Comparative analysis of eight cytotoxicity assays evaluated within the ACuteTox Project

Formation of adducts during digestion triggered dietary protein for alleviating cytotoxicity of 2-tert-butyl-1,4-benzoquinone

2-tert-butyl-1,4-benzoquinone (TBBQ), a degradation product of lipid antioxidant Tert-Butylhydroquinone (TBHQ), is a new hazardous compound in foods. This study investigated whether co-ingestion of dietary protein and TBBQ can alleviate the toxicity of TBBQ. The results indicated that soy protein isolate, whey protein isolate, and rice protein significantly reduced the residual amount of TBBQ during simulated gastrointestinal digestion. This result was attributed to the excellent elimination capacity of the released amino acids for TBBQ through formation of adducts. Among 20 amino acids, histidine, lysine, glycine, and cysteine showed better elimination capacity for TBBQ; they can eliminate 92.1%, 89.4%, 86.1%, and almost 100%, respectively, in 5 min at pH 8.0. Further study indicated that amino acids with lower ionization constant exhibited greater TBBQ elimination capacity. In addition, incubation of the cells with 50 μM TBBQ for 12 h decreased the cell viability to 28.95 ± 3.25%; while amino acids intervention was involved in the alleviation of TBBQ cytotoxicity via decreasing ROS. Particularly, cysteine showed 100 times more TBBQ detoxifying capacity than other amino acids. This work could provide a theoretical basis for the potential application of amino acids for detoxifying TBBQ in the food industry.

Antimicrobial Properties of New Polyamines Conjugated with Oxygen-Containing Aromatic Functional Groups

Antibiotic resistance is now a first-order health problem, which makes the development of new families of antimicrobials imperative. These compounds should ideally be inexpensive, readily available, highly active, and non-toxic. Here, we present the results of our investigation regarding the antimicrobial activity of a series of natural and synthetic polyamines with different architectures (linear, tripodal, and macrocyclic) and their derivatives with the oxygen-containing aromatic functional groups 1,3-benzodioxol, ortho/para phenol, or 2,3-dihydrobenzofuran. The new compounds were prepared through an inexpensive process, and their activity was tested against selected strains of yeast, as well as Gram-positive and Gram-negative bacteria. In all cases, the conjugated derivatives showed antimicrobial activity higher than the unsubstituted polyamines. Several factors, such as the overall charge at physiological pH, lipophilicity, and the topology of the polyamine scaffold were relevant to their activity. The nature of the lipophilic moiety was also a determinant of human cell toxicity. The lead compounds were found to be bactericidal and fungistatic, and they were synergic with the commercial antifungals fluconazole, cycloheximide, and amphotericin B against the yeast strains tested.

Toward Realistic Dosimetry In Vitro: Determining Effective Concentrations of Test Substances in Cell Culture and Their Prediction by an In Silico Mass Balance Model

Nominal concentrations (C_Nom) in cell culture media are routinely used to define concentration-effect relationships in the in vitro toxicology. The actual concentration in the medium (C_Medium) can be affected by adsorption processes, evaporation, or degradation of chemicals. Therefore, we measured the total and free concentration of 12 chemicals, covering a wide range of lipophilicity (log K_OW -0.07-6.84), in the culture medium (C_Medium) and cells (C_Cell) after incubation with Balb/c 3T3 cells for up to 48 h. Measured values were compared to predictions using an as yet unpublished in silico mass balance model that combined relevant equations from similar models published by others. The total C_Medium for all chemicals except tamoxifen (TAM) were similar to the C_Nom. This was attributed to the cellular uptake of TAM and accumulation into lysosomes. The free (i.e., unbound) C_Medium for the low/no protein binding chemicals were similar to the C_Nom, whereas values of all moderately to highly protein-bound chemicals were less than 30% of the C_Nom. Of the 12 chemicals, the two most hydrophilic chemicals, acetaminophen (APAP) and caffeine (CAF), were the only ones for which the C_Cell was the same as the C_Nom. The C_Cell for all other chemicals tended to increase over time and were all 2- to 274-fold higher than C_Nom. Measurements of C_Cytosol, using a digitonin method to release cytosol, compared well with C_Cell (using a freeze-thaw method) for four chemicals (CAF, APAP, FLU, and KET), indicating that both methods could be used. The mass balance model predicted the total C_Medium within 30% of the measured values for 11 chemicals. The free C_Medium of all 12 chemicals were predicted within 3-fold of the measured values. There was a poorer prediction of C_Cell values, with a median overprediction of 3- to 4-fold. In conclusion, while the number of chemicals in the study is limited, it demonstrates the large differences between C_Nom and total and free C_Medium and C_Cell, which were also relatively well predicted by the mass balance model.

Suitability of Human Mesenchymal Stem Cells Derived from Fetal Umbilical Cord (Wharton’s Jelly) as an Alternative In Vitro Model for Acute Drug Toxicity Screening

Preclinical toxicity screening is the first and most crucial test that assesses the safety of new candidate drugs before their consideration for further evaluation in clinical trials. In vitro drug screening using stem cells has lately arisen as a promising alternative to the “gold standard” of animal testing, but their suitability and performance characteristics in toxicological studies have so far not been comprehensively investigated. In this study, we focused on the evaluation of human mesenchymal stem cells isolated from the matrix (Wharton’s jelly) of fetal umbilical cord (WJSCs), which bear enhanced in vitro applicability due to their unique biological characteristics. In order to determine their suitability for drug-related cytotoxicity assessment, we adopted a high-throughput methodology that evaluated their sensitivity to a selected panel of chemicals in different culture environments. Cytotoxicity was measured within 48 h by means of MTS and/or NRU viability assays, and was compared directly (in vitro) or indirectly (in silico) to adult human mesenchymal stem cells and to reference cell lines of human and murine origin. Our data clearly suggest that human WJSCs can serve as a robust in vitro alternative for acute drug toxicity screening by uniquely combining rapid and versatile assay setup with high-throughput analysis, good representation of human toxicology, high reproducibility, and low cost.

Cytotoxicity and Mitochondrial Effects of Phenolic and Quinone-Based Mitochondria-Targeted and Untargeted Antioxidants on Human Neuronal and Hepatic Cell Lines: A Comparative Analysis

Mitochondriotropic antioxidants (MC3, MC6.2, MC4 and MC7.2) based on dietary antioxidants and analogs (caffeic, hydrocaffeic, trihydroxyphenylpropanoic and trihydroxycinnamic acids) were developed. In this study, we evaluate and compare the cytotoxicity profile of novel mitochondria-targeted molecules (generally known as MitoCINs) on human HepG2 and differentiated SH-SY5Y cells with the quinone-based mitochondria-targeted antioxidants MitoQ and SkQ1 and with two non-targeted antioxidants, resveratrol and coenzyme Q10 (CoQ10). We further evaluate their effects on mitochondrial membrane potential, cellular oxygen consumption and extracellular acidification rates. Overall, MitoCINs derivatives reduced cell viability at concentrations about six times higher than those observed with MitoQ and SkQ1. A toxicity ranking for both cell lines was produced: MC4 < MC7.2 < MC3 < MC6.2. These results suggest that C-6 carbon linker and the presence of a pyrogallol group result in lower cytotoxicity. MC3 and MC6.2 affected the mitochondrial function more significantly relative to MitoQ, SkQ1, resveratrol and CoQ10, while MC4 and MC7.2 displayed around 100-1000 times less cytotoxicity than SkQ1 and MitoQ. Based on the mitochondrial and cytotoxicity cellular data, MC4 and MC7.2 are proposed as leads that can be optimized to develop safe drug candidates with therapeutic application in mitochondrial oxidative stress-related diseases.

The design heatmap: A simple visualization of D -optimality design problems

Optimal experimental designs are often formal and specific, and not intuitively plausible to practical experimenters. However, even in theory, there often are many different possible design points providing identical or nearly identical information compared to the design points of a strictly optimal design. In practical applications, this can be used to find designs that are a compromise between mathematical optimality and practical requirements, including preferences of experimenters. For this purpose, we propose a derivative-based two-dimensional graphical representation of the design space that, given any optimal design is already known, will show which areas of the design space are relevant for good designs and how these areas relate to each other. While existing equivalence theorems already allow such an illustration in regard to the relevance of design points only, our approach also shows whether different design points contribute the same kind of information, and thus allows tweaking of designs for practical applications, especially in regard to the splitting and combining of design points. We demonstrate the approach on a toxicological trial where a D -optimal design for a dose-response experiment modeled by a four-parameter log-logistic function was requested. As these designs require a prior estimate of the relevant parameters, which is difficult to obtain in a practical situation, we also discuss an adaption of our representations to the criterion of Bayesian D -optimality. While we focus on D -optimality, the approach is in principle applicable to different optimality criteria as well. However, much of the computational and graphical simplicity will be lost.

An R-shiny application to calculate optimal designs for single substance and interaction trials in dose response experiments

BACKGROUND AND OBJECTIVE

Optimal experimental design theory proposes choosing specific settings in experimental trials in order to maximize the precision of the resulting parameter estimates. In dose response experiments, this corresponds to choosing the optimal dose levels for every available observation, and can be applied both to singular dose-response relationships and to interaction experiments where two substances are given simultaneously at several different mixture ratios ("ray designs"). While the theory of experimental design for this situation is well developed, the mathematical complexity prevents widespread use in practical applications. A simple to use application making the theory accessible to practitioners is thus very desirable.

METHODS

Results from established optimal experimental design theory are applied to dose response applications, focusing on log-logistic and Weibull class dose response functions. Suitable optimal design algorithms to solve these problems are implemented into an R-shiny based online application.

RESULTS

The application provides an interface to easily calculate D-optimal designs not only for singular dose experiments, but also for interaction trials with several combination rays of substances. Furthermore, the app also allows evaluating the efficiency of existing candidate designs, and finally allows construction of designs which perform robustly under different assumptions in regard to the true parameters.

Developing in vitro assays to transform gastrointestinal safety assessment: potential for microphysiological systems

Drug-induced gastrointestinal toxicities (DI-GITs) are among the most common adverse events in clinical trials. High prevalence of DI-GIT has persisted among new drugs due in part to the lack of robust experimental tools to allow early detection or to guide optimization of safer molecules. Developing in vitro assays for the leading GI toxicities (nausea, vomiting, diarrhoea, constipation, and abdominal pain) will likely involve recapitulating complex physiological properties that require contributions from diverse cell/tissue types including epithelial, immune, microbiome, nerve, and muscle. While this stipulation may be beyond traditional 2D monocultures of intestinal cell lines, emerging 3D GI microtissues capture interactions between diverse cell and tissue types. These interactions give rise to microphysiologies fundamental to gut biology. For GI microtissues, organoid technology was the breakthrough that introduced intestinal stem cells with the capability of differentiating into each of the epithelial cell types and that self-organize into a multi-cellular tissue proxy with villus- and crypt-like domains. Recently, GI microtissues generated using miniaturized devices with microfluidic flow and cyclic peristaltic strain were shown to induce Caco2 cells to spontaneously differentiate into each of the principle intestinal epithelial cell types. Second generation models comprised of epithelial organoids or microtissues co-cultured with non-epithelial cell types can successfully reproduce cross-'tissue' functional interactions broadening the potential of these models to accurately study drug-induced toxicities. A new paradigm in which in vitro assays become an early part of GI safety assessment could be realized if microphysiological systems (MPS) are developed in alignment with drug-discovery needs. Herein, approaches for assessing GI toxicity of pharmaceuticals are reviewed and gaps are compared with capabilities of emerging GI microtissues (e.g., organoids, organ-on-a-chip, transwell systems) in order to provide perspective on the assay features needed for MPS models to be adopted for DI-GIT assessment.

Use of LUCS (Light-Up Cell System) as an alternative live cell method to predict human acute oral toxicity

LUCS (Light-Up Cell System) is a new live cell test that allows assessment of a cell's homeostasis and its alteration by a toxic agent. To evaluate the effectiveness of LUCS as an alternative test method for acute oral toxicity, we compared EC₅₀s determined in HepG2 cells treated with 53 chemicals selected from the ACuteTox EU database with corresponding human blood LC₅₀s derived from human acute poisoning cases. Linear regression analysis showed that LUCS results predict human data to 69 %. Rodent oral LD₅₀s and LUCS EC₅₀s were then correlated to human LC₅₀s using shared data sets. Linear regression analyses comparing LUCS and animal data clearly showed that LUCS always predicts human toxicity better than animal data do. These successful prediction values prompted us to simplify the LUCS test, adapting it to regulatory and high throughput applications, resulting in a new protocol with consistent dose-response profiles and EC₅₀s. This study demonstrates that the LUCS test method could be relevant for assessing human acute oral toxicity with a simplified protocol adapted to commercially available fluorescence readers. We suggest that this new alternative method can be used for acute systemic toxicity testing in combination with other tests under European REACH and other regulations, wherever pertinent alternative methods are still lacking.

Modeling dose-response functions for combination treatments with log-logistic or Weibull functions

Dose-response curves of new substances in toxicology and related areas are commonly fitted using log-logistic functions. In more advanced studies, an additional interest is often how these substances will behave when applied in combination with a second substance. Here, an essential question for both design and analysis of these combination experiments is whether the resulting dose-response function will still be a member of the class of log-logistic functions, and, if so, what function parameters will result for the combined substances. Different scenarios might be considered in regard to whether a true interaction between the substances is expected, or whether the combination will simply be additive. In this paper, it is shown that the resulting function will in general not be a log-logistic function, but can be approximated very closely with one. Parameters for this approximation can be predicted from the parameters of both ingredients. Furthermore, some simple interaction structures can still be represented with a single log-logistic function. The approach can also be applied to Weibull-type dose-response functions, and similar results are obtained. Finally, the results were applied to a real data set obtained from cell culture experiments involving two cancer treatments, and the dose-response curve of a combination treatment was predicted from the properties of the singular substances.

Nonanimal Models for Acute Toxicity Evaluations: Applying Data-Driven Profiling and Read-Across

BACKGROUND

Low-cost, high-throughput in vitro bioassays have potential as alternatives to animal models for toxicity testing. However, incorporating in vitro bioassays into chemical toxicity evaluations such as read-across requires significant data curation and analysis based on knowledge of relevant toxicity mechanisms, lowering the enthusiasm of using the massive amount of unstructured public data.

OBJECTIVE

We aimed to develop a computational method to automatically extract useful bioassay data from a public repository (i.e., PubChem) and assess its ability to predict animal toxicity using a novel bioprofile-based read-across approach.

METHODS

A training database containing 7,385 compounds with diverse rat acute oral toxicity data was searched against PubChem to establish in vitro bioprofiles. Using a novel subspace clustering algorithm, bioassay groups that may inform on relevant toxicity mechanisms underlying acute oral toxicity were identified. These bioassays groups were used to predict animal acute oral toxicity using read-across through a cross-validation process. Finally, an external test set of over 600 new compounds was used to validate the resulting model predictivity.

RESULTS

Several bioassay clusters showed high predictivity for acute oral toxicity (positive prediction rates range from 62-100%) through cross-validation. After incorporating individual clusters into an ensemble model, chemical toxicants in the external test set were evaluated for putative acute toxicity (positive prediction rate equal to 76%). Additionally, chemical fragment -in vitro-in vivo relationships were identified to illustrate new animal toxicity mechanisms.

CONCLUSIONS

The in vitro bioassay data-driven profiling strategy developed in this study meets the urgent needs of computational toxicology in the current big data era and can be extended to develop predictive models for other complex toxicity end points. https://doi.org/10.1289/EHP3614.

UVA and UVB formulation phototoxicity in a three-dimensional human skin model: Photodegradation effect

In vitro three-dimensional human skin models are an innovative alternative to evaluate cytotoxicity and phototoxicity in the cosmetic industry. The aim of this study was to use a skin model to evaluate the potential toxicity of sunscreen formulations with or without exposure to UV radiation. In addition, the toxicity of these formulations was evaluated after exposure to photodegradation. The results showed toxicity with all formulations/conditions tested, including the control formulation, compared to PBS. Cell viability of photodegraded formulations - prior to the phototoxicity radiation process - was higher, indicating that some formulation components were degraded into products with reduced toxicity. The results also indicated that avobenzone was more unstable/toxic than octyl p-methoxycinnamate under the same test conditions. The sunscreens and their formulations were shown to be toxic to skin model cells to some extent, even when not exposed to UV irradiation; however the biological role of this toxicity is unclear. This result shows the importance of testing sunscreen formulations in real in-use conditions. Finally, since we used an in vitro assay based on a human cell model, this non-invasive technique represents a suitable alternative to animal models for phototoxicity tests in general and could have application in screening new sunscreen products.

LUCS (Light-Up Cell System), a universal high throughput assay for homeostasis evaluation in live cells

Observations of fluorescent cyanine dye behavior under illumination at 500 nm lead to a novel concept in cell biology allowing the development of a new live cell assay called LUCS, for Light-Up Cell System, measuring homeostasis in live cells. Optimization of the LUCS process resulted in a standardized, straightforward and high throughput assay with applications in toxicity assessment. The mechanisms of the LUCS process were investigated. Electron Paramagnetic Resonance experiments showed that the singlet oxygen and hydroxyl radical are involved downstream of the light effect, presumably leading to deleterious oxidative stress that massively opens access of the dye to its intracellular target. Reversible modulation of LUCS by both verapamil and proton availability indicated that plasma membrane proton/cation antiporters, possibly of the MATE drug efflux transport family, are involved. A mechanistic model is presented. Our data show that intracellular oxidation can be controlled by tuning light energy, opening applications in regulatory purposes, anti-oxidant research, chemotherapy efficacy and dynamic phototherapy strategies.

Discriminating modes of toxic action in mice using toxicity in BALB/c mouse fibroblast (3T3) cells

The objective of this study was to determine whether toxicity in mouse fibroblast cells (3T3 cells) could predict toxicity in mice. Synthesized data on toxicity was subjected to regression analysis and it was observed that relationship of toxicities between mice and 3T3 cells was not strong (R² = 0.41). Inclusion of molecular descriptors (e.g. ionization, pKa) improved the regression to R² = 0.56, indicating that this relationship is influenced by kinetic processes of chemicals or specific toxic mechanisms associated to the compounds. However, to determine if we were able to discriminate modes of action (MOAs) in mice using the toxicities generated from 3T3 cells, compounds were first classified into "baseline" and "reactive" guided by the toxic ratio (TR) for each compound in mice. Sequence, binomial and recursive partitioning analyses provided strong predictions of MOAs in mice based upon toxicities in 3T3 cells. The correct classification of MOAs based on these methods was 86%. Nearly all the baseline compounds predicted from toxicities in 3T3 cells were identified as baseline compounds from the TR in mice. The incorrect assignment of MOAs for some compounds is hypothesized to be due to experimental uncertainty that exists in toxicity assays for both mice and 3T3 cells. Conversely, lack of assignment can also arise because some reactive compounds have MOAs that are different in mice compared to 3T3 cells. The methods developed here are novel and contribute to efforts to reduce animal numbers in toxicity tests that are used to evaluate risks associated with organic pollutants in the environment.

Parametric modeling and optimal experimental designs for estimating isobolograms for drug interactions in toxicology

In toxicology and related areas, interaction effects between two substances are commonly expressed through a combination index [Formula: see text] evaluated separately at different effect levels and mixture ratios. Often, these indices are combined into a graphical representation, the isobologram. Instead of estimating the combination indices at the experimental mixture ratios only, we propose a simple parametric model for estimating the underlying interaction function. We integrate this approach into a joint model where both the parameters of the dose-response functions of the singular substances and the interaction parameters can be estimated simultaneously. As an additional benefit, this concept allows to determine optimal statistical designs for combination studies optimizing the estimation of the interaction function as a whole. From an optimal design perspective, finding the interaction parameters generally corresponds to a [Formula: see text]-optimality resp. [Formula: see text]-optimality design problem, while estimation of all underlying dose response parameters corresponds to a [Formula: see text]-optimality design problem. We show how optimal designs can be obtained in either case as well as how combination designs providing reasonable performance in regard to both criteria can be determined by putting a constraint on the efficiency in regard to one of the criteria and optimizing for the other. As all designs require prior information about model parameter values, which may be unreliable in practice, the effect of misspecifications is investigated as well.

Big data in chemical toxicity research: the use of high-throughput screening assays to identify potential toxicants

High-throughput screening (HTS) assays that measure the in vitro toxicity of environmental compounds have been widely applied as an alternative to in vivo animal tests of chemical toxicity. Current HTS studies provide the community with rich toxicology information that has the potential to be integrated into toxicity research. The available in vitro toxicity data is updated daily in structured formats (e.g., deposited into PubChem and other data-sharing web portals) or in an unstructured way (papers, laboratory reports, toxicity Web site updates, etc.). The information derived from the current toxicity data is so large and complex that it becomes difficult to process using available database management tools or traditional data processing applications. For this reason, it is necessary to develop a big data approach when conducting modern chemical toxicity research. In vitro data for a compound, obtained from meaningful bioassays, can be viewed as a response profile that gives detailed information about the compound's ability to affect relevant biological proteins/receptors. This information is critical for the evaluation of complex bioactivities (e.g., animal toxicities) and grows rapidly as big data in toxicology communities. This review focuses mainly on the existing structured in vitro data (e.g., PubChem data sets) as response profiles for compounds of environmental interest (e.g., potential human/animal toxicants). Potential modeling and mining tools to use the current big data pool in chemical toxicity research are also described.

Optimal experimental designs for dose-response studies with continuous endpoints

In most areas of clinical and preclinical research, the required sample size determines the costs and effort for any project, and thus, optimizing sample size is of primary importance. An experimental design of dose–response studies is determined by the number and choice of dose levels as well as the allocation of sample size to each level. The experimental design of toxicological studies tends to be motivated by convention. Statistical optimal design theory, however, allows the setting of experimental conditions (dose levels, measurement times, etc.) in a way which minimizes the number of required measurements and subjects to obtain the desired precision of the results. While the general theory is well established, the mathematical complexity of the problem so far prevents widespread use of these techniques in practical studies. The paper explains the concepts of statistical optimal design theory with a minimum of mathematical terminology and uses these concepts to generate concrete usable D-optimal experimental designs for dose–response studies on the basis of three common dose–response functions in toxicology: log-logistic, log-normal and Weibull functions with four parameters each. The resulting designs usually require control plus only three dose levels and are quite intuitively plausible. The optimal designs are compared to traditional designs such as the typical setup of cytotoxicity studies for 96-well plates. As the optimal design depends on prior estimates of the dose–response function parameters, it is shown what loss of efficiency occurs if the parameters for design determination are misspecified, and how Bayes optimal designs can improve the situation.

Benchmarking organic micropollutants in wastewater, recycled water and drinking water with in vitro bioassays

Thousands of organic micropollutants and their transformation products occur in water. Although often present at low concentrations, individual compounds contribute to mixture effects. Cell-based bioassays that target health-relevant biological endpoints may therefore complement chemical analysis for water quality assessment. The objective of this study was to evaluate cell-based bioassays for their suitability to benchmark water quality and to assess efficacy of water treatment processes. The selected bioassays cover relevant steps in the toxicity pathways including induction of xenobiotic metabolism, specific and reactive modes of toxic action, activation of adaptive stress response pathways and system responses. Twenty laboratories applied 103 unique in vitro bioassays to a common set of 10 water samples collected in Australia, including wastewater treatment plant effluent, two types of recycled water (reverse osmosis and ozonation/activated carbon filtration), stormwater, surface water, and drinking water. Sixty-five bioassays (63%) showed positive results in at least one sample, typically in wastewater treatment plant effluent, and only five (5%) were positive in the control (ultrapure water). Each water type had a characteristic bioanalytical profile with particular groups of toxicity pathways either consistently responsive or not responsive across test systems. The most responsive health-relevant endpoints were related to xenobiotic metabolism (pregnane X and aryl hydrocarbon receptors), hormone-mediated modes of action (mainly related to the estrogen, glucocorticoid, and antiandrogen activities), reactive modes of action (genotoxicity) and adaptive stress response pathway (oxidative stress response). This study has demonstrated that selected cell-based bioassays are suitable to benchmark water quality and it is recommended to use a purpose-tailored panel of bioassays for routine monitoring.

Validation of the hepatocyte-like HPCT-1E3 cell line as an in vitro model for the prediction of acute in vivo toxicity

In a pilot study, we tested 20 randomly-selected chemicals for their cytotoxicity toward the HPCT-1E3 cell model, in order to prove the ability of this in vitro model to predict human acute in vivo toxicity. The study revealed that, in contrast to most other in vitro models, results from the HPCT-1E3 cell-based system show better correlation with the more-relevant human acute lethal doses, whereas results from most other systems have a high predictivity for human lethal serum concentrations. For the prevalidation of the HPCT-1E3 model as a surrogate for regulatory acute in vivo toxicity tests, we have now expanded the list of tested chemicals to 57 substances, and have compared the results with data from the HepG2 cell assay. Again, a better correlation of HPCT-1E3 IC50 values with human oral lethal doses, as compared to correlation with human lethal serum concentrations, was observed after the pooling of all the tested substances (r(2) = 0.53 [P < 0.001] and r(2) = 0.41 [P = 0.009], respectively). Therefore, the HPCT-1E3 in vitro model may be a valuable tool for prediction of human oral toxicity, and may help to further reduce the number of animals used for in vivo toxicity tests.

Selection of test methods to be included in a testing strategy to predict acute oral toxicity: an approach based on statistical analysis of data collected in phase 1 of the ACuteTox project

More than 50 different in vitro and in silico methods assessing specific organ- and system-toxicity, such as haemato-, neuro-, nephro- and hepatotoxicity, as well as intestinal absorption, distribution and metabolism, have been used in the first phase of the ACuteTox project to test a common set of 57 chemicals. This paper describes the methods used for statistical evaluation of concentration-response data collected for each of the endpoint assays, and for the development of a testing strategy applicable for acute toxicity classification of chemicals based on the achieved results of the concentration-response analysis. A final list of in vitro test methods considered to be promising candidates for building blocks of the testing strategy is presented. Only these selected test methods were further investigated in the prevalidation phase of the project. The test methods were chosen according to their reproducibility and reliability and most importantly, according to their potential to classify chemicals into the official acute oral toxicity categories of the EU Classification, Labelling and Packaging (CLP) Regulation. The potential of the test methods to correctly classify the chemicals was assessed by Classification and Regression Trees (CART) analysis.