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Plasma and Hepatic Concentrations of Chemicals after Virtual Oral Administrations Extrapolated Using Rat Plasma Data and Simple Physiologically Based Pharmacokinetic Models Yusuke Kamiya, Shohei Otsuka, Tomonori Miura, Hiroka Takaku, Rio Yamada, Mayuko Nakazato, Hitomi Nakamura, Sawa Mizuno, Fumiaki Shono, Kimito Funatsu, and Hiroshi Yamazaki Chem. Res. Toxicol., Just Accepted Manuscript • DOI: 10.1021/acs.chemrestox.8b00307 • Publication Date (Web): 04 Dec 2018 Downloaded from http://pubs.acs.org on December 5, 2018

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Chemical Research in Toxicology

Plasma and Hepatic Concentrations of Chemicals after Virtual Oral Administrations Extrapolated Using Rat Plasma Data and Simple Physiologically Based Pharmacokinetic Models

Yusuke Kamiya,†,§ Shohei Otsuka,†,§ Tomonori Miura,† Hiroka Takaku,† Rio Yamada,† Mayuko Nakazato,† Hitomi Nakamura,† Sawa Mizuno,† Fumiaki Shono,‡ Kimito Funatsu,‡ and Hiroshi Yamazaki*,‡

†Laboratory

of Drug Metabolism and Pharmacokinetics, Showa Pharmaceutical University, 3-

3165 Higashi-tamagawa Gakuen, Machida, Tokyo 194-8543, Japan ‡Department

of Chemical System Engineering, School of Engineering, The University of

Tokyo, Bunkyo-ku, Tokyo 113-8656, Japan

§

These authors contributed equally.

*Corresponding

author:

Hiroshi Yamazaki, Ph.D. Laboratory of Drug Metabolism and Pharmacokinetics, Showa Pharmaceutical University, 3-3165 Higashi-tamagawa Gakuen, Machida, Tokyo 194-8543, Japan. Phone: +81-42721-1406. Fax: +81-42-721-1406. E-mail address: [email protected]

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Chemical Research in Toxicology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Table of Contents Graphic


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Abstract: Only a small fraction of chemicals possesses adequate in vivo toxicokinetic data for assessing potential hazards. The aim of the present study was to model the plasma and hepatic pharmacokinetics of more than 50 disparate types of chemicals and drugs after virtual oral administrations in rats. The models were based on reported pharmacokinetics determined after oral administration to rats. An inverse relationship was observed between no-observed-effect levels after oral administration and chemical absorbance rates evaluated for cell permeability (r = –0.98, p < 0.001, n = 17). For a varied selection of more than 30 chemicals, the plasma concentration curves and the maximum concentrations obtained using a simple onecompartment model (recently recommended as a high-throughput toxicokinetic model) and a simple physiologically based pharmacokinetic (PBPK) model (consisting of chemical receptor, metabolizing, and central compartments) were highly consistent. The hepatic and plasma concentrations and the hepatic and plasma areas under the concentration–time curves of more than 50 chemicals were roughly correlated; however, differences were evident between the PBPK-modeled values and empirically obtained values. Of the compounds selected for analysis, only seven had lowest observed effect level (LOEL) values for hepatoxicity listed in the Hazard Evaluation Support System Integrated Platform in Japan. For these seven compounds, the LOEL values and the areas under the hepatic concentration–time curves estimated using PBPK modeling were inversely correlated (r = –0.78, p < 0.05, n = 7). This study provides important information to help simulate the high hepatic levels of potent hepatotoxic compounds. Using suitable PBPK parameters, the present models could estimate the plasma/hepatic concentrations of chemicals and drugs after oral doses using both PBPK forward and reverse dosimetry, thereby indicating the potential value of this modeling approach in predicting hepatic toxicity as a part of risk assessments of chemicals absorbed in the human body.



Keywords: liver; PBPK; Cmax; AUC; modeling.

Introduction Current methods for estimating the health risks of general chemicals require animal testing studies that adhere to toxicity testing guidelines. Although big databases in chemical toxicity research have been setup, consequently, only a small fraction of chemicals possesses adequate in vivo data for assessing potential hazards.1 There are thousands of human-made chemicals in the environment that currently have limited toxicokinetic data. It is widely appreciated that drugs and their metabolites can cause adverse effects. The severity of such adverse effects is subject to intra- and interspecies differences mediated by the metabolic activities of enzymes in livers. These facts highlight the urgent need to develop more efficient and informative tools to determine hepatotoxicity. It is generally accepted that in vitro high-throughput toxicity screening assays combined with computational models may be able provide a suitable alternative to traditional animal testing studies.1 Scientists from US regulatory authorities have recommended the incorporation of dosimetry and exposure data supported by full physiologically based pharmacokinetic (PBPK) modeling to the tools available for interpreting in vitro toxicity screening data.2,3 It is important that these considerations inform the determination of chemical testing priorities. Furthermore, in vitro to in vivo extrapolation of high-throughput toxicokinetic screening data to predict toxicokinetics from rapid in vitro measurements and chemical structure-based properties has been recently recommended.4 Against this background, we developed a simplified PBPK modeling system that uses a combination of algorithms along with empirical in vitro and in vivo data and literature resources.5,6 Because of its simplicity and utility, this model could also be employed 4 ACS Paragon Plus Environment

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by industry researchers and regulatory authorities in risk assessment to replace complex multicompartment models that are only rarely used. The aim of the present study was to model the plasma and hepatic pharmacokinetics of more than 50 disparate types of chemicals and drugs after virtual oral administrations in rats. The models were based on reported rat plasma values and experimental pharmacokinetics determined after oral administration to rats. The current study employed two different models: one was a simple one-compartment model recently recommended by US authorities as a highthroughput toxicokinetic model, and the other was a simple PBPK model consisting of a chemical receptor compartment, a metabolizing compartment, and a central compartment. Although the number of compounds was limited in the present study, the lowest observed effect levels for hepatoxicity from the Hazard Evaluation Support System Integrated Platform (HESS) in Japan and the areas under the hepatic concentration–time curves estimated using our PBPK model were inversely correlated. Overlapping of chemicals with adequate in vivo data for assessing potential hazards1 and with the lowest observed effect levels for hepatoxicity was limited at this moment. We report herein that, with a view to predicting hepatic toxicity as a part of chemical risk assessment, the present models could estimate the plasma/hepatic concentrations of chemicals and drugs after oral doses using both forward and reverse dosimetry.

Experimental Procedures Chemical space

The sources of chemicals shown in Table 1 were Sigma-Aldrich (St. Louis, MO) or Fujifilm Wako Pure Chemical (Osaka, Japan). To ensure the diversity of chemical structures in the 5 ACS Paragon Plus Environment


original chemical space, the chemical structures described by 196 chemical descriptors were calculated using the open source chemoinformatics tool RDKit for approximately 50,000 randomly obtained molecules. The resulting chemical space was then projected onto a twodimensional plane for visualization using generative topographic mapping methods.7 The compounds selected for analysis in the current study represent a broad diversity of structures and are plotted in the two-dimensional plane shown in Figure 2. Permeability study The general procedures employed to prepare in vitro human intestinal caco-2 monolayers were described previously.8 Briefly, caco-2 cells obtained from (American Type Culture collection, Manassas, VA) were cultured on 12-well plates (1.0 × 105 cells/cm2) in Dulbecco's Modified Eagle Medium supplemented with 10% (v/v) fetal calf serum, non-essential amino acids solution, and penicillin–streptomycin–amphotericin B suspension (Fujifilm Wako Pure Chemical) for 3 weeks. Inserts were placed on the cell cultures and co-cultured in supplemented Dulbecco's Modified Eagle Medium. The cells formed a confluent monolayer that exhibits the same characteristics as the intestinal barrier. The permeability coefficients (Papp, nm/sec) were calculated for time-dependent in vitro absorption from the apical to basal sides of the caco-2 monolayer:

Papp 

dXp 1  dt A  C 0

where Xp is the amount of compound permeated across caco-2 cell monolayer (nmol)

determined by high-performance liquid chromatography, A is the surface area of the insert membrane (cm2), and C0 is the initial concentration of each compound (nmol/mL)


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PBPK study The current study employed both a simple one-compartment model (Figure 1A), recently recommended by US authorities as a high-throughput toxicokinetic model,4 and a simple physiologically based pharmacokinetic (PBPK) model (Figure 1B) consisting of a chemical receptor compartment, a metabolizing compartment, and a central compartment.5,6,9 Pharmacokinetic parameters were derived from the respective plasma concentrations versus time curves using non-compartmental analysis in Phoenix™ WinNonlin® 6.4 (Pharsight, Mountain View, CA). The area under the concentration–time curve from the time of virtual oral dosing to infinity with extrapolation of the terminal phase (AUC), the bioavailability (F), absorption constant (k1), the steady state volume of distribution (V/F), and the clearance (CL/F) were calculated using WinNonlin. For use in our simplified PBPK models, the values for the plasma unbound fraction (fu,p), the octanol–water partition coefficient, the liver-to-plasma concentration ratio (Kp,h), the bloodto-plasma concentration ratio (Rb) of test chemicals were estimated using in silico tools.5,6,9 The value of 0.853 L/h was used for the hepatic blood flow rate in rats (Qh). Values of the absorption rate constant (ka), the volume of the systemic circulation (V1), and the hepatic intrinsic clearance (CLh,int) were calculated to fit reported plasma substrate concentrations by simplex and modified Marquardt methods following previously established methods.5,6,9 Initiating values of hepatic clearance (CLh) and renal clearance (CLr) for PBPK modeling were derived the elimination constants in compartment modeling. Finally, in a similar way to that previously carried out,5,6,9 sets of differential equations were established for each test compound and solved to determine the concentrations in each compartment shown in Figure 1B:



dXg t   ka  Xg t  , when t  0, Xg  0   dose dt Vh

dCh Qh  Ch  Rb Ch  Qh  Cb   ka  Xg  CLh , int  fu , p dt Kp , h Kp , h

V1

dCb Qh  Ch  Rb  Qh  Cb   CLr  Cb dt Kp , h

where Xg, Vh, Ch, and Cb are the amount of compound in the gut, the volume of liver, the hepatic substrate concentration, and the blood substrate concentration, respectively. With regard to evaluation of the predictive ability of the current PBPK models, within 3-fold errors of outputs of Cmax and AUC by the PBPK models to the matched values calculated by empirical compartment models were used as criteria after virtual oral administrations of 1.0 mg doses/kg.

Statistical analysis Linear regression analyses were performed with the program Prism (GraphPad Software, San Diego, CA, USA) to investigate the relationship between drug concentrations/parameters and toxicological properties [the no-observed-effect level (NOEL) or the lowest observed effect level (LOEL) for hepatoxicity] obtained from the Hazard Evaluation Support System Integrated Platform (HESS) in Japan10 and listed in Table 1.

Results Seventeen chemicals selected because they had NOEL values were evaluated for caco-2 permeability (Table 1) to understand the relationship between NOEL dose levels and chemical absorbance rates after oral administration (Figure 3). Reported NOEL values were significantly inversely correlated with absorption rate (arbitrary units) of the 17 chemicals (Table 1) evaluated for caco-2 permeability (r = –0.98, p < 0.001, n = 17; Figure 3A). If both two 8 ACS Paragon Plus Environment

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compounds implying moderate absorption in Figure 3A would be omitted from the analysis, a correlation coefficient was apparently decreased to be 0.48, with a p value of 0.067. The absorption

constant values for 2-mercaptobenzimidazole, m-cresol, and bisphenol A calculated for PBPK modeling were apparently correlated (Figure 3B) with the permeability, although this correlation was not significant. In the present study, the plasma and hepatic pharmacokinetics after virtual oral administrations in rats of 53 chemicals and drugs of disparate types were estimated using PBPK modeling. The parameters (shown in Tables 2 and 3) that define the 53 PBPK models were based on reported rat plasma values and experimental pharmacokinetics determined after oral administration to rats. Based on pharmacokinetic data from the literature, the parameter values for the one-compartment models (k1, V/F, and CL/F) and the PBPK models (ka, V1, and CLh,int) were determined by curve fitting, as described in the Methods. The parameters for the onecompartment models for a varied selection of 34 chemicals are given in Table 2. The varied selection of 34 chemicals that underwent one-compartment modeling also underwent PBPK modeling (these 34 chemicals are included in the 53 chemicals mentioned above). The plasma concentrations after virtual oral administration of 1.0 mg/kg doses of the 34 chemicals were estimated using both one-compartment and PBPK models. The maximum plasma concentrations (Cmax, Figure 4A) and the areas under the concentration–time curves (AUC, Figure 4B) of the 34 disparate chemicals after virtual oral administration (1.0 mg/kg) to rats obtained using high-throughput one-compartment toxicokinetic models (Figure 1A) and those obtained using our simple PBPK models (Figure 1B) were highly consistent (r = 0.98 and 0.99, respectively). In contrast, for the combined 53 compounds shown in Tables 2 and 3, the maximum hepatic and plasma concentrations (data not shown in Figure 4) and AUC values (r = 0.62, Figure 4C) 9 ACS Paragon Plus Environment


were roughly correlated. However, the PBPK-modeled and empirically obtained values differed considerably. Finally, an inverse correlation was observed under the present conditions between the seven compounds with reported LOEL values for hepatotoxicity and PBPKmodeled liver AUC values (r = –0.78, p < 0.05, n = 7, Figure 5).

Discussion The current study employed the simple one-compartment model recently recommended by US authorities as a high-throughput toxicokinetic model4 and our simple (PBPK) model5,6,9 consisting of a chemical receptor compartment, a metabolizing compartment, and a central compartment (Figure 1). For a varied selection of 34 chemicals, the plasma AUC and maximum concentrations obtained using high-throughput toxicokinetic models and our simple PBPK models were quite consistent (Figure 4A, B). However, the maximum hepatic and plasma concentrations and the hepatic and plasma AUC values of 53 disparate chemicals obtained using the PBPK model were different (Figure 4C). Although the number of compounds (n = 7) for which represented a broad diversity of structures in the present chemical space and LOEL values for hepatoxicity were available [from the Hazard Evaluation Support System Integrated Platform (HESS)10] was limited, the LOEL values and the AUC values estimated using PBPK modeling were inversely correlated (Figure 5). Based on the present results, parameter estimation for PBPK modeling of new chemicals should be undertaken in future research projects. To establish a PBPK modeling-based hepatotoxicity prediction system, it would be worthwhile to set up the prediction equations for key parameters used in this simple PBPK modeling approach (Figure 1B). For example, using physiological parameters, we succeeded in estimating suitable albumin concentrations for 10 ACS Paragon Plus Environment

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enhanced drug oxidation activities mediated by liver microsomal cytochrome P450 enzymes.11 When more precise absorption constants of targeted compounds for one-compartment or PBPK models should be needed, it would be worth to measure permeability values in vitro which could be converted to absorption parameters in vivo. To establish the validity of estimated parameters required for

PBPK modeling, the variety of informative compounds in the chemical space should be as wide as possible, as shown in Figure 2. In conclusion, we found an inverse relationship between NOEL levels and chemical absorbance rates after oral administration and an inverse relationship between LOEL levels and PBPK-modeled hepatic AUC values. Furthermore, the maximum plasma concentrations and plasma AUCs estimated using the one-compartment model and our PBPK model were highly consistent. This study provides important information to help simulate, in addition to plasma levels, the high hepatic levels of potent hepatotoxic compounds. Although metabolic activation of protoxicants by livers could not be ruled out for hepatoxicity, estimated accumulation of intake compounds orally in livers by PBPK modeling should be one of determinant factors for potential of chemicals for hepatotoxicity. The present PBPK models with suitable parameters could estimate

the relationships between plasma and hepatic concentrations of chemicals and drugs after virtual oral doses using both forward and reverse dosimetry, thereby indicating their potential value in predicting hepatic toxicity as a part of risk assessments of chemicals absorbed in the human body.

ORCID Kimito Funatsu: 0000-0002-9368-0302 Hiroshi Yamazaki: 0000-0002-1068-4261 11 ACS Paragon Plus Environment


Funding This work was supported in part by the METI Artificial Intelligence–based Substance Hazard Integrated Prediction System Project, Japan. Notes The authors report no conflicts of interest. Acknowledgments We are grateful to Makiko Shimizu, Norie Murayama, Miyu Iwasaki, Yui Kobayashi, Ayane Nakano, Ushio Ohnishi, Tatsurou Sasaki, and Manae Yoshizawa for their assistance. We also thank David Smallbones for his advice on English language usage.

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the pharmacokinetics established in in vivo experiments with rats and chimeric mice with humanized liver and physiologically based pharmacokinetic modeling. Regul. Toxicol. Pharmacol. 65, 316–324. (28) Ma, Y. R., Rao, Z., Shi, A. X., Wang, Y. F., Huang, J., Han, M., Wang, X. D., Jin, Y. W., Zhang, G. Q., Zhou, Y., Zhang, F., Qin, H. Y., and Wu, X. A. (2016) Simultaneous determination of metformin, metoprolol and its metabolites in rat plasma by LC-MS-MS: Application to pharmacokinetic interaction study. J. Chromatogr. Sci. 54, 1–9. (29) Sakemi, K., Ito, R., Umemura, T., Ohno, Y., and Tsuda, M. (2002) Comparative toxicokinetic/toxicodynamic study of rubber antioxidants, 2-mercaptobenzimidazole and its methyl substituted derivatives, by repeated oral administration in rats. Arch. Toxicol. 76, 682–691. (30) Teirlynck, O. A., and Belpaire, F. (1985) Disposition of orally administered di-(2-ethylhexyl) phthalate and mono-(2-ethylhexyl) phthalate in the rat. Arch. Toxicol. 57, 226–230. (31) Doerge, D. R., Twaddle, N. C., Churchwell, M. I., Chang, H. C., Newbold, R. R., and Delclos, K. B. (2002) Mass spectrometric determination of p-nonylphenol metabolism and disposition following oral administration to Sprague-Dawley rats. Reprod. Toxicol. 16, 45–56. (32) Ogihara, T., Kano, T., Wagatsuma, T., Wada, S., Yabuuchi, H., Enomoto, S., Morimoto, K., Shirasaka, Y., Kobayashi, S., and Tamai, I. (2009) Oseltamivir (tamiflu) is a substrate of peptide transporter 1. Drug Metab. Dispos. 37, 1676–1681. (33) Iqbal, M., Ezzeldin, E., Al-Rashood, K. A., and Shakeel, F. (2015) A validated UPLC-MS/MS assay using negative ionization mode for high-throughput determination of pomalidomide in rat plasma. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 983–984, 76–82. (34) Withey, J. R. (1976) Quantitative analysis of styrene monomer in polystyrene and foods including some preliminary studies of the uptake and pharmacodynamics of the monomer in rats. Environ. Health Persp. 17, 125–133. (35) Schauer, U. M., Volkel, W., and Dekant, W. (2006) Toxicokinetics of tetrabromobisphenol A in humans and rats after oral administration. Toxicol. Sci. 91, 49–58. (36) Eriksson, T., Bjorkman, S., Fyge, A., and Ekberg, H. (1992) Determination of thalidomide in plasma and blood by high-performance liquid chromatography: avoiding hydrolytic degradation. J. 16 ACS Paragon Plus Environment

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pharmacokinetics established in in vivo experiments with chimeric mice with humanized liver and physiologically based pharmacokinetic modeling. Regul. Toxicol. Pharmacol. 70, 214–221. (45) Yamazaki, H., Horiuchi, K., Takano, R., Nagano, T., Shimizu, M., Kitajima, M., Murayama, N., and Shono, F. (2010) Human blood concentrations of cotinine, a biomonitoring marker for tobacco smoke, extrapolated from nicotine metabolism in rats and humans and physiologically based pharmacokinetic modeling. Int. J. Environ. Res. Public Health 7, 3406–3421. (46) Breen, K. J., Shaw, J., Alvin, J., Henderson, G. I., Hoyumpa, A. M., Jr., and Schenker, S. (1973) Effect of experimental hepatic injury on the clearance of phenobarbital and paraldehyde. Gastroenterology 64, 992–1004. (47) Sharma, R., Litchfield, J., Atkinson, K., Eng, H., Amin, N. B., Denney, W. S., Pettersen, J. C., Goosen, T. C., Di, L., Lee, E., Pfefferkorn, J. A., Dalvie, D. K., and Kalgutkar, A. S. (2014) Metabolites in safety testing assessment in early clinical development: a case study with a glucokinase activator. Drug Metab. Dispos. 42, 1926–1939. (48) Hooshfar, S., Mortuza, T. B., Rogers, C. A., Linzey, M. R., Gullick, D. R., Bruckner, J. V., White, C. A., and Bartlett, M. G. (2017) Gas chromatography/negative chemical ionization mass spectrometry of transfluthrin in rat plasma and brain. Rapid Commun. Mass Spectrom. 31, 1573– 1581. (49) Lee, C. H., Wang, C. L., Lin, H. F., Chai, C. Y., Hong, M. Y., and Ho, C. K. (2011) Toxicity of tetramethylammonium hydroxide: review of two fatal cases of dermal exposure and development of an animal model. Toxicol. Ind. Health 27, 497–503. (50) Nnane, I. P., and Damani, L. A. (2001) Pharmacokinetics of trimethylamine in rats, including the effects of a synthetic diet. Xenobiotica 31, 749–755. (51) Swiercz, R., Majcherek, W., and Wasowicz, W. (2016) Hemimellitene (1,2,3trimethylbenzene) in the liver, lung, kidney, and blood, and dimethylbenzoic acid isomers in the liver, lung, kidney and urine of rats after single and repeated inhalation exposure to hemimellitene. Int. J. Occup. Med. Environ. Health 29, 113–128. (52) Swiercz, R., Rydzynski, K., Wasowicz, W., Majcherek, W., and Wesolowski, W. (2002) 18 ACS Paragon Plus Environment

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Toxicokinetics and metabolism of pseudocumene (1,2,4-trimethylbenzene) after inhalation exposure in rats. Int. J. Occup. Med. Environ. Health 15, 37–42.



Figure legends Figure 1. The one-compartment (A) and PBPK (B) models used in this study to simulate virtual oral administrations of chemicals to rats. F, bioavailability; k1 or ka, absorption constant; V, V1 or Vh, volume of distribution; kel, elimination constant; and CLh or CLr, clearance. Figure 2. Coordinate values of individual chemicals in a two-dimensional plane illustrating variety in the chemical space. (A) The 17 chemicals selected for evaluation of caco-2 permeability are shown as triangles. (B) Indicated are the varied selection of 34 chemicals (closed circles), the plasma concentrations of which were evaluated using both onecompartment models and PBPK models, among the 53 chemicals (open and closed circles) that underwent PBPK modeling to calculate plasma and hepatic concentrations. Figure 3. (A) The reported NOEL values were inversely correlated to the absorption rates of compounds (arbitrary units). (B) The absorption constant (ka) values for the PBPK modeling are compared with the log of the measured permeability for 2-mercaptobenzimidazole, mcresol, and bisphenol A. The 17 selected chemicals (listed in Table 1) evaluated for caco-2 permeability are shown as circles. Figure 4. (A) Correlation between Cmax and (B) AUC values in plasma of 34 selected compounds after virtual oral administration (1.0 mg/kg) to rats estimated using onecompartment models and PBPK models. (C) Correlation between plasma and liver AUC values of 53 compounds obtained using PBPK models. The 53 compounds are made up of the 34 and 19 selected compounds shown in Tables 2 and 3, respectively. Figure 5. There was an inverse correlation between reported LOEL values and the liver AUC values of seven compounds. The seven chemicals with reported hepatic LOEL values (Table 1) were evaluated. 20 ACS Paragon Plus Environment

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Table 1. Selected Compounds with Reported NOEL or Hepatic LOEL Values for Evaluation of Apparent Permeably Using caco-2 Cell System NOEL LOEL mg/kg/day mg/kg/day 2-Mercaptobenzimidazole 583-39-1 2 10 2,4-Dimethylaniline 95-68-1 2 10 N-Methylaniline 100-61-8 5 25 3-Cyanopyridine 100-54-9 5 30 N-Ethylaniline 103-69-5 5 25 Isophthalonitrile 626-17-5 8 40 3,5-Dimethylaniline 108-69-0 10 60 2,3-Dimethylaniline 87-59-2 12 60 3-Nitroaniline 99-09-2 15 50 Terephthalonitrile 623-26-7 20 80 3,4-Dimethylaniline 95-64-7 50 250 m-Cresol 108-39-4 100 300 2-Aminobiphenyl 90-41-5 100 300 Bisphenol A 80-05-7 200 600 3-Aminophenol 591-27-5 240 720 5-Amino-2-chlorotoluene-4-sulfonic acid 88-53-9 1000 3-Aminobenzenesulfonic acid 121-47-1 1000 4-Nonylphenol 84852-15-3 250 1,2,4-Trimethylbenzene 95-63-6 300 1,2,3-Trimethylbenzene 526-73-8 300 Paraacetaldehyde 123-63-7 300 Compound

CAS No.

Papp, nm/s 673 661 463 569 660 805 674 624 520 573 541 851 576 321 513 20 21 Not determined Not determined Not determined Not determined

Values of NOEL and LOEL for hepatotoxicity for selected compounds were obtained from the Hazard Evaluation Support System Integrated Platform (HESS).10


Chemical Research in Toxicology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46

Page 22 of 29

Table 2. Plasma and Hepatic Concentrations of 34 Selected Compounds Obtained Using One-compartment and PBPK Models One-compartment parameters

PBPK parameters

k1, 1/h

ka, 1/h

Onecompartment modeling results AUC, Cmax, ng ng/mL h/mL 473 632

Substrate, pharmacokinetic data reference

CAS No.

Acetaminophen12

103-90-2

14.4

0.3

0.3

14.3

0.2

0.6

Alprazolam13

2898197-7 62-53-3

4.6

10.5

7.8

32.4

6.3

11.8

16

17.8

0.3

0.3

8.4

0.2

0.6

0.4

4.1

1.5

0.4

1.2

29.0

423

89.7

4.0

1.4

61.8

16.2

0.4

35.0

8.2

Aniline14 Apixaban15

PBPK modeling results

895

AUC, ng h/mL 829

Liver Cmax, ng/mL 877

Liver AUC, ng h/mL 444

28

12

101

288

117

393

505

682

674

571

399

11.8

22

161

42

280

79

353

0.4

0.2

1

3

1

3

5

13

0.9

1.4

5.2

3

15

3

16

53

118

1.6

0.5

6.4

2.1

5

97

12

177

233

484

3.7

1.5

6.3

1.2

0.5

54

152

66

176

3340

1180

V/F, L

CL/F, L/h

V1, L

CLh,int, L/h

Cmax, ng/mL

Benzydamine19

50361247-3 4137220-7 8301526-3 8390501-5 642-72-8

Caffeine20

58-08-2

7.2

0.6

0.3

5.7

0.3

0.2

291

651

392

894

463

579

Dabigatran21

21191451-1 125-71-3

4.1

13.2

4.0

2.5

0.7

0.1

15

60

24

164

35

115

15.4

286

116

14.4

4.1

28.1

1

2

1

4

158

22

1.9

3.6

0.9

2.1

1.9

0.6

50

270

61

356

1390

1810

1.0

1.6

0.2

1.1

1.0

0.5

111

966

115

809

109

451

1.9

1.6

0.2

1.9

0.5

0.2

134

1440

155

1300

1390

9610

11.0

2.0

0.6

9.6

0.9

13.1

105

382

183

477

3690

1501

Apomorphine16 Atomoxetine17 Azithromycin18

Dextromethorphan20 Disopyramide22 Edoxaban15 Fluvoxamine23 Itopride24

3737-095 48044970-5 6171882-9 12289267-3


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Lenalidomide25

19173272-6 11479826-4 108-39-4

2.7

1.0

1.0

1.0

0.2

0.1

138

232

149

281

133

230

5.3

3.1

0.7

6.7

2.1

39.7

69

348

77

417

5890

2630

3.5

0.8

1.2

2.9

0.1

2.0

143

161

459

421

913

668

2919-666 5138451-1 583-39-1

1.8

39.7

551

2.2

2.2

284

4

19

5

20

222

117

1.4

7.0

9.8

1.6

0.3

0.5

13

24

15

42

29

51

3.3

2.5

0.1

1.9

1.6

0.1

95

1560

172

2500

2550

11600

5946770-8 4376-209 8485215-3 19661813-0 106-44-5

2.7

27.0

18.9

5.6

0.1

73.8

6

12

8

4

81

20

3.7

1.3

0.1

0.7

1.0

14.2

168

1690

200

2190

1580

14800

1.3

1.9

0.4

1.4

1.2

48.4

90

569

203

1350

2440

6570

9.7

6.0

3.1

5.5

1.5

1.5

31

71

46

131

1050

377

4.2

0.9

1.5

3.1

0.1

1.9

116

122

510

431

1010

682

19.0

11.9

1.7

5.8

0.8

131

19

134

42

260

2600

1830

0.7

3.4

0.4

0.8

0.5

0.02

50

543

74

787

58

598

0.5

2.4

1.1

0.4

1.3

2.3

38

225

48

323

219

1080

Styrene34

84825927-8 1917119-8 36678902-8 100-42-5

65.1

0.2

0.3

24.0

0.1

14.5

17

21

568

128

2250

297

Tetrabromobisphenol A35

79-94-7

5.8

2.1

0.2

0.2

0.1

113

113

1070

182

1620

1240

10800

Thalidomide36

50-35-1

0.2

0.4

0.1

0.2

0.4

0.1

238

3150

249

3140

171

2160

Verapamil37

152-11-4

70.7

11.3

0.6

22.7

1.5

3.4

21

288

37

410

2470

2700

Warfarin38

81-81-2

1.6

0.1

0.04

1.5

0.0

0.1

3080

37000

3020

41600

13300

181000

Losartan20 m-Cresol26 Melengestrol acetate27 Metoprolol28 2-Mercaptobenzimidazole29 Midazolam20 Mono(2-ethylhexyl) phthalate30 4-Nonylphenol31 Oseltamivir32 p-Cresol26 Pemafibrate Pomalidomide33 Rivaroxaban15

Pharmacokinetics information for pemafibrate was taken from its package insert. 23 ACS Paragon Plus Environment


Page 24 of 29

Table 3. Plasma and Hepatic Concentrations of 19 Additional Compounds that Underwent PBPK Modeling PBPK parameters CLh,i Ka, V1, nt, 1/h L L/h 3.3 1.9 76.2

PBPK modeling results Cmax, AUC, Liver ng/m ng Cmax, L h/mL ng/mL 1.9 5.9 9.8

Substrate, pharmacokinetic data reference

CAS No.

Acrylonitrile6

107-13-1

Bisphenol A39

80-05-7

3.5

2.6

62.4

14.6

66.2

1070

373

Chlorpyrifos40

2921-882 50-29-3

3.7

8.1

67.4

9.7

115

1870

797

0.3

0.9

1.1

217

4520

1460

31700

75-09-2

121

0.2

11.4

111

33.3

9.7

3.1

1,4-Dioxane43

123-91-1

0.3

0.1

0.02

1150

12400

800

8520

Molinate44

2212-671 54-11-5

5.0

5.1

63.9

3.9

28.1

362

101

1.1

0.7

5.4

22.9

59.0

42.6

49.8

7359058-6 123-63-7

2.9

0.6

339

11.7

17.1

108

44.7

2.9

0.2

0.1

1070

7930

840

5960

8.5

1.6

0.2

6.2

50.8

64.8

59.1

2,3,5,6Tetrafluorobenzylalcohol48 Tetramethylammonium49

1245603 -92-2 4084-382 75-59-2

0.03

1.2

2.3

2.8

54.9

4.2

83.8

1.7

0.1

0.2

65.5

82.8

62.7

70.9

Tolbutamide12

64-77-7

1.8

0.04

0.2

6220

6640

2640

27800

Toluene42

108-88-3

131

0.7

33.5

43.5

39.3

18.1

16.3

Trichloroethylene42

79-01-6

12.1

0.1

23.0

103

37.5

259

41.2

Trimethylamine50

75-50-3

1.2

0.4

0.2

405

1790

336

1320

1,2,3-Trimethylbenzene51

526-73-8

1.4

1.6

7.3

30.3

180

630

1130

1,2,4-Trimethylbenzene52

95-63-6

1.5

1.7

7.6

29.2

175

683

1110

Dichlorodiphenyltrichloroethane 41 Dichloromethane42

Nicotine45 Omeprazole20 Paraacetaldehyde46 PF-0493731947


Liver AUC, ng h/mL 3.5

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Figure 1



Figure 2


Page 26 of 29

Page 27 of 29

Figure 3

A

1200

3

r = -0.98 p < 0.001 n = 17

B

800

400

0 1

2

logPapp

NOEL, mg/kg/day

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


2

3

1 0

Absorption rate (arbitrary units), logPapp

1

2

ka, 1/h


3

4

Chemical Research in Toxicology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46

Figure 4


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Figure 5


Plasma and Hepatic Concentrations of Chemicals ... - ACS Publications

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