https:\/\/doi.org\/10.1017\/S0963180115000079<\/a> <\/p>\n\n\n\nScientific Literacy 2: New Approach Methodologies <\/p>\n\n\n\n
New approach methodologies (NAM\u2019s) are innovative scientific technologies that aim to improve drug and chemical testing. The most modern and promising technologies are organ chips, organoids, and generative AI systems. These methodologies have been growing rapidly since 2006 and, conversely, have led to a decline in animal experimentation. Organ chips and 3D organoids, for example, can create more accurate molecular environments than genetically similar animals due to their ability to utilize human-derived induced pluripotent stem cells (iPSCs). The AI systems can take in necessary data to create predictive models of chemical and drug outcomes without the ethical concerns of animal testing. However, more complex scenarios require animal testing due to the NAM\u2019s inability to analyze drug and chemical effects on complex systems like the endocrine system or whole organs.\u00a0<\/p>\n\n\n\n
Microfluidic organs-on-chips and 3D organoids are essentially lab-grown organ tissues. They are created through the use of human-derived induced pluripotent stem cells or iPSCs. A person of interest who has the desired disease to be tested will provide a tissue sample, often from the skin or blood, and turn the cells into a stem cell by adding certain transcription factors known as Yamanaka factors (Liu et al., 2008). iPSCs are essential for the creation of organ chips and 3D organoids because of their ability to self-replicate into any desired cell (NIH, 2016). By modifying the cells, we can create organ tissue samples in vitro and test them. This is intrinsically better than animal testing because the variables are more controlled, and the gene expression of the cells is identical to humans, because it’s a human sample. Different organisms express their genome differently, so although the animals that are tested on have a fairly similar genome to humans, the difference in expression can complicate testing. This is why iPSCs are so important for 3D organoids and organ chips, and they can only become more accurate through the use of AI. <\/p>\n\n\n\n
AI systems have proved to be an instrumental tool for NAMs through the use of their multimodal learning, generative modeling, and causal inference. Toxicology has a large amount of data to work through, and that is an area where AI systems can excel. It can optimize workflows by organizing them and flagging any incongruent patterns (Luechtefeld & Hartung, 2025). It can also run many simulations to predict the effectiveness of certain drugs and chemicals. It is also important to note that it does not do this automatically and runs alongside toxicologists as a form of \u201ce-validation\u201d. It is effective at creating risk assessments and synthesizing evidence and is more accurate than animal testing (Luechtefeld & Hartung, 2025). An example of this is the use of SSL techniques to identify outliers in chemical graphs and ensure accuracy. Additionally, synthetic data generation can be used to create artificial data to fill in the gaps of analysis. In toxicology, there is often a lack of data necessary to experiment, so AI models can be used to fill that gap with synthetic data that is similar to the base data of the experiment. <\/p>\n\n\n\n
AI technology, organ chips, and 3D organelles are evidently valuable to drug testing and allow a more humane and accurate form of experimentation, but they can not completely replace animal testing. For example, AI systems suffer from the black box nature of their data calculations. The patterns and data it uses to come to its conclusions can not be viewed by scientists because of its complex neural network. AI systems can also be heavily affected by bias if the data sets they are trained on are not diverse enough (Luechtefeld & Hartung, 2025). This causes hesitancy of trust among many toxicological scientists, as these defects can heavily affect the results of experiments if the AI’s results are not validated. Additionally, 3D organoids and microfluidic organs-on-chips are limited to simpler structures that lack vasculature. Animal testing will continue to be used for testing on chemicals and drugs that have a wider range of effects. Organoids and organ chips also need a high degree of quality control in order to produce consistent and replicable results. This can often lead to very long production of these testing mediums (Park et al., 2024). <\/p>\n\n\n\n
Despite the flaws that prevent it from replacing animal testing, NAMs are a paradigm shift in toxicological technology. Creating artificial organ tissue from human samples creates the most accurate test results for chemicals and drugs, and AI systems streamline the analysis of results and fill in the gaps through simulations. Though they may not be able to simulate the results in more complex experiments and can take time to prepare, NAMs have plenty of room to improve as the most efficient and ethical form of toxicological testing. <\/p>\n\n\n\n
Refrances<\/p>\n\n\n\n
Kwon, D. (2026). The age of animal experiments is waning. Where will science go next? Nature<\/em>, 650<\/em>(8103), 812\u2013814. https:\/\/doi.org\/10.1038\/d41586-026-00563-3<\/p>\n\n\n\nLiu, X., Huang, J., Chen, T., Wang, Y., Xin, S., Li, J., Pei, G., & Kang, J. (2008). Yamanaka factors critically regulate the developmental signaling network in mouse embryonic stem cells. Cell Research<\/em>, 18<\/em>(12), 1177\u20131189. https:\/\/doi.org\/10.1038\/cr.2008.309<\/p>\n\n\n\nLuechtefeld, T., & Hartung, T. (2025). Navigating the AI Frontier in Toxicology: Trends, Trust, and Transformation. Current Environmental Health Reports<\/em>, 12<\/em>(1). https:\/\/doi.org\/10.1007\/s40572-025-00514-6<\/p>\n\n\n\nNIH. (2016). Stem Cell Basics<\/em>. Stemcells.nih.gov; National Institutes of Health. https:\/\/stemcells.nih.gov\/info\/basics\/stc-basics<\/p>\n\n\n\nPark, G., Yeri Alice Rim, Sohn, Y., Nam, Y., & Ji Hyeon Ju. (2024). Replacing Animal Testing with Stem Cell-Organoids : Advantages and Limitations. Stem Cell Reviews and Reports<\/em>, 20<\/em>(6). https:\/\/doi.org\/10.1007\/s12015-024-10723-5<\/p>\n\n\n\nEnd of term reflection <\/p>\n\n\n\n
Cell biology has allowed me to better understand how genetic diseases occur. With this knowledge, I can assist my future patients in the future more effectively. For example, Osteoarthritis is a congenital disease that weakens the bones in the body. This occurs due to a mutation in the LRP5 gene, which then creates overactivity in osteoclast cells. With this deeper knowledge of the cell and genome, I can further assist patients with the disease. <\/p>\n","protected":false},"excerpt":{"rendered":"
Draw me a cell! Draw me a biololecule! This is the Biomolecule Alpha helix with 3 amino acids Make a meme! Meme captionI created this meme because I never realized how much historical value the ribosomehas in cell biology. I feel as though I would have thought the ribosome was as cool asthe mitochondria in… <\/p>\n
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