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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
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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
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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
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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]
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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?
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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]
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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
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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
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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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