- On 6 Mar 2014 at 09:23:53, Andleeb shahzadi (shahzadiandleeb.-at-.yahoo.com) sent the message

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Dear Sir/Mam

Is it possible to study pharmacokinetic parameters with the single blood sample collected at the end

of one half life.

For example. I have given doxo 2.5 for 1 week, after completion of doses, I have collected a blood

sample after 48 hours of last dose. is it possible to calculate the pharmacokinetics parameters with

this data I have 80 rats in my study.

Kindly guide me obliged.

Regards

Andleeb Shahzadi

Ph.D. Scholar,

Dept. Pharmacology & Clinical Pharmacology

Faculty of Medicine,

Istanbul University,

Istanbul, Turkey - On 6 Mar 2014 at 16:52:25, Daniel Sitar (Daniel.Sitar.-a-.med.umanitoba.ca) sent the message

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If you have quality data on the whole pharmacokinetic profile, you can evaluate single data points

by using a Bayesian Pharmacokinetic approach. Lots of different software available to do this.

Dan Sitar

- On 7 Mar 2014 at 13:46:23, David Bourne sent the message

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Extrapolation is never a good idea

Edward O'Connor

--

Dear Shahzadi,

You can find plasma drug concentration from one sample using following equation but Cl and Vd are

unknown and in practice these two parameters are obtained from literature.

Cp = Cp0-e--kt and

K= Cl/Vd

Dr. Zafar

Prof. Dr. Zafar Iqbal

--

The following article may be of help to you.

Booth BP, Gobburu JV. Considerations in analyzing single-trough concentrations

using mixed-effects modeling. J Clin Pharmacol. 2003 Dec;43(12):1307-15. PubMed

PMID: 14615466.

Joga Gobburu

Gobburu, Joga

--

Dear Dan

I have only one blood sample no other sample is possible to find out the pharmacokinetic parameter.

Regards

Andleeb Shahzadi

Ph.D. Scholar,

Dept. Pharmacology & Clinical Pharmacology

Faculty of Medicine,

Istanbul University,

Istanbul, Turkey

Andleeb shahzadi

[Do you have data for this drug from the literature. Bayesian uses this information with your single

data point. Maybe it can be used in a PopPK analysis - db]

--

Back when I had a PK dosing service I used both traditional and Bayesian systems. I conducted

several comparative studies (never got around to publishing them) and found a couple of interesting

points that clinicians need to appreciate about Bayesian PK methods.

1) Bayesian 1 point methods usually hold the Vd relatively constant and only really fit Ke. When

using two points with Bayesian the predictive performance is was similar to two point traditional

methods.

2) Bayesian 1-point methods tend to drive the parameter estimates toward the mean values (Bayesian

priors) so it's not very good at identifying outliers, which potentially can be a patient safety

issue.

Thanks,

Mike

Michael A. Jones, Pharm.D.

Informatics - Clinical Decision Support

Co-chair CDS Governance Committee

University of Colorado Hospital

michael.jones.-at-.uchealth.org - On 7 Mar 2014 at 14:17:36, n.holford.aaa.auckland.ac.nz sent the message

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Edward's assertion should be qualified

"Extrapolation is never a good idea" Edward O'Connor

"Extrapolation is never a good ideas for anyone who does not understand science"

Fortunately intelligent people throughout the evolution of human discovery have used their

understanding of science to extrapolate and make new discoveries -- think of any navigation beyond

the visible horizon...

Nick

--

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

email: n.holford.aaa.auckland.ac.nz

http://holford.fmhs.auckland.ac.nz/

[Nick, any PK suggestions ;-) - db] - On 7 Mar 2014 at 15:53:55, Edward O'Connor (efoconnor.aaa.gmail.com) sent the message

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OK: I suppose it falls into the (and I paraphrase) "a second rate plan instituted early is superior

to a first rate plan implemented after the barbarians are over the wall"

Was the single point measurement planned as a savings or as a misunderstanding?

You are right Nick. Extrapolation's limited value is there in experimentation but to explore

anything with a single measure increases the risk of misinterpretation. Is this point "." rising,

falling, moving to the right or to the left? - On 20 Mar 2014 at 16:18:01, Andleeb shahzadi (shahzadiandleeb.-at-.yahoo.com) sent the message

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Hello

I am sorry I again came with problems could you please give me some information regarding Bayesain

Pk program. As I have download one program from Boomer website. It always asks for Vd or CL and I

dont have such data for doxorubicin. May be I am using it in wrong way. Kindly guide me I am in big

troble. I am again explaining my problem that I have only one blood sample collected from rats after

giving different doses of doxorubicin alone and in combination of ciprofloxacin. Can I able to

calculate any PK parameter from this sample. Obliged.

Regards

Andleeb Shahzadi

Ph.D. Scholar,

Dept. Pharmacology & Clinical Pharmacology

Faculty of Medicine,

Istanbul University,

Istanbul, Turkey

[What program did you download? You will need some population based PK parameter values, kel and V

or CL and V, maybe more and a suitable model to use the Bayesian method. - db] - On 20 Mar 2014 at 17:15:59, Xiao Quan Zhang (XiaoZ.aaa.amphastar.com) sent the message

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Bayesian approach needs prior, and likelihood to do the estimation. Prior means your previous data

set. You calculated the probability of the missing data. The result times the prior number will be

your estimation.

P(A/B)=P(B/A)*P(A)/P(B/A)*P(A)+(1-P(A)*(P(B/not A))]

Here, P(A) is prior, P(B/A) is likeliness.

Hope it help.

Kam - On 21 Mar 2014 at 09:05:41, shahzadiandleeb (shahzadiandleeb.at.yahoo.com) sent the message

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Dear Kam

Thank you very much for your precious time. Could you suggest me a research article regarding pk

parameters taken from one sample of rats. I think I dont have enough knowledge regarding bayesian

pk.

Could you guide me whether it will be useful to get the pk parameters in this way or not?.

Waiting for kind reply.

Regards

Andleeb - On 21 Mar 2014 at 11:21:02, Roger Jelliffe (jelliffe.aaa.usc.edu) sent the message

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Dear Andleeb:

If you do not know Bayesian methods of adaptive control of dosage regimens you most probably

need to. One cannot do modern PK/PD studies without them. Especially the nonparametric approach to

such modeling, and its natural linkage to the multiple model method of dosage design to obtain

maximally precise dosage regimens. Parametric approaches, of which NONMEM is the most widely used,

only obtain single point estimates of parameter values, such as mean, median, etc. The action taken

(the dosage regimen) is therefore based only on the central tendencies of the model parameter

distributions and not on the entire distributions, which is needed to obtain maximum precision in

the dosage regimen, to hit the desired therapeutic target goal with the least expected weighted

squared error. There is also a nonparametric approach within NONMEM, but it has been shown by Leary

not to be a very good one, and not to obtain the most likely answers given the data. The

nonparametric approach, as embodied in the Pmetrics software from the USC LAPK (www.lapk.org)

estimates the entire model parameter distributions, and then links naturally to multiple model (MM)

dosage design, which cannot be done without nonparametric models. I am enclosing a list of relevant

references below. Without such approaches, your work will not be optimal.

There is an upcoming workshop on this subject to be held in Barcelona on May 8-9, which is given

under the auspices of the International Association for Therapeutic Drug Monitoring and Clinical

Toxicology. You can get more info and can register at the web site above (link below).

Very best regards,

Roger Jelliffe

References:

1. Mallet A: A Maximum Likelihood Estimation Method for Random Coefficient Regression Models.

Biometrika. 1986; 73: 645-656.

2. Lindsay B: The Geometry of Mixture Likelihoods: A General Theory. Ann. Statist. 1983; 11:

86-94.

3. Schumitzky A: Nonparametric EM Algorithms for Estimating Prior Distributions. App.

Math. and Computation. 1991; 45: 143-157.

4. Bayard D, Milman M, and Schumitzky A: Design of Dosage Regimens: A Multiple Model Stochastic

Approach. Int. J. Biomed. Comput. 1994; 36: 103-115.

5. Bustad A, Terziivanov D, Leary R, Port R, Schumitzky A, and Jelliffe R: Parametric and

Nonparametric Population Methods: Their Comparative Performance in Analysing a Clinical Data Set and

Two Monte Carlo Simulation Studies. Clin. Pharmacokinet., 45: 365-383, 2006.

6. Jelliffe R, Schumitzky A, Bayard D, Milman M, Van Guilder M, Wang X, Jiang F, Barbaut X, and

Maire P: Model-Based, Goal-Oriented, Individualized Drug Therapy: Linkage of Population Modeling,

New "Multiple Model" Dosage Design, Bayesian Feedback, and Individualized Target Goals. Clin.

Pharmacokinet. 34: 57-77, 1998.

7. Jelliffe R, Bayard D, Milman M, Van Guilder M, and Schumitzky A: Achieving Target Goals most

Precisely using Nonparametric Compartmental Models and "Multiple Model" Design of Dosage

Regimens. Therap. Drug Monit. 22: 346-353, 2000.

8. Jelliffe R, Schumitzky A, and Van Guilder M: Population Pharmacokinetic / Pharmacodynamic

Modeling: Parametric and Nonparametric Methods. Therap. Drug Monit. 22: 354-365, 2000.

9. Leary, R., Jelliffe R., Schumitzky, A., and Van Guilder, M An adaptive grid non-parametric

approach to pharmacokinetic and dynamic(PK/PD) population models, 14-th IEEE Symposium on

Computer Based Medical Systems, 389-394, 2001.

10. Neely M, van Guilder M, Yamada W, Schumitzky A, and Jelliffe R: Accurate Detection of

Outliers and Subpopulations with Pmetrics, a Nonparametric and Parametric Pharmacometric Modeling

and Simulation Package for R. Therap. Drug Monit. 34: 467-476, 2012.

11. Leary R, Jelliffe R, Schumitzky A, and Van Guilder M: A Unified Parametric/ Nonparametric

Approach to Population PK/PD Modeling. Presented at the Annual Meeting of the Population Approach

Group in Europe, Paris, France, June 6-7, 2002.

[See

http://www.iatdmct.org/events/events-calendar/icalrepeat.detail/2014/05/08/17/-/3rd-annual-

individualized-therapeutic-approach-group-itag-meeting.html

- db]

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