelming body of evidence. Would you like to offer some guidelines for what *you* consider "an overwhelming body of evidence". How much evidence does I have to provide to persuade you that an allometric exponent of 3/4 is reasonable when you provide no plausible counter evidence (see below)? May I suggest, you add to the evidence I wish you to consider the recent generalization of the theoretical basis for the 3/4 exponent model by West et al (1999)? > Can I suggest that you take a look at: > > Nawaratne S. Brien JE. Seeman E. Fabiny R. Zalcberg J. Cosolo W. Angus P. > Morgan DJ. > Relationships among liver and > kidney volumes, lean body mass and drug clearance. British Journal of > Clinical Pharmacology. 46(5):447-52, 1998 Nov I do not consider this small study of 21 healthy volunteers, with less than a 2 fold range in any of the covariates and more than a 4 fold range in clearance, relevant to testing the hypothesis about the 3/4 power model. I would like to refer you to Beuchat (1997) with an accompanying comment from Brown et al. which deals with the problems of considering empirical alternative exponents based on sparse data and the lack of any theory. I note that the authors of this study make the classical error of accepting the null hypothesis that LBM was not a predictor of anitipyrine clearance via a relationship to liver volume without any consideration of the power they had for detecting such a prediction. I would think the signal to noise ratio is just too small for this kind of study to offer any insights. Beuchat CA. Allometric scaling laws in biology [letter; comment]. Science 1997;278(5337):371; discussion 372-3. West GB, Brown JH, Enquist BJ. The fourth dimension of life: fractal geometry and allometric scaling of organisms. Science 1999;284(5420):1677-9. -- Nick Holford, Dept Pharmacology & Clinical Pharmacology University of Auckland, Private Bag 92019, Auckland, New Zealand email:n.holford@auckland.ac.nz tel:+64(9)373-7599x6730 fax:373-7556 http://www.phm.auckland.ac.nz/Staff/NHolford/nholford.htm

Date: Fri, 19 Nov 1999 12:23:36 +0000 From: James <J.G.Wright@ncl.ac.uk> Subject: Re: Covariate Models Using Weight Dear Nick, The point where you and I differ, I think, is in what we consider relevant evidence. I don't consider between species scaling to have any relevance to the use of weight in a human population. Nor am I impressed by elaborate fractal models. As you have provided only two references that I consider relevant, (and you are an author on both of them), I remain totally underwhelmed. Steve raises several interesting issues, and the truth is that unless you have a truly massive sample there is always a certain amount of subjectivity in how you construct a covariate model (A Miller, Subset Selection in Regression, is a very scary book). If you require predictive accuracy, then you should be very wary of overfitting, so I guess I am not that keen on including covariates that are not justified by improvements in fit. Steve's example of creatinine clearance and the Cockcroft & Gault formula is an interesting one - the Cockcroft & Gault formula contains someone else's prior knowledge, but if your population isn't the same as theirs (or you are using a modern creatinine assay) maybe this will do more harm than good. Personally, I would use the raw covariate (and weight etc separately (if justified), although this will use more parameters) in this particular case. My view is we should use prior information, but evaluate its relevance critically. James

From: "Stephen Duffull" <sduffull@fs1.pa.man.ac.uk> Subject: Re: Covariate Models Using Weight Date: Mon, 22 Nov 1999 17:04:44 -0000 James Gallo wrote (in relation to a comment from Nick): > I feel you also are missing my point about covariate modeling related to > renal function or any other mechanism related to drug disposition. For > some reason you seem to want to adhere to PRE-DEFINED Formulas to describe > CRCL and subsequently drug clearance. This was my original point. If there is little difference in the fit between two models (one "predefined" and one not) to the same data then incorporating the "predefined" model which has proven generality seems more mechanistic than developing a "context-sensitive" model empirically (which has unknown generality). The choice of the "predefined" model (or "context-insensitive" model) is then up to the modeller (obviously no one model is going to solve all problems). Steve ===================== Stephen Duffull School of Pharmacy University of Manchester Manchester, M13 9PL, UK Ph +44 161 275 2355 Fax +44 161 275 2396

Date: Mon, 22 Nov 1999 13:42:12 -0600 From: James Gallo <JM_Gallo@fccc.edu> Subject: Re: Covariate Models Using Weight Steve, I disagree. I don't see how one can consider "PRE-Defined" models as mechanistic and "context-sensitive" [rather poor terminology] as empirical. Either type of model makes use of the same type of data [concentration-time or urine data and a mix of covairates], and it is only in how the final model shakes-out that one could attribute it to be 'mechanistic' or not. My concern is that the PRE-DEFINED models may be a priori applied as a surrogate for drug clearance when such application is unnecessary given a unique dataset for the drug of interest. I'd also be cautious of characterizing PRE-DEFINED models as being of "proven generality". Also, who cares if the so-called "context-sensitive" model is of "unknown generality" as long as it can most accurately predict clearance [for instance] for Drug X. Your last point that ultimately how one proceeds is up to the modeler is a good one. jim gallo

Date: Mon, 22 Nov 1999 12:02:11 -0800 From: LSheiner <lewis@c255.ucsf.edu> Subject: Re: Covariate Models Using Weight Seems to me that this whole controversy is settled be acting like a Bayesian: the "pre-defined" scientifically based model has a certain prior credibility, which can be stated using an informative prior distribution on, for example, the allometric exponent. Then, if the data really favor some other value, the exponent will adjust; if they do not, it will remain near it's prior. Almost always, controversies like this can be resolved by choosing a "model" (in this case the full Bayesian framework) which is a superset of both of the special cases that find themselves in conflict ... LBS. -- Lewis B Sheiner, MD Professor: Lab. Med., Biopharm. Sci., Med. Box 0626 voice: 415 476 1965 UCSF, SF, CA fax: 415 476 2796 94143-0626 email: lewis@c255.ucsf.edu

Date: Tue, 23 Nov 1999 09:34:45 +1300 From: Nick Holford <n.holford@auckland.ac.nz> Subject: Re: Covariate Models Using Weight James Gallo wrote: > Nick, > > I feel you also are missing my point about covariate modeling related to > renal function or any other mechanism related to drug disposition. For > some reason you seem to want to adhere to PRE-DEFINED Formulas to describe > CRCL and subsequently drug clearance. PRE-DEFINED = prior knowledge As Lewis Sheiner has pointed out we can always test using a Bayesian formulation if our data really has something to add to what we know already. I like to use prior knowledge if possible because in information about covariate relationships is usually sparse. > Given the problem of developing a population model for DRUG X that undergoes > appreciable renal clearance, I believe it is more rational to start from > scratch. That is your choice. I prefer to lean on all the scientific knowledge we have accumulated to date rather than reinvent the wheel. > the task was to model a drug that underwent 100% hepatic clearance. One > would not have [or want] PRE-DEFINED formulas to relate covariates and > clearance, and would start from scratch to identify significant covariate-CL > relationships. It seems you have not read the earlier portion of this thread in which I specifically pointed out the existence of a "pre-defined" allometric model which uses weight as the covariate to predict clearance such as hepatic clearance. Once again I prefer to rely on biology to help me understand rather than simply describe. -- Nick Holford, Dept Pharmacology & Clinical Pharmacology University of Auckland, Private Bag 92019, Auckland, New Zealand email:n.holford@auckland.ac.nz tel:+64(9)373-7599x6730 fax:373-7556 http://www.phm.auckland.ac.nz/Staff/NHolford/nholford.htm

Date: Mon, 22 Nov 1999 23:55:10 +0100 From: Pierre Maitre <pmaitre@freesurf.ch> Subject: Re: Covariate Models Using Weight >Nick Holford wrote: >> >> James Gallo wrote: >>> >>> James Wright wrote: >>> >>>> "Piotrovskij, Vladimir [JanBe]" wrote: .... ....May I too? Dear respected scientists May a modest clinician add a word of philosophy to your religious debate? You have been discussing the way body weight influences the elimination clearance and I liked this hot discussion very much. It is indeed important to understand the relationship between weight and elimination. I am just asking myself weither it is appropriate to plug into a pharmacokinetic model a formula like Males CLcr = (143.5 - (1.095*Age) * Wt * 0.07 Females CLCr = (119.5 - (0.915*Age) * Wt * 0.07 or any other equation you want, without some evidence in your data to support your choice. My NONMEM mentor, Sam Vozeh, used to tell me, back in 1984, that nonlinear regression is an art, not a science. So please allow me tu use unscientific words like " I believe". I believe that clinicians need that we tell them the average pharmacokinetics of a drug, the size of the inter-individual variability, and the influence of important covariates (and among them, possibly, body weight). "Important" is the keyword here and is the hardest to define. The size of "Important" has first to be compared with the size of the interpatient variability for the parameter in question. Who cares about a small 8% change of clearance due to a particular covariate, if the remaining inter-individual variability is 40% for that parameter? ... forget this covariate, it won't help the clinician. For a clinician, "important" would mean changing the dosing by at least 20% (this is not science, again, but my experience as an anesthesiologist administering drugs every day). How do we sort out the "important" covariates from those who are no important? The P value doesn't help in this matter: as you all know, statistically significant does not mean important. My answer to this question might be: what you see in your data is worth modeling, what you don't see in your data is not worth modeling. Graphical methods based on the plots of etas vs covariates are a good start (Xpose can be used for this purpose) and allow one to pick up the "Important" covariates and to find a simple model that would fit the data. Dosing recommendations for the clinician must be kept simple in order to be safe. The above equations are certainly very exact, but they are just too complex to be used at bedside. And their complexity give to the clinician a false sense of scientific truth (that the clinician translates into "precise prediction") whereas the prediction of the concentration is very vague and unprecise, due to the cloud of interpatient variability. To conclude, my point would be: for your model to be useful, keep is simple. Pierre Maitre Genolier / Geneva

From: "Piotrovskij, Vladimir [JanBe]" <VPIOTROV@janbe.jnj.com> To: nmusers@c255.ucsf.edu Subject: RE: Covariate Models Using Weight Date: Tue, 23 Nov 1999 08:41:03 +0100 MIME-Version: 1.0 I agree that having parameters of pre-defined formulas open for iterations may improve the fit, and this is perhaps a reasonable approach if a drug is eliminated exclusively via passive glomerular filtration. However, if the renal clearance is only a part of the total clearance, and the enzyme-dependent part may be affected by the same covariates (AGE, WT, etc.) differently, this approach will not work or will give misleading results. Vladimir ---------------------------------------------------------------------- Vladimir Piotrovsky, Ph.D. Janssen Research Foundation Clinical Pharmacokinetics B-2340 Beerse Belgium Email: vpiotrov@janbe.jnj.com

Date: Tue, 23 Nov 1999 09:14:00 +0000 From: James <J.G.Wright@ncl.ac.uk> Subject: Re: Covariate Models Using Weight Dear nmusers, I think everyone in this (extremely long) thread believes in using prior knowledge in some way. The problem is that this is inevitably a subjective process and having done some work with renal function in cancer patients I can safely say that none of the existing formula work terribly well in this population. As such, I don't want to use them (because of population specificity, problems with the assay method etc.). If you happen to be working in a population you consider exchangeable with that used in the original study then maybe you could use their information, but the way to do this may well be with prior that acknowledges uncertainty. However I can here the frequentists calling if you have a big sample the prior doesn't matter and if you have a little sample you will get biased results - it depends on the quality of your prior information. To return to Steve's original question - if both fit the model equally, then this probably isn't a big issue (not that you'd believe it). Personally I would stick to the covariates which come from a clearly defined source, rather than putting age & weight in a predefined formula and then interpreting this to mean their effects on renal function are completely accounted for. James PS Is this the longest thread ever on the nmuser list?

From: "Piotrovskij, Vladimir [JanBe]" <VPIOTROV@janbe.jnj.com> Subject: RE: Covariate Models Using Weight Date: Tue, 23 Nov 1999 11:36:22 +0100 I agree, it is already too long, but still, one more reason for not to rely very much on SCR and CLCR (irrespective to the formula used). Creatinine is excreted partly via an active process, tubular secretion, and its steady-state level may be affected by some drugs. One example I have found is given at the end of my mail. I presume there are more recent examples. Vladimir ---------------------------------------------------------------------- Vladimir Piotrovsky, Ph.D. Janssen Research Foundation Clinical Pharmacokinetics B-2340 Beerse Belgium Email: vpiotrov@janbe.jnj.com ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Authors Cahen R. Martin A. Francois B. Baltassat P. Louisot P. Institution Division of Nephrology, Lyon-South University Medical Center, Pierre Benite, France. Title Creatinine metabolism impairment by an anticonvulsant drug, phenacemide. Source Annals of Pharmacotherapy. 28(1):49-51, 1994 Jan. MeSH Subject Headings: Adult *Anticonvulsants/ae [Adverse Effects]Carbamazepine/tu [Therapeutic Use] Case Report *Creatinine/bl [Blood] Epilepsy, Temporal Lobe/bl [Blood] Epilepsy, Temporal Lobe/dt [Drug Therapy] Glomerular Filtration Rate Human Male Phenobarbital/tu [Therapeutic Use] *Urea/aa [Analogs & Derivatives] Urea/ae [Adverse Effects] Abstract: OBJECTIVE: To report two cases of increased true serum creatinine (Scr) without renal failure caused by an anticonvulsant drug, phenacemide, and to discuss the possible mechanisms. CASE SUMMARY: Two patients treated with phenacemide were investigated for markedly increased Scr and decreased creatinine clearance (Clcr) values. Glomerular filtration rates, as determined by 125I-iothalamate clearance, were normal in both patients and analytical interferences with the Jaffe reaction were excluded. After discontinuation of the drug, phenacemide concentrations became undetectable within 2 days but it took 7-14 days for Scr and Clcr to return to normal values. DISCUSSION: The Scr increase with phenacemide (120-170 percent) was higher than that reported with cimetidine or trimethoprim (10-40 percent) and could not be explained solely by inhibition of the tubular secretion of creatinine. The hypothesis of an overproduction of creatinine caused by phenacemide was ruled out by experimental studies in rats. Creatinine increase in tissues was lower than that in the serum of rats given phenacemide. In vitro creatinine influx into red blood cells was inhibited in a dose-dependent way by phenacemide. CONCLUSIONS: Increased Scr concentrations in these patients could be related to an inhibition of transport and a decrease in creatinine volume of distribution. Creatinine concentrations should not be considered when dosage adjustments of renally eliminated drugs are being calculated for patients with such metabolic interferences. ********************************************************************** Other thread subtopics: Centering Covariates Covariate Models Using Weight (Allometric Scaling) Covariate Models Using CrCL