- On 4 Feb 2000 at 23:40:47, "Shoaf, Susan" (susans.-at-.MOCR.OAPI.COM) sent the message

Back to the Top

Hello to the List,

Is there any other software besides ADAPT II that will help in the

selection of sampling points?

Susan E. Shoaf

[Have a look at

http://www.boomer.org/course/pk_bio/Ch9906c/sld004.htm and the next

few slides. Optimal sampling calculations look like sensitivity

analysis?? Also interesting is what happens when you add weights to

the data points - db] - On 6 Feb 2000 at 22:22:06, "David Foster" (dmfoster.-at-.u.washington.edu) sent the message

Back to the Top

There is a program which does optimal sampling produced by Alfredo Ruggeri

at the University of Padova in Italy. Part of this work was supported by

our NIH Resource Grant that produced SAAM II. I suggest you contact Dr.

Ruggeri for more information. - On 7 Feb 2000 at 19:42:52, David_Bourne (david.-at-.boomer.org) sent the message

Back to the Top

[Two replies - db]

X-Sender: jmlanao.aaa.gugu.usal.es (Unverified)

Date: Mon, 07 Feb 2000 11:35:35 +0100

To: PharmPK.at.boomer.org

From: jml

Subject: Re: PharmPK Re: Optimal sampling points

There is another program called DRUGTEST released by MEDISOFT for optimal

design of experiments in pharmacokinetics using Fisher Matrix information

and probability density functions of sampling times.

J.M.Lanao

Dpt. Pharmacy.

Univ. Salamanca. Spain

---

X-Originating-IP: [146.186.229.23]

From: "Vinay Desai"

To: PharmPK.at.boomer.org

Subject: Re: PharmPK Re: Optimal sampling points

Date: Mon, 07 Feb 2000 14:50:22 EST

WinNonlin allows you to plot various partial derivatives as part of

its modeling features. You can use these partial derivative plots

for optimizing time points.

Vinay Desai - On 8 Feb 2000 at 21:31:49, David_Bourne (david.-at-.boomer.org) sent the message

Back to the Top

From: exfamadu.aaa.savba.savba.sk

To: PharmPK.-a-.boomer.org

Date: Tue, 8 Feb 2000 10:28:11 +0100

Subject: Re: PharmPK Re: Optimal sampling points

X-Confirm-Reading-To: exfamadu.-at-.savba.savba.sk

X-pmrqc: 1

Priority: normal

> few slides. Optimal sampling calculations look like sensitivity

> analysis?? Also interesting is what happens when you add weights to

> the data points - db]---

No, optimal sampling calculations do not look like sensitivity analysis.

With best regards,

Maria Durisova

Dipl. Engineer Maria Durisova D.Sc.

Senior Research Worker

Scientific Secretary

Institute of Experimental Pharmacology

Slovak Academy of Sciences

http://nic.savba.sk/sav/inst/exfa/advanced.htm

SK-842 16 Bratislava

Slovak Republic

---

Date: Tue, 08 Feb 2000 13:38:02 -0500

From: Mark Lovern

Organization: Pharsight, Inc.

X-Accept-Language: en

To: PharmPK.aaa.boomer.org

Subject: [Fwd: [Fwd: Re: PharmPK Re: Optimal sampling points]]

> Dear Susan:

>

> The optimization of a sampling regimen can be a very complicated issue.

> What software package will suit your needs really depends upon how much

> information you wish to incorporate into your analysis. For instance,

> you may choose to base your sampling design based on simulations using a

> fixed set of parameter values that are assumed to be typical for your

> study population. This is an approach that can be implemented quite

> easily in WinNonlin. Other approaches based on simulation are discussed

> below.

>

> WinNonlin's simulation output includes both variance inflation factors

> (VIFs) as well as partial derivatives of the concentration function with

> respect to model parameters. Variance inflation factors provide a

> relative measure of how precisely parameters may be estimated using a

> particular sampling regimen. A parameter's VIF value may be compared

> across competing sampling designs. The design which minimizes the

> parameter's VIF will maximize the precision with which the parameter is

> estimated.

>

> The sensitivity of the dependent variable (ie plasma concentration) to

> changes in parameter values at particular times may be assessed using

> WinNonlin's partial derivative output. The sensitivity of the dependent

> variable to a particular parameter value increases as that parameter's

> partial derivative deviates from zero. Sampling in regions of greater

> sensitivity will result in more precise estimates for the parameter, and

> plots of partial derivatives over time can be used to suggest what

> sampling times will be most strategic for estimating model parameters.

>

> For a discussion of how model simulations may be employed to optimize

> study designs, you may want to read pp. 310-318 of Gabrielsson and

> Weiner's Pharmacokinetic and Pharmacodynamic Data Analysis 2nd Ed.

> (Swedish Pharmaceutical Press, 1997). If you do not have this reference

> available, I would be happy to FAX you a copy of these pages. I have

> found this example is particularly helpful, because the authors discuss

> how to select a sampling regime that will provide good information even

> when one is unsure of what model underlies the data. (Many sample time

> selection algorithms require a priori selection of a model.)

>

> While the "average value" approach to study design may convey useful

> information, it is rather simplistic and fails to consider any number of

> factors that may be important in determining what sampling design is

> "truly" optimal. Such factors include inter-subject variability in PK

> parameters, covariate relationships, and the error structure of the

> bioanalytical assay. Simulation approaches to study design that

> incorporate such information are collectively referred to as

> computer-aided trial design (CATD). Pharsight offers trial simulation

> software that is specifically designed to aid in the implementation of

> the CATD approach.

>

> The advantage of CATD is that it allows protocol designers to answer

> questions that cannot be answered by the "average value" approach to

> study design. When one uses "average values", one is able to determine

> what sampling times will minimize the uncertainty in parameter

> estimates, provided the true parameter values are somewhere in the

> neighborhood of the values used to perform the simulation. The question

> that is not answered by such an approach is how many of your study

> subjects actually have parameter values that fall within this

> neighborhood. With the CATD approach, one has the ability to answer

> questions such as "What (if any) sampling regimen will allow Clearance

> to be estimated with less than a 15% CV for all subjects?" It is such

> questions that are truly of interest when designing a study.

>

> If you have any questions regarding our software and how it may be

> applied to your problem, please feel free to contact me directly (email:

> mlovern.-at-.pharsight.com; phone: (919) 859-6868 ext. 4007).

>

> Best Regards,

Mark Lovern

Consulting Scientist

Pharsight, Inc.

Phone: (919) 859-6868 ext. 4007

FAX: (919) 859-6871 - On 23 Feb 2000 at 22:31:35, Daro Gross (maildrop.-a-.iname.com) sent the message

Back to the Top

There is no such thing as optimal design of experiments in PK/PD. PK/PD

analysis is a methodology that is insensitive, when used properly, to the

data set. The tools you refer to can only assist in identifying the

boundary values within which the analytical tools converge on a single

solution. Mathematical transformations do not necessarily simply

information---sometimes they add complexities that render the original data

even more difficult to understand.

Daro Gross - On 24 Feb 2000 at 22:40:45, David_Bourne (david.-a-.boomer.org) sent the message

Back to the Top

[Quite a few replies - hot topic! - db]

From: "Stephen Duffull"

To:

Subject: RE: PharmPK Re: Optimal sampling points

Date: Thu, 24 Feb 2000 15:57:07 +1000

X-Priority: 3 (Normal)

Importance: Normal

Daro

> There is no such thing as optimal design of

> experiments in PK/PD.

I am either missing the point - or I disagree with your

assertion. As I'm sure you're aware there are a number of

papers discussing optimal design of PK experiments both for

individuals, and more recently for populations (eg D'Argenio

JPB 1981;9:739-56 for individuals and Mentre et al

Biometrika 1997;84:429-442 for a population). Both papers

give an idea of how designs can be optimised - whether the

optimisation procedure will always locate the true optimum

is, however, up for debate.

> PK/PD

> analysis is a methodology that is insensitive,

> when used properly, to the

> data set.

Again - I can't agree. The standard errors of the parameter

estimates (computed from the square root of the diagonal

elements of the inverse of the Fisher information matrix)

are directly influenced by the design for non-linear mixed

effect models. Indeed it can be shown that as the number of

subjects in a population design increases the determinant of

the population Fisher information matrix increases.

On a more general note I would be worried if a PKPD analysis

was insensitive to the data - indeed this implies that the

model was selected independently of the data!

Regards

Steve

=================

Stephen Duffull

School of Pharmacy

University of Queensland

Brisbane, QLD 4072

Australia

Ph +61 7 3365 8808

Fax +61 7 3365 1688

---

From: "Della Pasqua, Oscar"

To: "'PharmPK.at.boomer.org'"

Subject: Re: Optimal sampling points

Date: Thu, 24 Feb 2000 08:22:16 -0000

Daro,

I wished you were right. I suggest you review the literature

(e.g.Park et al. J Pharmacokinet Biopharm 1998 Aug;26(4):471-92;

Gieschke et al. Int J Clin Pharmacol Ther 1997 Oct;35(10):469-74;

Palmer et al. Stat Med 1998 Jul 30;17(14):1613-22).

Oscar E. Della Pasqua

====================

Dr. O. E. Della Pasqua

Full Development Group

Worldwide Clinical Pharmacology

GlaxoWellcome Research & Developmet

* ++ 44 208 966 2404

fax ++ 44 208 966 2123

* odp72514.at.glaxowellcome.co.uk

====================

---

From: GLDrusano.-a-.aol.com

Date: Thu, 24 Feb 2000 10:23:08 EST

Subject: Re: PharmPK Re: Optimal sampling points

To: PharmPK.-a-.boomer.org

Such an arrogant resonse for an area with a huge literature that says exactly

the opposite and for which there is clinical validation!

George Drusano

---

X-Sender: n7950211.at.popin.ncl.ac.uk

Date: Thu, 24 Feb 2000 15:25:48 +0000

To: PharmPK.at.boomer.org

From: James

Subject: Re: Optimal sampling points

Dear Dr Gross,

When you say that there is no optimal design, do you mean that there are

many designs which give similar practical results? This may be true, but

there are still issues in how to design such experiments which may be

termed "optimal design". Personally, I dislike the word optimal because it

implies perfection and exists only in a perfectly defined sytem (however

that excludes essentially all real experiments). One could perhaps argue

that in any nonlinear system it is impossible to design an optimal

experiment without knowing what you want to find out in advance.

Much as I wish it was true PKPD modelling is not insensitive to the design

of the experiment. It is trivial to design a bad experiment.

A sensibly applied transformation (by which I take it you mean a mapping of

data onto summary parameters, using assumptions) should illuminate the key

features of the data - if it does not it is pointless.

James Wright

---

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

Date: Thu, 24 Feb 2000 09:58:03 -0800

To: PharmPK.at.boomer.org

From: Roger Jelliffe

Subject: Re: PharmPK Re: Optimal sampling points

Dear Daro:

Thanks for your note. It would be very interesting for me,

for example, to

understand better why you say what you say. For example, what do you mean

that PK/PD analysis is a method WHEN USED PROPERLY, that is insensitive to

the data set? It would be very interesting if you can expand on this

thought and support it, and your other thoughts, further. Further, how does

this relate to D- optimality in experimental design, for example?

Very best regards,

Roger Jelliffe

---

Date: Thu, 24 Feb 2000 13:59:07 -0500

From: "James Cherry"

To:

Cc:

Subject: PharmPK Re: Optimal sampling points

Thank you. I don't know who Daro Gross is, but he has restated things

that are well known and he has a firm grasp of the obvious. A cursory

reading of Belsley, Kuh and Welsch "Regression Diagnostics-

Identifying Influential Data and Sources of Collinearity" will show

the bias of his statement of the value of data transformations in

many cases. I have this book if you wish to read it. His statement

that transformations may make the info more complex is a one-sided

viewpoint which is already well known, that is why multiple

transformations are reviewed, and the ones that improve data

interpretation and prediction are kept and ones that make it more

meaningless are rejected. Duh. He is only partially right that our

tools only assist in identifying the boundary values( boundary values

are not easily determined from some non-lin models), but remember,

this is precisely what we are trying to do!

Remember, even the simplest aminoglycoside programs use log

transformation of the serum values to change the non-linear,

difficult to interpret picture of drug elimination to a simple,

straight linear graph. This is one obvious example of the use of

transformations to improve data interpretation. The cockcroft-gault

equation is nothing but a transformation of creatinine data to make

it simpler to interpret and decrease variance in the model. Can a

clinician as experienced as you deny the value of transformations in

those cases?

--- - On 27 Feb 2000 at 22:40:25, David_Bourne (david.aaa.boomer.org) sent the message

Back to the Top

[Two replies - db]

X-Sender: n7950211.aaa.popin.ncl.ac.uk

Date: Fri, 25 Feb 2000 10:15:21 +0000

To: PharmPK.-a-.boomer.org

From: James

Subject: Re: PharmPK Re: Optimal sampling points

Dear Dr Cherry,

It is interesting to see how people with different backgrounds have

different perspectives on these things. Personally, I would recommend the

text by Carroll & Rupert, Transformation and Weighting and Regression,

however none of these texts seem to emphasize the role of subject-specific

knowledge in deciding transformations. Both this and the Belsly, Kuh and

Welsch were written in the early eighties and may well be out-of-date

(although Carrol & Ruppert is a classic in my opinion). A lot of this

difference may be to do with terminology (what are boundary values exactly?

Why do they matter?) However, and without need to resort to my

undergraduate degree, I can confidently say that the only analysis that is

insensitive to the data (and how it is collected eg design) is one that

doesn't use it.

Incidentally, the Cockcroft & Gault formula is a misleading "transformation".

James Wright

---

X-Sender: mentor.at.hardlink.com

Date: Fri, 25 Feb 2000 06:41:12 -0500

To: PharmPK.aaa.boomer.org

From: Daro Gross

Subject: Re: Optimal sampling points

I feel like I have stirred up a hornet's nest by simply stating what is

already well understood---that PK/PD analytical techniques are mathematical

transformations that have been proven to be useful but for which there

cannot be an "optimal" data set, only an optimal choice of analytical tools.

The difficulty in choosing the correct PK/PD tools is a measure of a

person's experience in working with a particular data set (i.e., depth of

knowledge in a particular area of medicine) and is not related to the data

set itself.

Some persons have greater amounts of knowledge about the characteristics of

data sets related to a particular area of research. Other persons focus on

being able to adapt PK/PD tools to the parameters of data sets as presented

by the expert researcher.

Medicine is far too complicated to permit a person to acquire both in depth

expertise in an area of research as well as in the use of PK/PD analytical

tools required to analyze the data generated. No person can ever hope to

have the time or the energy in one life-time to absorb enough information

to become sufficiently expert in both specialty-specific data collection

and PK/PD analytical techniques.

An "optimal" data set implies that the researcher gathering the data is not

expert enough to gather the data rather than the more reasonable conclusion

that the selection of the PK/PD analytical techniques used to interpret the

data need conform to the parameters of the data gathered. Failure to chose

the correct data analysis tools does not render the data meaningless---but

may transform useful data into meaningless conclusions.

Daro Gross

P.S. I have run into too much good research that has been discarded because

it did not yield meaningful results when subjected to inappropriate

mathematical measures---PK/PD is intended to address this problem in

medicine, not preserve a status quo that discards valid research when it is

not parameterized in a manner that fits off-the-shelf computer software. (I

have always been under the impression that physicians were trained to

conduct medical research, not computer programmers.) - On 2 Mar 2000 at 22:28:18, "l.endrenyi" (l.endrenyi.at.utoronto.ca) sent the message

Back to the Top

A couple of clarifications about optimal designs for PK/PD studies.

With a given

optimality criterion and with stated modeling and statistical assumptions, an

optimal design can always be calculated. However, the design will

depend on the

assumptions. For instance, different variance model parameters can

yield widely

differing "optimal" designs.

Therefore, the robustness of optimal or even favorable experimental designs is

especially important. Fortunately, such designs are quite robust in

populations. The reason is that the optimal designs of the various

subjects are

different and tend to center around the best design that is calculated with the

population average parameters.

In the presence of various uncertainties, it is advantageous to pursue a

sequential strategy. Start with a classical sampling scheme which spreads the

observations around. Narrow the design toward a single-sample optimal plan as

information is gained about the parameters and their properties.

Laszlo Endrenyi

University of Toronto - On 3 Mar 2000 at 21:57:19, Roger Jelliffe (jelliffe.-a-.usc.edu) sent the message

Back to the Top

Dear All:

Dr. Endrenyi's remarks are very much to the point. Especially

appropriate

is the strategy of starting with a fairly conventional design, doing a few

subjects, making a population model, getting the parameter distributions,

and then defining an optimal design based on those results. Then repeat

this every so often, every 5 - 10 patients, for example, until the model

gets stable. In this way one never puts all one's eggs in one basket of an

experimental design of protocol, but refines it as the results come in.

This optimizes the information per subject and per level and per dollar,

for all of that. Well said, Laszlo!

Roger Jelliffe

Roger W. Jelliffe, M.D. Professor of Medicine, USC

USC Laboratory of Applied Pharmacokinetics

2250 Alcazar St, Los Angeles CA 90033, USA

Phone (323)442-1300, fax (323)442-1302, email= jelliffe.-a-.hsc.usc.edu

Our web site= http://www.usc.edu/hsc/lab_apk - On 5 Mar 2000 at 22:17:59, Guang Wu (Guang.Wu.-at-.pharmacie.univ-mrs.fr) sent the message

Back to the Top

Ref: Optimal sampling points

Dear All

In one of my earlier papers: Wu G. An explanation for failure to predict

cyclosporine area under the curve using a limited sampling strategy: a

beginner=EDs second note. Pharmacol Res 1997; 35: 547-52, I used the Fourie=

r

seires to decompose the blood drug concentration into linear and nonlinear

components, which is also an optimal sampling points.

Guang Wu, MD, PhD

Laboratoire de Toxicocinetique et Pharmacocinetique

=46aculte de Pharmacie

Universite de la Mediterranee Axi-Marseille II

27 Boulevard Jean Moulin

=46-13385 Marseile Cedex 05

=46rance

Tel: +33 4 91 83 56 45

=46ax: +33 4 91 80 26 12 - On 6 Mar 2000 at 17:44:49, Art Straughn (ASTRAUGHN.-at-.utmem.edu) sent the message

Back to the Top

I agree with the approach of Dr. Endrenyi endorsed by Dr. Jelliffe, however

there may be a slight problem in doing human research with this approach -

IRB's. At our institution, if one makes any change in a protocol it must be

approved, and adding or deleting samples or changing sample times requires an

amendment. A pharmacokinetic protocol with the approximate number of samples

and approximate sample times, from my experience, would not be approved, even

if the volume of blood did not exceed the our set limit. Although, minor

protocol changes do often represent major inconveniences, I totally agree with

keeping IRB's informed in every aspect of the research under their auspicious.

Just a reminder that conducting pharmacokinetic research (or really any human

research for that matter) can sometimes become a real nightmare for the

investigator and the institution when all the i's aren't dotted and the t's

crossed.

Art Straughn, Member

University of Tennessee IRB - On 6 Mar 2000 at 20:51:10, David_Bourne (david.aaa.boomer.org) sent the message

Back to the Top

[A few replies - db]

From: "Bruce CHARLES"

Organization: School of Pharmacy

To: PharmPK.at.boomer.org

Date: Tue, 7 Mar 2000 11:03:55 +1000

Subject: Re: PharmPK Re: Optimal sampling points

X-Confirm-Reading-To: "Bruce CHARLES"

X-pmrqc: 1

Priority: normal

Taking this a step further to the ops level, most (if not all) clinical

activities in Australian teaching hospitals are run on a cost-centre

basis where a unit is responsible for the costing of all their

activities. This could be a stumbling block for the optimal sampling

practice (which I do support).

Further, blood sampling for path, TDM etc is tied quite strictly to the

scheduled rounds of the phlebotomists which, for most drugs,

corresponds to the morning trough level at around 8 am. 'Non-

standard' sampling times can be a logistic headache especially

where there are multiple sampling regimens among patients and/or

which cross shifts of duty of the med and nursing staff.

I guess like most things in health the bottom line at the end of the

day is $ and if we can show favourable cost-effectiveness for such

a practice then optimal sampling will thrive.

Cheers,

BC

Bruce CHARLES, PhD

Associate Professor

Director, The Australian Centre for Paediatric Pharmacokinetics

University of Queensland, School of Pharmacy, QLD 4072

Australia

+61 7 336 53194 (TEL)

+61 7 336 51688 (FAX)

0403 022 252 (MOBILE)

bruce.-a-.pharmacy.uq.edu.au

---

Date: Tue, 07 Mar 2000 14:16:42 +1300

From: Nick Holford

X-Accept-Language: en

To: PharmPK.at.boomer.org

Subject: Re: PharmPK Re: Optimal sampling points

If the design of a study is not optimal then I would argue that it is

unethical. So changing the protocol to make the design better should

always be a priority for an ethical researcher. IMHO it is unethical to

use the burden of paperwork as an excuse for not improving the study

design.

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.htm

---

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

Date: Mon, 06 Mar 2000 17:21:19 -0800

To: PharmPK.-a-.boomer.org

From: Roger Jelliffe

Subject: Re: PharmPK Re: Optimal sampling points

Dear Art:

It is good to hear from you. As to the changes in the

protocol, describe

all this in the original protocol. State the number of samples, state the

original protocol, and state how the changes will be made. Dot all the i's

and cross all the t's. IRB's are able to do this stuff. Many have already

done so, as evidenced by a number of investigators who have done this already.

Very best regards,

Roger Jelliffe

Roger W. Jelliffe, M.D. Professor of Medicine, USC

USC Laboratory of Applied Pharmacokinetics

2250 Alcazar St, Los Angeles CA 90033, USA

Phone (323)442-1300, fax (323)442-1302, email= jelliffe.aaa.hsc.usc.edu

Our web site= http://www.usc.edu/hsc/lab_apk - On 7 Mar 2000 at 22:37:12, David_Bourne (david.aaa.boomer.org) sent the message

Back to the Top

[Two replies - db]

From: Laszlo Endrenyi

X-Accept-Language: en

To: PharmPK.aaa.boomer.org

Subject: Re: Optimal sampling points

Date: Mon, 6 Mar 2000 23:41:57 -0500

Nick,

The word "optimal" can be interpreted in different ways. If it means

"as good as

possible under the given constraints", I agree with you: we should

pursue studies

as much free of bias and as efficient as possible. If, however, it

means "optimal

in the statistical sense" then this is perhaps an ideal goal which is, however,

subject to several assumptions. I believe that it is more useful to consider

designs that are favorable under many assumptions and conditions, i.e., robust

designs.

Laszlo Endrenyi

University of Toronto

---

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

Date: Mon, 06 Mar 2000 21:51:04 -0800

To: PharmPK.-a-.boomer.org

From: Roger Jelliffe

Subject: Re: PharmPK Re: Optimal sampling points

Dear Bruce:

Thanks for your comments about optimal sampling times.

Another thing you

can do is to adjust the times of the doses so that the relation between the

dose and the level can be close to D-Optimal. For example, under many

circumstances, a pretty much D-Optimal strategy is to combine a peak level

with the first dose with another level, often after a different dose, which

is obtained at about 3 hours before the trough, for many patients with a

creatinine clearance above 40. Consider that if morning blood draw time is

at 8 am, for example, one can center the aminoglycoside dosing around an 11

am dose, so that the 8am blood sample is more informative than the trough,

and the pair, as outlined above, is then close to D-Optimal. It is just

another way to juggle things around to get a practical approach to

realizing optimal designs.

Veery best regards,

Roger Jelliffe

Roger W. Jelliffe, M.D. Professor of Medicine, USC

USC Laboratory of Applied Pharmacokinetics

2250 Alcazar St, Los Angeles CA 90033, USA

Phone (323)442-1300, fax (323)442-1302, email= jelliffe.aaa.hsc.usc.edu

Our web site= http://www.usc.edu/hsc/lab_apk

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

Want to post a follow-up message on this topic? If this link does not work with your browser send a follow-up message to PharmPK@boomer.org with "Optimal sampling points" as the subject

PharmPK Discussion List Archive Index page

Copyright 1995-2010 David W. A. Bourne (david@boomer.org)