Reduce Animal Testing Research Articles

A Novel Hydrogel Sponge for Three-Dimensional Cell Culture

Background/Objectives: Three-dimensional (3D) cell culture technologies allow us to overcome the constraints of two-dimensional methods in different fields like biochemistry and cell biology and in pharmaceutical in vitro tests. In this study, a novel 3D hydrogel sponge scaffold, composed of a crosslinked polyacrylic acid forming a porous matrix, has been developed and characterized. Methods: The scaffold was obtained via an innovative procedure involving thermal treatment followed by a salt-leaching step on a matrix-containing polymer along with a gas-forming agent. Based on experimental design for mixtures, a series of formulations were prepared to study the effect of the three components (polyacrylic acid, NaHCO3 and NaCl) on the scaffold mechanical properties, density, swelling behavior and morphological changes. Physical appearance, surface morphology, porosity, molecular diffusion, transparency, biocompatibility and cytocompatibility were also evaluated. Results: The hydrogel scaffolds obtained show high porosity and good optical transparency and mechanical resistance. The scaffolds were successfully employed to culture several cell lines for more than 20 days. Conclusions: The developed scaffolds could be an important tool, as such or with a specific coating, to obtain a more predictive cellular response to evaluate drugs in preclinical studies or for testing chemical compounds, biocides and cosmetics, thus reducing animal testing.

Predictive, integrative, and regulatory aspects of AI-driven computational toxicology – Highlights of the German Pharm-Tox Summit (GPTS) 2024

The 9th German Pharm-Tox Summit (GPTS) and the 90th Annual Meeting of the German Society for Experimental and Clinical Pharmacology and Toxicology (DGPT) took place in Munich from March 13–15, 2024. The event brought together over 700 participants from around the world to discuss cutting-edge developments in the fields of pharmacology and toxicology as well as scientific innovations and novel insights. A key focus of the conference was on the rapidly increasing role of computational toxicology, artificial intelligence (AI), and machine learning (ML) into the field, marking a shift away from traditional methods and allowing the reduction of animal testing as primary tool for toxicological risk assessment. Tools such as Toxometris.ai showcased the potential of AI-based risk assessments for predicting carcinogenicity, offering more ethical and efficient alternatives. Additionally, computer-driven models like computer-aided pattern analysis (C@PA) for drug toxicity prediction were presented, emphasizing the growing role of chem- and bioinformatic applications in computational sciences. Throughout the summit, there was a strong focus on the need for regulatory innovation to support the adoption of these advanced technologies and ensure the safety and sustainability of chemical substances and drugs.

Journeying Through Journals: The Publishing Process and How to Maximise Research Impact.

Publication is essential to share new ideas, knowledge, or recent findings with those who have an interest in a particular area. Selecting the most appropriate format and timing for dissemination is critical to ensuring the long-term impact of research. However, many researchers, particularly those in the early stages of their career, are unaware of how the publication process works and the different options available for promoting research to maximum effect. Understanding how to maximise impact is particularly important for research using animal models or alternative methods, to make the best use of any animal data generated and reduce animal testing in future. Herein, different publishing models are explained, including anonymised peer review, open review and data sharing initiatives. An overview is given of key resources available to assist authors, reviewers and editors in the process of writing, presenting, reviewing and publishing research. New challenges and opportunities in publishing are discussed, including the potential influence of Artificial Intelligence. A list of 'ten top tips' in publishing for early career researchers is presented, providing advice and recommendations for ensuring a successful and impactful publication record.

Chemicals regulation and non-animal methods: displacing the gold standard.

Regulating industrial chemicals in foodstuffs and consumer products is a major aspect of protecting populations against health risks. Non-animal testing methods are an essential part of the radical change to the framework for toxicity testing that is long overdue in global economies. This paper discusses reasons why the drive to reduce animal testing for chemical safety testing is so difficult to achieve, as perceived by those who are closely involved in chemicals regulations in different capacities. Progress is slow, despite the fact that the ethico-legal conditions for a move away from animal testing are largely in place, and despite scientific arguments for a radical change in the paradigm of toxicity testing, away from reliance on animal studies. I present empirical data drawn from two studies in a European Commission context promoting non-animal methods. The aim of the paper is modest. It is to foreground the voices of those who deal with the science and regulation of chemicals on a day-to-day basis, rather than to offer a theoretical framework for what I heard from them. I offer a synthesis of the main challenges faced by non-animal alternatives, as these are perceived by people in different stakeholder groups dealing with chemicals regulation. I show where there are pockets of agreement between different stakeholders, and where the main disagreements lie. In particular there is dispute and disagreement over what counts as validation of these alternative tests, and by implication of the traditional 'gold standard' of animal testing. Finally, I suggest that the shift to non-animal methods in chemicals regulation demonstrates the need for the concept of validation to be broadened from a purely techno-scientific definition, and be more explictly understood as a demand for trust and acceptance, with more attention given to the complex social, institutional and economic settings in which it operates.

Organ-On-A-Chip Devices: Technology Progress and Challenges.

Organ-On-a-Chip (OOC) is a multichannel 3D-microfluidic cell-culture system incorporated in a chip that simulates the behavior of an organ. This technology relies on a multidisciplinary science that benefits from and contributes in the progress of many fields including microbiology, microfluidics, biomaterials, and bioengineering. This review article summarizes the progress and achievements of various organ-on-chip technologies. It highlights the significant advantages of this technology in terms of reducing animal testing and providing personalized medical responses. In addition, this paper demonstrates how OOC is becoming a promising and powerful tool in pharmaceutical research to combat diseases. It predicts not only the effects of drugs on the target organs but also, using body-on-a-chip systems, it may provide insights into the side effects of the drug delivery on the other organs. Likewise, the models used for the construction of various organ-on-a-chip devices are investigated along with the design and materials of microfluidic devices. For each OOC, the integrated monitoring devices within the chips (e. g., sensors and biosensors) are discussed. We also discuss the evolution of FDA regulations and the potential in the near future for integrating OOCs into protocols that support and reduce the need for and the failure rates in preclinical and clinical studies.

Use of the threshold of toxicological concern (TTC) approach as an alternative tool for regulatory purposes: A case study with an inert ingredient used in pesticide products

This study investigates the potential of the Threshold of Toxicological Concern (TTC) as an alternative to traditional animal testing in pesticide regulatory risk assessments. The TTC is a principle that establishes exposure threshold values for chemicals with certain structural features, below which there is no appreciable risk to human health.A case study was conducted with α-terpineol, an inert ingredient proposed to be used at low concentrations in pesticide products, to compare a conventional risk assessment using animal data with one using the TTC method. For the conventional risk assessment, animal data showed that there was no toxicity endpoint of concern, which resulted in a qualitative assessment and no risks of concern identified. For the risk assessment using the TTC method, a 5th percentile no-observed-effect level (NOEL) selected based on α-terpineol's Cramer classification was used as a point of departure (POD) for a quantitative risk assessment that resulted in no risks of concern identified. Therefore, the same conclusion was reached with both approaches and α-terpineol is considered safe for use in pesticide products at low concentrations. A comparative analysis was also performed to determine the applicability of the TTC method in calculating potential dietary risk from common pesticide use patterns for chemicals that fall within different Cramer classes. Results showed that use of the TTC method may be feasible for inert ingredient risk assessments when chemicals are used in a pesticide product at concentrations below 1%.This research underscores the TTC as a valuable and robust tool for assessing the potential hazards from inert ingredient use in pesticide formulations, considering factors such as chemical properties and the concentrations at which a chemical may be used in pesticide products. These findings contribute to the ongoing efforts by the United States Environmental Protection Agency (US EPA) to reduce animal testing in chemical safety assessments. The TTC method presents a viable alternative for risk evaluations of chemicals used at low concentrations, with anticipated low exposure, and with a predicted low toxicity potential.

Evaluation of THP-1 and Jurkat Cell Lines Coculture for the In Vitro Assessment of the Effects of Immunosuppressive Substances.

The strong appeal to reduce animal testing calls for the development and validation of in vitro, in chemico and in silico models that would replace the need for in vivo testing and ex vivo materials. A category that requires such new approach methods is the assessment of immunosuppression that can be induced by chemicals including environmental pollutants. To assess the immunosuppressive action on monocytes and lymphocytes, we mimicked the whole-blood cytokine-release assay by preparing an in vitro coculture of THP-1 and Jurkat cell lines. We optimised its activation and investigated the effects of known immunosuppressive drugs with different mechanisms of action on the release of proinflammatory cytokines. Decreased secretion of IL-8 was achieved by several immunosuppressive mechanisms and was therefore selected as an appropriate marker of immunosuppression. A set of environmentally occurring bisphenols, BPA, BPAP, BPP, BPZ, BPE, TCBPA and BPS-MAE, were then applied to the model and BPP and BPZ were found to act as potent immunosuppressants at micromolar concentrations.

A novel multitask learning algorithm for tasks with distinct chemical space: zebrafish toxicity prediction as an example

Data scarcity is one of the most critical issues impeding the development of prediction models for chemical effects. Multitask learning algorithms leveraging knowledge from relevant tasks showed potential for dealing with tasks with limited data. However, current multitask methods mainly focus on learning from datasets whose task labels are available for most of the training samples. Since datasets were generated for different purposes with distinct chemical spaces, the conventional multitask learning methods may not be suitable. This study presents a novel multitask learning method MTForestNet that can deal with data scarcity problems and learn from tasks with distinct chemical space. The MTForestNet consists of nodes of random forest classifiers organized in the form of a progressive network, where each node represents a random forest model learned from a specific task. To demonstrate the effectiveness of the MTForestNet, 48 zebrafish toxicity datasets were collected and utilized as an example. Among them, two tasks are very different from other tasks with only 1.3% common chemicals shared with other tasks. In an independent test, MTForestNet with a high area under the receiver operating characteristic curve (AUC) value of 0.911 provided superior performance over compared single-task and multitask methods. The overall toxicity derived from the developed models of zebrafish toxicity is well correlated with the experimentally determined overall toxicity. In addition, the outputs from the developed models of zebrafish toxicity can be utilized as features to boost the prediction of developmental toxicity. The developed models are effective for predicting zebrafish toxicity and the proposed MTForestNet is expected to be useful for tasks with distinct chemical space that can be applied in other tasks.Scieific contributionA novel multitask learning algorithm MTForestNet was proposed to address the challenges of developing models using datasets with distinct chemical space that is a common issue of cheminformatics tasks. As an example, zebrafish toxicity prediction models were developed using the proposed MTForestNet which provide superior performance over conventional single-task and multitask learning methods. In addition, the developed zebrafish toxicity prediction models can reduce animal testing.

Hydrogel-based hybrid membrane enhances in vitro ophthalmic drug evaluation in the OphthalMimic device

Envisaging to improve the evaluation of ophthalmic drug products while minimizing the need for animal testing, our group developed the OphthalMimic device, a 3D-printed device that incorporates an artificial lacrimal flow, a cul-de-sac area, a moving eyelid, and a surface that interacts effectively with ophthalmic formulations, thereby providing a close representation of human ocular conditions. An important application of such a device would be its use as a platform for dissolution/release tests that closely mimic in vivo conditions. However, the surface that artificially simulates the cornea should have a higher resistance (10 min) than the previously described polymeric films (5 min). For this key assay upgrade, we describe the process of obtaining and thoroughly characterizing a hydrogel-based hybrid membrane to be used as a platform base to simulate the cornea artificially. Also, the OphthalMimic device suffered design improvements to fit the new membrane and incorporate the moving eyelid. The results confirmed the successful synthesis of the hydrogel components. The membrane’s water content (86.25 ± 0.35 %) closely mirrored the human cornea (72 to 85 %). Furthermore, morphological analysis supported the membrane’s comparability to the natural cornea. Finally, the performance of different formulations was analysed, demonstrating that the device could differentiate their drainage profile through the viscosity of PLX 14 (79 ± 5 %), PLX 16 (72 ± 4 %), and PLX 20 (57 ± 14 %), and mucoadhesion of PLXCS0.5 (69 ± 1 %), PLX16CS1.0 (65 ± 3 %), PLX16CS1.25 (67 ± 3 %), and the solution (97 ± 8 %). In conclusion, using the hydrogel-based hybrid membrane in the OphthalMimic device represents a significant advancement in the field of ophthalmic drug evaluation, providing a valuable platform for dissolution/release tests. Such a platform aligns with the ethical mandate to reduce animal testing and promises to accelerate the development of safer and more effective ophthalmic drugs.

Enhanced hepatotoxicity assessment through encapsulated HepG2 spheroids in gelatin hydrogel matrices: Bridging the gap from 2D to 3D culture

Conventional 2D drug screening often fails to accurately predict clinical outcomes. We present an innovative approach to improve hepatotoxicity assessment by encapsulating HepG2 spheroids in gelatin hydrogel matrices with different mechanical properties. Encapsulated spheroids exhibit sustained liver-specific functionality, enhanced expression of drug-metabolizing enzymes, and increased drug sensitivity compared to 2D cultures. The platform detects critical variations in drug response, with significant differences in IC50 values between 2D and spheroid cultures ranging from 1.3-fold to > 13-fold, particularly for acetaminophen. Furthermore, drug-metabolizing enzyme expression varies across hydrogel concentrations, suggesting a role for matrix mechanical properties in modulating hepatocyte function. This novel spheroid-hydrogel platform offers a transformative approach to hepatotoxicity assessment, providing increased sensitivity, improved prediction, and a more physiologically relevant environment. The use of such advanced in vitro models can accelerate drug development, reduce animal testing, and contribute to improved patient safety and clinical outcomes.

A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques

BackgroundThe growing abundance of in vitro omics data, coupled with the necessity to reduce animal testing in the safety assessment of chemical compounds and even eliminate it in the evaluation of cosmetics, highlights the need for adequate computational methodologies. Data from omics technologies allow the exploration of a wide range of biological processes, therefore providing a better understanding of mechanisms of action (MoA) related to chemical exposure in biological systems. However, the analysis of these large datasets remains difficult due to the complexity of modulations spanning multiple biological processes.ResultsTo address this, we propose a strategy to reduce information overload by computing, based on transcriptomics data, a comprehensive metabolic sub-network reflecting the metabolic impact of a chemical. The proposed strategy integrates transcriptomic data to a genome scale metabolic network through enumeration of condition-specific metabolic models hence translating transcriptomics data into reaction activity probabilities. Based on these results, a graph algorithm is applied to retrieve user readable sub-networks reflecting the possible metabolic MoA (mMoA) of chemicals. This strategy has been implemented as a three-step workflow. The first step consists in building cell condition-specific models reflecting the metabolic impact of each exposure condition while taking into account the diversity of possible optimal solutions with a partial enumeration algorithm. In a second step, we address the challenge of analyzing thousands of enumerated condition-specific networks by computing differentially activated reactions (DARs) between the two sets of enumerated possible condition-specific models. Finally, in the third step, DARs are grouped into clusters of functionally interconnected metabolic reactions, representing possible mMoA, using the distance-based clustering and subnetwork extraction method. The first part of the workflow was exemplified on eight molecules selected for their known human hepatotoxic outcomes associated with specific MoAs well described in the literature and for which we retrieved primary human hepatocytes transcriptomic data in Open TG-GATEs. Then, we further applied this strategy to more precisely model and visualize associated mMoA for two of these eight molecules (amiodarone and valproic acid). The approach proved to go beyond gene-based analysis by identifying mMoA when few genes are significantly differentially expressed (2 differentially expressed genes (DEGs) for amiodarone), bringing additional information from the network topology, or when very large number of genes were differentially expressed (5709 DEGs for valproic acid). In both cases, the results of our strategy well fitted evidence from the literature regarding known MoA. Beyond these confirmations, the workflow highlighted potential other unexplored mMoA.ConclusionThe proposed strategy allows toxicology experts to decipher which part of cellular metabolism is expected to be affected by the exposition to a given chemical. The approach originality resides in the combination of different metabolic modelling approaches (constraint based and graph modelling). The application to two model molecules shows the strong potential of the approach for interpretation and visual mining of complex omics in vitro data. The presented strategy is freely available as a python module (https://pypi.org/project/manamodeller/) and jupyter notebooks (https://github.com/LouisonF/MANA).

Bridging the Gap Between Human Toxicology and Ecotoxicology Under One Health Perspective by a Cross-Species Adverse Outcome Pathway Network for Reproductive Toxicity.

Although ecotoxicological and toxicological risk assessments are performed separately from each other, recent efforts have been made in both disciplines to reduce animal testing and develop predictive approaches instead, for example, via conserved molecular markers, and in vitro and in silico approaches. Among them, adverse outcome pathways (AOPs) have been proposed to facilitate the prediction of molecular toxic effects at larger biological scales. Thus, more toxicological data are used to inform on ecotoxicological risks and vice versa. An AOP has been previously developed to predict reproductive toxicity of silver nanoparticles via oxidative stress on the nematode Caenorhabditis elegans (AOPwiki ID 207). Following this previous study, our present study aims to extend the biologically plausible taxonomic domain of applicability (tDOA) of AOP 207. Various types of data, including in vitro human cells, in vivo, and molecular to individual, from previous studies have been collected and structured into a cross-species AOP network that can inform both human toxicology and ecotoxicology risk assessments. The first step was the collection and analysis of literature data to fit the AOP criteria and build a first AOP network. Then, key event relationships were assessed using a Bayesian network modeling approach, which gave more confidence in our overall AOP network. Finally, the biologically plausible tDOA was extended using in silico approaches (Genes-to-Pathways Species Conservation Analysis and Sequence Alignment to Predict Across Species Susceptibility), which led to the extrapolation of our AOP network across over 100 taxonomic groups. Our approach shows that various types of data can be integrated into an AOP framework, and thus facilitates access to knowledge and prediction of toxic mechanisms without the need for further animal testing. Environ Toxicol Chem 2024;00:1-14. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Development of a novel high-throughput culture system for hypoxic 3D hydrogel cell culture.

Animal models lack physiologic relevance to the human system which results in low clinical translation of results derived from animal testing. Besides spheroids or organoids, hydrogel-based 3D in vitro models are used to mimic the in vivo situation increasing the relevance while reducing animal testing. However, to establish hydrogel-based 3D models in applications such as drug development or personalized medicine, high-throughput culture systems are required. Furthermore, the integration of oxygen-reduced (hypoxic) conditions has become increasingly important to establish more physiologic culture models. Therefore, we developed a platform technology for the high-throughput generation of miniaturized hydrogels for 3D cell culture. The Oli-Up system is based on the shape of a well-plate and allows for the parallel culture of 48 hydrogel samples, each with a volume of 15µl. As a proof-of-concept, we established a 3D culture of gelatin-methacryloyl (GelMA)-encapsulated mesenchymal stem/stromal cells (MSCs). We used a hypoxia reporter cell line to establish a defined oxygen-reduced environment to precisely trigger cellular responses characteristicof hypoxia in MSCs. In detail, the expression of hypoxia response element (HRE) increased dependent on the oxygen concentration and cell density. Furthermore, MSCs displayed an altered glucose metabolism and increased VEGF secretion upon oxygen-reduction. In conclusion, the Oli-Up system is a platform technology for the high-throughput culture of hydrogel-based 3D models in a defined oxygen environment. As it is amenable for automation, it holds the potential for high-throughput screening applications such as drug development and testing in more physiologic 3D in vitro tissue models.

Exploring a mechanism-based approach for the identification of endocrine disruptors using Adverse Outcome Pathways (AOPs) and New Approach Methodologies (NAMs) : A perfluorooctane sulfonic acid case study

Endocrine disruptors (EDs) pose a serious threat to human health and the environment and require a comprehensive evaluation to be identified. The identification of EDs require a substantial amount of data, both in vitro and in vivo, due to the current scientific criteria in the EU. At the same time, the EU strives to reduce animal testing due to concerns regarding animal welfare and sensitivity of animal studies to adequately detect adverse effects relevant for human health. Perfluorooctane sulfonic acid (PFOS) is a persistent organic pollutant that is suspected to be an ED based on academic research, however it is not identified as such from a regulatory perspective. It has previously been shown that PFOS has the potential to cause neurotoxicity as well as affect the thyroid system, and it is known that specific thyroid hormone levels are critical in the development of the brain during. In this work, the aim was to evaluate a mechanism-based approach to identify ED properties of PFOS based on the Adverse Outcome Pathway (AOP) framework and using New Approach Methods (NAMs), by comparing this approach to an ED assessment based on the currently available guidance document. An AOP network (AOPN) was generated for the thyroid modality, and AOPs leading to developmental neurotoxicity (DNT) were identified. A literature search and screening process based on the AOPN, and systematic review methodology, was performed, followed by a rigorous Weight-of-Evidence (WoE) assessment. Evidence was mapped back onto the AOPN used for the literature search, to identify possible endocrine Modes-of-Action (MoAs) for PFOS and data gaps in the two assessments. It could be concluded that PFOS fulfils the criteria for ED classification in the standard ED assessment, but not in the mechanism-based assessment. The need for quantitative information, such as quantitative AOPs, for the mechanism-based approach is discussed. The possibility of a directly neurotoxic alternative MoA was also highlighted based on available in vitro data. Opportunities and challenges with implementing AOPs and NAMs into the regulatory assessment of EDs, and assessing hazard in the Next Generation Risk Assessment, is discussed. This case study exploring the mechanism-based approach to ED identification represents an important step toward more accurate and predictive assessment of EDs based on AOPs and NAMs, and to the Next Generation Risk Assessment (NGRA) concept.

Energy metabolism disturbance, altered neuronal development and glutamatergic signalling in human derived neuronal cell models of ADHD

Abstract Despite major advances in research into the neurobiological basis of mental illness, there have been hardly any new developments in new drug therapies. As there are approximately 30% of affected individuals that do not respond sufficiently to available treatments, there is a significant unmet medical need for new therapeutic approaches. About 90% of novel substances that have shown promise in animal studies are not effective in clinical trials. Recent research on human induced pluripotent stem cells (hiPSC) could lead to the use of more human-tailored models in this field. IPSC-derived cell models and organoids may be very attractive for preclinical screening and bridge the gap between in vitro and in vivo studies, reducing animal testing. However, the next steps must first demonstrate the validity and reproducibility of the initial functional results from the hIPSC models of mental illness. In our own studies on neuronal cell models of patients with attention-deficit/hyperactivity disorder (ADHD) with rare PARK2 gene variants, we were able to show evidence of mitochondrial dysfunction and impaired energy metabolism. Additionally, we have first hints at a oxidative dysbalance which could be as well targeted by medication. In a model of cortical development of ADHD patients with common variants in the ADGRL3 gene, we found first evidence for altered neuronal maturation as well as abnormalities in calcium metabolism and glutamatergic functionality compared to cells from healthy controls. In summary, these first results are promising that hIPSC models can contribute new insights into cellular pathomechanisms of mental and neurodevelopmental disorders and the development of new, individualised therapeutic approaches.Disclosure of InterestS. Kittel-Schneider Grant / Research support from: The studies are funded by IZKF Wuerzburg and BBRF fund (to SKS and RMcN). SKS received speaker’s honoraria from Takeda, Medice and Janssen.

Creating Tiny Human “Organs” to Test medicines… and More!

Scientists have developed tiny cell models called microphysiological systems (MPSs) that mimic human organs, allowing medicines to be tested without using animals. MPS contain human cells carefully arranged to simulate a real organ’s structure and function. One type of MPS, called an organ-on-chip, also pumps fluids containing nutrients and oxygen through the model, similar to the function of blood flow in our bodies. These MPSs can test how medicines affect human cells and help scientists develop safer, more effective treatments for diseases. MPSs can also be personalized using a patient’s own cells, to find the best treatment for each person. While challenges remain, like cost and reliability, MPSs are steadily improving. Beyond testing medicines, they can be used to study dangerous environmental chemicals and to model diseases. We can even connect multiple “organs” to simulate the whole body. As these revolutionary technologies improve and become widely accepted, they could speed up drug development and reduce animal testing.

Chemicals regulation and non-animal methods: displacing the gold standard

Regulating industrial chemicals in foodstuffs and consumer products is a major aspect of protecting populations against health risks. Non-animal testing methods are an essential part of the radical change to the framework for toxicity testing that is long overdue in global economies. This paper discusses reasons why the drive to reduce animal testing for chemical safety testing is so difficult to achieve, as perceived by those who are closely involved in chemicals regulations in different capacities. Progress is slow, despite the fact that the ethico-legal conditions for a move away from animal testing are largely in place, and despite scientific arguments for a radical change in the paradigm of toxicity testing, away from reliance on animal studies. I present empirical data drawn from two studies in a European Commission context promoting non-animal methods. The aim of the paper is modest. It is to foreground the voices of those who deal with the science and regulation of chemicals on a day-to-day basis, rather than to offer a theoretical framework for what I heard from them. I offer a synthesis of the main challenges faced by non-animal alternatives, as these are perceived by people in different stakeholder groups dealing with chemicals regulation. I show where there are pockets of agreement between different stakeholders, and where the main disagreements lie. In particular there is dispute and disagreement over what counts as validation of these alternative tests, and by implication of the traditional ‘gold standard’ of animal testing. Finally, I suggest that the shift to non-animal methods in chemicals regulation demonstrates the need for the concept of validation to be broadened from a purely techno-scientific definition, and be more explictly understood as a demand for trust and acceptance, with more attention given to the complex social, institutional and economic settings in which it operates.

Improving in vivoassays in snake venom and antivenom research: A community discussion.

On the 26 th January 2023, a free to attend, 'improving in vivo snake venom research: a community discussion' meeting was held virtually. This webinar brought together researchers from around the world to discuss current neutralisation of venom lethality mouse assays that are used globally to assess the efficacy of therapies for snakebite envenoming. The assay's strengths and weaknesses were highlighted, and we discussed what improvements could be made to refine and reduce animal testing, whilst supporting preclinical antivenom and drug discovery for snakebite envenoming. This report summarises the issues highlighted, the discussions held, with additional commentary on key perspectives provided by the authors.

Lab on chip for testing of repurposed drugs.

The lab-on-chip technique broadly comprises of microfluidics and aims to progress multidimensionally by changing the outlook of medicine and pharmaceuticals as it finds it roots in miniaturization. Moreover, microfluidics facilitates precise physiological simulation and possesses biological system-mimicking capabilities for drug development and repurposing. Thus, organs on chip could pave a revolutionary pathway in the field of drug development and repurposing by reducing animal testing and improving drug repurposing.

E-validation - Unleashing AI for validation.

The validation of new approach methods (NAMs) in toxicology faces significant challenges, including the integration of diverse data, selection of appropriate reference chemicals, and lengthy, resource-intensive consensus processes. This article proposes an artificial intelligence (AI)-based approach, termed e-validation, to optimize and accelerate the NAM validation process. E-vali-dation employs advanced machine learning and simulation techniques to systematically design validation studies, select informative reference chemicals, integrate existing data, and provide tailored training. The approach aims to shorten current decade-long validation timelines, using fewer resources while enhancing rigor. Key components include the smart selection of reference chemicals using clustering algorithms, simulation of validation studies, mechanistic validation powered by AI, and AI-enhanced training for NAM education and implementation. A centralized dashboard interface could integrate these components, streamlining workflows and providing real-time decision support. The potential impacts of e-validation are extensive, promising to accel-erate biomedical research, enhance chemical safety assessment, reduce animal testing, and drive regulatory and commercial innovation. While the integration of AI and machine learning offers sig-nificant advantages, challenges related to data quality, complexity of implementation, scalability, and ethical considerations must be addressed. Real-world validation and pilot studies are crucial to demonstrate the practical benefits and feasibility of e-validation. This transformative approach has the potential to revolutionize toxicological science and regulatory practices, ushering in a new era of predictive, personalized, and preventive health sciences.