Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model.
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ABSTRACT:
SUBMITTER: Kuepfer L
PROVIDER: S-EPMC5080648 | biostudies-literature | 2016 Oct
REPOSITORIES: biostudies-literature
Publications
Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model.
Kuepfer L L Niederalt C C Wendl T T Schlender J-F JF Willmann S S Lippert J J Block M M Eissing T T Teutonico D D
CPT: pharmacometrics & systems pharmacology 20161019 10
The aim of this tutorial is to introduce the fundamental concepts of physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling with a special focus on their practical implementation in a typical PBPK model building workflow. To illustrate basic steps in PBPK model building, a PBPK model for ciprofloxacin will be constructed and coupled to a pharmacodynamic model to simulate the antibacterial activity of ciprofloxacin treatment. ...[more]
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Project description:This tutorial presents the workflow of adapting an adult physiologically based pharmacokinetic (PBPK) model to the pregnant populations using the Open Systems Pharmacology (OSP) software suite (www.open-systems-pharmacology.org). This workflow is illustrated using a previously published PBPK model for metronidazole that is extrapolated to pregnancy by parameterizing and extending the model structure in terms of pregnancy-induced physiological changes. Importantly, this workflow can be applied to other scenarios where PBPK models need to be re-parameterized or structurally modified.
| S-EPMC6063743 | biostudies-literature
Project description:BackgroundNicotine, the pharmacologically active substance in both tobacco and many electronic cigarette (e-cigarette) liquids, is responsible for the addiction that sustains cigarette smoking. With 8 million deaths worldwide annually, smoking remains one of the major causes of disability and premature death. However, nicotine also plays an important role in smoking cessation strategies.ObjectivesThe aim of this study was to develop a comprehensive, whole-body, physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model of nicotine and its major metabolite cotinine, covering various routes of nicotine administration, and to simulate nicotine brain tissue concentrations after the use of combustible cigarettes, e-cigarettes, nicotine gums, and nicotine patches.MethodsA parent-metabolite, PBPK/PD model of nicotine for a non-smoking and a smoking population was developed using 91 plasma and brain tissue concentration-time profiles and 11 heart rate profiles. Among others, cytochrome P450 (CYP) 2A6 and 2B6 enzymes were implemented, including kinetics for CYP2A6 poor metabolizers.ResultsThe model is able to precisely describe and predict both nicotine plasma and brain tissue concentrations, cotinine plasma concentrations, and heart rate profiles. 100% of the predicted area under the concentration-time curve (AUC) and maximum concentration (Cmax) values meet the twofold acceptance criterion with overall geometric mean fold errors of 1.12 and 1.15, respectively. The administration of combustible cigarettes, e-cigarettes, nicotine patches, and nicotine gums was successfully implemented in the model and used to identify differences in steady-state nicotine brain tissue concentration patterns.ConclusionsOur PBPK/PD model may be helpful in further investigations of nicotine dependence and smoking cessation strategies. As the model represents the first nicotine PBPK/PD model predicting nicotine concentration and heart rate profiles after the use of e-cigarettes, it could also contribute to a better understanding of the recent increase in youth e-cigarette use.
| S-EPMC7467963 | biostudies-literature
Project description:There are many types of biomarkers; the two common ones are biomarkers of exposure and biomarkers of effect. The utility of a biomarker for estimating exposures or predicting risks depends on the strength of the correlation between biomarker concentrations and exposure/effects. In the current study, a combined exposure and physiologically-based pharmacokinetic/pharmacodynamic (PBPK/PD) model of carbaryl was used to demonstrate the use of computational modeling for providing insight into the selection of biomarkers for different purposes. The Cumulative and Aggregate Risk Evaluation System (CARES) was used to generate exposure profiles, including magnitude and timing, for use as inputs to the PBPK/PD model. The PBPK/PD model was then used to predict blood concentrations of carbaryl and urine concentrations of its principal metabolite, 1-naphthol (1-N), as biomarkers of exposure. The PBPK/PD model also predicted acetylcholinesterase (AChE) inhibition in red blood cells (RBC) as a biomarker of effect. The correlations of these simulated biomarker concentrations with intake doses or brain AChE inhibition (as a surrogate of effects) were analyzed using a linear regression model. Results showed that 1-N in urine is a better biomarker of exposure than carbaryl in blood, and that 1-N in urine is correlated with the dose averaged over the last 2 days of the simulation. They also showed that RBC AChE inhibition is an appropriate biomarker of effect. This computational approach can be applied to a wide variety of chemicals to facilitate quantitative analysis of biomarker utility.
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Project description:Using laser capture microdissection to isolate over 100 specific cell populations, we report the profiling of prostate cancer progression from benign epithelium to metastatic disease. By analyzing these expression signatures in the context of over 15,000 “molecular concepts”, or sets of biologically connected genes, we generated an integrative model of prostate cancer progression. Keywords: disease state analysis
2006-12-17 | GSE6099 | GEO
Project description:Using laser capture microdissection to isolate over 100 specific cell populations, we report the profiling of prostate cancer progression from benign epithelium to metastatic disease. By analyzing these expression signatures in the context of over 15,000 â??molecular conceptsâ??, or sets of biologically connected genes, we generated an integrative model of prostate cancer progression. Keywords: disease state analysis Experiment Design: â?¢ Type of experiment: This study used microarray experiments to profile prostate cancer progression through the isolation of pure cell populations using laser capture microdissection (LCM) and OmniPlex Whole Transcriptome (WTA) Amplification. â?¢ Experimental factors: This study profiled various pure cell populations isolated by LCM from prostate tissue. â?¢ The number of hybridizations performed in the experiment: A total of 104 hybridizations were performed. â?¢ The type of reference used for the hybridizations: A single commercially available pool of benign prostate tissue (CPP, Clontech) was used as the reference in all hybridizations. For the reference sample, 10 ng of CPP total RNA was converted into a WTA cDNA library in a volume of 25 µl and five 5 µl aliquots were amplified by WTA PCR and purified. Five ng aliquots of the pooled products were subjected to a second WTA PCR amplification in the presence of amino-allyl dUTP and all products were pooled before labeling to ensure equal representation across all of the hybridizations. â?¢ Hybridization design: For all hybridizations, the experimental prostate sample isolated by LCM was labeled with Cy5 and the common reference of benign pooled prostate as described above was labeled with Cy3. â?¢ Quality control steps taken: In order to assess biological and technical reproducibility, we captured identical regions from two specimens (PCA_11 and MET_HR_3) and subjected one sample, PCA_30, to two hybridizations. For PCA_11, adjacent regions of the same section were captured and treated as separate samples. For MET_HR_3, two separate foci of the liver metastasis (from separate frozen blocks) were captured and treated as separate samples. For PCA_30, two aliquots of the primary WTA PCR products were separately subjected to a second round of WTA PCR amplification. Replicates from PCA_30 and MET_HR_3 were also hybridized to arrays from each of the print runs utilized in this study. Samples used, extract preparation and labeling: â?¢ The origin of the biological sample (for instance, name of the organism, the provider of the sample) and its characteristics: All samples used were of human origin. The table in the MIAME Checklist (Supplmentary Methods) summarizes the samples used. Hybridizations are named according to the class of sample profiled and the background color is the same as shown in Figure S3. Two print runs were used during the course of the study and the indicated run for each hybridization is given. For prostate cancer samples, the Gleason patterns present on the sample section used are indicated, as well as the Gleason patterns of the actual cells isolated by LCM. A unique case number for all profiled samples is given. Descriptions of samples are given where applicable, including the location of metastatic foci. â?¢ Manipulation of biological samples and protocols used: Laser Capture Microdissection (LCM) was performed from frozen tissue sections with the SL Microtest device using µCUT software (MMI). Approximately 10,000 cells were captured for each sample. Serial sections were used if cells could not be obtained from a single section. Total RNA was isolated from captured cells with the RNAqueous Micro kit (Ambion) and treated with DNAse I according to the manufacturer's instructions. RNA quantification was perfomed using Ribogreen (Molecular Probes). â?¢ Protocol for preparing the hybridization extract: For each sample, total RNA (~10ng) in a volume of 25 µl was converted into an OmniPlex WTA cDNA library and amplified by WTA PCR using reagents and protocols according to the beta-commercial TransPlex WTA kit (Rubicon Genomics, Ann Arbor, MI). For each sample, a single 10 µl aliquot of the WTA cDNA library was amplified by WTA PCR and products were purified. Four or five 5 ng aliquots of product were subjected to a second WTA PCR amplification in the presence of amino-allyl dUTP for post amplification labeling and products were pooled before proceeding with the hybridization. Yields after all WTA PCR amplifications were between 2 to 5 μg per reaction, and reaction progress was monitored in real time using SYBR green I (Molecular Probes) on the iCycler IQ (BioRad) or ABI 7300 Real Time PCR system (Applied Biosystems). Real-time PCR amplifications were terminated at or before plateau phase as measured by fluorescence incorporation to preserve maximum representation. â?¢ Labeling protocol(s): For all WTA amplified cDNA samples with amino-allyl dUTP incorporated (10-20 ï?g), nuclease free water was added to 500 ï?l, the sample was transferred to a Microcon YM-30 filter, and centrifuged at 13,000 rcf at RT for 12 minutes. The spin column was inverted in a new collection tube and centrifuged at 13,000 rcf for 2 min at RT. The amount of sample was measured and the volume was adjusted to 5 ï?l with nuclease free water. For labeling, 5 ï?l of 1 M sodium bicarbonate (pH 9.0) was added and allowed to incubate at RT for 15 min. Nine ï?l of anhydrous DMSO were added to Mono Reactive Cy3 and Cy5 dye packs (Amersham, Buckinghamshire, England), mixed thoroughly, and 1 ï?l of the proper dye was added to each sample. The labeling mixture was incubated in the dark at RT for 1 hour. The reaction was stopped by the addition of 9 ï?l of 4 M Hydroxylamine for 15 min. For each labeling mixture, RNase free water was added to 100 ï?l, 500 ï?l of PB buffer was added and Cy3 and Cy5 mixtures were added to separate Qiaquick (Qiagen, Valencia, CA) spin columns and centrifuged at 13,000 rcf. Columns were washed twice with 700 ï?l of buffer PE, and centrifuged dry at 13,000 rcf for 2 min. To elute, 60 ï?l of EB buffer was placed on the column, incubated for 5 min and centrifuged at 13,000 rcf for 1 min. The Cy3 labeled and Cy5 labeled cDNA for each hybridization were mixed and 1.5 ï?l were used for analysis on a ND-1000 spectrophotometer. â?¢ External controls (spikes): Not utilized Hybridization procedures and parameters: â?¢ The protocol and conditions used during hybridization, blocking and washing: For hybridization, 40 ï?g of human Cot-1 DNA (Invitrogen, Carlsbad, CA) was added to 120 ï?l of labeled cDNA, the probe was transferred to a Microcon YM-30 filter and centrifuged at 13,000 rcf for 6 min at RT. The spin column was inverted in a new collection tube and centrifuged at 13,000 rcf for 2 min at RT. The eluted probe volume was adjusted to 18.6 ï?l with nuclease free water and the following were added: 4 ï?l of yeast tRNA (10 ï?g/ï?l, Invitrogen, Carlsbad, CA), 4.9 ï?l of 20X SSC and 0.84 ï?l of 10% SDS. The probe was denatured at 100 Co for 3 min and centrifuged at 13,000 rcf for 45 sec. The probe was added directly to the microarray and a cover slip was added. Slides were hybridized overnight in DIE-TECH 1 or 5 slide hybridization chambers in a 65 Co water bath. After hybridization, cover slips were removed by incubation in 2x SSC / 0.05% SDS. Slides were then washed in 2x SSC / 0.05% SDS for 5 min and 0.2x SSC / 0.05% SDS for five minutes. Slides were then washed in 0.2x SSC for 10 sec and centrifuged dry at 500 rpm for 5 min. Measurement data and specifications: â?¢ Type of scanning hardware and software used: Microarrays were scanned using a GenePix 4000B scanner (Axon Instruments, Union City, CA). â?¢ Type of image analysis software used: Images were gridded and spots were quantified using GenePix Pro 4.0 software (Axon Instruments, Union City, CA). â?¢ A description of the measurements produced by the image-analysis software and a description of which measurements were used in the analysis: The measurements made by GenePix Pro 4.0 are described in the sample files. For all hybridizations, the log2 normalized Median of Ratios (as described below) was used. â?¢ Data selection and transformation procedures: Arrays were auto-gridded by GenePix 4.0. Features flagged by GenePix as not found during grid
2010-06-05 | E-GEOD-6099 | biostudies-arrayexpress
Project description:Background and objectiveMore than half of all drugs are still prescribed off-label to children. Pharmacokinetic (PK) data are needed to support off-label dosing, however for many drugs such data are either sparse or not representative. Physiologically-based pharmacokinetic (PBPK) models are increasingly used to study PK and guide dosing decisions. Building compound models to study PK requires expertise and is time-consuming. Therefore, in this paper, we studied the feasibility of predicting pediatric exposure by pragmatically combining existing compound models, developed e.g. for studies in adults, with a pediatric and preterm physiology model.MethodsSeven drugs, with various PK characteristics, were selected (meropenem, ceftazidime, azithromycin, propofol, midazolam, lorazepam, and caffeine) as a proof of concept. Simcyp® v20 was used to predict exposure in adults, children, and (pre)term neonates, by combining an existing compound model with relevant virtual physiology models. Predictive performance was evaluated by calculating the ratios of predicted-to-observed PK parameter values (0.5- to 2-fold acceptance range) and by visual predictive checks with prediction error values.ResultsOverall, model predicted PK in infants, children and adolescents capture clinical data. Confidence in PBPK model performance was therefore considered high. Predictive performance tends to decrease when predicting PK in the (pre)term neonatal population.ConclusionPragmatic PBPK modeling in pediatrics, based on compound models verified with adult data, is feasible. A thorough understanding of the model assumptions and limitations is required, before model-informed doses can be recommended for clinical use.
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Project description:Population pharmacodynamic (PD) models describe the time course of drug effects, relating exposure to response, and providing a more robust understanding of drug action than single assessments. PD models can test alternative dose regimens through simulation, allowing for informed assessment of potential dose regimens and study designs. This is the third paper in a three-part series, providing an introduction into methods for developing and evaluating population PD models. Example files are available in the Supplementary Data.
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Project description:Modeling and simulation of drug disposition has emerged as an important tool in drug development, clinical study design and regulatory review, and the number of physiologically based pharmacokinetic (PBPK) modeling related publications and regulatory submissions have risen dramatically in recent years. However, the extent of use of PBPK modeling by researchers, and the public availability of models has not been systematically evaluated. This review evaluates PBPK-related publications to 1) identify the common applications of PBPK modeling; 2) determine ways in which models are developed; 3) establish how model quality is assessed; and 4) provide a list of publically available PBPK models for sensitive P450 and transporter substrates as well as selective inhibitors and inducers. PubMed searches were conducted using the terms "PBPK" and "physiologically based pharmacokinetic model" to collect published models. Only papers on PBPK modeling of pharmaceutical agents in humans published in English between 2008 and May 2015 were reviewed. A total of 366 PBPK-related articles met the search criteria, with the number of articles published per year rising steadily. Published models were most commonly used for drug-drug interaction predictions (28%), followed by interindividual variability and general clinical pharmacokinetic predictions (23%), formulation or absorption modeling (12%), and predicting age-related changes in pharmacokinetics and disposition (10%). In total, 106 models of sensitive substrates, inhibitors, and inducers were identified. An in-depth analysis of the model development and verification revealed a lack of consistency in model development and quality assessment practices, demonstrating a need for development of best-practice guidelines.
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