Basic concepts in population modeling, simulation, and model-based drug development: part 3-introduction to pharmacodynamic modeling methods.
Ontology highlight
ABSTRACT:
SUBMITTER: Upton RN
PROVIDER: S-EPMC3917320 | biostudies-literature | 2014
REPOSITORIES: biostudies-literature
Publications
Basic concepts in population modeling, simulation, and model-based drug development: part 3-introduction to pharmacodynamic modeling methods.
CPT: pharmacometrics & systems pharmacology 20140102
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 avail ...[more]
Similar Datasets
Project description:Modeling is an important tool in drug development; population modeling is a complex process requiring robust underlying procedures for ensuring clean data, appropriate computing platforms, adequate resources, and effective communication. Although requiring an investment in resources, it can save time and money by providing a platform for integrating all information gathered on new therapeutic agents. This article provides a brief overview of aspects of modeling and simulation as applied to many areas in drug development.CPT: Pharmacometrics & Systems Pharmacology (2012) 1, e6; doi:10.1038/psp.2012.4; advance online publication 26 September 2012.
| S-EPMC3606044 | biostudies-other
Project description:BackgroundComputer simulation models are used increasingly to support public health research and policy, but questions about their quality persist. The purpose of this article is to review the principles and methods for validation of population-based disease simulation models.MethodsWe developed a comprehensive framework for validating population-based chronic disease simulation models and used this framework in a review of published model validation guidelines. Based on the review, we formulated a set of recommendations for gathering evidence of model credibility.ResultsEvidence of model credibility derives from examining: 1) the process of model development, 2) the performance of a model, and 3) the quality of decisions based on the model. Many important issues in model validation are insufficiently addressed by current guidelines. These issues include a detailed evaluation of different data sources, graphical representation of models, computer programming, model calibration, between-model comparisons, sensitivity analysis, and predictive validity. The role of external data in model validation depends on the purpose of the model (e.g., decision analysis versus prediction). More research is needed on the methods of comparing the quality of decisions based on different models.ConclusionAs the role of simulation modeling in population health is increasing and models are becoming more complex, there is a need for further improvements in model validation methodology and common standards for evaluating model credibility.
| S-EPMC3001435 | biostudies-literature
Project description:Metabolic networks supply the energy and building blocks for cell growth and maintenance. Cells continuously rewire their metabolic networks in response to changes in environmental conditions to sustain fitness. Studies of the systemic properties of metabolic networks give insight into metabolic plasticity and robustness, and the ability of organisms to cope with different environments. Constraint-based stoichiometric modeling of metabolic networks has become an indispensable tool for such studies. Herein, we review the basic theoretical underpinnings of constraint-based stoichiometric modeling of metabolic networks. Basic concepts, such as stoichiometry, chemical moiety conservation, flux modes, flux balance analysis, and flux solution spaces, are explained with simple, illustrative examples. We emphasize the mathematical definitions and their network topological interpretations.
| S-EPMC4671265 | biostudies-literature
Project description:Optimal dosing of propofol to maintain appropriate anesthetic depth is challenging in severely obese (SO) adolescents. We previously reported that total body weight (TBW) is predictive of propofol clearance. This study was aimed at characterizing pharmacokinetics (PK) and pharmacodynamics (PD) of propofol in SO adolescents, using bispectral index (BIS), and toward developing PK/PD model-based dosing guidelines.A prospective PK/PD study was conducted in 26 SO children and adolescents aged 9-18 years (body mass index 31-69 kg·m(-2)), undergoing surgery with intravenous propofol anesthesia clinically titrated by providers blinded to BIS. BIS data and propofol infusion schemes were recorded. Venous blood samples collected during and after propofol infusion were assayed for propofol concentrations. A propofol PK/PD model was developed using NONMEM and model-based simulations were performed to determine propofol dosing regimens targeting BIS of 50 ± 10.A three-compartment PK model linked to a sigmoidal inhibitory Emax PD model by a first-order rate constant, adequately described the propofol concentration (n = 375) and BIS (n = 3334) data. TBW was the most predictive covariate for propofol clearance [CL (l·min(-1) ) = 1.65 × (TBW/70)(0.75)]. An effect-site propofol concentration of 3.19 ?g·ml(-1) was estimated for half-maximal effect, with no identifiable predictive covariates. The proposed maintenance dosing regimen targeted to a BIS of 50 ± 10, based on our PK/PD model, was able to predict desired propofol concentrations and BIS in a representative obese teen when used in conjunction with accepted PK/PD models for children/obese adults (PK:Eleveld/PD: Cortinez), further supporting evidence for the dosing based on TBW.This is the first study to describe the PK/PD of propofol in SO adolescents. The proposed maintenance dosing regimen for propofol uses TBW in an allometric function as a dosing scalar, with an exponent of 0.75. Our results suggest no relevant effect of obesity on the propofol concentration-BIS relationship.
| S-EPMC4516654 | biostudies-literature
Project description:BackgroundChronic obstructive pulmonary disease (COPD) patients may develop metabolic alkalosis during weaning from mechanical ventilation. Acetazolamide is one of the treatments used to reverse metabolic alkalosis.Methods619 time-respiratory (minute ventilation, tidal volume and respiratory rate) and 207 time-PaCO2 observations were obtained from 68 invasively ventilated COPD patients. We modeled respiratory responses to acetazolamide in mechanically ventilated COPD patients and then simulated the effect of increased amounts of the drug.ResultsThe effect of acetazolamide on minute ventilation and PaCO2 levels was analyzed using a nonlinear mixed effect model. The effect of different ventilatory modes was assessed on the model. Only slightly increased minute ventilation without decreased PaCO2 levels were observed in response to 250 to 500 mg of acetazolamide administered twice daily. Simulations indicated that higher acetazolamide dosage (>1000 mg daily) was required to significantly increase minute ventilation (P<.001 vs pre-acetazolamide administration). Based on our model, 1000 mg per day of acetazolamide would increase minute ventilation by >0.75 L min(-1) in 60% of the population. The model also predicts that 45% of patients would have a decrease of PaCO2>5 mmHg with doses of 1000 mg per day.ConclusionsSimulations suggest that COPD patients might benefit from the respiratory stimulant effect after the administration of higher doses of acetazolamide.
| S-EPMC3895035 | biostudies-literature
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:The aim of this study was to develop a population pharmacokinetic model for piperacillin (PIPC)/tazobactam (TAZ) in late elderly patients with pneumonia and to optimize the administration planning by applying pharmacokinetic/pharmacodynamic (PK/PD) criteria. PIPC/TAZ (total dose of 2.25 or 4.5 g) was infused intravenously three times daily to Japanese patients over 75 years old. The plasma concentrations of PIPC and TAZ were determined using high-performance liquid chromatography and modeled using the NONMEM program. PK/PD analysis with a random simulation was conducted using the final population PK model to estimate the probability of target attainment (PTA) profiles for various PIPC/TAZ-regimen-minimum-inhibitory-concentration (MIC) combinations. The PTAs for PIPC and TAZ were determined as the fraction that achieved at least 50% free time > MIC and area under the free-plasma-concentration-time curve over 24 h ? 96 ?g h/mL, respectively. A total of 18 cases, the mean age of which was 86.5 ± 6.0 (75-101) years, were investigated. The plasma-concentration-time profiles of PIPC and TAZ were characterized by a two-compartment model. The parameter estimates for the final model, namely the total clearance, central distribution volume, peripheral distribution volume, and intercompartmental clearance, were 4.58 + 0.061 × (CLcr - 37.4) L/h, 5.39 L, 6.96 L, and 20.7 L/h for PIPC, and 5.00 + 0.059 × (CLcr - 37.4) L/h, 6.29 L, 7.73 L, and 24.0 L/h for TAZ, respectively, where CLcr is the creatinine clearance. PK/PD analysis using the final model showed that in drug-resistant strains with a MIC > 8 ?g/mL, 4.5 g of PIPC/TAZ every 6 h was required, even for the patients with a CLcr of 50-60 mL/min. The population PK model developed in this study, together with MIC value, can be useful for optimizing the PIPC/TAZ dosage in the over-75-year-old patients, when they are administered PIPC/TAZ. Therefore, the findings of present study may contribute to improving the efficacy and safety of the administration of PIPC/TAZ therapy in late elderly patients with pneumonia.
| S-EPMC7148462 | biostudies-literature
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:Stochasticity is a key characteristic of intracellular processes such as gene regulation and chemical signalling. Therefore, characterizing stochastic effects in biochemical systems is essential to understand the complex dynamics of living things. Mathematical idealizations of biochemically reacting systems must be able to capture stochastic phenomena. While robust theory exists to describe such stochastic models, the computational challenges in exploring these models can be a significant burden in practice since realistic models are analytically intractable. Determining the expected behaviour and variability of a stochastic biochemical reaction network requires many probabilistic simulations of its evolution. Using a biochemical reaction network model to assist in the interpretation of time-course data from a biological experiment is an even greater challenge due to the intractability of the likelihood function for determining observation probabilities. These computational challenges have been subjects of active research for over four decades. In this review, we present an accessible discussion of the major historical developments and state-of-the-art computational techniques relevant to simulation and inference problems for stochastic biochemical reaction network models. Detailed algorithms for particularly important methods are described and complemented with Matlab® implementations. As a result, this review provides a practical and accessible introduction to computational methods for stochastic models within the life sciences community.
| S-EPMC6408336 | biostudies-literature
Project description:This study aimed to investigate the pharmacokinetic and pharmacodynamic (PK/PD) profiles of dotinurad, a novel uricosuric agent, and to construct a PK/PD model to predict serum urate (SUA) levels after dotinurad administration in healthy men. PK/PD model was constructed using single-dose study data considering the physiological features of urate handling. Model validation was performed by comparing the predicted SUA levels with the SUA levels in a multiple-dose study. Dotinurad was absorbed rapidly, and its exposure increased proportionally in the tested dose ranges (0.5-20 mg) after a single-dose administration. The PK model after oral administration was described using a one-compartment model with first-order absorption. Effects on SUA and renal urate excretion of dotinurad increased with dose escalation but were apparently saturable at a dose >5 mg. The simple maximal effect (E max) model was selected as the PD model of dotinurad on renal urate reabsorption, resulting in an estimated E max of 0.51. The plasma concentration at the half-maximal effect of dotinurad was 196 ng/mL. Other PD parameters were calculated from the change in SUA level or urinary excretion of urate before and after dotinurad administration. The predicted SUA levels, using the PK/PD model, were well-fitted with the observed values. The constructed PK/PD model of dotinurad appropriately described the profiles of dotinurad plasma concentrations and SUA level in multiple administration study.
| S-EPMC6880184 | biostudies-literature