Toxicological Sciences Research Articles

Toxicometabolomics-based cardiotoxicity evaluation of Thiazolidinedione exposure in human-derived cardiomyocytes

IntroductionThiazolidinediones (TZDs), represented by pioglitazone and rosiglitazone, are a class of cost-effective oral antidiabetic agents posing a marginal hypoglycaemia risk. Nevertheless, observations of heart failure have hindered the clinical use of both therapies.ObjectiveSince the mechanism of TZD-induced heart failure remains largely uncharacterised, this study aimed to explore the as-yet-unidentified mechanisms underpinning TZD cardiotoxicity using a toxicometabolomics approach.MethodsThe present investigation included an untargeted liquid chromatography–mass spectrometry-based toxicometabolomics pipeline, followed by multivariate statistics and pathway analyses to elucidate the mechanism(s)of TZD-induced cardiotoxicity using AC16 human cardiomyocytes as a model, and to identify the prognostic features associated with such effects.ResultsAcute administration of either TZD agent resulted in a significant modulation in carnitine content, reflecting potential disruption of the mitochondrial carnitine shuttle. Furthermore, perturbations were noted in purine metabolism and amino acid fingerprints, strongly conveying aberrations in cardiac energetics associated with TZD usage. Analysis of our findings also highlighted alterations in polyamine (spermine and spermidine) and amino acid (L-tyrosine and valine) metabolism, known modulators of cardiac hypertrophy, suggesting a potential link to TZD cardiotoxicity that necessitates further research. In addition, this comprehensive study identified two groupings – (i) valine and creatine, and (ii) L-tryptophan and L-methionine – that were significantly enriched in the above-mentioned mechanisms, emerging as potential fingerprint biomarkers for pioglitazone and rosiglitazone cardiotoxicity, respectively.ConclusionThese findings demonstrate the utility of toxicometabolomics in elaborating on mechanisms of drug toxicity and identifying potential biomarkers, thus encouraging its application in the toxicological sciences. (245 words)

Machine learning in toxicological sciences: opportunities for assessing drug toxicity

Machine learning (ML) in toxicological sciences is growing exponentially, which presents unprecedented opportunities and brings up important considerations for using ML in this field. This review discusses supervised, unsupervised, and reinforcement learning and their applications to toxicology. The application of the scientific method is central to the development of a ML model. These steps involve defining the ML problem, constructing the dataset, transforming the data and feature selection, choosing and training a ML model, validation, and prediction. The need for rigorous models is becoming more of a requirement due to the vast number of chemicals and their interaction with biota. Large datasets make this task possible, though selecting databases with overlapping chemical spaces, amongst other things, is an important consideration. Predicting toxicity through machine learning can have significant societal impacts, including enhancements in assessing risks, determining clinical toxicities, evaluating carcinogenic properties, and detecting harmful side effects of medications. We provide a concise overview of the current state of this topic, focusing on the potential benefits and challenges related to the availability of extensive datasets, the methodologies for analyzing these datasets, and the ethical implications involved in applying such models.

ACC says regulatory systems must rely on sturdy foundation of sound science

On February 27, 2024, the American Chemistry Council reports on a recent commentary published in the Society of Toxicology's Toxicological Sciences journal which examines the European Food Safety Authority's (EFSA) opinion for a reduction of the tolerable daily intake (TDI) level of bisphenol A (BPA) in food contact materials (compared with the temporary TDI established in 2015), which the authors of the study say is based on an insufficient foundation for regulatory action.

Metamorphosis of human health risk assessment with artificial intelligence (AI) - a new paradigm in pharmaco-toxicological sciences

Toxicological Science, especially in the last five decades, has witnessed rapid evolution of different tools and techniques developed to address diverse issues related to studies dealing with adverse health effects of a variety of poisons, drugs, chemicals,ever-growing list of xenobiotics and human diseases. Traditionally these studies are performed using suitable animal (in vivo) models. There was a time when toxicologists/pharmacologists were searching models alternate to animal toxicity testing (Doke and Dhawale, 2015). Improved cell culture techniques, knowledge on stem cells and other microbiological systems led to the development of in vitro toxicology. It was soon followed by DNA chips, micro fluidics, in silico toxicology , toxicogenomics and computational toxicology. Several platforms are now discussing machine learning (ML) and artificial intelligence (AI) together as future tools of computational toxicology. For decades, quantitative structure-activity relationship (QSAR) methods have been employed to study the effects of drugs/chemicals (Cai et al., 2022). However, AI methods for toxicity assessment ranging from ADMEtox to AI4TOX provide evidence to the immense potential of AI. Intriguingly, a few problems between theoretical developments and practice of AI by end users have been recognized. AI is now being employed in cancer care. According to WHO (2022), cancer is responsible for 9.3 million deaths per year. AI is being used for cancer grading, classification, follow up services and diagnostic accuracy. However, certain limitations viz. testing, validation, certification and auditing need to be addressed (Cabral et al., (2023). Potential of AI in diabetic care and management has recently been recognized. The huge burden of diabetic patients in India can be managed through AI tools. Diabetic risk can be predicted using genomic data, to diagnose diabetes using EHR data and to identify diabetes related complications i.e. retinopathy and nephropathy (Singhla et al., 2019). Application of AI in the management of cardiovascular diseases like myocardial infarction has been highlighted with special reference to Chinese medicine (Chen et al., 2022). There exists experimental evidence that AI tools can be used to assess, monitor and manage Parkinsons' disease (Bounsall et al., 2023). Perspectives of the application of AI in complimentary and alternative medicine were reviewed by Chu et al. (2022). Several regulatory agencies are now adopting the concept of 3R ie., replacement, reduction and refinement of animal testing (EU REACH/3R principles; Toxicology 21 of U.S. Government) ( Maestri, 2021).The application of AI in clinical toxicology through converging data resources, algorithms, real world information from sensors and health records has also been discussed (Sinha et al. 2021). Plausibility of toxicity prediction using AI tools was recently reviewed by Santin et al. (2021). Application of AI in recently emerged science of nanotoxicology is also being sought. The need for nanotoxicity databases, powerful nano descriptors, new modeling approaches, molecular mechanism analyses and designing of next generation nanomaterials are being debated ( Jha et al., 2014; Yan et al., 2023). U.S. Food and Drug Administration (USFDA) has recently initiated and AI program in Toxicology known as AI4TOX. This program mainly consists of four initiatives- AnimalGAN- to predict animal toxicology data for untested chemicals ( /about –fda/nctr-research-focus-areas/animalgan-initiative); SafetAI- to develop novel deep learning methods for toxicological endpoints (/about-fda/nctr-research-focus-areas/safetai-initiative); BERTox- to develop the most advanced AI powered Natural Language Processing (NPL) (/about-fda/nctr-research-focus-areas/bertox initiative) and PathologAI- to develop an effective and accurate framework for analysis of histopathological data from animal studies (/ about-fda/nctr-research-focus-areas/pathologai-initiative). Recently, Society of Toxicology (SOT) annual meeting held at Nashville from March 19-23, focused on a question-“How could AI be used for risk assessment?” There exists some skepticism weather AI may be used in human health risk assessment? How AI could be applied -to prioritize pharmaceutical/environmental chemicals, to identify potential off targets and decipher the mechanisms of toxicity and detect pathological effects? A Symposium session devoted to AI summarized international collaborative computational projects like CERaPP, COmPara and CATOMOS that have been designed to streamline the regulatory and safety assessments (Hartung, 2023). High throughput screening data (HTS) to predict drug induced liver injury (DILI) using AI is also being generated. AI can identify mechanisms for off target effects in drug development. AI can also be plausibly used to predict genotoxicity. Can AI tools automate the analysis of developmental or physiologically based assays? These discussions held during the symposium indicate exciting potential of AI in health risk assessment. It is speculated that, tools of nanotechnology hybridized with AI can metamorphose human health risk assessment to an extent that has never been achieved before.

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.

Brazilian National Network of Alternative Methods (RENAMA) 10th Anniversary: Meeting of the Associated Laboratories, May 2022.

The Brazilian National Network of Alternative Methods (RENAMA), which is linked to the Ministry of Science, Technology and Innovation, is currently comprised of 51 laboratories from CROs, academia, industry and government. RENAMA's aim is to develop and validate new approach methodologies (NAMs), as well as train researchers and disseminate information on their use - thus reducing Brazilian, and consequently Latin American, dependence on external technology. Moreover, it promotes the adoption of NAMs by educators and trained researchers, as well as the implementation of good laboratory practice (GLP) and the use of certified products. The RENAMA network started its activities in 2012, and was originally comprised of three central laboratories - the National Institute of Metrology, Quality and Technology (INMETRO); the National Institute of Quality Control in Health (INCQS); and the National Brazilian Biosciences Laboratory (LNBio) - and ten associated laboratories. In 2022, RENAMA celebrated its 10th anniversary, a milestone commemorated by the organisation of a meeting attended by different stakeholders, including the RENAMA-associated laboratories, academia, non-governmental organisations and industry. Ninety-six participants attended the meeting, held on 26 May 2022 in Balneário Camboriú, SC, Brazil, as part of the programme of the XXIII Brazilian Congress of Toxicology 2022. Significant moments of the RENAMA were remembered, and new goals and discussion themes were established. The lectures highlighted recent innovations in the toxicological sciences that have translated into the assessment of consumer product safety through the use of human-relevant NAMs instead of the use of existing animal-based approaches. The challenges and opportunities in accepting such practices for regulatory purposes were also presented and discussed.

Artificial Intelligence in Toxicology and Pharmacology

Methods that utilize machine learning and artificial intelligence have transformed a wide variety of fields, including the field of toxicology. Physiologically based pharmacokinetic (PBPK) modeling, quantitative structure-activity relationship modeling for toxicity prediction, adverse outcome pathway analysis, high-throughput screening, toxicogenomics, big data, and toxicological databases are just some of the areas that are covered in this review. By leveraging machine learning and artificial intelligence approaches, it is now possible to develop PBPK models for hundreds of chemicals in an efficient manner, to create in silico models to predict toxicity for a large number of chemicals with similar accuracies compared with In vivo animal experiments, and to analyze a large amount of data of various types (toxicogenomics, high-content image data, etc.) to generate new insights into toxicity mechanisms rapidly, which was previously impossible. This is an improvement over the previous situation The field of toxicological sciences faces a number of challenges that must be overcome before it can make further progress. These challenges include the following: (1) not all machine learning models are equally useful for a particular type of toxicology data; therefore, it is important to test different methods to determine the optimal approach; (2) the current toxicity prediction is primarily based on bioactivity classification (yes/no); therefore, additional studies are required to predict the intensity of effect or dose-response relationship.

Toxicology Certification: The Need, the Value, and an Evolving Landscape.

Certification in toxicology remains a subject of interest to those in the field, as evidenced by the number of presentations at major meetings and publications in the past decade. In 2009, Brock and colleagues summarized the certifications available in the field of toxicology and provided an international perspective of the pros and cons of gaining certification. Though that article has been viewed thousands of times, the certification processes have evolved over the past decade. Therefore, the purpose of the article is to build upon the prior work and present current information for toxicologists seeking certification with the American Board of Toxicology (DABT), Academy of Toxicological Sciences (ATS), as a European Registered Toxicologist (ERT), as a Diplomate, Japanese Society of Toxicology (DJSOT), and as a Diplomate of the Chinese Society of Toxicology (DCST). In addition, other certifications are presented, but available details are limited.

Eleventh Triennial Toxicology Salary Survey.

This is the eleventh in a series of salary surveys for toxicologists conducted at three-year intervals that began in 1988. Previous salary surveys were conducted in 1988, 1991, 1995, 1998, 2001, 2004, 2007 (which was posted electronically, but not published), 2012, 2016 and 2020. In addition to presenting the 2022 results, herein we are providing additional data and an analysis of the trends for employment and pay in toxicology over the last 35 years. The eleventh Triennial Toxicology Salary Survey was conducted as a joint project by the American College of Toxicology (ACT) and the Society of Toxicology (SOT). In addition to the two parent organizations, eight others (the Society for Birth Defects Research, the Society of Toxicologic Pathology, the Safety Pharmacology Society, the American Board of Toxicology (ABT), the Academy of Toxicological Sciences, the Roundtable of Toxicology Consultants, the Society of Environmental Toxicology and Chemistry (SETAC), and the Environmental Mutagenesis and Genomics Society) supported the effort by distributing the Survey Monkey-based instrument to their memberships. Surveys were not distributed to regional SOT chapters, causing there to be minimal responses below the doctorate and master's degree levels. It should be noted that there continues to be a significant increase in the number of individuals reporting six-figure incomes and in those receiving significant sums as bonuses.

2021-2022 Toxicological Sciences paper of the year.

2021-2022 Toxicological Sciences paper of the year.

Assessing machine learning approaches for predicting failures of investigational drug candidates during clinical trials

One of the major challenges in drug development is having acceptable levels of efficacy and safety throughout all the phases of clinical trials followed by the successful launch in the market. While there are many factors such as molecular properties, toxicity parameters, mechanism of action at the target site, etc. that regulates the therapeutic action of a compound, a holistic approach directed towards data-driven studies will invariably strengthen the predictive toxicological sciences. Our quest for the current study is to find out various reasons as to why an investigational candidate would fail in the clinical trials after multiple iterations of refinement and optimization. We have compiled a dataset that comprises of approved and withdrawn drugs as well as toxic compounds and essentially have used time-split based approach to generate the training and validation set. Five highly robust and scalable machine learning binary classifiers were used to develop the predictive models that were trained with features like molecular descriptors and fingerprints and then validated rigorously to achieve acceptable performance in terms of a set of performance metrics. The mean AUC scores for all the five classifiers with the hold-out test set were obtained in the range of 0.66–0.71. The models were further used to predict the probability score for the clinical candidate dataset. The top compounds predicted to be toxic were analyzed to estimate different dimensions of toxicity. Apparently, through this study, we propose that with the appropriate use of feature extraction and machine learning methods, one can estimate the likelihood of success or failure of investigational drugs candidates thereby opening an avenue for future trends in computational toxicological studies. The models developed in the study can be accessed at https://github.com/gnsastry/predicting_clinical_trials.git.

Letter to the editor.

We appreciate the opportunity to respond to the recent article published in Toxicological Sciences describing a new TeraTox assay for teratogenicity assessment (Jaklin et al., 2022). The performance of the assay is compared with in silico prediction of toxicity using 2 different models implemented in the open-source Virtual models for property Evaluation of chemicals within a Global Architecture (VEGA) tool and a list of 20 benchmark chemicals. One of these models consists of the VEGA version of the Procter & Gamble framework for developmental and reproductive toxicity described in our paper (Wu et al., 2013). We would like to express our concern surrounding the use of the Procter & Gamble model for such benchmarking activity and the reporting of a poor performance metric. Of particular concern is the listing of chemicals that are clearly out of the domain of the model (as indicated by the “Low reliability” warning in the model output) as false negatives. This is an erroneous application of the model. As stated in our paper, the Procter & Gamble decision tree was developed “to broadly capture chemicals with features that are similar to chemicals with precedent for developmental and reproductive toxicity effects.” It was not intended to be used as a stand-alone prediction tool: if there are no analogous chemicals with developmental and reproductive toxicity precedent in the model, there can be no prediction. Developed using compounds predominantly positive for developmental and reproductive toxicity, boundaries between active and inactive chemicals are not well defined. According to the guidance, we provided on the use of the model, a chemical producing no match to any chemical of the developmental and reproductive toxicity precedent library of structures is not assumed to be negative for developmental and reproductive toxicity but is a compound for which no conclusions on developmental and reproductive toxicity may be drawn without additional data. This “no conclusion” result also is appropriate for the chemicals determined to be “out of domain” in the VEGA tool which is the case for the “false negative” chemicals listed in Jaklin et al. Although we appreciate usage of the VEGA implementation of the Procter & Gamble developmental and reproductive toxicity model, we note that the authors did not properly categorize model output.

Rhazes's methodology in the science of toxicology.

Toxicology has been one of the most important topics throughout the history of medicine. Persian medicine (PM) textbooks such as Al-Hawi fi Al-Tib of Rhazes (Razi) can be a useful source for novel information about toxicology and thus we aimed to elucidate Rhazes's methodology in toxicology based on this textbook. This research is a historical descriptive study. Data were obtained from the book Al-Hawi fi Al-Tib using keywords of poison, poisoning, and relevant terminologies in ArabicAQ1, Persian, and English and also from appropriate literature in PubMed, Scopus, Web of Science, Scientific Information Database (SID), Magiran, and IranDoc. After introducing the types of common poisons in his era, Rhazes categorized them into three main categories of plants, animals, and minerals, which cause human poisoning orally or via stings and bites. To identify the poison and make a diagnosis of the corresponding toxidrome, he conducted a thorough physical examination of the patient, carefully observing signs and symptoms, and then treated the poisoning using pharmaceutical and non-pharmaceutical modalities. In the book Al-Hawi fi Al-Tib, Rhazes has provided comprehensive information about the types of poisons, their effects on the human body, the signs and symptoms of poisonings, and relevant diagnostic and therapeutic methods. Rhazes had a great contributing role to the science of toxicology. We suggest future research on an in-depth analysis of other PM references for toxicology knowledge and how they may foster the science of toxicology.

Poisoning at the court of the Sun King, the affair of the poisons

Versailles, 1679, at the apogee of the reign of King Louis XIV, one of the largest and most sordid criminal case of all time breaks out: the affair of the poisons. Love, hatred, greed or jealousy affected all classes of society, including the King's entourage. The doctors Fresquières and Dugué, as well as the apothecaries Simon and Geoffroy, will be the experts in charge of the medical and toxicological reports of the Chambre Ardente (“burning court”), a special jurisdiction in charge of cases of poisoning and witchcraft, set up on 7th April 1679 by King Louis XIV. The latter wished to shed light on all these criminal attempts, no matter how highly placed the culprits were; on condition that all interrogations involving the most prominent personalities were kept out of the proceedings, as he did not wish to cause a scandal. Olympe Mancini, one of the young King's first loves, Madame de Montespan, King's ex-favourite, her maid of honour, La Des Oeillets and her chambermaid Catau, or even Nicolas Fouquet's supporters, could all have had their finger on the secret plan to oust the King. At a time when toxicological science was still in its beginning, poisoners such as La De La Grange, La Bosse, La Voisin, La Filastre and Lesage used arsenic, sulphurous or mercurous compounds, plant or animal poisons to make decoctions, to spice up a glass of wine, to sprinkle beatillis on a pie, to impregnate clothes or to coat paper. Among their expert opinions, the apothecaries of the Chambre Ardente examined powders of La Filastre, reputed to be “dangerous with a dagger, with poison and in everything”. They found dried menstrual blood, cantharides mixed with two ounces of henbane grains “whose quantity taken internally is very dangerous”, a small bag of blackish powder that they identified as powder of crushed herbs or plants and, in a scapular bound with thread, a small quantity of grey powder whose composition could not be determined. Despite these inconclusive toxicological results, La Filastre was nevertheless found guilty of abortion, infanticide, Satanism and poisoning and was sentenced to death, burned alive at the stake in the Place de Grève. Madame de Montespan, who was guilty, according to the Lieutenant General of Police, of sacrilege (by participating in diabolical ceremonies), attempted crimes (on the King's new favourite, Madame de Fontange), crimes (by offering newborn babies during black masses) and lèse-majesté (administration of aphrodisiacs and poisoning) but defended by Colbert, left the court in 1691. In 3 years, after 210 sessions of the Chambre Ardente, 319 decrees of arrest, 194 arrests and 104 judgements including 36 death sentences, on 21st July 1692, the King closed the Chambre de l’Arsenal. On 31st July 1692, the Edict of Colbert and De La Reynie banished soothsayers, diviners and horoscope readers from the Kingdom, severely punished superstitious practices, so-called magic, which was henceforth considered pure charlatanism and sacrilege, regulated the trade in toxic or poisonous products and animals, herbs, snakes, toads and vipers, and reserved the right to have laboratories solely for apothecaries and professors of chemistry. The affair of the poisons, if it was not the occasion for the fledging of toxicology, at least allowed the foundations to be laid…of the officinal pharmacy!

Machine Learning and Artificial Intelligence in Toxicological Sciences.

Machine learning and artificial intelligence approaches have revolutionized multiple disciplines, including toxicology. This review summarizes representative recent applications of machine learning and artificial intelligence approaches in different areas of toxicology, including physiologically based pharmacokinetic (PBPK) modeling, quantitative structure-activity relationship modeling for toxicity prediction, adverse outcome pathway analysis, high-throughput screening, toxicogenomics, big data, and toxicological databases. By leveraging machine learning and artificial intelligence approaches, now it is possible to develop PBPK models for hundreds of chemicals efficiently, to create in silico models to predict toxicity for a large number of chemicals with similar accuracies compared with in vivo animal experiments, and to analyze a large amount of different types of data (toxicogenomics, high-content image data, etc.) to generate new insights into toxicity mechanisms rapidly, which was impossible by manual approaches in the past. To continue advancing the field of toxicological sciences, several challenges should be considered: (1) not all machine learning models are equally useful for a particular type of toxicology data, and thus it is important to test different methods to determine the optimal approach; (2) current toxicity prediction is mainly on bioactivity classification (yes/no), so additional studies are needed to predict the intensity of effect or dose-response relationship; (3) as more data become available, it is crucial to perform rigorous data quality check and develop infrastructure to store, share, analyze, evaluate, and manage big data; and (4) it is important to convert machine learning models to user-friendly interfaces to facilitate their applications by both computational and bench scientists.

IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making.

During the past few decades, the science of toxicology has been undergoing a transformation from observational to predictive science. New approach methodologies (NAMs), including in vitro assays, in silico models, read-across, and in vitro to in vivo extrapolation (IVIVE), are being developed to reduce, refine, or replace whole animal testing, encouraging the judicious use of time and resources. Some of these methods have advanced past the exploratory research stage and are beginning to gain acceptance for the risk assessment of chemicals. A review of the recent literature reveals a burst of IVIVE publications over the past decade. In this review, we propose operational definitions for IVIVE, present literature examples for several common toxicity endpoints, and highlight their implications in decision-making processes across various federal agencies, as well as international organizations, including those in the European Union (EU). The current challenges and future needs are also summarized for IVIVE. In addition to refining and reducing the number of animals in traditional toxicity testing protocols and being used for prioritizing chemical testing, the goal to use IVIVE to facilitate the replacement of animal models can be achieved through their continued evolution and development, including a strategic plan to qualify IVIVE methods for regulatory acceptance.

Contribution of Toxicology in Sustainable Development

Contribution of Toxicology in Sustainable Development

About the threshold concentration of nickel oxide nanoparticles in long-term inhalation exposure of rats

Introduction. Nickel oxide nanoparticles are of interest for toxicological science, not only as engineered nanoparticles, producing for industrial and scientific needs, but also as spontaneous pollutants of the atmosphere and the working area in industrial processes related to metallurgy and welding. Materials and methods. Rats were exposed to nickel-oxide aerosol at a concentration of 2.4 ± 0.4 µg/m3 in a “nose only” inhalation setup for 4 hours at a time, 5 times a week, during an overall period of 2 weeks to 6 months. Results. Of the several dozen examined parameters, only a few statistically significant manifestations associated with the reaction of the deep airways to inhaled nanoparticles were noted. However, in the biochemical and morphometric parameters of the lungs, even at the longest periods of exposure, the intergroup differences were insignificant. At the same time, even from the first weeks of the exposure period, genotoxic and allergic indices shifts are detected. Conclusion. For most of the evaluated effects, this level of exposure to nickel oxide nanoparticles may be considered as close to LOAEL, or even to NOAEL. However, according to some indicators, there are effects that suggest a non-threshold nature.

Rethinking agrochemical safety assessment: A perspective

Agrochemical safety assessment has traditionally relied on the use of animals for toxicity testing, based on scientific understanding and test guidelines developed in the 1980s. However, since then, there have been significant advances in the toxicological sciences that have improved our understanding of mechanisms underpinning adverse human health effects. The time is ripe to ‘rethink’ approaches used for human safety assessments of agrochemicals to ensure they reflect current scientific understanding and increasingly embrace new opportunities to improve human relevance and predictivity, and to reduce the reliance on animals. Although the ultimate aim is to enable a paradigm shift and an overhaul of global regulatory data requirements, there is much that can be done now to ensure new opportunities and approaches are adopted and implemented within the current regulatory frameworks. This commentary reviews current initiatives and emerging opportunities to embrace new approaches to improve agrochemical safety assessment for humans, and considers various endpoints and initiatives (including acute toxicity, repeat dose toxicity studies, carcinogenicity, developmental and reproductive toxicity, exposure-driven approaches, inhalation toxicity, and data modelling). Realistic aspirations to improve safety assessment, incorporate new technologies and reduce reliance on animal testing without compromising protection goals are discussed.

ENDOBRONCHIAL TISSUE PLASMINOGEN ACTIVATOR (TPA) ADMINISTRATION TO RESOLVE ENDOBRONCHIAL OBSTRUCTION DUE TO SIGNIFICANT BLOOD CLOT BURDEN IN A SAR COV 2 POSITIVE PATIENT ON EXTRACORPOREAL MEMBRANE OXYGENATION (ECMO)

TOPIC: Critical Care TYPE: Fellow Case Reports INTRODUCTION: Endobronchial obstruction can be a potentially life-threatening condition leading to respiratory failure. A significant cause includes endobronchial blood clots. The presence of blood clots in a patient with SARS COV 2 pneumonia and ARDS is even more detrimental. If the clot burden is obstructing central airways, it may result in fatal respiratory failure. This report will describe a case where removing endobronchial clots was unsuccessful by suctioning during bronchoscopy;therefore, endobronchial tPA was administered to reduce the clot burden successfully. CASE PRESENTATION: Fifty-six-year-old African American male with a past medical history of multiple myeloma;on chemotherapy, was positive for SARS COV 2 by rapid PCR. He presented with severe respiratory failure. He was intubated, and due to the severity of ARDS not responding to salvage techniques, he was started on ECMO via Avalon catheter.Thirty days into ECMO treatment, patients' tidal volumes suddenly dropped to 40mL/breath. Bronchoscopy revealed extensive clot burden extending from the endotracheal tube distally. After two unsuccessful attempts at clot extraction, endobronchial tPA for thrombolysis was administered. The patient received three such treatments. There was no evidence of acute pulmonary hemorrhage or other adverse effects throughout and between each procedure due to tPA administration. The patient showed significant improvement in tidal volumes. DISCUSSION: The presence of large clots in the bronchial tree can lead to compromised oxygenation. The presence of ARDS requiring ECMO highlights the severity of illness in a patient. Conservative management is reasonable in a patient without instability. Otherwise, more aggressive management will be needed. The gold standard therapy includes bronchoscopy with suction, forceps, and basket extraction. When these strategies do not give desired results, administering tPA is another option.Other invasive methods to dissolve the bronchial blood clots have been described in literature including cryo-adhesion and topical thrombolysis with streptokinase or urokinase. CONCLUSIONS: Management of endobronchial blood clots using tPA appears to be safe and effective, clearing the blot burden from the larger airways without resulting in any acute or worsening pulmonary hemorrhage. REFERENCE #1: Anderson, D., De la Cruz, P., Dellavolpe, J., & Walter, R. (2016). Endobronchial blood Clot extraction with Tissue plasminogen activator. Chest, 150(4). doi:10.1016/j.chest.2016.08.1102 REFERENCE #2: Veress, L. A., Anderson, D. R., Hendry-Hofer, T. B., Houin, P. R., Rioux, J. S., Garlick, R. B.,… White, C. W. (2014). Airway tissue plasminogen activator prevents acute mortality due to lethal sulfur mustard inhalation. Toxicological Sciences, 143(1), 178-184. doi:10.1093/toxsci/kfu225 DISCLOSURES: No relevant relationships by Muhammad Ehtesham, source=Web Response No relevant relationships by Waseem Farra, source=Web Response No relevant relationships by Elise Landa, source=Web Response