Liver and Plasma Concentrations of Food Chemicals after Virtual Oral Doses Extrapolated Using in Silico Estimated Input Pharmacokinetic Parameters to Confirm Reported Liver Toxicity in Rats

Abstract

The estimation of health risks of chemical substances was historically investigated using animal studies; however, current research focuses on reducing the number of animal experiments. The toxicity of chemicals in fish screening systems is reportedly correlated with their hydrophobicity. The inverse relationship between absorption rates (intestinal cell permeability) and virtual hepatic/plasma pharmacokinetics of diverse chemicals has been previously evaluated by modeling oral administration in rats. In the current study, internal exposures, i.e., virtual maximum plasma concentrations (C_max) and areas under the concentration–time curves (AUC), of 56 food chemicals with reported hepatic lowest-observed-effect levels (LOELs) of ≤1000 mg/kg/d in rats were pharmacokinetically modeled using in silico estimated input pharmacokinetic parameters. After a virtual single oral dose of 1.0 mg/kg of 56 food chemicals, the output C_max and AUC values in rat plasma generated by modeling using the corresponding in silico estimated input parameters were not significantly correlated with the reported hepatic LOEL values. However, significant inverse relationships between hepatic/plasma concentrations of selected lipophilic food chemicals (i.e., octanol–water partition coefficient log P > 1) using forward dosimetry and reported LOEL values (≤300 mg/kg/d) were observed (n = 14, r=−0.52–0.66, p ≤ 0.05). This simple modeling approach, which uses no experimental pharmacokinetic data, has the potential to play a significant role in reducing the use of animals to estimate toxicokinetics or internal exposures of lipophilic food components after oral doses. Therefore, these methods are valuable for estimating hepatic toxicity by using forward dosimetry in animal toxicity experiments.

INTRODUCTION

Estimation of the health risks of a variety of chemical substances has historically followed guidelines for investigatory studies by administering repeated oral doses to experimental animals.¹⁾ In general, repeated-dose toxicity studies for a variety of chemicals may involve significant costs and time. For animal welfare, the current movement has been focused on reducing animal experiments and developing alternative methods, such as in vitro or in silico studies.²⁾ Recent high-throughput in vitro screening assays combined with in silico computational models might provide suitable alternative methods to conventional animal testing.³⁾ This highlights the urgent and significant need to develop more efficient and informative tools for determining the toxicity of food chemicals.

The pharmacokinetics and/or toxicokinetics (including intestinal absorption) of food or industrial chemicals are generally not considered when evaluating their toxicological potential.⁴⁾ We demonstrated that the calculated absorbance rates of such chemicals based on intestinal cell permeability studies were inversely correlated with the no-observed-effect levels for hepatotoxicity after oral administration taken from the Hazard Evaluation Support System Integrated Platform in Japan (r=−0.98 to −0.55, p < 0.01, n = 17–29).^5–7) Physiologically based toxicokinetic models are important tools for in vitro to in vivo or inter-species extrapolations in the health risk assessment of foodborne and non-foodborne chemicals.⁸⁾ A simplified systematic physiologically based pharmacokinetic (PBPK) model, with its benefits of utility and simplicity, has been established that uses several input parameters derived from in vitro and in vivo data and the literature.^5,9) To evaluate internal exposure in rats and humans without any reference to in vitro or in vivo experimental data, PBPK modeling can be used if the model input parameters can be estimated in silico. The in silico estimation of input parameters for rat and human PBPK models (i.e., fraction absorbed × intestinal availability, F_aF_g; absorption rate constants, k_a; volumes of the systemic circulation, V₁; and hepatic intrinsic clearance, CL_h,int) has been proposed^10,11) and updated using panels of 372¹²⁾ and 355¹³⁾ chemicals, respectively. Input parameter estimation was carried out using light gradient boosting machine learning algorithms (LightGBM) based on between 11 and 29 in silico calculated chemical descriptors. Our analysis also revealed that the virtual areas under the hepatic concentration–time curves (AUC) of limited numbers of chemicals obtained using PBPK models were also inversely correlated with their hepatic lowest-observed-effect levels (LOEL, r=−0.78, p < 0.05, n = 8),^6,14) suggesting that estimation of oral exposure could be a useful tool to indicate hepatotoxicity in vivo, but this needs to be confirmed with more examples.

A literature search for the hepatotoxicity of food additives and functional food ingredients yielded approximately 100 reports giving levels of hepatotoxicity after repeated oral administrations, the majority for administrations lasting 4 weeks and some lasting 13 weeks. In this study, we first focused on 56 selected food chemicals with reported LOEL doses in rats, as shown in Table 1. In a zebrafish toxicity screening model, the toxicity of chemicals was correlated with their hydrophobicity (i.e., the octanol–water partition coefficient,¹⁵⁾ log P), and neutral species of potentially toxic chemicals have been almost certainly taken up into cells according to their pH-dependent log D-based distribution.¹⁶⁾ The aim of the present study was to estimate internal exposures, i.e., virtual maximum plasma concentrations (C_max) and AUC, of food chemicals and their correlation with reported liver toxicity in rats. We report herein that, using forward dosimetry, the present simplified PBPK models could estimate the relationships between hepatic/plasma concentrations of lipophilic food chemicals (log P > 1) that have reported LOEL values of ≤1000 mg/kg/d. The approach that applies simple PBPK modeling without any reference to experimental pharmacokinetic data has the potential to play a significant role in reducing the use of animals for estimating toxicokinetics or internal exposures of lipophilic food components after oral doses.

Table 1. Source Literature for Liver Toxicity in Rats and in Silico Derived Input Values for Rat PBPK Models of the 56 Food Chemicals

Compound	Cas No.	LOEL, mg/kg/d	Log P	FaFg, Caco-2 model	FaFg, machine learning	ka, 1/h	V1, L	CLh,int, L/h
Deoxynivalenol	51481-10-8	0.2522)	−3.20	0.823	0.387	2.30	1.00	0.135
Nivalenol	23282-20-4	1.523)	−3.92	0.776	0.546	0.931	0.914	0.132
Val-Pro-Pro	58872-39-2	1.924)	−1.24	0.749	0.347	2.84	0.320	0.095
Lasiocarpine	303-34-4	2.525)	0.650	0.547	0.663	1.42	0.884	1.00
Senecionine	130-01-8	3.326)	0.476	0.724	0.326	1.84	0.348	0.469
Senkirkine	2318-18-5	3.326)	−0.017	0.589	0.418	2.43	0.782	0.929
Isopsoralen	523-50-2	3.527)	1.97	0.942	0.909	0.991	0.043	0.0083
Hydroxytyrosol	10597-60-1	528)	0.069	0.900	0.748	7.87	0.237	0.377
N-Nitrosomorpholine	59-89-2	629)	−0.744	0.916	0.615	1.66	0.179	0.100
Tartrazine acid	34175-08-1	7.530)	−2.49	0.0024	0.674	1.80	0.806	0.321
Mitragynine	4098-40-2	1031)	4.00	0.875	0.791	0.907	0.730	3.00
L-Aspartic acid	56-84-8	2732)	−2.41	0.853	0.809	3.75	0.844	0.408
Isoeugenol	97-54-1	37.533)	2.58	0.820	0.238	17.1	0.967	4.84
3-Chloro-1,2-propanediol	96-24-2	4034)	−0.847	0.736	0.772	2.17	0.174	0.473
Harman	486-84-0	4135)	3.06	0.915	0.293	8.88	1.09	3.32
5-Aminolevulinic acid	106-60-5	44a)	−3.41	0.808	0.900	10.8	0.252	0.205
Norharman	244-63-3	5035)	2.56	0.782	0.410	3.49	0.168	0.459
Emodin	518-82-1	8036)	3.62	0.888	0.215	0.543	7.00	31.5
Ethyl acetoacetate	141-97-9	10037)	0.333	0.944	0.596	2.36	0.134	0.149
2-(Aminomethyl)phenol	932-30-9	10038)	0.377	0.951	0.581	15.6	0.327	0.262
β-Carotene	7235-40-7	12539)	15.2b)	0.783	0.218	0.803	1.19	297
3-Chloro-1,2-propanediol 1-palmitate	30557-04-1	13040)	7.46	0.948	0.703	1.19	0.348	51.6
Dioscin	19057-60-4	15041)	3.66	0.957	0.175	6.52	0.557	2.80
Coptisine	3486-66-6	15642)	−0.464	0.963	0.161	1.06	0.551	0.271
Epiberberine	6873-09-2	15642)	−0.771	0.852	0.198	8.29	0.736	0.490
tert-Butylhydroquinone	1948-33-0	20043)	2.53	0.889	0.461	4.44	0.262	9.63
Isoflavone	574-12-9	20044)	2.98	0.0699	0.806	0.430	0.280	1.29
Norbixin	542-40-5	20445)	5.89	0.931	0.779	2.07	0.114	64.6
3-Chloropropane-1,2-diol dipalmitate	51930-97-3	22040)	15.8b)	0.943	0.097	0.816	0.510	221
3-Chloro-1,2-propanediol dioleate	69161-73-5	24040)	16.9b)	0.954	0.108	0.830	0.626	227
Turmeric oleoresin	8024-37-1	25046)	2.25	0.864	0.350	2.56	0.989	6.88
2-Ethylhexanol	104-76-7	25047)	2.81	0.846	0.565	9.47	0.358	1.84
trans-Cinnamaldehyde	14371-10-9	27548)	2.05	0.695	0.473	44.4	0.086	4.00
Ubiquinol-10	992-78-9	30049)	20.9b)	0.970	0.184	0.722	1.51	431
Tartaric acid	133-37-9	30050)	−3.22	0.931	0.900	2.28	0.679	0.286
N-Acetyl-D-glucosamine	7512-17-6	30251)	−1.77	0.842	0.832	7.94	0.726	0.0622
Glycyrrhizic acid	1405-86-3	310c)	3.03	Not availabled)	0.508	1.49	0.716	4.83
Citral	5392-40-5	34552)	2.95	0.900	0.439	21.1	0.237	10.1
Gallic acid	149-91-7	35753)	0.425	0.914	0.713	36.2	0.529	1.66
trans-Resveratrol	501-36-0	40054)	2.83	0.932	0.212	7.42	0.174	0.495
Castor oil	8001-79-4	40455)	17.8b)	0.918	0.819	0.885	0.247	157
beta-Caryophyllene	87-44-5	45656)	6.45	0.914	0.319	1.27	0.453	279
Spermine	71-44-3	47557)	−1.59	0.168	0.255	2.47	0.704	0.072
Crocetin	27876-94-4	50058)	4.50	0.954	0.639	7.38	0.672	4.37
Epigallocatechin gallate	989-51-5	50059)	1.49	0.764	0.719	1.29	2.71	8.17
α-Isomethylionone	127-51-5	50060)	4.02	0.91	0.382	1.10	0.688	21.8
α-Tocopherol	59-02-9	50061)	12.0	0.963	0.111	0.743	2.43	694
Stevioside	57817-89-7	57762)	1.27	0.03	0.293	2.07	0.445	0.589
L-Methionine	63-68-3	70563)	−1.73	0.879	0.718	5.66	0.373	1.32
Spermidine	124-20-9	83057)	−1.28	0.445	0.318	4.47	0.649	0.112
Tyramine	51-67-2	90057)	0.766	0.754	0.534	10.2	0.237	0.475
Cadaverine	462-94-2	90057)	−0.435	0.668	0.928	10.1	0.375	0.071
Hesperidin	520-26-3	100064)	−0.291	0.417	0.241	5.34	0.551	0.359
Dihydrocapsiate	205687-03-2	100065)	5.24	0.942	0.525	1.48	0.111	7.16
Dihydrocapsaicin	19408-84-5	100066)	4.23	0.909	0.621	1.66	0.127	8.10
Chondroitin	9007-27-6	100067)	−3.91	0.479	0.346	10.9	0.626	0.219

a) https://www.pmda.go.jp/drugs/2017/P20171006001/171155000_22900AMX00989000_H100_1.pdf. b) Because a log P that was too high yielded extremely small f_u,p values (<10⁻⁷ to 10⁻⁹), these chemicals were omitted from further internal exposure estimations. c) https://cir.nii.ac.jp/crid/1570291224875741056. d) A F_aF_g value for glycyrrhizic acid was not available because of its small minus value for predicted log P_app A to B²¹⁾ (−0.173 nm/s).

MATERIALS AND METHODS

A literature search for the hepatotoxicity of food additives and functional food ingredients found reports of levels of hepatotoxicity estimated after repeated oral administration for 4 or 13 weeks. Among the available information, the hepatic LOEL levels of 56 food chemicals (Table 1 and Fig. 1) were available with consistent units of mg/kg body weight/d. To confirm the broad diversity of tested compounds depicting a wide chemical space,^5–7) the molecular structures described by 196 chemical descriptors were previously calculated using the open-source software RDKit for approximately 50000 randomly obtained general chemicals. To allow visualization of the variety of their chemical structures, the chemical space^9–14) represented by the 56 food chemicals was projected onto a two-dimensional plane with 25 subdivisions (with no axis labels) using generative topographic mapping (Fig. 2).

Fig. 1. Histogram of Reported LOEL Values of 56 Food Chemicals (≤1000 mg/kg/d) on a Raw Dose Scale (mg/kg/d, A) and a Logarithm-Transformed Scale (B) and the Relationship between Log LOEL and Log P (C)

A significant linear regression line with 95% confidence intervals for the 56 food chemicals is shown. The names of the chemicals and their LOEL and log P values are listed in Table 1.

Fig. 2. Coordinate Values in a Two-Dimensional Plane Illustrating the Variety of Chemical Structures (n = 56)

Chemicals with reported hepatic LOEL values of ≤30 and ≤300 mg/kg/d are illustrated in dark and light gray colors, respectively.

The acid dissociation constant, plasma unbound fraction (f_u,p), and log P for the test chemicals were obtained by in silico estimation using ACD/Percepta (Advanced Chemistry Development, Toronto, ON, Canada), Simcyp (Certara U.K., Sheffield, U.K.), and ChemDraw (PerkinElmer, Inc., Waltham, PA, U.S.A.) software, respectively.^17–19) The liver (kidney)-to-plasma concentration ratios (K_p,h/K_p,r) and blood-to-plasma concentration ratio (R_b) were calculated from the f_u,p, and log P values as follows:

  

The input parameters for the rat PBPK models (i.e., F_aF_g, k_a,V₁, and CL_h,int) were calculated using chemical descriptors calculated in silico,^10,20) as shown in Table 1. The hepatic and plasma C_max and AUC values of a variety of food chemicals were estimated using simplified PBPK models consisting of chemical receptor (gut), metabolizing (liver), excreting (kidney), and central (main) compartments with two sets of F_aF_g values, namely, in silico estimation based on the in vitro permeability (F_aF_{g, Caco-2 model})²¹⁾ or derived from the direct machine learning system with no reference to empirical values (F_aF_{g, machine learning}),¹²⁾ as described previously. The values used in this study for the hepatic/renal volumes and the hepatic/renal blood flow rates (Q_h/Q_r), respectively, in rats were 8.5 mL, 3.7 mL, 0.853 L/h, and 0.853 L/h.^10,20) A set of differential equations representing the simplified PBPK model^10,20) was solved to derive the plasma and hepatic C_max and AUC values from 0 to 24 h after a single virtual 1.0 mg/kg dose of test chemicals. Correlations with hepatic LOEL levels were evaluated using Prism 9 software (GraphPad Software, La Jolla, CA, U.S.A.).

RESULTS AND DISCUSSION

A wide range of LOEL values was obtained for a diverse range of food chemicals, as illustrated by the raw doses and logarithm-transformed doses in the two histograms (Fig. 1). The shape of the histogram with logarithm-transformed doses (Fig. 1B) appeared to have an approximately normal distribution. Although an inverse correlation was expected between the chemical LOEL and log P values, because the toxicity of chemicals has been correlated with their log P,¹⁵⁾ there was a positive correlation (n = 56) under the present conditions (Fig. 1C). To allow visualization of the variety of chemical structures among the test substances, they were projected onto a two-dimensional plane¹⁴⁾ representing the chemical space using generative topographic mapping (Fig. 2). In this figure, closer plots in the chemical space generally indicate greater similarity in chemical properties.¹⁴⁾ Under the present conditions, there was no evident relationship between the reported LOEL values, i.e., strong toxicity (<30 mg/kg/d, dark gray circles) or moderate toxicity (< 300 mg/kg/d, light gray circles), and the location in the chemical space represented by the two-dimensional plane in this study.

The values of F_a·F_g and k_a, V₁, and CL_h,int for the 56 food chemicals tested were generated in silico using estimation methods based on physicochemical properties,^10,20) as shown in Table 1. The hepatic and plasma C_max and AUC values of a variety of food chemicals were estimated using simplified PBPK models with the two sets of F_aF_g along with calculated K_p,h/K_p,r, and R_b values. As shown in Fig. 3, with the F_aF_g values derived solely from machine learning systems, the estimated C_max (Fig. 3A) and AUC (Fig. 3B) values in the rat plasma and C_max (Fig. 3C) and AUC (Fig. 3D) values in rat livers after virtual single oral doses of 1.0 mg/kg of 56 food chemicals generated by PBPK models using the corresponding in silico-estimated input parameters were not correlated with the reported hepatic LOEL values. Similarly, no relationship was observed between the PBPK-modeled C_max and AUC values in rat plasma/livers after 1.0 mg/kg doses of 56 food chemicals and the reported hepatic LOEL values when the PBPK models used F_aF_g values derived from Caco-2 estimation systems (data not shown).

Fig. 3. Relationship between the Log Hepatic LOEL Values and Rat Plasma C_max (A) and AUC (B) and Hepatic C_max (C) and AUC (D) for Food Chemicals (n = 56) Estimated with PBPK Models Using F_aF_g Values Derived from the Machine-Learning Model

The toxicity of chemicals in fish screening systems correlates with their hydrophobicity.^15,16) The relationships between the reported hepatic LOEL values and the present estimated plasma C_max and AUC and hepatic C_max and AUC values for selected hydrophobic food chemicals (log P ≥ 1) generated by rat PBPK models using F_aF_g values derived from the in vitro Caco-2 model (n = 24) were evaluated (Fig. 4), because chemicals with log P ≤ 0 reportedly tend to be nontoxic in fish.¹⁵⁾ Although the C_max and AUC values in rat plasma (Figs. 4A, B) and livers (Figs. 4C, D) after virtual single oral doses of 1.0 mg/kg of 24 food chemicals generated by PBPK models were not correlated with the reported hepatic LOEL values, correlation coefficients of −0.66 (Fig. 4B) and −0.61 (Fig. 4D) were significant for the virtual AUC values in rat plasma and livers of 14 hydrophobic food chemicals with moderate or strong hepatotoxicity (LOEL values ≤300 mg/kg/d). Similarly, the C_max and AUC values in rat plasma (Figs. 5A, B) and livers (Figs. 5C, D) of 25 food chemicals after virtual 1.0 mg/kg doses estimated by PBPK models using F_aF_g values derived from the machine-learning system generated were not correlated with the reported hepatic LOEL values; however, significant correlation coefficients were observed in the relationship between the virtual AUC values in rat plasma (Fig. 5B) and the liver (Fig. 5D) of 14 hydrophobic food chemicals with reported hepatic LOEL values ≤300 mg/kg/d.

Fig. 4. Relationship between the Log Hepatic LOEL Values and Rat Plasma C_max (A) and AUC (B) and Hepatic C_max (C) and AUC (D) for Selected Hydrophobic Food Chemicals (Log P > 1) Generated by Rat PBPK Models Using F_aF_g Values Derived from the in Vitro Caco-2 Model (n = 24)

In panels B and D, significant linear regression lines with 95% confidence intervals are shown for 14 hydrophobic food chemicals (gray circles) with hepatic LOEL values of ≤300 mg/kg/d.

Fig. 5. Relationship between the Log Hepatic LOEL Values and the Rat Plasma C_max (A) and AUC (B) and Hepatic C_max (C) and AUC (D) for Selected Hydrophobic Food Chemicals (Log P > 1) Generated by Rat PBPK Models Using F_aF_g Values Derived from the Machine-Learning Model (n = 25)

In panels B and D, significant linear regression lines with 95% confidence intervals are shown for 14 hydrophobic food chemicals (gray circles) with hepatic LOEL values of ≤300 mg/kg/d.

It has been noted in fish toxicity screening systems that chemicals with log P ≤ 0 tend to be nontoxic,¹⁵⁾ and a full log D-based model based on log P as a membrane passage descriptor¹⁶⁾ can be used to predict potential toxicities. Absorption rates evaluated as Caco-2 cell permeability and hepatic/plasma pharmacokinetics of diverse chemicals estimated by modeling virtual oral administration in rats were previously evaluated.^5–7) Under similar conditions, the LOEL and virtual hepatic AUC values obtained using our PBPK models were also inversely correlated for a limited number of industrial chemicals.^6,14) In the current study, simplified PBPK models using forward dosimetry demonstrated an inverse relationship between hepatic/plasma concentrations of lipophilic food chemicals (log P > 1) and their reported moderate LOEL values (≤300 mg/kg/d). Although it should be noted that the current study size of lipophilic food chemicals with reported moderate LOEL values (≤300 mg/kg/d) is limited, the simple PBPK modeling approach using input parameters generated without any reference to experimental pharmacokinetic data has the potential to play a significant role in replacing and reducing the use of animals for estimating the toxicokinetics or internal exposures of lipophilic food components after oral doses. Therefore, these methods are valuable for estimating hepatic toxicity from internal exposure by using reverse dosimetry in animal toxicity experiments.

Acknowledgments

This study was supported in part by the Japan Chemical Industry Association Long-Range Research Initiative Program. We thank Drs. Satoshi Uwagawa, Haruka Nishimura, Norie Murayama, Fumiaki Shono, and Kimito Funatsu for their assistance. We are also grateful to David Smallbones for copy-editing a draft of this article.

Conflict of Interest

The authors declare no conflict of interest.

REFERENCES

• 1) Törnqvist E, Annas A, Granath B, Jalkesten E, Cotgreave I, Oberg M. Strategic focus on 3R principles reveals major reductions in the use of animals in pharmaceutical toxicity testing. PLOS ONE, 9, e101638 (2014).
• 2) Wambaugh JF, Hughes MF, Ring CL, MacMillan DK, Ford J, Fennell TR, Black SR, Snyder RW, Sipes NS, Wetmore BA, Westerhout J, Setzer RW, Pearce RG, Simmons JE, Thomas RS. Evaluating in vitro-in vivo extrapolation of toxicokinetics. Toxicol. Sci., 163, 152–169 (2018).
• 3) Krewski D, Andersen ME, Tyshenko MG, Krishnan K, Hartung T, Boekelheide K, Wambaugh JF, Jones D, Whelan M, Thomas R, Yauk C, Barton-Maclaren T, Cote I. Toxicity testing in the 21st century: progress in the past decade and future perspectives. Arch. Toxicol., 94, 1–58 (2020).
• 4) Bell SM, Chang X, Wambaugh JF, et al. In vitro to in vivo extrapolation for high throughput prioritization and decision making. Toxicol. In Vitro, 47, 213–227 (2018).
• 5) Kamiya Y, Otsuka S, Miura T, Takaku H, Yamada R, Nakazato M, Nakamura H, Mizuno S, Shono F, Funatsu K, Yamazaki H. Plasma and hepatic concentrations of chemicals after virtual oral administrations extrapolated using rat plasma data and simple physiologically based pharmacokinetic models. Chem. Res. Toxicol., 32, 211–218 (2019).
• 6) Yamazaki H, Kamiya Y. Extrapolation of hepatic concentrations of industrial chemicals using pharmacokinetic models to predict hepatotoxicity. Toxicol. Res., 35, 295–301 (2019).
• 7) Kamiya Y, Takaku H, Yamada R, Akase C, Abe Y, Sekiguchi Y, Murayama N, Shimizu M, Kitajima M, Shono F, Funatsu K, Yamazaki H. Determination and prediction of permeability across intestinal epithelial cell monolayer of a diverse range of industrial chemicals/drugs for estimation of oral absorption as a putative marker of hepatotoxicity. Toxicol. Rep., 7, 149–154 (2020).
• 8) Tebby C, van der Voet H, de Sousa G, Rorije E, Kumar V, de Boer W, Kruisselbrink JW, Bois FY, Faniband M, Moretto A, Brochot C. A generic PBTK model implemented in the MCRA platform: predictive performance and uses in risk assessment of chemicals. Food Chem. Toxicol., 142, 111440 (2020).
• 9) Kamiya Y, Handa K, Miura T, Ohori J, Shimizu M, Kitajima M, Shono F, Funatsu K, Yamazaki H. An updated in silico prediction method for volumes of systemic circulation of 323 disparate chemicals for use in physiologically based pharmacokinetic models to estimate plasma and tissue concentrations after oral doses in rats. Chem. Res. Toxicol., 34, 2180–2183 (2021).
• 10) Kamiya Y, Handa K, Miura T, Yanagi M, Shigeta K, Hina S, Shimizu M, Kitajima M, Shono F, Funatsu K, Yamazaki H. In silico prediction of input parameters for simplified physiologically based pharmacokinetic models for estimating plasma, liver, and kidney exposures in rats after oral doses of 246 disparate chemicals. Chem. Res. Toxicol., 34, 507–513 (2021).
• 11) Kamiya Y, Handa K, Miura T, Ohori J, Kato A, Shimizu M, Kitajima M, Yamazaki H. Machine learning prediction of the three main input parameters of a simplified physiologically based pharmacokinetic model subsequently used to generate time-dependent plasma concentration data in humans after oral doses of 212 disparate chemicals. Biol. Pharm. Bull., 45, 124–128 (2022).
• 12) Adachi K, Shimizu M, Yamazaki H. Updated in silico prediction methods for fractions absorbed and absorption rate constants of 372 disparate chemicals for use in physiologically based pharmacokinetic models for estimating internal concentrations in rats. J. Toxicol. Sci., 47, 453–456 (2022).
• 13) Adachi K, Shimizu M, Yamazaki H. Updated in silico prediction methods for fractions absorbed and key input parameters of 355 disparate chemicals for physiologically based pharmacokinetic models for time-dependent plasma concentrations after virtual oral doses in humans. Biol. Pharm. Bull., 45, 1812–1817 (2022).
• 14) Kamiya Y, Otsuka S, Miura T, Yoshizawa M, Nakano A, Iwasaki M, Kobayashi Y, Shimizu M, Kitajima M, Shono F, Funatsu K, Yamazaki H. Physiologically based pharmacokinetic models predicting renal and hepatic concentrations of industrial chemicals after virtual oral doses in rats. Chem. Res. Toxicol., 33, 1736–1751 (2020).
• 15) Padilla S, Corum D, Padnos B, Hunter DL, Beam A, Houck KA, Sipes N, Kleinstreuer N, Knudsen T, Dix DJ, Reif DM. Zebrafish developmental screening of the ToxCast Phase I chemical library. Reprod. Toxicol., 33, 174–187 (2012).
• 16) Köhler HR, Graff T, Schweizer M, et al. Log D-based modelling and Δlog D as a proxy for pH-dependent action of ionizable chemicals reveal the relevance of both neutral and ionic species for fish embryotoxicity and possess great potential for practical application in the regulation of chemicals. Water Res., 235, 119864 (2023).
• 17) Kato M, Shitara Y, Sato H, Yoshisue K, Hirano M, Ikeda T, Sugiyama Y. The quantitative prediction of CYP-mediated drug interaction by physiologically based pharmacokinetic modeling. Pharm. Res., 25, 1891–1901 (2008).
• 18) Uchimura T, Kato M, Saito T, Kinoshita H. Prediction of human blood-to-plasma drug concentration ratio. Biopharm. Drug Dispos., 31, 286–297 (2010).
• 19) Takano R, Murayama N, Horiuchi K, Kitajima M, Kumamoto M, Shono F, Yamazaki H. Blood concentrations of acrylonitrile in humans after oral administration extrapolated from in vivo rat pharmacokinetics, in vitro human metabolism, and physiologically based pharmacokinetic modeling. Regul. Toxicol. Pharmacol., 58, 252–258 (2010).
• 20) Kamiya Y, Handa K, Miura T, Ohori J, Shimizu M, Kitajima M, Shono F, Funatsu K, Yamazaki H. Correction to “An updated in silico prediction method for volumes of systemic circulation of 323 disparate chemicals for use in physiologically based pharmacokinetic models to estimate plasma and tissue concentrations after oral doses in rats”. Chem. Res. Toxicol., 35, 1433 (2022).
• 21) Kamiya Y, Omura A, Hayasaka R, Saito R, Sano I, Handa K, Ohori J, Kitajima M, Shono F, Funatsu K, Yamazaki H. Prediction of permeability across intestinal cell monolayers for 219 disparate chemicals using in vitro experimental coefficients in a pH gradient system and in silico analyses by trivariate linear regressions and machine learning. Biochem. Pharmacol., 192, 114749 (2021).
• 22) Arnold D, Karpinski KF, McGuire PF, Nera EA, Zawidzka ZZ, Lok E, Campbell JS, Tryphonas L, Scott PM. A short-term feeding study with deoxynivalenol (vomitoxin) using rats. Fundam. Appl. Toxicol., 6, 691–696 (1986).
• 23) Takahashi M, Shibutani M, Sugita-Konishi Y, Aihara M, Inoue K, Woo GH, Fujimoto H, Hirose M. A 90-day subchronic toxicity study of nivalenol, a trichothecene mycotoxin, in F344 rats. Food Chem. Toxicol., 46, 125–135 (2008).
• 24) Maeno M, Nakamura Y, Mennear JH, Bernard BK. Studies of the toxicological potential of tripeptides (L-valyl-L-prolyl-L-proline and L-isoleucyl-L-prolyl-L-proline): III. Single- and/or repeated-dose toxicity of tripeptides-containing Lactobacillus helveticus-fermented milk powder and casein hydrolysate in rats. Int. J. Toxicol., 24 (Suppl. 4), 13–23 (2005).
• 25) Dalefield RR, Gosse MA, Mueller U. A 28-day oral toxicity study of echimidine and lasiocarpine in Wistar rats. Regul. Toxicol. Pharmacol., 81, 146–154 (2016).
• 26) Ebmeyer J, Rasinger JD, Hengstler JG, Schaudien D, Creutzenberg O, Lampen A, Braeuning A, Hessel-Pras S. Hepatotoxic pyrrolizidine alkaloids induce DNA damage response in rat liver in a 28-day feeding study. Arch. Toxicol., 94, 1739–1751 (2020).
• 27) Zhang Y, Yuan XM, Wang YF, Jiang MM, Bi YN, Liu Y, Pu WL, Song L, Huang JY, Sun LK, Zhou ZX, Zhou K. Isopsoralen induces different subchronic toxicities and metabolomic outcomes between male and female Wistar rats. Regul. Toxicol. Pharmacol., 103, 1–9 (2019).
• 28) Auñon-Calles D, Canut L, Visioli F. Toxicological evaluation of pure hydroxytyrosol. Food Chem. Toxicol., 55, 498–504 (2013).
• 29) Weber E, Bannasch P. Dose and time dependence of the cellular phenotype in rat hepatic preneoplasia and neoplasia induced by continuous oral exposure to N–nitrosomorpholine. Carcinogenesis, 15, 1235–1242 (1994).
• 30) Himri I, Bellahcen S, Souna F, Belmekki F, Aziz M, Zoheir J, Mekhfi H, Saalaoui E. A 90 day oral toxicity study of tartrazine, a synthetic food dye, in Wistar rats. Int. J. Pharm. Pharm. Sci., 3, 159–169 (2011).
• 31) Sabetghadam A, Ramanathan S, Sasidharan S, Mansor SM. Subchronic exposure to mitragynine, the principal alkaloid of Mitragyna speciosa, in rats. J. Ethnopharmacol., 146, 815–823 (2013).
• 32) Tada Y, Yano N, Takahashi H, Yuzawa K, Ando H, Kubo Y, Nagasawa A, Uehara S, Ogata A, Nakae D. Toxic effects of l-aspartic acid at high dose levels on kidneys and salivary glands in Fischer 344 rats detected in a 90-day feeding study. Food Chem. Toxicol., 46, 2789–2795 (2008).
• 33) National Toxicology Program. Toxicology and carcinogenesis studies of isoeugenol (CAS No. 97-54-1) in F344/N rats and B6C3F1 mice (gavage studies). Natl. Toxicol. Program Tech. Rep. Ser., 551, 1–178 (2010).
• 34) Li Y, Liu S, Wang C, Li K, Shan YJ, Wang XJ, Sun CH. Novel biomarkers of 3-chloro-1,2-propanediol exposure by ultra performance liquid chromatography/mass spectrometry based metabonomic analysis of rat urine. Chem. Res. Toxicol., 23, 1012–1017 (2010).
• 35) Hagiwara A, Asakawa E, Kurata Y, Sano M, Hirose M, Ito N. Dose-dependent renal tubular toxicity of harman and norharman in male F344 rats. Toxicol. Pathol., 20, 197–204 (1992).
• 36) National Toxicology Program. NTP Toxicology and carcinogenesis studies of EMODIN (CAS NO. 518-82-1) feed studies in F344/N rats and B6C3F₁ mice. Natl. Toxicol. Program Tech. Rep. Ser., 493, 1–278 (2001).
• 37) Cook WM, Purchase R, Ford GP, Creasy DM, Brantom PG, Gangolli SD. A 28-day feeding study with ethyl acetoacetate in rats. Food Chem. Toxicol., 30, 567–573 (1992).
• 38) Fuller JC Jr, Pitchford LM, Abumrad NN, Rathmacher JA. Subchronic (90-day) repeated dose toxicity study of 2-hydroxybenzylamine acetate in rats. Regul. Toxicol. Pharmacol., 99, 225–232 (2018).
• 39) Nabae K, Ichihara T, Hagiwara A, Hirota T, Toda Y, Tamano S, Nishino M, Ogasawara T, Sasaki Y, Nakamura M, Shirai T. A 90-day oral toxicity study of beta-carotene derived from Blakeslea trispora, a natural food colorant, in F344 rats. Food Chem. Toxicol., 43, 1127–1133 (2005).
• 40) Onami S, Cho YM, Toyoda T, Mizuta Y, Yoshida M, Nishikawa A, Ogawa K. A 13-week repeated dose study of three 3-monochloropropane-1,2-diol fatty acid esters in F344 rats. Arch. Toxicol., 88, 871–880 (2014).
• 41) Xu T, Zhang S, Zheng L, Yin L, Xu L, Peng J. A 90-day subchronic toxicological assessment of dioscin, a natural steroid saponin, in Sprague–Dawley rats. Food Chem. Toxicol., 50, 1279–1287 (2012).
• 42) Yi J, Ye X, Wang D, He K, Yang Y, Liu X, Li X. Safety evaluation of main alkaloids from Rhizoma Coptidis. J. Ethnopharmacol., 145, 303–310 (2013).
• 43) National Toxicology Program. NTP toxicology and carcinogenesis studies of t-butylhydroquinone (CAS No. 1948-33-0) in F344/N rats and B6C3F₁ mice (feed studies). Natl. Toxicol. Program Tech. Rep. Ser., 459, 1–326 (1997).
• 44) Zhang WZ, Cui WM, Zhang X, Wang W, Jia XD, Zhang XP, Li N. Subchronic toxicity study on soy isoflavones in rats. Biomed. Environ. Sci., 22, 259–264 (2009).
• 45) Hagiwara A, Imai N, Ichihara T, Sano M, Tamano S, Aoki H, Yasuhara K, Koda T, Nakamura M, Shirai T. A thirteen-week oral toxicity study of annatto extract (norbixin), a natural food color extracted from the seed coat of annatto (Bixa orellana L.), in Sprague–Dawley rats. Food Chem. Toxicol., 41, 1157–1164 (2003).
• 46) National Toxicology Program. NTP Toxicology and carcinogenesis studies of turmeric oleoresin (CAS No. 8024-37-1) (major component 79%–85% curcumin, CAS No. 458-37-7) in F344/N rats and B6C3F₁ mice (feed studies). Natl. Toxicol. Program Tech. Rep. Ser., 427, 1–275 (1993).
• 47) Astill BD, Deckardt K, Gembardt C, Gingell R, Guest D, Hodgson JR, Mellert W, Murphy SR, Tyler TR. Prechronic toxicity studies on 2-ethylhexanol in F334 rats and B6C3F1 mice. Fundam. Appl. Toxicol., 29, 31–39 (1996).
• 48) National Toxicology Program. NTP toxicology and carcinogenesis studies of trans-cinnamaldehyde (CAS No. 14371-10-9) in F344/N rats and B6C3F₁ mice (feed studies). Natl. Toxicol. Program Tech. Rep. Ser., 514, 1–281 (2004).
• 49) Deshmukh G, Venkataramaiah SB, Doreswamy CM, Umesh MC, Subbanna RB, Pradhan BK, Seekallu S, Sekar R, Prabhu K, Sadagopan S, Arumugam SN, Sharma S, Gavara G, Balaraman S, Sambasivam G, Chandrappa RK, Flynn S, Shivarudraiah P. Safety assessment of ubiquinol acetate: subchronic toxicity and genotoxicity studies. J. Toxicol., 2019, 3680757 (2019).
• 50) Inoue K, Morikawa T, Takahashi M, Yoshida M, Ogawa K. Obstructive nephropathy induced with DL-potassium hydrogen tartrate in F344 rats. J. Toxicol. Pathol., 28, 89–97 (2015).
• 51) Lee KY, Shibutani M, Takagi H, Arimura T, Takigami S, Uneyama C, Kato N, Hirose M. Subchronic toxicity study of dietary N-acetylglucosamine in F344 rats. Food Chem. Toxicol., 42, 687–695 (2004).
• 52) National Toxicology Program. NTP toxicology and carcinogenesiss studies of citral (microencapsulated) (CAS No. 5392-40-5) in F344/N rats and B6C3F₁ mice (feed studies). Natl. Toxicol. Program Tech. Rep. Ser., 505, 1–268 (2003).
• 53) Niho N, Shibutani M, Tamura T, Toyoda K, Uneyama C, Takahashi N, Hirose M. Subchronic toxicity study of gallic acid by oral administration in F344 rats. Food Chem. Toxicol., 39, 1063–1070 (2001).
• 54) Johnson WD, Morrissey RL, Usborne AL, Kapetanovic I, Crowell JA, Muzzio M, McCormick DL. Subchronic oral toxicity and cardiovascular safety pharmacology studies of resveratrol, a naturally occurring polyphenol with cancer preventive activity. Food Chem. Toxicol., 49, 3319–3327 (2011).
• 55) Irwin R. NTP technical report on the toxicity studies of castor oil (CAS No. 8001-79-4) in F344/N rats and B6C3F₁ mice (dosed feed studies). Toxic. Rep. Ser., 12, 1–B5 (1992).
• 56) Bastaki M, Api AM, Aubanel M, Bauter M, Cachet T, Demyttenaere JCR, Diop MM, Harman CL, Hayashi SM, Krammer G, Lu V, Marone PA, Mendes O, Renskers KJ, Schnabel J, Tsang SY, Taylor SV. Dietary administration of beta-caryophyllene and its epoxide to Sprague-Dawley rats for 90 days. Food Chem. Toxicol., 135, 110876 (2020).
• 57) Til HP, Falke HE, Prinsen MK, Willems MI. Acute and subacute toxicity of tyramine, spermidine, spermine, putrescine and cadaverine in rats. Food Chem. Toxicol., 35, 337–348 (1997).
• 58) Tang X, Wang Y, Yang W, Zheng Y, Liu C, Qu M, Xu H, Zhang L, Liang J, Fan B. Acute and subchronic oral toxicity study of gardenia yellow e500 in Sprague-Dawley rats. Int. J. Environ. Res. Public Health, 17, 531 (2020).
• 59) Isbrucker RA, Edwards JA, Wolz E, Davidovich A, Bausch J. Safety studies on epigallocatechin gallate (EGCG) preparations. Part 2: dermal, acute and short-term toxicity studies. Food Chem. Toxicol., 44, 636–650 (2006).
• 60) Politano VT, Lapczynski AA, Ritacco G, Api AM. Ninety-day toxicity study of alpha-iso-methylionone in rats. Int. J. Toxicol., 31, 595–601 (2012).
• 61) EL-Hak HNG, ELaraby EE, Hassan AK, Abbas OA. Study of the toxic effect and safety of vitamin E supplement in male albino rats after 30 days of repeated treatment. Heliyon, 5, e02645 (2019).
• 62) Aze Y, Toyoda K, Imaida K, Hayashi S, Imazawa T, Hayashi Y, Takahashi M. Subchronic oral toxicity study of stevioside in F344 rats. Eisei Shikenjo Hokoku, 109, 48–54 (1991).
• 63) Chin K, Toue S, Kawamata Y, Watanabe A, Miwa T, Smriga M, Sakai R. A 4-week toxicity study of methionine in male rats. Int. J. Toxicol., 34, 233–241 (2015).
• 64) Li Y, Kandhare AD, Mukherjee AA, Bodhankar SL. Acute and sub-chronic oral toxicity studies of hesperidin isolated from orange peel extract in Sprague Dawley rats. Regul. Toxicol. Pharmacol., 105, 77–85 (2019).
• 65) Kodama T, Watanabe E, Tsubuku S, Otabe A, Mochizuki M, Masuyama T, Bernard BK. Studies of the toxicological potential of capsinoids: VII. A 13-week toxicity study of dihydrocapsiate in rats. Int. J. Toxicol., 27 (Suppl. 3), 79–100 (2008).
• 66) Watanabe E, Kodama T, Masuyama T, Tsubuku S, Otabe A, Mochizuki M, Bernard BK. Studies of the toxicological potential of capsinoids: VIII. A 13-week toxicity study of commercial-grade dihydrocapsiate in rats. Int. J. Toxicol., 27 (Suppl. 3), 101–118 (2008).
• 67) Miraglia N, Bianchi D, Trentin A, Volpi N, Soni MG. Safety assessment of non-animal chondroitin sulfate sodium: subchronic study in rats, genotoxicity tests and human bioavailability. Food Chem. Toxicol., 93, 89–101 (2016).