Optimization of the TeraTox assay for preclinical teratogenicity assessment

Predictive biomarkers for embryotoxicity: a machine learning approach to mitigating multicollinearity in RNA-Seq

Multicollinearity, characterized by significant co-expression patterns among genes, often occurs in high-throughput expression data, potentially impacting the predictive model's reliability. This study examined multicollinearity among closely related genes, particularly in RNA-Seq data obtained from embryoid bodies (EB) exposed to 5-fluorouracil perturbation to identify genes associated with embryotoxicity. Six genes-Dppa5a, Gdf3, Zfp42, Meis1, Hoxa2, and Hoxb1-emerged as candidates based on domain knowledge and were validated using qPCR in EBs perturbed by 39 test substances. We conducted correlation studies and utilized the variance inflation factor (VIF) to examine the existence of multicollinearity among the genes. Recursive feature elimination with cross-validation (RFECV) ranked Zfp42 and Hoxb1 as the top two among the seven features considered, identifying them as potential early embryotoxicity assessment biomarkers. As a result, a t test assessing the statistical significance of this two-feature prediction model yielded a p value of 0.0044, confirming the successful reduction of redundancies and multicollinearity through RFECV. Our study presents a systematic methodology for using machine learning techniques in transcriptomics data analysis, enhancing the discovery of potential reporter gene candidates for embryotoxicity screening research, and improving the predictive model's predictive accuracy and feasibility while reducing financial and time constraints.

Perspectives on chick embryo models in developmental and reproductive toxicity screening

Teratology, the study of congenital anomalies and their causative factors intersects with developmental and reproductive toxicology, employing innovative methodologies. Evaluating the potential impacts of teratogens on fetal development and assessing human risk is an essential prerequisite in preclinical research. The chicken embryo model has emerged as a powerful tool for understanding human embryonic development due to its remarkable resemblance to humans. This model offers a unique platform for investigating the effects of substances on developing embryos, employing techniques such as ex ovo and in ovo assays, chorioallantoic membrane assays, and embryonic culture techniques. The advantages of chicken embryonic models include their accessibility, cost-effectiveness, and biological relevance to vertebrate development, enabling efficient screening of developmental toxicity. However, these models have limitations, such as the absence of a placenta and maternal metabolism, impacting the study of nutrient exchange and hormone regulation. Despite these limitations, understanding and mitigating the challenges posed by the absence of a placenta and maternal metabolism are critical for maximizing the utility of the chick embryo model in developmental toxicity testing. Indeed, the insights gained from utilizing these assays and their constraints can significantly contribute to our understanding of the developmental impacts of various agents. This review underscores the utilization of chicken embryonic models in developmental toxicity testing, highlighting their advantages and disadvantages by addressing the challenges posed by their physiological differences from mammalian systems.

The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review

BACKGROUND

Stem cell research, particularly in the domain of induced pluripotent stem cell (iPSC) technology, has shown significant progress. The integration of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has played a pivotal role in refining iPSC classification, monitoring cell functionality, and conducting genetic analysis. These enhancements are broadening the applications of iPSC technology in disease modelling, drug screening, and regenerative medicine. This review aims to explore the role of AI in the advancement of iPSC research.

METHODS

In December 2023, data were collected from three electronic databases (PubMed, Web of Science, and Science Direct) to investigate the application of AI technology in iPSC processing.

RESULTS

This systematic scoping review encompassed 79 studies that met the inclusion criteria. The number of research studies in this area has increased over time, with the United States emerging as a leading contributor in this field. AI technologies have been diversely applied in iPSC technology, encompassing the classification of cell types, assessment of disease-specific phenotypes in iPSC-derived cells, and the facilitation of drug screening using iPSC. The precision of AI methodologies has improved significantly in recent years, creating a foundation for future advancements in iPSC-based technologies.

CONCLUSIONS

Our review offers insights into the role of AI in regenerative and personalized medicine, highlighting both challenges and opportunities. Although still in its early stages, AI technologies show significant promise in advancing our understanding of disease progression and development, paving the way for future clinical applications.

Human Pluripotent Stem Cell-Based Assays to Predict Developmental Toxicity

Human beings are continuously exposed to various toxic substances throughout their lives, which affect their reproductive health and eventually the offspring they give birth to. Mainly, these toxins damage the heart and neurological development of the newborn, but most recently, they have begun to affect the musculoskeletal system as well. These toxins are usually present in food, pharmaceuticals, cosmetics, or even the polluted air that people breathe; as a result, the prevalence of birth defects is steadily rising. For this reason, it becomes a necessity to deploy a new set of assays to test for such toxins in industries to decrease the occurrence of developmental toxicity. These assays are exceedingly expensive when carried out conventionally using animal models or cells from such sources and have a lower predictive value due to the vast variation between animals and humans. To overcome such major problems, human pluripotent stem cells are now frequently used for these assays. These cells are easily available, are quickly generated from somatic cells (induce pluripotent stem cells), can be of human origin without harming people, and eliminate animal harm, which makes them the top choice of scientists for carrying out any in vitro developmental toxicity assays.This chapter, therefore, provides an overview of several steps that can be used to predict a compound's developmental toxicity by utilizing human pluripotent stem cells. Here, the easiest and most effective procedure has been outlined that can screen many compounds simultaneously.

Advancing the use of new approach methodologies for assessing teratogenicity: Building a tiered approach

Many New Approach Methodologies (NAMs) have been developed for the safety assessment of new ingredients. Research into reproductive toxicity and teratogenicity is a particularly high priority, especially given their mechanistic complexity. Forty-six non-teratogenic and 39 teratogenic chemicals were screened for teratogenic potential using the in silico DART model from the OECD QSAR Toolbox; the devTox quickPredict™ (devTox assay) test and the Zebrafish Embryotoxicity Test (ZET). The sensitivity and specificity were 94.7% and 84.1%, respectively, for the DART tree (83 chemicals), 86.1% and 35.6% for the devTox (81 chemicals) and 77.8% and 76.7% for the ZET (57 chemicals). Fifty-three chemicals were tested in all three assays and when results were combined and based on a "2 out of 3 rule", the sensitivity and specificity were 96.0% and 71.4%, respectively. The specificity of the devTox assay for a sub-set of 43 chemicals was increased from 26.1% to 82.6% by incorporating human plasma concentrations into the assay interpretation. When all 85 chemicals were assessed in a decision tree approach, there was an excellent predictivity and assay robustness of 90%. In conclusion, all three models exhibited a good sensitivity and specificity, especially when outcomes from all three were combined or used in "2 out of 3" or a tiered decision tree approach. The latter is an interesting predictive approach for evaluating the teratogenic potential of new chemicals. Future investigations will extend the number of chemicals tested, as well as explore ways to refine the results and obtain a robust Integrated Testing Strategy to evaluate teratogenic potential.

Transcriptome-based prediction of drugs, inhibiting cardiomyogenesis in human induced pluripotent stem cells

Animal studies for embryotoxicity evaluation of potential therapeutics and environmental factors are complex, costly, and time-consuming. Often, studies are not of human relevance because of species differences. In the present study, we recapitulated the process of cardiomyogenesis in human induced pluripotent stem cells (hiPSCs) by modulation of the Wnt signaling pathway to identify a key cardiomyogenesis gene signature that can be applied to identify compounds and/or stress factors compromising the cardiomyogenesis process. Among the 23 tested teratogens and 16 non-teratogens, we identified three retinoids including 13-cis-retinoic acid that completely block the process of cardiomyogenesis in hiPSCs. Moreover, we have identified an early gene signature consisting of 31 genes and associated biological processes that are severely affected by the retinoids. To predict the inhibitory potential of teratogens and non-teratogens in the process of cardiomyogenesis we established the "Developmental Cardiotoxicity Index" (CDI31g) that accurately differentiates teratogens and non-teratogens to do or do not affect the differentiation of hiPSCs to functional cardiomyocytes.

embryoTox: Using Graph-Based Signatures to Predict the Teratogenicity of Small Molecules

Teratogenic drugs can lead to extreme fetal malformation and consequently critically influence the fetus's health, yet the teratogenic risks associated with most approved drugs are unknown. Here, we propose a novel predictive tool, embryoTox, which utilizes a graph-based signature representation of the chemical structure of a small molecule to predict and classify molecules likely to be safe during pregnancy. embryoTox was trained and validated using in vitro bioactivity data of over 700 small molecules with characterized teratogenicity effects. Our final model achieved an area under the receiver operating characteristic curve (AUC) of up to 0.96 on 10-fold cross-validation and 0.82 on nonredundant blind tests, outperforming alternative approaches. We believe that our predictive tool will provide a practical resource for optimizing screening libraries to determine effective and safe molecules to use during pregnancy. To provide a simple and integrated platform to rapidly screen for potential safe molecules and their risk factors, we made embryoTox freely available online at https://biosig.lab.uq.edu.au/embryotox/.

Developmental toxicity of remdesivir, an anti-COVID-19 drug, is implicated by in vitro assays using morphogenetic embryoid bodies of mouse and human pluripotent stem cells

BACKGROUND

Remdesivir is an antiviral drug approved for the treatment of COVID-19, whose developmental toxicity remains unclear. More information about the safety of remdesivir is urgently needed for people of childbearing potential, who are affected by the ongoing pandemic. Morphogenetic embryoid bodies (MEBs) are three-dimensional (3D) aggregates of pluripotent stem cells that recapitulate embryonic body patterning in vitro, and have been used as effective embryo models to detect the developmental toxicity of chemical exposures specifically and sensitively.

METHODS

MEBs were generated from mouse P19C5 and human H9 pluripotent stem cells, and used to examine the effects of remdesivir. The morphological effects were assessed by analyzing the morphometric parameters of MEBs after exposure to varying concentrations of remdesivir. The molecular impact of remdesivir was evaluated by measuring the transcript levels of developmental regulator genes.

RESULTS

The mouse MEB morphogenesis was impaired by remdesivir at 1-8 μM. Remdesivir affected MEBs in a manner dependent on metabolic conversion, and its potency was higher than GS-441524 and GS-621763, presumptive anti-COVID-19 drugs that act similarly to remdesivir. The expressions of developmental regulator genes, particularly those involved in axial and somite patterning, were dysregulated by remdesivir. The early stage of MEB development was more vulnerable to remdesivir exposure than the later stage. The morphogenesis and gene expression profiles of human MEBs were also impaired by remdesivir at 1-8 μM.

CONCLUSIONS

Remdesivir impaired mouse and human MEBs at concentrations that are comparable to the therapeutic plasma levels in humans, urging further investigation into the potential impact of remdesivir on developing embryos.

Establishment of a human cell-based in vitro battery to assess developmental neurotoxicity hazard of chemicals

Developmental neurotoxicity (DNT) is a major safety concern for all chemicals of the human exposome. However, DNT data from animal studies are available for only a small percentage of manufactured compounds. Test methods with a higher throughput than current regulatory guideline methods, and with improved human relevance are urgently needed. We therefore explored the feasibility of DNT hazard assessment based on new approach methods (NAMs). An in vitro battery (IVB) was assembled from ten individual NAMs that had been developed during the past years to probe effects of chemicals on various fundamental neurodevelopmental processes. All assays used human neural cells at different developmental stages. This allowed us to assess disturbances of: (i) proliferation of neural progenitor cells (NPC); (ii) migration of neural crest cells, radial glia cells, neurons and oligodendrocytes; (iii) differentiation of NPC into neurons and oligodendrocytes; and (iv) neurite outgrowth of peripheral and central neurons. In parallel, cytotoxicity measures were obtained. The feasibility of concentration-dependent screening and of a reliable biostatistical processing of the complex multi-dimensional data was explored with a set of 120 test compounds, containing subsets of pre-defined positive and negative DNT compounds. The battery provided alerts (hit or borderline) for 24 of 28 known toxicants (82% sensitivity), and for none of the 17 negative controls. Based on the results from this screen project, strategies were developed on how IVB data may be used in the context of risk assessment scenarios employing integrated approaches for testing and assessment (IATA).

Human Maternal-Fetal Interface Cellular Models to Assess Antiviral Drug Toxicity during Pregnancy

Pregnancy is a period of elevated risk for viral disease severity, resulting in serious health consequences for both the mother and the fetus; yet antiviral drugs lack comprehensive safety and efficacy data for use among pregnant women. In fact, pregnant women are systematically excluded from therapeutic clinical trials to prevent potential fetal harm. Current FDA-recommended reproductive toxicity assessments are studied using small animals which often do not accurately predict the human toxicological profiles of drug candidates. Here, we review the potential of human maternal-fetal interface cellular models in reproductive toxicity assessment of antiviral drugs. We specifically focus on the 2- and 3-dimensional maternal placental models of different gestational stages and those of fetal embryogenesis and organ development. Screening of drug candidates in physiologically relevant human maternal-fetal cellular models will be beneficial to prioritize selection of safe antiviral therapeutics for clinical trials in pregnant women.