Establishment of metrics for evaluation of in vitro test method performance
To evaluate the performance of an in vitro test method, two new metrics—the toxicity separation index (TSI) and toxicity estimation index (TEI)—were introduced, where TSI considers the separation of hepatotoxic from non-hepatotoxic compounds, and TEI estimates how well hepatotoxic blood concentrations in vivo can be estimated for hepatotoxic compounds (Fig. 3). Assessment of in vitro test methods using these two indices may be advantageous, because the general performance of different methodological alternatives can be compared, for example different cytotoxicity cutoffs or the inclusion of additional readouts for a given set of compounds. Once an optimized test method has been established, it can then be applied to independent compounds where the performance is assessed in standard terms, such as sensitivity and specificity.
Both TSI and TEI are calculated based on the projection of a predefined battery of test compounds onto a two-dimensional coordinate system, where the x-axis indicates the lowest concentrations that cause a positive test result (‘in vitro alert’, such as decreased viability or increased expression of genes) of any test method, and the y-axis indicates the in vivo blood concentrations (e.g., Cmax) that result from a specific dosing schedule. In this in vitro-to-in vivo extrapolation plot (shortened: ‘extrapolation plot’), each test compound is represented by a symbol. Red and green color indicate whether the individual compounds cause an increased risk of hepatotoxicity (red) or are non-hepatotoxic (green) at the corresponding Cmax. For ease of understanding, the principles of TSI and TEI are illustrated with hypothetical scenarios (Fig. 3a–d). TSI measures how well a test method differentiates between hepatotoxic and non-hepatotoxic compounds. It covers a range from 0.5 to 1.0, where a TSI of 1.0 indicates a perfect separation, while 0.5 represents a random result. The hypothetical examples illustrate both good (Fig. 3a, b) and poor (Fig. 3c, d) separation of hepatotoxic and non-hepatotoxic compounds. The concept of separation in such a plot is based on the assumption that the difference between the in vitro alert concentration and its corresponding concentration in vivo is larger for non-hepatotoxic than for hepatotoxic compounds. The diagonal line in the extrapolation plot indicates a hypothetical situation where the in vitro alert concentration exactly corresponds to the in vivo hepatotoxic blood concentration for the hepatotoxic compounds (‘iso-concentration line’).
TEI measures how accurately an in vitro test method estimates hepatotoxic blood concentrations in vivo; in other words—it measures how far the red points are below the iso-concentration line, e.g., a TEI of 1 indicates a position where all hepatotoxic compounds (red points) are on the iso-concentration line or above. Therefore, shifting all points in Fig. 3a downwards leaves the TSI unchanged, but decreases the TEI, as shown in Fig. 3b. If all points lie on, or very close to, the iso-concentration line, as in Fig. 3c, the TEI is high, but the test method has no, or only little, discriminatory power (i.e., low TSI). Finally, shifting the points in Fig. 3c downwards results in both poor TSI and poor TEI (Fig. 3d). When designing a good in vitro method, priority is given to obtaining a high TSI, since the first objective is to discriminate whether a compound is hepatotoxic or not. A high TEI is also desirable but should not be achieved at the expense of a worse TSI, since this metric is only relevant once hepatotoxic compounds have been reliably identified.
Once an extrapolation plot has been established for a set of hepatotoxic and non-hepatotoxic compounds, it can then be used to assess additional substances without having information on their hepatotoxicity by adding them to the existing plot. The position on the x-axis is determined in vitro, while additional knowledge is required for the y-axis location.
In vitro data generation and PBPK modeling
This chapter gives an overview over the generated data, while the actual application as summarized in the working pipeline (Fig. 2) follows in the next chapters. For in vitro test system optimization with concrete TSI and TEI values, the lowest concentrations of 28 test compounds that caused a positive result in vitro were presented on the x-axis of the extrapolation plot, and the blood concentrations (Cmax) established by PBPK modeling were plotted on the y-axis. To generate the required data, PHH from three donors were used to perform concentration-dependent cytotoxicity analyses of the 28 compounds using the (CTB) assay according to a published standard operation procedure (Fig. 4a, left panel; Supplement 3A). After fitting a sigmoidal dose–response curve, EC values ranging from EC10 to EC80 with a stepwise increase of 10 were calculated. Figure 4b illustrates the example of the EC10 for one compound (clonidine, CLON) in PHH. The raw data generated from the 28 compounds tested in PHH from three donors at five concentrations plus solvent controls are available in Supplement 4. An overview of the EC10 values for all compounds is given in Table 1. In a subsequent step, expression of a previously published seven-gene panel (CYP1B1, CYP3A7, TUBB2B, SULT1C2, G6PD, RGCC and FBXO32) (Grinberg et al. 2014) was determined in a concentration-dependent manner in cultivated PHH from three donors for the 28 compounds. The results for one compound (valproic acid, VPA) are shown in Fig. 4c; data obtained for all compounds and donors are available in Supplement 4 and 6. Cytotoxicity and the expression of the seven genes was also determined in a concentration-dependent manner for HepG2 cells in three independent experiments, as done for PHH (Fig. 4a, right panel, d, e, Table 1, Supplement 5, 6). Moreover, GSH depletion was measured as an additional in vitro endpoint in HepG2 cells for evaluation in a pilot study (Fig. 4f) as described below. The processed data for PHH and HepG2, all fitted curves and the goodness of fit for these curves are given in Supplements 6, 7 and 8.
Pharmacokinetic modeling was performed for oral dosing schedules used in clinical routine (Supplement 9). For all compounds, the blood Cmax, Cmax at the steady state (Cmax,ss) and the average concentration at steady state (Cav,ss) (Fig. 4g) were calculated for (a) total concentration (protein bound plus free compound) in blood from the general circulation, (b) free, non-protein bound concentrations in blood from the general circulation, and (c) total concentrations (protein bound plus free compound) in blood from the portal vein (Supplement 9). Besides pharmacokinetic modeling, a comprehensive literature search was performed for experimentally analyzed blood concentrations for the different test compounds (Supplement 6). All pharmacokinetic parameters correlated with one another (Supplement 10). One example of the correlation plots for total (protein-bound and unbound) concentrations in the general circulation of Cmax versus Cmax,ss is illustrated in Fig. 4h, where Cmax,ss was only slightly higher than Cmax for most compounds. In a second example, the correlation plot of Cmax in the general circulation versus the corresponding concentration in the portal vein shows that portal vein concentrations can be higher than concentrations in the general circulation, which is plausible for orally administered compounds with a high first pass effect (Fig. 4h). Correlation plots for pharmacokinetic parameters and physicochemical properties with blood concentrations of the study compounds are given in Supplement 10. As expected, the daily dose of the test compounds strongly correlates with the Cmax in blood (Supplement 10A); Cmax of the hepatotoxic compounds is higher compared to the non-hepatotoxic substances. Moreover, a weak inverse correlation between hydrophobicity and Cmax was observed (Supplement 10B), whereas Cmax showed a weak inverse correlation with the molecular weight of the tested compounds (Supplement 10C). Key parameters, including Cmax, EC10 (median of the three donors), and the lowest positively tested concentrations of the seven genes are summarized in Table 1, and the complete set of data is available in Supplements 1, 6, and 9.
An important aspect for test development is whether specific dosing regimens of drugs (or specific levels of exposure to environmental compounds) lead to an increased probability of hepatotoxicity. This information is given in Table 1 (sources and details in Supplement 1) for the dosing schedules summarized in Supplement 9. For most of the drugs in Table 1, reliable information was only available for one (or for a few similar) therapeutic dosing schedule. An exception is acetaminophen where not only non-hepatotoxic doses and therapeutic blood concentrations are available, but also comprehensive data from overdoses that lead to hepatotoxicity (Table 1 and, Supplement 9). Therefore, acetaminophen appears twice in Table 1, with a hepatotoxic and a non-hepatotoxic blood concentration. Besides pharmaceutical compounds, certain chemicals (ethanol, dimethyl sulfoxide, glucose monohydrate, methylparaben and triclosan) were also included (Table 1). Ethanol was considered, because large studies are available that provide information on doses, and associated blood concentrations, leading to liver damage when exposure continues over longer periods of time (Supplement 9). In contrast, the very low ethanol blood concentrations observed after transdermal exposure during hand disinfection can be considered non-hepatotoxic. Therefore, ethanol also appears in Table 1 with both a hepatotoxic and a non-hepatotoxic Cmax.
In vitro test optimization based on cytotoxicity
The above-introduced concept of TSI and TEI was applied to the 28 test compounds to determine which cytotoxicity parameter (EC-threshold, incubation period) is optimal (x-axis), while Cmax (total concentration; 95% population percentile; y-axis) was kept constant. When PHH from three donors were tested for cytotoxicity, the first question to be answered was from which donor the cytotoxicity data should be used, the median, minimum or maximum. A second important question was whether the often-used EC50 value is optimal or if other EC values (EC10up to EC80) are superior. To systematically address these questions, extrapolation plots were generated, considering all the different parameters for the x-axis, and the corresponding TSI and TEI were determined and plotted against each other (Fig. 5a). Higher TSI values were obtained when the median donor values were used, compared to the corresponding minima and maxima (Fig. 5a). Moreover, a consistent and relatively strong decrease in TEI was obtained when EC values were increased from EC10 to EC80. This was observed for the minimum, maximum, as well as the median values (Fig. 5a). Based on these results, the median EC10 value was chosen for further analysis of cytotoxicity.
A third question was how long cultivated PHH should be exposed to the test compounds for cytotoxicity testing. In the present study, an incubation period of 48 h was used. Additionally, incubations of 24 h and 7 days (with repeated culture medium changes with fresh test compound) were performed as previously reported (Gu et al. 2018). A higher TSI was obtained for the 48 h compared to the 24 h and 7 day incubation periods (Fig. 5b). This observation was independent of the EC cutoff, as exemplified for EC10, EC20 and EC50 in Fig. 5b. Therefore, the median EC10 with 48 h of compound exposure were used to generate an extrapolation plot (Fig. 5c). Each compound was identified by an abbreviation defined in Table 1, where red and green symbols indicate hepatotoxic and non-hepatotoxic compounds, respectively. In general, hepatotoxic compounds were located above the non-hepatotoxic compounds. This resulted in an almost optimal TSI of 0.996 but a lower TEI of 0.844, since most of the hepatotoxic compounds clustered below the iso-concentration line (Fig. 5c).
Integration of gene expression into the in vitro test
Next, we evaluated if adding gene expression as an additional readout to the optimized version of the test method obtained above could further improve its performance. For this purpose, seven genes (Fig. 4c, e) were selected from a previously published study analyzing genome-wide expression data in cultivated human hepatocytes of 143 compounds (Grinberg et al. 2014). The selection criteria were (1) gene expression increased by many compounds; (2) gene expression increased in human liver disease (steatosis, fibrosis and cirrhosis) to support in vivo relevance; and (3) gene expression not altered by the hepatocyte isolation and cultivation procedure. Although the goal was to include gene expression into the test battery together with cytotoxicity, the seven genes were initially analyzed alone and in combination (without cytotoxicity). Extrapolation plots were generated, which included each of the seven genes individually, with the lowest as well as the median and the maximum compound concentrations that induced gene expression plotted on the x-axis, and the Cmax on the y-axis (Fig. 6a–g). To identify an appropriate cutoff, an analysis of TSI and TEI was performed where all seven genes were considered. Gene expression at a specific test compound concentration was defined as positive if the expression of the most sensitive gene increased 1.5-fold; this cutoff value was increased to 5.0-fold in steps of 0.1 (Fig. 6i). A maximal TSI was obtained for cutoffs ranging between 2.1 and 2.6; therefore, a cutoff of 2.5 was further used to define a positive test result. A comparison of the median and the minimum alert concentration among the three donors (with a 2.5-fold cutoff) demonstrated that using the median leads to a higher TSI (Fig. 6j). The maximum gene expression alert concentrations (the most resistant donor) are not shown, because more compounds did not reach the cutoff of 2.5-fold, which would lead to the disadvantage of a high number of compounds without in vitro alert. Together, these results justify the use of a 2.5-fold gene expression cutoff for the median donor for further analyses.
The data of the individual genes (Fig. 6a–g) showed that only some of the compounds generated a positive test result in the gene expression assay. Despite this limitation, a relatively good separation of hepatotoxic and non-hepatotoxic properties was possible for the substances that did reach an alert concentration. Therefore, a combined analysis of all seven genes was performed, and a concentration defined as positive when at least a 2.5-fold increase was obtained for the median alert concentration per compound, for at least one gene (Fig. 6h). Even under these conditions, three hepatotoxic (ETOHhigh, LAB, LEV) and seven non-hepatotoxic (ETOHlow, FAM, GLC, HYZ, MEL, PPL, and TSN) compounds did not generate an alert. The results show that gene expression may support the differentiation of hepatotoxic and non-hepatotoxic compounds, but only if an alert concentration is observed; however, with only seven genes not all test compounds can be assessed.
A subsequent goal was to study if TSI or TEI can be improved by combining cytotoxicity (median EC10, 48 h incubation) with gene expression. For this purpose, all possible combinations (n = 128) of gene expression—for zero up to seven genes—with cytotoxicity were analyzed (Supplement 11). In each combination, the readout (alert of median gene expression or EC10) that resulted in the lowest positively tested concentration was considered. None of the combinations improved TSI beyond 0.996, which was already achieved by the median EC10 alone. However, the TEI of EC10 (0.844) was improved by additionally considering gene expression (Fig. 7a). CYP1B1 and CYP3A7 were of particular relevance in the combined scenario. When three genes were considered—CYP1B1 and CYP3A7 and a third gene (G6PD, SULT1C2, or TUBB2B)—a maximal TEI of 0.887 was obtained. Adding further genes, up to all seven, did not further improve the TEI (Fig. 7a). In conclusion, a combination of cytotoxicity (median EC10, 48 h incubation) and the expression of three genes as specified above resulted in optimal TSI and TEI for the analyzed set of compounds, as illustrated in Fig. 7b.
Choice of pharmacokinetic parameter to represent in vivo blood concentration
To determine the best factor to represent in vivo blood concentration, the pharmacokinetic parameter on the y-axis was varied, while keeping the cytotoxicity parameter (median EC10, 48 h incubation) constant on the x-axis. Key questions were to identify which of the following parameters are superior (Fig. 7c): total or free concentrations; concentrations in blood of the general circulation or in the portal vein; the use of pharmacokinetic parameters (e.g. Cmax) of the 95% percentile, mean or 5% percentile of a population; maximal blood concentrations (Cmax), maximal blood concentrations in the steady state (Cmax ss) or average blood concentrations in the steady state (Cav, ss). The modeled pharmacokinetic parameters are available in Supplement 9. Plotting TSI against TEI for all pharmacokinetic parameters, led to the following findings for the analyzed set of compounds (Fig. 7c): (1) the use of total blood concentrations (orange symbols) resulted in higher TSI and TEI than free drug concentrations (blue symbols); (2) blood concentrations of the test compounds in the general circulation (e.g., the orange and red symbols) resulted in higher TSI, but lower TEI than concentrations in the portal vein after oral uptake (green symbols); (3) PBPK modeling allows simulation of interindividual differences, differentiating for example, the mean blood concentrations, as well as concentrations in the highest 5% (95th percentile) and the lowest 5% (5th percentile) of a human population. The use of the 95th percentiles led to higher TSI and TEI than the corresponding 5th percentiles and the mean; and (4) the use of Cmax led to a slightly higher TSI than Cmax ss and clearly higher TSI and TEI than Cav, ss. In summary, the use of total (free and protein bound) Cmax of the 95th percentile leads to the best TSI based on in vitro data (cytotoxicity, EC10, median), while portal vein concentrations lead to higher TEI at the expense of a reduced TSI (Supplement 11). Therefore, the total Cmax of the 95th percentile was used for the next step in the pipeline (Fig. 2), the establishment of the classifier for the prediction of hepatotoxicity status.
Prediction of hepatotoxicity and non-hepatotoxicity by SVM classification
Once the optimized parameters had been established based on TSI and TEI, the test system was used to evaluate whether compounds were hepatotoxic or non-hepatotoxic. When projected onto the known compounds in the extrapolation plot, the location of a compound with unknown hepatotoxicity status allows for a visual assessment (qualitative) of its potential toxicity. However, an objective categorization of compounds as hepatotoxic or non-hepatotoxic requires the use of a classification algorithm. Here, a SVM classifier was used to classify compounds as either toxic and non-toxic by finding a separation line that maximizes the minimal distance to any of the compounds while constraining the errors by a constant. Based on the 28 training compounds (Fig. 7d) the classification performance was assessed using leave-one-out cross-validation.
The in vitro EC10 (median, 48 h incubation) and Cmax (total, general circulation, 95% CI) were used as input parameters. This resulted in 28 out of 30 correct predictions (Fig. 7d), and thus a sensitivity, specificity and accuracy of 100, 87.3, and 93.3%, respectively. The incorrect predictions were for APAP, which at a therapeutic dose (14 mg/kg) was falsely predicted as hepatotoxic. The second false positive was glucose. A rich meal may increase blood glucose levels from approximately 90 to 219 mg/dl (5.0–12.2 mM), which despite a prediction of ‘hepatotoxic’, is not expected to have adverse effects on the liver. The accuracy was not improved when expression of the seven genes was additionally included as an input parameter (Supplement 11), which agrees with the observation that TSI did not improve when gene expression was considered in addition to cytotoxicity.
An overall classifier was obtained by fitting an SVM classifier on all 30 compounds using the same input variables (EC10 median, 48 h incubation and Cmax, total, general circulation, 95% percentile) as for the leave-one-out classifiers. This classifier was applied to eight independent test substances known to be either hepatotoxic (leflunomide, nevirapine, tolcapone and troglitazone) or non-hepatotoxic (ethyl-, propyl-, butyl- and isobutylparaben) at specific blood concentrations (Fig. 7d). The classifier properties reflect the proportion of hepatotoxic compounds (here: 14 of total 30). Therefore, the present classifier was calibrated for test data with a similar number of hepatotoxic and non-hepatotoxic compounds. If this proportion will differ in future studies, adjustment of the predicted probabilities derived from the SVM will be required. Tested concentrations, toxicity information, pharmacokinetics, raw data and EC10 values are given in Supplement 12. The eight independent test compounds were added to the optimized extrapolation plot (Fig. 8a). All non-hepatotoxic compounds were located in a region at least three orders of magnitude below the iso-concentration line in the extrapolation plot. Blood concentrations of ethyl-, propyl-, butyl- and isobutylparaben are known from biomonitoring studies, and such exposure levels are not expected to cause an increased risk of hepatotoxicity (Azzouz et al. 2016; Frederiksen et al. 2011; Shekar et al. 2016; Sandanger et al. 2011; Mulla et al. 2015; Nellis et al. 2013). In contrast, the four hepatotoxic compounds were located in the hepatotoxic area delineated by the original set of compounds (Fig. 8a). Using the SVM classifier trained on the 28 original compounds, the independent eight compounds were all correctly classified as either hepatotoxic or non-hepatotoxic (Fig. 7d).
The purpose of the analysis with eight independent compounds was to check whether the separation line between hepatotoxic and non-hepatotoxic compounds established by the SVM classifier is plausible. A real validation of the predictive performance in terms of sensitivity, specificity, etc., would require the testing of more compounds with different mechanisms of action and varying degrees of hepatotoxicity, not chosen to be only on the opposite extremes of the spectrum with regard to hepatotoxicity as for the present set of compounds. Nevertheless, the successful separation of the hepatotoxic and non-hepatotoxic compounds studied here allowed us to proceed with the next step in the working pipeline (Fig. 2), the extrapolation from in vitro alert concentrations to in vivo blood concentrations.
Estimation of the risk of hepatotoxicity at specific blood concentrations of test compounds
An important question is whether the probability of hepatotoxicity caused by specific in vivo blood concentrations can be extrapolated based on in vitro alert concentrations. The systematic degree of separation of hepatotoxic and non-hepatotoxic compounds observed across the entire in vitro concentration range (Fig. 5c) suggests that such an extrapolation may be possible. As described above, a SVM classifier was used to identify the line that best separates the hepatotoxic and the non-hepatotoxic compounds. A compound located exactly on this line has a 50% probability of belonging to the hepatotoxic category (Fig. 8b). As a consequence, the intersection of the in vitro concentration (EC10, median, 48 h incubation) with this line can be used to estimate an in vivo concentration (Cmax) with a probability of 50% that it belongs to the hepatotoxic category (red symbols). It should be considered that the 50% probability of hepatotoxicity scenario does not mean that 50% of the individuals will suffer from hepatotoxicity; belonging to the hepatotoxic category means a risk far below 50% for individual patients. The risk for each hepatotoxic compound has been defined in Supplement 1 (‘hepatotoxicity information’). For example, oral doses of ketoconazole (one of the hepatotoxic compounds) caused hepatotoxicity in 0.007–0.05% and liver enzyme elevations in 4–20% of all treated patients.
Application of the extrapolation procedure to the four hepatotoxic test compounds (leflunomide, nevirapine, tolcapone and troglitazone) led to calculated in vivo blood concentrations that are related with a 50% probability of hepatotoxicity of 0.050, 2.55, 1.46, and 0.61 µM, respectively. Using a similar procedure, blood concentrations with a lower probability of hepatotoxicity, e.g., based on a 5% or 1% probability of hepatotoxicity, can also be calculated (Fig. 8b).
Estimation of an acceptable daily intake based on in vitro data
For all compounds studied so far, human hepatotoxicity and associated blood concentrations were known. However, often this knowledge is not available (ab initio toxicity evaluation). Pulegone was chosen as an example to establish an acceptable daily intake concerning hepatotoxicity. Pulegone is a naturally occurring organic compound used in flavoring agents and in the fragrance industry. High doses caused hepatotoxicity in rats (Khojasteh et al. 2012; Chen et al. 2011). Therefore, knowing which concentrations in blood increase the risk of human hepatotoxicity is of interest. Cytotoxicity testing in PHH from three donors resulted in a median EC10 of 1.27 mM (Fig. 8b, c). Application of the above-mentioned extrapolation procedure identified 30.3 µM as the blood concentration (Cmax) corresponding to a 50% probability of belonging to the hepatotoxic category, and 1.57 µM as the concentration corresponding to a 5% probability (Fig. 8c, d). The latter concentration associated with a 5% probability of hepatotoxicity may serve as a basis for derivation of an acceptable daily intake dose (ADI). Reverse PBPK modeling for repeated oral doses indicated that a blood concentration (Cmax) of 1.57 µM corresponds to 268 µg pulegone/kg body weight/day (Fig. 8c), which may be considered an in vitro derived ADI. This extrapolation can also be performed for stricter probability levels, for example, a 1% probability will result in an in vitro derived ADI of 51 µg/kg/day (Fig. 8d). ADIs based on 28 days oral toxicity studies in rats by the established method ranged between 100 and 750 µg/kg/day (HMPC 2016; CEFS/SCF/CS 2002). Tested concentrations, raw data and EC10 values, as well as reverse modelling of the pulegone case study are given in Supplement 12.
Comparison with publicly available data and analysis by margin of safety
Recently, the cytotoxicity of 110 compounds (69 hepatotoxic and 41 non-hepatotoxic) was tested in spheroid cultures of human hepatocytes (Proctor et al. 2017). Besides information on hepatotoxicity, the EC50 of cytotoxicity and the Cmax of the test compounds were also published (Supplementary material 2 in Proctor et al. 2017). For comparison, we prepared an extrapolation plot using the data provided by Proctor et al. (2017) in their supplementary information (Fig. 9a), resulting in a TSI of 0.773 and a TEI of 0.788. Although the compounds were not perfectly separated, the trend is that the non-hepatotoxic compounds are further below the iso-concentration line compared to the hepatotoxic compounds.
Previous studies have mainly used the margin of safety (MoS) method to evaluate hepatotoxicity in vitro. To allow a comparison, our data was also analyzed by MoS, where the ratio of the EC50 or EC10 to the Cmax in blood were plotted on the y-axis; whereas, hepatotoxic compounds (DILI concern) and controls (no DILI concern) are shown on the x-axis (Fig. 9b–d). This method was applied to plot the published data of Proctor et al. (2017), together with the data used in the present study. Proctor et al. subdivided “DILI concern” compounds into “severe”, “high”, and “low” (Fig. 9b). While the most severe DILI concern compounds showed a MoS lower than 20, a relatively high fraction of the high and low DILI concern compounds were above 20. Data from the present study (Fig. 9c, d) showed that using the median EC10 of three donors allows for a better differentiation between hepatotoxic and non-hepatotoxic compounds than the EC10 minimum or maximum (Fig. 9c). Moreover, EC10 values (Fig. 9c) differentiated better than EC50 values (Fig. 9d); the EC50 resulted in more hepatotoxic compounds with a MoS > 20 compared to the corresponding EC10 data.
HepG2 instead of PHH for in vitro testing
The human hepatocyte-based test method established in this study successfully distinguished hepatotoxic and non-hepatotoxic compounds. However, PHH are expensive, compared to HepG2 cells. It is unknown whether using the same approach with HepG2 cells would generate comparable results to those obtained with PHH. Therefore, the same experiments as described for PHH were performed using HepG2 cells, including cytotoxicity and expression of the seven genes. Extrapolation plots using the median EC10 (Fig. 10a) and the combination of median EC10 with CYP3A7, CYP1B1 and G6PD (Fig. 10b) showed that lower metrics for test performance were obtained for HepG2 than for PHH. The TSI for PHH was 0.996 compared to 0.911 for HepG2. The TEI was also lower for HepG2 (0.844 and 0.810 for PHH and HepG2, respectively). The difference between HepG2 cells and PHH is illustrated in Fig. 10c where ratios of the median EC10 values of HepG2 and PHH are plotted for each compound (corresponding analysis for EC50: Supplement 6). The highest ratios were obtained for NAC, CBZ, BPR and ETOH, where PHH were at least tenfold more susceptible than HepG2 (Fig. 10c). In contrast, HepG2 cells were more susceptible to other compounds, including TSN and VPA. The results show that 6 out of 28 tested compounds resulted in more than tenfold differences between PHH and HepG2.
Despite the inferior performance metrics of the HepG2-based in vitro test method, HepG2 cells may still be used for exploratory studies. In an attempt to improve the performance of the HepG2-based test, a glutathione (GSH) assay was performed with all test compounds. A combined analysis of cytotoxicity (median EC10) and GSH depletion (median EC10), where combination means the use of the more sensitive of the two readouts for each compound, improved TSI and TEI compared to either GSH depletion or cytotoxicity alone (Fig. 10d). In addition, addition of measuring GSH content to the combined cytotoxicity- and gene expression-based test battery (CYP3A7, CYP1B1 and G6PD) improved TSI and TEI (all analyses based on median values) (Fig. 10d). Therefore, it should be evaluated in the future, whether the GSH assay also improves hepatotoxicity analysis in PHH.