Development of genotoxicity test procedures with Episkin, a reconstructed human skin model: towards new tools for in vitro risk assessment of dermally applied compounds?

Today reconstructed skin models that simulate human skin, such as Episkin, are widely used for safety or efficacy pre-screening. Moreover, they are of growing interest for regulatory purposes in the framework of alternatives to animal testing. In order to reduce and eventually replace results of in vivo genotoxicity testing with in vitro data, there is a need to develop new complementary biological models and methods with improved ability to predict genotoxic risk. This can be achieved if these new assays do take into account exposure conditions that are more relevant than in the current test systems. In an attempt to meet this challenge, two new applications using a human reconstructed skin model for in vitro genotoxicity assessment are proposed. The skin is the target organ for dermally exposed compounds or environmental stress. Although attempts have been made to develop genotoxicity test procedures in vivo on mouse skin, human reconstructed skin models have not been used for in vitro genotoxicity testing so far, although they present clear advantages over mouse skin for human risk prediction. This paper presents the results of the development of a specific protocol allowing to perform the comet assay, a genotoxicity test procedure, on reconstructed skin. The comet assay was conducted after treatment of Episkin with UV, Lomefloxacin and UV or 4-nitroquinoline-N-oxide (4NQO). Treatment with the sunscreen Mexoryl was able to reduce the extent of comet signal. A second approach to use reconstructed epidermis in genotoxicity assays is also proposed. Indeed, the skin is a biologically active barrier driving the response to exposure to chemical agents and their possible metabolites. A specific co-culture system (Figure 1) using Episkin to perform the regular micronucleus assay is presented. Micronucleus induction in L5178Y cells cultured underneath Episkin was assessed after treatment of the reconstructed epidermis with mitomycin C, cyclophosphamide or apigenin. This second way of using human reconstructed skin for genotoxicity testing aims at improving the relevance of exposure conditions in in vitro genotoxicity assays for dermally applied compounds.     

Optimizing classification of drug-drug interaction potential for CYP450 isoenzyme inhibition assays in early drug discovery

In drug discovery, the potential of cytochrome P450 inhibition of new chemical entities is frequently quantified in terms of IC50 values. In early drug discovery, a risk classification into low, medium, or high potential inhibitors is often sufficient for ranking and prioritizing of compounds. Although often 6 or more inhibitor concentrations are used to determine the IC50 value, the question arises whether it is possible to predict the risk class based on fewer inhibitor concentrations with comparable reliability. In this article, the authors propose a new integrated 2-point method with inhibitor concentrations chosen in accordance with the risk classification. They analyze its predictive power and the feasibility of not only classifying the compounds into different risk classes but also ranking those compounds that have been binned into the middle risk class. The proposed integrated 2-point method is thus highly suitable for automation. Altogether, it maintains the quality of the prediction while considerably reducing time and cost. The proposed method is applicable to other IC50 assays and risk classifications.     

Modelling tissues in 3D: the next future of pharmaco-toxicology and food research?

The development and validation of reliable in vitro methods alternative to conventional in vivo studies in experimental animals is a well-recognised priority in the fields of pharmaco-toxicology and food research. Conventional studies based on two-dimensional (2-D) cell monolayers have demonstrated their significant limitations: the chemically and spatially defined three-dimensional (3-D) network of extracellular matrix components, cell-to-cell and cell-to-matrix interactions that governs differentiation, proliferation and function of cells in vivo is, in fact, lost under the simplified 2-D condition. Being able to reproduce specific tissue-like structures and to mimic functions and responses of real tissues in a way that is more physiologically relevant than what can be achieved through traditional 2-D cell monolayers, 3-D cell culture represents a potential bridge to cover the gap between animal models and human studies. This article addresses the significance and the potential of 3-D in vitro systems to improve the predictive value of cell-based assays for safety and risk assessment studies and for new drugs development and testing. The crucial role of tissue engineering and of the new microscale technologies for improving and optimising these models, as well as the necessity of developing new protocols and analytical methods for their full exploitation, will be also discussed.     

Predicting conotoxin superfamily and family by using pseudo amino acid composition and modified Mahalanobis discriminant

The conotoxin proteins are disulfide rich small peptides that target ion channels and G protein coupled receptors. And they provide promising application in treating some chronic pain, epilepsy, cardiovascular diseases, and so on. Conotoxins may be classified into 11 superfamilies: A, D, I1, I2, J, L, M, O, P, S, and T according to the disulfide connectivity, highly conserved N-terminal precursor sequence and similar mode of actions. Successful prediction mature conotoxin superfamily peptide has important signification for the biological and pharmacological functions of the toxins. In this study, a new algorithm of increment of diversity combined with modified Mahalanobis discriminant is presented to predict five superfamilies by using the pseudo amino acid composition. The results of jackknife cross-validation test show that the overall prediction sensitivity and specificity are 88% and 91%, respectively. The predictive algorithm is also used to predict three O-conotoxin families. The 72% sensitivity and 78% specificity are obtained. These results indicate that the conotoxin superfamily peptides correlate with their amino acid compositions.     

POTENTIAL FOR MEASUREMENT OF LYSINE BIO AVAILABILITY IN POULTRY FEEDS BY RAPID MICROBIOLOGICAL ASSAYS — A REVIEW

Optimizing economic utilization of feed protein sources for poultry nutritional requiremens is difficult to achieve given the varied protein quality of the respective sources. Although there are several limiting amino acids in feeds that would benefit from development of rapid and more reliable bioavailability assays, lysine is of key importance since this amino acid is usually the first or second limiting amino acid in poultry feeds and is susceptible to processing treatments. However, to incorporate incoming sources in the most cost-effective manner, accurate and timely prediction of lysine bioavailability prior to use is desired to achieve a consistent nutritional value. Animal bioassays involving chicks are one of the standard accepted practices for evaluating protein quality and amino acid bioavailability, but such assays have several limitations. Alternative in vitro tests that accurately predict lysine bioavailability for feed proteins would solve many of the problems associated with currently used animal bioassays. The expected focus should be on the development of more rapid in vitro lysine bioavailability assays that could be easily used for evaluation of poultry feed protein sources.     

Prediction of drug-target interactions and drug repositioning via network-based inference

Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 μM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning.     

Biostatistical methods for the validation of alternative methods for in vitro toxicity testing

Statistical methods for the validation of toxicological in vitro test assays are developed and applied. Validation is performed either in comparison with in vivo assays or in comparison with other in vitro assays of established validity. Biostatistical methods are presented which are of potential use and benefit for the validation of alternative methods for the risk assessment of chemicals, providing at least an equivalent level of protection through in vitro toxicity testing to that obtained through the use of current in vivo methods. Characteristic indices are developed and determined. Qualitative outcomes are characterised by the rates of false-positive and false-negative predictions, sensitivity and specificity, and predictive values. Quantitative outcomes are characterised by regression coefficients derived from predictive models. The receiver operating characteristics (ROC) technique, applicable when a continuum of cut-off values is considered, is discussed in detail, in relation to its use for statistical modelling and statistical inference. The methods presented are examined for their use for the proof of safety and for toxicity detection and testing. We emphasise that the final validation of toxicity testing is human toxicity, and that the in vivo test itself is only a predictor with an inherent uncertainty. Therefore, the validation of the in vitro test has to account for the vagueness and uncertainty of the "gold standard" in vivo test. We address model selection and model validation, and a four-step scheme is proposed for the conduct of validation studies. Gaps and research needs are formulated to improve the validation of alternative methods for in vitro toxicity testing.     

Extent of N-terminal modifications in cytosolic proteins from eukaryotes

Most proteins in all organisms undergo crucial N-terminal modifications involving N-terminal methionine excision, N-alpha-acetylation or N-myristoylation (N-Myr), or S-palmitoylation. We investigated the occurrence of these poorly annotated but essential modifications in proteomes, focusing on eukaryotes. Experimental data for the N-terminal sequences of animal, fungi, and archaeal proteins, were used to build dedicated predictive modules in a new software. In vitro N-Myr experiments were performed with both plant and animal N-myristoyltransferases, for accurate prediction of the modification. N-terminal modifications from the fully sequenced genome of Arabidopsis thaliana were determined by MS. We identified 105 new modified protein N-termini, which were used to check the accuracy of predictive data. An accuracy of more than 95% was achieved, demonstrating (i) overall conservation of the specificity of the modification machinery in higher eukaryotes and (ii) robustness of the prediction tool. Predictions were made for various proteomes. Proteins that had undergone both N-terminal methionine (Met) cleavage and N-acetylation were found to be strongly overrepresented among the most abundant proteins, in contrast to those retaining their genuine unblocked Met. Here we propose that the nature of the second residue of an ORF is a key marker of the abundance of the mature protein in eukaryotes.     

Predicting protein interaction sites: binding hot-spots in protein-protein and protein-ligand interfaces

MOTIVATION: Protein assemblies are currently poorly represented in structural databases and their structural elucidation is a key goal in biology. Here we analyse clefts in protein surfaces, likely to correspond to binding 'hot-spots', and rank them according to sequence conservation and simple measures of physical properties including hydrophobicity, desolvation, electrostatic and van der Waals potentials, to predict which are involved in binding in the native complex. RESULTS: The resulting differences between predicting binding-sites at protein-protein and protein-ligand interfaces are striking. There is a high level of prediction accuracy (< or =93%) for protein-ligand interactions, based on the following attributes: van der Waals potential, electrostatic potential, desolvation and surface conservation. Generally, the prediction accuracy for protein-protein interactions is lower, with the exception of enzymes. Our results show that the ease of cleft desolvation is strongly predictive of interfaces and strongly maintained across all classes of protein-binding interface.     

SPR for molecular interaction analysis: a review of emerging application areas

PubMed searches identified four emerging application areas for surface plasmon resonance systems. Food analysis, proteomics, immunogenicity and drug discovery. These application areas are reviewed. In connection with the review of drug discovery applications a case study is presented. This study demonstrates the value of combining results from drug-target and ADME predictive assays for compound selection.     

Relevance and follow-up of positive results in in vitro genetic toxicity assays: an ILSI-HESI initiative

In vitro genotoxicity assays are often used to screen and predict whether chemicals might represent mutagenic and carcinogenic risks for humans. Recent discussions have focused on the high rate of positive results in in vitro tests, especially in those assays performed in mammalian cells that are not confirmed in vivo. Currently, there is no general consensus in the scientific community on the interpretation of the significance of positive results from the in vitro genotoxicity assays. To address this issue, the Health and Environmental Sciences Institute (HESI), held an international workshop in June 2006 to discuss the relevance and follow-up of positive results in in vitro genetic toxicity assays. The goals of the meeting were to examine ways to advance the scientific basis for the interpretation of positive findings in in vitro assays, to facilitate the development of follow-up testing strategies and to define criteria for determining the relevance to human health. The workshop identified specific needs in two general categories, i.e., improved testing and improved data interpretation and risk assessment. Recommendations to improve testing included: (1) re-examine the maximum level of cytotoxicity currently required for in vitro tests; (2) re-examine the upper limit concentration for in vitro mammalian studies; (3) develop improved testing strategies using current in vitro assays; (4) define criteria to guide selection of the appropriate follow-up in vivo studies; (5) develop new and more predictive in vitro and in vivo tests. Recommendations for improving interpretation and assessment included: (1) examine the suitability of applying the threshold of toxicological concern concepts to genotoxicity data; (2) develop a structured weight of evidence approach for assessing genotoxic/carcinogenic hazard; and (3) re-examine in vitro and in vivo correlations qualitatively and quantitatively. Conclusions from the workshop highlighted a willingness of scientists from various sectors to change and improve the current paradigm and move from a hazard identification approach to a "realistic" risk-based approach that incorporates information on mechanism of action, kinetics, and human exposure..     

Translating clinical findings into knowledge in drug safety evaluation--drug induced liver injury prediction system (DILIps)

Drug-induced liver injury (DILI) is a significant concern in drug development due to the poor concordance between preclinical and clinical findings of liver toxicity. We hypothesized that the DILI types (hepatotoxic side effects) seen in the clinic can be translated into the development of predictive in silico models for use in the drug discovery phase. We identified 13 hepatotoxic side effects with high accuracy for classifying marketed drugs for their DILI potential. We then developed in silico predictive models for each of these 13 side effects, which were further combined to construct a DILI prediction system (DILIps). The DILIps yielded 60-70% prediction accuracy for three independent validation sets. To enhance the confidence for identification of drugs that cause severe DILI in humans, the "Rule of Three" was developed in DILIps by using a consensus strategy based on 13 models. This gave high positive predictive value (91%) when applied to an external dataset containing 206 drugs from three independent literature datasets. Using the DILIps, we screened all the drugs in DrugBank and investigated their DILI potential in terms of protein targets and therapeutic categories through network modeling. We demonstrated that two therapeutic categories, anti-infectives for systemic use and musculoskeletal system drugs, were enriched for DILI, which is consistent with current knowledge. We also identified protein targets and pathways that are related to drugs that cause DILI by using pathway analysis and co-occurrence text mining. While marketed drugs were the focus of this study, the DILIps has a potential as an evaluation tool to screen and prioritize new drug candidates or chemicals, such as environmental chemicals, to avoid those that might cause liver toxicity. We expect that the methodology can be also applied to other drug safety endpoints, such as renal or cardiovascular toxicity.     

Benchmarking protein–protein interface predictions: Why you should care about protein size

A number of predictive methods have been developed to predict protein–protein binding sites. Each new method is traditionally benchmarked using sets of protein structures of various sizes, and global statistics are used to assess the quality of the prediction. Little attention has been paid to the potential bias due to protein size on these statistics. Indeed, small proteins involve proportionally more residues at interfaces than large ones. If a predictive method is biased toward small proteins, this can lead to an over‐estimation of its performance. Here, we investigate the bias due to the size effect when benchmarking protein‐protein interface prediction on the widely used docking benchmark 4.0. First, we simulate random scores that favor small proteins over large ones. Instead of the 0.5 AUC (Area Under the Curve) value expected by chance, these biased scores result in an AUC equal to 0.6 using hypergeometric distributions, and up to 0.65 using constant scores. We then use real prediction results to illustrate how to detect the size bias by shuffling, and subsequently correct it using a simple conversion of the scores into normalized ranks. In addition, we investigate the scores produced by eight published methods and show that they are all affected by the size effect, which can change their relative ranking. The size effect also has an impact on linear combination scores by modifying the relative contributions of each method. In the future, systematic corrections should be applied when benchmarking predictive methods using data sets with mixed protein sizes. Proteins 2014; 82:1444–1452. © 2014 Wiley Periodicals, Inc.     

Predicting the actigraphy-based acrophase using the Morningness–Eveningness Questionnaire (MEQ) in college students of North Italy

Actigraphy is the reference objective method to measure circadian rhythmicity. One simpler subjective approach to assess the circadian typology is the Morningness–Eveningness Questionnaire (MEQ) by Horne and Ostberg. In this study, we compared the MEQ score against the actigraphy-based circadian parameters MESOR, amplitude and acrophase in a sample of 54 students of the University of Milan in Northern Italy. MEQ and the acrophase resulted strongly and inversely associated (r = ?0.84, p < 0.0001), and their relationship exhibited a clear-cut linear trend. We thus used linear regression to develop an equation enabling us to predict the value of the acrophase from the MEQ score. The parameters of the regression model were precisely estimated, with the slope of the regression line being significantly different from 0 (p < 0.0001). The best-fit linear equation was: acrophase (min) = 1238.7–5.49·MEQ, indicating that each additional point in the MEQ score corresponded to a shortening of the acrophase of approximately 5 min. The coefficient of determination, R², was 0.70. The residuals were evenly distributed and did not show any systematic pattern, thus indicating that the linear model yielded a good, balanced prediction of the acrophase throughout the range of the MEQ score. In particular, the model was able to accurately predict the mean values of the acrophase in the three chronotypes (Morning-, Neither-, and Evening-types) in which the study subjects were categorized. Both the confidence and prediction limits associated to the regression line were calculated, thus providing an assessment of the uncertainty associated with the prediction of the model. In particular, the size of the two-sided prediction limits for the acrophase was about ±100 min in the midrange of the MEQ score. Finally, k-fold cross-validation showed that both the model’s predictive ability on new data and the model’s stability to changes in the data set used for parameter estimation were good. In conclusion, the actigraphy-based acrophase can be predicted using the MEQ score in a population of college students of North Italy.     

A Decision-Tree Strategy for Combining Diagnostic Tests for Prediction

In the context of medical screening, various diagnostic tests have been developed for determining whether a disease is present in an individual. Similarly, in the context of toxicological screening, a variety of short-term assays have been developed to predict whether a chemical would be carcinogenic if tested in a long-term bioassay. In both contexts, it is a challenge to combine the results of several predictive tests in a way that improves on the predictivity of the individual tests. Increases in positive predictivity can be accompanied by decreases in negative predictivity, and vice versa. This article presents a decision-tree classification model for combining results from several independent short-term or diagnostic tests to quantify the likelihood of a true positive result (patient has disease, or chemical is carcinogenic). The decision-tree strategy determines the most advantageous sequence for conducting the predictive tests. The classification model is based on statistical confidence limits on the predictive probability of disease (carcinogenicity) rather than on the central estimate of the predictive probability. This model is applied to the assessment of the abilities of four short-term tests in the prediction of chemical carcinogenicity under the assumption of independence among the four tests, and is used to demonstrate a testing strategy for the application of three pancreatic cancer diagnostic tests.     

Comparing solid tumors with cell lines: implications for identifying drug resistance genes in cancer

Gene expression arrays allow researchers to profile the differences between cell lines or tissues and they may identify genetic markers of development, organ maturation, or tumor progression. Although a primary tumor that grows in a host and a tumor-cell-line derived from that primary tumor and grown in vitro share similar gene expression profiles, there are, not unexpectedly, some important differences. In fact, Stein and colleagues have found that genes that are differentially expressed in primary tumors as compared to the specific genes expressed in their cell-line derivatives are more reliably predictive of tumor tractability. Thus, sensitivity in vitro might not reflect sensitivity in vivo. Because anti-tumor compounds are largely evaluated in cell culture assays, these compounds' therapeutic utility must be judged in light of genes described by Stein et al. that better predict tractability.     

Cross-platform comparison of microarray-based multiple-class prediction

High-throughput microarray technology has been widely applied in biological and medical decision-making research during the past decade. However, the diversity of platforms has made it a challenge to re-use and/or integrate datasets generated in different experiments or labs for constructing array-based diagnostic models. Using large toxicogenomics datasets generated using both Affymetrix and Agilent microarray platforms, we carried out a benchmark evaluation of cross-platform consistency in multiple-class prediction using three widely-used machine learning algorithms. After an initial assessment of model performance on different platforms, we evaluated whether predictive signature features selected in one platform could be directly used to train a model in the other platform and whether predictive models trained using data from one platform could predict datasets profiled using the other platform with comparable performance. Our results established that it is possible to successfully apply multiple-class prediction models across different commercial microarray platforms, offering a number of important benefits such as accelerating the possible translation of biomarkers identified with microarrays to clinically-validated assays. However, this investigation focuses on a technical platform comparison and is actually only the beginning of exploring cross-platform consistency. Further studies are needed to confirm the feasibility of microarray-based cross-platform prediction, especially using independent datasets.     

Prediction of protein subcellular locations using fuzzy k-NN method

MOTIVATION: Protein localization data are a valuable information resource helpful in elucidating protein functions. It is highly desirable to predict a protein's subcellular locations automatically from its sequence. RESULTS: In this paper, fuzzy k-nearest neighbors (k-NN) algorithm has been introduced to predict proteins' subcellular locations from their dipeptide composition. The prediction is performed with a new data set derived from version 41.0 SWISS-PROT databank, the overall predictive accuracy about 80% has been achieved in a jackknife test. The result demonstrates the applicability of this relative simple method and possible improvement of prediction accuracy for the protein subcellular locations. We also applied this method to annotate six entirely sequenced proteomes, namely Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Oryza sativa, Arabidopsis thaliana and a subset of all human proteins. AVAILABILITY: Supplementary information and subcellular location annotations for eukaryotes are available at http://166.111.30.65/hying/fuzzy_loc.htm