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ABSTRACT:Estimation of xenobiotic kinetics in humans frequently relies upon extrapolation from experimental data generated in animals. In an accompanying paper, we have presented a unique, generic, physiologically based pharmacokinetic model and described its application to the prediction of rat plasma pharmacokinetics from in vitro data alone. Here we demonstrate the application of the same model, parameterized for human physiology, to the estimation of plasma pharmacokinetics in humans and report a comparative evaluation against some recently published predictive methods that involve scaling from in vivo animal data. The model was parameterized through an optimization process, using a training set of in vivo data taken from the literature, and validated using a separate test set of published in vivo data. On average, the vertical divergence of the predicted plasma concentrations from the observed data, on a semilog concentration-time plot, was 0.47 log unit. For the training set, more than 80% of the predicted values of a standardized measure of the area under the concentration-time curve were within 3-fold of the observed values; over 70% of the test set predictions were within the same margin. Furthermore, in terms of predicting human clearance for the test set, the model was found to match or exceed the performance of three published interspecies scaling methods, all of which showed a distinct bias toward overprediction. We conclude that the generic physiologically based pharmacokinetic model, as a means of integrating readily determined in vitro and/or in silico data, is potentially a powerful, cost-effective tool for predicting human xenobiotic kinetics in drug discovery and risk assessment.Physiologically based pharmacokinetic (PBPK) models are mathematical descriptions of the flow of blood throughout the body, developed for the simulation of xenobiotic absorption, distribution, and elimination. Such models have been used by scientists from a number of different disciplines who are interested in the simulation and prediction of exposure (Grass and Sinko, 2002;Leahy, 2003).The application of a generic form of a PBPK model to the prediction of xenobiotic plasma levels in rat following an intravenous dose has been reported in an accompanying publication (Brightman et al., 2006). Here we describe the work that we have done to parameterize the same PBPK model for humans and to assess the reliability of the model in estimating plasma levels of xenobiotics, where these values are known from experimentation. In addition, we draw comparisons with alternative methods for predicting human pharmacokinetic properties that involve extrapolation from experimental data generated in animals.Just as there are numerous published compound-specific PBPK models for the rat that use data derived from in vivo studies (Sugita et al., 1982;Igari et al., 1983;Tsuji et al., 1983;Bernareggi and Rowland, 1991;Kawai et al., 1994;Blakey et al., 1997), there are many examples of comparable PBPK models for humans that rely upon scaling f...

ABSTRACT:The routine assessment of xenobiotic in vivo kinetic behavior is currently dependent upon data obtained through animal experimentation, although in vitro surrogates for determining key absorption, distribution, metabolism, and elimination properties are available. Here we present a unique, generic, physiologically based pharmacokinetic (PBPK) model and demonstrate its application to the estimation of rat plasma pharmacokinetics, following intravenous dosing, from in vitro data alone. The model was parameterized through an optimization process, using a training set of in vivo data taken from the literature and validated using a separate test set of in vivo discovery compound data. On average, the vertical divergence of the predicted plasma concentrations from the observed data, on a semilog concentration-time plot, was approximately 0.5 log unit. Around 70% of all the predicted values of a standardized measure of area under the concentration-time curve (AUC) were within 3-fold of the observed values, as were over 90% of the training set t 1/2 predictions and 60% of those for the test set; however, there was a tendency to overpredict t 1/2 for the test set compounds. The capability of the model to rank compounds according to a given criterion was also assessed: of the 25% of the test set compounds ranked by the model as having the largest values for AUC, 61% were correctly identified. These validation results lead us to conclude that the generic PBPK model is potentially a powerful and cost-effective tool for predicting the mammalian pharmacokinetics of a wide range of organic compounds, from readily available in vitro inputs only.Physiologically based pharmacokinetic (PBPK) models are mathematical descriptions of the flow of blood throughout the body, developed for the simulation of xenobiotic absorption, distribution, and elimination. The essential concepts were outlined over 60 years ago in a farsighted paper (Teorell, 1937) that presented many of the mathematical relationships required to simulate blood flow and tissue distribution.Simulation modeling ideas were developed further by Mapleson (1973), to explain the effect of anesthetics, and early attempts to apply the approach to drugs were published in the 1960s by Bellman et al. (1961). Probably the most important contributions in that period were made by Bischoff and Dedrick (1968), who demonstrated that PBPK models could be used for the a priori prediction of the pharmacokinetics of thiopental. During the following decades, developments were made by academics such as Rowland (Rowland, 1986), Sugiyama (Sugiyama and Ito, 1998), and Amidon (Yu and Amidon, 1999), as well as scientists working in the environmental health field, in particular Anderson and Clewell (Andersen et al., 2002). Recent reviews (Grass and Sinko, 2002;Leahy, 2003) have discussed the application of these approaches to the prediction of pharmacokinetics in drug discovery.It is of interest to us to apply the PBPK approach to the estimation of plasma levels in animals from in vitro d...

Endocrine disruptors (EDs) are chemicals that contribute to health problems by interfering with the physiological production and target effects of hormones, with proven impacts on a number of endocrine systems including the thyroid gland. Exposure to EDs has also been associated with impairment of the reproductive system and incidence in occurrence of obesity, type 2 diabetes, and cardiovascular diseases during ageing. SCREENED aims at developing in vitro assays based on rodent and human thyroid cells organized in three different three-dimensional (3D) constructs. Due to different levels of anatomical complexity, each of these constructs has the potential to increasingly mimic the structure and function of the native thyroid gland, ultimately achieving relevant features of its 3D organization including: (1) a 3D organoid based on stem cell-derived thyrocytes, (2) a 3D organoid based on a decellularized thyroid lobe stromal matrix repopulated with stem cell-derived thyrocytes, and (3) a bioprinted organoid based on stem cell-derived thyrocytes able to mimic the spatial and geometrical features of a native thyroid gland. These 3D constructs will be hosted in a modular microbioreactor equipped with innovative sensing technology and enabling precise control of cell culture conditions. New superparamagnetic biocompatible and biomimetic particles will be used to produce “magnetic cells” to support precise spatiotemporal homing of the cells in the 3D decellularized and bioprinted constructs. Finally, these 3D constructs will be used to screen the effect of EDs on the thyroid function in a unique biological sex-specific manner. Their performance will be assessed individually, in comparison with each other, and against in vivo studies. The resulting 3D assays are expected to yield responses to low doses of different EDs, with sensitivity and specificity higher than that of classical 2D in vitro assays and animal models. Supporting the “Adverse Outcome Pathway” concept, proteogenomic analysis and biological computational modelling of the underlying mode of action of the tested EDs will be pursued to gain a mechanistic understanding of the chain of events from exposure to adverse toxic effects on thyroid function. For future uptake, SCREENED will engage discussion with relevant stakeholder groups, including regulatory bodies and industry, to ensure that the assays will fit with purposes of ED safety assessment. In this project review, we will briefly discuss the current state of the art in cellular assays of EDs and how our project aims at further advancing the field of cellular assays for EDs interfering with the thyroid gland.

The global pharmaceutical industry is estimated to use close to 20 million animals annually, in in vivo studies which apply the results of fundamental biomedical research to the discovery and development of novel pharmaceuticals, or to the application of existing pharmaceuticals to novel therapeutic indications. These applications of in vivo experimentation include: a) the use of animals as disease models against which the efficacy of therapeutics can be tested; b) the study of the toxicity of those therapeutics, before they are administered to humans for the first time; and c) the study of their pharmacokinetics —i.e. their distribution throughout, and elimination from, the body. In vivo pharmacokinetic (PK) studies are estimated to use several hundred thousand animals annually. The success of pharmaceutical research currently relies heavily on the ability to extrapolate from data obtained in such in vivo studies to predict therapeutic behaviour in humans. Physiologically-based modelling has the potential to reduce the number of in vivo animal studies that are performed by the pharmaceutical industry. In particular, the technique of physiologically-based pharmacokinetic (PBPK) modelling is sufficiently developed to serve as a replacement for many in vivo PK studies in animals during drug discovery. Extension of the technique to incorporate the prediction of in vivo therapeutic effects and/or toxicity is less well-developed, but has potential in the longer-term to effect a significant reduction in animal use, and also to lead to improvements in drug discovery via the increased rationalisation of lead optimisation.