Physiologically based pharmacokinetic (PBPK) modeling of pitavastatin in relation to SLCO1B1 genetic polymorphism

Pitavastatin, a potent 3-hydroxymethylglutaryl coenzyme A reductase inhibitor, is indicated for the treatment of hypercholesterolemia and mixed dyslipidemia. Hepatic uptake of pitavastatin is predominantly occupied by the organic anion transporting polypeptide 1B1 (OATP1B1) and solute carrier organic anion transporter family member 1B1 (SLCO1B1) gene, which is a polymorphic gene that encodes OATP1B1. SLCO1B1 genetic polymorphism significantly alters the pharmacokinetics of pitavastatin. This study aimed to establish the physiologically based pharmacokinetic (PBPK) model to predict pitavastatin pharmacokinetics according to SLCO1B1 genetic polymorphism. PK-Sim® version 10.0 was used to establish the whole-body PBPK model of pitavastatin. Our pharmacogenomic data and a total of 27 clinical pharmacokinetic data with different dose administration and demographic properties were used to develop and validate the model, respectively. Physicochemical properties and disposition characteristics of pitavastatin were acquired from previously reported data or optimized to capture the plasma concentration-time profiles in different SLCO1B1 diplotypes. Model evaluation was performed by comparing the predicted pharmacokinetic parameters and profiles to the observed data. Predicted plasma concentration-time profiles were visually similar to the observed profiles in the non-genotyped populations and different SLCO1B1 diplotypes. All fold error values for AUC and Cmax were included in the two fold range of observed values. Thus, the PBPK model of pitavastatin in different SLCO1B1 diplotypes was properly established. The present study can be useful to individualize the dose administration strategy of pitavastatin in individuals with various ages, races, and SLCO1B1 diplotypes.

PBPK modeling to predict the pharmacokinetics of pantoprazole in different CYP2C19 genotypes

Pantoprazole is used to treat gastroesophageal reflux disease (GERD), maintain healing of erosive esophagitis (EE), and control symptoms related to Zollinger-Ellison syndrome (ZES). Pantoprazole is mainly metabolized by cytochrome P450 (CYP) 2C19, converting to 4'-demethyl pantoprazole. CYP2C19 is a genetically polymorphic enzyme, and the genetic polymorphism affects the pharmacokinetics and/or pharmacodynamics of pantoprazole. In this study, we aimed to establish the physiologically based pharmacokinetic (PBPK) model to predict the pharmacokinetics of pantoprazole in populations with various CYP2C19 metabolic activities. A comprehensive investigation of previous reports and drug databases was conducted to collect the clinical pharmacogenomic data, physicochemical data, and disposition properties of pantoprazole, and the collected data were used for model establishment. The model was evaluated by comparing the predicted plasma concentration-time profiles and/or pharmacokinetic parameters (AUC and Cmax) with the clinical observation results. The predicted plasma concentration-time profiles in different CYP2C19 phenotypes properly captured the observed profiles. All fold error values for AUC and Cmax were included in the two-fold range. Consequently, the minimal PBPK model for pantoprazole related to CYP2C19 genetic polymorphism was properly established and it can predict the pharmacokinetics of pantoprazole in different CYP2C19 phenotypes. The present model can broaden the insight into the individualized pharmacotherapy for pantoprazole.

Physiologically based pharmacokinetic (PBPK) modeling to predict the pharmacokinetics of irbesartan in different CYP2C9 genotypes

Irbesartan, a potent and selective angiotensin II type-1 (AT1) receptor blocker (ARB), is one of the representative medications for the treatment of hypertension. Cytochrome P450 (CYP) 2C9 is primarily involved in the oxidation of irbesartan. CYP2C9 is highly polymorphic, and genetic polymorphism of this enzyme is the leading cause of significant alterations in the pharmacokinetics of irbesartan. This study aimed to establish the physiologically based pharmacokinetic (PBPK) model to predict the pharmacokinetics of irbesartan in different CYP2C9 genotypes. The irbesartan PBPK model was established using the PK-Sim® software. Our previously reported pharmacogenomic data for irbesartan was leveraged in the development of the PBPK model and collected clinical pharmacokinetic data for irbesartan was used for the validation of the model. Physicochemical and ADME properties of irbesartan were obtained from previously reported data, predicted by the modeling software, or optimized to fit the observed plasma concentration-time profiles. Model evaluation was performed by comparing the predicted plasma concentration-time profiles and pharmacokinetic parameters to the observed results. Predicted plasma concentration-time profiles were visually similar to observed profiles. Predicted AUCinf in CYP2C9*1/*3 and CYP2C9*1/*13 genotypes were increased by 1.54- and 1.62-fold compared to CYP2C9*1/*1 genotype, respectively. All fold error values for AUC and Cmax in non-genotyped and CYP2C9 genotyped models were within the two-fold error criterion. We properly established the PBPK model of irbesartan in different CYP2C9 genotypes. It can be used to predict the pharmacokinetics of irbesartan for personalized pharmacotherapy in individuals of various races, ages, and CYP2C9 genotypes.

Development of a risk assessment method for the detailed consideration of the effects of liquid toxic substance leakage incidents on the human body: ethanol as a model substance

To ensure safety in chemical plants handling a wide variety of liquid and gaseous hazardous substances, it is necessary to carry out highly accurate risk assessments and take appropriate measures. In this study, a risk assessment method was developed for the problem of the leakage of liquid hazardous substances. The risk assessment of toxic liquid leaks must consider the exposure of workers to the liquid and toxic gases produced by vaporization. The absorption and subsequent metabolism of hazardous substances in the body via multiple pathways after exposure to liquids and gases was calculated using a pharmacokinetic model. Estimation of exposure concentrations of toxic gases volatilized from leaked liquids was reproduced by computational fluid dynamics simulation. In this study, ethanol was selected as the hazardous substance and the risk of hazardous liquid leakage was assessed. The results of the analysis, which considered liquid and gas exposure under the conditions of the assumed scenario, showed that the maximum blood concentration of ethanol was 1640 µmol/L, which is sufficiently low compared to the concentration of 10,900 µmol/L at which acute toxic effects become apparent. These results suggest that work can be carried out safely under the conditions of the assumed scenario. The risk assessment methodology for liquid spills in this study confirms that risk assessment is possible under multiple scenarios, including individual differences, activity conditions, and the use of protective equipment.

Simulation-based risk assessment for the leakage of toxic substances in a chemical plant and the effects on the human body: ethanol as a working model

Chemical plants must handle a wide variety of hazardous substances. To ensure safety in such plants, it is necessary to conduct extensive and highly accurate risk assessments. In this study, we aimed at developing a method that enables flexible and accurate risk assessment. We combined two different simulation tools to reproduce the phenomena of toxic gas leakage and diffusion as well as its impact on human health. The atmospheric diffusion after the leakage of toxic gas was simulated by computational fluid dynamics (CFD). Assuming the movement line of the person, toxic gas absorption and subsequent metabolism were calculated by a physiologically based pharmacokinetic (PBPK) model. From this, changes in blood concentration of toxic substances with time were simulated and we evaluated the effects of toxic gases on human body. Ethanol was selected as a toxic gas in this study. Based on the assumed scenario, the diffusion of leaked ethanol gas was calculated by CFD leading to the confirmation that the concentration of ethanol gas varies significantly with wind speed, human position, and elapsed time. The PBPK model showed that the maximum blood concentration of ethanol was 161 µmol/L, which is sufficiently low compared to that of ethanol poisoning (i.e., 10,900 µmol/L). These results suggest that the effects on the human body are relatively low and the evacuation can be performed safely. Compared to conventional methods of risk assessment, our new method allows the risk assessment of multiple scenarios, namely interindividual differences, activity status and the used of protective equipment.

Physiologically based pharmacokinetic (PBPK) modeling of flurbiprofen in different CYP2C9 genotypes

The aim of this study was to establish the physiologically based pharmacokinetic (PBPK) model of flurbiprofen related to CYP2C9 genetic polymorphism and describe the pharmacokinetics of flurbiprofen in different CYP2C9 genotypes. PK-Sim® software was used for the model development and validation. A total of 16 clinical pharmacokinetic data for flurbiprofen in different CYP2C9 genotypes, dose regimens, and age groups were used for the PBPK modeling. Turnover number (k_cat) of CYP2C9 values were optimized to capture the observed profiles in different CYP2C9 genotypes. In the simulation, predicted fraction metabolized by CYP2C9, fraction excreted to urine, bioavailability, and volume of distribution were similar to previously reported values. Predicted plasma concentration-time profiles in different CYP2C9 genotypes were visually similar to the observed profiles. Predicted AUC_inf in CYP2C9*1/*2, CYP2C9*1/*3, and CYP2C9*3/*3 genotypes were 1.44-, 2.05-, and 3.67-fold higher than the CYP2C9*1/*1 genotype. The ranges of fold errors for AUC_inf, C_max, and t_1/2 were 0.84-1.00, 0.61-1.22, and 0.74-0.94 in development and 0.59-0.98, 0.52-0.97, and 0.61-1.52 in validation, respectively, which were within the acceptance criterion. Thus, the PBPK model was successfully established and described the pharmacokinetics of flurbiprofen in different CYP2C9 genotypes, dose regimens, and age groups. The present model could guide the decision-making of tailored drug administration strategy by predicting the pharmacokinetics of flurbiprofen in various clinical scenarios.

Intestinal uptake and low transformation increase the bioaccumulation of inorganic arsenic in freshwater zebrafish

Arsenate [As(V)] is the main form of arsenic (As) present in freshwater taken up by freshwater fish. Data on the main uptake tissue, biotransformation, and bioaccumulation in freshwater fish exposed to As(V) were limited, and the reasons for its bioaccumulation in the muscle tissue of freshwater fish remain undetermined. Accordingly, we simulated bioaccumulation and depuration in zebrafish (Danio rerio) exposed to waterborne As(V) by employing a six-compartment physiologically based pharmacokinetic model and As speciation analysis. Modeling and biotransformation suggested that intestines were the main uptake site for waterborne As(V), instead of the gills. This novel finding was evidenced by the higher As transfer constant from water to intestines (k₀₃ = 1.52 × 10^-4 L d^-1) compared to gills (k₀₂ = 5.28 × 10^-5 L d^-1). The low concentration and percentage of arsenobetaine (AsB) in the intestines suggested a weak ability to synthesize AsB. Our results showed a substantial proportion of inorganic As in intestines and a relatively substantial percentage in muscle tissue. Therefore, high As(V) uptake in the intestines and lack of biotransformation contributed to high bioaccumulation of inorganic As in freshwater fish. Inorganic As posed concerns due to the human health risks associated with consuming As(V)-contaminated fish and should be addressed.

Human risk assessment of 4-n-nonylphenol (4-n-NP) using physiologically based pharmacokinetic (PBPK) modeling: analysis of gender exposure differences and application to exposure analysis related to large exposure variability in population

As a toxic substance, 4-n-nonylphenol (4-n-NP) or 4-nonylphenol (4-NP) is widely present in the environment. 4-n-NP is a single substance with a linear-alkyl side chain, but 4-NP usually refers to a random mixture containing various branched types. Unfortunately, human risk assessment and/or exposure level analysis for 4-n-NP (or 4-NP) were almost nonexistent, and related research was urgently needed. This study aimed to analyze the various exposures of 4-n-NP (or 4-NP) through development of a physiologically based-pharmacokinetic (PBPK) model considering gender difference in pharmacokinetics of 4-n-NP and its application to human risk assessment studies. A PBPK model was newly developed considering gender differences in 4-n-NP pharmacokinetics and applied to a human risk assessment for each gender. Exposure analysis was performed using a PBPK model that considered gender differences in 4-n-NP (or 4-NP) exposure and high variabilities in several countries. Furthermore, an extended application was attempted as a human risk assessment for random mixture 4-NP, which is difficult to accurately evaluate in reality. External-exposure and margin-of-safety estimated with the same internal exposure amount differed between genders, meaning the need for a differentiated risk assessment considering gender. Exposure analysis based on biomonitoring data confirmed large variability in exposure to 4-n-NP (or 4-NP) by country, group, and period. External-exposures estimated using PBPK model varied widely, ranging from 0.039 to 63.875 mg/kg/day (for 4-n-NP or 4-NP). By country, 4-n-NP (or 4-NP) exposure was higher in females than in males and the margin-of-safety tended to be low. Overall, exposure to 4-n-NP (or 4-NP) in populations was largely not safe, suggesting need for ongoing management and monitoring. Considering low in vivo accumulation confirmed by PBPK model, risk reduction of 4-n-NP is possible by reducing its use.

Physiologically based pharmacokinetic (PBPK) modeling of piroxicam with regard to CYP2C9 genetic polymorphism

Piroxicam is a non-steroidal anti-inflammatory drug used to alleviate symptoms of osteoarthritis and rheumatoid arthritis. CYP2C9 genetic polymorphism significantly influences the pharmacokinetics of piroxicam. The objective of this study was to develop and validate the piroxicam physiologically based pharmacokinetic (PBPK) model related to CYP2C9 genetic polymorphism. PK-Sim^® version 10.0 was used for the PBPK modeling. The PBPK model was evaluated by predicted and observed plasma concentration-time profiles, fold errors of predicted to observed pharmacokinetic parameters, and a goodness-of-fit plot. The turnover number (k_cat) of CYP2C9 was adjusted to capture the pharmacokinetics of piroxicam in different CYP2C9 genotypes. The population PBPK model overall accurately described and predicted the plasma concentration-time profiles in different CYP2C9 genotypes. In our simulations, predicted AUC_inf in CYP2C9*1/*2, CYP2C9*1/*3, and CYP2C9*3/*3 genotypes were 1.83-, 2.07-, and 6.43-fold higher than CYP2C9*1/*1 genotype, respectively. All fold error values for AUC, C_max, and t_1/2 were included in the acceptance criterion with the ranges of 0.57-1.59, 0.63-1.39, and 0.65-1.51, respectively. The range of fold error values for predicted versus observed plasma concentrations was 0.11-3.13. 93.9% of fold error values were within the two-fold range. Average fold error, absolute average fold error, and root mean square error were 0.93, 1.27, and 0.72, respectively. Our model accurately captured the pharmacokinetic alterations of piroxicam according to CYP2C9 genetic polymorphism.

Physiologically based pharmacokinetic (PBPK) modeling of perfluorohexane sulfonate (PFHxS) in humans

Per- and polyfluoroalkyl substances (PFAS) are persistent, man-made compounds prevalent in the environment and consistently identified in human biomonitoring samples. In particular, perfluorooctane sulfonic acid (PFOS), perfluorooctanoic acid (PFOA), and perfluorohexane sulfonic acid (PFHxS) have been identified at U.S. Air Force installations. The study of human toxicokinetics and physiologically based pharmacokinetic (PBPK) modeling of PFHxS has been less robust and has been limited in scope and application as compared to PFOS and PFOA. The primary goal of the current effort was to develop a PBPK model describing PFHxS disposition in humans that can be applied to retrospective, current, and future human health risk assessment of PFHxS. An existing model developed for PFOS and PFOA was modified and key parameter values for exposure and toxicokinetics were calibrated for PFHxS prediction based on human biomonitoring data, particularly general population serum levels from the U.S. Centers for Disease Prevention and Control (CDC) National Health and Nutrition Examination Survey (NHANES). Agreement between the model and the calibration and evaluation data was excellent and recapitulated observed trends across sex, age, and calendar years. Confidence in the model is greatest for application to adults in the 2000-2018 time frame and for shorter-term future projections.

Physiologically based pharmacokinetic model revealed the distinct bio-transportation and turnover of arsenobetaine and arsenate in marine fish

Arsenobetaine (AsB) is the major form of arsenic in marine fish; however, its biodynamics within the fish tissues is not well understood. This study simulated the biodynamics and biotransportation (absorption, distribution, and elimination) of dietary AsB and arsenate [As(V)] in the marine grouper Epinephelus fuscoguttatus, by constructing a physiologically based pharmacokinetic (PBPK) model. The transfer rates between different compartments (gill, intestine, liver, heart, kidney, and muscle) and blood were modeled during exposure (14 d) and depuration (20 d). The model showed that AsB had a weak ability to cross the intestinal membranes and circulated slowly in the blood. The newly AsB absorbed from the blood did not enter the hepatointestinal circulation for elimination, but was effectively distributed in liver. Thereafter, it was slowly absorbed and finally stored in the muscle, the most important organ for AsB deposition, at a constant rate of 63.5 d^-1. In contrast, As(V) displayed a dynamic behavior, including rapid crossing through the intestinal membranes, quick circulation in the blood and transportation to other tissues, and elimination. Biodynamics coupled with biotransformation illustrated, for the first time, the unique strategies of dietary AsB that passed slowly through the fish intestine with the highest deposition rate in the muscle, thereby contributing to the high AsB bioaccumulation in the muscle tissue of marine fish. CAPSULE: AsB displayed a weaker ability to cross the intestine membranes, slowly absorbed and finally stored in muscle, whereas As(V) displayed rapid crossing the intestine membranes, quick transportation, and elimination.

Two-pore physiologically based pharmacokinetic model validation using whole-body biodistribution of trastuzumab and different-size fragments in mice

In the past, our lab proposed a two-pore PBPK model for different-size protein therapeutics using de novo derived parameters and the model was validated using plasma PK data of different-size antibody fragments digitized from the literature (Li Z, Shah DK, J Pharmacokinet Pharmacodynam 46(3):305-318, 2009). To further validate the model using tissue distribution data, whole-body biodistribution study of 6 different-size proteins in mice were conducted. Studied molecules covered a wide MW range (13-150 kDa). Plasma PK and tissue distribution profiles is 9 tissues were measured, including heart, lung, liver, spleen, kidney, skin, muscle, small intestine, large intestine. Tumor exposure of different-size proteins were also evaluated. The PBPK model was validated by comparing percentage predictive errors (%PE) between observed and model predicted results for each type of molecule in each tissue. Model validation showed that the two-pore PBPK model was able to predict plasma, tissues and tumor PK of all studied molecules relatively well. This model could serve as a platform for developing a generic PBPK model for protein therapeutics in the future.

Quantitative analysis of an impact of P-glycoprotein on edoxaban's disposition using a human physiologically based pharmacokinetic (PBPK) model

The purpose of this study was to evaluate the impact of P-glycoprotein (P-gp) efflux on edoxaban absorption in gastrointestinal tracts quantitatively by a physiologically based pharmacokinetic (PBPK) model constructed with clinical and non-clinical observations (using GastroPlus™ software). An absorption process was described by the advanced compartmental absorption and transit model with the P-gp function. A human PBPK model was constructed by integrating the clinical and non-clinical observations. The constructed model was demonstrated to reproduce the data observed in the mass-balance study. Thus, elimination pathways can be quantitatively incorporated into the model. A constructed model successfully described the difference in slopes of plasma concentration (Cp)-time curve at around 8 - 24 hr post-dose between intravenous infusion and oral administration. Furthermore, the model without P-gp efflux activity can reproduce the Cp-time profile in the absence of P-gp activity observed from the clinical DDI study results. Since the difference of slopes between intravenous infusion and oral administration also disappeared by the absence of P-gp efflux activity, P-gp must be a key molecule to govern edoxaban's PK behavior. The constructed PBPK model will help us to understand the significant contribution of P-gp in edoxaban's disposition in gastrointestinal tracts quantitatively.

A study to assess the correlation between plasma, oral fluid and urine concentrations of flunixin meglumine with the tissue residue depletion profile in finishing-age swine

Background

Flunixin meglumine (FM) was investigated for the effectiveness of plasma, oral fluid, and urine concentrations to predict tissue residue depletion profiles in finishing-age swine, along with the potential for untreated pigs to acquire tissue residues following commingled housing with FM-treated pigs. Twenty pigs were housed in groups of three treated and one untreated control. Treated pigs received one 2.2 mg/kg dose of FM intramuscularly. Before treatment and at 1, 3, 6, 12, 24, 36, and 48 h (h) after treatment, plasma samples were taken. At 1, 4, 8, 12 and 16 days (d) post-treatment, necropsy and collection of plasma, urine, oral fluid, muscle, liver, kidney, and injection site samples took place. Analysis of flunixin concentrations using liquid chromatography/tandem mass spectrometry was done. A published physiologically based pharmacokinetic (PBPK) model for flunixin in cattle was extrapolated to swine to simulate the measured data.

Results

Plasma concentrations of flunixin were the highest at 1 h post-treatment, ranging from 1534 to 7040 ng/mL, and were less than limit of quantification (LOQ) of 5 ng/mL in all samples on Day 4. Flunixin was detected in the liver and kidney only on Day 1, but was not found 4–16 d post-treatment. Flunixin was either not seen or found less than LOQ in the muscle, with the exception of one sample on Day 16 at a level close to LOQ. Flunixin was found in the urine of untreated pigs after commingled housing with FM-treated pigs. The PBPK model adequately correlated plasma, oral fluid and urine concentrations of flunixin with residue depletion profiles in liver, kidney, and muscle of finishing-age pigs, especially within 24 h after dosing.

Conclusions

Results indicate untreated pigs can be exposed to flunixin by shared housing with FM-treated pigs due to environmental contamination. Plasma and urine samples may serve as less invasive and more easily accessible biological matrices to predict tissue residue statuses of flunixin in pigs at earlier time points (≤24 h) by using a PBPK model.

Risk assessment for humans using physiologically based pharmacokinetic model of diethyl phthalate and its major metabolite, monoethyl phthalate

Diethyl phthalate (DEP) belongs to phthalates with short alkyl chains. It is a substance frequently used to make various products. Thus, humans are widely exposed to DEP from the surrounding environment such as food, soil, air, and water. As previously reported in many studies, DEP is an endocrine disruptor with reproductive toxicity. Monoethyl phthalate (MEP), a major metabolite of DEP in vivo, is a biomarker for DEP exposure assessment. It is also an endocrine disruptor with reproductive toxicity, similar to DEP. However, toxicokinetic studies on both MEP and DEP have not been reported in detail yet. Therefore, the objective of this study was to evaluate and develop physiologically based pharmacokinetic (PBPK) model for both DEP and MEP in rats and extend this to human risk assessment based on human exposure. This study was conducted in vivo after intravenous or oral administration of DEP into female (2 mg/kg dose) and male (0.1-10 mg/kg dose) rats. Biological samples consisted of urine, plasma, and 11 different tissues. These samples were analyzed using UPLC-ESI-MS/MS method. For DEP, the tissue to plasma partition coefficient was the highest in the kidney, followed by that in the liver. For MEP, the tissue to plasma partition coefficient was the highest in the liver. It was less than unity in all other tissues. Plasma, urine, and fecal samples were also obtained after IV administration of MEP (10 mg/kg dose) to male rats. All results were reflected in a model developed in this study, including in vivo conversion from DEP to MEP. Predicted concentrations of DEP and MEP in rat urine, plasma, and tissue samples using the developed PBPK model fitted well with observed values. We then extrapolated the PBPK model in rats to a human PBPK model of DEP and MEP based on human physiological parameters. Reference dose of 0.63 mg/kg/day (or 0.18 mg/kg/day) for DEP and external doses of 0.246 μg/kg/day (pregnant), 0.193 μg/kg/day (fetus), 1.005-1.253 μg/kg/day (adults), 0.356-0.376 μg/kg/day (adolescents), and 0.595-0.603 μg/kg/day (children) for DEP for human risk assessment were estimated using Korean biomonitoring values. Our study provides valuable insight into human health risk assessment regarding DEP exposure.

Global optimization of the Michaelis-Menten parameters using physiologically-based pharmacokinetic (PBPK) modeling and chloroform vapor uptake data in F344 rats

Objective: To quantify metabolism, a physiologically based pharmacokinetic (PBPK) model for a volatile compound can be calibrated with the closed chamber (i.e. vapor uptake) inhalation data. Here, we introduce global optimization as a novel component of the predictive process and use it to illustrate a procedure for metabolic parameter estimation.Materials and methods: Male F344 rats were exposed in vapor uptake chambers to initial concentrations of 100, 500, 1000, and 3000 ppm chloroform. Chamber time-course data from these experiments, in combination with optimization using a chemical-specific PBPK model, were used to estimate Michaelis-Menten metabolic constants. Matlab^® simulation software was used to integrate the mass balance equations and to perform the global optimizations using MEIGO (MEtaheuristics for systems biology and bIoinformatics Global Optimization - Version 64 bit, R2016A), a toolbox written for Matlab®. The cost function used the chamber time-course data and least squares to minimize the difference between data and simulation values.Results and discussion: The final values estimated for V_max (maximum metabolic rate) and K_m (affinity constant) were 1.2 mg/h and a range between 0.0005 and 0.6 mg/L, respectively. Also, cost function plots were used to analyze the dose-dependent capacity to estimate V_max and K_m within the experimental range used. Sensitivity analysis was used to assess identifiability for both parameters and show these kinetic data may not be sufficient to identify K_m.Conclusion: In summary, this work should help toxicologists interested in optimization techniques understand the overall process employed when calibrating metabolic parameters in a PBPK model with inhalation data.

A physiologically based pharmacokinetic model of doxycycline for predicting tissue residues and withdrawal intervals in grass carp (Ctenopharyngodon idella)

The extensive use of doxycycline in aquaculture results in drug residue violations that negatively impact human food safety. This study aimed to develop a physiologically based pharmacokinetic (PBPK) model for doxycycline to predict drug residues and withdrawal times (WTs) in grass carp (Ctenopharyngodon idella) after daily oral administration for 3 days. Physiological parameters including cardiac output and organ weights were measured experimentally. Chemical-specific parameters were obtained from the literature or estimated by fitting to the observed data. The model properly captured the observed kinetic profiles of doxycycline in tissues (i.e., liver, kidney, muscle + skin and gill). The predicted WT in muscle + skin by Monte Carlo analysis based on sensitive parameters identified at 24 h after drug administration was 41 d, which was similar to 43 d calculated using the tolerance limit method. Sensitivity analysis identified two additional sensitive parameters at 6 weeks: intestinal transit rate constant and urinary elimination rate constant. The predicted WT in muscle + skin based on sensitive parameters identified at 6 weeks was 54 d. This model provides a useful tool to estimate tissue residues and withdrawal times for doxycycline in grass carp and also serves a foundation for extrapolation to other fish species and other tetracyclines.

Dynamics of PCB exposure in the past 50 years and recent high concentrations in human breast milk: Analysis of influencing factors using a physiologically based pharmacokinetic model

In this study we reconstruct the long-term exposure of Czech mothers to polychlorinated biphenyls (PCBs) and determine the causes of high contamination of breast milk by indicator PCBs (iPCBs). A data set containing information from more than 1000 primiparous women from the Czech Republic was used, including iPCB concentrations in breast milk, individual physiology and living characteristics. The time series of PCB intakes for the whole period from the beginning of PCB production in 1958 until 2011 were reconstructed. We estimated the individual lifetime exposure of mothers for all iPCBs, i.e. congeners 28, 52, 101, 118, 138, 153 and 180, using a physiologically based pharmacokinetic (PBPK) model. Various model scenarios were investigated to determine the influence of physiology, age at delivery, past dietary exposure, and food composition on concentrations in breast milk for all iPCBs. The highest contributions to the presence of iPCBs in breast milk were observed for food composition. The main factor determining the concentration of higher-chlorinated PCBs (138, 153 and 180) was past exposure. The most important parameter for identification of children's postnatal exposure through breast milk was the time-span from the maximum of the exposure peak to the birth of the child. The current concentrations of iPCBs in breast milk in the Czech population are still high because the maximum of the exposure peak occurred more than 10 years later than in other European countries and was very broad, e.g. covered more than 10 years.

Two-pore physiologically based pharmacokinetic model with de novo derived parameters for predicting plasma PK of different size protein therapeutics

Two-pore PBPK models have been used for characterizing the PK of protein therapeutics since 1990s. However, widespread utilization of these models is hampered by the lack of a priori parameter values, which are typically estimated using the observed data. To overcome this hurdle, here we have presented the development of a two-pore PBPK model using de novo derived parameters. The PBPK model was validated using plasma PK data for different size proteins in mice. Using the "two pore theory" we were able to establish the relationship between protein size and key model parameters, such as: permeability-surface area product (PS), vascular reflection coefficient (σ), peclet number (Pe), and glomerular sieving coefficient (θ). The model accounted for size dependent changes in tissue extravasation and glomerular filtration. The model was able to a priori predict the PK of 8 different proteins: IgG (150 kDa), scFv-Fc (105 kDa), F(ab)₂ (100 kDa, minibody (80 kDa), scFv₂ (55 kDa), Fab (50 kDa), diabody (50 kDa), scFv (27 kDa), and nanobody (13 kDa). In addition, the model was able to provide unprecedented quantitative insight into the relative contribution of convective and diffusive pathway towards trans-capillary mass transportation of different size proteins. The two-pore PBPK model was also able to predict systemic clearance (CL) versus Molecular Weight relationship for different size proteins reasonably well. As such, the PBPK model proposed here represents a bottom-up systems PK model for protein therapeutics, which can serve as a generalized platform for the development of truly translational PBPK model for protein therapeutics.

Incorporation of the glutathione conjugation pathway in an updated physiologically-based pharmacokinetic model for perchloroethylene in mice

BACKGROUND

Perchloroethylene (perc) induced target organ toxicity has been associated with tissue-specific metabolic pathways. Previous physiologically-based pharmacokinetic (PBPK) modeling of perc accurately predicted oxidative metabolites but suggested the need to better characterize glutathione (GSH) conjugation as well as toxicokinetic uncertainty and variability.

OBJECTIVES

We updated the previously published "harmonized" perc PBPK model in mice to better characterize GSH conjugation metabolism as well as the uncertainty and variability of perc toxicokinetics.

METHODS

The updated PBPK model includes expanded models for perc and its oxidative metabolite trichloroacetic acid (TCA), and physiologically-based sub-models for conjugative metabolites. Previously compiled mouse kinetic data in B6C3F1 and Swiss-Webster mice were augmented to include data from a recent study in male C57BL/6J mice that measured perc and metabolites in serum and multiple tissues. Hierarchical Bayesian population analysis using Markov chain Monte Carlo was conducted to characterize uncertainty and inter-strain variability in perc metabolism.

RESULTS

The updated model fit the data as well or better than the previously published "harmonized" PBPK model. Tissue dosimetry for both oxidative and conjugative metabolites was successfully predicted across the three strains of mice, with estimated residuals errors of 2-fold for majority of data. Inter-strain variability across three strains was evident for oxidative metabolism; GSH conjugation data were only available for one strain.

CONCLUSIONS

This updated PBPK model fills a critical data gap in quantitative risk assessment by predicting the internal dosimetry of perc and its oxidative and GSH conjugation metabolites and lays the groundwork for future studies to better characterize toxicokinetic variability.

Prenatal exposure estimation of BPA and DEHP using integrated external and internal dosimetry: A case study

Prenatal exposure to Endocrine disruptors (EDs), such as Bisphenol A (BPA) and di (2-ethylhexyl) phthalate (DEHP), has been associated with obesity and diabetes diseases in childhood, as well as reproductive, behavioral and neurodevelopment problems. The aim of this study was to estimate the prenatal exposure to BPA and DEHP through food consumption for pregnant women living in Tarragona County (Spain). Probabilistic calculations of prenatal exposure were estimated by integrated external and internal dosimetry modelling, physiologically based pharmacokinetic (PBPK) model, using a Monte-Carlo simulation. Physical characteristic data from the cohort, along with food intake information from the questionnaires (concentrations of BPA and DEHP in different food categories and the range of the different food ratios), were used to estimate the value of the total dietary intake for the Tarragona pregnancy cohort. The major contributors to the total dietary intake of BPA were canned fruits and vegetables, followed by canned meat and meat products. In turn, milk and dairy products, followed by ready to eat food (including canned dinners), were the most important contributors to the total dietary intake of DEHP. Despite the dietary variations among the participants, the intakes of both chemicals were considerably lower than their respective current tolerable daily intake (TDI) values established by the European Food Safety Authority (EFSA). Internal dosimetry estimates suggest that the plasma concentrations of free BPA and the most important DEHP metabolite, mono (2-ethylhexyl) phthalate (MEHP), in pregnant women were characterized by transient peaks (associated with meals) and short half-lives (< 2h). In contrast, fetal exposure was characterized by a low and sustained basal BPA and MEHP concentration due to a lack of metabolic activity in the fetus. Therefore, EDs may have a greater effect on developing organs in young children or in the unborn child.

The margin of internal exposure (MOIE) concept for dermal risk assessment based on oral toxicity data - A case study with caffeine

Route-to-route extrapolation is a common part of human risk assessment. Data from oral animal toxicity studies are commonly used to assess the safety of various but specific human dermal exposure scenarios. Using theoretical examples of various user scenarios, it was concluded that delineation of a generally applicable human dermal limit value is not a practicable approach, due to the wide variety of possible human exposure scenarios, including its consequences for internal exposure. This paper uses physiologically based kinetic (PBK) modelling approaches to predict animal as well as human internal exposure dose metrics and for the first time, introduces the concept of Margin of Internal Exposure (MOIE) based on these internal dose metrics. Caffeine was chosen to illustrate this approach. It is a substance that is often found in cosmetics and for which oral repeated dose toxicity data were available. A rat PBK model was constructed in order to convert the oral NOAEL to rat internal exposure dose metrics, i.e. the area under the curve (AUC) and the maximum concentration (C_max), both in plasma. A human oral PBK model was constructed and calibrated using human volunteer data and adapted to accommodate dermal absorption following human dermal exposure. Use of the MOIE approach based on internal dose metrics predictions provides excellent opportunities to investigate the consequences of variations in human dermal exposure scenarios. It can accommodate within-day variation in plasma concentrations and is scientifically more robust than assuming just an exposure in mg/kg bw/day.

Predicting points of departure for risk assessment based on in vitro cytotoxicity data and physiologically based kinetic (PBK) modeling: The case of kidney toxicity induced by aristolochic acid I

Aristolochic acids are naturally occurring nephrotoxins. This study aims to investigate whether physiologically based kinetic (PBK) model-based reverse dosimetry could convert in vitro concentration-response curves of aristolochic acid I (AAI) to in vivo dose response-curves for nephrotoxicity in rat, mouse and human. To achieve this extrapolation, PBK models were developed for AAI in these different species. Subsequently, concentration-response curves obtained from in vitro cytotoxicity models were translated to in vivo dose-response curves using PBK model-based reverse dosimetry. From the predicted in vivo dose-response curves, points of departure (PODs) for risk assessment could be derived. The PBK models elucidated species differences in the kinetics of AAI with the overall catalytic efficiency for metabolic conversion of AAI to aristolochic acid Ia (AAIa) being 2-fold higher for rat and 64-fold higher for mouse than human. Results show that the predicted PODs generally fall within the range of PODs derived from the available in vivo studies. This study provides proof of principle for a new method to predict a POD for in vivo nephrotoxicity by integrating in vitro toxicity testing with in silico PBK model-based reverse dosimetry.