Improved Data Interpretation Research Articles

LMS-based continuous pediatric reference values for soluble receptor activator of nuclear factor kappa B ligand (sRANKL) and osteoprotegerin (OPG) in the HARP cohort

SummarySoluble RANKL (sRANKL) and osteoprotegerin (OPG) are regulators of osteoclast differentiation and activation, but adequate pediatric reference values are lacking. Here we provide LMS (Lambda-Mu-Sigma)-based continuous pediatric reference percentiles for sRANKL, OPG and sRANKL/OPG ratio that will allow calculation of standardized patient z-scores to assess bone modeling in children.PurposeSoluble receptor activator of nuclear factor kappa B ligand (sRANKL) and osteoprotegerin (OPG) are regulators of osteoclast differentiation and activation and thus bone metabolic turnover in children. Adequate pediatric reference values for their serum/plasma concentrations are lacking. The development of Lambda-Mu-Sigma (LMS)-based continuous reference percentiles for laboratory parameters allow improved data interpretation in clinical practice.MethodsA total of 300 children aged 0.1–18 years (166 boys) were enrolled in the HAnnover Reference values for Pediatrics (HARP) study. sRANKL and OPG were assessed by ELISA. LMS-based continuous reference percentiles were generated using RefCurv software.ResultsLMS-based percentiles were established for sRANKL, OPG and sRANKL/OPG ratio, which were all found to be age-dependent. sRANKL and sRANKL/OPG associated with sex. In boys, sRANKL percentiles were highest during infancy, followed by a continuous decline until the age of 7 years and a second peak around age 12–13 years. In girls, a continuous, slow decline of sRANKL percentiles was noticed from infancy onwards until the age of 13 years, followed by a rapid decline until adulthood. OPG percentiles continuously declined from infancy to adulthood. The percentiles for sRANKL/OPG ratio paralleled those of sRANKL. Serum concentrations of sRANKL correlated with OPG and serum phosphate z-scores, while OPG concentrations inversely associated with standardized body weight, BMI, and urinary phosphate to creatinine ratio (each p < 0.05).ConclusionThis is the first report of LMS-based continuous pediatric reference percentiles for sRANKL, OPG and sRANKL/OPG ratio that allows calculation of standardized patient z-scores to assess bone metabolic turnover in children.

Automated Deep Learning-Based Diagnosis and Molecular Characterization of Acute Myeloid Leukemia Using Flow Cytometry

The current flow cytometric analysis of blood and bone marrow samples for diagnosis of acute myeloid leukemia (AML) relies heavily on manual intervention in the processing and analysis steps, introducing significant subjectivity into resulting diagnoses and necessitating highly trained personnel. Furthermore, concurrent molecular characterization via cytogenetics and targeted sequencing can take multiple days, delaying patient diagnosis and treatment. Attention-based multi-instance learning models (ABMILMs) are deep learning models that make accurate predictions and generate interpretable insights regarding the classification of a sample from individual events/cells; nonetheless, these models have yet to be applied to flow cytometry data. In this study, we developed a computational pipeline using ABMILMs for the automated diagnosis of AML cases based exclusively on flow cytometric data. Analysis of 1820 flow cytometry samples shows that this pipeline provides accurate diagnoses of acute leukemia (area under the receiver operating characteristic curve [AUROC] 0.961) and accurately differentiates AML vs B- and T-lymphoblastic leukemia (AUROC 0.965). Models for prediction of 9 cytogenetic aberrancies and 32 pathogenic variants in AML provide accurate predictions, particularly for t(15;17)(PML::RARA) [AUROC 0.929], t(8;21)(RUNX1::RUNX1T1) (AUROC 0.814), and NPM1 variants (AUROC 0.807). Finally, we demonstrate how these models generate interpretable insights into which individual flow cytometric events and markers deliver optimal diagnostic utility, providing hematopathologists with a data visualization tool for improved data interpretation, as well as novel biological associations between flow cytometric marker expression and cytogenetic/molecular variants in AML. Our study is the first to illustrate the feasibility of using deep learning-based analysis of flow cytometric data for automated AML diagnosis and molecular characterization.

A combined liquid chromatography – trapped ion mobility – tandem high-resolution mass spectrometry and multivariate analysis approach for the determination of enzymatic reactivity descriptors in biomass hydrolysates

Intermediate products such as oxygenated compounds may interfere with bioconversion kinetics of lignocellulosic biomass into bioethanol. This work presents a multidimensional approach, based on liquid chromatography (LC), trapped ion mobility spectrometry (TIMS), tandem high-resolution mass spectrometry (HRMS/MS), and multivariate analysis, for the identification of enzymatic reactivity descriptors in 22 industrial biomass samples, called hydrolysates. The first part of the study is dedicated to the improvement of the chemical diversity assessment of the hydrolysates through an original three-dimensional Van Krevelen diagram displaying the double bond equivalent (DBE) as third dimension. In a second part, the evaluation of data by multivariate data analysis allowed the discrimination of sample according to the biomass type and the level of enzymatic reactivity. In the last part, a potential descriptor of low enzymatic reactivity was selected and used in a case study. An in-depth structural analysis was performed on the feature annotated as carbohydrate derivative. Considering the intricate fragmentation spectrum exhibited by the selected feature, trapped ion mobility was employed to enhance separation prior to the HRMS/MS experiments. This final step improved data interpretation and increased the identification confidence level leading to the characterization of xylotriose, 3,5-dimethoxy-4-hydroxybenzaldehyde and 4-hydroxy-3-methoxy-cinnamaldehyde. This is the first study to present an untargeted multidimensional approach for the identification of enzymatic hydrolysis inhibitors in industrial hydrolysate samples.

Advancing Condition Assessment of Reinforced Concrete Bridge Elements Through Automation, Visualization, and Improved Interpretation of Multi-NDE Technology Data

Economic bridge management requires accurate information about the condition of bridges in the network. Nondestructive evaluation (NDE)has shown high potential in providing accurate condition assessment and, through periodic surveys, development of accurate deterioration, predictive, and life-cycle cost models. To achieve wide adoption by transportation agencies, further advances should be made that would lead to the accuracy of NDE-based condition assessment, reduced costs and traffic interruptions, and minimized risk to transportation workers. The paper discusses the following areas of improvement: increased speed and safety of data collection through the use of robotic systems, and improved data interpretation through visualization and joint analysis of data collected by multiple NDE technologies.

A data-driven model for pressure distribution measurements by a four-electrode polymer sensor

Machine learning techniques have significantly enhanced signal handling and prediction accuracy in electronic skins by facilitating the extraction of useful information hidden in the sensory outputs. We present a polymer sensor with four irregularly shaped electrodes enabling energy-efficient sensing and improved data interpretation. We first compute the resistance change for the sensing element under pressure. The finite element method is used to solve the three-dimensional nonlinear elasticity. The electric potential distribution is simulated using an arbitrary Lagrangian-Eulerian formulation. We then build reduced-order models for detecting pressure distribution for different pressure cases. The inverse models built using deep neural networks showed good prediction accuracy and resolution. The irregular arrangement of the electrodes resulted in low correlation coefficients between the input resistances, and therefore, efficient predictions of the four-electrode sensor. It is demonstrated that the present four-electrode sensor could replace at least four sensors in an array. For arbitrary pressure distributions over a 2 × 2 surface resolution, a model is trained with a mean accuracy of 22 Pa in the range of 1–20 kPa. Additionally, for a single square-shaped pressure with arbitrary magnitude and surface area, position prediction accuracies of 99% and 96% are obtained at 4 × 4 and 8 × 8 resolutions, respectively. Moreover, the models showed low sensitivity to the uncertainty in the measured signals.

Cheating on forensic hair testing? Detection of potential biomarkers for cosmetically altered hair samples using untargeted hair metabolomics.

Hair analysis has become an integral part in forensic toxicological laboratories for e.g. assessment of drug or alcohol abstinence. However, hair samples can be manipulated by cosmetic treatments, altering drug concentrations which eventually leads to false negative hair test results. In particular oxidative bleaching of hair samples under alkaline conditions significantly affects incorporated drug concentrations. To date, current techniques to detect cosmetic hair adulterations bear limitations such as the implementation of cut-off values or the requirement of specialized instrumentations. As a new approach, untargeted hair metabolomics analysis was applied to detect altered, endogenous biomolecules that could be used as biomarkers for oxidative cosmetic hair treatments. For this, genuine hair samples were treated in vitro with 9% hydrogen peroxide (H2O2) for 30 minutes. Untreated and treated hair samples were analyzed using liquid-chromatography high-resolution time-of-flight mass spectrometry. In total, 69 metabolites could be identified as significantly altered after hair bleaching. The majority of metabolites decreased after bleaching, yet totally degraded metabolites were most promising as suitable biomarkers. The formation of biomarker ratios of metabolites decreasing and increasing in concentrations improved the discrimination of untreated and treated hair samples. With the results of this study, the high variety of identified biomarkers now offers the possibility to include single biomarkers or biomarker selections into routine screening methods for improved data interpretation of hair test results.

Polarization Analysis in Mössbauer Reflectometry with Synchrotron Mössbauer Source

Polarization selection of the reflected radiation has been employed in Mössbauer reflectivity measurements with a synchrotron Mössbauer source (SMS). The polarization of resonantly scattered radiation differs from the polarization of an incident wave so the Mössbauer reflectivity contains a scattering component with 90° rotated polarization relative to the π-polarization of the SMS for some hyperfine transitions. We have shown that the selection of this rotated π→σ component from total reflectivity gives an unusual angular dependence of reflectivity characterized by a peak near the critical angle of the total external reflection. In the case of collinear antiferromagnetic interlayer ordering, the “magnetic” maxima on the reflectivity angular curve are formed practically only by radiation with this rotated polarization. The first experiment on Mössbauer reflectivity with a selection of the rotated polarization discovers the predicted peak near the critical angle. The measurement of the rotated π→σ polarization component in Mössbauer reflectivity spectra excludes the interference with non-resonant electronic scattering and simplifies the spectrum shape near the critical angle allowing for an improved data interpretation in the case of poorly resolved spectra. It is shown that the selected component of Mössbauer reflectivity with rotated polarization is characterized by enhanced surface sensitivity, determined by the “squared standing waves” depth dependence. Therefore, the new approach has interesting perspectives for investigations of surfaces, ultrathin layers and multilayers having complicated magnetic structures.

Review and investigations on geothermal energy extraction from abandoned petroleum wells

Geothermal energy is a sustainable and renewable energy source, which can be used in electricity production, space heating/cooling, and other industrial applications. In the recent years, it has been gathering more and more attention due to its numerous advantages as low impact on the surrounding environment, continuous power outputs, low greenhouse gas emissions, and worldwide availabilities. All make the geothermal energy a significant contributor to the global energy productions in an environmentally friendly way. One big concern of geothermal sources’ exploration is the expensive investment costs of geothermal wells. Utilizing abandoned petroleum wells for the purpose of geothermal extraction is a novel idea. Well temperature profiles help to estimate how much heat can be transferred and produced from the wells. In this paper, a literature review has been done to investigate the existing applications on geothermal energy extraction utilizing abandoned petroleum wells. Then, the case study demonstrates the importance of properties of working fluids, wellbore architecture, and operational parameters (circulation rate, inlet temperature, etc.) in geothermal energy production. The obtained results can be used to achieve an improved data interpretation and generate more optimal solutions. In geothermal projects, extensive knowledge of the heat transfer is of great importance for the economical aspect and the performance of wells. Our work demonstrates that it is a good approach to provide cost-effective solutions to enhance heat extraction from geothermal wells.

Identification of sudden transitions in sensor data from rocket tests using wavelet transforms within an integrated health monitoring system

Under a project undertaken at NASA’s Stennis Space Center, an integrated framework has been developed for intelligent monitoring of smart elements. Integrated Systems Health Monitoring is an implementation of a monitoring system which is robust, user friendly, and adaptable. This paper focuses on smart sensors, and shows the advantage of utilizing an enhanced version of a previously developed intelligent system, DATA-SIMLAMT, called Enhanced DATA-SIMLAMT or EDATA-SIMLAMT. This new version contains additional properties and states for improved data interpretation. The additional properties are based on wavelets. The major advantage provided by adding wavelet analysis is the ability to detect sudden transitions as well as obtaining the frequency content using a much smaller data set then that required by the traditional Fourier transform method. Historically, sudden transitions could only be detected by a visual method or by offline analysis of the data. EDATA-SIMLAMT provides an opportunity to automatically detect sudden transitions as well as many additional data anomalies, and provide improved data-correction and sensor health diagnostic abilities. The newly developed system has been tested on actual rocket test data from NASA’s Stennis Space Center.

Structurally coupled inversion of ERT and refraction seismic data combined with cluster-based model integration

Electrical resistivity tomography (ERT) and refraction seismics are among the most frequently used geophysical methods for site-investigations and the combined results can be very helpful to fill in the gaps between the point measurements made by traditional geotechnical methods such as Cone Penetration Test (CPT), core-drilling and geophysical borehole logging. The interpretation of the results from a geophysical investigation constituting a single method often yields ambiguous results. Hence, an approach utilizing multiple techniques is often necessary. To facilitate interpretation of such a combined dataset, we propose a more controlled and objective approach and present a method for a structurally coupled inversion of 2D electrical resistivity and refraction seismic data using unstructured meshes. Mean shift clustering is used to combine the two images and to compare the separate and coupled inversion methodologies. Two synthetic examples are used to demonstrate the method, and a field-data example is included as a proof of concept. In all cases a significant improvement by the coupling is visible. The methodology can be used as a tool for improved data interpretation and for obtaining a more comprehensive and complete picture of the subsurface by combining geophysical methods.

Evaluation of an acoustic telemetry transmitter designed to identify predation events

Summary The field of acoustic telemetry has evolved rapidly and now permits the remote sensing of animal behaviour, movement, physiology and survival in environments, and species not previously possible. However, an inability to detect when a telemetered animal is consumed by a predator can complicate accurate interpretation of the telemetry data. In this paper, we describe the efforts taken to test the two generations of a novel prototype acoustic telemetry transmitter designed specifically to detect predation. Testing involved either staged predation events where tagged prey (Rainbow Trout Oncorhynchus mykiss and Yellow Perch Perca flavescens) were fed to captive Largemouth Bass Micropterus salmoides, or false‐positive testing where prey fish were tagged and held without the risk of predation. Metrics of interest were (i) the rate of correctly identifying the predation events, (ii) signal lag (i.e. the time required to detect a predation event), (iii) tag retention time in the predator's gut, and (iv) the rate of false‐positive triggering in both live and dead prey fishes. Staged predation events were successfully identified in 61/65 and 52/55 trials for generation 1 and 2 tags, respectively. Signal lag time was reduced in generation 1 tags (generally between 1 and 9 h) relative to generation 2 (3–29 h); although signal lag was highly variable. A generalized linear mixed model (GLMM) indicated strong evidence that signal lag and tag retention were both negatively correlated with water temperature, but were not affected by prey species and only slightly by individual predator traits. There was preliminary evidence that prey size may be an important determinant of both signal lag and tag retention. False‐positives in live fish were absent after 120 days for generation 1 tags (n = 31), however, the false‐positive rates were significantly higher (10/44) after only 66 days for generation 2 tags. False‐positives in dead fish showed that 20% of the generation 2 predation tags would falsely trigger 2–3 days post‐mortem. Testing of the novel predation tags was encouraging, however, further testing is recommended. Predation tags will be an important contribution to the field of acoustic telemetry, thus, permitting the improved data interpretation and less‐subjective estimates of predation rates in biotelemetry studies.

Initial clinical evaluation of PET-based ion beam therapy monitoring under consideration of organ motion.

Intrafractional organ motion imposes considerable challenges to scanned ion beam therapy and demands for a thorough verification of the applied treatment. At the Heidelberg Ion-Beam Therapy Center (HIT), the scanned ion beam delivery is verified by means of postirradiation positron-emission-tomography (PET) imaging. This work presents a first clinical evaluation of PET-based treatment monitoring in ion beam therapy under consideration of target motion. Three patients with mobile liver lesions underwent scanned carbon ion irradiation at HIT and postirradiation PET/CT (x-ray-computed-tomography) imaging with a commercial scanner. Respiratory motion was recorded during irradiation and subsequent image acquisition. This enabled a time-resolved (4D) calculation of the expected irradiation-induced activity pattern and, for one patient where an additional 4D CT was acquired at the PET/CT scanner after treatment, a motion-compensated PET image reconstruction. For the other patients, PET data were reconstructed statically. To verify the treatment, calculated prediction and reconstructed measurement were compared with a focus on the ion beam range. Results in the current three patients suggest that for motion amplitudes in the order of 2 mm there is no benefit from incorporating respiratory motion information into PET-based treatment monitoring. For a target motion in the order of 10 mm, motion-related effects become more severe and a time-resolved modeling of the expected activity distribution can lead to an improved data interpretation if a sufficient number of true coincidences is detected. Benefits from motion-compensated PET image reconstruction could not be shown conclusively at the current stage. The feasibility of clinical PET-based treatment verification under consideration of organ motion has been shown for the first time. Improvements in noise-robust 4D PET image reconstruction are deemed necessary to enhance the clinical potential.

In Situ Quantification of Surface Chemistry in Porous Collagen Biomaterials.

Cells inside a 3D matrix (such as tissue extracellular matrix or biomaterials) sense their insoluble environment through specific binding interactions between their adhesion receptors and ligands present on the matrix surface. Despite the critical role of the insoluble matrix in cell regulation, there exist no widely-applicable methods for quantifying the chemical stimuli provided by a matrix to cells. Here, we describe a general-purpose technique for quantifying in situ the density of ligands for specific cell adhesion receptors of interest on the surface of a 3D matrix. This paper improves significantly the accuracy of the procedure introduced in a previous publication by detailed marker characterization, optimized staining, and improved data interpretation. The optimized methodology is utilized to quantify the ligands of integrins α 1 β 1, α 2 β 1 on two kinds of matched porous collagen scaffolds, which are shown to possess significantly different ligand density, and significantly different ability to induce peripheral nerve regeneration in vivo. Data support the hypothesis that cell adhesion regulates contractile cell phenotypes, recently shown to be inversely related to organ regeneration. The technique provides a standardized way to quantify the surface chemistry of 3D matrices, and a means for introducing matrix effects in quantitative biological models.

Implementation of toxicokinetics in toxicity studies – Toxicokinetics of 4-methylanisole and its metabolites in juvenile and adult rats

The current risk assessment of compounds is generally based on external exposure and effect relationships. External doses are often not representative for internal exposure concentrations. The aim of this study was to show how the implementation of toxicokinetics in a scheduled toxicity study contributes to improved data interpretation without additional use of animals and to the three goals of the 3R principles for animal testing. Toxicokinetic analyses were implemented in a rat developmental immunotoxicity study with 4-methylanisole without interfering with the outcome of the study and without the use of additional animals. 4-Methylanisole and its metabolites were analysed in plasma of adult rats and in pups at postnatal day 10. 4-Methylanisole has a short half-life in adult animals and the plasma concentrations increased more than proportional with increasing dose. The metabolic profile appeared to be different at low dose as compared to high dose. This information on the dose-proportionality of the internal exposure is crucial for the interpretation of the toxicity data and helps to identify the toxic agent and the appropriate dose metric. The metabolism was similar in adult and juvenile animals. Large inter-individual variability in adult animals, as observed for 4-methylanisole, may hamper dose–response analyses of the results. In addition, 4-metylanisole was excreted via milk, but concentrations in the juvenile animals appeared to be 20- to 100-fold lower than via direct gavage exposure. The toxicokinetic parameters support the data interpretation, among others by providing better insight into internal exposures. Subsequently, it will help to prevent testing of irrelevant exposure scenarios and exposure concentrations. Overall, implementation of kinetics with limited effort provides useful information to support the interpretation of toxicological data and can contribute to reduction and refinement of animal testing.

ARXPS measurement simulation for improved data interpretation at complex Ta/Li‐niobate interfaces

For surface acoustic wave devices, piezoelectric substrates have to be coated with barrier and metallization layers. The interface formation plays an important role for the functionality of the layer systems. We used angle‐resolved XPS for nondestructive interface characterization of such material systems. However, the analyses are challenging for complex systems as considered here, because model calculations always have to be performed for data interpretation. To eliminate measuring uncertainties, in a first step, Monte Carlo simulations of angle‐resolved XPS measurement were performed. By use of such reliable data, system inherent uncertainties can be revealed. It is shown that the application of appropriate boundary conditions is inevitable the more complex the structures are. The use of high (near‐surface) measuring angles is discussed as one possible advantageous way. Copyright © 2014 John Wiley &amp; Sons, Ltd.

Combination of seismic and resistivity tomography for the detection of abandoned mine workings in Münster/Westfalen, Germany: Improved data interpretation by cluster analysis

ABSTRACTThe detection of abandoned mine workings is a frequent objective of geophysical investigations. Several non‐destructive geophysical methods have the potential to image these structures, although in practice the interpretation of the results may be complicated. In this study, an abandoned mine was investigated using different geophysical methods on the same site. Seismic refraction tomography and resistivity tomography was undertaken on numerous profiles. With both methods it was possible to detect the mine workings, although interpretation of the resistivity distributions was difficult. To achieve a more meaningful interpretation of the results, seismic modelling was done. By cluster analysis, even further information about the condition of the galleries could be gained.

Infrared spectroscopic imaging: the next generation.

Infrared (IR) spectroscopic imaging seemingly matured as a technology in the mid-2000s, with commercially successful instrumentation and reports in numerous applications. Recent developments, however, have transformed our understanding of the recorded data, provided capability for new instrumentation, and greatly enhanced the ability to extract more useful information in less time. These developments are summarized here in three broad areas--data recording, interpretation of recorded data, and information extraction--and their critical review is employed to project emerging trends. Overall, the convergence of selected components from hardware, theory, algorithms, and applications is one trend. Instead of similar, general-purpose instrumentation, another trend is likely to be diverse and application-targeted designs of instrumentation driven by emerging component technologies. The recent renaissance in both fundamental science and instrumentation will likely spur investigations at the confluence of conventional spectroscopic analyses and optical physics for improved data interpretation. While chemometrics has dominated data processing, a trend will likely lie in the development of signal processing algorithms to optimally extract spectral and spatial information prior to conventional chemometric analyses. Finally, the sum of these recent advances is likely to provide unprecedented capability in measurement and scientific insight, which will present new opportunities for the applied spectroscopist.

Digestion models: predicting the allergenic risk of novel food proteins

There is no definitive method to determine if a novel food protein will become an allergen. For this reason, the allergenic risk of a novel food protein is currently evaluated using a weight-of-evidence (WOE) approach. The components of this WOE approach were arrived at based on expert opinion rather than evidential science. Digestive stability is considered a physiochemical property of food allergens. For food allergens to interact with the immune system via the intestinal mucosa, they must survive the gastric digestion process in an immunoreactive state. An in-vitro assay based on simulated gastric fluid (SGF; 0.32% pepsin at pH 1.2) has been adapted for use with purified proteins to evaluate the stability of the primary structure of novel food proteins. SGF results are currently used as one component in a WOE approach for predicting the risk that a novel food protein will become an allergen. The SGF assay was adopted based an initial investigation that indicated several known allergens were more resistant to SGF compared with several non-allergens. Subsequent investigations have called this correlation into question. In addition, artifacts associated with interpretation of stability results using semi-quantitative approaches have been identified and more classical stability models have been used to enable quantitative comparisons among proteins; however, improved data interpretation has not supported a strong correlation between digestive stability in SGF and the allergenic status of food proteins. Potential interactions between proteins and food matrices have been identified as one factor that may contribute the poor predictive capability of this assay, as has the non-physiological conditions present in SGF. More physiological digestion models that include the food matrix have been suggested based on expert opinion, but as yet, no such assays have been shown to better discriminate food allergens from non-allergens compared with the SGF assay. More useful elements of the WOE approach include the source of the gene/protein, the prevalence of the protein in food, and the structural relationship with known allergens.

Multivariate analysis of ToF‐SIMS data for biological applications

AbstractApplications of time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) were demonstrated, focusing on multivariate analysis (MVA) such as principal component analysis (PCA), principal component regression (PCR), and maximum autocorrelation factors (MAF). Three main categories are presented in this report: quantitative analysis of protein and chemical derivatives, surface characterization of chemical composition, and image‐based analysis in disease‐related tissues. The use of MVA in a ToF‐SIMS study showed improved data interpretation of chemicals or biomolecules on a surface, and consequently enabled the straightforward analysis of ToF‐SIMS data. Even on biological samples with high complexity, the MVA method effectively contributed to obtaining valuable information, including chemical distribution of biomolecules. It is anticipated that MVA with ToF‐SIMS data will be widely used for exploring biological studies in a reliable and simple way. Copyright © 2009 John Wiley &amp; Sons, Ltd.

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..