
In this review, we describe the absorption rates (Caco-2 cell permeability) and hepatic/plasma pharmacokinetics of 53 diverse chemicals estimated by modeling virtual oral administration in rats. To ensure that a broad range of chemical structures is present among the selected substances, the properties described by 196 chemical descriptors in a chemoinformatics tool were calculated for 50,000 randomly selected molecules in the original chemical space. To allow visualization, the resulting chemical space was projected onto a two-dimensional plane using generative topographic mapping. The calculated absorbance rates of the chemicals based on cell permeability studies were found to be inversely correlated to the no-observed-effect levels for hepatoxicity after oral administration, as obtained from the Hazard Evaluation Support System Integrated Platform in Japan (
There is a wide variety of human-made chemicals in the environment. Organic compounds originating from myriad natural processes and industrial sources (1) are also ubiquitous in the human environment. Because long-term exposure to some volatile organic compounds may increase the risk of cancer or birth defects, estimation of the exposures to such compounds is a research area that can have a significant positive impact on human health (2). Estimation of health risks due to chemical substances has historically followed guidelines on investigatory studies with experimental animals. The general toxicities of industrial chemicals have been extensively investigated by administering repeated oral doses of the chemicals to rodents. Nonetheless, in general, repeated-dose toxicity studies for many chemicals may require significant cost and time. For animal welfare, the current movement has been focused on developing alternative methods such as
Recently developed high-throughput
To interpret toxicity data obtained from high-throughput
A five-year project has started in April 2017, sponsored by the Ministry of Economy and Trade Industry in Japan. The aim is to develop a high-accuracy
The
The procedures used for preparing
To investigate the relevance of pH-dependent Caco-2 monolayer assays in the
The current study employed two modeling systems, the simple one-compartment model (Fig. 4A) that was recently recommended by US authorities as a high-throughput toxicokinetic screening tool, and a simplified PBPK model (Fig. 4B) made up of chemical receptor (gut), metabolizing (liver), and central (main) compartments. Examples of the plasma concentration curves of individual general chemicals estimated by the two models after virtual oral doses were reported previously (8,17–28).
The simplified PBPK models were established as previously described (9,15). For some chemicals, a peripheral compartment was used in addition to the central compartment. The general procedure for PBPK modeling is as follows: the octanol-water partition coefficient (log
The physiological values, such as the hepatic blood flow rate (
where
The calculated maximum plasma concentrations and areas under the concentration-time curves (AUC) of 34 diverse chemicals (15) and an updated selection of 70 chemicals (unpublished) obtained using the one-compartment models and our simplified PBPK models were found to be consistent (
Among the compounds with a broad diversity of structures that were used in the PBPK modeling, seven had reported hepatoxicity NOEL or lowest-observed-effect level (LOEL) values in the HESS database (Table 2) (16). The LOEL values in rats for oral administrations of 2-mercaptobenzimidazole (CAS No. 583-39-1), 4-nonylphenol (84852-15-3), paraacetaldehyde (123-63-7), 1,2,3-trimethylbenzene (526-73-8), 1,2,4-trimethylbenzene (95-63-6),
In this review, the broad chemical diversity of the substances tested is illustrated in a two-dimensional plane depicting the chemical space (Fig. 2). We determined the permeability across the intestinal epithelial cell monolayers (in a pH-dependent Caco-2 cell system) of a diverse range of industrial chemicals/drugs. These permeability values provided a good estimation of oral absorbance as a putative marker of hepatotoxicity. Further analysis revealed that the cell permeability coefficients of the industrial chemicals were inversely correlated to their hepatic NOELs (Fig. 3), suggesting that the estimation of oral absorbance could be a useful tool to indicate hepatotoxicity
We thank Drs. Kimito Funatsu and Fumiaki Shono (Tokyo University) for chemical space diversity evaluation and their general support and Drs. Makiko Shimizu and Norie Murayama (Showa Pharmaceutical University) for their collaboration in our laboratory. We also thank David Smallbones for copyediting a draft of this article. This original work was supported by the Ministry of Economy and Trade Industry (METI) Artificial Intelligence-based Substance Hazard Integrated Prediction System Project (AI-SHIPS project), Japan.
Names of the chemicals tested in the current PBPK modeling
Acetaminophen | Chlorpyrifos | Itopride | Omeprazole | Thalidomide |
Acrylonitrile | Lenalidomide | Oseltamivir | Tolbutamide | |
Alprazolam | Losartan | Paraacetaldehyde | Toluene | |
Aniline | Dabigatran | Melengestrol acetate | Pemafibrate | Trichloroethylene |
Apixaban | Dextromethorphan | 2-Mercaptobenzimidazole | PF-04937319 | Trimethylamine |
Apomorphine | Dichlorodiphenyltrichloroethane | Metoprolol | Pomalidomide | 1,2,3-Trimethylbenzene |
Atomoxetine | Dichloromethane | Midazolam | Rivaroxaban | 1,2,4-Trimethylbenzene |
Azithromycin | 1,4-Dioxane | Molinate | Styrene | Verapamil |
Benzydamine | Disopyramide | Mono(2-ethylhexyl) phthalate | Tetrabromobisphenol A | Warfarin |
Bisphenol A | Edoxaban | Nicotine | 2,3,5,6-Tetrafluorobenzylalcohol | |
Caffeine | Fluvoxamine | 4-Nonylphenol | Tetramethylammonium |
These chemicals are those examined in our previous report (15).
PBPK parameters and modeling results of seven compounds after virtual oral administration (1.0 mg/kg) and their reported hepatic NOEL and LOEL values in rats
Substrate | CAS No. | Log | PBPK parameters | PBPK modeling results | NOEL LOEL | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|
AUC, ng h/mL | Liver Cmax, ng/g | Liver AUC, ng h/g | mg/kg/day | ||||||||
2-Mercaptobenzimidazole | 583-39-1 | 2.8 | 1.9 | 1.6 | 0.1 | 172 | 2500 | 2550 | 11600 | 2 | 10 |
4-Nonylphenol | 84852-15-3 | NA | 1.4 | 1.2 | 48.4 | 203 | 1350 | 2440 | 6570 | 15 | 250 |
Paraacetaldehyde | 123-63-7 | NA | 2.9 | 0.2 | 0.1 | 1070 | 7930 | 840 | 5960 | 100 | 300 |
1,2,3-Trimethylbenzene | 526-73-8 | NA | 1.4 | 1.6 | 7.3 | 30 | 180 | 630 | 1130 | 30 | 300 |
1,2,4-Trimethylbenzene | 95-63-6 | NA | 1.5 | 1.7 | 7.6 | 29 | 175 | 683 | 1110 | 100 | 300 |
108-39-4 | 2.9 | 2.9 | 0.1 | 2.0 | 459 | 421 | 913 | 668 | 100 | 300 | |
Bisphenol A | 80-05-7 | 2.5 | 3.5 | 2.6 | 62.4 | 15 | 66 | 1070 | 373 | 200 | 600 |
NA, not available. Data were obtained from our previous report (15).