New approach methodologies for metabolic disruption: a high-throughput human liver cell phospholipidosis assay

TL;DR

This paper introduces a high-throughput screening method using human liver cells to detect chemicals that cause phospholipid accumulation, a condition linked to drug toxicity.

Contribution

The study develops and validates a new in vitro assay using differentiated HepaRG cells for high-throughput screening of phospholipidosis inducers.

Findings

The LipidTox assay in differentiated HepaRG cells reliably detects phospholipid accumulation in a 96-well format.

The assay achieved 86% predictivity when compared to existing in vivo, in vitro, and in silico datasets.

The method is suitable for identifying potential PLD inducers among diverse chemical groups.

Abstract

Phospholipidosis (PLD) is the excessive accumulation of phospholipids within cells, which is, for example, observed in the liver following exposure to certain chemicals or drugs. Although the precise molecular mechanisms underlying PLD remain incompletely elucidated, it is documented that PLD is frequently induced by cationic amphiphilic compounds. To facilitate the identification of potential PLD inducers across diverse groups of chemicals, the development of novel high-throughput screening methodologies is imperative. Here, an in vitro human liver cell culture system was established for PLD screening, employing the fluorescence-based LipidTox assay in differentiated HepaRG cells in a 96-well format. Cells were treated with a selection of 35 compounds characterized as either PLD inducers or non-inducers, based on previously published in vivo data. Concentration-response curves of phospholipid accumulation were assessed and the results were compared with existing in vivo, in vitro and in silico datasets. The findings confirm the applicability of the LipidTOX assay in differentiated HepaRG cells as a reliable in vitro-based high-throughput screening method for PLD prediction, achieving an overall predictivity of 86%.

The online version contains supplementary material available at 10.1007/s00204-025-04254-2.

Figures

Representative fluorescence images and phospholipid quantification results. Differentiated HepaRG cells were treated for 24 h with the indicated concentrations of each test substance and with LipidTOX™. Fluorescent images (phospholipids in red, nuclei in blue) for the highest effect concentration of the corresponding substance (left hand side) and concentration-response curves (right hand side) showing phospholipid accumulation for the selected substances with high (a: haloperiol), medium (b: triparanol), low (c: erythromycin), and no (d: carbamazepine) potency. Scale bar = 100 µm. The phospho

Full text

Introduction

Phospholipidosis (PLD) is the excessive intracellular accumulation of phospholipids of predominantly lysosomal origin. It has been reported that various drugs are able to induce PLD in different organs such as the lung, kidney, lymph nodes, liver, and others (Lüllmann et al. 1975). The accumulating phospholipids form so-called lamellar bodies, which are known from inheritable lipid storage diseases containing non-degradable phospholipids and sphingolipids (Anderson & Borlak 2006; Lüllmann et al. 1975). Many of the reported PLD-inducing substances are so-called cationic amphiphilic drugs (CADs), which are characterized by a hydrophobic ring and a hydrophilic amine group. Nonetheless, the underlying mechanisms leading to PLD are largely unknown. Various molecular initiating events are discussed for PLD, mainly related to the inhibition of phospholipid degradation and/or elevated phospholipid biosynthesis. The majority of studies agree on the following possible modes of action of PLD-inducing compounds: 1. Binding of phospholipids and complex formation that prevent phospholipid degradation; 2. Inhibition of phospholipases, thereby preventing phospholipid degradation; 3. Stimulation of phospholipid biosynthesis; 4. Increased cholesterol biosynthesis, which is reported to have a substantial influence on the accumulation of phospholipids (Donato & Gómez-Lechón, 2012; Hiraoka et al. 2006; Lüllmann et al. 1975; Reasor et al. 2006).

The accumulation of phospholipids in vivo has been demonstrated to be a dose-dependent process proportional to the accumulation of CADs in the tissue (Reasor et al. 2006). The lamellar bodies regress upon drug cessation of the causing CAD, thereby confirming the reversibility of PLD (Donato et al. 2022). However, the consequences of PLD for cellular functions have not yet been fully elucidated.

The detection of lamellar bodies is conventionally performed using transmission electron microscopy of tissue sections (Lüllmann et al. 1975), which is a time-consuming and low-throughput method. With the rise of questioning conventional animal experiments for ethical reasons, as well as for their scientific relevance and efficiency, more efforts are dedicated to develop so-called “New Approach Methodologies” (NAMs) to assess the safety of chemicals. NAMs cover a wide range of different approaches based on, for example, in silico predictions or in vitro assays, promising faster and more efficient toxicity testing. In the last two decades, different NAMs have been published aimed to improve the prediction of the PLD-inducing potential for drugs and other chemicals. The development of in silico models is hereby focusing on physicochemical properties (e.g., pK_a_ and ClogP) of compounds (Hanumegowda et al. 2010; Pelletier et al. 2007; Ploemen et al. 2004). While these models demonstrate satisfactory performance, they fail to incorporate factors including bioavailability, compound accumulation, metabolic transformation, different modes of action, or the magnitude of effect caused by the different compounds. Various studies have been published using different cell lines or primary cells as in vitro models for PLD. Focusing on liver models, the predominantly used in vitro model is the human liver carcinoma cell line HepG2 (Nioi et al. 2008; Nioi et al. 2007; Park et al. 2012; Sawada et al. 2005; Tilmant et al. 2011; van de Water et al. 2011). HepG2 is a cell line that mimics physiological liver functions only to a very limited degree, as it lacks a lot of liver-specific functions, for example sufficiently high levels of xenobiotic-metabolizing enzymes (Arzumanian et al. 2021; Guguen-Guillouzo & Guillouzo 2010). Primary human or rat hepatocytes have been used as in vitro models for the detection of PLD in a few publications (Bhandari et al. 2008; Kasahara et al. 2006; Takagi et al. 2016). Primary cells display higher functionality, but tend to be phenotypically unstable, exhibit large inter-donor variability (human) or pose the problem of possible inter-species differences (animal hepatocytes) (Guguen-Guillouzo & Guillouzo 2010). In contrast, the human cell line HepaRG displays stable liver-specific functionality, like the induction and expression of xenobiotic-metabolizing enzymes, without inter-donor variability (Guguen-Guillouzo & Guillouzo 2010). Therefore, HepaRG cells are utilized frequently as an in vitro model for predicting drug-induced liver injury (Lichtenstein et al. 2021; Wu et al. 2016). This study aims to investigate the potential of HepaRG cells in a high-throughput manner to predict PLD in comparison to other NAMs.

We demonstrate the suitability of the fluorescence-based LipidTOX assay in HepaRG cells as a cell-based in vitro method to predict PLD induction. Accordingly, the method was applied to test 20 substances known to induce PLD in vivo, as well as 15 substances which do not induce PLD in vivo.

Material and methods

Chemicals

All substances were purchased in the highest available purity. In Table S1, all test compounds (95–99% purity) are listed with their CAS number, supplier, used solvent, and stock concentration. Compounds were dissolved in water or dimethyl sulfoxide (DMSO; purity > 99%; Carl Roth, Karlsruhe, Germany) and stored at − 20 °C.

Cell culture

Undifferentiated HepaRG cells (Biopredic International, Sant Grégoire, France) were seeded in 96-well plates at a density of 9000 cells/well. For the cultivation, William’s E medium with 2 mM stable glutamine (PAN-Biotech, Aidenbach, Germany) was supplemented with 10% fetal bovine serum (FBS; FBS Superior S0615, Lot. 0001637726, Sigma-Aldrich, Taufkirchen, Germany), 100 U/mL penicillin and 100 µg/mL streptomycin (Capricorn Scientific, Ebsdorfergrund, Germany), 5 μg/ml human insulin (PAN-Biotech, Aidenbach, Germany) and 50 µM hydrocortisone-hemisuccinate (Sigma-Aldrich, Taufkirchen, Germany). Cells were cultured in the described medium for two weeks for proliferation. Afterwards, cell differentiation was induced by adding 1% DMSO to the culture medium for 48 h, then switching to culture medium containing 1.7% DMSO (differentiation medium) for the remaining two weeks. Differentiated HepaRG cells were adapted for 48 h before substance incubation to treatment medium (differentiation medium without phenol red (PAN-Biotech, Aidenbach, Germany)).

Cell viability

Cytotoxic effects of the tested substances were analyzed using the WST-1 Assay (Immunservice GmbH, Hamburg, Germany). Differentiated HepaRG cells were incubated for 24 h with the different substances. As solvent control, treatment medium containing 1.7% DMSO was used, and 0.01% Triton X–100 served as positive control. One hour prior to the end of incubation, 10 µL WST-1 reagent were added to each well. Absorbance of WST-1 was measured at 450 nm together with a reference wavelength at 620 nm using the Infinite M200 Pro plate reader from Tecan (Tecan, Männedorf, Switzerland). Cytotoxicity was analyzed for all tested substances to determine non-toxic concentrations (cell viability > 80%) for all subsequent experiments. IC_50_ values were calculated based on nonlinear regression applying sigmoidal dose-response curve fitting (variable slope) with a bottom constraint set to 0 using GraphPad Prism (Version 10.1.2).

LipidTOX assay

For the detection of PLD, the HCS LipidTOX™ Red Phospholipidosis Detection Reagent (Invitrogen, Carlsbad, United States of America) was utilized; a fluorescent dye designed to detect changes in phospholipid metabolism in cells. The dye was aliquoted and stored at  – 20 °C. Prior to cell treatment, LipidTOX was solved in treatment medium and filtered with a 0.2 µm filter. Differentiated HepaRG cells were treated with non-toxic concentrations of the tested substances or controls, and all conditions containing a final concentration of 0.5x LipidTOX. As a positive control, cells were treated with 12.5 µM chloroquine (data not shown, induction range: 270–460%); treatment medium containing 1.7% DMSO served as a solvent control. After 24 h of incubation at 37 °C, cells were fixed for 10 min at 37 °C with 3% (v/v) formaldehyde in phosphate-buffered saline containing 5 μg/ml Hoechst 33342 to stain cell nuclei (Invitrogen, Carlsbad, United States of America). Plates were imaged the same day using the high content screening system ZEISS Celldiscoverer 7 (Zeiss AG, Oberkochen, Germany) with a 10x objective to detect fluorescence signals of LipidTOX (Ex: 584 nm; Em: 608 nm) and Hoechst 33342 (Ex: 348 nm; Em: 455 nm). Three wells were incubated with each condition, and three images per well were acquired. The ZEN 3.1 lite software was used to analyzed obtained images. For relative phospholipid quantification, the “region count” of the LipidTOX signal was analyzed and normalized to the “region intensity sum” of the Hoechst signal.

Statistics

All presented data are the mean of three independent experiments. Statistical analysis was performed using GraphPad Prism (Version 10.1.2). Data was analyzed for normal distribution with the Shapiro–Wilk test. Significance was determined by one-way ANOVA analysis followed by Dunnett’s multiple comparisons test against the solvent control. Statistical significance was assumed at *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, and ****p ≤ 0.0001.

Results

Phospholipid accumulation in HepaRG cells after exposure to test compounds

A set of 35 substances from different chemical classes was selected for PLD induction testing in HepaRG cells in vitro. Based on available in vivo data, mainly obtained in rodents, 20 of the selected substances were classified as PLD inducers and 15 substances as non-inducers. Initial cytotoxicity testing was performed to identify the highest non-toxic concentrations (cell viability > 80%) after a 24 h incubation period for all compounds, and IC_50_ values were determined (Fig. S1). Compounds were then tested, using only non-cytotoxic concentrations, for their ability and potency to cause phospholipid accumulation in human HepaRG liver cells.

Each substance was screened in the LipidTOX assay to determine concentration-dependent phospholipid accumulation in HepaRG cells after 24 h of incubation. Representative fluorescence images and phospholipid quantification for selected substances with varying degrees of phospholipid accumulation are shown in Fig. 1. Quantitative data for all tested substances are provided in Fig. S2 for in vivo PLD-inducing substances, and in Fig. S3 for in vivo non-PLD-inducing substances. The induction strength and the resulting concentration-response curves differed among the various substances tested. Some substances, such as haloperidol, displayed a high potency resulting in an increased phospholipid content of over 600% of the solvent control (Fig. 1a), whereas others, such as erythromycin, showed lower potencies with a maximum of, e.g., approximately 300% phospholipid content (Fig. 1c). The curve progression exhibited distinct variations across the different PLD inducers. The results demonstrate that for some substances the phospholipid content steadily increased with higher concentrations, others displayed a maximum accumulation followed by a decline in phospholipid content, while others plateaued (Figs. S2 and S3). An overview of the overall results of PLD testing is presented in Table 1. In total, 23 compounds were tested positive in HepaRG cells, while 12 compounds were tested negative.

Fig. 1. Representative fluorescence images and phospholipid quantification results. Differentiated HepaRG cells were treated for 24 h with the indicated concentrations of each test substance and with LipidTOX™. Fluorescent images (phospholipids in red, nuclei in blue) for the highest effect concentration of the corresponding substance (left hand side) and concentration-response curves (right hand side) showing phospholipid accumulation for the selected substances with high (a: haloperiol), medium (b: triparanol), low (c: erythromycin), and no (d: carbamazepine) potency. Scale bar = 100 µm. The phospholipid content was normalized to the solvent control. Data are presented as the mean of three biological replicates ± SD (one-way ANOVA against solvent control: *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, and ****p ≤ 0.0001)

Table 1. Heatmap of measured PLD induction by all tested substances in different modelsR: rat; H: human; M: mouse; D: dog; Ha: hamster; Mo: monkey; L: liver. Color code: reported in silico prediction for PLD induction is likely (bright red) or unlikely (blue); PLD induction in vitro is high (dark red), medium (red), low (bright red), negative (blue); or has not been tested (white). Divided blocks: two assays with different outcome were performed in the same study. Detailed information for defined PLD induction classification can be found in Table S2. References: ^a^(Toubeau et al., 1986); ^b^(Yudate et al., 2012); ^c^(Guigui et al., 1988); ^d^(Lüllmann-Rauch, 1974); ^e^(Hruban et al., 1972); ^f^(Shaikh et al., 1987); ^g^(Pappu and Hauser, 1981); ^h^(Gray et al., 1971); ^i^(Whitehouse et al., 1994); ^j^(Lüllmann and Lüllmann-Rauch, 1981); ^k^(Weiss et al., 1980); ^l^(Chen and Yates, 1967); ^m^(Waters et al., 2001); ^n^(Goasduff et al., 1996); ^o^(Sahini et al., 2014); ^p^(Becker and Harris, 1983)

Comparison of phospholipid accumulation in different PLD models

The results on PLD induction in HepaRG using the LipidTOX assay are summarized in Table 1 and aligned with data of other published in vivo, in silico and in vitro methods. The induction potential for each substance was categorized into high, medium, low, and no potency for all selected in vitro studies, whereas in vivo and in silico data were only categorized into PLD-positive or -negative. Detailed information of defined thresholds and ranges for the PLD induction classification can be found in Table S2. In brief, PLD values were divided into four approximately equal ranges to improve the comparability of the various studies regarding their induction potential. A comparison of the data from the published methods reveals a strong overall correlation between in silico predictions, in vitro results, and in vivo PLD induction (Table 1). While the different in silico methods largely align in their predictions, also some discrepancies for individual substances were observed between the different in silico methods, for instance for clozapine. In comparison with in vivo observations and PLD induction in HepaRG cells, several false-negative in silico predictions appeared, for example for antibiotics such as erythromycin. Comparing the different in vitro assays, observed PLD induction data mostly align regarding positive or negative responses, with only minor disparities in their potency categorization. For the in vivo PLD-inducers haloperidol and erythromycin, in vitro results range from negative responses to strong induction, displaying a very heterogenous outcome. In vitro assays exhibit false-positive results for several compounds, for example disopyramide and methapyrilene. Noteworthy, the limited overlap between tested in vivo-negative compounds in the different in vitro assays hinders performance comparison in this regard.

The present results on PLD induction in HepaRG were collated to in vivo findings from the literature (Table 2). Comparison with in vivo data revealed a sensitivity of 95% (19 out of 20) and a specificity of 73% (11 out of 15). Therefore, the LipidTOX assay conducted in HepaRG cells shows an overall predictivity of 86% (30 out of 35).Table 2. Comparison of phospholipidosis induction in vivo vs. in vitro (in HepaRG cells)in vitro positivein vitro negativetotalin vivo positive19120sensitivity95%in vivo negative41115specificity73%total231235predictivity86%

Discussion

In previous studies, HepaRG cells have been successfully used to analyze chemically induced changes in hepatic lipid metabolism, mainly focusing on neutral fatty acid changes (Karaca et al. 2023; Lichtenstein et al. 2021). So far, only few studies have investigated PLD induction in HepaRG cells, and if so, a focus has been placed on few selected substances (Anthérieu et al. 2011; Tomida et al. 2017; Zhang et al. 2022). To our knowledge, this is the first study to investigate phospholipid accumulation in HepaRG cells using the LipidTOX assay as an in vitro cell-based high-throughput method for predicting PLD for a large number of compounds.

In the present study, the assay performance identifying PLD-inducing compounds was evaluated. A panel of 35 substances was examined for their ability to induce phospholipid accumulation. The tested compounds comprise a selection of PLD-inducing substances, with the majority being classified as CADs, as well as a set of non-PLD-inducing substances. Compared to in vivo data, results revealed a sensitivity of 95% and a specificity of 73% with an overall predictivity of 86% (Table 2). Although the results accord very well with in vivo findings on PLD induction (Table 1), some discrepancies were observed between in vitro and in vivo data. As an example, disopyramide and methapyrilene exhibited PLD induction in our assay, as well as in HepG2 cells (Tilmant et al. 2011; van de Water et al. 2011), but no induction in rats (Sahini et al. 2014; Yudate et al. 2012). Those discrepancies are not unexpected as the majority of in vivo data originates from rodent experiments which may exhibit limited comparability to human cells. One reason could be that oral absorption rates in vivo limit compound bioavailability, which would impair the identification of substance-specific effects. Moreover, the detection of PLD in vivo is mainly done by electron microscopy. This method provides high resolution, but has only a low throughput without quantification, and bears risks of subjective interpretation sampling bias, which could cause false negative categorization. Another factor might be that the ability of certain compounds to induce PLD might differ between humans and other species. It is well established that the activity and expression of metabolic enzymes involved in the biotransformation of substances and the expression and regulation of hepatic drug transporters mediating drug uptake and efflux differ across species, which impacts substance uptake, metabolism, and clearance (Martignoni et al. 2006; Wang et al. 2015). Interspecies differences become also apparent in the context of PLD considering in vitro studies comparing PLD induction in the same cell type of different species. For example, human and murine macrophages exhibited in vitro different phospholipid and lysosome accumulation (Öhlinger et al. 2020). Bhandari et al. could also demonstrate differences in PLD induction in vitro between the human HepG2 cell line and hepatocytes from rats and rhesus monkeys, confirming inter-species differences in PLD induction (Bhandari et al. 2008). Therefore, comparisons of in vitro data on PLD induction in this work have been limited to in vitro studies utilizing human-derived hepatic-like cells, while in vitro studies using other cell types have not been included in the comparative analyses.

To overcome the limitations of in vivo assessment of PLD, attempts have made to predict the PLD-inducing potential of chemicals with in silico NAMs, as fast, cost-efficient, and animal-free alternatives. Published in silico models focus mainly on physicochemical ClogP and pK_a_ calculations of substances, as those seem to be the shared characteristics among CADs (Fischer et al. 2000). Here, in silico methodologies discussed are the Ploemen model utilizing basic physicochemical calculations (pK_a_ and ClogP) (Pelletier et al. 2007; Ploemen et al. 2004), the Bayesian model which incorporates pK_a_, ClogP, amphiphilic moment, number of basic and acidic centers, structural and atom classes (Pelletier et al. 2007), and the model by Hanumegowda focusing pK_a_ and ClogP as well as tissue distribution factors (Hanumegowda et al. 2010). As shown by the good concordance of those models compared to in vivo data (75–88% predictivity), in silico NAMs offer a powerful and efficient tool for PLD prediction. The advantage of their high throughput and cost-effectiveness enables rapid screening of large compound libraries for early risk identification and prioritization. However, looking at the here investigated substances, in silico predictions show several false negatives when compared to in vivo data (Table 1), which negatively impacts method sensitivity. As all models are based on physicochemical characteristics, they probably can not fully capture all biological mechanisms leading to PLD (esp. for non-CAD compounds); this is why relatively low ClogP and tissue distribution values lead to false negative predictions as for several antibiotics, such as amikacin, erythromycin, and gentamicin (Hanumegowda et al. 2010). The inclusion of amphiphilic moments, number of basic and acidic centers, structural and atom classes seem to improve the prediction for this substance class (Pelletier et al. 2007). Interestingly, the negative PLD induction for amikacin aligns with results of the here established in vitro assay in HepaRG cells. Moreover, also for disopyramide and methapyrilene, which displayed PLD induction in HepaRG cells, were positively predicted by one of the in silico models. Moreover, factors like bioavailability, substance accumulation, and xenobiotic metabolism are neglected in most prediction tools which might result in false classification (Hanumegowda et al. 2010). For example, ketoconazole, haloperidol, and erythromycin are metabolized by various cytochrome P450 enzymes. Their metabolites can differ in their PLD-inducing properties from the parent compound, as for ketoconazole, which is extensively metabolized to de-N-acetyl ketoconazole, a potent PLD inducer (Whitehouse et al. 1994). In silico methods also lack information on the potency of substances to induce PLD, and the models are not suitable for assessing mixture effects. While in silico NAMs are highly useful for early-stage screening, their limitations advocate their use in combination with experimental approaches to enhance overall predictive accuracy for PLD.

In vitro assays represent a good alternative to in vivo and in silico methods. Previous to the here established assay, several studies have been published using the human liver carcinoma cell line HepG2 with different PLD detection methods. For the comparative analysis of PLD induction in vitro, only those studies which tested a larger set of substances and not only individual compounds were considered in this work. Across these studies, the reported accuracies were ranging from approximately 80% to 100%. Data obtained from the different HepG2 studies discussed in this work revealed differences in PLD induction potency among substances, similar to the results observed in HepaRG cells. Comparing results per substance, the induction strength varied between the different HepG2 assays leading partly to a categorization of three different PLD induction classes for the same substance (Table 1). The selection of seeded cell numbers, used dye, and incubation conditions seems to influence assay performance and measured PLD induction potency in HepG2 cells. Moreover, the HepG2 cell line is deficient in many liver-specific functions essential for mediating cellular responses to toxicological stressors, such as sufficiently high levels of xenobiotic-metabolizing enzymes (Guguen-Guillouzo & Guillouzo 2010). This limitation is particularly relevant, as metabolites can exhibit different toxicological effects compared to the parent compound. For instance, haloperidol and erythromycin do not exhibit PLD induction in the studies conducted by Sawada et al. and van de Water et al., although they are classified as PLD inducers according to in vivo data. In this study, haloperidol and erythromycin displayed PLD induction in HepaRG cells indicating the importance to use a model that provides xenobiotic metabolism functionality for PLD prediction. Comparing our findings with PLD induction in HepaRG cells with the LYSO-ID Red dye revealed a high degree of concordance for the majority of compounds tested in both assays, including induction potencies (Tomida et al. 2017). In contrast to this study, Tomida et al. reported no PLD induction for dexamethasone, but the tested concentrations in their study were considerably lower. The strong concordance between Tomida’s and our study emphasizes the reproducibility and utility of HepaRG cells for predicting PLD.

A key advantage offered by in vitro methods in comparison to other methodologies is the possibility to systematically investigate PLD induction potency and its concentration-dependent characteristics. As observed in HepaRG cells, curve shapes of concentration-dependent PLD induction in HepG2 cells seem to differ amongst substances. For the majority of the substances, a continuous increase in phospholipid content with rising concentration was observed, while for others, like chloroquine and clomipramine, the phospholipid content plateaued at a certain level (Nioi et al. 2007; Park et al. 2012). The appearance of plateaus may be due to a saturation threshold within cells beyond which no additional phospholipid accumulation is possible. However, these plateaus are primarily observed at phospholipid levels substantially lower than those associated with maximal PLD induction for other compounds. For example, chloroquine treatment reveals a plateau at 300% phospholipid content, whereas tilorone induces a maximum of 600% (Fig. S2). Therefore, the differences in PLD curve progressions could be an indicator for different modes of action instead of an indicator for saturation with phospholipids. Even though, no studies have been published correlating this observation to underlying mechanisms of PLD or specific chemical groups. In this study, PLD induction by some substances displayed a maximum accumulation followed by a decline in phospholipid content at higher concentrations. A drop in the phospholipid content was previously associated with cytotoxic effects (Bhandari et al. 2008). In contrast, viability measurement in HepaRG cells in our study revealed no cytotoxic effects (Fig. S1), suggesting the involvement of alternative mechanisms. Moreover, PLD induction in HepaRG cells using the LYSO-ID Red dye exhibited curve shapes similar to what has been observed in our study (Tomida et al. 2017). This reinforces the hypothesis that the appearance of plateaus or a declining content of phospholipids correlates with different underlying modes of action instead of saturation and cytotoxicity and is highlighting the complexity of PLD induction and the utility of in vitro HepaRG assays.

While applied concentrations of test compounds in vitro might exceed their typical serum levels in vivo, the objective of this study was to confirm the utility of the assay as a NAM, thereby providing framework for future refinement and broader applicability, including its potential use with non-pharmaceutical chemicals. This assay holds significant promise for future applications, such as the pre-screening of substances prior to animal testing, the evaluation of testing substance batteries to assess metabolic disruption of hepatic lipid metabolism, the investigation of effects of compound mixtures, and mechanistic studies aimed at elucidating the modes of action of PLD. The reliability of the here developed in vitro assay assessing PLD accumulation in HepaRG cells strongly supports the utility of in vitro NAMs. Furthermore, these findings emphasize the importance of integrating human-relevant in vitro models, such as the HepaRG cell line, alongside with in silico methods within a framework of NAM-based testing strategies.

Conclusion

In conclusion, data from this study underline that the LipidTOX assay in differentiated HepaRG cells is a useful in vitro NAM for the analysis of hepatic PLD induction, able to mimic responses observed in vivo with an overall predictivity of 86%. Results accord well with previously published PLD induction data, and compared to published in silico data, our assay exhibited higher sensitivity for PLD prediction, especially regarding detection of effects of metabolized substances.

Supplementary Information

Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 3093 kb)