By D.W.G. Kalani Tharanga, JadeTimes News
The Challenge of Animal Testing
From animal lovers to laboratory technicians, no one enjoys subjecting animals to scientific tests. However, these tests are currently necessary to ensure the safety of drugs and other substances for human use. Researchers have long sought alternatives to animal testing, and artificial intelligence (AI) systems are now accelerating this search.
AI as a Tool for Data Analysis
One promising application of AI in this field involves using it to analyze existing global animal testing results, thereby reducing the need for new tests. According to Joseph Manuppello, a senior research analyst at the Physicians Committee of Responsible Medicine, this is crucial because it can be challenging for scientists to sift through decades of data to find the necessary information. He is enthusiastic about AI models like ChatGPT, which can extract and synthesize this data effectively.
AI's Role in Toxicity Testing
Thomas Hartung, a toxicology professor at Johns Hopkins University and director of the Center for Alternatives to Animal Testing, states that AI is proving to be as good as or better than humans at extracting information from scientific papers. One major reason for current animal testing is the need to evaluate new chemicals, with over 1,000 new compounds entering the market annually. Hartung highlights that trained AI systems are now capable of assessing a new chemical's toxicity. These AI tools can provide preliminary assessments and flag potential issues, which is a significant advancement.
The Advancements and Limitations of AI in Toxicology
AI represents a substantial leap forward in the power and accuracy of toxicology testing. Hartung notes that AI's involvement at every stage of toxicity testing is creating unprecedented opportunities. Furthermore, AI is now even being used to develop new drugs. However, AI systems are not infallible. Data bias remains a significant challenge, as AI models trained on data from one ethnic group may not produce accurate results for other populations.
Comparing AI and Animal Testing
Despite these challenges, AI can sometimes offer more accurate results than animal testing. For instance, the arthritis medication Vioxx passed animal tests but was later withdrawn from the market after it was found to increase the risk of heart attack and stroke in humans. Conversely, some widely used medicines, such as aspirin, would have failed animal tests due to their toxicity to rat embryos.
Innovative AI Projects
Several innovative AI projects aim to replace animal testing. One such project is AnimalGAN, developed by the US Food and Drug Administration, which aims to predict how rats would react to various chemicals using data from over 6,000 real rats across 1,317 treatment scenarios. Another project, Virtual Second Species, is creating an AI-powered virtual dog trained on historical dog test data. Cathy Vickers, head of innovation at the UK's National Centre for the Replacement, Refinement and Reduction of Animals in Research, explains that new medicines are currently tested on rats and dogs before human trials begin.
Regulatory Challenges and Future Prospects
The major challenge for AI testing is obtaining regulatory approval. Dr. Vickers acknowledges that full acceptance of AI methods will take time. Emma Grange, director of science and regulatory affairs at Cruelty Free International, emphasizes the need to phase out animal testing. While the impact of new technologies like AI on completely ending animal testing is still uncertain, Grange hopes that AI can contribute to transitioning away from using animals in any test or experiment.
