Reduce Animal Testing Research Articles

Greener chemicals for the future: QSAR modelling of the PBT index using ETA descriptors

Persistent, bioaccumulative and toxic (PBT) chemicals symbolize a group of substances that are not easily degraded; instead, they accumulate in different organisms and exhibit an acute or chronic toxicity. The limited empirical data on PBT chemicals, the high cost of testing together with the regulatory constraints and the international push for reduced animal testing motivate a greater reliance on predictive computational methods like quantitative structure–activity relationship (QSAR) models in PBT assessment. Papa and Gramatica have recently proposed a PBT index that could be computed directly from structural features. In the current study, we have modelled the experimentally derived PBT index data using an extended topological atom (ETA) along with constitutional descriptors to show the usefulness of the ETA indices in modelling the endpoint. The models developed through a double cross-validation (DCV) method gave the best results in terms of both internal and external validation metrics. The developed models were comparable in predictive quality to those previously reported. The current models were further used for consensus predictions of PBT behaviour for a set of pharmaceuticals and a set of synthetic drug-like compounds. The developed models can be used in PBT hazard screening for identification and prioritization of chemicals from the structural information alone.

Corneal Tissue Engineering: An <em>In Vitro</em> Model of the Stromal-nerve Interactions of the Human Cornea

Tissue engineering has gained substantial recognition due to the high demand for human cornea replacements with an estimated 10 million people worldwide suffering from corneal vision loss1. To address the demand for viable human corneas, significant progress in three-dimensional (3D) tissue engineering has been made2,3,4. These cornea models range from simple monolayer systems to multilayered models, leading to 3D full-thickness corneal equivalents2. However, the use of a 3D tissue-engineered cornea in the context of in vitro disease models studied to date lacks resemblance to the multilayered 3D corneal tissue structure, function, and the networking of different cell types (i.e., nerve, epithelium, stroma, and endothelium)2,3. In addition, the demand for in vitro cornea tissue models has increased in an attempt to reduce animal testing for pharmaceutical products. Thus, more sophisticated models are required to better match systems to human physiological requirements, and the development of a model that is more relevant to the patient population is absolutely necessary. Given that multiple cell types in the cornea are affected by diseases and dystrophies, such as Keratoconus, Diabetic Keratopathy, and Fuchs, this model includes a 3D co-culture model of primary human corneal fibroblasts (HCFs) from healthy donors and neurons from the SH-SY5Y cell line. This allows us for the first time to investigate the interactions between the two cell types within the human corneal tissue. We believe that this model could potentially dissect the underlying mechanisms associated with the stromal-nerve interactions of corneal diseases that exhibit nerve damages. This 3D model mirrors the basic anatomical and physiological nature of the corneal tissue in vivo and can be used in the future as a tool for investigating corneal defects as well as screening the efficacy of various agents before animal testing.

Developing predictive models for toxicity of organic chemicals to green algae based on mode of action

Organic chemicals in the aquatic ecosystem may inhibit algae growth and subsequently lead to the decline of primary productivity. Growth inhibition tests are required for ecotoxicological assessments for regulatory purposes. In silico study is playing an important role in replacing or reducing animal tests and decreasing experimental expense due to its efficiency. In this work, a series of theoretical models was developed for predicting algal growth inhibition (log EC50) after 72 h exposure to diverse chemicals. In total 348 organic compounds were classified into five modes of toxic action using the Verhaar Scheme. Each model was established by using molecular descriptors that characterize electronic and structural properties. The external validation and leave-one-out cross validation proved the statistical robustness of the derived models. Thus they can be used to predict log EC50 values of chemicals that lack authorized algal growth inhibition values (72 h). This work systematically studied algal growth inhibition according to toxic modes and the developed model suite covers all five toxic modes. The outcome of this research will promote toxic mechanism analysis and be made applicable to structural diversity.

Bioinformatic pipelines to predict respiratory toxicity and reduce animal testing

In central nervous system cells, low molecular weight fractions (LMWF) from snake venoms can inhibit changes in mitochondrial membrane permeability, preventing the diffusion of cytochrome c to the cytoplasm, inhibiting the activation of pro-apoptotic factors. Here, we evaluated the neuroprotective activity of LMWF from Bothrops jararaca (Bj) snake venom in H2O2-induced cytotoxicity in cultured hippocampal cells. SDS-PAGE, FT-IR and MALDI-TOF analysis of LMWF (<14 kDa) confirmed the absence of high-molecular-weight proteins in the fraction. LMWF did not present cytotoxicity in all concentrations and time tested by MTT assay. Neuroprotection was evaluated in cells pretreated with LMWF for 4 h prior to the addition of 50 μM H2O2 for 20 h. We demonstrated that LMWF reduced the argininosuccinate synthase (AsS) and superoxide dismutase (SOD1) expressions, suggesting that this fraction as an effective neuroprotective compound that could increase the hippocampal cells viability by attenuation of oxidative stress. In addition, LMWF protects against apoptosis induced by H2O2, reducing the expression of caspase-3 and caspase-8. Overall, this study opens new perspectives for the identification of new molecules for the development of drugs applied to the treatment of neurodegenerative diseases.

Understanding the limitations of, and improving, read-across predictions for repeated-dose toxicity by learning from case studies

The use of in silico tools within the drug development process to predict a wide range of properties including absorption, distribution, metabolism, elimination and toxicity has become increasingly important due to changes in legislation and both ethical and economic drivers to reduce animal testing. Whilst in silico tools have been used for decades there remains reluctance to accept predictions based on these methods particularly in regulatory settings. This apprehension arises in part due to lack of confidence in the reliability, robustness and applicability of the models. To address this issue we propose a scheme for the verification of in silico models that enables end users and modellers to assess the scientific validity of models in accordance with the principles of good computer modelling practice. We report here the implementation of the scheme within the Innovative Medicines Initiative project “eTOX” (electronic toxicity) and its application to the in silico models developed within the frame of this project.

Jaqpot Quattro: A Novel Computational Web Platform for Modeling and Analysis in Nanoinformatics.

Engineered nanomaterials (ENMs) are increasingly infiltrating our lives as a result of their applications across multiple fields. However, ENM formulations may result in the modulation of pathways and mechanisms of toxic action that endanger human health and the environment. Alternative testing methods such as in silico approaches are becoming increasingly popular for assessing the safety of ENMs, as they are cost- and time-effective. Additionally, computational approaches support the industrial safer-by-design challenge and the REACH legislation objective of reducing animal testing. Because of the novelty of the field, there is also an evident need for harmonization in terms of databases, ontology, and modeling infrastructures. To this end, we present Jaqpot Quattro, a comprehensive open-source web application for ENM modeling with emphasis on predicting adverse effects of ENMs. We describe the system architecture and outline the functionalities, which include nanoQSAR modeling, validation services, read-across predictions, optimal experimental design, and interlaboratory testing.

In Vitro Microfluidic Models for Neurodegenerative Disorders.

Microfluidic devices enable novel means of emulating neurodegenerative disease pathophysiology in vitro. These organ-on-a-chip systems can potentially reduce animal testing and substitute (or augment) simple 2D culture systems. Reconstituting critical features of neurodegenerative diseases in a biomimetic system using microfluidics can thereby accelerate drug discovery and improve our understanding of the mechanisms of several currently incurable diseases. This review describes latest advances in modeling neurodegenerative diseases in the central nervous system and the peripheral nervous system. First, this study summarizes fundamental advantages of microfluidic devices in the creation of compartmentalized cell culture microenvironments for the co-culture of neurons, glial cells, endothelial cells, and skeletal muscle cells and in their recapitulation of spatiotemporal chemical gradients and mechanical microenvironments. Then, this reviews neurodegenerative-disease-on-a-chip models focusing on Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. Finally, this study discusses about current drawbacks of these models and strategies that may overcome them. These organ-on-chip technologies can be useful to be the first line of testing line in drug development and toxicology studies, which can contribute significantly to minimize the phase of animal testing steps.

SMoBAICS

Simulation and modelling are powerful methods in computer aided therapy, rehabilitation monitoring, identification and control. The smart modular biosignal acquisition and identification system (SMoBAICS) provides methods and techniques to acquire electromyogram (EMG)- and electroneurogram (ENG)-based data for the evaluation and identification of biosignals. In this paper the author focuses on the development, integration and verification of platform technologies which support this entire data processing. Simulation and verification approaches are integrated to evaluate causal relationships between physiological and bioinformatical processes. Based on this we are stepping up of efforts to develop substitute methods and computer-aided simulation models with the objective of reducing animal testing. This work continues the former work about system identification and biosignal acquisition and verification systems presented in (Bohlmann et al., 2010), (Klinger and Klauke, 2013), (Klinger, 2014). This paper focuses on the next generation of an embedded data acquisition and identification system and its flexible platform architecture. Different application scenarios are shown to illustrate the system in different application fields. The author presents results of the enhanced closed-loop verification approach and of the signal quality using the Cuff-electrode-based ENG-data acquisition system.

Mass Spectrometry and Luminogenic-based Approaches to Characterize Phase I Metabolic Competency of &lt;em&gt;In Vitro&lt;/em&gt; Cell Cultures

Xenobiotic metabolizing enzymes play a key function in the biotransformation of medicines and toxicants by adding functional groups that increase solubility and facilitate excretion. On some occasions those structural modifications lead to the formation of new toxic products. In order to reduce animal testing, chemical risk can be assessed using metabolically competent cells. The expression of metabolic enzymes, however, is not stable over time in many in vitro primary culture systems and is often partial or absent in cell lines. Therefore, the study of medicines, additives, and environmental pollutants metabolism in vitro should ideally be conducted in cell systems where metabolic activity has been characterized. We explain here an approach to measure the activity of a class of metabolic enzymes (Human Phase I) in 2D cell lines and primary 3D cultures using chemical probes and their metabolic products quantifiable by UPLC mass spectrometry and luminometry. The method can be implemented to test the metabolic activity in cell lines and primary cells derived from a variety of tissues.

Hybrid optimal descriptors as a tool to predict skin sensitization in accordance to OECD principles

Skin sensitization (allergic contact dermatitis) is a widespread problem arising from the contact of chemicals with the skin. The detection of molecular features with undesired effect for skin is complex task owing to unclear biochemical mechanisms and unclearness of conditions of action of chemicals to skin. The development of computational methods for estimation of this endpoint in order to reduce animal testing is recommended (Cosmetics Directive EC regulation 1907/2006; EU Regulation, Regulation, 1223/2009). The CORAL software (http://www.insilico.eu/coral) gives good predictive models for the skin sensitization. Simplified molecular input-line entry system (SMILES) together with molecular graph are used to represent the molecular structure for these models. So-called hybrid optimal descriptors are used to establish quantitative structure-activity relationships (QSARs). The aim of this study is the estimation of the predictive potential of the hybrid descriptors. Three different distributions into the training (≈70%), calibration (≈15%), and validation (≈15%) sets are studied. QSAR for these three distributions are built up with using the Monte Carlo technique. The statistical characteristics of these models for external validation set are used as a measure of predictive potential of these models. The best model, according to the above criterion, is characterized by nvalidation=29, r2validation=0.8596, RMSEvalidation=0.489. Mechanistic interpretation and domain of applicability for these models are defined.

Mass Spectrometry and Luminogenic-based Approaches to Characterize Phase I Metabolic Competency of In Vitro Cell Cultures.

Xenobiotic metabolizing enzymes play a key function in the biotransformation of medicines and toxicants by adding functional groups that increase solubility and facilitate excretion. On some occasions those structural modifications lead to the formation of new toxic products. In order to reduce animal testing, chemical risk can be assessed using metabolically competent cells. The expression of metabolic enzymes, however, is not stable over time in many in vitro primary culture systems and is often partial or absent in cell lines. Therefore, the study of medicines, additives, and environmental pollutants metabolism in vitro should ideally be conducted in cell systems where metabolic activity has been characterized. We explain here an approach to measure the activity of a class of metabolic enzymes (Human Phase I) in 2D cell lines and primary 3D cultures using chemical probes and their metabolic products quantifiable by UPLC mass spectrometry and luminometry. The method can be implemented to test the metabolic activity in cell lines and primary cells derived from a variety of tissues.

Software targets animal testing

The testing and certifying company UL has introduced software aimed at reducing the need for animal testing when assessing chemicals for Europe’s Registration, Evaluation, Authorisation & Restriction of Chemicals (REACH) program. The product, REACHAcross software, uses machine learning, a method whereby data continually enhances software, to assess the behavior of any chemical of interest to European regulators, UL says. REACHAcross, which debuted at the Society of Toxicology’s annual meeting in Baltimore last week, is the latest in an effort by professional organizations, chemical makers, and equipment suppliers to develop improved means of assessing chemicals’ effects on human health and the environment. Many of the efforts target a reduction in animal testing. Managing regulatory assessment was a prominent topic at the toxicologists’ meeting, in part because of changes last year to the U.S.’s Toxic Substances Control Act requiring more data reporting. The Physicians Committee for Responsible Medicine, ...

In silico cytotoxicity assessment on cultured rat intestinal cells deduced from cellular impedance measurements

Early and reliable identification of chemical toxicity is of utmost importance. At the same time, reduction of animal testing is paramount. Therefore, methods that improve the interpretability and usability of in vitro assays are essential. xCELLigence's real-time cell analyzer (RTCA) provides a novel, fast and cost effective in vitro method to probe compound toxicity. We developed a simple mathematical framework for the qualitative and quantitative assessment of toxicity for RTCA measurements. Compound toxicity, in terms of its 50% inhibitory concentration IC50 on cell growth, and parameters related to cell turnover were estimated on cultured IEC-6 cells exposed to 10 chemicals at varying concentrations. Our method estimated IC50 values of 113.05, 7.16, 28.69 and 725.15 μM for the apparently toxic compounds 2-acetylamino-fluorene, aflatoxin B1, benzo-[a]-pyrene and chloramphenicol in the tested cell line, in agreement with literature knowledge. IC50 values of all apparent in vivo non-toxic compounds were estimated to be non-toxic by our method. Corresponding estimates from RTCA's in-built model gave false positive (toxicity) predictions in 5/10 cases. Taken together, our proposed method reduces false positive predictions and reliably identifies chemical toxicity based on impedance measurements. The source code for the developed method including instructions is available at https://git.zib.de/bzfgupta/toxfit/tree/master.

Predicting acute contact toxicity of pesticides in honeybees (Apis mellifera) through a k-nearest neighbor model

Ecological risk assessment of plant protection products (PPPs) requires an understanding of both the toxicity and the extent of exposure to assess risks for a range of taxa of ecological importance including target and non-target species. Non-target species such as honey bees (Apis mellifera), solitary bees and bumble bees are of utmost importance because of their vital ecological services as pollinators of wild plants and crops. To improve risk assessment of PPPs in bee species, computational models predicting the acute and chronic toxicity of a range of PPPs and contaminants can play a major role in providing structural and physico-chemical properties for the prioritisation of compounds of concern and future risk assessments.Over the last three decades, scientific advisory bodies and the research community have developed toxicological databases and quantitative structure-activity relationship (QSAR) models that are proving invaluable to predict toxicity using historical data and reduce animal testing. This paper describes the development and validation of a k-Nearest Neighbor (k-NN) model using in-house software for the prediction of acute contact toxicity of pesticides on honey bees.Acute contact toxicity data were collected from different sources for 256 pesticides, which were divided into training and test sets. The k-NN models were validated with good prediction, with an accuracy of 70% for all compounds and of 65% for highly toxic compounds, suggesting that they might reliably predict the toxicity of structurally diverse pesticides and could be used to screen and prioritise new pesticides.

Tiered application of the neutral red release and EpiOcular™ assays for evaluating the eye irritation potential of agrochemical formulations

Agrochemical formulations have been underrepresented in validation efforts for implementing alternative eye irritation approaches but represent a significant opportunity to reduce animal testing. This study assesses the utility of the neutral red release assay (NRR) and EpiOcular™ assay (EO) for predicting the eye irritation potential of 64 agrochemical formulations relative to Draize data. In the NRR, formulations with an NRR50 value ≤ 50 mg/mL were categorized as UN GHS Cat 1 and those >250 mg/mL were classified as UN GHS Non Classified (NC). The accuracy, sensitivity, and specificity were 78, 85 and 76% and 73, 85 and 61% for identifying UN GHS 1 and NC formulations, respectively. Specificity was poor for formulations with NRR50 > 50 to ≤250 mg/mL. The EO (ET-40 method) was explored to differentiate formulations that were UN GHS 1/2 and UN GHS NC. The EO resulted in accuracy, sensitivity, and specificity of 65%, 58% and 75% for identifying UN GHS NC formulations. To improve the overall performance, the assays were implemented using a tiered-approach where the NRR was run as a first-tier followed by the EO. The tiered-approach resulted in improved accuracy (75%) and balanced sensitivity (73%) and specificity (77%) for distinguishing between irritating and non-irritating agrochemical formulations.

Optimizing neuronal differentiation of human pluripotent NT2 stem cells in monolayer cultures.

Human pluripotent embryonal carcinoma (NT2) cells are increasingly considered as a suitable model for invitro developmental toxicity and neurotoxicity (DT/DNT) studies as they undergo neuronal differentiation upon stimulation with retinoic acid (RA) and allow toxicity testing at different stages of maturation. However, differentiation of NT2 cells is not straightforward. There are different protocols available in the literature reporting varying results with regard to differentiation efficiency, expression of neuronal markers and morphological characteristics of differentiated cells. Yet, the efficiency of available protocols has not been systematically compared. To address this question, we quantified the number and size of cell cluster formed during differentiation using published and modified protocols and analyzed the abundance of neuronal and non-neuronal expression markers using immunocytochemistry. In the course of the experiments we observed that differentiation results strongly depend on the cell density at differentiation-initiation as well as on the type of used cell culture plastic ware. Based on those observations and the results from our comparative analysis, we created our own optimized and robust protocol that reproducibly reveals differentiated cells with high yield. We conclude that our method may be superior to differentiation of NT2 cells for systematic invitro-based primary screening for developmental toxicants and neurotoxicants at different stages of maturation over previous protocols used. Our approach will also contribute to reduce animal testing in the context of the 3Rs.

ZFminus1: A strategy to reduce animal tests for developmental toxicity testing by a combined use of mammalian models and the zebrafish embryotoxicity test (ZFET or ZETA). Selection of model compounds

ZFminus1: A strategy to reduce animal tests for developmental toxicity testing by a combined use of mammalian models and the zebrafish embryotoxicity test (ZFET or ZETA). Selection of model compounds

ZFminus1: A strategy to reduce animal tests for developmental toxicity testing by a combined use of mammalian models and the zebrafish embryotoxicity test (ZFET or ZETA)

ZFminus1: A strategy to reduce animal tests for developmental toxicity testing by a combined use of mammalian models and the zebrafish embryotoxicity test (ZFET or ZETA)

Human barrier models for the in vitro assessment of drug delivery.

In vitro test systems gain increasing importance in preclinical studies to increase the predictivity and reduce animal testing. Of special interest herein are barrier tissues that guard into the human body. These barriers are formed by highly specialized tissues such as the skin, the airways, and the intestine. However, to recapitulate these tissues, researchers are currently restricted by a lack of suitable supporting scaffolds. In this study, we present biological scaffolds based on decellularized porcine gut segments that offer a natural environment for cell growth and differentiation. Employing these scaffolds, human barrier models of the skin, the airways, and the intestine that mimic the natural histological architecture of the respective tissue are generated. These models show tissue specific barrier properties, such as the stratification of the skin, the mucociliary phenotype of the airways, and polarization of the intestinal epithelium. To investigate the transport characteristics of the intestinal test system, we incubated the tissue models with fluorescein (P app <1×106 cm/s), propranolol (P app >7×106 cm/s), and rhodamin123 (ratio 2.45). The here presented biological scaffolds facilitate the in vitro generation of human barrier models that might represent useful tools for drug delivery studies.

Design, characterization, and modeling of microcirculation systems with integrated oxygenators

Abstract. Here, we describe a microfluidic system for hypoxia assays on human cell culture models. These systems are developed to replace or reduce animal testing in biomedical basic research. The presented system uses a gas-permeable membrane as a gas–liquid interface and a micropump for media actuation to influence the oxygen content in two cell culture chambers. To apply well-defined hypoxic conditions to the cells, a good understanding of the mass transport phenomena is necessary. Therefore, a complete network model of the microfluidic system is presented. This model is validated by means of micro-particle image velocimetry (µPIV) and optical oxygen measurement with fluorescence lifetime detection. Finally, the impact of several process parameters, e.g., the gas permeability of the pump, is discussed using the developed model.