PBPK modelling of inter-individual variability in the pharmacokinetics of environmental chemicals

International audienceGeneric PBPK models, applicable to a large number of substances, coupled to parameter databases and QSAR modules, are now available for predictive modelling of inter-individual variability in the absorption, distribution, metabolism and excretion of environmental chemicals. When needed, Markov chain Monte Carlo methods and multilevel population models can be jointly used for a Bayesian calibration of a PBPK model, to improve our understanding of the determinants of population heterogeneity and differential susceptibility. This article reviews those developments and illustrates them with recent applications to environmentally relevant questions

Computational modeling of the pharmacokinetics and pharmacodynamics of selected xenobiotics

2016 Fall.Includes bibliographical references.The determination of important endpoints in toxicology and pharmacology continues to involve the acquisition of large amounts of data through resource-intensive experimental studies involving a large number of resources. Because of this, only a small fraction of chemicals in the environment and marketplace can reasonably be evaluated for safety, and many promising drug candidates must be eliminated from consideration based on inadequate evaluation. Promisingly, advances in biologically-based computational models are beginning to allow researchers to estimate these endpoints and make useful extrapolations using a limited set of experimental data. The work described in this dissertation examined how computational models can provide meaningful insight and quantitation of important pharmacological and toxicological endpoints related to toxicity and pharmacological efficacy. To this end, physiologically-based pharmacokinetic and pharmacodynamic models were developed and applied for several pharmaceutical agents and environmental toxicants to predict significant, and diverse, biological endpoints. First, physiologically-based modeling allowed for the evaluation of various dosing regimens of rifapentine, a drug that is showing great promise for the treatment of tuberculosis, by comparing lung-specific concentration predictions to experimentally-derived thresholds for antibacterial activity. Second, physiologically-based pharmacokinetic modeling, coupled with Bayesian inference, was used as part of a methodology to characterize genetic differences in acetaminophen pharmacokinetics and also to help clinicians predict an ingested dose of this drug under overdose conditions. Third, a methodology for using physiologically-based pharmacokinetic modeling to predict health-based cognitive endpoints was demonstrated for chronic exposure to chlorpyrifos, an organophosphorus insecticide. The environmental public health indicators derived from this work allowed for biomarkers of exposure to be used to predict neurobehavioral changes following long-term exposure to this chemical. Finally, computational modeling was used to develop a mechanistically-plausible pharmacodynamic model for hepatoprotective and pro-inflammatory events to relate trichloroethylene dosing conditions to observed pathologies associated with auto-immune hepatitis

Population variability in animal health: Influence on dose-exposure-response relationships: Part II: Modelling and simulation

During the 2017 Biennial meeting, the American Academy of Veterinary Pharmacology and Therapeutics hosted a 1‐day session on the influence of population variability on dose‐exposure‐response relationships. In Part I, we highlighted some of the sources of population variability. Part II provides a summary of discussions on modelling and simulation tools that utilize existing pharmacokinetic data, can integrate drug physicochemical characteristics with species physiological characteristics and dosing information or that combine observed with predicted and in vitro information to explore and describe sources of variability that may influence the safe and effective use of veterinary pharmaceuticals

Performance-Based Quality Specifications: The Link between Product Development and Clinical Outcomes

The design of drug delivery systems and their corresponding dosing guidelines are critical product development functions supported by clinical pharmacokinetic (PK) and pharmacodynamic (PD) data. Largely, the importance of variance and covariance in product and patient attributes is poorly understood. The existence of PK/PD diversity among myriad patient sub-populations further complicates efforts to gauge the importance of product quality variation. Nevertheless, a platform capable of evaluating the effects of product and patient variability on clinical performance was constructed. This dissertation was predicated on requests to re-define pharmaceutical quality in terms of risk by relating clinical attributes to production characteristics. To avoid in vivo studies, simulated experimental trials were conducted using the model drug, theophylline, for which data and models could be acquired from the literature. Where comprehensive data were unavailable (e.g., production variability statistics), initial estimates were acquired via laboratory-scale experiments. Model asthmatic patients were generated using Monte Carlo simulation and published population distributions of various anothropometric measurements, disease rates, and lifestyle factors. Mathematical constructs for in vitro-in vivo correlations provide a linkage between Quality by Design (QbD) product and process models, PK/PD models, and patient population statistics. The combined models formed the foundation for Monte Carlo risk assessments, which characterized the risk of inefficacy and toxicity for dosing of extended-release theophylline tablets. Sensitivity analyses revealed that patient compliance and content uniformity significantly influenced the probability of observing an adverse event. The Monte Carlo risk assessment platform defined the link between the critical quality attributes (CQAs) and clinical performance (i.e., performance-based quality specifications (PBQS)). The PBQS were subsequently utilized to generate process independent design spaces conditioned on inefficacy and toxicity risk. These design spaces, which directly account for the conditional relationships between product quality and patient variability, can be transferred to a specific process via models that relate process critical control parameters to the CQAs. Process Analytical Technology, therefore, can be integrated into the QbD production environment to control the safety and efficacy of the final product. This work demonstrated that process and product knowledge can be used to estimate the risk that final product quality imparts to clinical performance

Dynamic modelling of blood glucose concentration in people with type 1 diabetes

The behaviour of blood glucose concentration (BGC) in free living conditions is not well understood in people with type 1 diabetes; in particular, the effect of different types of activity experienced in everyday life has not been fully investigated. Better understanding of the effect of major disturbances to BGC can improve treatment regimes and delay or prevent complications associated with diabetes. The current research investigates approaches to modelling BGC, based on blood glucose, physical activity, food and insulin data collected from a Diabetes UK study. Exploratory analysis of the study data found that BGC is non-stationary and exhibits strong autocorrelation, which varies among and within individuals. Analysis of BGC in the frequency domain also highlights indistinct low-frequency periodicities. However, BGC measurements alone are not enough to predict BGC over several hours using autoregressive models. Dynamic linear models are used to model BGC empirically using inputs from measured physical activity, and estimates of glucose and insulin absorption after food intake and injections, respectively, derived from physiological models in the literature. Dynamic linear models are used for parameter learning and predicting BGC over several hours: the models show some capability for predicting BGC for up to one hour, in particular highlighting periods of low and high BGC, but parameter estimates do not comply with established physiological knowledge. A new semi-empirical compartmental model is developed to impose a structure that incorporates well established physiology. A set of differential equations are converted into a probabilistic Bayesian framework, suitable for simultaneous, model-wide parameter estimation and prediction. A simulation study is conducted to determine the feasibility of using Markov chain Monte Carlo methods as a means for parameter estimation, and test performance in the predictive space. The methods show an ability to estimate a subset of the parameters simultaneously with good coverage, robustness to parameter misspecification, and insensitivity to specification of prior distributions. The current research represents a new paradigm for analysing mathematical models of BGC, and highlights important practical and theoretical issues not previously addressed in the quest for an artificial pancreas as treatment for type 1 diabetes

Investigation of the Effects of Image Signal-to-Noise Ratio on TSPO PET Quantification of Neuroinflammation

Neuroinflammation may be imaged using positron emission tomography (PET) and the tracer [11C]-PK11195. Accurate and precise quantification of 18 kilodalton Translocator Protein (TSPO) binding parameters in the brain has proven difficult with this tracer, due to an unfavourable combination of low target concentration in tissue, low brain uptake of the tracer and relatively high non-specific binding, all of which leads to higher levels of relative image noise. To address these limitations, research into new radioligands for the TSPO, with higher brain uptake and lower non-specific binding relative to [11C]-PK11195, is being conducted world-wide. However, factors other than radioligand properties are known to influence signal-to-noise ratio in quantitative PET studies, including the scanner sensitivity, image reconstruction algorithms and data analysis methodology. The aim of this thesis was to investigate and validate computational tools for predicting image noise in dynamic TSPO PET studies, and to employ those tools to investigate the factors that affect image SNR and reliability of TSPO quantification in the human brain. The feasibility of performing multiple (n≥40) independent Monte Carlo simulations for each dynamic [11C]-PK11195 frame- with realistic modelling of the radioactivity source, attenuation and PET tomograph geometries- was investigated. A Beowulf-type high performance computer cluster, constructed from commodity components, was found to be well suited to this task. Timing tests on a single desktop computer system indicated that a computer cluster capable of simulating an hour-long dynamic [11C]-PK11195 PET scan, with 40 independent repeats, and with a total simulation time of less than 6 weeks, could be constructed for less than 10,000 Australian dollars. A computer cluster containing 44 computing cores was therefore assembled, and a peak simulation rate of 2.84x105 photon pairs per second was achieved using the GEANT4 Application for Tomographic Emission (GATE) Monte Carlo simulation software. A simulated PET tomograph was developed in GATE that closely modelled the performance characteristics of several real-world clinical PET systems in terms of spatial resolution, sensitivity, scatter fraction and counting rate performance. The simulated PET system was validated using adaptations of the National Electrical Manufacturers Association (NEMA) quality assurance procedures within GATE. Image noise in dynamic TSPO PET scans was estimated by performing n=40 independent Monte Carlo simulations of an hour-long [11C]-PK11195 scan, and of an hour- long dynamic scan for a hypothetical TSPO ligand with double the brain activity concentration of [11C]-PK11195. From these data an analytical noise model was developed that allowed image noise to be predicted for any combination of brain tissue activity concentration and scan duration. The noise model was validated for the purpose of determining the precision of kinetic parameter estimates for TSPO PET. An investigation was made into the effects of activity concentration in tissue, radionuclide half-life, injected dose and compartmental model complexity on the reproducibility of kinetic parameters. Injecting 555 MBq of carbon-11 labelled TSPO tracer produced similar binding parameter precision to 185 MBq of fluorine-18, and a moderate (20%) reduction in precision was observed for the reduced carbon-11 dose of 370 MBq. Results indicated that a factor of 2 increase in frame count level (relative to [11C]-PK11195, and due for example to higher ligand uptake, injected dose or absolute scanner sensitivity) is required to obtain reliable binding parameter estimates for small regions of interest when fitting a two-tissue compartment, four-parameter compartmental model. However, compartmental model complexity had a similarly large effect, with the reduction of model complexity from the two-tissue compartment, four-parameter to a one-tissue compartment, two-parameter model producing a 78% reduction in coefficient of variation of the binding parameter estimates at each tissue activity level and region size studied. In summary, this thesis describes the development and validation of Monte Carlo methods for estimating image noise in dynamic TSPO PET scans, and analytical methods for predicting relative image noise for a wide range of tissue activity concentration and acquisition durations. The findings of this research suggest that a broader consideration of the kinetic properties of novel TSPO radioligands, with a view to selection of ligands that are potentially amenable to analysis with a simple one-tissue compartment model, is at least as important as efforts directed towards reducing image noise, such as higher brain uptake, in the search for the next generation of TSPO PET tracers

Steady-State Kinetic Modeling Constrains Cellular Resting States and Dynamic Behavior

A defining characteristic of living cells is the ability to respond dynamically to external stimuli while maintaining homeostasis under resting conditions. Capturing both of these features in a single kinetic model is difficult because the model must be able to reproduce both behaviors using the same set of molecular components. Here, we show how combining small, well-defined steady-state networks provides an efficient means of constructing large-scale kinetic models that exhibit realistic resting and dynamic behaviors. By requiring each kinetic module to be homeostatic (at steady state under resting conditions), the method proceeds by (i) computing steady-state solutions to a system of ordinary differential equations for each module, (ii) applying principal component analysis to each set of solutions to capture the steady-state solution space of each module network, and (iii) combining optimal search directions from all modules to form a global steady-state space that is searched for accurate simulation of the time-dependent behavior of the whole system upon perturbation. Importantly, this stepwise approach retains the nonlinear rate expressions that govern each reaction in the system and enforces constraints on the range of allowable concentration states for the full-scale model. These constraints not only reduce the computational cost of fitting experimental time-series data but can also provide insight into limitations on system concentrations and architecture. To demonstrate application of the method, we show how small kinetic perturbations in a modular model of platelet P2Y1 signaling can cause widespread compensatory effects on cellular resting states

Relevance of accurate Monte Carlo modeling in nuclear medical imaging

Monte Carlo techniques have become popular in different areas of medical physics with advantage of powerful computing systems. In particular, they have been extensively applied to simulate processes involving random behavior and to quantify physical parameters that are difficult or even impossible to calculate by experimental measurements. Recent nuclear medical imaging innovations such as single-photon emission computed tomography (SPECT), positron emission tomography (PET), and multiple emission tomography (MET) are ideal for Monte Carlo modeling techniques because of the stochastic nature of radiation emission, transport and detection processes. Factors which have contributed to the wider use include improved models of radiation transport processes, the practicality of application with the development of acceleration schemes and the improved speed of computers. This paper presents derivation and methodological basis for this approach and critically reviews their areas of application in nuclear imaging. An overview of existing simulation programs is provided and illustrated with examples of some useful features of such sophisticated tools in connection with common computing facilities and more powerful multiple-processor parallel processing systems. Current and future trends in the field are also discussed

PBTK modeling of the pyrrolizidine alkaloid retrorsine to predict liver toxicity in mouse and rat

Retrorsine is a hepatotoxic pyrrolizidine alkaloid (PA) found in herbal supplements and medicines, food and livestock feed. Dose-response studies enabling the derivation of a point of departure including a benchmark dose for risk assessment of retrorsine in humans and animals are not available. Addressing this need, a physiologically based toxicokinetic (PBTK) model of retrorsine was developed for mouse and rat. Comprehensive characterization of retrorsine toxicokinetics revealed: both the fraction absorbed from the intestine (78%) and the fraction unbound in plasma (60%) are high, hepatic membrane permeation is dominated by active uptake and not by passive diffusion, liver metabolic clearance is 4-fold higher in rat compared to mouse and renal excretion contributes to 20% of the total clearance. The PBTK model was calibrated with kinetic data from available mouse and rat studies using maximum likelihood estimation. PBTK model evaluation showed convincing goodness-of-fit for hepatic retrorsine and retrorsine-derived DNA adducts. Furthermore, the developed model allowed to translate in vitro liver toxicity data of retrorsine to in vivo dose-response data. Resulting benchmark dose confidence intervals (mg/kg bodyweight) are 24.1–88.5 in mice and 79.9–104 in rats for acute liver toxicity after oral retrorsine intake. As the PBTK model was built to enable extrapolation to different species and other PA congeners, this integrative framework constitutes a flexible tool to address gaps in the risk assessment of PA