On 08-Dec-17 02:00, STANDING, Joseph (GREAT ORMOND STREET HOSPITAL FOR
CHILDREN NHS FOUNDATION TRUST) wrote:
> Dear Rob,
> Why do you want to use a model to predict the value of a covariate to add into your model? Apart from glomerular filtration rate, what other situations would you do this?
I'm not sure what you are trying to say here. What model for GFR are you
thinking about as the only example?
I could mention using a model for FFM based on covariates of WT, HT and
SEX as an example but I don't know if this is what you mean.
> Unless I was trying to do some fancy separation of renal and non-renal clearance,
Unless you want to assume that the drug is completely eliminated renally
then there should always be a "fancy separation" of renal and non-renal
clearance. I don't know of any clear cut case where I could assume a
drug can only be eliminated by the kidneys.
> I would simply ignore the fact there is a model to predict glomerular fltration, and include its component parts e.g.
> CL = THETA * (WT/70)**0.75 * FCREAT * FAGE
> where FCREAT and FAGE are covariate functions for creatinine (e.g. (SECR/median value)**THETA ) and age.
No explicit assumption of the pathway of elimination but just an
empirical functions of SCR. This can include the bizarre MDRD and
CKD-EPI functions which include skin colour as a covariate. I consider
this kind of empiricism when there are mechanistic alternatives to be
> Some examples of not using a model to predict GFR still gave an acceptable model of CL (what we were interested in):
> Creatinie e.g.
> Hennig S. Population pharmacokinetics of tobramycin in patients with and without cystic fibrosis. Clin Pharmacokinet. 2013 Apr;52(4):289-301
> Cystatin C example:
> De Cock PA. Augmented renal clearance implies a need for increased amoxicillin-clavulanic acid dosing in critically ill children. Antimicrob Agents Chemother. 2015 Nov;59(11):7027-35.
Just because a model can provide a local fit to the data does not mean
it has good properties for generalization / extrapolation. Incorporating
biological mechanism and sensible extrapolation properties should be
used whenever possible.
> Joseph F Standing
> MRC Fellow, UCL Institute of Child Health
> Antimicrobial Pharmacist, Great Ormond Street Hospital
> Honorary Senior Lecturer, St George's University of London
> Tel: +44(0)207 905 2370
> Mobile: +44(0)7970 572435
> From: owner-nmusers_at_globomaxnm.com [owner-nmusers_at_globomaxnm.com] on behalf of R.terHeine_at_radboudumc.nl [R.terHeine_at_radboudumc.nl]
> Sent: 07 December 2017 11:56
> To: nmusers_at_globomaxnm.com
> Cc: n.holford_at_auckland.ac.nz; max.taubert_at_uk-koeln.de; ruben.faelens_at_gmail.com; j.h.proost_at_rug.nl
> Subject: RE: [NMusers] Allometric scaling of renal clearance with estimated glomerular filtration rate
> Dear Nick, Hans, Ruben, Max,
> Great to hear several approaches and opinions on the use of glomerular filtration approximations in PK modeling and scaling to body size. Thank you!
> I have a hard time ignoring the CKD-EPI equations (with or without cystatin C), as they are well established and proven better predictors for GFR, when compared to the Cockroft-Gault. In general, sample sizes of pharmacokinetic studies are smaller than those where the CKD-EPI and MDRD equations were developed. I am not convinced that developing a new creatinine/cystatin c equation for GFR for each PK analysis is the right approach. Then again, I also have a hard time scaling to BSA, as in, for example, obese patients this is likely a poor body size descriptor to scale renal function.
> Also, depending on the population and drug one may choose one equation above another. For example: if a drug is completely filtrated (no active secretion), a cystatin C based equation is likely better explain variability in clearance of completely filtrated drugs (e.g. carboplatin). Another example: in cachectic patients one may argue that there is not enough muscle mass (and thus serum creatinine) to provide accurate GFR estimations and then creatinine-independent equations may provide better equations.
> Thinking about this the last couple of days and with your feedback, I am inclined to choose the equation based on population (e.g. cachectic or not?) and drug (e.g. filtration/active secretion) and, if the equation scales renal function to BSA, convert it to scaling to FFM. Nonetheless, open to any other suggestion or discuss cons and pro's anytime!
> -----Oorspronkelijk bericht-----
> Van: owner-nmusers_at_globomaxnm.com [mailto:owner-nmusers_at_globomaxnm.com] Namens Nick Holford
> Verzonden: woensdag 6 december 2017 20:06
> Aan: nmusers_at_globomaxnm.com
> Onderwerp: Re: [FORGED] [NMusers] Allometric scaling of renal clearance with estimated glomerular filtration rate
> Hi Rob,
> Thanks for bringing this up again. I don't think much has changed since I wrote this in 2013
> 1. Theory Based Allometry or Surface Area
> "Note that using surface area as a form of size standardization forglomerular filtration rate has no theoretical nor experimental support when compared to theory based allometry (Rhodin et al. 2009). So I donot agree with standardizing CLCR to 1.73 m^2. I know this is frequently done but in fact this is just based on tradition and an out of datetheory of scaling based on surface area (see Anderson & Holford 2008)."
> There is no biological or experimental support for using surface area to scale renal function markers such as GFR and CLcr. In contrast, there is strong biological based theory and experimental support for using theory based allometry (see Holford & Anderson 2017 for a recent review).
> 2. Mechanism Based Models for CLcr
> I also wrote in 2013:
> "The MDRD method of predicting glomerular filtration rate is astatistical absurdity which does not include any measurement of size for its prediction. I would certainly not recommend using it for anyscientific purpose."
> This applies equally well to the CKD-EPI method. Let me explain why it is a absurdity generated by a naive statistician using CLcr as an example.
> CLcr can be calculated from the creatinine excretion rate (CER) and the serum creatiniine. This is based on the definition of clearance and is true without any assumptions.
> If we then assume Scr is at steady state then CER will be equal to creatinine production rate (CPR) and we can use this:
> All rational models for predicting CLcr without measurement of CER use models to predict CPR e.g.
> CPR=(140-Age)*Weight/72 use Cockcroft & Gault to predict CPR in males then CLcr=CPR/Scr is Cockcroft & Gault CLcr ml/min
> Dividing CPR by Scr gives the CLcr. This can be written equivalently but less clearly:
> The empirical models such as MDRD and CKI-EPI (see below) involve the absurdity of estimating the known exponent for Scr of -1. These estimates must be wrong based on the theory I have outlined above (unless the estimate is exactly -1). The reported estimates are -1.209 for CKI-EPI and -1.154 for MDRD.
> In addition, and more importantly,they have no direct measure of body size which seriously limits the value outside the typical weight distribution and they are only applicable to adults. GFR can be described from premature neonates to adults using theory based allometry and maturation based on post-menstrual age so GFR predicttions should try to follow the concepts used there (Rhodin 2008).
> So what to do?
> First -- don't use MDRD or CKI-EPI unless you are sure you are applying them to a population similar to that used to develop these empirical predictions. You could add allometric scaling to the eGFR by assuming the 1.72m^2 value is equivalent to 70 kg with a fat free mass (FFM) of
> 56.1 kg. Then scaling the eGFR by (WT/70)^(3/4) or (FFM/56.1)^(3/4).
> I use the Schwartz (1992) equations for neonates, children and teenagers then the Matthews (2004) equation for adults. I am working on an integrated method for CPR prediction which was presented as a work in progress at PAGE this year. Watch this space...
> Best wishes,
> eGFR =175 x (SCr)^-1.154 x (age)-0.203 x 0.742 [if female] x
> 1.212 [if Black]
> eGFR = 141 x min(SCr/k, 1)^alpha x max(SCr /kappa, 1)^-1.209 x 0.993^Age x 1.018 [if female] x 1.159 [if Black]
> kappa = 0.7 (females) or 0.9 (males)
> alpha = -0.329 (females) or -0.411 (males)
> eGFR (estimated glomerular filtration rate) = mL/min/1.73 m2; SCr (standardized serum creatinine) = mg/dL
> Holford NHG, Anderson BJ. Allometric size: The scientific theory and
> extension to normal fat mass. Eur J Pharm Sci. 2017;109(Supplement):S59-S64.
> Rhodin MM, Anderson BJ, Peters AM, Coulthard MG, Wilkins B, Cole M,
> Chatelut E, Grubb A, Veal GJ, Keir MJ, Holford NH
> Human renal function maturation – a quantitative description using
> weight and postmenstrual age. Pediatr Nephrol. 2008
> Schwartz GJ. Does kL/PCr estimate GFR, or does GFR determine k? Pediatr
> Nephrol. 1992;6(6):512-5.
> Matthews I, Kirkpatrick C, Holford N. Quantitative justification for
> target concentration intervention -- parameter variability and
> predictive performance using population pharmacokinetic models for
> aminoglycosides. Br J Clin Pharmacol. 2004;58(1):8-19.
> Holford N, Sherwin CM. Scaling renal function in neonates and infants to
> describe the pharmacodynamics of antibiotic nephrotoxicity. PAGE 26
> Abstr 7208 [wwwpage-meetingorg/?abstract=7208]. 2017.
> On 06-Dec-17 23:52, R.terHeine_at_radboudumc.nl wrote:
>> Hi Ruben,
>> Interesting work, Ruben. One may indeed question the validity of
>> glomerular filtration rate markers like cystatin C (that is only
>> filtrated and not actively secreted) to predict PK of drugs that
>> undergo active tubular secretion in patients with decreased renal
>> function. When glomerular filtration rate drops, the relative
>> contribution of active tubular secretion to renal clearance increases.
>> To me, it appears logical that creatinine is a better marker for
>> clearance drugs that are actively secreted, as creatinine also
>> undergoes active tubular secretion.
>> Nonetheless, I’m also interested whether other people have considered
>> allometric scaling of MDRD/CKD-EPI derived GFR’s?
>> *Van: *Ruben Faelens <ruben.faelens_at_gmail.com>
>> *Datum: *dinsdag 5 december 2017 om 7:13 PM
>> *Aan: *"Heine, Rob ter" <R.terHeine_at_radboudumc.nl>
>> *CC: *"nmusers_at_globomaxnm.com" <nmusers_at_globomaxnm.com>
>> *Onderwerp: *Re: [NMusers] Allometric scaling of renal clearance with
>> estimated glomerular filtration rate
>> Dear Rob,
>> At PMX Benelux, there was an interesting talk about the correlation
>> between different metrics describing renal function by Stijn Jonckheere.
>> A part of the work presented was published:
>> This may provide some perspective, or rather complicate things even
>> more, depending on your viewpoint.
>> Best regards
>> Ruben Faelens
> On 06-Dec-17 06:17, R.terHeine_at_radboudumc.nl wrote:
>> Dear all,
>> I am wondering what your thoughts are on the allometric scaling of
>> clearance of renally extreted drugs, where we have estimations renal
>> Simply scaling the predicted glomerular filtration rate from, for
>> example, the Cockroft-gault equation seems inappropriate, since weight
>> is already a part of the equation. Standardizing this to weight in the
>> Cockroft-gault equation can be done, a solution has been discussed
>> here: http://cognigencorp.com/nonmem/current/2013-August/4697.html
>> However, in the recent years some new equations to calculate
>> glomerular filtration rate from endogenous markers have emerged. For
>> example the CKD-EPI CREATININE CYSTATIN C equation
>> . As the addition of a muscle mass independent endogenous marker like
>> cystatin C is known to provide better estimations of GFR in, for
>> example, cachectic patients, it is likely that this equation may
>> outperform to predict renally filtrated compounds in this patient
>> group. It is rather odd that this CKD-EPI equation does not contain
>> any measure of body size. The outcome of this equation is a GFR scaled
>> to a BSA of 1.73m^2.
>> I am wondering how you would allometrically scale the eGFRs from these
>> CKD EPI equations to, for example, fat-free mass.
>> R. ter Heine, PhD, PharmD
>> Hospital Pharmacist-Clinical Pharmacologist
>> Radboudumc, Nijmegen, The Netherlands
>> Het Radboudumc staat geregistreerd bij de Kamer van Koophandel in het
>> handelsregister onder nummer 41055629.
>> The Radboud university medical center is listed in the Commercial
>> Register of the Chamber of Commerce under file number 41055629.
> 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
> office:+64(9)923-6730 mobile:NZ+64(21)46 23 53 FR+33(6)62 32 46 72
> Read the question, answer the question, attempt all questions
> Het Radboudumc staat geregistreerd bij de Kamer van Koophandel in het handelsregister onder nummer 41055629.
> The Radboud university medical center is listed in the Commercial Register of the Chamber of Commerce under file number 41055629.
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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
office:+64(9)923-6730 mobile:NZ+64(21)46 23 53 FR+33(6)62 32 46 72
Read the question, answer the question, attempt all questions
Received on Thu Dec 07 2017 - 15:58:05 EST