PKPD

acikgoz_2009a.cellml

Tommy Yu — 2012-02-13T23:56:13Z

Model Status

Made in COR. Model runs in OpenCell to recreate results from figure 3a in published paper. This is the control version of the experiment.

Model Structure

ABSTRACT: Drug biotransformation is one of the most important parameters of preclinical screening tests for the registration of new drug candidates. Conventional existing tests rely on nonhuman models which deliver an incomplete metabolic profile of drugs due to the lack of proper CYP450 expression as seen in human liver in vivo. In order to overcome this limitation, we used an organotypical model of human primary hepatocytes for the biotransformation of the drug diazepam with special reference to metabolites in both the cell matrix phase and supernatant and its interaction of three inducers (phenobarbital, dexamethasone, aroclor 1254) in different time responses (1, 2, 4, 8, 24 h). Phenobarbital showed the strongest inducing effect in generating desmethyldiazepam and induced up to a 150 fold increase in oxazepam-content which correlates with the increased availability of the precursor metabolites (temazepam and desmethyldiazepam). Aroclor 1254 and dexamethasone had the strongest inducing effect on temazepam and the second strongest on oxazepam. The strong and overlapping inductive role of phenobarbital strengthens the participation of CYP2B6 and CYP3A in diazepam N-demethylation and CYP3A in temazepam formation. Aroclor 1254 preferentially generated temazepam due to the interaction with CYP3A and potentially CYP2C19. In parallel we represented these data in the form of a mathematical model with two compartments explaining the dynamics of diazepam metabolism with the effect of these other inducers in human primary hepatocytes. The model consists of ten differential equations, with one for each concentration c(i,j) (i=diazepam, temazepam, desmethyldiazepam, oxazepam, other metabolites) and one for each compartment (j= cell matrix phase, supernatant), respectively. The parameters p(k) (k=1, 2, 3, 4, 13) are rate constants describing the biotransformation of diazepam and its metabolites and the other parameters (k=5, 6, 7, 8, 9, 10, 11, 12, 14, 15) explain the concentration changes in the two compartments.

The complete original paper reference is cited below:

Two compartment model of diazepam biotransformation in an organotypical culture of primary human hepatocytes, Ali Acikgoz et al, 2009, Toxicology and applied pharmacology, 234, 179-191. PubMed ID: 18983865

Structure of the two compartment model of diazepam biotransformation and appropriated model paprameter.

acikgoz_2009b.cellml

Tommy Yu — 2012-02-13T23:56:15Z

Model Status

Made in COR. Model runs in OpenCell to recreate results from figure 3b in published paper. This is the aroclor version of the experiment.

Model Structure

The complete original paper reference is cited below:

Structure of the two compartment model of diazepam biotransformation and appropriated model paprameter.

Two compartment model of diazepam biotransformation in an organotypical culture of primary human hepatocytes

Tommy Yu — 2012-02-13T23:56:17Z

Model Status

Made in COR. Model runs in OpenCell to recreate results from figure 3c in published paper. This is the dexamethasone version of the experiment.

Model Structure

The complete original paper reference is cited below:

Structure of the two compartment model of diazepam biotransformation and appropriated model paprameter.

Two compartment model of diazepam biotransformation in an organotypical culture of primary human hepatocytes

Tommy Yu — 2012-02-13T23:56:19Z

Model Status

Made in COR. Model runs in OpenCell to recreate results from figure 3d in published paper. This is the Phenobarbital version of the experiment.

Model Structure

The complete original paper reference is cited below:

Structure of the two compartment model of diazepam biotransformation and appropriated model paprameter.

A pharmacokinetic model to predict the PK interaction of L-dopa and benserazide in rats (L-dopa)

Catherine Lloyd — 2012-02-14T00:54:06Z

Model Status

This CellML model runs in PCenv, COR and OpenCell to recreate the published results (Figure 6). This model is simulates the administration of L-dopa only, without benserazide added. This model was created using the paper equations supplemented by suggestions made by Dr. Holford, as the original code was not available.

Model Structure

PURPOSE: To study the PK interaction of L-dopa/benserazide in rats. METHODS: Male rats received a single oral dose of 80 mg/kg L-dopa or 20 mg/kg benserazide or 80/20 mg/kg L-dopa/benserazide. Based on plasma concentrations the kinetics of L-dopa, 3-O-methyldopa (3-OMD), benserazide, and its metabolite Ro 04-5127 were characterized by noncompartmental analysis and a compartmental model where total L-dopa clearance was the sum of the clearances mediated by amino-acid-decarboxylase (AADC), catechol-O-methyltransferase and other enzymes. In the model Ro 04-5127 inhibited competitively the L-dopa clearance by AADC. RESULTS: The coadministration of L-dopa/benserazide resulted in a major increase in systemic exposure to L-dopa and 3-OMD and a decrease in L-dopa clearance. The compartmental model allowed an adequate description of the observed L-dopa and 3-OMD concentrations in the absence and presence of benserazide. It had an advantage over noncompartmental analysis because it could describe the temporal change of inhibition and recovery of AADC. CONCLUSIONS: Our study is the first investigation where the kinetics of benserazide and Ro 04-5127 have been described by a compartmental model. The L-dopa/benserazide model allowed a mechanism-based view of the L-dopa/benserazide interaction and supports the hypothesis that Ro 04-5127 is the primary active metabolite of benserazide.

The original paper reference is cited below:

A pharmacokinetic model to predict the PK interaction of L-dopa and benserazide in rats, Susan Grange, Nicholas H. G. Holford, Theodor W. Guentert, 2001, Pharm Res., volume 18, 1174-1184. PubMed ID: 11587490

Schematic diagram of the conceptual model to describe kinetics of L-dopa and 3-OMD.

A pharmacokinetic model to predict the PK interaction of L-dopa and benserazide in rats (L-dopa + Benserazide)

Catherine Lloyd — 2012-02-14T00:54:08Z

Model Status

This CellML model runs in PCenv, COR and OpenCell to recreate the published results (Figures 5 and 7). This model describes the metabolism of both L-dopa and benserazide. This model was created using the paper equations supplemented by suggestions made by Dr. Holford, as the original code was not available.

Model Structure

The original paper reference is cited below:

Schematic diagram of the conceptual model to describe kinetics of benserazide and Ro 04-5127.

Application of clinical trial simulation to compare proof-of-concept study designs for drugs with a slow onset of effect; an example in Alzheimer's disease

admin — 2012-02-14T01:38:31Z

Model Status

This CellML model runs in both PCEnv and COR to replicate the published results (as confirmed by the original model author). The units have been checked and they are consistent.

Model Structure

ABSTRACT: OBJECTIVE: Clinical trial simulation (CTS) was used to select a robust design to test the hypothesis that a new treatment was effective for Alzheimer's disease (AD). Typically, a parallel group, placebo controlled, 12-week trial in 200-400 AD patients would be used to establish drug effect relative to placebo (i.e., Ho: Drug Effect = 0). We evaluated if a crossover design would allow smaller and shorter duration trials. MATERIALS AND METHODS: A family of plausible drug and disease models describing the time course of the AD assessment scale (ADAS-Cog) was developed based on Phase I data and litreature reports of other treatments for AD. The models included pharmacokinetic, pharmacodynamic, disease progression, and placebo components. Eight alternative trial designs were explored via simulation. One hundred replicates of each combination of drug and disease model and trial design were simulated. A 'positive trial' reflecting drug activity was declared considering both a dose trend test (p less than 0.05) and pair-wise comparisons to placebo (p less than 0.025). RESULTS: A 4 x 4 Latin Square design was predicted to have at least 80% power to detect activity across a range of drug and disease models. The trial design was subsequently implemented and the trial was completed. Based on the results of the actual trial, a conclusive decision about further development was taken. The crossover design provided enhanced power over a parallel group design due to the lower residual variability. CONCLUSION: CTS aided the decision to use a more efficient proof of concept trial design, leading to savings of up to US 4 M dollars in direct costs and a firm decision 8-12 months earlier than a 12-week parallel group trial.

The original paper reference is cited below:

Application of clinical trial simulation to compare proof-of-concept study designs for drugs with a slow onset of effect; an example in Alzheimer's disease, Peter Lockwood, Wayne Ewy, David Hermann and Nick Holford, 2006, Pharmaceutical Research, 23, (9), 2050-2059. PubMed ID: 16906456

PKPD model of interleukin-21 effects on thermoregulation in monkeys (Baseline)

Catherine Lloyd — 2012-02-14T01:12:05Z

Model Status

This CellML model represents the "Baseline Model" in the published paper. The model runs in PCEnv and has been unit checked. All the units are balanced. Note that this model will not run in COR due to the presence of a "remainder" function. Note there are no figures in the paper showing the simuation results of this model. However we can say that the simulation output from the CellML model looks reasonable - the body temperature and metabolic rates have physiologically realistic values and oscillate over a 24 hour period according to day and night changes.

Model Structure

ABSTRACT: PURPOSE: To describe the pharmacodynamic effects of recombinant human interleukin-21 (IL-21) on core body temperature in cynomolgus monkeys using basic mechanisms of heat regulation. A major effort was devoted to compare the use of ordinary differential equations (ODEs) with stochastic differential equations (SDEs) in pharmacokinetic pharmacodynamic (PKPD) modelling. METHODS: A temperature model was formulated including circadian rhythm, metabolism, heat loss, and a thermoregulatory set-point. This model was formulated as a mixed-effects model based on SDEs using NONMEM. RESULTS: The effects of IL-21 were on the set-point and the circadian rhythm of metabolism. The model was able to describe a complex set of IL-21 induced phenomena, including 1) disappearance of the circadian rhythm, 2) no effect after first dose, and 3) high variability after second dose. SDEs provided a more realistic description with improved simulation properties, and further changed the model into one that could not be falsified by the autocorrelation function. CONCLUSIONS: The IL-21 induced effects on thermoregulation in cynomolgus monkeys are explained by a biologically plausible model. The quality of the model was improved by the use of SDEs.

Schematic diagram of the model for IL-21 induced regulation of core body temperature.

The original paper reference is cited below:

PKPD model of interleukin-21 effects on thermoregulation in monkeys - application and evaluation of stochastic differential equations, Rune Viig Overgaard, Nick Holford, Klaus A. Rytved and Henrik Madsen, 2007, Pharmaceutical Research, 24, (2), 298-309. PubMed ID: 17009101

PKPD model of interleukin-21 effects on thermoregulation in monkeys (Pharmacodynamic)

Catherine Lloyd — 2012-02-14T01:12:09Z

Model Status

This CellML model represents the "Pharmacodynamic Model" in the published paper. The model runs in PCEnv to recreate figure3 in the published paper. The units have been chaecked and they balance. Note that this model will not run in COR due to the presence of a "remainder" function. Please also note that we have chosen "high dose levels" (deltahigh_dose=1), and IL-21 is administered on days 1, 3 and 5, at a dose of 3mg/kg. The f2 function in equation 3 of the paper has been ignored as this is only relevant for drugs directly affecting heat conduction, which IL-21 is not expected to. Thank you to the original model author Rune Viig Overgaard for providing us with the NONMEM code and for clarifying certain aspects of the model.

Model Structure

Schematic diagram of the model for IL-21 induced regulation of core body temperature.

The original paper reference is cited below:

A pharmacodynamic model for the time course of tumor shrinkage by gemcitabine + carboplatin in non-small cell lung cancer patients

Catherine Lloyd — 2012-02-14T00:43:27Z

Model Status

This CellML model runs in PCenv and OpenCell to recreate the published results, but not COR because of the presence of the modulo operator. The maximum step size must be constrained to be 0.001 weeks or smaller, or the software will ignore the pulsatile administration of gemcitabine. This model describes the response of tumor size to administration of gemcitabine, and was based off original Berkeley Madonna code provided by the model authors.

Model Structure

ABSTRACT: PURPOSE: This tumor response pharmacodynamic model aims to describe primary lesion shrinkage in non-small cell lung cancer over time and determine if concentration-based exposure metrics for gemcitabine or that of its metabolites, 2',2'-difluorodeoxyuridine or gemcitabine triphosphate, are better than gemcitabine dose for prediction of individual response. EXPERIMENTAL DESIGN: Gemcitabine was given thrice weekly on days 1 and 8 in combination with carboplatin, which was given only on day 1 of every cycle. Gemcitabine amount in the body and area under the concentration-time curves of plasma gemcitabine, 2',2'-difluorodeoxyuridine, and intracellular gemcitabine triphosphate in white cells were compared to determine which best describes tumor shrinkage over time. Tumor growth kinetics were described using a Gompertz-like model. RESULTS: The apparent half-life for the effect of gemcitabine was 7.67 weeks. The tumor turnover time constant was 21.8 week.cm. Baseline tumor size and gemcitabine amount in the body to attain 50% of tumor shrinkage were estimated to be 6.66 cm and 10,600 mg. There was no evidence of relapse during treatment. CONCLUSIONS: Concentration-based exposure metrics for gemcitabine and its metabolites were no better than gemcitabine amount in predicting tumor shrinkage in primary lung cancer lesions. Gemcitabine dose-based models did marginally better than treatment-based models that ignored doses of drug administered to patients. Modeling tumor shrinkage in primary lesions can be used to quantify individual sensitivity and response to antitumor effects of anticancer drugs.

The original paper reference is cited below:

A pharmacodynamic model for the time course of tumor shrinkage by gemcitabine + carboplatin in non-small cell lung cancer patients, Lai-San Tham, Lingzhi Wang, Ross A Soo, Soo-Chin Lee, How-Sung Lee, Wei-Peng Yong, Boon-Cher Goh and Nicholas H.G. Holford, 2008, Clin Cancer Res, volume 14, 4213-4218. PubMed ID: 18594002

Schematic diagram of the model.

Population-Based Analysis of Methadone Distribution and Metabolism Using an Age-Dependent Physiologically Based Pharmacokinetic Model

Catherine Lloyd — 2012-02-14T02:07:08Z

Model Status

This CellML version of the model has been checked in COR and PCEnv. It will recreate published results, although it only models a single organ (the liver) as well as the veins and arteries, and is based on a 5 year old boy. The model cannot replicate the population analysis described in the paper, which needs stochastic tools that are unavailable to CellML at present.

Model Structure

Abstract: Limited pharmacokinetic (PK) and pharmacodynamic (PD) data are available to use in methadone dosing recommendations in pediatric patients for either opioid abstinence or analgesia. Considering the extreme inter-individual variability of absorption and metabolism of methadone, population-based PK would be useful to provide insight into the relationship between dose, blood concentrations, and clinical effects of methadone. To address this need, an age-dependent physiologically based pharmacokinetic (PBPK) model has been constructed to systematically study methadone metabolism and PK. The model will facilitate the design of cost-effective studies that will evaluate methadone PK and PD relationships, and may be useful to guide methadone dosing in children. The PBPK model, which includes whole-body multi-organ distribution, plasma protein binding, metabolism, and clearance, is parameterized based on a database of pediatric PK parameters and data collected from clinical experiments. The model is further tailored and verified based on PK data from individual adults, then scaled appropriately to apply to children aged 0-24 months. Based on measured variability in CYP3A enzyme expression levels and plasma orosomucoid (ORM2) concentrations, a Monte-Carlo-based simulation of methadone kinetics in a pediatric population was performed. The simulation predicts extreme variability in plasma concentrations and clearance kinetics for methadone in the pediatric population, based on standard dosing protocols. In addition, it is shown that when doses are designed for individuals based on prior protein expression information, inter-individual variability in methadone kinetics may be greatly reduced.

The complete original paper reference is cited below:

Population-based analysis of methadone distribution and metabolism using an age-dependent physiologically based pharmacokinetic model, Feng Yang, Xianping Tong, D. Gail. McCarver, Ronald N. Hines and Daniel A. Beard, 2006, Journal of Pharmacokinetics and Pharmacodynamics , volume 33, issue 4. PubMed ID: 16758333

Schematic diagram of a PBPK model consisting of 17 compartments. The lines represent blood flow while the boxes represent organs or systems. Methadone is primarily metabolised in the liver and gastro-intestinal (GI) system, while its elimination mainly occurs through the kidneys. Organs in which methadone are not distributed include skin, adipose, thyroid, pancreas, and bone marrow are grouped together as others.