A microelectric cell sensing technique for in vitro assessment of ocular irritation.

The animal-based Draize test remains the gold standard for assessment of ocular irritation. However, subjective scoring methods, species differences, and animal welfare concerns have spurred development of alternative test methods. In this study, a novel in vitro method for assessing ocular irritancy was developed using a microelectric cell sensing technology, real-time cell analysis (RTCA). The cytotoxicity of sixteen compounds was assessed in two cell lines: ARPE-19 (human retina) and SIRC (rabbit cornea). In vitro inhibitory (IC50 and AUC50) values were determined at 6, 12, 24, 48, 72, and 96 h exposure, with a subset of values confirmed with MTT testing. The values displayed comparable predictivity of in vivo ocular irritation on the basis of a linear regression between the calculated values and each compounds' corresponding Draize-determined modified maximum average score (MMAS), but the ARPE-19 derived values were more strongly correlated than those from SIRC cells. Hence, IC50 values derived from ARPE-19 cells were used to predict the UN GHS/EU CLP classification of each test compound. The method was determined to have sensitivity of 90%, specificity of 50%, and overall concordance of 75%. Thus, RTCA testing may be best incorporated into a top-down tiered testing strategy for identification of ocular irritants in vitro.

A Novel Edge Effect Detection Method for Real-Time Cellular Analyzer Using Functional Principal Component Analysis.

Real-time cellular analyzer (RTCA) has been generally applied to test the cytotoxicity of chemicals. However, several factors impact the experimental quality. A non-negligible factor is the abnormal time-dependent cellular response curves (TCRCs) of the wells located at the edge of the E-plate which is defined as edge effect. In this paper, a novel statistical analysis is proposed to detect the edge effect. First, TCRCs are considered as observations of a random variable in a functional space. Then, functional principal component analysis (FPCA) is adopted to extract the principal component (PC) functions of the TCRCs, and the first and second PCs of these curves are selected to distinguish abnormal TCRCs. The average TCRC of the inner wells with the same culture environment is set as the standard. If the distance between the scoring point of the standard curve and one designated scoring point exceeds the defined threshold, the corresponding TCRC of the designated point should be removed automatically. The experimental results demonstrate the effectiveness of the proposed algorithm. This method can be used as a standard method to resolve general time-dependent series issues.

Modeling and simulation for toxicity assessment.

The effect of various toxicants on growth/death and morphology of human cells is investigated using the xCELLigence Real-Time Cell Analysis High Troughput in vitro assay. The cell index is measured as a proxy for the number of cells, and for each test substance in each cell line, time-dependent concentration response curves (TCRCs) are generated. In this paper we propose a mathematical model to study the effect of toxicants with various initial concentrations on the cell index. This model is based on the logistic equation and linear kinetics. We consider a three dimensional system of differential equations with variables corresponding to the cell index, the intracellular concentration of toxicant, and the extracellular concentration of toxicant. To efficiently estimate the model's parameters, we design an Expectation Maximization algorithm. The model is validated by showing that it accurately represents the information provided by the TCRCs recorded after the experiments. Using stability analysis and numerical simulations, we determine the lowest concentration of toxin that can kill the cells. This information can be used to better design experimental studies for cytotoxicity profiling assessment.

Machine learning algorithms for mode-of-action classification in toxicity assessment.

BACKGROUND: Real Time Cell Analysis (RTCA) technology is used to monitor cellular changes continuously over the entire exposure period. Combining with different testing concentrations, the profiles have potential in probing the mode of action (MOA) of the testing substances. RESULTS: In this paper, we present machine learning approaches for MOA assessment. Computational tools based on artificial neural network (ANN) and support vector machine (SVM) are developed to analyze the time-concentration response curves (TCRCs) of human cell lines responding to tested chemicals. The techniques are capable of learning data from given TCRCs with known MOA information and then making MOA classification for the unknown toxicity. A novel data processing step based on wavelet transform is introduced to extract important features from the original TCRC data. From the dose response curves, time interval leading to higher classification success rate can be selected as input to enhance the performance of the machine learning algorithm. This is particularly helpful when handling cases with limited and imbalanced data. The validation of the proposed method is demonstrated by the supervised learning algorithm applied to the exposure data of HepG2 cell line to 63 chemicals with 11 concentrations in each test case. Classification success rate in the range of 85 to 95 % are obtained using SVM for MOA classification with two clusters to cases up to four clusters. CONCLUSIONS: Wavelet transform is capable of capturing important features of TCRCs for MOA classification. The proposed SVM scheme incorporated with wavelet transform has a great potential for large scale MOA classification and high-through output chemical screening.

Predicting GHS toxicity using RTCA and discrete-time Fourier transform.

In order to promote the acceptance of cell-based toxicity testings, the accuracy of cytotoxicity test must be determined when compared to in vivo results. Traditional methods of cytotoxicity analysis, such as LC[Formula: see text] (concentration where 50% of the cells are killed) can be problematic since they have been found to vary with time. Technological advances in cytotoxicity testing make it easy to record the dynamic data on changes in cell proliferation, morphology, and damage. To effectively and reasonably analyze the dynamic data, we present a new in vitro toxicity assessed method using the discrete-time Fourier transform (DTFT) which maps the measured cell index from the time domain to the frequency domain. The direct current (DC) component of the DTFT is extracted as a feature which reflects the intensity of cytotoxicity. The smaller the value, the higher the cytotoxicity. Then, a novel toxicity index, as expressed in terms of DC[Formula: see text], is calculated. Results generated with selected test chemicals are compared favorably with data obtained from The Interagency Coordinating Committee on the Validation of Alternative Method (ICCVAM) report concerning the prediction of acute systemic toxicity in rodents. The method can be applied with the standard and high throughput to estimate acute rodent oral toxicity which reduces the number of animals required in subsequent pharmacological/toxicological studies.

Analysis of inter-/intra-E-plate repeatability in the real-time cell analyzer.

Over the past decade, the real-time cell analyzer (RTCA) has provided a good tool to the cell-based in vitro assay. Unlike the traditional systems that label the target cells with luminescence, fluorescence, or light absorption, RTCA monitors cell properties using noninvasive and label-free impedance measuring. However, realization of the maximum value of RTCA for applications will require assurance of within-experiment repeatability, day-to-day repeatability, and robustness to variations in conditions that might occur from different experiments. In this article, the performance and variability of RTCA is evaluated and a novel repeatability index (RI) is proposed to analyze the intra-/inter-E-plate repeatability of RTCA. The repeatability assay involves six cell lines and two media (water [H2O] and dimethyl sulfoxide [DMSO]). First, six cell lines are exposed to the media individually, and time-dependent cellular response curves characterized as a cell index (CI) are recorded by RTCA. Then, the variations along sampling time and among repeated tests are calculated and RI values are obtained. Finally, a discriminating standard is set up to evaluate the degree of repeatability. As opposed to the standardized methodologies, it is shown that the presented index can give the quantitative evaluation for repeatability of RTCA within E-plate and variation on different days.

Cytotoxicity assessment based on the AUC50 using multi-concentration time-dependent cellular response curves.

Although many indices have been developed to quantify chemical toxicity, substantial shortcoming is inherent in most of them, such as observation time dependence, insufficient robustness, and no comparison with the negative control. To assess the extent of exposure of the tested substance, a cytotoxicity assay named AUC(50) was developed to describe the time and concentration-dependent cellular responses. By monitoring the dynamic cytotoxicity response profile of living cells via the xCELLigence real-time cell analysis high-throughput (RTCA HT) system, changes in cell number (named cell index, CI) were recorded and analyzed subsequently. A normalized cell index (NCI) is introduced to reduce the influence of inter-experimental variations. The log-phase of cellular growth is considered, which alleviates the cell's spontaneous effect. The area between the control line and the assessed time-dependent cellular response curve (TCRC) of the tested substance was calculated, and the corresponding exponential kill model (concentration-response curve) was developed along with exploiting the concept of AUC(50). The validation of the proposed method is demonstrated by exposing HepG2 cell line to seven chemical compounds. Our findings suggested that the proposed AUC-based toxicity assay could be an alternative to the traditional single time-point assay, and it has potential to become routine settings for evaluating the cell-based in vitro assay. Furthermore, the AUC(50) combined with RTCA HT assay can be used to achieve a high-throughput screening that conventional cellular assay cannot achieve.

Dynamic cytotoxic response to microcystins using microelectronic sensor arrays.

Microcystins are bioactive metabolites produced by cyanobacteria in water. These cyclic heptapeptides have caused public health concern worldwide. By interfering with cellular phosphorylation and signaling, microcystins can cause acute and chronic liver diseases. Therefore, the World Health Organization (WHO) has set the provisional drinking water guideline value at 1.0 microg/L for microcystin-LR (free plus cell-bound). Microcystins do not readily cross cell membranes in in vitro cell-based assays, except for those using freshly isolated hepatocytes. However, the sensitivity of in vitro cell-based assays is not adequate for testing samples at low environmental concentrations. Hence, there is a need to develop a sensitive and stable cytotoxicity assay for use in environmental studies. On the basis of the observation that microcystin-LR can be transported by the liver-specific members of the organic anion transporting polypeptides (OATPs), we investigated the potential of using an OATP1B3-expressing cell line in a cytotoxicity assay for microcystins. Using a novel cell electronic sensing system (RT-CES), we were able to monitor the real-time, dynamic cytotoxic response to microcystins at microgram per liter concentrations. We demonstrated that the cytotoxicity of the most common microcystins, -LR, -YR, -RR, -LF, and -LW, was mediated by OATP1B3 transporters. Microcystin-LF is the most potent toxin among the five congeners. In conclusion, we have established a highly automated, real-time, sensitive, and stable assay for measuring microcystin cytoxicity.