- On 12 Sep 1997 at 10:26:40, "Faruq H Noormohamed" (f.noormohamed.at.s1.cxwms.ac.uk) sent the message

Back to the Top

Hi

Would anyone in the PK group be able to briefly (or in depth, if

appropriate) give some sort of guidelines as to what are the relative

merits of modeling PK data or using non-compartmental PK analysis for

obtaining the usual PK parameters eg F, AUC, elimination rate constant(s),

accumulation, clearance etc..

A second but related question is would the answers to above query still

hold if the analyses were extended to include PK/PD.

My thanks in advance

Faruq H Noormohamed

Department of Therapeutics

Chelsea and Westminster Hospital

369 Fulham Road

LONDON

SW10 9NH

Tel +44 (0)181 746 8141

Fax +44 (0)181 746 8887

email f.noormohamed.aaa.cxwms.ac.uk - On 15 Sep 1997 at 15:23:06, JWALD.at.quintiles.com (Jeff Wald) sent the message

Back to the Top

The clearest "guideline" to me is that noncompartmental analysis is used

to describe PK parameters and PK modeling is used to decribe plasma (or

some other matrix) concentrations. Nevertheless, the same parameters

obtained via noncompartmental analysis can be obtained from PK modeling.

Take these examples:

1. Noncompartmental analysis gives you apparent clearance which allows

you to compute mean steady-state concentrations. When done correctly and

with appropriate assumptions, PK modeling will allow you to compute the

concentration at any time point following a dose, or multiple doses of a

drug.

2. Noncompartmental analysis will give you the Cmax of a drug. PK

modeling will give you Cmax and allow you to compute how long it will

take for concnentrations to decline to a sub-toxic (or sub-therapeutic)

threshold

Cases where noncompartmental analysis works best are when a predefined PK

hypothesis (bioequivalence, drug IX, ...) is defined and answered in a

well designed study. PK modeling can be used in these cases too, but its

complexity is often not warranted. PK modeling is very handy when

analyzing sparse data, when complicated dosing regimens are administered,

or when extrapolations (new doses, regimens, etc...) are required.

When doing PK/PD modeling, one associates concentrations with effects.

Therefore, PK modeling is usually used, even if only as a "smoothing"

function to supply plasma concentrations to the PD model.

Hope this helps.

Regards,

Jeffrey Wald, Ph.D.

_____________________________________________________________

Quintiles, Incorporated

Post Office Box 13979

Research Triangle Park, North Carolina

27709-3979

Phone 919-941-7245

Fax 919-941-0493

>============== Reply: BEGIN Original Message ==============<

PharmPK - Discussions about Pharmacokinetics

Pharmacodynamics and related topics

Hi

Would anyone in the PK group be able to briefly (or in depth, if

appropriate) give some sort of guidelines as to what are the relative

merits of modeling PK data or using non-compartmental PK analysis for

obtaining the usual PK parameters eg F, AUC, elimination rate

constant(s),

accumulation, clearance etc..

A second but related question is would the answers to above query still

hold if the analyses were extended to include PK/PD.

My thanks in advance

Faruq H Noormohamed

Department of Therapeutics

Chelsea and Westminster Hospital

369 Fulham Road

LONDON

SW10 9NH

Tel +44 (0)181 746 8141

Fax +44 (0)181 746 8887

email f.noormohamed.at.cxwms.ac.uk

>============== Reply: END Original Message ================< - On 15 Sep 1997 at 15:10:55, Nick Holford (n.holford.-at-.auckland.ac.nz) sent the message

Back to the Top

> Would anyone in the PK group be able to briefly (or in depth, if

> appropriate) give some sort of guidelines as to what are the relative

> merits of modeling PK data or using non-compartmental PK analysis for

> obtaining the usual PK parameters eg F, AUC, elimination rate constant(s),

> accumulation, clearance etc..

IMHO non-compartmental estimates are convenient ways of *describing* PK

expts. They have uses for those who enjoy stamp collecting and for

satisfying regulatory requirements. However, they are of limited use for

*predictive* and *explanatory* purposes. For this, a physiological/

compartmental model approach is more useful.

>

> A second but related question is would the answers to above query still

> hold if the analyses were extended to include PK/PD.

PKPD analyses seem to me illustrate very clearly the severe limitations

of the Cmax,Tmax,AUC (elementary) school of pharmacokinetics. It is only by

using a

model capable of describing the time course of drug concentrations that

any progress can be made in understanding the time course of drug effect

in vivo. I prefer physiological / compartmental models to the black box

models which rely on convolution or splines because the parameters of

the former classes of models are often closely linked to structure and

function and thus the influence of changes in say body size or blood

flow on PK and PD can be predicted.

--

Nick Holford, Dept Pharmacology & Clinical Pharmacology

University of Auckland, Private Bag 92019, Auckland, New Zealand

email:n.holford.at.auckland.ac.nz tel:+64(9)373-7599x6730 fax:373-7556

http://www.phm.auckland.ac.nz/Staff/NHolford/nholford.html - On 16 Sep 1997 at 13:27:16, "Robert D. Phair, Ph.D." (rphair.-at-.ix.netcom.com) sent the message

Back to the Top

In reply to Faruq Noormohamed and Hans Bender:

I have found it fascinating that Nick Holford and I have come to such

similar positions by such different routes. I agree so strongly with Nick's

response to this inquiry, that I considered not replying so as not to use

up bandwidth with "me too."

But then I saw the post from Hans Bender regarding PK in transgenic

animals, and I thought a combined response to the two inquiries might be

useful.

I have been working with a group at the US National Heart Lung and Blood

Institute's Molecular Disease Branch on a kinetic model of lipoprotein

metabolism in human LCAT transgenic rabbits. The principal experimental

investigators are Drs. Meg Brousseau and Jeff Hoeg and the lab is guided by

Dr. Bryan Brewer. An abstract on this work will be presented at the 1997

meeting of the American Heart Association in Orlando in November.

My experience is that analysis of data from transgenic animals is

tremendously more informative in the context of a

physiological/compartmental model than it could be using noncompartmental

methods. This is because, as Nick emphasized, the parameters of a

compartmental model are explicitly associated with known or hypothesized

physiological processes. This means that a powerful constraint is available

to the modeler because he or she can point to a rate constant or a

clearance and say with conviction that *this* process should be

up-regulated in the transgenic animal because this process is mediated by

the gene product of the transgene. Such constraints make it much easier to

extract new and useful information from the available experimental data.

There may well be additional control mechanisms that lead to secondary

changes in other clearances, but it is really powerful to see if the effect

of the transgene alone is sufficient to account for the data.

Because the parameters of noncompartmental analysis are combinations of

many rate constants or clearances, they can be used efficiently to make

predictions. But they will not permit you to impose constraints such as the

one I allude to above, and they will keep you in the dark concerning the

underlying physiological mechanisms. Biologists think mechanistically, and

medicine, as Lewis Thomas has eloquently emphasized, is at its best when

based on mechanism. To build strong working relationships with these two

constituencies, mechanistic compartmental models are highly desirable. And

while Nick is entirely correct that noncompartmental parameters are all you

currently need for the FDA, there are already well-placed people in the FDA

who see the power of compartmental approaches and the prudent

pharmacokineticist should certainly consider adding this approach to his or

her professional toolbox.

Regards,

Bob

----------

Robert D. Phair, Ph.D. rphair.-a-.bioinformaticsservices.com

BioInformatics Services http://www.bioinformaticsservices.com

12114 Gatewater Drive

Rockville, MD 20854 U.S.A. Phone: 1.301.315.8114

Partnering and Outsourcing for Computational Biology - On 16 Sep 1997 at 13:28:55, "Zutshi, Anup" (zutshi.-at-.BATTELLE.ORG) sent the message

Back to the Top

I agree with Nick on the advantages and power of using physiologically

based/compartmental modeling of PK data. There is one significant reason

for the development and success of Non-Compartmental modeling of kinetic

data. As the name suggests, there is no mathematical modeling of the

data i.e. no judgements have to be made regarding the selection of the

appropriate number of physiological compartments or exponentials to fit

the data. Additionally, since the mathematical `best' fit of sum of

exponential data is based on statistical parameters such as `goodness of

fit', sum of squares, selection of optimal weighting factors, etc. this

interpretation can be subjective and kineticist dependent. I am sure

many kineticists have experienced difficulty in modeling oral data which

falls through a 3-4 log orders of magnitude--where in attempting to fit

the terminal phase, the profile around Cmax is not well fit (It is also

for this reason the FDA prefers an observed Cmax rather than a

calculated Cmax). Non-Compartmental analysis of the data allows one to

estimate most of the basic kinetic parameters for characterizing the

disposition of the drug. Granted that mechanistic interpretation of the

kinetic data is limited.

I prefer to use Non-compartmental analysis for basic kinetic

interpretations of pilot studies etc. If there is a need for further

kinetic evaluation modeling is recommended. I do not see how

Non-Compartmental estimates can be extended to PK-PD modeling since we

have not established the time-course of drug disposition. At best we can

make qualitative (non-predictive) interpretations on the kinetic and

dynamic data.

Anup Zutshi - On 17 Sep 1997 at 10:57:27, David_Bourne (david.-at-.pharm.cpb.uokhsc.edu) sent the message

Back to the Top

From: "Robert D. Phair, Ph.D."

To: "'PharmPK.-at-.pharm.cpb.uokhsc.edu'"

Subject: Modeling vs non-compartmental PK Analysis

Date: Tue, 16 Sep 1997 16:07:35 -0400

MIME-Version: 1.0

In reply to Anup Zutshi:

I am always surprised when people assert that no judgements have to be made

with noncompartmental approaches. It appears to me than many of the most

common noncompartmental parameters depend critically on the kineticist's

judgement as to how to extrapolate the tail of the plasma disappearance

curve to infinity. If we guess wrong or miss a slow exponential because we

didn't have data points for enough hours or days, our noncompartmental

answers are simply wrong. Moreover, they are wrong precisely because our

judgement was faulty.

We all make errors if we think that there are no assumptions and no calls

for judgement in the application of noncompartmental analysis. If PK did

not require judgement, but only required plugging into published formulas,

then the discipline of pharmacokinetics could be reduced to an algorithm.

Noncompartmental analysis has made tremendous contributions to the rational

design of therapy, but it is not magic.

The bottom line is that it's impossible to hide from insufficient data.

Noncompartmental advocates do the field of PK a disservice by pretending

that one method of analysis requires no assumptions or judgement while the

other requires both.

Regards,

Bob

----------

Robert D. Phair, Ph.D. rphair.at.bioinformaticsservices.com

BioInformatics Services http://www.bioinformaticsservices.com

12114 Gatewater Drive

Rockville, MD 20854 U.S.A. Phone: 1.301.315.8114

Partnering and Outsourcing for Computational Biology

*

X-Sender: jelliffe.-at-.hsc.usc.edu

Date: Tue, 16 Sep 1997 13:10:43 -0700

To: PharmPK.aaa.pharm.cpb.uokhsc.edu

From: Roger Jelliffe

Subject: Modeling vs non-compartmental PK Analysis

Mime-Version: 1.0

I would like to second Dr. Holford's desctiption of the strengths and

weaknesses on noncompartmental vs physiological / compartmental models. The

latter give structure to the analysis, and result in a controllable system

for which one can plan dosage regimens not just to achieve a desired goal

in a steady state situation, but which can grab a patient (if you will) in

an unsteady state and achieve a desired goal then and thereafter, until the

steady state is reached. As far as I kow, noncompartmental models are good

for writing papers, but not so good for taking care of real people in

unstable and changing clinical situations. Models having structure are

better suited for developing individualized regimens to achieve, and then

to maintain, chosen target goals.

Roger Jelliffe

************************************************

Roger W. Jelliffe, M.D.

USC Lab of Applied Pharmacokinetics

CSC 134-B, 2250 Alcazar St, Los Angeles CA 90033

Phone (213)342-1300, Fax (213)342-1302

email=jelliffe.at.hsc.usc.edu

************************************************

Take a look at our Web page for announcements of

new software and upcoming workshops and events!!

It is http://www.usc.edu/hsc/lab_apk/

************************************************

*

Mime-Version: 1.0

Date: Tue, 16 Sep 1997 17:16:30 -0700

From: Jun_Shi.-a-.berlex.com (Jun Shi)

Subject: Modeling vs non-compartmental PK Analysis

To: PharmPK.at.pharm.cpb.uokhsc.edu

PharmPK - Discussions about Pharmacokinetics

Pharmacodynamics and related topics

I agree with Nick on the advantages and power of using physiologically

based/compartmental modeling of PK data. There is one significant reason

for the development and success of Non-Compartmental modeling of kinetic

data. As the name suggests, there is no mathematical modeling of the

data i.e. no judgements have to be made regarding the selection of the

appropriate number of physiological compartments or exponentials to fit

the data. Additionally, since the mathematical `best' fit of sum of

exponential data is based on statistical parameters such as `goodness of

fit', sum of squares, selection of optimal weighting factors, etc. this

interpretation can be subjective and kineticist dependent. I am sure

many kineticists have experienced difficulty in modeling oral data which

falls through a 3-4 log orders of magnitude--where in attempting to fit

the terminal phase, the profile around Cmax is not well fit (It is also

for this reason the FDA prefers an observed Cmax rather than a

calculated Cmax). Non-Compartmental analysis of the data allows one to

estimate most of the basic kinetic parameters for characterizing the

disposition of the drug. Granted that mechanistic interpretation of the

kinetic data is limited.

I prefer to use Non-compartmental analysis for basic kinetic

interpretations of pilot studies etc. If there is a need for further

kinetic evaluation modeling is recommended. I do not see how

Non-Compartmental estimates can be extended to PK-PD modeling since we

have not established the time-course of drug disposition. At best we can

make qualitative (non-predictive) interpretations on the kinetic and

dynamic data.

Anup Zutshi

*

X-Sender: dfarrier.aaa.mail.bright.net (Unverified)

Date: Wed, 17 Sep 1997 01:28:21 -0400

To: PharmPK.-at-.pharm.cpb.uokhsc.edu

From: "David S. Farrier"

Subject: Modeling vs non-compartmental PK Analysis

Mime-Version: 1.0

Regarding Faruq Noormohamed's query and the flourish of excellent comments

that followed:

In the course of planning our pharmacokinetics analysis software, "PK

Solutions", we took a look at what parameters researchers are publishing

and what methods they favor. A survey of all the articles dealing with

pharmacokinetics appearing during the last 5 years in the Drug Metabolism

and Dispoistion and in the Journal of Pharmaceutical Sciences revealed a

>96% use of noncompartmental methods. Most papers included results derived

from both graphic (AUC, etc.) and summed exponential calculations.

Observation: you are in good company with noncompartmental methods.

A free survey of 75 noncompartmental PK equations can be downloaded from my

web site or viewed at:

http://www.bright.net/~dfarrier/equations/equations.htm

David

Dr. David S. Farrier

Summit Research Services

1374 Hillcrest Drive

Ashland, OH 44805 USA

Telephone: (419)-289-9207

E-mail: dfarrier.at.bright.net

Internet: http://www.bright.net/~dfarrier

*

Date: Wed, 17 Sep 1997 08:59:37 +0200 (MET DST)

From: Maria Durisova

To: PharmPK.-a-.pharm.cpb.uokhsc.edu

Subject: RE: modeling vs non-compartmental PK Analysis

Dear colleagues,

Our article:

Durisova, M., Dedik, L., Balan, M.:

Building a structured model of a complex pharmacokinetic

system with time delays, Bull. Math. Biol., 57, 1995,

787-808,

devoted to non-compartmental modeling, describes in

a tutorial manner a procedure for building a structured model

of a complex pharmacokinetic system on using its transfer

function. The example employed is that of the pharmacokinetic

system based on gentamicin plasma concentrations after

intravenous and intratracheal administration to guinea pigs,

describing the pathway of the drug into the systemic

circulation after the extravascular injection mentioned. The

structured model selected consisted of a submodel of

a proportional linear subsystem, two submodels of simple

linear dynamic subsystems with time constants of 0.135+-0.065

hr (95 % I.C.) and 0.052+-0.049 hr, and two submodels of

parallel subsystems with time delays of 0.254+-0.046 hr and

1.135+-0.288 hr, connected in serial. The serio-parallel

structure of the model selected allowed to estimate mean

residence times for four fractions of gentamicin. From the

methodological point of view, our paper demonstrates the

efficiency of combination of modeling in the frequency and

in the time domain, designed to facilitate studies of complex

pharmacokinetic systems.

Regards,

Maria

*************************************************************

Diploma Engineer Maria Durisova CSc.,

Scientific Secretary

Institute of Experimental Pharmacology

Slovak Academy of Sciences

Dubravska cesta 9

842 16 Bratislava

Slovak Republic

Europe

Phone/Fax: 004217375928

Note:

Diploma Engineer is comparable to M.S. from a technical university

CSc., is comparable to Ph.D.

*

From: Nick Holford

Subject: Modeling vs non-compartmental PK Analysis

To: PharmPK.at.pharm.cpb.uokhsc.edu

Date: Wed, 17 Sep 1997 22:33:45 +1200 (NZT)

reply-to: n.holford.-at-.auckland.ac.nz

MIME-Version: 1.0

Anup,

Thanks for your comments and I think we basically agree but in case

there are still some die hard anti-modelling types left out there...

> There is one significant reason

> for the development and success of Non-Compartmental modeling of kinetic

> data. As the name suggests, there is no mathematical modeling of the

> data i.e. no judgements have to be made regarding the selection of the

> appropriate number of physiological compartments or exponentials to fit

> the data. Additionally, since the mathematical `best' fit of sum of

> exponential data is based on statistical parameters such as `goodness of

> fit', sum of squares, selection of optimal weighting factors, etc. this

> interpretation can be subjective and kineticist dependent.

The very fact that there is some subjectivity and kineticist dependency is

one of the reasons why modelling is valuable. Having to THINK about what

you are doing instead of blindly cranking out Tmax,Cmax, AUC is the way

to understand what the data is trying to say. When the model does not

fit an opportunity presents itself to ask why. Indeed models that fit

the data are less interesting in the spirit of scientific enquiry than

those that don't.

> I am sure many kineticists have experienced difficulty in modeling oral

>data which

> falls through a 3-4 log orders of magnitude--where in attempting to fit

> the terminal phase, the profile around Cmax is not well fit

Sure. I recognise the problem. And it has made me more aware of the need

to think more carefully about the processes that govern drug input. The

difference between the necessarily simple prediction of the model and

the observed reality is the only way to learn more about the PK of

absorption. Tmax and Cmax are even more naive than the elementary one

compartment first-order input, first-order elimination model and will

teach you next to nothing about a drug's absorption rate.

> (It is also for this reason the FDA prefers an observed Cmax rather than a

> calculated Cmax).

The FDA prefers observed Cmax rather then 'calculated' Cmax because the

guidelines are written for commercial (i.e. generic drug) not scientific

reasons. FDA reviewers in this situation don't want to spend time

thinking about yet another me too generic (and I agree with them in this

context). There is also a certain element of ignorance among the FDA

Biometric staff who offer advice on these issues.

Almost none of them have any practical experience of PK

modelling so of course they can only see the problems and not the

advantages. If any FDA Biometrician reads this I challenge them to tell

the world what fraction of the biometrics staff have attempted any PK

or PKPD modelling in the last 12 months (Bob O'Neill are you out there :-)).

--

Nick Holford, Dept Pharmacology & Clinical Pharmacology

University of Auckland, Private Bag 92019, Auckland, New Zealand

email:n.holford.-a-.auckland.ac.nz tel:+64(9)373-7599x6730 fax:373-7556

http://www.phm.auckland.ac.nz/Staff/NHolford/nholford.html - On 18 Sep 1997 at 10:17:06, "Aziz Karim" (AAKARI.-a-.classic.msn.com) sent the message

Back to the Top

Further comments on compartmental vs noncompartmenta PK

I have used noncompartmental approach in PK analysis for considerable time and

feel compelled to express my opinion. I certainly agree with the merits of

compartmental PK/PD analysis in understanding mechanism of drug kinetics and

dynamics. However, let's not discard and ridicule noncomparmental approach

entirely. One can do very sophisticated PK analysis (with effects of

covariance included) using noncomparmental approach. One can even do

simulation of concentration time curves using superposition method.

The greatest advantage of noncomparmental approach is in drug development

where one needs an accurate measure of drug exposure across different species.

One of the most powerful noncompartmental PK parameter is AUC(tau) at steady

state which allows us to determine CLss/F as well as drug exposure. This

approach requires minimum assumptions .

In my opinion inaccuracies in the error and structural model in the

compartmental PK approach especially with sparse sampling is grossly

underestimated and there are times where I get the feeling that compartmental

PK is being reduced to number crunching.

Both compartmental and noncompartmental analysis have their place in

pharmacokinetics and serve as useful tools in understanding drug kinetics.

Aziz Karim, PhD - On 19 Sep 1997 at 10:26:47, Hans Proost (J.H.Proost.aaa.farm.rug.nl) sent the message

Back to the Top

As far as I have seen, the replies on this topic did not deal with

the parameters Mean Residence Time (MRT) and Vss in non-compartmental

PK analysis. Therefore I would like to add two notes:

1. Calculation of MRT and Vss in non-compartmental PK analysis

(using AUC and AUMC) implies the assumption that the rate of

elimination from the body is proportional to the (plasma)

concentration (equivalent to: elimination from the central

compartment in compartmental analysis). If this is not the case (for

many drugs, e.g. atracurium which breaks down spontaneously in any

body fluid), the values obtained for MRT and Vss are meaningless.

Of course, the same is true in compartmental analysis, but in that

case one is 'forced' to choose a model, including the route of

elimination (do we all realize this every time we use an open two- or

three-compartment model ....?).

2. Calculation of MRT and Vss in non-compartmental PK analysi implies

calculation of AUMC (area under the first moment curve). Calculation

of AUMC without a curve-fitting procedure is prone to large

extrapolation errors (much more than AUC!).

In conclusion, be very careful in calculating MRT and Vss by non-

compartmental PK analysis!

Johannes H. Proost

Dept. of Pharmacokinetics and Drug Delivery

University Centre for Pharmacy

Groningen, The Netherlands

tel. 31-50 363 3292

fax 31-50 363 3247

Email: j.h.proost.-at-.farm.rug.nl - On 24 Sep 1997 at 11:01:25, Elena Strocchi (strocchi.-a-.antlab.cineca.it) sent the message

Back to the Top

Dear Faruq hi!

only some observations for your first question:

1) it's important remember that non-compartmental methods (NCM) ASSUME

LINEAR PHARMACOKINETICS for observed drug.

2) NCM are useful to estimate some PK parameters, useful for clinical

practice (bioavailability, clearance, apparent volume of distribution,

fraction of a dose converted to a metabolite, rates of absorption), but

they do not describe the time course of drug in the blood (different

half-lives, k-rates, etc)

NCM do not require the assumption of a specific compartmental model for

drug and are

based essentially on the theory of statistical methods, and

concentration-time course after single administration can be considered a

statistical distribution curve. The results are also independent enough on

changes of metabolism/distribution/elimination during the time.

Compartmental methods required for PK of a drug, depends in part on the

experimental design and you can have problems of accuracy when the

frequency and timing of samples are not correct (too sparse, interrupted

too early, etc).

In addition calculation of PK parameters and of distribution rates are

dependent on the model selected, but "many" models sometimes are

structurally identifiable and give you "acceptable" solution. You should

therefore perform a robust error-analysis on your data.

Elena Strocchi

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

Laboratorio di Farmacocinetica ANT

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

Dipartimento di Chimica Organica | Tel +39 51 6443645

Universita' di Bologna | FAX +39 51 6443654

Viale Risorgimento, 4 |

40136 Bologna - Italy | EMAIL strocchi.-a-.antlab.cineca.it

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

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