The concept of physiologically based pharmacokinetic (PBPK) modeling was introduced years ago, but it has not been practiced significantly. However, interest in and implementation of this modeling technique have grown, as evidenced by the increased number of publications in this field. This paper demonstrates briefly the methodology, applications, and limitations of PBPK modeling with special attention given to discuss the use of PBPK models in pediatric drug development and some examples described in detail. Although PBPK models do have some limitations, the potential benefit from PBPK modeling technique is huge. PBPK models can be applied to investigate drug pharmacokinetics under different physiological and pathological conditions or in different age groups, to support decision-making during drug discovery, to provide, perhaps most important, data that can save time and resources, especially in early drug development phases and in pediatric clinical trials, and potentially to help clinical trials become more “confirmatory” rather than “exploratory”.
Propofol is widely used for both short-term anesthesia and long-term sedation. It has unusual pharmacokinetics because of its high lipid solubility. The standard approach to describing the pharmacokinetics is by a multi-compartmental model. This paper presents the first detailed human physiologically based pharmacokinetic (PBPK) model for propofol.
PKQuest, a freely distributed software routine , was used for all the calculations. The "standard human" PBPK parameters developed in previous applications is used. It is assumed that the blood and tissue binding is determined by simple partition into the tissue lipid, which is characterized by two previously determined set of parameters: 1) the value of the propofol oil/water partition coefficient; 2) the lipid fraction in the blood and tissues. The model was fit to the individual experimental data of Schnider et. al., Anesthesiology, 1998; 88:1170 in which an initial bolus dose was followed 60 minutes later by a one hour constant infusion.
The PBPK model provides a good description of the experimental data over a large range of input dosage, subject age and fat fraction. Only one adjustable parameter (the liver clearance) is required to describe the constant infusion phase for each individual subject. In order to fit the bolus injection phase, for 10 or the 24 subjects it was necessary to assume that a fraction of the bolus dose was sequestered and then slowly released from the lungs (characterized by two additional parameters). The average weighted residual error (WRE) of the PBPK model fit to the both the bolus and infusion phases was 15%; similar to the WRE for just the constant infusion phase obtained by Schnider et. al. using a 6-parameter NONMEM compartmental model.
A PBPK model using standard human parameters and a simple description of tissue binding provides a good description of human propofol kinetics. The major advantage of a PBPK model is that it can be used to predict the changes in kinetics produced by variations in physiological parameters. As one example, the model simulation of the changes in pharmacokinetics for morbidly obese subjects is discussed.
Physiologically based pharmacokinetic (PBPK) models are composed of a series of differential equations and have been implemented in a number of commercial software packages. These models require species-specific and compound-specific input parameters and allow for the prediction of plasma and tissue concentration time profiles after intravenous and oral administration of compounds to animals and humans. PBPK models allow the early integration of a wide variety of preclinical data into a mechanistic quantitative framework. Use of PBPK models allows the experimenter to gain insights into the properties of a compound, helps to guide experimental efforts at the early stages of drug discovery, and enables the prediction of human plasma concentration time profiles with minimal (and in some cases no) animal data. In this review, the application and limitations of PBPK techniques in drug discovery are discussed. Specific reference is made to its utility (1) at the lead development stage for the prioritization of compounds for animal PK studies and (2) at the clinical candidate selection and “first in human” stages for the prediction of human PK.
absorption; clearance; distribution; PBPK; pharmacokinetics
Physiologically based pharmacokinetics (PBPK) uses a realistic organ model to describe drug kinetics. The blood-tissue exchange of each organ is characterized by its volume, perfusion, metabolism, capillary permeability and blood/tissue partition coefficient. PBPK applications require both sophisticated mathematical modeling software and a reliable complete set of physiological parameters. Currently there are no software packages available that combine ease of use with the versatility that is required of a general PBPK program.
The program is written in Java and is available for free download at . Included in the download is a detailed tutorial that discusses the pharmacokinetics of 6 solutes (D2O, amoxicillin, desflurane, propofol, ethanol and thiopental) illustrated using experimental human pharmacokinetic data. The complete PBPK description for each solute is stored in Excel spreadsheets that are included in the download. The main features of the program are: 1) Intuitive and versatile interactive interface; 2) Absolute and semi-logarithmic graphical output; 3) Pre-programmed optimized human parameter data set (but, arbitrary values can be input); 4) Time dependent changes in the PBPK parameters; 5) Non-linear parameter optimization; 6) Unique approach to determine the oral "first pass metabolism" of non-linear solutes (e.g. ethanol); 7) Pulmonary perfusion/ventilation heterogeneity for volatile solutes; 8) Input and output of Excel spreadsheet data; 9) Antecubital vein sampling.
PKQuest_Java is a free, easy to use, interactive PBPK software routine. The user can either directly use the pre-programmed optimized human or rat data set, or enter an arbitrary data set. It is designed so that drugs that are classified as "extracellular" or "highly fat soluble" do not require information about tissue/blood partition coefficients and can be modeled by a minimum of user input parameters. PKQuest_Java, along with the included tutorial, could be used as the basis of an interactive, on-line, pharmacokinetic course.
The European Medicines Agency (EMA) and the Federation of Pharmaceutical Industries and Associations (EFPIA) hosted a workshop on modeling and simulation (M&S).1 Representatives from industry, academia, and regulatory agencies from Europe and beyond discussed the role of M&S in the development and registration of medicinal products within plenary and breakout sessions (BOS). This manuscript summarizes the plenary discussion (Table 1) focusing on the European perspective. Deliverables from each BOS are included in separate papers.
The expression and activity of P-glycoproteins due to genetic or environmental factors may have a significant impact on drug disposition, drug effectiveness or drug toxicity. Hence, characterization of drug disposition over a wide range of conditions of these membrane transporters activities is required to better characterize drug pharmacokinetics and pharmacodynamics. This work aims to improve our understanding of the impact of P-gp activity modulation on tissue distribution of P-gp substrate.
A PBPK model was developed in order to examine activity and expression of P-gp transporters in mouse brain and heart. Drug distribution in these tissues was first represented by a well-stirred (WS) model and then refined by a mechanistic transport-based (MTB) model that includes P-gp mediated transport of the drug. To estimate transport-related parameters, we developed an original three-step procedure that allowed extrapolation of in vitro measurements of drug permeability to the in vivo situation. The model simulations were compared to a limited set of data in order to assess the model ability to reproduce the important information of drug distributions in the considered tissues.
This PBPK model brings insights into the mechanism of drug distribution in non eliminating tissues expressing P-gp. The MTB model accounts for the main transport mechanisms involved in drug distribution in heart and brain. It points out to the protective role of P-gp at the blood-brain barrier and represents thus a noticeable improvement over the WS model.
Being built prior to in vivo data, this approach brings an interesting alternative to fitting procedures, and could be adapted to different drugs and transporters.
The physiological based model is novel and unique and brought effective information on drug transporters.
Dose selection for “first in children” trials often relies on scaling of the pharmacokinetics from adults to children. Commonly used approaches are physiologically-based pharmacokinetic modeling (PBPK) and allometric scaling (AS) in combination with maturation of clearance for early life. In this investigation, a comparison of the two approaches was performed to provide insight into the physiological meaning of AS maturation functions and their interchangeability. The analysis focused on the AS maturation functions established using paracetamol and morphine paediatric data after intravenous administration. First, the estimated AS maturation functions were compared with the maturation functions of the liver enzymes as used in the PBPK models. Second, absolute clearance predictions using AS in combination with maturation functions were compared to PBPK predictions for hypothetical drugs with different pharmacokinetic properties. The results of this investigation showed that AS maturation functions do not solely represent ontogeny of enzyme activity, but aggregate multiple pharmacokinetic properties, as for example extraction ratio and lipophilicity (log P). Especially in children younger than 1 year, predictions using AS in combination with maturation functions and PBPK were not interchangeable. This highlights the necessity of investigating methodological uncertainty to allow a proper estimation of the “first dose in children” and assessment of its risk and benefits.
Allometric scaling; Physiologically-based pharmacokinetic models; Pediatrics; Bridging; Scaling; Extrapolation
Physiologically based Pharmacokinetic (PBPK) models are used for predictions of internal or target dose from environmental and pharmacologic chemical exposures. Their use in human risk assessment is dependent on the nature of databases (animal or human) used to develop and test them, and includes extrapolations across species, experimental paradigms, and determination of variability of response within human populations. Integration of state-of-the science PBPK modeling with emerging computational toxicology models is critical for extrapolation between in vitro exposures, in vivo physiologic exposure, whole organism responses, and long-term health outcomes. This special issue contains papers that can provide the basis for future modeling efforts and provide bridges to emerging toxicology paradigms. In this overview paper, we present an overview of the field and introduction for these papers that includes discussions of model development, best practices, risk-assessment applications of PBPK models, and limitations and bridges of modeling approaches for future applications. Specifically, issues addressed include: (a) increased understanding of human variability of pharmacokinetics and pharmacodynamics in the population, (b) exploration of mode of action hypotheses (MOA), (c) application of biological modeling in the risk assessment of individual chemicals and chemical mixtures, and (d) identification and discussion of uncertainties in the modeling process.
This is a summary report of the workshop, organized by the European Federation of Pharmaceutical Scientists in association with the American Association of Pharmaceutical Scientists, the European Agency for the Evaluation of Medicinal Products, the European Pharmacopoeia, the US Food and Drug Administration and the United States Pharmacopoeia, on “Assuring Quality and Performance of Sustained and Controlled Release Parenterals” held in Basel, Switzerland, February 2003. Experts from the pharmaceutical industry, regulatory authorities and academia participated in this workshop to review, discuss and debate formulation, processing and manufacture of sustained and controlled release parenterals, and identify critical process parameters and their control. This workshop was a follow-up workshop to a previous workshop on Assuring Quality and Performance of Sustained and Controlled Release Parenterals that was held in Washington, DC in April 2001. This report reflects the outcome of the Basel 2003 meeting and the advances in the field since the Washington, DC meeting in 2001. As necessary, the reader is referred to the report on the 2001 meeting. Areas were identified at the 2003 Basel meeting where research is needed in order to understand the performance of these drug delivery systems and to assist in the development of appropriate testing procedures. Recommendations were made for future workshops and meetings.
Physiologically Based Pharmacokinetic (PBPK) models can be used to determine the internal dose and strengthen exposure assessment. Many PBPK models are available, but they are not easily accessible for field use. The Agency for Toxic Substances and Disease Registry (ATSDR) has conducted translational research to develop a human PBPK model toolkit by recoding published PBPK models. This toolkit, when fully developed, will provide a platform that consists of a series of priority PBPK models of environmental pollutants. Presented here is work on recoded PBPK models for volatile organic compounds (VOCs) and metals. Good agreement was generally obtained between the original and the recoded models. This toolkit will be available for ATSDR scientists and public health assessors to perform simulations of exposures from contaminated environmental media at sites of concern and to help interpret biomonitoring data. It can be used as screening tools that can provide useful information for the protection of the public.
volatile organic compounds; VOCs; metals; PBPK; toxicokinetic; National Health and Nutrition Examination Survey (NHANES)
The emergence of multidrug-resistant tuberculosis (MDR-TB) has led to a renewed interest in the use of second-line antibiotic agents. Unfortunately, there are currently dearths of information, data, and computational models that can be used to help design rational regimens for administration of these drugs. To help fill this knowledge gap, an exploratory physiologically based pharmacokinetic (PBPK) model, supported by targeted experimental data, was developed to predict the absorption, distribution, metabolism, and excretion (ADME) of the second-line agent capreomycin, a cyclic peptide antibiotic often grouped with the aminoglycoside antibiotics. To account for interindividual variability, Bayesian inference and Monte Carlo methods were used for model calibration, validation, and testing. Along with the predictive PBPK model, the first for an antituberculosis agent, this study provides estimates of various key pharmacokinetic parameter distributions and supports a hypothesized mechanism for capreomycin transport into the kidney.
The renin-angiotensin-aldosterone system (RAAS) plays a key role in the pathogenesis of cardiovascular disorders including hypertension and is one of the most important targets for drugs. A whole body physiologically based pharmacokinetic (wb PBPK) model integrating this hormone circulation system and its inhibition can be used to explore the influence of drugs that interfere with this system, and thus to improve the understanding of interactions between drugs and the target system. In this study, we describe the development of a mechanistic RAAS model and exemplify drug action by a simulation of enalapril administration. Enalapril and its metabolite enalaprilat are potent inhibitors of the angiotensin-converting-enzyme (ACE). To this end, a coupled dynamic parent-metabolite PBPK model was developed and linked with the RAAS model that consists of seven coupled PBPK models for aldosterone, ACE, angiotensin 1, angiotensin 2, angiotensin 2 receptor type 1, renin, and prorenin. The results indicate that the model represents the interactions in the RAAS in response to the pharmacokinetics (PK) and pharmacodynamics (PD) of enalapril and enalaprilat in an accurate manner. The full set of RAAS-hormone profiles and interactions are consistently described at pre- and post-administration steady state as well as during their dynamic transition and show a good agreement with literature data. The model allows a simultaneous representation of the parent-metabolite conversion to the active form as well as the effect of the drug on the hormone levels, offering a detailed mechanistic insight into the hormone cascade and its inhibition. This model constitutes a first major step to establish a PBPK-PD-model including the PK and the mode of action (MoA) of a drug acting on a dynamic RAAS that can be further used to link to clinical endpoints such as blood pressure.
physiologically based pharmacokinetic model; cardiovascular; renin-angiotensin-aldosterone system; enalapril; enalaprilat
In October 2007, a National Center for Complementary and Alternative Medicine (NCCAM)–sponsored workshop, entitled “Applying Principles from Complex Systems to Studying the Efficacy of CAM Therapies,” was held at Georgetown University in Washington, DC. Over a 2-day period, the workshop engaged a small group of experts from the fields of complementary and alternative medicine (CAM) research and complexity science to discuss and examine ways in which complexity science can be applied to CAM research. After didactic presentations and small-group discussions, a number of salient themes and ideas emerged. This paper article describes the workshop program and summarizes these emergent ideas, which are divided into five broad categories: (1) introduction to complexity; (2) challenges to CAM research; (3) applications of complexity science to CAM; (4) CAM as a model of complexity applied to medicine; and (5) future directions. This discusses possible benefits and challenges associated with applying complexity science to CAM research. By providing an introductory framework for this collaboration and exchange, it is hoped that this article may stimulate further inquiry into this largely unexplored area of research.
The well-stirred tank (WST) has been the predominant flow-limited tissue compartment model in physiologically-based pharmacokinetic (PBPK) modeling. Recently, we developed a two-region asymptotically reduced (TAR) PBPK tissue compartment model through an asymptotic approximation to a two-region vascular-extravascular system to incorporate more biophysical detail than the WST model. To determine the relevance of the novel flow-limited approach (F-TAR), 75 structurally diverse drugs are evaluated herein using a priori predicted tissue:plasma partition coefficients along with hybrid and whole-body PBPK of eight rat tissues to determine the impact of model selection on simulation and optimization. Simulations show the F-TAR model significantly improves the ability to predict drug exposure, with hybrid and whole-body WST model error approaching 50% for tissues with larger vascular volumes. When optimization is used to fit F-TAR and WST models to pseudo data, WST-optimized drug partition coefficients more appropriately represent curve-fitting parameters rather than biophysically meaningful partition coefficients. Median F-TAR-optimized error ranged from -0.4 to 0.3%, while WST-optimized median error ranged from -22.2 to 1.8%. These studies demonstrate the use of F-TAR represents a more accurate, biophysically realistic PBPK tissue model for predicting tissue exposure to drug and should be considered for use in drug development and regulatory review.
Pharmacokinetics; Physiological model; Well stirred model; Tissue partition; In silico modeling; Physicochemical; Singular perturbation; Asymptotic matching; Flow-limited; Compartmental modeling
Much progress has been made in understanding the complex pharmacokinetics of trichloroethylene (TCE). Qualitatively, it is clear that TCE is metabolized to multiple metabolites either locally or into systemic circulation. Many of these metabolites are thought to have toxicologic importance. In addition, efforts to develop physiologically based pharmacokinetic (PBPK) models have led to a better quantitative assessment of the dosimetry of TCE and several of its metabolites. As part of a mini-monograph on key issues in the health risk assessment of TCE, this article is a review of a number of the current scientific issues in TCE pharmacokinetics and recent PBPK modeling efforts with a focus on literature published since 2000. Particular attention is paid to factors affecting PBPK modeling for application to risk assessment. Recent TCE PBPK modeling efforts, coupled with methodologic advances in characterizing uncertainty and variability, suggest that rigorous application of PBPK modeling to TCE risk assessment appears feasible at least for TCE and its major oxidative metabolites trichloroacetic acid and trichloroethanol. However, a number of basic structural hypotheses such as enterohepatic recirculation, plasma binding, and flow- or diffusion-limited treatment of tissue distribution require additional evaluation and analysis. Moreover, there are a number of metabolites of potential toxicologic interest, such as chloral, dichloroacetic acid, and those derived from glutathione conjugation, for which reliable pharmacokinetic data is sparse because of analytical difficulties or low concentrations in systemic circulation. It will be a challenge to develop reliable dosimetry for such cases.
metabolism; pharmacokinetics; physiologically based pharmacokinetic model; risk assessment; trichloroethylene
The workshop “Pharmacogenetics in Individualized Medicine: Methods, Regulatory, and Clinical Applications” was held November 15–16, 2008 in Atlanta, Georgia, USA. This workshop provided an opportunity for pharmaceutical scientists, clinical practitioners, clinical laboratory scientists, and FDA to discuss methods, regulatory, and the application of pharmacogenetics in clinical practice and drug discovery. Key highlights of the workshop were: (a) the use of genetic information in individualized medicine has significant potential in advancing drug development and human health by optimizing drug response, drug efficacy, and preventing adverse drug reactions; (b) various barriers exist preventing the advance of the individualized medicine in the society, industry, and clinical practice; and (c) the barriers may be overcome by integrated approaches; the education of researchers, clinical practitioners, and patients and fostering interactive communication among stakeholders. By targeting the AAPS audience, this workshop was one step among many steps that AAPS–FIP is intending to take towards removing the barriers to widespread uptake of pharmacogenetics in drug discovery and clinical practice.
clinical pharmacology; drug discovery; drug metabolism; individualized medicine; pharmacogenetics; pharmacogenomics
One of the fundamental challenges facing the development of new chemical entities within the pharmaceutical industry is the extrapolation of key in vivo parameters from in vitro cell culture assays and animal studies. Development of microscale devices and screening assays incorporating primary human cells can potentially provide better, faster and more efficient prediction of in vivo toxicity and clinical drug performance. With this goal in mind, large strides have been made in the area of microfluidics to provide in vitro surrogates that are designed to mimic the physiological architecture and dynamics. More recent advancements have been made in the development of in vitro analogues to physiologically-based pharmacokinetic (PBPK) models – a mathematical model that represents the body as interconnected compartments specific for a particular organ. In this review we highlight recent advancements in human hepatocyte microscale culture, and describe the next generation of integrated devices, whose potential allows for the high throughput assessment of drug metabolism, distribution and pharmacokinetics.
Microfluidics; human hepatocyte; bioreactor; coculture
The available data on binary interactions are yet to be considered within the context of mixture risk assessment because of our inability to predict the effect of a third or a fourth chemical in the mixture on the interacting binary pairs. Physiologically based pharmacokinetic (PBPK) models represent a potentially useful framework for predicting the consequences of interactions in mixtures of increasing complexity. This article highlights the conceptual basis and validity of PBPK models for extrapolating the occurrence and magnitude of interactions from binary to more complex chemical mixtures. The methodology involves the development of PBPK models for all mixture components and interconnecting them at the level of the tissue where the interaction is occurring. Once all component models are interconnected at the binary level, the PBPK framework simulates the kinetics of all mixture components, accounting for the interactions occurring at various levels in more complex mixtures. This aspect was validated by comparing the simulations of a binary interaction-based PBPK model with experimental data on the inhalation kinetics of m-xylene, toluene, ethyl benzene, dichloromethane, and benzene in mixtures of varying composition and complexity. The ability to predict the kinetics of chemicals in complex mixtures by accounting for binary interactions alone within a PBPK model is a significant step toward the development of interaction-based risk assessment for chemical mixtures.
MeHg and PCB exposure to lactating mice were analyzed and a physiologically-based pharmacokinetic (PBPK) model was developed to describe the lactational transfer of MeHg in mice. The influence of albumin on the lactational transfer of MeHg was incorporated into the PBPK model. Experimental results with lactating mice and their pups showed that co-exposure with PCB congeners increased the lactational transfer of MeHg to the pups, which was associated with the rise of albumin levels in maternal blood. Observed results were matched with PBPK model simulations conducted under the assumptions that (1) MeHg bound to plasma albumin is transferred to maternal milk, and (2) PCB congeners may increase the lactational transfer of MeHg by escalating albumin levels in maternal blood. Further refinement of PBPK model quantitatively described the pharmacokinetic changes of MeHg by co-exposure with PCBs in pup’s tissues.
MeHg; PCBs; PBPK; lactational transfer
A Bayesian hierarchical model was developed to estimate the parameters in a physiologically based pharmacokinetic (PBPK) model for chloroform using prior information and biomarker data from different exposure pathways. In particular, the model provides a quantitative description of the changes in physiological parameters associated with hot-water bath and showering scenarios. Through Bayesian inference, uncertainties in the PBPK parameters were reduced from the prior distributions. Prediction of biomarker data with the calibrated PBPK model was improved by the calibration. The posterior results indicate that blood flow rates varied under two different exposure scenarios, with a two-fold increase of the skin's blood flow rate predicted in the hot-bath scenario. This result highlights the importance of considering scenario-specific parameters in PBPK modeling. To demonstrate the application of a probability approach in toxicological assessment, results from the posterior distributions from this calibrated model were used to predict target tissue dose based on the rate of chloroform metabolized in liver. This study demonstrates the use of the Bayesian approach to optimize PBPK model parameters for typical household exposure scenarios.
Bayesian; chloroform; dermal-only exposure; inhalation-only exposure; MCMC; multiroute; PBPK
A physiologically based pharmacokinetic (PBPK) model would make it possible to simulate the dynamics of chemical absorption, distribution, metabolism, and elimination (ADME) from different routes of exposures and, in theory, could be used to evaluate associations between exposures and biomarker measurements in blood or urine.
We used a PBPK model to predict urinary excretion of 3,5,6-trichloro-2-pyridinol (TCPY), the specific metabolite of chlorpyrifos (CPF), in young children.
We developed a child-specific PBPK model for CPF using PBPK models previously developed for rats and adult humans. Data used in the model simulation were collected from 13 children 3–6 years of age who participated in a cross-sectional pesticide exposure assessment study with repeated environmental and biological sampling.
The model-predicted urinary TCPY excretion estimates were consistent with measured levels for 2 children with two 24-hr duplicate food samples that contained 350 and 12 ng/g of CPF, respectively. However, we found that the majority of model outputs underpredicted the measured urinary TCPY excretion.
We concluded that the potential measurement errors associated with the aggregate exposure measurements will probably limit the applicability of PBPK model estimates for interpreting urinary TCPY excretion and absorbed CPF dose from multiple sources of exposure. However, recent changes in organophosphorus (OP) use have shifted exposures from multipathways to dietary ingestion only. Thus, we concluded that the PBPK model is still a valuable tool for converting dietary pesticide exposures to absorbed dose estimates when the model input data are accurate estimates of dietary pesticide exposures.
chlorpyrifos; dietary pesticide exposure; PBPK; pesticide risk assessment; TCPY; urinary biomarker
Recently, a variety of physiologically based pharmacokinetic (PBPK) models have been developed for the essential element manganese. This paper reviews the development of PBPK models (e.g., adult, pregnant, lactating, and neonatal rats, nonhuman primates, and adult, pregnant, lactating, and neonatal humans) and relevant risk assessment applications. Each PBPK model incorporates critical features including dose-dependent saturable tissue capacities and asymmetrical diffusional flux of manganese into brain and other tissues. Varied influx and efflux diffusion rate and binding constants for different brain regions account for the differential increases in regional brain manganese concentrations observed experimentally. We also present novel PBPK simulations to predict manganese tissue concentrations in fetal, neonatal, pregnant, or aged individuals, as well as individuals with liver disease or chronic manganese inhalation. The results of these simulations could help guide risk assessors in the application of uncertainty factors as they establish exposure guidelines for the general public or workers.
The angiotensin-converting enzyme (ACE) inhibitors have complicated and poorly characterized pharmacokinetics. There are two binding sites per ACE (high affinity "C", lower affinity "N") that have sub-nanomolar affinities and dissociation rates of hours. Most inhibitors are given orally in a prodrug form that is systemically converted to the active form. This paper describes the first human physiologically based pharmacokinetic (PBPK) model of this drug class.
The model was applied to the experimental data of van Griensven et. al for the pharmacokinetics of ramiprilat and its prodrug ramipril. It describes the time course of the inhibition of the N and C ACE sites in plasma and the different tissues. The model includes: 1) two independent ACE binding sites; 2) non-equilibrium time dependent binding; 3) liver and kidney ramipril intracellular uptake, conversion to ramiprilat and extrusion from the cell; 4) intestinal ramipril absorption. The experimental in vitro ramiprilat/ACE binding kinetics at 4°C and 300 mM NaCl were assumed for most of the PBPK calculations. The model was incorporated into the freely distributed PBPK program PKQuest.
The PBPK model provides an accurate description of the individual variation of the plasma ramipril and ramiprilat and the ramiprilat renal clearance following IV ramiprilat and IV and oral ramipril. Summary of model features: Less than 2% of total body ACE is in plasma; 35% of the oral dose is absorbed; 75% of the ramipril metabolism is hepatic and 25% of this is converted to systemic ramiprilat; 100% of renal ramipril metabolism is converted to systemic ramiprilat. The inhibition was long lasting, with 80% of the C site and 33% of the N site inhibited 24 hours following a 2.5 mg oral ramipril dose. The plasma ACE inhibition determined by the standard assay is significantly less than the true in vivo inhibition because of assay dilution.
If the in vitro plasma binding kinetics of the ACE inhibitor for the two binding sites are known, a unique PBPK model description of the Griensven et. al. experimental data can be obtained.
Setting: In summer 2002, the Health Sciences Library System (HSLS) at the University of Pittsburgh initiated an information service in molecular biology and genetics to assist researchers with identifying and utilizing bioinformatics tools.
Program Components: This novel information service comprises hands-on training workshops and consultation on the use of bioinformatics tools. The HSLS also provides an electronic portal and networked access to public and commercial molecular biology databases and software packages.
Evaluation Mechanisms: Researcher feedback gathered during the first three years of workshops and individual consultation indicate that the information service is meeting user needs.
Next Steps/Future Directions: The service's workshop offerings will expand to include emerging bioinformatics topics. A frequently asked questions database is also being developed to reuse advice on complex bioinformatics questions.
The presence of antimicrobial agents in edible tissues of food-producing animals remains a major public health concern. Probabilistic modeling techniques incorporated into a physiologically based pharmacokinetic (PBPK) model were used to predict the amounts of sulfamethazine residues in edible tissues in swine. A PBPK model for sulfamethazine in swine was adapted to include an oral dosing route. The distributions for sensitive parameters were determined and were used in a Monte Carlo analysis to predict tissue residue times. Validation of the distributions was done by comparison of the results of a Monte Carlo analysis to those obtained with an external data set from the literature and an in vivo pilot study. The model was used to predict the upper limit of the 95% confidence interval of the 99th percentile of the population, as recommended by the U.S. Food and Drug Administration (FDA). The external data set was used to calculate the withdrawal time by using the tolerance limit algorithm designed by FDA. The withdrawal times obtained by both methods were compared to the labeled withdrawal time for the same dose. The Monte Carlo method predicted a withdrawal time of 21 days, based on the amounts of residues in the kidneys. The tolerance limit method applied to the time-limited data set predicted a withdrawal time of 12 days. The existing FDA label withdrawal time is 15 days. PBPK models can incorporate probabilistic modeling techniques that make them useful for prediction of tissue residue times. These models can be used to calculate the parameters required by FDA and explore those conditions where the established withdrawal time may not be sufficient.