Advancing Prediction of Tissue Distribution and Volume of Distribution of Highly Lipophilic Compounds from a Simplified Tissue-Composition-Based Model as a Mechanistic Animal Alternative Method

Application of partition coefficient methods to predict tissue:plasma affinities in common farm animals: Influence of ionisation state

Tissue affinities are conventionally determined from in vivo steady-state tissue and plasma or plasma-water chemical concentration data. In silico approaches were initially developed for preclinical species but standardly applied and tested in human physiologically-based kinetic (PBK) models. Recently, generic PBK models for farm animals have been made available and require partition coefficients as input parameters. In the current investigation, data for species-specific tissue compositions have been collected, and prediction of chemical distribution in various tissues of livestock species for cattle, chicken, sheep and swine have been performed. Overall, tissue composition was very similar across the four farm animal species. However, small differences were observed in moisture, fat and protein content in the various organs within each species. Such differences could be attributed to factors such as variations in age, breed, and weight of the animals and general conditions of the animal itself. With regards to the predictions of tissue:plasma partition coefficients, 80 %, 71 %, 77 % of the model predictions were within a factor 10 using the methods of Berezhkovskiy (2004), Rodgers and Rowland (2006) and Schmitt (2008). The method of Berezhkovskiy (2004) was often providing the most reliable predictions except for swine, where the method of Schmitt (2008) performed best. In addition, investigation of the impact of chemical classes on prediction performance, all methods had very similar reliability. Notwithstanding, no clear pattern regarding specific chemicals or tissues could be detected for the values predicted outside a 10-fold change in certain chemicals or specific tissues. This manuscript concludes with the need for future research, particularly focusing on lipophilicity and species differences in protein binding.

Relative Performance of Volume of Distribution Prediction Methods for Lipophilic Drugs with Uncertainty in LogP Value

PURPOSE

The goal was to assess, for lipophilic drugs, the impact of logP on human volume of distribution at steady-state (VDss) predictions, including intermediate fut and Kp values, from six methods: Oie-Tozer, Rodgers-Rowland (tissue-specific Kp and only muscle Kp), GastroPlus, Korzekwa-Nagar, and TCM-New.

METHOD

A sensitivity analysis with focus on logP was conducted by keeping pKa and fup constant for each of four drugs, while varying logP. VDss was also calculated for the specific literature logP values. Error prediction analysis was conducted by analyzing prediction errors by source of logP values, drug, and overall values.

RESULTS

The Rodgers-Rowland methods were highly sensitive to logP values, followed by GastroPlus and Korzekwa-Nagar. The Oie-Tozer and TCM-New methods were only modestly sensitive to logP. Hence, the relative performance of these methods depended upon the source of logP value. As logP values increased, TCM-New and Oie-Tozer were the most accurate methods. TCM-New was the only method that was accurate regardless of logP value source. Oie-Tozer provided accurate predictions for griseofulvin, posaconazole, and isavuconazole; GastroPlus for itraconazole and isavuconazole; Korzekwa-Nagar for posaconazole; and TCM-New for griseofulvin, posaconazole, and isavuconazole. Both Rodgers-Rowland methods provided inaccurate predictions due to the overprediction of VDss.

CONCLUSIONS

TCM-New was the most accurate prediction of human VDss across four drugs and three logP sources, followed by Oie-Tozer. TCM-New showed to be the best method for VDss prediction of highly lipophilic drugs, suggesting BPR as a favorable surrogate for drug partitioning in the tissues, and which avoids the use of fup.

A New Version of the Tissue Composition-Based Model for Improving the Mechanism-Based Prediction of Volume of Distribution at Steady-State for Neutral Drugs

In-vitro models are available in the literature for predicting the volume of distribution at steady-state (Vdss) of drugs. The mechanistic model refers to the tissue composition-based model (TCM), which includes important factors that govern Vdss such as drug physiochemistry and physiological data. The recognized TCM published by Rodgers and Rowland (TCM-RR) and a subsequent adjustment made by Simulations Plus Inc. (TCM-SP) have been shown to be generally less accurate with neutral compared to ionized drugs. Therefore, improving these models for neutral drugs becomes necessary. The objective of this study was to propose a new TCM for improving the prediction of Vdss for neutral drugs. The new TCM included two modifications of the published models (i) accentuate the effect of the blood-to-plasma ratio (BPR) that should cover permeated molecules across the biomembranes, which is lacking in these models for neutral compounds, and (ii) use a different approach to estimate the binding in tissues. The new TCM was validated with a large dataset of 202 commercial and proprietary compounds including preclinical and clinical data. All scenario datasets were predicted more accurately with the TCM-New, whereas all statistical parameters indicate that the TCM-New showed significant improvements in terms of accuracy over the TCM-RR and TCM-SP. Predictions of Vdss were frequently more accurate for the TCM-new with 83% within twofold error versus only 50% for the TCM-RR. And more than 95% of the predictions were within threefold error and patient interindividual differences can be predicted with the TCM-New, greatly exceeding the accuracy of the published models. Overall, the new TCM incorporating BPR significantly improved the Vdss predictions in animals and humans for neutral drugs, and, hence, has the potential to better support the drug discovery and facilitate the first-in-human predictions.

The Application of a Physiologically Based Toxicokinetic Model in Health Risk Assessment

Toxicokinetics plays a crucial role in the health risk assessments of xenobiotics. Classical compartmental models are limited in their ability to determine chemical concentrations in specific organs or tissues, particularly target organs or tissues, and their limited interspecific and exposure route extrapolation hinders satisfactory health risk assessment. In contrast, physiologically based toxicokinetic (PBTK) models quantitatively describe the absorption, distribution, metabolism, and excretion of chemicals across various exposure routes and doses in organisms, establishing correlations with toxic effects. Consequently, PBTK models serve as potent tools for extrapolation and provide a theoretical foundation for health risk assessment and management. This review outlines the construction and application of PBTK models in health risk assessment while analyzing their limitations and future perspectives.

Considering developmental neurotoxicity in vitro data for human health risk assessment using physiologically-based kinetic modeling: deltamethrin case study

Developmental neurotoxicity (DNT) is a potential hazard of chemicals. Recently, an in vitro testing battery (DNT IVB) was established to complement existing rodent in vivo approaches. Deltamethrin (DLT), a pyrethroid with a well-characterized neurotoxic mode of action, has been selected as a reference chemical to evaluate the performance of the DNT IVB. The present study provides context for evaluating the relevance of these DNT IVB results for the human health risk assessment of DLT by estimating potential human fetal brain concentrations after maternal exposure to DLT. We developed a physiologically based kinetic (PBK) model for rats which was then translated to humans considering realistic in vivo exposure conditions (acceptable daily intake [ADI] for DLT). To address existing uncertainties, we designed case studies considering the most relevant drivers of DLT uptake and distribution. Calculated human fetal brain concentrations were then compared with the lowest benchmark concentration achieved in the DNT IVB. The developed rat PBK model was validated on in vivo rat toxicokinetic data of DLT over a broad range of doses. The uncertainty based case study evaluation confirmed that repeated exposure to DLT at an ADI level would likely result in human fetal brain concentrations far below the in vitro benchmark. The presented results indicate that DLT concentrations in the human fetal brain are highly unlikely to reach concentrations associated with in vitro findings under realistic exposure conditions. Therefore, the new in vitro DNT results are considered to have no impact on the current risk assessment approach.

Development, testing, parameterisation, and calibration of a human PBK model for the plasticiser, di (2-ethylhexyl) adipate (DEHA) using in silico, in vitro and human biomonitoring data

Introduction: A physiologically based biokinetic model for di (2-ethylhexyl) adipate (DEHA) based on a refined model for di-(2-propylheptyl) phthalate (DPHP) was developed to interpret the metabolism and biokinetics of DEHA following a single oral dosage of 50 mg to two male and two female volunteers. Methods: The model was parameterized using in vitro and in silico methods such as, measured intrinsic hepatic clearance scaled from in vitro to in vivo and algorithmically predicted parameters such as plasma unbound fraction and tissue:blood partition coefficients (PCs). Calibration of the DEHA model was achieved using concentrations of specific downstream metabolites of DEHA excreted in urine. The total fractions of ingested DEHA eliminated as specific metabolites were estimated and were sufficient for interpreting the human biomonitoring data. Results: The specific metabolites of DEHA, mono-2-ethyl-5-hydroxyhexyl adipate (5OH-MEHA), mono-2-ethyl-5-oxohexyl adipate (5oxo-MEHA), mono-5-carboxy-2-ethylpentyl adipate (5cx-MEPA) only accounted for ∼0.45% of the ingested DEHA. Importantly, the measurements of adipic acid, a non-specific metabolite of DEHA, proved to be important in model calibration. Discussion: The very prominent trends in the urinary excretion of the metabolites, 5cx-MEPA and 5OH-MEHA allowed the important absorption mechanisms of DEHA to be modelled. The model should be useful for the study of exposure to DEHA of the general human population.

Development, testing, parameterisation, and calibration of a human PBPK model for the plasticiser, di-(2-ethylhexyl) terephthalate (DEHTP) using in silico, in vitro and human biomonitoring data

A physiologically based pharmacokinetic model for di-(2-ethylhexyl) terephthalate (DEHTP) based on a refined model for di-(2-propylheptyl) phthalate (DPHP) was developed to interpret the metabolism and biokinetics of DEHTP following a single oral dose of 50 mg to three male volunteers. In vitro and in silico methods were used to generate parameters for the model. For example, measured intrinsic hepatic clearance scaled from in vitro to in vivo and plasma unbound fraction and tissue:blood partition coefficients (PCs) were predicted algorithmically. Whereas the development and calibration of the DPHP model was based upon two data streams, blood concentrations of parent chemical and first metabolite and the urinary excretion of metabolites, the model for DEHTP was calibrated against a single data stream, the urinary excretion of metabolites. Despite the model form and structure being identical significant quantitative differences in lymphatic uptake between the models were observed. In contrast to DPHP the fraction of ingested DEHTP entering lymphatic circulation was much greater and of a similar magnitude to that entering the liver with evidence for the dual uptake mechanisms discernible in the urinary excretion data. Further, the absolute amounts absorbed by the study participants, were much higher for DEHTP relative to DPHP. The in silico algorithm for predicting protein binding performed poorly with an error of more than two orders of magnitude. The extent of plasma protein binding has important implications for the persistence of parent chemical in venous blood-inferences on the behaviour of this class of highly lipophilic chemicals, based on calculations of chemical properties, should be made with extreme caution. Attempting read across for this class of highly lipophilic chemicals should be undertaken with caution since basic adjustments to PCs and metabolism parameters would be insufficient, even when the structure of the model itself is appropriate. Therefore, validation of a model parameterized entirely with in vitro and in silico derived parameters would need to be calibrated against several human biomonitoring data streams to constitute a data rich source chemical to afford confidence for future evaluations of other similar chemicals using the read-across approach.

PBPK Modeling to Simulate the Fate of Compounds in Living Organisms

Pharmacokinetics study the fate of xenobiotics in a living organism. Physiologically based pharmacokinetic (PBPK) models provide realistic descriptions of xenobiotics' absorption, distribution, metabolism, and excretion processes. They model the body as a set of homogeneous compartments representing organs, and their parameters refer to anatomical, physiological, biochemical, and physicochemical entities. They offer a quantitative mechanistic framework to understand and simulate the time-course of the concentration of a substance in various organs and body fluids. These models are well suited for performing extrapolations inherent to toxicology and pharmacology (e.g., between species or doses) and for integrating data obtained from various sources (e.g., in vitro or in vivo experiments, structure-activity models). In this chapter, we describe the practical development and basic use of a PBPK model from model building to model simulations, through implementation with an easily accessible free software.

Development, Testing, Parameterisation and Calibration of a Human PBPK Model for the Plasticiser, Di-(2-propylheptyl) Phthalate (DPHP) Using in Silico, in vitro and Human Biomonitoring Data

A physiologically based pharmacokinetic model for Di-(2-propylheptyl) phthalate (DPHP) was developed to interpret the biokinetics in humans after single oral doses. The model was parameterized with in vitro and in silico derived parameters and uncertainty and sensitivity analysis was used during the model development process to assess structure, biological plausibility and behaviour prior to simulation and analysis of human biological monitoring data. To provide possible explanations for some of the counter-intuitive behaviour of the biological monitoring data the model included a simple lymphatic uptake process for DPHP and enterohepatic recirculation (EHR) for DPHP and the mono ester metabolite mono-(2-propylheptyl) phthalate (MPHP). The model was used to simultaneously simulate the concentration-time profiles of blood DPHP, MPHP and the urinary excretion of two metabolites, mono-(2-propyl-6-hydroxyheptyl) phthalate (OH-MPHP) and mono-(2-propyl-6-carboxyhexyl) phthalate (cx-MPHP). The availability of blood and urine measurements permitted a more robust qualitative and quantitative investigation of the importance of EHR and lymphatic uptake. Satisfactory prediction of blood DPHP and urinary metabolites was obtained whereas blood MPHP was less satisfactory. However, the delayed peak of DPHP concentration relative to MPHP in blood and second order metabolites in urine could be explained as a result of three processes: 1) DPHP entering the systemic circulation from the lymph, 2) rapid and very high protein binding and 3) the efficiency of the liver in removing DPHP absorbed via the hepatic route. The use of sensitivity analysis is considered important in the evaluation of uncertainty around in vitro and in silico derived parameters. By quantifying their impact on model output sufficient confidence in the use of a model should be afforded. This approach could expand the use of PBPK models since parameterization with in silico techniques allows for rapid model development. This in turn could assist in reducing the use of animals in toxicological evaluations by enhancing the utility of “read across” techniques.

Current and prospective computational approaches and challenges for developing COVID-19 vaccines

SARS-CoV-2, which causes COVID-19, was first identified in humans in late 2019 and is a coronavirus which is zoonotic in origin. As it spread around the world there has been an unprecedented effort in developing effective vaccines. Computational methods can be used to speed up the long and costly process of vaccine development. Antigen selection, epitope prediction, and toxicity and allergenicity prediction are areas in which computational tools have already been applied as part of reverse vaccinology for SARS-CoV-2 vaccine development. However, there is potential for computational methods to assist further. We review approaches which have been used and highlight additional bioinformatic approaches and PK modelling as in silico methods which may be useful for SARS-CoV-2 vaccine design but remain currently unexplored. As more novel viruses with pandemic potential are expected to arise in future, these techniques are not limited to application to SARS-CoV-2 but also useful to rapidly respond to novel emerging viruses.

Systematic Analysis of the Whole-Body Tissue Distribution and Fatty Acid Compositions of Membrane Lipids in CD1 and NMRI Mice and Wistar Rats

Understanding the tissue distribution of phospholipids and glycerolipids in animal models enables promoting the pharmacokinetic study of drugs and related PK predictions. The measurement of lipid compositions in animal models, usually mice and rats, without a standardized approach hindered the accuracy of PBPK investigation. In this work, high resolution mass spectrometry was applied to profile the tissue distribution of phospholipids and glycerolipids in 12 organs/tissues of mice and rats. Using this method, not only the amounts of phospholipids and glycerolipids in each organ/tissue but also the fatty acid compositions were acquired. In order to explore the interspecies specificity of lipid distribution in different organs/tissues, three animal species including CD1 mice, NMRI mice, and Wister rats were used in this systematic study. Globally, more organ specificity was observed. It was found that the brain is the organ containing the most abundant phosphatidylserine lipids (PSs) in all three animal models, leading to brain tissues having the most concentrated acidic phospholipids. Diverse fatty acid compositions in each lipid class were clearly revealed. Certain tissues/organs also had a specific selection of unique fatty acid compositions, for example, unreferenced FA(18 : 2) in the brain. It turned out that the access of free fatty acids affects the incorporation of acyl chain in phospholipids and glycerolipids. In the analysis, ether lipids were also profiled with the observation of dominant ePEs in brain tissues. However, little interspecies difference was found for fatty acid constituents and tissues distribution of phospholipids and glycerolipids.

Application of physiologically based pharmacokinetic modeling for sertraline dosing recommendations in pregnancy

Pregnancy is a period of significant change that impacts physiological and metabolic status leading to alterations in the disposition of drugs. Uncertainty in drug dosing in pregnancy can lead to suboptimal therapy, which can contribute to disease exacerbation. A few studies show there are increased dosing requirements for antidepressants in late pregnancy; however, the quantitative data to guide dose adjustments are sparse. We aimed to develop a physiologically based pharmacokinetic (PBPK) model that allows gestational-age dependent prediction of sertraline dosing in pregnancy. A minimal physiological model with defined gut, liver, plasma, and lumped placental-fetal compartments was constructed using the ordinary differential equation solver package, ‘mrgsolve’, in R. We extracted data from the literature to parameterize the model, including sertraline physicochemical properties, in vitro metabolism studies, disposition in nonpregnant women, and physiological changes during pregnancy. The model predicted the pharmacokinetic parameters from a clinical study with eight subjects for the second trimester and six subjects for the third trimester. Based on the model, gestational-dependent changes in physiology and metabolism account for increased clearance of sertraline (up to 143% at 40 weeks gestational age), potentially leading to under-dosing of pregnant women when nonpregnancy doses are used. The PBPK model was converted to a prototype web-based interactive dosing tool to demonstrate how the output of a PBPK model may translate into optimal sertraline dosing in pregnancy. Quantitative prediction of drug exposure using PBPK modeling in pregnancy will support clinically appropriate dosing and increase the therapeutic benefit for pregnant women.

Interpretation of Drug Interaction Using Systemic and Local Tissue Exposure Changes

Systemic exposure of a drug is generally associated with its pharmacodynamic (PD) effect (e.g., efficacy and toxicity). In this regard, the change in area under the plasma concentration-time curve (AUC) of a drug, representing its systemic exposure, has been mainly considered in evaluation of drug-drug interactions (DDIs). Besides the systemic exposure, the drug concentration in the tissues has emerged as a factor to alter the PD effects. In this review, the status of systemic exposure, and/or tissue exposure changes in DDIs, were discussed based on the recent reports dealing with transporters and/or metabolic enzymes mediating DDIs. Particularly, the tissue concentration in the intestine, liver and kidney were referred to as important factors of PK-based DDIs.

Undernutrition combined with dietary mineral oil hastens depuration of stored dioxin and polychlorinated biphenyls in ewes. 2. Tissue distribution, mass balance and body burden

Food safety crises involving persistent organic pollutants (POPs) lead to systematic slaughter of livestock to prevent contaminants from entering the food chain. Therefore, there is a need to develop strategies to depurate livestock moderately contaminated with POPs to reduce economic and social damage. This study aimed to test undernutrition (37% of energy requirements) combined with mineral oil (10% in total dry matter intake) in nine non-lactating ewes contaminated with 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and polychlorinated biphenyls (PCBs) 126 and 153 as a strategy to enhance the depuration of POPs through faecal excretion. To better understand the underlying mechanisms of the depuration process, lipophilic POPs and lipid fluxes were co-monitored in various body and excretion compartments. Body compartments (adipose tissues, muscle, liver and blood) and the total empty body were analyzed for lipids and POPs concentrations and burdens at slaughter, as well as excretion compartments (faeces and wool) collected during the depuration period. Decreases in empty body total and lipid weights were 6-fold higher in underfed and supplemented ewes compared to control ewes. In addition, over the depuration period undernutrition and supplementation treatment increased faecal TCDD, PCBs 126 and 153 excretions by 1.4- to 2.1-fold but tended to decrease wool PCB 153 excretion by 1.4-fold. This induced 2- to 3-fold higher decreases in the empty body POPs burdens for underfed and supplemented ewes. Nonetheless, when expressed relative to the calculated initial empty body burdens, burdens at slaughter decreased only slightly from 97%, 103% and 98% for control ewes to 92%, 97% and 94% for underfed and supplemented ones, for TCDD, PCBs 126 and 153, respectively. Fine descriptions at once of POPs kinetic (companion paper 1) and mass balance (companion paper 2), and of body lipid dynamics were very useful in improving our understanding of the fate of POPs in the ruminants.

Global Sensitivity Analysis of the Rodgers and Rowland Model for Prediction of Tissue: Plasma Partitioning Coefficients: Assessment of the Key Physiological and Physicochemical Factors That Determine Small-Molecule Tissue Distribution

In physiologically based pharmacokinetic (PBPK) modelling, the large number of input parameters, limited amount of available data and the structural model complexity generally hinder simultaneous estimation of uncertain and/or unknown parameters. These parameters are generally subject to estimation. However, the approaches taken for parameter estimation vary widely. Global sensitivity analyses are proposed as a method to systematically determine the most influential parameters that can be subject to estimation. Herein, a global sensitivity analysis was conducted to identify the key drug and physiological parameters influencing drug disposition in PBPK models and to potentially reduce the PBPK model dimensionality. The impact of these parameters was evaluated on the tissue-to-unbound plasma partition coefficients (Kpus) predicted by the Rodgers and Rowland model using Latin hypercube sampling combined to partial rank correlation coefficients (PRCC). For most drug classes, PRCC showed that LogP and fraction unbound in plasma (fu_p) were generally the most influential parameters for Kpu predictions. For strong bases, blood:plasma partitioning was one of the most influential parameter. Uncertainty in tissue composition parameters had a large impact on Kpu and Vss predictions for all classes. Among tissue composition parameters, changes in Kpu outputs were especially attributed to changes in tissue acidic phospholipid concentrations and extracellular protein tissue:plasma ratio values. In conclusion, this work demonstrates that for parameter estimation involving PBPK models and dimensionality reduction purposes, less influential parameters might be assigned fixed values depending on the parameter space, while influential parameters could be subject to parameters estimation.

Development, Testing, Parameterization, and Calibration of a Human Physiologically Based Pharmacokinetic Model for the Plasticizer, Hexamoll® Diisononyl-Cyclohexane-1, 2-Dicarboxylate Using In Silico, In Vitro, and Human Biomonitoring Data

A physiologically based pharmacokinetic model for Hexamoll^® diisononyl-cyclohexane-1, 2-dicarboxylate was developed to interpret the biokinetics in humans after single oral doses. The model was parameterized with in vitro and in silico derived parameters and uncertainty and sensitivity analysis was used during the model development process to assess structure, biological plausibility and behavior prior to simulation and analysis of human biological monitoring data. The model provided good simulations of the urinary excretion (Curine) of two metabolites; cyclohexane-1,2-dicarboxylic acid mono hydroxyisononyl ester (OH-MINCH) and cyclohexane-1, 2-dicarboxylic acid mono carboxyisononyl ester (cx-MINCH) from the biotransformation of mono-isononyl-cyclohexane-1, 2-dicarboxylate (MINCH), the monoester metabolite of di-isononyl-cyclohexane-1,2-dicarboxylate. However, good simulations could be obtained, with and without, a lymphatic compartment. Selection of an appropriate model structure was informed by sensitivity analysis which could identify and quantify the contribution to variability in Curine by parameters, such as, the fraction of oral dose that directly entered the lymphatic compartment and therefore by-passed the liver and the fraction of MINCH bio-transformed to cx-MINCH and OH-MINCH. By constraining these parameters within biologically plausible limits the presence of a lymphatic compartment was deemed an important component of model structure. Furthermore, the use of sensitivity analysis is important in the evaluation of uncertainty around in silico derived parameters. By quantifying their impact on model output sufficient confidence in the use of a model should be afforded. This type of approach could expand the use of physiologically based pharmacokinetic models since parameterization with in silico techniques allows for rapid model development. This in turn could assist in reducing the use of animals in toxicological evaluations by enhancing the utility of “read across” techniques.

Assessing Toxicokinetic Uncertainty and Variability in Risk Prioritization

High(er) throughput toxicokinetics (HTTK) encompasses in vitro measures of key determinants of chemical toxicokinetics and reverse dosimetry approaches for in vitro-in vivo extrapolation (IVIVE). With HTTK, the bioactivity identified by any in vitro assay can be converted to human equivalent doses and compared with chemical intake estimates. Biological variability in HTTK has been previously considered, but the relative impact of measurement uncertainty has not. Bayesian methods were developed to provide chemical-specific uncertainty estimates for 2 in vitro toxicokinetic parameters: unbound fraction in plasma (fup) and intrinsic hepatic clearance (Clint). New experimental measurements of fup and Clint are reported for 418 and 467 chemicals, respectively. These data raise the HTTK chemical coverage of the ToxCast Phase I and II libraries to 57%. Although the standard protocol for Clint was followed, a revised protocol for fup measured unbound chemical at 10%, 30%, and 100% of physiologic plasma protein concentrations, allowing estimation of protein binding affinity. This protocol reduced the occurrence of chemicals with fup too low to measure from 44% to 9.1%. Uncertainty in fup was also reduced, with the median coefficient of variation dropping from 0.4 to 0.1. Monte Carlo simulation was used to propagate both measurement uncertainty and biological variability into IVIVE. The uncertainty propagation techniques used here also allow incorporation of other sources of uncertainty such as in silico predictors of HTTK parameters. These methods have the potential to inform risk-based prioritization based on the relationship between in vitro bioactivities and exposures.

Effects of migration and reproduction on the variation in persistent organic pollutant levels in Kentish Plovers from Cangzhou Wetland, China

Migratory Birds have been considered biovectors of persistent organic pollutants (POPs) from sources to remote areas. In the present study, Kentish Plovers (Charadrius alexandrines) were collected in different periods, including immigration, breeding and emigration, to investigate the effects of migration and reproduction on POP variations in this bird species. Significant differences were found for dichlorodiphenyltrichloroethane (DDT) and hexachlorobenzene (HCB) concentrations in muscles between the immigration and emigration periods (p < 0.01 and p < 0.001, respectively), which could be attributed to the higher pesticide residues in the wintering grounds of plovers. Female plovers could excrete about 20.8-42.7% of POP load into eggs. Nevertheless, the POP levels didn't exhibit great reduction during the breeding period compared with other seasons, which suggested that the breeding status had little impact on POP levels in female plovers. The estimated mean transport masses of POPs driven by plover migration were at the milligram level (range: 0.02-7.05 mg), suggesting that the migration of plovers had limited impacts on the redistributions of POPs along their migratory routes.

A framework for application of quantitative property-property relationships (QPPRs) in physiologically based pharmacokinetic (PBPK) models for high-throughput prediction of internal dose of inhaled organic chemicals

New generation of toxicological tests and assessment strategies require validated toxicokinetic data or models that are lacking for most chemicals. This study aimed at developing a quantitative property-property relationship (QPPR)-based human physiologically based pharmacokinetic (PBPK) modeling framework for high-throughput predictions of inhalation toxicokinetics of organic chemicals. A PBPK model was parameterized with QPPR-derived values for hepatic clearance (CL_h) and partition coefficients (P) [blood:air (P_ba) and tissue:air (P_ta) and tissue:blood (P_tb)]. The model was initially applied to an evaluation dataset of 40 organic chemicals in the applicability domain, and then to an expanded dataset of 249 organic chemicals from diverse chemical classes. 'Batch' analyses were performed for rapid assessments of hundreds of chemicals. The simulations of inhalation toxicokinetics following an 8-h exposure to 1 ppm of each chemical were successful. The mean ratios of their predicted-to-experimental values were within a factor of 1.36-2.36 for P_tb and 1.18 for CL_h, for 80% of the chemicals in the evaluation dataset. The predicted 24-h area under the venous blood concentration-time curve (AUC₂₄) values were within the predicted envelopes obtained while using experimental values of P_ba and considering either no or maximal hepatic extraction. The reliability analysis (based on combined sensitivity and uncertainty analyses) indicated that AUC₂₄ predictions for 55% of the expanded dataset were moderately to highly reliable, with 46% exhibiting highly reliable values. Overall, the modeling framework suggests that molecular structure and chemical properties can together be effectively used to obtain first-cut estimates of the toxicokinetics of data-poor organic chemicals for screening and prioritization purposes.

Application of the Tissue Composition-Based Model to Minipig for Predicting the Volume of Distribution at Steady State and Dermis-to-Plasma Partition Coefficients of Drugs Used in the Physiologically Based Pharmacokinetics Model in Dermatology

The minipig continues to build a reputation as a viable alternative large animal model to predict humans in dermatology and toxicology studies. Therefore, it is essential to describe and predict the pharmacokinetics in that species to speed up the clinical candidate selection. Essential input parameters in whole-body physiologically based pharmacokinetic models are the tissue-to-plasma partition coefficients and the resulting volume of distribution at steady-state (V_ss). Mechanistic in vitro- and in silico-based models used for predicting these parameters of tissue distribution of drugs refer to the tissue composition-based model (TCM). Robust TCMs were initially developed for some preclinical species (e.g., rat and dog) and human; however, there is currently no model available for the minipig. Therefore, the objective of this present study was to develop a TCM for the minipig and to estimate the corresponding tissue composition data. Drug partitioning into the tissues was predominantly governed by lipid and protein binding effects in addition to drug solubilization and pH gradient effects in the aqueous phase on both sides of the biological membranes; however, some more complex tissue distribution processes such as drug binding to the collagen-laminin material in dermis and a restricted drug partitioning into membranes of tissues for compounds that are amphiphilic and contain sulfur atom(s) were also challenged. The model was validated by predicting V_ss and the dermis-to-plasma partition coefficients (K_p-dermis) of 68 drugs. The prediction of K_p-dermis was extended to humans for comparison with the minipig. The results indicate that the extended TCM provided generally good agreements with observations in the minipig showing that it is also applicable to this preclinical species. In general, up to 86% and 100% of the predicted V_ss values are respectively within 2-fold and 3-fold errors compared to the experimentally determined values, whereas these numbers are 78% and 94% for K_p-dermis when the anticipated outlier compounds are not included. Binding data to dermis are comparable between minipigs and humans. Overall, this study is a first step toward developing a mechanistic TCM for the minipig, with the aim of increasing the use of physiologically based pharmacokinetic models of drugs for that species in addition to rats, dogs, and humans because such models are used in preclinical and clinical transdermal studies.

Comparing Mechanistic and Preclinical Predictions of Volume of Distribution on a Large Set of Drugs

PURPOSE

Volume of distribution at steady state (Vd_ss) is a fundamental pharmacokinetic (PK) parameter driven predominantly by passive processes and physicochemical properties of the compound. Human Vd_ss can be estimated using in silico mechanistic methods or empirically scaled from Vd_ss values obtained from preclinical species. In this study the accuracy and the complementarity of these two approaches are analyzed leveraging a large data set (over 150 marketed drugs).

METHODS

For all the drugs analyzed in this study experimental in vitro measurements of LogP, plasma protein binding and pKa are used as input for the mechanistic in silico model to predict human Vd_ss. The software used for predicting human tissue partition coefficients and Vd_ss based on the method described by Rodgers and Rowland is made available as supporting information.

RESULTS

This assessment indicates that overall the in silico mechanistic model presented by Rodgers and Rowland is comparably accurate or superior to empirical approaches based on the extrapolation of in vivo data from preclinical species.

CONCLUSIONS

These results illustrate the great potential of mechanistic in silico models to accurately predict Vd_ss in humans. This in silico method does not rely on in vivo data and is, consequently, significantly time and resource sparing. The success of this in silico model further suggests that reasonable predictability of Vd_ss in preclinical species could be obtained by a similar process.

High-throughput in-silico prediction of ionization equilibria for pharmacokinetic modeling

Chemical ionization plays an important role in many aspects of pharmacokinetic (PK) processes such as protein binding, tissue partitioning, and apparent volume of distribution at steady state (Vdss). Here, estimates of ionization equilibrium constants (i.e., pKa) were analyzed for 8132 pharmaceuticals and 24,281 other compounds to which humans might be exposed in the environment. Results revealed broad differences in the ionization of pharmaceutical chemicals and chemicals with either near-field (in the home) or far-field sources. The utility of these high-throughput ionization predictions was evaluated via a case-study of predicted PK Vdss for 22 compounds monitored in the blood and serum of the U.S. population by the U.S. Centers for Disease Control and Prevention National Health and Nutrition Examination Survey (NHANES). The chemical distribution ratio between water and tissue was estimated using predicted ionization states characterized by pKa. Probability distributions corresponding to ionizable atom types (IATs) were then used to analyze the sensitivity of predicted Vdss on predicted pKa using Monte Carlo methods. 8 of the 22 compounds were predicted to be ionizable. For 5 of the 8 the predictions based upon ionization are significantly different from what would be predicted for a neutral compound. For all but one (foramsulfuron), the probability distribution of predicted Vdss generated by IAT sensitivity analysis spans both the neutral prediction and the prediction using ionization. As new data sets of chemical-specific information on metabolism and excretion for hundreds of chemicals are being made available (e.g., Wetmore et al., 2015), high-throughput methods for calculating Vdss and tissue-specific PK distribution coefficients will allow the rapid construction of PK models to provide context for both biomonitoring data and high-throughput toxicity screening studies such as Tox21 and ToxCast.

Estrogenic effects following larval exposure to the putative anti-estrogen, fulvestrant, in the fathead minnow (Pimephales promelas)

The objective of the present study was to investigate the consequences of early-life exposure to fulvestrant on estrogenic gene expression in fathead minnow larvae. To address this objective, fathead minnow larvae were exposed to fulvestrant (ICI 182,780) during the window of sexual differentiation between 0 to 30 days post-hatch (dph). The four treatment groups in this study included: filtered water controls (never exposed), solvent controls (ethanol 0.01%), and nominally low (0.10μg/L) and high (10.0μg/L) doses of fulvestrant. Following 30 d exposure to their respective treatment, larvae were transferred to filtered water aquaria and assessed for alterations in endocrine-responsive gene expression (i.e., RT-qPCR), body size and survival. The remaining fish depurated in filtered water until reaching sexual maturity (180dph) for assessment of persistent effects on sex characteristics, reproductive performance and sex ratio. Following the 30-d early life exposure, larvae showed upregulations of the endocrine-responsive genes ar, erβ and vtg in response to both low and high doses of fulvestrant, but showed no differences in survival or body mass. Upon reaching sexual maturity under depuration conditions, male minnows previously exposed to fulvestrant as larvae showed reductions in gonad mass along with the feminization of secondary sex characteristics with no observed effects in females. Exposure to fulvestrant had no effects on gonadal histology, reproductive performance or final sex ratio as adults. Results from this study demonstrate that aqueous exposure to fulvestrant is estrogenic in fathead minnow larvae and is capable of feminizing male fish as adults following early life exposure.

Modeling Pharmacokinetics

Pharmacokinetics is the study of the fate of xenobiotics in a living organism. Physiologically based pharmacokinetic (PBPK) models provide realistic descriptions of xenobiotics' absorption, distribution, metabolism, and excretion processes. They model the body as a set of homogeneous compartments representing organs, and their parameters refer to anatomical, physiological, biochemical, and physicochemical entities. They offer a quantitative mechanistic framework to understand and simulate the time-course of the concentration of a substance in various organs and body fluids. These models are well suited for performing extrapolations inherent to toxicology and pharmacology (e.g., between species or doses) and for integrating data obtained from various sources (e.g., in vitro or in vivo experiments, structure-activity models). In this chapter, we describe the practical development and basic use of a PBPK model from model building to model simulations, through implementation with an easily accessible free software.

Evaluation and calibration of high-throughput predictions of chemical distribution to tissues

Toxicokinetics (TK) provides critical information for integrating chemical toxicity and exposure assessments in order to determine potential chemical risk (i.e., the margin between toxic doses and plausible exposures). For thousands of chemicals that are present in our environment, in vivo TK data are lacking. The publicly available R package "httk" (version 1.8, named for "high throughput TK") draws from a database of in vitro data and physico-chemical properties in order to run physiologically-based TK (PBTK) models for 553 compounds. The PBTK model parameters include tissue:plasma partition coefficients (Kp) which the httk software predicts using the model of Schmitt (Toxicol In Vitro 22 (2):457-467, 2008). In this paper we evaluated and modified httk predictions, and quantified confidence using in vivo literature data. We used 964 rat Kp measured by in vivo experiments for 143 compounds. Initially, predicted Kp were significantly larger than measured Kp for many lipophilic compounds (log10 octanol:water partition coefficient > 3). Hence the approach for predicting Kp was revised to account for possible deficiencies in the in vitro protein binding assay, and the method for predicting membrane affinity was revised. These changes yielded improvements ranging from a factor of 10 to nearly a factor of 10,000 for 83 Kp across 23 compounds with only 3 Kp worsening by more than a factor of 10. The vast majority (92%) of Kp were predicted within a factor of 10 of the measured value (overall root mean squared error of 0.59 on log10-transformed scale). After applying the adjustments, regressions were performed to calibrate and evaluate the predictions for 12 tissues. Predictions for some tissues (e.g., spleen, bone, gut, lung) were observed to be better than predictions for other tissues (e.g., skin, brain, fat), indicating that confidence in the application of in silico tools to predict chemical partitioning varies depending upon the tissues involved. Our calibrated model was then evaluated using a second data set of human in vivo measurements of volume of distribution (Vss) for 498 compounds reviewed by Obach et al. (Drug Metab Dispos 36(7):1385-1405, 2008). We found that calibration of the model improved performance: a regression of the measured values as a function of the predictions has a slope of 1.03, intercept of - 0.04, and R2 of 0.43. Through careful evaluation of predictive methods for chemical partitioning into tissues, we have improved and calibrated these methods and quantified confidence for TK predictions in humans and rats.

Revolutionizing Therapeutic Drug Monitoring with the Use of Interstitial Fluid and Microneedles Technology

While therapeutic drug monitoring (TDM) that uses blood as the biological matrix is the traditional gold standard, this practice may be impossible, impractical, or unethical for some patient populations (e.g., elderly, pediatric, anemic) and those with fragile veins. In the context of finding an alternative biological matrix for TDM, this manuscript will provide a qualitative review on: (1) the principles of TDM; (2) alternative matrices for TDM; (3) current evidence supporting the use of interstitial fluid (ISF) for TDM in clinical models; (4) the use of microneedle technologies, which is potentially minimally invasive and pain-free, for the collection of ISF; and (5) future directions. The current state of knowledge on the use of ISF for TDM in humans is still limited. A thorough literature review indicates that only a few drug classes have been investigated (i.e., anti-infectives, anticonvulsants, and miscellaneous other agents). Studies have successfully demonstrated techniques for ISF extraction from the skin but have failed to demonstrate commercial feasibility of ISF extraction followed by analysis of its content outside the ISF-collecting microneedle device. In contrast, microneedle-integrated biosensors built to extract ISF and perform the biomolecule analysis on-device, with a key feature of not needing to transfer ISF to a separate instrument, have yielded promising results that need to be validated in pre-clinical and clinical studies. The most promising applications for microneedle-integrated biosensors is continuous monitoring of biomolecules from the skin's ISF. Conducting TDM using ISF is at the stage where its clinical utility should be investigated. Based on the advancements described in the current review, the immediate future direction for this area of research is to establish the suitability of using ISF for TDM in human models for drugs that have been found suitable in pre-clinical experiments.

httk: R Package for High-Throughput Toxicokinetics

Thousands of chemicals have been profiled by high-throughput screening programs such as ToxCast and Tox21; these chemicals are tested in part because most of them have limited or no data on hazard, exposure, or toxicokinetics. Toxicokinetic models aid in predicting tissue concentrations resulting from chemical exposure, and a "reverse dosimetry" approach can be used to predict exposure doses sufficient to cause tissue concentrations that have been identified as bioactive by high-throughput screening. We have created four toxicokinetic models within the R software package httk. These models are designed to be parameterized using high-throughput in vitro data (plasma protein binding and hepatic clearance), as well as structure-derived physicochemical properties and species-specific physiological data. The package contains tools for Monte Carlo sampling and reverse dosimetry along with functions for the analysis of concentration vs. time simulations. The package can currently use human in vitro data to make predictions for 553 chemicals in humans, rats, mice, dogs, and rabbits, including 94 pharmaceuticals and 415 ToxCast chemicals. For 67 of these chemicals, the package includes rat-specific in vitro data. This package is structured to be augmented with additional chemical data as they become available. Package httk enables the inclusion of toxicokinetics in the statistical analysis of chemicals undergoing high-throughput screening.

Accumulation of α-hexabromocyclododecane (α-HBCDD) in tissues of fast- and slow-growing broilers (Gallus domesticus)

The aim of the current study was to describe the fate of ingested α-hexabromocyclododecane (α-HBCDD) in fast-growing (FG) and slow-growing (SG) broilers, through an exposure to a dietary concentration of 50 ng α-HBCDD g^-1 feed during 42 and 84 days, respectively. Depuration parameters were assessed in SG broilers successively exposed during 42 days and depurated during 42 days. At market age, SG broilers had ingested 42% more feed than FG broilers, while their body weight gain per g of feed ingested was 34% lower. No isomerization of α- to β- or γ-HBCDD forms occurred, while OH-HBCDD was identified as a product of α-HBCDD metabolism. Irrespective of the strain, abdominal fat displayed the highest α-HBCDD concentration on a lipid weight basis, followed leg muscles and then breast muscle, liver and plasma. The accumulation ratios of α-HBCDD were slightly higher in SG (6.7, 2.1, 2.6 and 9.9 in leg muscles, breast muscle, liver and abdominal fat, respectively) than in FG broilers (5.2, 2.2, 1.1 and 8.4, respectively). The elimination half-lives in SG broilers were 20, 12 and 19 d in leg muscles, breast muscle and abdominal fat, respectively, to which dilution through growth contributed for around 50%. The overall assimilation efficiency of α-HBCDD was estimated at 58 and 50% in FG and SG broilers, respectively, while 22 and 17% of α-HBCDD ingested were estimated to be eliminated in excreta as metabolites.

A physiologically based pharmacokinetic model of vitamin D

Despite the plethora of studies discussing the benefits of vitamin D on physiological functioning, few mathematical models of vitamin D predict the response of the body on low-concentration supplementation of vitamin D under sunlight-restricted conditions. This study developed a physiologically based pharmacokinetic (PBPK) model utilizing published human data on the metabolic cascade of orally derived, low-concentration (placebo, 5 μg and 10 μg) supplementation of vitamin D over the course of 28 days in the absence of sunlight. Vitamin D and its metabolites are highly lipophilic and binding assays of these compounds in serum may not account for binding by lipids and additional proteins. To compensate for the additional bound amounts, this study allowed the effective adipose-plasma partition coefficient to vary dynamically with the concentration of each compound in serum utilizing the Hill equation for binding. Through incorporating the optimized parameters with the adipose partition coefficient adaptation to the PBPK model, this study was able to fit serum concentration data for circulating vitamin D at all three supplementation concentrations within confidence intervals of the data. Copyright © 2017 John Wiley & Sons, Ltd.

IMI - Oral biopharmaceutics tools project - Evaluation of bottom-up PBPK prediction success part 3: Identifying gaps in system parameters by analysing In Silico performance across different compound classes

Three Physiologically Based Pharmacokinetic software packages (GI-Sim, Simcyp® Simulator, and GastroPlus™) were evaluated as part of the Innovative Medicine Initiative Oral Biopharmaceutics Tools project (OrBiTo) during a blinded "bottom-up" anticipation of human pharmacokinetics. After data analysis of the predicted vs. measured pharmacokinetics parameters, it was found that oral bioavailability (F_oral) was underpredicted for compounds with low permeability, suggesting improper estimates of intestinal surface area, colonic absorption and/or lack of intestinal transporter information. F_oral was also underpredicted for acidic compounds, suggesting overestimation of impact of ionisation on permeation, lack of information on intestinal transporters, or underestimation of solubilisation of weak acids due to less than optimal intestinal model pH settings or underestimation of bile micelle contribution. F_oral was overpredicted for weak bases, suggesting inadequate models for precipitation or lack of in vitro precipitation information to build informed models. Relative bioavailability was underpredicted for both high logP compounds as well as poorly water-soluble compounds, suggesting inadequate models for solubility/dissolution, underperforming bile enhancement models and/or lack of biorelevant solubility measurements. These results indicate areas for improvement in model software, modelling approaches, and generation of applicable input data. However, caution is required when interpreting the impact of drug-specific properties in this exercise, as the availability of input parameters was heterogeneous and highly variable, and the modellers generally used the data "as is" in this blinded bottom-up prediction approach.

A mechanistic approach to modelling the formation of a drug reservoir in the skin

It has been shown that prolonged systemic presence of a drug can cause a build-up of that drug in the skin. This drug 'reservoir', if properly understood, could provide useful information about recent drug-taking history of the patient. We create a pair of coupled mathematical models which combine to explore the potential for a drug reservoir to establish based on the kinetic properties of the drug. The first compartmental model is used to characterise time-dependent drug concentrations in plasma and tissue following a customisable drug regimen. Outputs from this model provide boundary conditions for the second, spatio-temporal model of drug build-up in the skin. We focus on drugs that are highly bound as this will restrict their potential to move freely into the skin but which are lipophilic so that, in the unbound form, they would demonstrate an affinity to the outer layers of the skin. Buprenorphine, a drug used to treat opiate addiction, is one example of a drug satisfying these properties. In the discussion we highlight how our study might be used to inform future experimental design and data collection to provide relevant parameter estimates for reservoir formation and its potential to contribute to enhanced drug monitoring techniques.

Towards a Best Practice Approach in PBPK Modeling: Case Example of Developing a Unified Efavirenz Model Accounting for Induction of CYPs 3A4 and 2B6

In this study, we present efavirenz physiologically based pharmacokinetic (PBPK) model development as an example of our best practice approach that uses a stepwise approach to verify the different components of the model. First, a PBPK model for efavirenz incorporating in vitro and clinical pharmacokinetic (PK) data was developed to predict exposure following multiple dosing (600 mg q.d.). Alfentanil i.v. and p.o. drug‐drug interaction (DDI) studies were utilized to evaluate and refine the CYP3A4 induction component in the liver and gut. Next, independent DDI studies with substrates of CYP3A4 (maraviroc, atazanavir, and clarithromycin) and CYP2B6 (bupropion) verified the induction components of the model (area under the curve [AUC] ratios within 1.0–1.7‐fold of observed). Finally, the model was refined to incorporate the fractional contribution of enzymes, including CYP2B6, propagating autoinduction into the model (Racc 1.7 vs. 1.7 observed). This validated mechanistic model can now be applied in clinical pharmacology studies to prospectively assess both the victim and perpetrator DDI potential of efavirenz.

Evaluation of the GastroPlus™ Advanced Compartmental and Transit (ACAT) Model in Early Discovery

PURPOSE

The aim of this study was to evaluate the oral exposure predictions obtained early in drug discovery with a generic GastroPlus Advanced Compartmental And Transit (ACAT) model based on the in vivo intravenous blood concentration-time profile, in silico properties (lipophilicity, pKa) and in vitro high-throughput absorption-distribution-metabolism-excretion (ADME) data (as determined by PAMPA, solubility, liver microsomal stability assays).

METHODS

The model was applied to a total of 623 discovery molecules and their oral exposure was predicted in rats and/or dogs. The predictions of Cmax, AUClast and Tmax were compared against the observations.

RESULTS

The generic model proved to make predictions of oral Cmax, AUClast and Tmax within 3-fold of the observations for rats in respectively 65%, 68% and 57% of the 537 cases. For dogs, it was respectively 77%, 79% and 85% of the 124 cases. Statistically, the model was most successful at predicting oral exposure of Biopharmaceutical Classification System (BCS) class 1 compounds compared to classes 2 and 3, and was worst at predicting class 4 compounds oral exposure.

CONCLUSION

The generic GastroPlus ACAT model provided reasonable predictions especially for BCS class 1 compounds. For compounds of other classes, the model may be refined by obtaining more information on solubility and permeability in secondary assays. This increases confidence that such a model can be used in discovery projects to understand the parameters limiting absorption and extrapolate predictions across species. Also, when predictions disagree with the observations, the model can be updated to test hypotheses and understand oral absorption.

Tissue Distribution and Transfer to Eggs of Ingested α-Hexabromocyclododecane (α-HBCDD) in Laying Hens (Gallus domesticus)

The aim of the current study was to describe the fate of ingested α-hexabromocyclododecane (α-HBCDD) in laying hens. Individuals were exposed to two dietary concentrations of α-HBCDD (50 and 5 ng g(-1) feed) for 18 or 11 weeks followed by a 7-week decontamination period. The results show that no isomerization of α- to β- or γ-HBCDD forms occurred, whereas OH-HBCDD was identified as a product of α-HBCDD metabolism. Irrespective of the level of feed contamination, estimates of steady-state accumulation ratios were 5.2, 3.6, and 9.2 and half-lives were estimated at 17.4, 22.8, and 35.3 days in egg yolk, liver tissue, and abdominal fat, respectively. The steady-state carry-over rate to eggs was 22.9%. Thus, α-HBCDD ingested by laying hens is readily transferred to eggs and significantly accumulates in adipose tissue.

A paradigm shift in pharmacokinetic-pharmacodynamic (PKPD) modeling: rule of thumb for estimating free drug level in tissue compared with plasma to guide drug design

A basic assumption in pharmacokinetics-pharmacodynamics research is that the free drug concentration is similar in plasma and tissue, and, hence, in vitro plasma data can be used to estimate the in vivo condition in tissue. However, in a companion manuscript, it has been demonstrated that this assumption is violated for the ionized drugs. Nonetheless, these observations focus on in vitro static environments and do not challenge data with an in vivo dynamic system. Therefore, an extension from an in vitro to an in vivo system becomes the necessary next step. The objective of this study was to perform theoretical simulations of the free drug concentration in tissue and plasma by using a physiologically based pharmacokinetics (PBPK) model reproducing the in vivo conditions in human. Therefore, the effects of drug ionization, lipophilicity, and clearance have been taken into account in a dynamic system. This modeling exercise was performed as a proof of concept to demonstrate that free drug concentration in tissue and plasma may also differ in a dynamic system for passively permeable drugs that are ionized at the physiological pH. The PBPK model simulations indicated that free drug concentrations in tissue cells and plasma significantly differ for the ionized drugs because of the pH gradient effect between cells and interstitial space. Hence, a rule of thumb for potentially performing more accurate PBPK/PD modeling is suggested, which states that the free drug concentration in tissue and plasma will differ for the ionizable drugs in contrast to the neutral drugs. In addition to the pH gradient effect for the ionizable drugs, lipophilicity and clearance effects will increase or decrease the free drug concentration in tissue and plasma for each class of drugs; thus, higher will be the drug lipophilicity and clearance, lower would be the free drug concentration in plasma, and, hence, in tissue, in a dynamic in vivo system. Therefore, only considering the value of free fraction in plasma derived from a static in vitro environment might be biased to guide drug design (the old paradigm), and, hence, it is recommended to use a PBPK model to reproduce more accurately the in vivo condition in tissue (the new paradigm). This newly developed approach can be used to predict free drug concentration in diverse tissue compartments for small molecules in toxicology and pharmacology studies, which can be leveraged to optimize the pharmacokinetics drivers of tissue distribution based upon physicochemical and physiological input parameters in an attempt to optimize free drug level in tissue. Overall, this present study provides guidance on the application of plasma and tissue concentration information in PBPK/PD research in preclinical and clinical studies, which is in accordance with the recent literature.

Drug Distribution to Human Tissues: Prediction and Examination of the Basic Assumption in In Vivo Pharmacokinetics-Pharmacodynamics (PK/PD) Research

The tissue:plasma partition coefficients (Kp ) are good indicators of the extent of tissue distribution. Therefore, advanced tissue composition-based models were used to predict the Kp values of drugs under in vivo conditions on the basis of in vitro and physiological input data. These models, however, focus on animal tissues and do not challenge the predictions with human tissues for drugs. The first objective of this study was to predict the experimentally determined Kp values of seven human tissues for 26 drugs. In all, 95% of the predicted Kp values are within 2.5-fold error of the observed values in humans. Accordingly, these results suggest that the tissue composition-based model used in this study is able to provide accurate estimates of drug partitioning in the studied human tissues. Furthermore, as the Kp equals to the ratio of total concentration between tissue and plasma, or the ratio of unbound fraction between plasma (fup ) and tissue (fut ), this parameter Kp would deviate from the unity. Therefore, the second objective was to examine the corresponding relationships between fup and fut values experimentally determined in humans for several drugs. The results also indicate that fup may significantly deviate to fut ; the discrepancies are governed by the dissimilarities in the binding and ionization on both sides of the membrane, which were captured by the tissue composition-based model. Hence, this violated the basic assumption in in vivo pharmacokinetics-pharmacodynamics (PK/PD) research, since the free drug concentration in tissue and plasma was not equal particularly for the ionizable drugs due to the pH gradient effect on the fraction of unionized drug in plasma (fuip ) and tissue (fuit ) (i.e., fup × fuip × total plasma concentration = fut × fuit × total tissue concentration, and, hence, the free drug concentration in plasma and tissue differed by fuip/fuit). Therefore, this assumption should be adjusted for the ionized drugs, and, hence, a mathematical correction to the basic assumption of similar free drug concentration in plasma and tissues can be derived from the tissue composition-based model. Note that this assumption will be further challenged in a dynamic in vivo system in a companion manuscript. Overall, this study was a first attempt to predict the in vivo Kp values for specific human tissues by considering separately the effect of fup and fut , with the aim of facilitating the use of physiologically-based PK (PBPK) model in PK/PD studies.

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients

BACKGROUND

Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values.

RESULTS

Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied.

CONCLUSIONS

Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Graphical AbstractDecision trees for the prediction of tissue partition coefficient and volume of distribution of drugs.

Improving the risk assessment of lipophilic persistent environmental chemicals in breast milk

Lipophilic persistent environmental chemicals (LPECs) have the potential to accumulate within a woman's body lipids over the course of many years prior to pregnancy, to partition into human milk, and to transfer to infants upon breastfeeding. As a result of this accumulation and partitioning, a breastfeeding infant's intake of these LPECs may be much greater than his/her mother's average daily exposure. Because the developmental period sets the stage for lifelong health, it is important to be able to accurately assess chemical exposures in early life. In many cases, current human health risk assessment methods do not account for differences between maternal and infant exposures to LPECs or for lifestage-specific effects of exposure to these chemicals. Because of their persistence and accumulation in body lipids and partitioning into breast milk, LPECs present unique challenges for each component of the human health risk assessment process, including hazard identification, dose-response assessment, and exposure assessment. Specific biological modeling approaches are available to support both dose-response and exposure assessment for lactational exposures to LPECs. Yet, lack of data limits the application of these approaches. The goal of this review is to outline the available approaches and to identify key issues that, if addressed, could improve efforts to apply these approaches to risk assessment of lactational exposure to these chemicals.

Prediction of drug disposition on the basis of its chemical structure

The chemical structure of any drug determines its pharmacokinetics and pharmacodynamics. Detailed understanding of relationships between the drug chemical structure and individual disposition pathways (i.e., distribution and elimination) is required for efficient use of existing drugs and effective development of new drugs. Different approaches have been developed for this purpose, ranging from statistics-based quantitative structure-property (or structure-pharmacokinetic) relationships (QSPR) analysis to physiologically based pharmacokinetic (PBPK) models. This review critically analyzes currently available approaches for analysis and prediction of drug disposition on the basis of chemical structure. Models that can be used to predict different aspects of disposition are presented, including: (a) value of the individual pharmacokinetic parameter (e.g., clearance or volume of distribution), (b) efficiency of the specific disposition pathway (e.g., biliary drug excretion or cytochrome P450 3A4 metabolism), (c) accumulation in a specific organ or tissue (e.g., permeability of the placenta or accumulation in the brain), and (d) the whole-body disposition in the individual patients. Examples of presented pharmacological agents include "classical" low-molecular-weight compounds, biopharmaceuticals, and drugs encapsulated in specialized drug-delivery systems. The clinical efficiency of agents from all these groups can be suboptimal, because of inefficient permeability of the drug to the site of action and/or excessive accumulation in other organs and tissues. Therefore, robust and reliable approaches for chemical structure-based prediction of drug disposition are required to overcome these limitations. PBPK models are increasingly being used for prediction of drug disposition. These models can reflect the complex interplay of factors that determine drug disposition in a mechanistically correct fashion and can be combined with other approaches, for example QSPR-based prediction of drug permeability and metabolism, pharmacogenomic data and tools, pharmacokinetic-pharmacodynamic modeling approaches, etc. Moreover, the PBPK models enable detailed analysis of clinically relevant scenarios, for example the effect of the specific conditions on the time course of the analyzed drug in the individual organs and tissues, including the site of action. It is expected that further development of such combined approaches will increase their precision, enhance the effectiveness of drugs, and lead to individualized drug therapy for different patient populations (geriatric, pediatric, specific diseases, etc.).

Development of a decision tree to classify the most accurate tissue-specific tissue to plasma partition coefficient algorithm for a given compound

Physiologically based pharmacokinetic (PBPK) modeling is a tool used in drug discovery and human health risk assessment. PBPK models are mathematical representations of the anatomy, physiology and biochemistry of an organism and are used to predict a drug's pharmacokinetics in various situations. Tissue to plasma partition coefficients (Kp), key PBPK model parameters, define the steady-state concentration differential between tissue and plasma and are used to predict the volume of distribution. The experimental determination of these parameters once limited the development of PBPK models; however, in silico prediction methods were introduced to overcome this issue. The developed algorithms vary in input parameters and prediction accuracy, and none are considered standard, warranting further research. In this study, a novel decision-tree-based Kp prediction method was developed using six previously published algorithms. The aim of the developed classifier was to identify the most accurate tissue-specific Kp prediction algorithm for a new drug. A dataset consisting of 122 drugs was used to train the classifier and identify the most accurate Kp prediction algorithm for a certain physicochemical space. Three versions of tissue-specific classifiers were developed and were dependent on the necessary inputs. The use of the classifier resulted in a better prediction accuracy than that of any single Kp prediction algorithm for all tissues, the current mode of use in PBPK model building. Because built-in estimation equations for those input parameters are not necessarily available, this Kp prediction tool will provide Kp prediction when only limited input parameters are available. The presented innovative method will improve tissue distribution prediction accuracy, thus enhancing the confidence in PBPK modeling outputs.

Tissue distribution kinetics of 2,2',4,4',5,5'-hexachlorobiphenyl in ringdoves after oral dosing

Ring doves were provided contaminated food spiked with [(13)C]-2,2',4,4',5,5'-hexachlorobiphenyl (PCB 153) over a period of 63 days. Animals were sacrificed after 0.33, 0.5, 1, 2, 4, 8, 18, 36 and 63 days following access to contaminated food. At each time point, chemical concentrations in blood, liver, brain, gonad, adipose and remaining whole carcass was determined. Whole body concentrations of PCB 153 increased linearly with time over the experiment indicating that the birds did not reach steady state with their food after 63 days. Tissue/plasma concentration ratios were plotted as a function of time to determine time to inter-tissue steady state for fast and slowly perfused tissues. Liver, brain and gonad achieved steady state concentrations with plasma in less than 3 days, whereas fat and carcass tissues required 9.7 and 11.5 days, respectively. The results indicate that inter-tissue distribution kinetics for PCBs in birds is relatively rapid and completed within a little over a week following exposure to a contaminated diet.

Dose selection based on physiologically based pharmacokinetic (PBPK) approaches

Physiologically based pharmacokinetic (PBPK) models are built using differential equations to describe the physiology/anatomy of different biological systems. Readily available in vitro and in vivo preclinical data can be incorporated into these models to not only estimate pharmacokinetic (PK) parameters and plasma concentration-time profiles, but also to gain mechanistic insight into compound properties. They provide a mechanistic framework to understand and extrapolate PK and dose across in vitro and in vivo systems and across different species, populations and disease states. Using small molecule and large molecule examples from the literature and our own company, we have shown how PBPK techniques can be utilised for human PK and dose prediction. Such approaches have the potential to increase efficiency, reduce the need for animal studies, replace clinical trials and increase PK understanding. Given the mechanistic nature of these models, the future use of PBPK modelling in drug discovery and development is promising, however some limitations need to be addressed to realise its application and utility more broadly.