From: Martin Bergstrand <*martin.bergstrand*>

Date: Wed, 31 Mar 2010 09:55:05 +0200

Dear Leonid,

As I have pointed out once before on NMusers

(http://www.cognigencorp.com/nonmem/current/2009-April/1661.html) the error

model that you are using can be very problematic. The RUV model only have

the desired properties as long as THETA(7) is larger than TY (TY=IPRED in

your example). If TY << THETA(7) this error model will give rise to an

almost infinite RUV and hence completely unrealistic predictions (eg. DV:

10^-50 to 10^50). If you don't understand what I mean you can take the

dataset associated with this model and add an observations at a very late

time point (where TY typically is << TY). If you simulate out this sample a

number of times you will see that DV takes on values in an almost infinite

range. Even though this is perhaps primarily a problem during simulation but

it is of course also potentially harmful to estimations.

In contrast to Nick I do sometimes see the benefit of modeling a

log-transformed DV since it in many cases improve the runtimes and "model

stability" of NONMEM. However even though I have been playing around with it

quite a lot I haven't found a really good RUV model for the so typical

"combined error" structure of bioanaytical data. I feel that the downside of

simulating some negative DVs with the additive + proportional RUV model for

non transformed data generally is less of a problem. The sad part of this is

as Nick and others has pointed out numerous times that it is a constructed

problem. Isn't it about time that we as the customers of bioanalytical

analysis enforce a better data reporting standard that doesn't imply non

random censoring of data?

Best regards,

Martin Bergstrand, MSc, PhD student

-----------------------------------------------

Pharmacometrics Research Group,

Department of Pharmaceutical Biosciences,

Uppsala University

-----------------------------------------------

martin.bergstrand

-----------------------------------------------

Work: +46 18 471 4639

Mobile: +46 709 994 396

-----Original Message-----

From: owner-nmusers

Behalf Of Leonid Gibiansky

Sent: den 31 mars 2010 00:07

To: Nick Holford

Cc: nmusers

Subject: Re: [NMusers] Parallel first order and Michaelis-Menten elimination

Nick,

The form that I use directly follows from the TMDD model suggested in

2001 in:

Mager DE, Jusko WJ. General pharmacokinetic model for drugs exhibiting

target-mediated drug disposition. J. Pharmacokinetic and Pharmacodynamic

Vol. 28, pp. 507-532 (2001).

Derivation of the MM model from the TMDD equations can be found in:

Gibiansky L, Gibiansky E, Kakkar T, Ma P: Approximations of the

target-mediated drug disposition model and identifiability of model

parameters. J Pharmacokinet Pharmacodyn. (2008) 35(5):573-91.

(this is not the only paper that discusses the TMDD/MM correspondence,

but I like it, for the obvious reasons :) ). TMDD model assumes that all

elimination occurs in the same volume as the central volume.

You can check that in this case the nonlinear part of elimination is

volume-proportional (so, mass-proportional) with VM being WT-independent

[in VM*A(1)/(KM+A(1)/V1) ].

As to the error model with negative predictions, I will start to use it

as soon as I get my first dataset with negative concentrations :) Note

that in many if not all cases, most of the residual error comes not from

the assay error but from other unexplained factors. While assay error

could lead to negative concentrations, "other unexplained factors"

cannot result in negative values, so I prefer to use the error model

that provides only positive values. Of course, this is the matter of

preferences / style. Everyone has their own favorite models, including

the error models.

Best !

Leonid

--------------------------------------

Leonid Gibiansky, Ph.D.

President, QuantPharm LLC

web: www.quantpharm.com

e-mail: LGibiansky at quantpharm.com

tel: (301) 767 5566

Nick Holford wrote:

*> Leonid,
*

*>
*

*> I accept that the unit volume parameterization of VM is quite reasonable
*

*> if you think that all elimination occurs in the same volume as the
*

*> distribution volume. This is the usual test tube model that gives rise
*

*> to the unit volume belief system. It is not a realistic view of
*

*> elimination from the human body
*

*>
*

*> I would also accept that if all elimination occurs in some blood cell
*

*> distribution volume (e.g.white cells) then this will be highly
*

*> correlated with the drug distribution volume and the unit volume
*

*> parameterization will appear to work fine but will fail if there is some
*

*> covariate that determines blood cell volume distribution differently
*

*> from drug distribution volume.
*

*>
*

*> However, I don't accept that the elimination of a biological can be
*

*> independent of weight if we refer to the actual mass eliminated per unit
*

*> time. The first order part of the model for elimination which uses
*

*> clearance is certainly not independent of weight so why would you
*

*> imagine that the mixed order elimination process would be independent of
*

*> weight?
*

*>
*

*> Note that the oldest example of a mixed order elimination process is for
*

*> ethanol (Widmark essentially invented the science of pharmacokinetics
*

*> with a mixed order elimination model). Ethanol elimination largely
*

*> occurs in the liver while it distributes in total body water. Liver
*

*> metabolism scales allometrically in a different way from total body
*

*> water so I do not believe that the mixed order elimination of ethanol
*

*> should be described with the unit volume belief system. I much prefer
*

*> the unit body belief system.
*

*>
*

*> Sorry - I was confused by your residual error model which at first sight
*

*> seemed to be a transform both sides model. However, the model you use
*

*> restricts all residual errors to be non-negative which is not a
*

*> realistic description of any non-censored residual error process. All
*

*> real world uncensored measurement errors should have an error
*

*> distribution on both sides of zero when the true value is zero.
*

*>
*

*> Nick
*

*>
*

*> Leonid Gibiansky wrote:
*

*>> Hi Nick
*

*>>
*

*>> This form of equations can be derived from the target-mediated drug
*

*>> disposition equations. In that, VM=Kint*Rmax, where Kint is the
*

*>> internalization rate and Rmax is the concentration of the target
*

*>> (receptor). Target-mediated clearance is believed to be carried out in
*

*>> the central volume (in that particular form of the equations), and
*

*>> thus, VM is coming from the enzyme theory equations (maximum reaction
*

*>> rate). In my experience, if you follow parametrization that I use, VM
*

*>> is independent on weight (and thus, random effect on volume does not
*

*>> correlate with the random effect on VM - if the one is needed- unlike
*

*>> the parameterization that you propose). For biologics, if we believe
*

*>> that the mechanism of non-linear clearance is TMDD, it is more
*

*>> mechanistic to use parameterization suggested in my e-mail.
*

*>>
*

*>> Alternatively, the same MM equation can be derived from TMDD using
*

*>> slightly different assumptions. In that form, VM=ksyn, where ksyn is
*

*>> the target production rate per unit volume. Both forms (Vm=ksyn or
*

*>> VM=Kint*Rmax) interpret MM constants in terms of mechanistic
*

*>> parameters of the TMDD system.
*

*>>
*

*>> As to the error model, I used non-transformed variables but borrowed
*

*>> the error model from the log-transformed case. I like it because it
*

*>> would not deliver negative results on simulations.
*

*>>
*

*>> While the log-transformation may or may not provide much benefits, it
*

*>> is the only way to implement true exponential (rather than
*

*>> proportional) error model in nonmem. This is purely technical
*

*>> (mathematical-statistical-numerical method-related) problem, no
*

*>> biology behind this transformation, so I cannot see much sense to
*

*>> argue for or against using this trick.
*

*>>
*

*>> Any way, the question was about MM part, not the error model. We can
*

*>> supplement MM model with any more conventional error model of personal
*

*>> choice (hopefully supported by the data and confirmed by the VPC).
*

*>>
*

*>> Best !
*

*>> Leonid
*

*>>
*

*>>
*

*>>
*

*>>
*

*>> --------------------------------------
*

*>> Leonid Gibiansky, Ph.D.
*

*>> President, QuantPharm LLC
*

*>> web: www.quantpharm.com
*

*>> e-mail: LGibiansky at quantpharm.com
*

*>> tel: (301) 767 5566
*

*>>
*

*>>
*

*>>
*

*>>
*

*>> Nick Holford wrote:
*

*>>> Leonid,
*

*>>>
*

*>>> Thanks for the code example which illustrates one side of a religious
*

*>>> debate which took place a few weeks ago on PharmPK. The essence of
*

*>>> this debate was should one normalize PK parameters to a unit volume
*

*>>> or to a unit body.
*

*>>>
*

*>>> The unit volume believers feel that the rate constant is the
*

*>>> 'natural' way to describe pharmacokinetics while the unit body
*

*>>> believers feel that clearance is more 'natural'. Both groups agree
*

*>>> that the two systems are just reparameterizations and make identical
*

*>>> numerical predictions.
*

*>>>
*

*>>> Your coding of Vmax for the mixed order elimination process has the
*

*>>> implicit units of mass/time per unit volume e.g. mg/h/L. This is the
*

*>>> unit volume belief system.
*

*>>>
*

*>>> I am a unit body believer so I would code this system differently
*

*>>> with a very simple change- substituting A(1) with C1 to multiply the
*

*>>> mixed order expression. I have also changed VM to VMUB to indicate
*

*>>> that the dimensions of the Vmax parameter are per unit body i.e. mg/h
*

*>>> per body.
*

*>>>
*

*>>> DADT(1) = -K10*A(1)-C1*VMUB/(KM+C1)-K12*A(1)+K21*A(2)
*

*>>>
*

*>>> It could also be written like this to emphasize that the mixed order
*

*>>> process has the same units as CL (for unit body believers) when C1
*

*>>> tends to 0:
*

*>>>
*

*>>> DADT(1) = -C1*(CL+VMUB/(KM+C1) - K12*A(1)+K21*A(2)
*

*>>>
*

*>>> I note also that your residual error model implies that the DV has
*

*>>> been log transformed. This reflects yet another belief system which I
*

*>>> think you have shown has little, if any, practical merit. I prefer to
*

*>>> keep the DV in the original units.
*

*>>>
*

*>>> Best wishes,
*

*>>>
*

*>>> Nick
*

*>>>
*

*>>>
*

*>>>
*

*>>> Leonid Gibiansky wrote:
*

*>>>> ADVAN6 ADVAN8 or (nm7) ADVAN13
*

*>>>>
*

*>>>> The code is below
*

*>>>>
*

*>>>> Leonid
*

*>>>>
*

*>>>> -------------------
*

*>>>> $SUBROUTINE ADVAN6 TOL=9
*

*>>>>
*

*>>>> $MODEL
*

*>>>> NCOMP = 2
*

*>>>> COMP = (CENTRAL) ;1
*

*>>>> COMP = (PERIPH) ;2
*

*>>>>
*

*>>>> $PK
*

*>>>> CL= THETA(1)*EXP(ETA(1))
*

*>>>> V1= THETA(2)*EXP(ETA(2))
*

*>>>> Q = THETA(3)*EXP(ETA(3))
*

*>>>> V2= THETA(4)*EXP(ETA(4))
*

*>>>> VM= THETA(5)*EXP(ETA(5))
*

*>>>> KM= THETA(6)
*

*>>>>
*

*>>>> K10 = CL/V1
*

*>>>> K12 = Q/V1
*

*>>>> K21 = Q/V2
*

*>>>> S1 = V1
*

*>>>> S2 = V2
*

*>>>>
*

*>>>> $DES
*

*>>>> C1 = A(1)/S1
*

*>>>> DADT(1) = -K10*A(1)-A(1)*VM/(KM+C1)-K12*A(1)+K21*A(2)
*

*>>>> DADT(2) = K12*A(1)-K21*A(2)
*

*>>>>
*

*>>>> $ERROR
*

*>>>> TY = A(1)/V1
*

*>>>> IPRED=TY
*

*>>>> W = SQRT(THETA(7)**2/TY**2+THETA(8)**2)
*

*>>>> Y = IPRED*EXP(W*ERR(1))
*

*>>>>
*

*>>>> $THETA
*

*>>>> .....
*

*>>>>
*

*>>>> $OMEGA
*

*>>>> .....
*

*>>>>
*

*>>>> $SIGMA
*

*>>>> 1 FIX ; ~ERR
*

*>>>>
*

*>>>> --------------------------------------
*

*>>>> Leonid Gibiansky, Ph.D.
*

*>>>> President, QuantPharm LLC
*

*>>>> web: www.quantpharm.com
*

*>>>> e-mail: LGibiansky at quantpharm.com
*

*>>>> tel: (301) 767 5566
*

*>>>>
*

*>>>> chenyuhong *

*>>>>> Dear All,
*

*>>>>>
*

*>>>>> I am working with a Biologic and would like to have a PK model with
*

*>>>>> parallel first order and Michaelis-Menten elimination. Any
*

*>>>>> suggestion about which subroutine I am supposed to use? If you can
*

*>>>>> share an example for the control stream, that will be a great help.
*

*>>>>>
*

*>>>>> Thanks,
*

*>>>>>
*

*>>>>> Yuhong
*

*>>>>>
*

*>>>>>
*

*>>>>>
*

*>>>
*

*>>> --
*

*>>> Nick Holford, Professor Clinical Pharmacology
*

*>>> Dept Pharmacology & Clinical Pharmacology
*

*>>> 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: n.holford *

*>>> http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford
*

*>>>
*

*>
*

*> --
*

*> Nick Holford, Professor Clinical Pharmacology
*

*> Dept Pharmacology & Clinical Pharmacology
*

*> 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: n.holford *

*> http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford
*

*>
*

Received on Wed Mar 31 2010 - 03:55:05 EDT

Date: Wed, 31 Mar 2010 09:55:05 +0200

Dear Leonid,

As I have pointed out once before on NMusers

(http://www.cognigencorp.com/nonmem/current/2009-April/1661.html) the error

model that you are using can be very problematic. The RUV model only have

the desired properties as long as THETA(7) is larger than TY (TY=IPRED in

your example). If TY << THETA(7) this error model will give rise to an

almost infinite RUV and hence completely unrealistic predictions (eg. DV:

10^-50 to 10^50). If you don't understand what I mean you can take the

dataset associated with this model and add an observations at a very late

time point (where TY typically is << TY). If you simulate out this sample a

number of times you will see that DV takes on values in an almost infinite

range. Even though this is perhaps primarily a problem during simulation but

it is of course also potentially harmful to estimations.

In contrast to Nick I do sometimes see the benefit of modeling a

log-transformed DV since it in many cases improve the runtimes and "model

stability" of NONMEM. However even though I have been playing around with it

quite a lot I haven't found a really good RUV model for the so typical

"combined error" structure of bioanaytical data. I feel that the downside of

simulating some negative DVs with the additive + proportional RUV model for

non transformed data generally is less of a problem. The sad part of this is

as Nick and others has pointed out numerous times that it is a constructed

problem. Isn't it about time that we as the customers of bioanalytical

analysis enforce a better data reporting standard that doesn't imply non

random censoring of data?

Best regards,

Martin Bergstrand, MSc, PhD student

-----------------------------------------------

Pharmacometrics Research Group,

Department of Pharmaceutical Biosciences,

Uppsala University

-----------------------------------------------

martin.bergstrand

-----------------------------------------------

Work: +46 18 471 4639

Mobile: +46 709 994 396

-----Original Message-----

From: owner-nmusers

Behalf Of Leonid Gibiansky

Sent: den 31 mars 2010 00:07

To: Nick Holford

Cc: nmusers

Subject: Re: [NMusers] Parallel first order and Michaelis-Menten elimination

Nick,

The form that I use directly follows from the TMDD model suggested in

2001 in:

Mager DE, Jusko WJ. General pharmacokinetic model for drugs exhibiting

target-mediated drug disposition. J. Pharmacokinetic and Pharmacodynamic

Vol. 28, pp. 507-532 (2001).

Derivation of the MM model from the TMDD equations can be found in:

Gibiansky L, Gibiansky E, Kakkar T, Ma P: Approximations of the

target-mediated drug disposition model and identifiability of model

parameters. J Pharmacokinet Pharmacodyn. (2008) 35(5):573-91.

(this is not the only paper that discusses the TMDD/MM correspondence,

but I like it, for the obvious reasons :) ). TMDD model assumes that all

elimination occurs in the same volume as the central volume.

You can check that in this case the nonlinear part of elimination is

volume-proportional (so, mass-proportional) with VM being WT-independent

[in VM*A(1)/(KM+A(1)/V1) ].

As to the error model with negative predictions, I will start to use it

as soon as I get my first dataset with negative concentrations :) Note

that in many if not all cases, most of the residual error comes not from

the assay error but from other unexplained factors. While assay error

could lead to negative concentrations, "other unexplained factors"

cannot result in negative values, so I prefer to use the error model

that provides only positive values. Of course, this is the matter of

preferences / style. Everyone has their own favorite models, including

the error models.

Best !

Leonid

--------------------------------------

Leonid Gibiansky, Ph.D.

President, QuantPharm LLC

web: www.quantpharm.com

e-mail: LGibiansky at quantpharm.com

tel: (301) 767 5566

Nick Holford wrote:

Received on Wed Mar 31 2010 - 03:55:05 EDT