- On 22 Oct 2014 at 21:59:21, Jim Zheng (pgscientist.-a-.gmail.com) sent the message

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Hi, I have the following problem, and I wonder whether any one can give me some tips. We do a

pharmacokinetic study on a drug. When we do dense sampling for single dose, we find that AUC is

approximately linear with the dose (from 10 mg to 70 mg), but Cmax is substantially less than that

predicted by linear trend. I wonder what would be an appropriate model for such PK data. In

addition, we also have PK data with sparse sampling between 10 mg and 20 mg. I wonder whether the

dense sampling data and the sparse sampling data can be combined. If so, then what kind of model

would be appropriate.

Thank you a bundle for your help. - On 23 Oct 2014 at 10:36:55, Parag Kumar (prgkmr.-at-.gmail.com) sent the message

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Jim,

I'm assuming this is an oral drug. This sounds like a rate-limited absorption. If the AUC is still

increasing linearly, then the drug is still being absorbed to the same extent. A model with a fixed

rate constant for absorption from the GI into the central compartment could be helpful here.

Parag - On 24 Oct 2014 at 09:33:43, Sam Liao (sliao.at.pharmaxresearch.com) sent the message

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Hi Jim,

Sounds like a oral dose with saturable rate of absorption.

Yes, the sparse sampling data and the dense sampling data could be

combined in a population PK analysis. But, you would need to use the

dense sampling data to develop the structural PK model first.

Best regards,

Sam Liao, Ph.D.

Pharmax Research, Inc. - On 24 Oct 2014 at 09:36:01, Ahmed Suleiman (ahmed_bisso.-at-.hotmail.com) sent the message

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Jim,

As Parag suggested, could the absorption be limited?

Do you have any information about the solubility of the drug? Perhaps the absorption rate is limited

by the solubility of the drug in the gut fluid (assuming the drug was orally given). This would mean

that at concentrations higher than the solubility equilibrium, a portion of the dose could

precipitate and therefore the absorption rate becomes constant (zero-order) until the drug

concentration drops down to its solubility limit and consequently the absorption shifts to a

first-order process. You can find a description of this model (besides others) in the following

paper by Holford et al. (1992).

http://www.ncbi.nlm.nih.gov/pubmed/1287195

I would also be glad if other members of the community share their ideas and experience.

Ahmed Suleiman - On 24 Oct 2014 at 14:43:01, Walt Woltosz (walt.-a-.simulations-plus.com) sent the message

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Jim,

This is an ideal type of problem for mechanistic absorption models (MAMs)

and physiologically based pharmacokinetics (PBPK). Forget the calls to use

empirical statistical models and use a model that gets to the underlying

phenomena that are affecting absorption and PK. You may have any number of

factors that are affecting your drug formulation and they should become

evident when you use a good simulation to integrate all the factors

(pH-dependent solubility, dissolution, permeability, transit time through

different regions of the intestine, etc.).

GastroPlus is used throughout the industry for this kind of model and its

Advanced Compartmental Absorption and Transit (ACAT) model of

gastrointestinal absorption and PK has been shown to be the most accurate

model available for such predictions.

Best regards,

Walt

Walt Woltosz

Chairman and CEO

Simulations Plus, Inc. (NASDAQ: SLP)

and Cognigen Corp, a wholly owned subsidiary of Simulations Plus

42505 10th Street West

Lancaster, CA 93534 - On 27 Oct 2014 at 11:58:32, Roger Jelliffe (jelliffe.aaa.usc.edu) sent the message

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Dear Jim:

You can make any type of model you wish to make (MAM, PBPK, anything) using Pmetrics. What does

it get you? This nonparametric (NP) approach gets you the most likely results given the data,

because it does not constrain the likelihood with the assumptions made by parametric approaches

(NONMEM, etc) that assume a shape for the probability distributions of the model parameters. You can

easily detect unsuspected skewed or bimodal distributions. You have the guarantee as well of

statistical consistency (study more subjects, the results approach the true distributions). The real

payoff is that you can develop maximally precise dosage regimens that will hit a desired target goal

(target serum concentration or PBPK effect) specifically with maximum precision (minimum expected

weighted squared error) Parametric modeling approaches cannot do that. Anything you can do with any

other approach (PBPK, etc.) you can do with the NP approach as is Pmetrics (see below), and you then

have the ability to develop maximally precise dosage regimens using multiple model (MM) dosage

design), followed by NP Bayesian stochastic Bayesian adaptive control and MM dosage.

You might look at:

1. 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.

2. Jelliffe R: Goal-Oriented, Model-Based Drug Regimens: Setting Individualized Goals for each

Patient. Therap. Drug Monit. 22: 325-329, 2000.

3. 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.

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

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

5. 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.

6. Jelliffe R, Schumitzky A, Bayard D, Leary R, Botnen A, Van Guilder M, Bustad A, and Neely M:

Human Genetic variation, Population Pharmacokinetic – Dynamic Models, Bayesian feedback control, and

Maximally precise Individualized drug dosage regimens. Current Pharmacogenomics and Personalized

Medicine, 7: 249-262, 2009.

7. Bayard D, and Jelliffe R: A Bayesian Approach to Tracking Patients having Changing

Pharmacokinetic Parameters. J. Pharmacokin. Pharmacodyn. 31 (1): 75-107, 2004.

8. Macdonald I, Staatz C, Jelliffe R, and Thomson A: Evaluation and Comparison of Simple

Multiple Model, Richer Data Multiple Model, and Sequential Interacting Multiple Model (IMM) Bayesian

Analyses of Gentamicin and Vancomycin Data Collected From Patients Undergoing Cardiothoracic

Surgery. Ther. Drug Monit. 30:67–74, 2008.

Again, you can use Pmetrics, the NP modeling software, available free at www.lapk.org, to make

any type of model you wish, including, for example, models of two or more drugs acting together with

whatever interaction you wish to describe as one drug affects the other and vice versa, any type of

absorption model you wish, and any type of PBPK model.

Hope this helps, and very best regards,

Roger Jelliffe

Roger W. Jelliffe, M.D., F.C.P., F.A.A.C.P.

Professor of Medicine Emeritus,

Founder and Director Emeritus

Laboratory of Applied Pharmacokinetics

USC School of Medicine

Consultant in Infectious Diseases,

Children’s Hospital of Los Angeles

4650 Sunset Blvd, MS 51

Los Angeles CA 90027

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